Treatment with E-52862 (20 mg/kg, twice daily during 25 days) did not prevent the joint damage. (C) The semiquantitative scoring system for joint histopathology. The scores for each image are (first value represents femur condyle and second value represents tibial plateau): (a) 1, 0.5; (b) 0.5, 0; (c) 2, 5; (d) 2, 5; (e) 2, 0.5; (f) 0, 0.5; (g) 3, 6; (h) 2, 3. Data are expressed as the mean ± SEM (n = 5–7 animals per group). Scale bar: 100 µm. ∗∗∗p < 0.001 for saline vs. MIA (2-way ANOVA). MIA, monosodium iodoacetate; SEM, standard error of the mean.

increase in despair-like behavior (p < 0.01 vs. MIA vehicle; **Figure 4G**). Therefore, anxiety-like behavior was not sensitive to σ1R antagonism, whereas MIA-induced depressive-like behavior was prevented after E-52862 treatment.

#### E-52862 Modulates Microglial Expression in the Medial Prefrontal Cortex

A possible central role of σ1R modulating microglial activity was assessed through quantification of the density of microglial cells and the perimeter of the somas in the prelimbic and infralimbic areas of the medial prefrontal cortex (**Figure 5**). The analysis of the cellular density showed a significant increase on the total number of microglial cells in the prelimbic and the infralimbic areas of mice with osteoarthritis pain receiving vehicle (p < 0.001 vs. saline) (**Figures 5A,C,D,F**). Repeated administration of the σ1R antagonist (20 mg/kg, twice daily during 25 days) significantly reduced the microglial density in both cortical areas (p < 0.01 vs. MIA vehicle; **Figures 5A,C,D,F**). MIA-injected mice had an increase of the perimeter of microglial cells in the infralimbic (p < 0.05;

**Figure 5E**), but not in the prelimbic area (**Figure 5B**) when compared to saline-injected mice. This increase was not significantly affected by the treatment with E-52862. Therefore, E-52862 modulated the density of microglial cells in the medial prefrontal cortices without affecting microglia activation.

### DISCUSSION

The present study reveals the involvement of the σ1R in the nociceptive, emotional and cognitive alterations associated with osteoarthritis pain in mice. Mechanical allodynia and gait impairments induced by MIA injection were partly prevented by chronic administration of the σ1R antagonist E-52862. This treatment also inhibited the cognitive deficits and depressive-like behavior of mice with osteoarthritis pain, although anxiogeniclike responses were not modified. Modulation of the paininduced behavioral alterations by E-52862 was not due to an inhibition of joint damage produced by MIA, and there was a concomitant decrease on MIA-induced microgliosis in the medial prefrontal cortex.

σ1R is highly expressed in key areas for pain control (Alonso et al., 2000; Bangaru et al., 2013). Behavioral studies have shown analgesic efficacy of the σ1R antagonist E-52862 in acute (Romero et al., 2012; Gris et al., 2014; Tejada et al., 2014) and chronic (Gris et al., 2014) models of inflammatory pain, and in neuropathic pain models induced by partial sciatic nerve ligation (Romero et al., 2012), chemotherapy (Nieto et al., 2012), or streptozotocin-induced diabetes (Gris et al., 2016). However, the role of σ1R has not been previously assessed in models of osteoarthritis pain, one of the most prevalent and disabling chronic pain conditions. We showed that E-52862 inhibited both mechanical hypersensitivity and gait alterations in the MIA model of osteoarthritis pain. Gait alterations could be associated to structural modifications of the joint or to the increased mechanical sensitivity (Boettger et al., 2009). Previous studies using the antigen-induced arthritis model in rats suggested that specific gait parameters, such as the angle between the paws, were exclusively influenced by the structural damage of the joint as indicated by its correlation with cartilage destruction (Boettger et al., 2009). However, other parameters, such as the paw print area, represent good measures of pain (Boettger et al., 2009). The correlation with mechanical allodynia would be in agreement with previous work showing that nerve-injured rats with decreased mechanical thresholds to punctate stimulation had also altered walking patterns (Vrinten and Hamers, 2003). In the same line, the MIA model of osteoarthritis pain in rodents showed that celecoxib and morphine reduced mechanical allodynia and gait abnormalities (Ferland et al., 2011; Ferreira-Gomes et al., 2012), suggesting that both parameters are associated in this

monosodium iodoacetate; SEM, standard error of the mean.

chronic pain model. Such correlation has also been described in higher order mammals with osteoarthritis pain (Haussler et al., 2007; Frost-Christensen et al., 2008; Moreau et al., 2011; Cake et al., 2013). Thus, the reduction of the paw print area and the maximal contact area parameters observed in our study in osteoarthritic mice were probably a consequence of an unwillingness of the animal to bear weight on the injured limb, while the normalization of such parameters after E-52862 treatment might be related to reduced pain perception. In agreement, the effect of E-52862 on the walking patterns of mice with osteoarthritis was not accompanied by a normalization of the structural alterations observed in the histological assessments. This absence of effect on cartilage damage is in agreement with the low expression levels of σ1R in chondrocytes and bone marrow when compared to its expression in the peripheral and central nervous system1,2 . The relief of mechanical hypersensitivity and pain-associated comorbidities after the treatment with E-52862 coexisted with the cartilage degradation, in agreement with the widely recognized fact that the presence and severity of joint pain poorly correlates with structural joint damage in osteoarthritis patients (Lawrence et al., 1966; Dieppe, 2004). Thus, the pain-relieving

effects of the σ1R antagonist probably rely on a modulatory role on the nervous system and are independent of the site of the primary lesion.

(two-way ANOVA). MIA, monosodium iodoacetate; SEM, standard error of the mean.

We observed a cognitive deficit associated to osteoarthritis induced by MIA, which was significantly reduced by the repeated administration of E-52862. Our result suggests that the blockade of σ1R plays a protective role in this long-term memory impairment produced by chronic pain. Previous studies also showed impaired memory function in other chronic pain models (Zhao et al., 2006; Kodama et al., 2011) and specifically during MIA-induced joint pain (La Porta et al., 2015; Negrete et al., 2017). Selective σ1R ligands failed to modify learning, consolidation or retention phases of the mnemonic process when administered to naïve animals (Hashimoto et al., 2007; Antonini et al., 2011), but σ1R activation reduced cognitive deficits associated with schizophrenia (Hashimoto et al., 2007), Alzheimer disease (Maurice et al., 1998; Antonini et al., 2011) or scopolamine treatment (Hiramatsu et al., 2002). In contrast, we observed that σ1R blockade reversed the memory impairment induced after MIA injection. The overlap between the neuroanatomical substrates implicated in both pain control and cognitive functions provides information about the development of memory deficits in patients with chronic pain (Moriarty et al., 2011). However, the precise causal mechanisms underlying the pain-related cognitive impairment are still unclear, and the role of the σ1R on this specific type of memory deficits has not been studied. Our data suggest that σ1R antagonists are efficient improving cognitive functions under a chronic pain state.

We obtained increased anxiety-like responses after the intra-knee injection of MIA, as previously reported in other murine models of inflammatory (Schellinck et al., 2003; Chen et al., 2013) and neuropathic pain (Benbouzid et al., 2008; Matsuzawa-Yanagida et al., 2008; La Porta et al., 2016). Anxiety-like behavior was present 3 weeks after MIA, but not at earlier time points (11 days). Previous studies suggested that persistent pain may trigger alterations in brain areas involved in affective responses, which over time may lead to emotional comorbidities including anxiety and depressive-like behavior (Narita et al., 2006; Suzuki et al., 2007;

Seminowicz et al., 2009; Sellmeijer et al., 2018). In agreement, 25 days after the intra-knee injection of MIA depressivelike responses were observed in animals with osteoarthritis pain, as in previous studies investigating inflammatory and neuropathic pain (Hasnie et al., 2007; Suzuki et al., 2007; Norman et al., 2010; Negrete et al., 2017). Depressivelike responses were abolished after chronic administration of E-52862, although anxiety-like behavior was not modified with this σ1R antagonist. These results are in line with previous works studying affective behavior in σ1R knockout mice. In these studies, σ1R knockouts exhibited increased immobility in the forced swimming test, but normal anxietylike behavior (Sabino et al., 2009), suggesting distinct roles of the receptor modulating depressive and anxiety responses. Common neuroplastic changes associated with chronic pain and emotional disorders were proposed as important routes for the onset and reciprocal aggravation of both pathologies (Sheng et al., 2017). Consequently, analgesic drugs such as opioids (Mague et al., 2003; Tenore, 2008) or benzodiazepines (Vollenweider et al., 2011) have been proposed as a treatment for chronic pain-induced depression, and antidepressants like selective serotonin reuptake inhibitors (SSRIs) (Tasmuth et al., 2002; Gebhardt et al., 2016) or tricyclic antidepressants (Rowbotham et al., 2005; Kopsky and Keppel Hesselink, 2012) exhibited antinociceptive effects under chronic pain conditions. The interest of σ1R ligands for the treatment of depressive states raised from the observation that several antidepressants had moderate to high affinity for σ1R sites (Schmidt et al., 1989; Itzhak et al., 1991; Narita et al., 1996). While some SSRIs such as fluvoxamine or venlafaxine have shown agonism for σ1R, others like sertraline may act as antagonists (Ishima et al., 2014). Moreover, the antidepressant efficacy of σ1R ligands may depend on the affective status of the animal, since the selective σ1R agonist PRE-084 reduced depressive-like behavior in adrenalectomized mice but lacked effect in naïve animals (Urani et al., 2001).

We observed an increased microgliosis in the medial prefrontal cortex produced by the injection of MIA. This result agrees with a previous study showing increases of microglial density in the infralimbic cortex of nerve-injured rats (Chu Sin Chung et al., 2017; Xu et al., 2017). Other brain areas such as the amygdala, periaqueductal gray (PAG) or hippocampus, have also shown increased gliosis during chronic pain conditions (Humo et al., 2019). Interestingly, a recent study on neuropathic pain showed enhanced expression of microglial markers in the prefrontal cortex accompanied by depressive-like behavior. Chronic minocycline attenuated both microglial activation and depressive-like responses (Xu et al., 2017). Previous studies have shown that the σ1R antagonist BD1047 attenuated microglial activation in the spinal cord in a model of bone cancer pain (Zhu et al., 2015), but the effect of σ1R on supraspinal microglia has not been assessed in chronic pain models. Our data show that σ1R antagonist E-52862 significantly reduced the density of microglia in medial prefrontal cortices of mice with osteoarthritis pain. This effect was not accompanied by a reduction of anxiety-like behavior, suggesting that this affective disturbance is not directly related to cortical microgliosis. However, these anatomical changes correlated with the cognitive performance and the depressive-like behavior, pointing toward an involvement of cortical microglia on both pain comorbidities. Therefore, σ1R-regulated cortical microgliosis might be crucial for the manifestation of cognitive and emotional alterations often present in chronic pain conditions. Indeed, antidepressant drugs such as SSRIs also have activity modulating microgliosis and reducing microglial production of tumor necrosis factor α and nitric oxide (Chung et al., 2011; Tynan et al., 2012). It is well known that σ1R modulates several signal transduction pathways, including the production of ATP, reactive oxygen species or mitogenactivated protein kinases (MAPK) (Zamanillo et al., 2013; Hayashi, 2015; Zhao et al., 2017). All these molecules have been identified as effective signals for microglial migration and activation (Biber et al., 2007; Fan et al., 2017), suggesting an indirect modulatory role of σ1R. In agreement, σ1R activation by methamphetamine induces a microgliosis that involves generation of reactive oxygen species and activation of the MAPK pathway (Chao et al., 2017).

The present study reveals that E-52862 alleviates the nociceptive, cognitive and emotional manifestations associated to chronic osteoarthritis pain. We provide evidence showing that the effect of σ1R over these manifestations of chronic pain is not associated to local changes in articular damage but is accompanied by modulation of microglial activity in the medial prefrontal cortex. Our data highlight the blockade of σ1R as an interesting pharmacological strategy for the simultaneous management of multiple aspects of chronic osteoarthritis pain.

#### ETHICS STATEMENT

All experimental procedures and animal husbandry were conducted following the ARRIVE (Animal Research: Reporting In Vivo Experiments) guidelines and according to the ethical principles of the International Association for the Study of Pain (I.A.S.P.) for the evaluation of pain in conscious animals (Zimmermann, 1986) and the European Parliament and the Council Directive (2010/63/EU), and were approved by the Animal Care and Use Committees of the PRBB and Departament de Territori i Habitatge of Generalitat de Catalunya.

#### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

#### ACKNOWLEDGMENTS

We acknowledge the financial support of the European Commission (FP7, NeuroPain #2013-602891), the Catalan Government (AGAUR, #SGR2017-669), the Spanish Instituto

de Salud Carlos III (RTA, #RD16/0017/0020), and AGAUR (ICREA Academia Award 2015). MC is the recipient of an Industrial Doctorate contract from the Catalan Government and Laboratorios Esteve (AGAUR, #2014-DI-040). Partial support from FEDER funds is also acknowledged.

#### REFERENCES

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2019.00468/full#supplementary-material

Holmes, A., and Rodgers, R. J. (1998). Responses of swiss-webster mice to repeated plus-maze experience: further evidence for a qualitative shift in emotional state? Pharmacol. Biochem. Behav. 60, 473–488. doi: 10.1016/s0091-3057(98)00008-2

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling Editor declared a past co-authorship with several of the authors.

Copyright © 2019 Carcolé, Zamanillo, Merlos, Fernández-Pastor, Cabañero and Maldonado. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Anti-tumor Efficacy Assessment of the Sigma Receptor Pan Modulator RC-106. A Promising Therapeutic Tool for Pancreatic Cancer

, Sara Pignatta1

, Chiara Bigogno2

, Alessia Chiorazzi3

#### Edited by:

Anna Tesei1

\* †

Giulio Massimo Dondio2

Section, University of Pavia, Pavia, Italy

Cristina Meregalli3

, Michela Cortesi1†

Tangui Maurice, INSERM U1198 Mécanismes Moléculaires dans les Démences Neurodégénératives, France

#### Reviewed by:

Franck Borgese, INSERM U1091 Institut de Biologie de Valrose, France Elia Ranzato, University of Eastern Piedmont, Italy

#### \*Correspondence:

Anna Tesei<EMAIL_ADDRESS>Simona Collina<EMAIL_ADDRESS>†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

Received: 22 January 2019 Accepted: 17 April 2019 Published: 14 May 2019

#### Citation:

Tesei A, Cortesi M, Pignatta S, Arienti C, Dondio GM, Bigogno C, Malacrida A, Miloso M, Meregalli C, Chiorazzi A, Carozzi V, Cavaletti G, Rui M, Marra A, Rossi D and Collina S (2019) Anti-tumor Efficacy Assessment of the Sigma Receptor Pan Modulator RC-106. A Promising Therapeutic Tool for Pancreatic Cancer. Front. Pharmacol. 10:490. doi: 10.3389/fphar.2019.00490 Annamaria Marra4 , Daniela Rossi4 and Simona Collina4 \* 1 Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRCCS), Meldola, Italy, 2 Aphad S.r.l., Milan, Italy, 3 Experimental Neurology Unit, School of Medicine and Surgery, Milan Center for Neuroscience, University of Milano-Bicocca, Monza, Italy, 4 Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology

, Valentina Carozzi3

, Chiara Arienti1

, Alessio Malacrida3

,

, Guido Cavaletti3

, Mariarosaria Miloso3

,

,

, Marta Rui4

Introduction: Pancreatic cancer (PC) is one of the most lethal tumor worldwide, with no prognosis improvement over the past 20-years. The silent progressive nature of this neoplasia hampers the early diagnosis, and the surgical resection of the tumor, thus chemotherapy remains the only available therapeutic option. Sigma receptors (SRs) are a class of receptors proposed as new cancer therapeutic targets due to their over-expression in tumor cells and their involvement in cancer biology. The main localization of these receptors strongly suggests their potential role in ER unfolded protein response (ER-UPR), a condition frequently occurring in several pathological settings, including cancer. Our group has recently identified RC-106, a novel pan-SR modulator with good in vitro antiproliferative activities toward a panel of different cancer cell lines. In the present study, we investigated the in vitro properties and pharmacological profile of RC-106 in PC cell lines with the aim to identify a potential lead candidate for the treatment of this tumor.

Methods: Pancreatic cancer cell lines Panc-1, Capan-1, and Capan-2 have been used in all experiments. S1R and TMEM97/S2R expression in PC cell lines was quantified by Real-Time qRT-PCR and Western Blot experiments. MTS assay was used to assess the antiproliferative effect of RC-106. The apoptotic properties of RC-106 was evaluated by TUNEL and caspase activation assays. GRP78/BiP, ATF4, and CHOP was quantified to evaluate ER-UPR. Proteasome activity was investigated by a specific fluorescent-based assay. Scratch wound healing assay was used to asses RC-106 effect on cell migration. In addition, we delineated the in vivo pharmacokinetic profile and pancreas distribution of RC-106 in male CD-1 mice.

Results: Panc-1, Capan-1, and Capan-2 express both SRs. RC-106 exerts an antiproliferative and pro-apoptotic effect in all examined cell lines. Cells exposure to

**81**

RC-106 induces the increase of the expression of ER-UPR related proteins, and the inhibition of proteasome activity. Moreover, RC-106 is able to decrease PC cell lines motility. The in vivo results show that RC-106 is more concentrated in pancreas than plasma.

Conclusion: Overall, our data evidenced that the pan-SR modulator RC-106 is an optimal candidate for in vivo studies in animal models of PC.

Keywords: Pancreatic cancer, pan-sigma receptor modulators, endoplasmic reticulum stress, unfolded protein response, proteasome inhibition

#### INTRODUCTION

Pancreatic cancer remains one of the most lethal tumor types for both men and women and, it represents the 11th most common cancer worldwide (Ilic and Ilic, 2016). WCRF reported that in 2018 there were 460,000 new cases, which mainly affected developed countries (Weledji et al., 2016). For this type of tumor, beneficial pharmaceutical approaches result challenging to develop, since the etiology as well as the triggering factors associated with PC remain undefined (Kim and Ahuja, 2015). Relying on the negative prognosis – the average 5-year survival rate is 6% or less (Siegel et al., 2014) – and on the lack of a concrete cure, PC urgently requires effective therapeutic strategies.

Over the past few decades, SRs, have been widely associated with aging- and mitochondria-associated disorders, such as Parkinson's and Alzheimer's disease and cancer (Martin et al., 1976; Su, 1982; Vaupel, 1983; Quirion et al., 1987; Maurice and Lockhart, 1997; Skuza, 2003; Peviani et al., 2014; Collina et al., 2017a,b). Moreover, although no endogenous SRs ligands have ever been found, and the specific role played by this orphan receptor family in cell biology has yet to be clarified, SRs are considered as potential therapeutic targets for neurodegenerative diseases and cancer. Accumulating evidence strongly suggests a pivotal role of these proteins in ER-UPR pathways, whose activation is frequently detected in many solid tumors (Shuda et al., 2003; Corazzari et al., 2017). In particular, the triggering of the UPR machinery in cancer is the result of neoplastic cells spreading in unfavorable environments characterized by hypoxia, low pH, high levels of ROS and inadequate glucose and amino acid supply, conditions that could compromise the correct ER protein folding. Under such stress conditions, SRs are activated to allow the cells survival, as broadly demonstrated by the direct involvement of S1R in UPR pathways (Hayashi, 2015; Penke et al., 2017). The decrease of Ca++ ion level in ER, the accumulation of misfolded or aggregated protein within the ER, the rise of ROS level due to stress conditions promote the exit of S1R from a dormant state and its activation as chaperon protein. Accordingly, the correct Ca++ signaling from the ER to the mitochondria, the transmission of the ER stress signal to the nucleus and the consequent increase of antistress and antioxidant proteins production are guaranteed (Hayashi and Su, 2007; Mori et al., 2013; Wang et al., 2015).

Only recently S2R has been cloned and its identity as TMEM97 has been postulated (Alon et al., 2017). TMEM97 is a transmembrane protein involved in cholesterol homeostasis, and its dysregulation has been associated to ER stress and to activation of the UPR, thus causing cellular lipid accumulation (Colgan et al., 2011). Notably, UPR is classically related to the maintenance of cellular homeostasis in secretory cells (i.e., pancreatic and immune cells), where the high demand for protein synthesis and secretion leads to proteostasis and cellular stress (Hetz, 2012; Moore and Hollien, 2012). Indeed, pancreatic cells have high hormone and enzyme secretory functions and possess highly developed ER. The role of ER stress in PC pathobiology and inflammation has been increasingly recognized as an important factor in tumorigenesis and chemoresistance (Yadav et al., 2014). Nonetheless, PC is extremely rich in stroma, is hypoxic and deficient in metabolites (Vasseur et al., 2010). A similar behavior can be found when cells grow under chronic metabolic stress conditions, favoring the activation of adaptive mechanisms, such as UPR and autophagy (Kondo et al., 2005; Moenner et al., 2007) the latter frequently associated to SR overexpression (Zeng et al., 2012; Mir et al., 2013). Altogether, these findings pointed out SRs as potential targets useful for inhibiting UPR machinery in PC.

Our research team is active in the SR modulation and recently we identified compound **RC-106** endowed with pan-SR modulatory activity (S1R antagonist and S2R agonist profile) and in vitro antiproliferative properties toward a panel of cancer cell lines (i.e., Capan-2, MDA-MB 231, PC3, and U87) (Rui et al., 2016; Rossi et al., 2017). These encouraging results led us to further investigate its potential in PC treatment. After preparing **RC-106** in a suitable amount to support the whole study, we deepened its antitumor properties and evaluated its capability to interfere with ER stress conditions. Lastly preliminary PK and biodistribution studies have been performed, to verify if **RC-106** is able to reach the target tissue.

**Abbreviations:** ATCC, American Type Culture Collection; ATF4, activating transcription factor 4; CHOP, C/EBP homologous protein; CTR, control; DMSO, dimethyl sulfoxide; ER, endoplasmic reticulum; ESI, electrospray ionization; FC, flash chromatography; FITC, fluorescein isothiocyanate; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GRP78, 78-kDa glucose regulated protein; HPRT-1, hypoxanthine phosphoribosyltransferase 1; IS, internal standard; LC, liquid chromatography; MAM, mitochondria associated ER membrane; M-PER, Mammalian Protein Extraction Reagent; MRM, multiple reaction monitoring; MS, mass spectrometry; NMR, nuclear magnetic resonance; OD, optical density; PBS, phosphate buffered saline; PC, Pancreatic cancer; PK, pharmacokinetic; QC, quality control; RCCS, rotary cell culture system; ROS, reactive oxygen species; S1R, sigma 1 receptor; S2R, sigma 2 receptor; SD, standard deviation; SRs, sigma receptors; TdT, terminal deoxynucleotidyl transferase; TLC, thin layer chromatography; TMEM97, transmembrane protein 97; UFLC, ultra-fast liquid chromatography; UPLC, ultra performance liquid chromatography; UPR, unfolded protein response; UV, ultraviolet; WCRF, World Cancer Research Fund.

#### MATERIALS AND METHODS

#### RC-106 Synthesis

fphar-10-00490 May 10, 2019 Time: 14:46 # 3

Reagents and solvents for synthesis were obtained from Sigma-Aldrich (Italy). Solvents were purified according to the guidelines in Purification of Laboratory Chemicals. Melting points were measured on SMP3 Stuart Scientific apparatus and are uncorrected. For FT-IR analysis a Spectrum One PerkinElmer spectrophotometer equipped with a MIRacleTM ATR device was used. The IR spectra were scanned over wavenumber range of 4000–650 cm−1 with a resolution of 4 cm−1 . Analytical thin-layer chromatography (TLC) was carried out on silica gel precoated glass backed plates (Fluka Kieselgel 60 F254, Merck); visualized by UV radiation, acidic ammonium molybdate (IV), or potassium permanganate. FC was performed with Silica Gel 60 (particle size 230e400 mesh, purchased from Merck). Proton NMR spectra were recorded on Bruker Avance 400 spectrometer operating at 400 MHz. 13C NMR spectra were recorded on 500 MHz spectrometer, operating at 125 MHz, with complete proton decoupling. UPLC-UV-ESI/MS analyses were carried out on a Acuity UPLC Waters LCQ FLEET system using an ESI source operating in positive ion mode, controlled by ACQUIDITY PDA and 4 MICRO (Waters). Analyses were run on a ACQUITY BEH C18 (50 mm × 2.1 mm, 1.7 mm) column, at room temperature, with gradient elution (solvent A: water containing 0.1% of formic acid; solvent B: methanol containing 0.1% of formic acid; gradient: 10% B in A to 100% B in 3 min, followed by isocratic elution 100% B for 1.5 min, return to the initial conditions in 0.2 min) at a flow rate of 0.5 mL min−1 . Detailed synthetic procedure and characterization of intermediates and **RC-106** are reported in the **Supplementary Material**.

#### Cell Cultures 2D Cell Culture

Pancreatic adenocarcinoma Panc-1, Capan-1, and Capan-2, cell lines were purchased by the ATCC. All cell lines were grown in culture medium composed of DMEM/Ham's F12 (1:1; Euroclone) supplemented with fetal calf serum (10%; Euroclone), glutamine (2 mM; Euroclone), and insulin (10 µg/mL; Sigma-Aldrich, St. Louis, MO, United States). All experiments were performed on cells in the exponential growth phase and checked periodically for mycoplasma contamination by MycoAlertTM Mycoplasma Detection Kit (Lonza, Basel, Switzerland).

#### 3D-Cell Culture

Spheroids were obtained as previously described (Zanoni et al., 2016). Briefly, a rotatory cell culture system RCCS (Synthecon Inc., Houston, TX, United States) was used. The rotary systems were placed inside a humidified 37◦C, 5% CO2 incubator and all procedures were performed in sterile conditions. Single cell suspensions of about 1 × 106 cells/ml of Panc-1 were placed in the 50 mL rotating chamber at an initial speed of 12 rpm. Speed was increased as cells formed aggregates to avoid sedimentation. The culture medium was changed every 4 days and tumor spheroids with an equivalent diameter ranging from about 500–1300 µm were obtained in around 15 days. After formation, spheroids were transferred into a 96-well low-attachment culture plates (Corning Inc., Corning, NY, United States; one spheroid/well), containing 100 µL of fresh culture medium per well.

#### Cell Viability Assays MTS Assay

Cytotoxicity was assayed using CellTiter 96 R AQueous One Solution Cell Proliferation Assay (Promega, Milan, Italy). Cells were seeded onto a 96-well plate at a density of 3 × 103 cells per well. Cell lines were exposed to increasing concentrations of the drug, ranging from 0.1 to 100 µM. The effect of the drug was evaluated after 24, 48, and 72 h of continued exposure. Two independent experiments were performed in octuplicate. The OD of treated and untreated cells was determined at a wavelength of 490 nm using a fluorescence plate reader.

Dose response curves were created by Excel software. IC50 values were determined graphically from the plot.

#### CellTiter-Glo R 3D

Cell viability of Panc-1 spheroids was measured using a 3D cell viability assay (Promega, Milan, Italy). Briefly, homogeneous spheroids were removed from the 96-well low-attachment culture plate and placed separately in single wells of a 96-well opaque culture plate (BD Falcon). CellTiter-Glo R 3D reagent was added to each well and the luminescence signal was read after 30 min with the GloMax R bioluminescent reader (Promega).

#### Analysis of Morphological Parameters of 3D Tumor Spheroids

The analysis of morphological parameters were performed as previously described (Piccinini et al., 2017). Briefly, an inverted Olympus IX51 microscope (Olympus Corporation, Tokyo, Japan), equipped with a Nikon Digital Sight DS-Vi1 camera (CCD vision sensor, square pixels of 4.4 µm side length, 1600 × 1200 pixel resolution, 8-bit gray level; Nikon Instruments, Spa. Florence, Italy) was used to take images and for morphological analyses. The open-source ReViSM software tools was used to achieve morphological 3D, such as volume and sphericity, and to select morphologically homogeneous spheroids. For the experiments, Panc-1 spheroids characterized by spherical shape and by a diameter size ranging from 500 to 600 µM were selected.

#### Real Time RT-PCR

Total cellular RNA was extracted using TRIzol reagent (Life technologies) in accordance with manufacturer's instruction and quantified using the Nanodrop MD-1000 spectrophotometer system. Reverse transcription reactions were performed in 20 µL of nuclease free water containing 400 ng of total RNA using iScript cDNA Synthesis kit (Bio-Rad Laboratories, Hercules, CA). Real-Time PCR was run using 7500 Fast Real-Time PCR system (Applied Biosystems) and TaqMan assays to detect the expression of SIGMAR1, TMEM97, GRP78/BiP, ATF4, and CHOP genes.

Reactions were carried out in triplicate at a final volume of 20 µL containing 40 ng of cDNA template, TaqMan universal PCR Master Mix (2X), and selected TaqMan assays (20X). Samples were maintained at 50◦C for 2 min, then at 95◦C for 10 min followed by 40 amplification cycles at 95◦C for 15 s, and at 60◦C for 30 s.

The amount of mRNA was normalized to the endogenous genes GAPDH and HPRT-1.

#### TUNEL Assay

fphar-10-00490 May 10, 2019 Time: 14:46 # 4

TUNEL assay was performed as previously described (Tesei et al., 2007). Briefly, cells were fixed in 1% formaldehyde in PBS on ice for 15 min, suspended in 70% ice cold ethanol and stored overnight at 20◦C. Cells were then washed twice in PBS and re-suspended in PBS containing 0.1% Triton X-100 for 5 min at 48◦C. Thereafter, samples were incubated in 50 µL of solution containing TdT and FITC conjugated dUTP deoxynucleotides 1:1 (Roche Diagnostic GmbH, Mannheim, Germany) in a humidified atmosphere for 90 min at 37◦C in the dark, washed in PBS, counterstained with propidium iodide (2.5 µg/mL, MP Biomedicals, Verona, Italy) and RNAse (10 kU/mL, Sigma–Aldrich) for 30 min at 48◦C in the dark and analyzed by flow cytometry. Flow cytometric analysis was performed using a FACS Canto flow cytometer (Becton Dickinson, San Diego, CA, United States). Data acquisition and analysis were performed using FACSDiva software (Becton Dickinson). Samples were run in triplicate and 10,000 events were collected for each replicate.

#### Western Blot

Western Blot were performed as previously described (Arienti et al., 2016). Briefly, Cell proteins were extracted with M-PER (Thermo Fisher Scientific) supplemented with Halt Protease Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific).

Mini-PROTEANTGXTM precast gels (4–20% and any kD; BIO-RAD) were run using Mini-PROTEAN Tetra electrophoresis cells and then electroblotted by Trans-Blot TurboTM Mini PVDF Transfer Packs (BIORAD). The unoccupied membrane sites were blocked with T-TBS 1X (Tween 0.1%) and 5% non-fat dry milk to prevent non-specific binding of antibodies and probed with specific primary antibodies overnight at 4◦C. This was followed by incubation with the respective secondary antibodies. The antibody-antigen complexes were detected with Immun-StarTM WesternCTM kit (BIO-RAD).

The following primary antibodies were used: anti-sigma receptor (S18): sc-22948 (Santa Cruz Biotechnology inc.), anti-TMEM97, anti-caspase-3, and anti-caspase-9. Anti-vinculin (sc-5573) from Santa Cruz Biotechnology and anti-actin from Sigma Aldrich Inc., were used as housekeeping. Quantity One Software was used for analysis.

#### Proteasome Activity Assay

Cells were seeded in 6-well plates at density of 250 × 103 cells/well. Cells were treated with increasing concentration of **RC-106** and after 24 h total protein extracts were obtained: cells were washed 2 times with PBS and lysed with 100 µL of lysis buffer (Hepes 5 mM pH 7.5, NaCl 150 mM, Glycerol 10%, Triton X100 1%, MgCl2 1.5 mM, EGTA 5 mM). Protein concentration of samples was quantified using Bradford method. Proteasome activity was quantified as described below. Proteasome solution was composed by 40 µg of proteins, 10 µL of 10X proteasome buffer (Hepes pH 7.5 250 mM, EDTA pH 8.0 5 mM, NP-40 0.5%, SDS 0.01%) and 10 µL of 10 mM proteasome substrate (N-Succinyl-Leu-Leu-Val-Tyr-7-Amido-4- Methylcoumarin, 7.6 mg/mL; Sigma-Aldrich, United States). 100 µL of proteasome solution was loaded in wells of a black 96-well plate. The plate was then incubated at 37◦C for 2 h and the fluorescence was measured in a microplate reader (excitation 380 nm, emission 460 nm; BMG-Labtech, Germany).

#### Migration Scratch Wound Healing Assay

Cells were seeded in a 6-well plate and were incubated at 37◦C until confluence of 90–100% was reached. Culture medium was then replaced by serum free medium. After 24 h, a scratch was made on cell monolayer using a plastic tip and wells were washed 2 times with PBS to remove detached cells and debris. Culture medium, with or without **RC-106**, was added to each well. Micrographs of the scratches were taken at 0 h, immediately after the scratch, and at 24, 48, and 72 h. Cell migration area was quantified using IMAGEJ software.

### Pharmacokinetic and Pancreas Distribution Studies

#### Animals and Biological Matrix Preparation

The experiments were performed in agreement with the Italian Law D. L.vo 4 marzo 2014, n. 26. The treatments involved male CD-1 mice and a unique number on the tail identified each animal. Mice were housed, in groups of four, in cages suitable for the species. After 5 days of adaptation to the local housing conditions, animals were housed in a single, exclusive, air-conditioned room to provide a minimum of 15 air changes/hour. The environmental controls were set to maintain the temperature at around 22◦C and the relative humidity within the range 50 to 60%, along with an approximate 12:12 h light/dark cycle automatically controlled. Food (Mucedola Standard GLP diet) and water were available ad libitum throughout the entire duration of the study. All animals were weighted on the day of the treatment.

Mice (n = 4/time point) received an intraperitoneal administration (i.p., 10 mL/kg) of **RC-106** at 10 mg/kg. CD-1 male mice were exsanguinated under anesthesia (isoflurane) from the aorta at the following time points: 5, 10, 30, 120, 240, and 480 min. Blood samples were collected in tubes containing heparin, gently mixed and immediately placed on ice. Afterward, they have been centrifuged (3500 × g, at 4◦C for 15 min), the obtained plasma has been collected and transferred to individually labeled tubes and frozen at −20◦C until the analysis. Plasma samples were used for quantification of **RC-106**. Pancreas was taken by surgical resection after 20 min from the last treatment, washed in saline, dried on paper, weighted and frozen at −20◦C. The organ was homogenized using a Velp OV5 homogenizer with 20 mM ammonium formiate buffer in a ratio of 1 g of tissue per 10 mL of buffer.

#### Sample Preparation

20 mg/mL stock solution (s.s.) of **RC-106** was prepared by dissolving the compound in DMSO. 1 mL of 5% Tween80 in H2O

was slowly added to 500 µL of s.s. under stirring. Then 8.5 mL of water was gently spiked to obtain the 1 mg/mL formulation of **RC-106**.

Standard curves of **RC-106** were prepared for plasma and pancreas homogenate, and analyzed together with each QC and unknown sample set. For the PK and pancreas distribution sample analysis, plasma and pancreas homogenate samples (50 µL) were spiked in 200 µL of IS in MeOH (0.1 µg/mL of **RC-33**), followed by 2 min vortex mixing. Samples were centrifuged and transferred in UFLC vials. 5 µL aliquots of the collected samples were injected into the LC-MS/MS system. Standard calibration graphs were constructed by linear least-squares regression analysis on the analyte/IS area ratio plotted against sample concentration. Calibration ranges were from 5 to 1000 ng/mL for plasma, and from 5 to 500 ng/mL for pancreas homogenate. Accuracy values were determined in triplicates at three different concentrations (high, medium, and low) in the range of linearity of the calibration curves.

#### LC-MS/MS Conditions

Analyses were acquired on a Shimadzu AC20 UFLC system interfaced with an API 3200 Triple Quadrupole detector (AB Sciex). Data acquisition and control were performed using the AnalystTM 6.1 (Applied Biosystems) Software. A Phenomenex Gemini-NX C18 (50 mm × 2 mm, 5 µm) column was selected to carry out the analytical evaluations. A gradient method was set up (**Supplementary Table S1**) and it provided the employment of water and methanol, both containing 0.1% of formic acid, at a flow rate of 0.3 mL/min. The LC eluate was directly introduced into the MS interface using the ESI in the positive ion mode. The MRM transitions m/z 181.2 were tracked (**Supplementary Table S2**).

#### RESULTS

#### Chemistry

We studied an easy to handle synthetic route suitable to dispose of **RC-106** in a g-scale amount. The synthetic route is outlined in **Scheme 1**. Briefly, a Heck reaction between 4-bromobiphenyl and (E)-ethyl crotonate, using Palladium acetate microencapsulated in polyurea matrix (Pd EnCat R ) as catalyst allowed to obtain the α,β-unsaturated ester (E)-**1** which was easily reduced to give allyl alcohol (E)-**2**, and then converted into **RC-106** according to Frøyen and Juvvik (1995). The use of Pd EnCat R simplified the work-up procedure and more important avoided the heavy metal contamination of the product, which could compromise the in vitro and in vivo studies.

#### Cell Biology

#### SRs Expression in Pancreatic Adenocarcinoma

We explored the expression of S1R and TMEM97/S2R genes in a panel of cell lines representative of pancreatic adenocarcinoma. The expression of S1R and TMEM97/S2R was evaluated in cells derived both from primary tumor and metastatic site (i.e., liver), characterized by different doubling time and different mutational status of p53, KRAS, P16/CDKN2A, and SMAD 4 (**Table 1**), the major driver-genes involved in the pathogenesis of PC (Sipos et al., 2003).

The expression level of SRs was determined by Real-Time qRT-PCR. We used cervix adenocarcinoma HeLa as reference sample, because of its high expression of both S1R and TMEM97/S2R (Bartz et al., 2009; Ebrahimi-Fakhari et al., 2015; Miki et al., 2015). All analyzed cell lines express SRs and no correlation between the tumor site and the expression level of both targets, as well as respect to the mutational status of p53 and KRAS was evidenced. In particular, S1R was expressed at similar levels in the PC cell lines. Conversely, differences about the expression of TMEM97/S2R have been evidenced in the three cell lines investigated, with the highest expression in Capan-1 (4-fold respect to the control line) and the lowest in Capan-2 cells (**Figure 1**). Basing on these results, we took into account the three cell lines to perform the biological evaluation.

#### In vitro Cytotoxic Activity

We evaluated the in vitro cytotoxic activity of **RC-106** by MTS assay. Cells were treated for 24, 48, and 72 h with increasing concentrations, ranging from 0.1 to 100 µM. **RC-106** was effective in all cell lines tested independently from the exposure time (IC50 values ranging from 33 to 57 µM, **Figure 2A**).

All cell lines were purchased from ATCC.

Encouraged by these results, we investigated the capability of **RC-106** to penetrate three dimensional structures mimicking tumor micronodules of about 500–600 µm in diameter. Panc-1 cells grown as 3D spheroids were treated with increasing concentrations of **RC-106** (12.5–50 µM for 48 h, **Figure 2B**). The results obtained with Panc-1 spheroids with a diameter up to 600 µm (IC50 = 39.55 µM, **Figures 2B,C**) are in line with those observed in 2D culture.

#### Pro-apoptotic Effect

fphar-10-00490 May 10, 2019 Time: 14:46 # 7

The apoptotic properties of **RC-106** was evaluated by TUNEL assay. The exposure time (48 h) and the drug concentration (50 µM) have been chosen according to the data resulting from cell viability assay. TUNEL assay showed a significant induction of apoptosis in treated samples compared to the untreated controls, with a percentage of apoptotic cells ranging from 53.25% ± 4.7 (Panc-1) to 78.55% ± 5.6 (Capan-1) (**Figure 3A**). Hence, we investigated the activation of caspase cascade by Western Blot analysis, treating cells with **RC-106** at different exposure times. We found that both caspases 3 and 9 were cleaved, in all cell lines after the treatment, indicating the activation of the intrinsic apoptotic pathway. To sum up, **RC-106** was able to activate both caspases in all the considered cell lines, but after different exposure times and concentrations. In detail, in Panc-1 and Capan-2 cell lines this event occurred after an exposure of 48 h to **RC-106** at 25 µM concentration, whereas in Capan-1 cell line after 12 h at 50 µM concentration (**Figure 3B**).

#### ER Stress and Unfolded Protein Response

The expression of the ER stress master proteins GRP78/BiP, ATF4, and CHOP, commonly used for the detection of UPR activation (Samali et al., 2010), was analyzed by Real-Time qRT-PCR. In general, the mRNA expression of all the investigated ER markers highly increased after the exposure to 50 µM of **RC-106**. In the two cell lines derived from primitive pancreatic tumor, Panc-1 and Capan-2, the trend is similar. In particular, GRP78/BiP and ATF4 mRNA levels increased after 24 h of treatment, while CHOP mRNA levels considerably increased after 12 h, then slightly declined after 24 h (**Figure 4A**). The highest increase in expression of CHOP was individuated in Capan-2 (70 fold higher than untreated cells). A different behavior was observed for the metastatic cell line Capan-1, where a faster switch-off of all ER markers was evidenced already starting from 12 h after the beginning of treatment.

All the cell lines were treated with increasing concentrations of **RC-106** (20–100 µM) to evaluate in vitro **RC-106** proteasome

RC-106 25 µM (Panc-1 and Capan-2) and after 12 h treatment with RC-106 50 µM (Capan-1). Images are representative of two independent experiments.

effect. After 24 h of treatment, **RC-106** was able to reduce proteasome activity in a dose dependent manner in all the PC investigated (**Figure 4B**). Capan-2 resulted the most sensible cell line as showed by the lowest concentration used to inhibit proteasome activity (20 µM). Instead the greatest proteasome inhibition is observed in Capan-1 cells but at highest concentration used (100 µM).

#### Cell Migration

Scratch wound healing assay was performed to assess the effect of **RC-106** on cell migration. After the scratch, cells were treated with increasing concentration of **RC-106** (20–60 µM) and cell migration was evaluated after 24, 48, and 72 h. Capan-1 untreated cells migrated normally to refill the scratch present on cell monolayer. Cell migration was significantly reduced after 48 h of treatment with **RC-106** (c = 20 µM). Conversely, **RC-106** at concentrations of 40 and 60 µM reduced cell migration already after 24 h of treatment, whereas at major times these concentrations resulted too toxic, promoting cellular death. Capan-2 untreated cells migrated normally and continued to fill the empty space of the scratch for all considered times. **RC-106** 20 and 40 µM significatively reduceded Capan-2 cells migration ability after 48 and 72 h of treatment. **RC-106** 60 µM is too toxic and, as for Capan-1 cells, it was not possibile to quantify cell migration inhibition at 48 and 72 h. Panc-1 untreated cells migrated only for the first 24 h, then they slow down and stop migration. **RC-106** reduced Panc-1 cell migration in a dose dependent manner, but only in cells treated with 60 µM, migration was significatively reduced for all considered time points (**Figure 5**).

#### In vivo Pharmacokinetic and Pancreas Distribution Studies

fphar-10-00490 May 10, 2019 Time: 14:46 # 9

We investigated the in vivo PK profile and pancreas distribution of **RC-106** in male CD-1 mice. Basing on our experience, we developed a rapid and sensitive UFLC-MS/MS method for detecting and quantifying **RC-106** in biological matrices (Rossi et al., 2013; Marra et al., 2016a,b). Briefly, chromatographic elutions were achieved on a reverse phase column and eluting under a gradient conditions (**Supplementary Table S1**). LC eluates were directly introduced into the MS interface using the ESI source and detected in positive ion mode (**Supplementary Table S2**). According to the structure of **RC-106**, parent ion m/z 356.5 and product ion m/z 181.2 – MRM transitions – were monitored during the analyses. Quantification of **RC-106** in plasma or pancreas homogenate were performed by generating 7 concentrations-calibration curves (5–1000 ng/mL for plasma, and 5–500 ng/mL for pancreas homogenate), employing **RC-33** as IS, 0.1 µg/mL in MeOH. Accordingly, concentrations of **RC-106** at each time point were extrapolated from the corresponding calibration curve. The developed method resulted suitable to separate **RC-106** from endogenous interferences. Afterward, CD-1 male mice received intraperitoneal administration at a concentration of 10 mg/kg. Plasma PK parameters are listed in **Supplementary Table S3**. **RC-106** showed a maximal concentration (Cmax) in plasma of 973.3 ng/mL (Tmax of 5 min) with an area under the curve (AUC0−t) of 67986.7 ng/mL∗min (**Figure 6** and **Supplementary Table S4**). Interestingly, **RC-106** reached high concentrations also in pancreas with AUC0−t of 1729315.7 ng/mL∗min, thus showing AUC0−t pancreas/AUC0−t plasma of about 25 times (**Figure 6** and **Supplementary Table S4**).

#### DISCUSSION

Chemotherapy is the only therapeutic strategy effective in counteracting PC. Nevertheless, the pharmaceutical panorama counts very few effective molecules, since the etiology of this tumor is still elusive and specific therapeutic targets have not been identified yet. Recently, our research team highlighted that the PC cell lines express both S1R and S2R/TMEM97. Therefore, molecules acting via SRs pathway may play a positive role in counteracting PC. In the present work, we deepened the in vitro properties of the pan-SR modulator **RC-106** and evaluated its PK profile to define its potential as lead compound.

Pancreatic cancer cell lines Panc-1, Capan-1, and Capan-2, harboring a mutational status representative of clinical tumors and expressing both SRs, have been selected to delineate the in vitro **RC-106** profile, and used in all the experiments. The citoxicity tests clearly showed that **RC-106** exerts a strong antiproliferative and pro-apoptotic action in all considered cell lines, with IC50 values in the micromolar range. To straighten these data, we exploit the 3D cell culture spheroids, an in vitro model mimicking in vivo features, thus providing better read-outs for drug screening (Carragher et al., 2018). The analysis of 3D morphological parameters of Panc-1 cells, the only able to grow as 3D structure, showed a complete disaggregation of spheroid organization and cytoarchitecture, thus confirming both the strong cytotoxic activity of **RC-106** and its good penetration capability.

The cytotoxic activity of **RC-106** seems to be mostly attributable to the induction of the intrinsic apoptotic pathways. Herein, we focused on the failure of the adaptive response to restore protein-folding homeostasis. In fact, when UPR is inadequate to restore ER proteostasis, the pathway alternates its signaling toward a terminal UPR, leading to cellular death. To study the role of SRs in ER stress, we measured the expression of the key factors GRP78/BiP, ATF4, and CHOP. In detail, GRP78/BiP is one of the best characterized ER chaperones (Lee, 2005), whereas ATF4 and CHOP are both markers for the shift of the UPR signaling into the alternate signaling program called the "terminal UPR" (Oyadomari and Mori, 2004; Maly and Papa, 2014; Hetz and Papa, 2018).

The cellular exposure to **RC-106** induces a relevant increase of the considered key regulators of ER stress, being GRP78/BiP, ATF4, and CHOP overexpressed. To sum up, results of our experiments demonstrated that the antitumor activity of **RC-106** is related to the triggering of the "terminal UPR," confirming the key role of SRs as ER Stress gatekeepers (Tesei et al., 2018). It is worth noting that some compounds able to activate the terminal "UPR" have already reached the clinic for the treatment of several neoplasia, including PC (Hetz et al., 2013; Wang et al., 2018). Among them, bortezomib an inhibitor of proteasome enzyme complex (Chen et al., 2011) deserve to be mentioned, even if its therapeutic use is hampered by its toxic side effects (Field-Smith et al., 2006; Chen et al., 2011; Kharel et al., 2018). Since previous works reported that the silencing or the presence of lossof-function mutations of S1R lead to an imbalance of protein degradation (Fukunaga et al., 2015; Dreser et al., 2017; Kim, 2017), we extent the evaluation to proteasome inhibition activity. **RC-106** resulted able to inhibit the proteasome activity in all the examined cell lines in a dose dependent manner. As a last step of cell biology investigation, we performed the scratch wound healing assay suitable for estimating the local spreading of cancer cells in the tissues/organs. The results showed that **RC-106** is able to decrease PC cell motility in a dose dependent manner, suggesting its therapeutic efficacy also in advanced disease.

Taken together the aforementioned results suggest **RC-106** as a valuable candidate for the treatment of PC. Considering that tissue distribution in target organ is at the core of drug discovery and development process, having a direct impact on pharmacology, we conclude our study performing PK and pancreas distribution evaluations. The results show that **RC-106** is 25 times more concentrated in pancreas than plasma, reaching a concentration similar or even higher (Cmax about 70 µM) than those required to be effective in all the in vitro experiments considered in this work.

#### CONCLUSION

Pancreatic cancer treatment is one of the most relevant challenges that the scientific community will have to face in the 21st century. Although novel approaches for PC have been recently proposed, chemotherapy still remains the only effective option to mitigate and counteract the devastating outcome. We herein

#### REFERENCES

propose **RC-106**, a pan-SR modulator with S1R antagonist and S2R agonist profile discovered by our research team, as a valuable compound for in vivo investigation. Obtained results clearly demonstrated that it is effective against PC, via apoptotic pathways, driven by both SR modulation and proteasome complex inhibition. We also deepen the mechanism of action, studying the role played by SR as ER gatekeepers. The so-obtained results demonstrated that **RC-106** is able to modulate UPR in response to ER stress, enhancing the expression of GRP78/BiP, ATF4, and CHOP. Furthermore, **RC-106** affected not only the viability of PC lines, but also their metastatic potential. Not last in importance, our lead compound it is able to reach the target tissue.

In conclusion, basing on pharmacological and PK profile we suggest the pan-SR modulator **RC-106**, as an optimal candidate for proof of concept in vivo studies in animal models of PC.

#### ETHICS STATEMENT

Autorizzazione ministeriale 433/2016-PR: the procedures were authorized by the national authority (Istituto Superiore di Sanità, authorization number 433/2016-PR) and adhered to all the applicable institutional and governmental guidelines for the treatment of laboratory animals (Italian D.L.vo n. 26/2014).

#### AUTHOR CONTRIBUTIONS

AT and SC: conceptualization. AT, MM, GD, DR, and SC: experimental design and methodology. MC, SP, CA, AlM, CM, AC, VC, MR, and AnM: investigation. MC, AlM, MR, and AnM: writing-original draft preparation. AT, MC, GD, MM, MR, and SC: writing-review and editing. AT, GC, and SC: supervision. AT and SC: project administration. All authors contributed to manuscript revision, and read and approved the submitted version.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2019.00490/full#supplementary-material

A patent overview (1996-2016). Expert Opin. Ther. Pat. 27, 565–578. doi: 10.1080/13543776.2017.1276569

towards the establishment of an in vitro research platform. Virchows Arch. 442, 444–452. doi: 10.1007/s00428-003-0784-4

Crit. Rev. Oncol. Hematol. 127, 66–79. doi: 10.1016/j.critrevonc.2018. 05.003

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Tesei, Cortesi, Pignatta, Arienti, Dondio, Bigogno, Malacrida, Miloso, Meregalli, Chiorazzi, Carozzi, Cavaletti, Rui, Marra, Rossi and Collina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Trends in Sigma-1 Receptor Research: A 25-Year Bibliometric Analysis

#### Luz Romero\* and Enrique Portillo-Salido\*

Drug Discovery and Preclinical Development, Esteve Pharmaceuticals, Parc Científic de Barcelona, Barcelona, Spain

Purpose: There are previous reviews focused on Sigma-1 receptor but no bibliometric studies examining this field as a whole. This article aims to present a global view of Sigma-1 receptor research and its intellectual structure.

Methods: We used bibliometric indicators of a basic nature as well as techniques for the visualization and analysis of networks of scientific information extracted from Scopus database.

#### Edited by:

Ebru Aydar, University College London, United Kingdom

#### Reviewed by:

Elena Martín-García, Universidad Pompeu Fabra, Spain Zaida Chinchilla-Rodríguez, Spanish National Research Council (CSIC), Spain

#### \*Correspondence:

Luz Romero<EMAIL_ADDRESS>Enrique Portillo-Salido<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

> Received: 27 February 2019 Accepted: 06 May 2019 Published: 24 May 2019

#### Citation:

Romero L and Portillo-Salido E (2019) Trends in Sigma-1 Receptor Research: A 25-Year Bibliometric Analysis. Front. Pharmacol. 10:564. doi: 10.3389/fphar.2019.00564 Results: In total, 1,102 articles from 1992 to 2017 were identified. The growth in the production of articles is not constant over time, with periods of stagnation of approximately 5 years. Only 247 authors have five or more publications. The authors appear grouped in relatively independent clusters, thus suggesting a low level of collaborations between those dedicated to the Sigma-1 receptor. The United States was the country with the highest production followed by Japan and Germany. Spain, Japan, and Italy showed the highest per million inhabitants ratio. The highest citation/article ratio was reached in France, United States, and Canada. The leading institutions were the University of Münster, the National Institutes of Health, ESTEVE, and INSERM. The top authors in number of publications were Wünsch-B, Schepmann-D, and Maurice-T. Hayashi-T, Su-TP and Bowen-WD showed the highest citations per article. The article by Hayashi-T and Su-TP in Cell (2007) describing the Sigma-1 receptor as a chaperone protein is the top cited reference. Cluster labeling from author cocitation analysis shows that research has been focused on specific diseases such as addiction, neuroprotection and neurodegenerative diseases, psychiatric disorders, and pain. High-frequency terms in author keywords suggest that the research efforts in some areas such as neuroimaging, cocaine addiction or psychiatric disorders have declined over time, while others such as neurodegenerative diseases or pain are currently most popular.

Perspective: A greater involvement of the scientific community, with an increase in the scientific production related to Sigma-1, is desirable. Additional boost needed to improve research performance is likely to come from combining data from different laboratories to overcome the limitations of individual approaches. The resulting maps are a useful and attractive tool for the Sigma-1 receptor research community, as they reveal the main lines of exploration at a glance.

Keywords: sigma-1 receptor, bibliometrics, scopus, VOSviewer, co-occurrence analysis, co-citation analysis, co-authorship analysis

### INTRODUCTION

The Sigma-1 receptor is considered a unique ligand-operated chaperone protein which regulates protein folding/degradation, ER/oxidative stress, and cell survival (Hayashi, 2015). Sigma-1 receptor ligands have long been expected to serve as drugs for the treatment of human diseases such as neurodegenerative disorders, depression, chronic pain, drug abuse, retinal disease, and cancer (Cobos et al., 2008; Katz et al., 2017; Kim and Maher, 2017; Maurice and Goguadze, 2017a,b; Merlos et al., 2017a,b; Sabino et al., 2017; Sanchez-Fernandez et al., 2017; Smith et al., 2018). Two subtypes of Sigma receptors have been identified, Sigma-1 and Sigma-2 (Hellewell et al., 1994).  The Sigma-2 receptor (Sigma2, Sig2R, σ2 receptor) was cloned very recently from calf liver (Alon et al., 2017) and identified as transmembrane protein 97 (TMEM97). Very recently, the first crystal structure of the full-length human Sig-1R was reported in a complex with two different ligands, PD144418 and 4-IBP (Schmidt et al., 2016). As shows in the **Figure 1**, representing a chronological view of these important milestones, the research on Sigma-1 receptor has evolved since the cloning of the receptor. The Sigma-1 receptor shares no homology with any mammalian protein (Hanner et al., 1996). It is widely distributed in peripheral organs and different areas of the central nervous system involved in memory, emotion, sensory and motor function (Wolfe et al., 1989; Brust et al., 2014). The generation of the Sigma-1 knockout mice in 2003 contributed to better understand the in vivo role of Sigma-1 receptors (Langa et al., 2003). The concept of the Sigma-1 receptor has evolved significantly over the past decades. Today, it seems clear that the Sigma-1 is not a traditional receptor. It is considered to be a non-G-protein coupled, non-ionotropic intracellular chaperone at the endoplasmic reticulum (ER) that modulates Ca2+-signaling (Hayashi and Su, 2007; Kim, 2017; Penke et al., 2018). Nevertheless, we are only just beginning to understand what the Sigma-1 protein does and how it works.

The most popular methods to study the function of a specific protein mainly include molecular, cellular and pharmacological approaches, and bioinformatic analysis. However, very few researchers utilize systematic bibliometric analytical approaches to study a specific protein or gene. Bibliometric analysis is a widely used quantitative method to examine the knowledge structure and development in research fields (Portillo-Salido, 2010; Guler et al., 2016; Munoz-Ecija et al., 2017). It is widely used in various areas to estimate the productivity of institutions, countries, and authors; and identify international collaborations and geographic distributions (Chinchilla-Rodríguez et al., 2015).  To our knowledge, no previous bibliometric analysis on Sigma-1 receptor has been published. When searching for the term "sigma-1 receptor" in the Scopus or Pubmed scientific databases, the first two references that appear are from Basile et al. (1992a,b). Previously, the more general term "sigma receptor" was used for many years but the use of this generic term progressively declined when, 4 years after Basile's articles, the Sigma-1 receptor was cloned. In the current study, we performed a bibliometric analysis to qualitatively and quantitatively specifically evaluate the Sigma-1 receptor studies until 2017. We take advantage of new visualization techniques based on bibliometric analysis of scientific publications to better approach the research focused on Sigma-1 receptor in the last 25 years. Our objectives were to describe the scientific outputs of Sigma-1 receptor research and identify trends and hotspots. The main questions that became our guidelines for analysis were:

#### METHODS

#### Study Design

A bibliometric analysis using documents published until December 2017 in journals indexed in Scopus (https://www. scopus.com/) was performed. While there are a variety of document types, only articles were included.

#### Source of Information

Scopus (Elsevier BV Company, USA) is the largest abstract and citation database of scientific peer-review literature, including more than 22,000 titles from international publishers. We decided to use this database because it includes all MEDLINE documents and other characteristics, such as country of all authors and citations per document—this information being relevant to this study (Falagas et al., 2008; Kulkarni et al., 2009; Agarwal et al., 2016).

#### Search Strategy

A literature search was conducted by the authors in Scopus for publications on a single day, May 15, 2018, and used the following search: [TITLE-ABS-KEY ("Sigma-1 receptor") OR TITLE-ABS-KEY ("SigmaR1") OR TITLE-ABS-KEY ("Sigma type 1") OR TITLE-ABS-KEY ("Sig1r") OR TITLE-ABS-KEY ("Sigma1 receptor") OR TITLE-ABS-KEY ("Sigma-1 agonist" ) OR TITLE-ABS-KEY ("Sigma-1 antagonist") OR TITLE-ABS-KEY ("Sigma-1 ligand") OR TITLE-ABS-KEY ("Sigma1 agonist") OR TITLE-ABS-KEY ("Sigma1 antagonist") OR TITLE-ABS-KEY ("Sigma1 ligand") OR TITLE-ABS-KEY ("Sigma1-binding")] AND DOCTYPE (ar) AND PUBYEAR < 2018 AND [LIMIT-TO (LANGUAGE, "English")]. The validity of the search strategy was tested by manually reviewing the retrieved articles.

#### Data Analysis

All data were collected by the authors and downloaded in csv format. The data were imported to Microsoft Excel 2013 and quantitatively and qualitatively analyzed. Some data had to be standardized because documents mistakenly attributed to the domain of author name and affiliation were detected. Therefore, standardization was carried out manually by the authors. Different outputs were extracted from Scopus, including annual research, countries, journals, authors, institutions, and citation frequency. The annual publications and average citations per year per publication, the relationship between the average number of times cited per paper and the number of years since its publication was calculated. The mean number of citations per publication (CPP), including article lifespan for all 1,102 articles and for those with more than 150 citations was also analyzed. For journal analysis we used Bradford's law as a bibliometric indicator for the dispersion of scientific information. This law first described by Samuel C. Bradford in 1934 is to show the distribution of the scientific literature in a particular discipline, and Bradford proposed a model of concentric zones of productivity (Bradford zones) with decreasing density of information that can be used to identify the "core" journals in a field (Brookes, 1969; Desai et al., 2018). One formulation is that if journals in a field are sorted by the number of articles into three zones, each with approximately one-third of all articles, then the number of journals in each zone will be proportional to 1:n:n2. We also summarized the number of journal articles and percentage of total, cumulative number of articles published by the journals and percentage of Sigma-1 receptor articles, SCImago Journal Rank (SJR), CiteScore, best quartile, and categories.

To analyse the impact factor of the journals we used CiteScore and SCImago Journal Rank (SJR) from Scopus. CiteScore is a new journal metric recently launched by Elsevier which is similar to the Journal Impact Factor (Journal Citations Reports, Clarivate Analytics). CiteScore is the number of citations received by a journal in 1 year to documents published in the 3 previous years, divided by the number of documents indexed in Scopus published in those same 3 years (see https://service.elsevier.com/ app/answers/detail/a\_id/14880/supporthub/scopus/ for details). SCImago Journal Rank indicator expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years. Citation weighting depends on subject field and prestige of the citing serial (see https://www.scimagojr.com/ for further details). The contributions of countries were evaluated based on paper and citation numbers, and the research output of each country was adjusted according to population size (http:// www.worldbank.org/). For author and cited reference analysis, the top 15 productive and cited authors on Sigma-1 receptor research, along with their h-index, period of activity, and number and citations per article was analyzed. The research areas (e.g., Neuroscience, Medicine, Chemistry, etc.) were defined as described in SCOPUS.

#### Visualization Maps

Several visualization tools for bibliometrics have been developed and are now frequently used, including Vosviewer, Citespace, Bicomb, and BibExcel (Chen, 2004). These tools have been developed to help researchers create knowledge maps, evaluate the collective state of the art about a subject, and identify hotspots in a research field. The two most common methods are co-occurrence and co-citation analysis. Co-occurrence analysis helps researchers identify the hot topics and trends in a discipline. If two words co-occur frequently in an article, they may have a closer relationship than other pairs of words. The citations in an article can provide important insight into what is currently known about a given topic. The most common method to acquire this information is by co-citation analysis: two papers that are both cited by a third article have a co-citation relationship. The strength of this relationship between articles can help researchers identify the intellectual base of the discipline, research frontiers, important authors, and other relevant bibliometric information (Chen, 2004; Vargas-Quesada et al., 2017).

We used VOSviewer version 1.6.9 for viewing and creating the desired bibliometric maps (http://www.vosviewer.com/; Leiden University, Netherlands; van Eck and Waltman, 2010). It is a software tool for building and depicting networks based on bibliometric data. It features a text mining instrument that can be used to depict co-occurrence networks of terms extracted from any part of scientific literature. The terms maps were used to explore trends and active growth areas. To explore the knowledge structure and main lines of Sigma-1 receptor research, we selected Author Keywords as the unit of analysis; their co-occurrence was, as we mentioned before, the unit of measurement (full counting). When two different keywords were used to define the same concept, normalization was applied (for instance the different variants used for the term "positron emission tomography" were normalized to "PET"). Country co-authorship, author co-authorship and author cocitation were also presented as network visualization maps. In VOSviewer maps, the size of the label and the circle of an item are determined by the weight of the item. The higher its weight, the larger its label, and circle. The color of an item is determined by the cluster to which the item belongs. Lines between items represent links. By default, at most 1,000 lines are displayed and represent the 1,000 strongest links between items. The distance between two items in the visualization approximately indicates the relatedness of the items in terms of co-authorship, co-occurrence, citation, bibliographic coupling, or co-citation links.

#### Research Ethics

The data were downloaded from Scopus; these being secondary data, no interaction with animal or human subjects was involved.

There were no ethical questions about the data. Approval by an ethics committee was not necessary.

#### RESULTS AND DISCUSSION

#### Analysis of Publication Outputs and Citations

Annual publications and average citations per year per publication on the Sigma-1 receptor are summarized in **Figure 2**. From 1992 to 2017, there were 1,102 publications on the Sigma-1 receptor, including 1,084 articles published in Journals and 18 Book Series. While the annual number of publications increased over time, growth rate fluctuations were observed. Thus, the distribution of publications can be divided into different time stages. Sigma-1 receptor research was initiated in 1992–1995, with increased research in 1996–2001; twice as many publications were found in 2001 (38 articles) vs. 1996 (18 articles). As compared to the past 5 years (1996–2001), the publication growth rate suddenly decreased in the period of 2001–2009 (1.2-fold). From 2009 to 2012 the growth rate partially recovered again (1.73-fold). Finally, there was no growth in the number of articles from 2012 to 2016. Interestingly, the last year analyzed (2017) reached a peak of 107 publications, which represent the largest increase in the number of articles with respect to previous year. The sum of all citation numbers is 29,646. Thus, the average citation value was 27 per paper and the h-index was 79. A total of 3,531 authors in 2,697 organizations from 68 countries were found. When analyzing the average number of citations per year per published item since 1992, the 10 articles published in 1995 and the 44 articles published in 2007 reached peaks of 5.09 and 4.86 citations per year, respectively. These citation peaks were due to three articles that were frequently cited: (Monnet et al., 1995) (16.35); (Vilner et al., 1995) (16.26); (Hayashi and Su, 2007) (66.55) (discussed below).

**Figure 3** shows the histogram of the citation data for all Sigma-1 receptor papers. Among all 1,102 Sigma-1 related articles in the pool indexed in the Scopus publication database, 89 papers (∼8% of the pool) had no citations at all, 86% received fewer than 50 citations, and 75% received fewer than 32 citations. The median research paper on the Sigma-1 receptor received 16 citations, and only 15 articles received more than 150 citations. The most highly cited paper was cited 732 times (discussed below).

The citation of an article usually follows a time course. The article lifespan demonstrates the influence of the article on scientific research. The impact of normal publications increases during the first years after publication, peaks after 3–5 years and then decreases over time (Costas et al., 2011). **Figure 4** shows the citation pattern of the Sigma-1 receptor publications, i.e., the relationship between the average number of times cited per paper and the number of years since its publication. **Figure 4A** shows the mean number of citations per publication (CPP), including article lifespan for all 1102 articles and for those with more than 150 citations (insert graph). CPP values for all 1102 articles significantly increased over the first 2 years, peaked in the 3rd year (CPP ∼4) and declined thereafter. Only 15 papers received more than 150 citations. These articles were published between 1995 and 2011. CPP values from these highly cited articles also significantly increased over the first 2 years but peaked only in

TABLE 1 | Bradford's Law of Scattering for journals that published articles on Sigma-1 receptor research from 1992 to 2017.

Each zone represents about 33% of the total articles (1,102); n, the number of journals in each zone; N, the number of all journals (306).

the 6th year (CPP ∼25). The mean number of citations for the 15 most cited articles was ∼261 times.

Citation frequency curves of individual articles can exhibit one of the following patterns: 1) initially much praised work, 2) basic recognized work, 3) scarcely reflected work, 4) wellreceived but later erroneous qualified work, and 5) genius work (Avramescu, 1979). **Figure 4B** illustrates the CPP values of each highly cited article over the years. Two types of citing patterns can be observed, including basic recognized work and one genius work. The article published by Hayashi and Su (2007), the genius work, stands out prominently. It is the most highly cited recent article and does not seem to have reached its peak yet, as evidenced by the steady CPP value increase over the years. The other 14 highly cited articles had lower CPP values and remained essentially constant.

#### Journal Analysis

More than 300 scholarly journals have published articles on Sigma-1 research. Bradford's law of scattering is a pattern first described by Samuel C. Bradford in 1934 that estimates the exponentially diminishing returns of extending a search for references in science journals, and that can be used to identify the "core" journals in a field (Brookes, 1969; Desai et al., 2018). One formulation is that if journals in a field are sorted by the number of articles into three zones, each with approximately onethird of all articles, then the number of journals in each zone will be proportional to 1:n:n2 . **Table 1** shows the Bradford zones of scattering for Sigma-1 receptor literature. Our sample from 1992 to 2017 includes 306 journals. Of these, 170 journals have published only 1 paper on the Sigma-1 receptor. **Table 1** lists the nucleus and the successive zones of journals. Three zones, each

TABLE 2 | The 15 most active journals that published articles on Sigma-1 receptor research from 1992 to 2017.

publishing approximately 33% (367 articles) of the total Sigma-1 receptor articles (1,102 articles), constitute the most specific subdivisions. We found that 3.6% (11 journals) of the journals that published articles on Sigma-1 research were distributed in zone 1, 14% (44 journals) were distributed in zone 2, and 82% (251 journals) were distributed in zone 3, which had a lower influence than zone 1 or 2 (**Table 1**).

To more closely examine the leading journals, **Table 2** lists the number of journal articles in descending order and percentage of total, cumulative number of articles published by the journal from 1992 to 2017 and percentage of Sigma-1 receptor articles, CiteScore, SCImago Journal Rank (SJR), best journal quartile, and categories. We used CiteScore and SJR from Scopus as an indicator of the publication's repercussion. As indicated in **Table 2**, the core Sigma-1 receptor literature concentrates on a small number of Pharmacology, Drug Discovery and Chemistry related journals. Others categories are Radiology Nuclear Medicine and Imaging, Neurosciences, Biochemistry, and Biology. Out of the top 15 journals analyzed, the European Journal of Pharmacology (CiteScore2017: 3.18, SJR2017: 1.06, 73 articles, 7%) ranks first in the number of Sigma-1 receptor publications, followed by the Journal of Medicinal Chemistry (CiteScore2017: 6.25, SJR2017: 2.75, 56 articles, 5%) and the Journal of Pharmacology and Experimental Therapeutics (CiteScore2017: 3.70, SJR: 1.59, 47 articles, 4%). These three journals published 16% of the total articles. Additionally, Nuclear Medicine and Biology (CiteScore2017: 2.12; SJR2017: 0.70) devoted 24 articles (0.8% of its publications) to Sigma-1 research, followed by Synapse (CiteScore2017: 2.18; SJR2017: 0.97, 23 articles, 0.7% of its publications). As compared to other journals, articles on Sigma-1 research were more likely to be accepted by these active journals. The 15 most active journals (80% in Quartile 1; 13% in Quartile 2; 7% in Quartile 3) published approximately the 40% of the Sigma-1 receptor articles.

All 15 most active journals have a CiteScore ranging from 1.67 (Advances in Experimental Medicine and Biology) to 6.25 (Journal of Medicinal Chemistry). The top journals with a CiteScore > 5 (2/15; 13% of the top journals) published 7% of the total number of Sigma-1-related articles. The top journals with CiteScore 3–5 (8/15; 53% of the top journals) published 21% of the total number of Sigma-1-related articles. The top journals with CiteScore < 3 (5/15; 33% of the top journals) published 10% of the total number of Sigma-1-related articles. In summary, when comparing the rate of Sigma-1 receptor articles medium-CiteScore journals (CiteScore 3–5) to that of all journals (rate of journals with CiteScore > 10, 0.7%; CiteScore 5–10, 3.1%;

TABLE 3 | Top countries, country institutions and institution authors with more publications on Sigma-1 receptor research from 1992 to 2017.

CiteScore 3–5, 8.8%; and CiteScore < 3, 87.4%) (https://www. scopus.com/sources), Sigma-1 receptor articles were relatively intensively published in medium CiteScore journals.

#### Country, Institution, and International Collaboration Analysis

The top countries, country institutions and institution authors with more publications on Sigma-1 receptor research from 1992 to 2017 are shown in **Table 3**. Sigma-1 receptor publications were produced by countries from different world geographies, including countries outside Europe and North America. The United States was the country with the highest production with 405 (37%) documents, followed by Japan and Germany with 228 (21%) and 103 (9%) documents, respectively. Spain (1.8), Japan (1.8) and Italy (1.6) showed the highest per million inhabitants ratio. The United States had the highest h-index of 63, followed by Japan (h-index 43) and France (h-index 40). The highest citation/article ratio was reached in France (56), United States (34) and Canada (33). The leading institutions were the University of Münster (67; 65% of Germany documents), the National Institutes of Health (65; 16% of The United States documents), ESTEVE (48; 58% of Spain documents) and INSERM (45; 54% of France documents). The main research area within these four institutions was related to chemistry, drug addiction, pain, and cognition/neuroprotection, respectively.

Nineteen companies published 119 articles (11% of the total articles) (**Table 4**). Out of 19 companies publishing about the Sigma-1 receptor, 2 companies contributed 63% of the papers. The most productive company was ESTEVE (now Esteve Pharmaceuticals) with 48 publications, followed by Santen Pharmaceutical, which contributed 27 articles until 2004, thus suggesting that they are not currently active in Sigma-1 receptor research. Santen Pharmaceutical is the originator company of Cutamesine (SA-4503), a Sigma-1 receptor denoted as agonist/activator that was under development for the treatment of amyotrophic lateral sclerosis, age-related macular degeneration, acute ischemic stroke, major depressive disorder, traumatic brain injury, multiple sclerosis, stroke, and retinitis pigmentosa. While the drug was tested in phase II in patients with major depressive disorders and in patients recovering from stroke, its development was terminated for the given conditions. Currently, M's science corporation (under license from Santen) is supposedly developing cutamesine for the potential treatment of amyotrophic lateral sclerosis and retinitis pigmentosa as more suitable target diseases (Source: https://pharma.globaldata. com/). Esteve Pharmaceuticals is currently active in the development of Sigma-1 receptor antagonists/inhibitors for the treatment of neuropathic pain (Source: https://www.esteve.com/ en/\hboxresearch-development).

We used VOSviewer to visualize the network map of country co-authorship (international collaboration) for Sigma-1 receptor publications. Distribution maps provide valuable information and help researchers identify potential collaborators. The largest set of connected countries consists of 26 countries in 7 clusters. **Figure 5** illustrates the collaborative network of countries publishing more than five documents (26 of the 68

countries). Clusters are formed by the frequency of co-occurring terms representing each country, the more often the terms tend to co-occur they get colored into clusters. The size of circles represents the number of publications of the country and the thickness of lines depicts the size of collaboration. For example, the link strength (collaboration) between the United States and Japan was 20 and it represents a thick line. On the other hand, the line between the United States and India had a link strength of 2. Countries with similar color form one cluster. The blue cluster shows collaborative links between the largest circles of the United States, Japan (green color) and Italy representing authors affiliated to these countries. The United States collaborated the most with other countries worldwide. Other international researchers who collaborated the most with the United States researchers were from South Korea, France, Italy, and China. Spain is associated with France (red color), and to a lesser extent other countries such as the United Kingdom and Canada. The

TABLE 5 | The 15 most active authors in Sigma-1 receptor research from 1992 to 2017.

purple cluster is leaded by China collaborating with Japan and the United States.

### Author and Cited Reference Analysis

The 1,102 articles on the Sigma-1 receptor were drafted by more than 3,000 authors. **Table 5** presents the top 15 productive and cited authors on Sigma-1 receptor research, along with their hindex, period of activity, and number and citations per article. **Table 6** shows the 15 most frequently cited references. Eleven out of the 15 most frequently cited articles were published in journals with a high impact factor (CiteScore > 7). Hayashi-T and Su-TP (Cell. 2007) is the top cited reference (732 citations, 66.5 citations per year). Each of the 15 most active authors contributed at least 22 articles to Sigma-1 research. Wünsch (73 articles), who mainly participated in chemistry-related papers, ranks first, followed by Schepmann-D (57 articles), who is a coauthor in the Wünsch's articles, and by Maurice-T (45 articles), who mainly focused on the role and applications of Sigma-1 receptor and ligands to cognitive/behavioral diseases. Most of the productive authors were also the most cited ones, with some particularities. Wünsch, who is the most productive, ranks thirteenth in terms of citations per article. Most cited authors were Hayashi-T and Su-TP, who had the most cited publication in the field of the Sigma-1 receptor (**Table 6** and **Figure 4B**). Their paper identified the Sigma-1 receptor as a novel "ligandoperated" chaperone and characterized the important role played by the Sigma-1 receptor in endoplasmic reticulum-mitochondrial interorganelle Ca2+ signaling and also in cell survival. These two authors have two more articles among the most cited ones (Hayashi and Su, 2001; Kourrich et al., 2013; **Table 6**) which demonstrated that Sigma-1 receptor modulation of different proteins contribute to the effects of cocaine, neurosteroids and other drugs by regulating intracellular Ca2+ signaling. The third most cited author is Bowen-WD, who ranks twelfth in number of articles, followed by Maurice-T, who also ranks third in number of articles. Maurice-T also scores first in h-index (h-index = 30). As shown in **Table 6**, Bowen-WD contributed an early article, which is among the most cited ones, describing the expression of Sigma-1 and Sigma-2 receptors in a wide variety of human and rodent tumor cell lines (Vilner et al., 1995). A paper authored by Maurice-T describing the involvement of the Sigma-1 receptor in the anti-amnesic and neuroprotective effects of donepezil in a mouse model of Alzheimer's disease in mice is also among the most cited (Meunier et al., 2006) (**Table 6**). The paper by Schmidt et al. (2016), published in Nature and reporting the crystal structure of the human σ1 receptor, ranks second and is followed by a paper in Science by Fontanilla et al. (2009) in terms of average citations per year. Schmidt-HR showed the overall architecture, oligomerization state and molecular basis for ligand recognition of the Sigma-1 receptor, and Fontanilla-D described N,N-dimethyltryptamine (DMT), which has shown hallucinogenic properties, as an endogenous Sigma-1 receptor

of 224 authors in 16 clusters. (B) Co-citation analysis (the relatedness of items is determined based on the number of times they are cited together). Of the 51,564 authors, 1291 had at least 20 citations; the largest set of authors with greatest total link strength selected consists of 1000 authors in 7 clusters.

#### TABLE 6 | The 15 most frequently cited references in Sigma-1 receptor research from 1992 to 2017.

regulator. Other highly cited articles on Sigma-1 focused on the role of Sigma-1 in a number of diseases with important medical needs such as Alzheimer's disease (Hedskog et al., 2013), amyotrophic lateral sclerosis (Al-Saif et al., 2011), cancer (Vilner et al., 1995), and Parkinson disease (Francardo et al., 2014). Finally, two articles on Sigma-1 receptor cloning and expression (Hanner et al., 1996; Kekuda et al., 1996) and one on Sigma-1 and Sigma-2 receptor characterization (Hellewell et al., 1994) also appear as highly cited in **Table 6**.

Citation networks have been applied to information science analysis. Here, VOSviewer was also used to analyse author citations in Sigma-1 research. VOSviewer is primarily intended to be used for bibliometric network analysis. The program can be used to create maps of publications, authors or journals based on a citation, co-citation, or bibliographic coupling network, or to create keyword maps based on a co-occurrence network. Two network visualization maps provided by VOSviewer are shown in **Figure 6**. In the co-authorship analysis (**Figure 6A**)

the relatedness of items is determined based on the number of co-authored documents. Each node represents an author with at least five publications, the node label is the last name of the author, and the node size indicates the number of published articles. The link connecting two nodes stands for the cooperative relationship between two authors, and the thickness of the link stands for the intensity of cooperation. Another important information regarding authors is the degree of cocitation. Author co-citation analysis measures the number of times a particular group of authors was cited together within the collection (van Eck and Waltman, 2017). In the co-citation analysis (**Figure 6B**) the relatedness of items is determined based on the number of times they are cited together. Each node represents an author with at least 20 citations, the node label is the last name of the author, and edges represent citation relations. When co-authorship analysis was performed (**Figure 6A**), 247 out of the 3,529 authors had at least 5 publications, and the largest set of connected authors consists of 224 authors in 16 clusters. An inspection of the publications from each cluster allows displaying the main area for each research group. There are several independent clusters working in the same area. For instance, there are six clusters publishing articles on "Chemistry," four clusters on "Neuroprotection," three clusters on "PET," two clusters on "Pain," and one cluster related to those publishing mainly in "Addiction."

The co-citation analysis by author included 51,564 cited authors, of which 1,291 were cited at least 20 times (**Figure 6B**); the largest set of authors with greatest total link strength selected consists of 1,000 authors in 7 clusters. In this case, each color represents a community of authors within the same subject of interest, and authors within a cluster represent a set of strongly connected authors in terms of co-citation relations. These clusters, as can be seen from **Figure 6B** relate to "Chemistry," "PET," "Neuroprotection," "Addiction," "Neurodegenerative diseases," "Psychiatric disorders," and "Pain." These clusters point toward the pivotal scholars whose works were cited in the references of our collection and who contributed to build on each field studied (**Figure 6B**).

#### Research Area Analysis

Research on the Sigma-1 receptor occurred in 20 special research areas. These 20 research areas, appearing in publications of Sigma-1 receptor research from 1992 to 2017, are shown in **Figure 7**. Here, the research areas (e.g., Neuroscience, Medicine, Chemistry, etc.) were defined as described in SCOPUS. "Pharmacology, Toxicology and Pharmaceutics" and "Biochemistry, Genetics, and Molecular Biology" accounted for the largest number of publications (49 and 47%, respectively), followed by "Neuroscience" (28%), "Medicine" (28%), and "Chemistry" (14%).

#### Keyword Co-occurrence Cluster Analysis

The topics involved in Sigma-1 receptor research can be outlined in the keywords assigned to each article. Keywords have as their main objective to provide rapid access to scientific works and are highly effective in terms of bibliometric analysis when investigating the knowledge structure of scientific fields (Zhang et al., 2016; Vargas-Quesada et al., 2017). Keywords provide a reasonable description of research hotspots (attention by a number of scientific researchers focused on a set of related research problems and concepts).

In the present study, VOSviewer was used to create a knowledge map of keyword co-occurrence with 114 terms in 11 clusters (**Figure 8**) and to identify the top 25 keywords

times. Omitting the term Sigma-1 receptor and the variants thereof, the largest set of connected keywords with greatest total link strength consists of 114 terms in 11 clusters. (A) VOSviewer network co-occurrence visualization map. (B) VOSviewer overlay visualization by time.

in Sigma-1 research articles from 1992 to 2017, according to occurrences and citation counts, and ordered by average publication year (**Figure 9**). The top 5 keywords were "Positron Emission Tomography (PET)," "Neuroprotection," "Cocaine," "Rat," and "Haloperidol" (**Figure 9**). High-frequency terms in author keywords such as "Learning and Memory" (2004),

"Schizophrenia" (2006), "Positron Emission Tomography" (2008), "Cocaine" (2008), and "Depression" (2010) were found in the early years, as compared to terms such as "Alzheimer's disease" (2011), "Neuroprotection" (2011), "Addiction" (2012), and "Neuropathic Pain" (2014) in more recent years (**Figure 8B**).

#### CONCLUSIONS

In this work, the global scientific outputs of Sigma-1 receptor research, its main research lines and its evolution are studied for the first time by means of bibliometric indicators of a basic nature and modern information visualization techniques. The resulting maps are a useful and attractive tool for the Sigma-1 receptor research community, as they reveal the main lines of exploration at a glance. The global view provided by our study shows that researchers have intended to explore the potential involvement of Sigma-1 receptors in some specific physiological processes and diseases. The hope that the modulation of the Sigma-1 receptor could be a therapeutic strategy is likely driving the Sigma-1 research community. Consistent with this, intensive activity has been carried out in the area of Medicinal Chemistry to obtain selective ligands that could be developed as drugs in the future. According to our analysis, the main diseases in which the Sigma-1 receptors have been explored include addiction, neuroprotection and neurodegenerative diseases, psychiatric disorders, and pain. Keyword co-occurrence analysis suggests that the research efforts made in some indications such as those in cocaine (addiction), learning and memory or depression/schizophrenia/ haloperidol (psychiatric conditions) have declined over time, while others such as those focusing on neuroprotection/Alzheimer's disease (neurodegenerative diseases) or pain are currently most popular. Early studies on psychiatric disorders, learning and memory or cocaine did not translate into marketed drugs, and hopes now seem to be placed on studies relating to neurodegenerative diseases and pain. Leaving aside the well-known low success in transforming basic research into new drugs with a clear therapeutic potential and difficulties related to drug discovery programs (Paul et al., 2010), the lack of selective Sigma-1 ligands approved for use in humans could be the result of insufficient research effort/interest by the scientific community, including biopharmaceutical companies. In fact, only 247 authors have five or more publications and the growth in the production of articles is not constant over time, with periods of stagnation of approximately 5 years. The authors appear grouped in relatively independent clusters, thus suggesting a low level of collaboration between those devoted to the Sigma-1 receptor. Furthermore, as evidenced by the number of recent articles, only one pharmaceutical company, developing selective Sigma-1 receptor drug ligands, is currently actively publishing in the field. Additionally, the very nature of the Sigma-1 receptor may also be influencing the low success in transforming basic research into new drugs with a clear therapeutic potential. It is common in papers to describe the Sigma-1 receptor as an "enigmatic protein whose molecular mechanism of action remains elusive." The most cited article we found in this bibliometric analysis (Hayashi and Su, 2007) proposed that the Sigma-1 receptor acts as a molecular chaperone and therefore is not a traditional receptor. Chaperones ensure that all proteins obtain their correct folding and functionalities in the right localization at the right time. They recognize and bind their protein clients in conformational ensembles that are locally highly dynamic and interconvert, while in other cases clients bind in unique conformations (Hiller, 2019). Thus, to transform basic research into new drugs with a clear therapeutic potential, the intrinsic difficulty when trying to understand the mysteries of this unique ligandregulated molecular chaperone should be considered in drug discovery programs.

In summary, a greater interest and involvement of the scientific community for this enigmatic chaperone, accompanied by a parallel increase in the scientific production would help, hopefully in coming years, to the discovery of new functions and deepening in those already known. Additional boost needed to improve research performance are likely to come from new conceptual frameworks and breakthrough discoveries derived from recent and future advances in the "chaperone field," and from collaborative, synergistic initiatives by combining resources and knowhow from different laboratories to overcome the

#### REFERENCES

limitations of the individual approaches. This study may provide a valuable basis for identifying important topics for future research, and create opportunities for collaboration between research groups with complementary scientific interest in the field of Sigma-1 receptor.

#### DATA AVAILABILITY

The datasets generated for this study are available on request to the corresponding author.

#### AUTHOR CONTRIBUTIONS

EP-S and LR designed the study, performed the search, analyzed the data, and wrote the paper.

#### FUNDING

This work was a part of activities in R&D projects IDI20150914 and IDI20150915 supported by the Spanish Ministerio de Economía y Competitividad (MINECO), through the Centro para el Desarrollo Tecnológico Industrial (CDTI), co-financed by the European Union through the European Regional Development Fund (ERDF; Fondo Europeo de Desarrollo Regional, FEDER).

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#### **Conflict of Interest Statement:** LR and EP-S are employees of ESTEVE Pharmaceuticals.

Copyright © 2019 Romero and Portillo-Salido. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset

Rosalia Pascual\*, Carmen Almansa, Carlos Plata-Salamán and José Miguel Vela

ESTEVE Pharmaceuticals S.A., Drug Discovery and Preclinical Development, Barcelona, Spain

The recent publication of the σ1R crystal structure is an important cornerstone for the derivation of more accurate activity prediction models. We report here a comparative study involving a set of more than 25,000 structures from our internal database that had been screened for σ1R affinity. Using the recently published crystal structure, 5HK1, two new pharmacophore models were generated. The first one, 5HK1–Ph.A, was obtained by an algorithm that identifies the most important receptor-ligand interactions including volume restrictions enforced by the atomic structure of the recognition site. The second, 5HK1–Ph.B, resulted from a manual edition of the first one by the fusion of two hydrophobic (HYD) features. Finally, we also docked the database using a high throughput docking technique and scored the resulting poses with seven different scoring functions. Statistical performance measures were obtained for the two models, comparing them with previously published σ1R pharmacophores (Hit Rate, sensitivity, specificity, and Receiver Operator Characteristic) and 5HK1–Ph.B emerged as the best one in discriminating between active and inactive compounds, with a ROC-AUC value above 0.8 and enrichment values above 3 at different fractions of screened samples. 5HK1–Ph.B also showed better results than the direct docking, which may be due to the rigidity of the crystal structure in the docking process (i.e., feature tolerances in the pharmacophore model). Additionally, the impact of the HYD interactions and the penalty for desolvating ligands with polar atoms may be not adequately captured by scoring functions, whereas HYD groups filling up such regions of the binding site are entailed in the pharmacophore model. Altogether, using annotated data from a large and diverse compound collection together with crystal structure information provides a sound basis for the generation and validation of predictive models to design new molecules.

### Keywords: sigma-1, crystal structure, 5HK1, pharmacophore model, docking, virtual screening

#### INTRODUCTION

The sigma-1 receptor (σ1R) is an intracellular chaperone protein, expressed in CNS regions and known to regulate Ca2+ signaling and cell survival. The σ1R gene encodes a 24 kDa protein of 223 amino acids anchored to the endoplasmic reticulum (ER) and plasma membranes (Maurice and Su, 2009). The σ1R sequence has no homology with other mammalian proteins and is structurally and

#### Edited by:

Enrique José Cobos, University of Granada, Spain

#### Reviewed by:

Pramod C. Nair, Flinders University, Australia Rajnish Kumar, Karolinska Institute (KI), Sweden

> \*Correspondence: Rosalia Pascual<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

Received: 04 February 2019 Accepted: 24 April 2019 Published: 31 May 2019

#### Citation:

Pascual R, Almansa C, Plata-Salamán C and Vela JM (2019) A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset. Front. Pharmacol. 10:519. doi: 10.3389/fphar.2019.00519

**Abbreviations:** 2/3D, 2/3-Dimensional; EF, Enrichment Factor; HR, Hit Rate; HYD, Hydrophobic; HBA, Hydrogen Bond Acceptor; PI, Positive Ionizable; AR, Aromatic Ring; HYD-AR, Hydrophobic Aromatic; ROC, Receiver Operator Characteristic; ROC-AUC, Area under the ROC curve; σ1R, Sigma-1-Receptor; TRP, Sensitivity; TNR, Specificity; ECFP, extended connectivity fingerprints; FCFP, functional class fingerprints.

functionally different from other target classes. The σ1R is also unique in that it exerts molecular chaperone activity and interacts with diverse proteins to modulate their functions. Accordingly, the σ1R is involved in many physiological functions, including inter-organelle signaling (Su et al., 2010). Its activity can be regulated by ligands in an agonist/antagonist manner (Hayashi et al., 2011). Just as examples, the σ1R modulates opioid analgesia through physical protein-protein interactions, with σ1R antagonists enhancing and σ1R agonists inhibiting the antinociceptive effect of opioids, and σ1R antagonists reproduce the pain-protective phenotype of σ1R knockout mice when administered to wild-type mice (Zamanillo et al., 2013).

Until its recent crystallization, little was known about the σ1R 3-dimensional (3D) structure and the rational design of σ1R modulators mostly relied on ligand-based approaches. Based on a series of diphenylalkylamines, a first 2D-pharmacophore model (Glennon–Ph) for the σ1R was designed in the early 90's (Glennon et al., 1994) consisting in a positive ionizable (PI) group (i.e., a basic amino group) and two opposite hydrophobic (HYD) regions at 2.5–3.9 Å and 6–10 Å without any angle constrain (**Figure 1**). This qualitative model has been very useful as a guide to medicinal chemists for the design of new ligands. In 2004, a Sybyl 3D-pharmacophore model (Gund–Ph) was derived based on the alignment of PD144418, spipethiane, haloperidol and (+)-pentazocine (Gund et al., 2004). It consists in an aromatic region and a nitrogen atom that acts as hydrogen bond acceptor, as primary requirement for binding, and a polar feature representing an oxygen or sulfur atom as secondary binding interaction. In 2005, Langer's group developed a 3D-pharmacophore model (Langer–Ph) based upon 23 structurally diverse molecules with σ1R Ki values between 10 pM and 100 µM (Laggner et al., 2005). The model was generated with the HypoGen algorithm of Catalyst (Catalyst 4.9, 2003) and it consists in one PI and four HYD features (**Figure 1**). The model is in good agreement with Glennon's one but lacks the secondary polar binding region of Gund–Ph. Another HypoGen derived model (Zampieri–Ph) was published in 2009 using a series of 31 benzo[d]oxazol-2(3H)-one derivatives (Zampieri et al., 2009). The model contains one hydrogen bond acceptor (HBA), two hydrophobic aromatic features (HYD-AR), one HYD feature and one PI group. It is also in agreement with Glenon–Ph concerning distances among the PI feature and any HYD group, but it includes an additional polar/hydrogen bond acceptor feature as hypothesized by Gund. Langer–Ph and Zampieri–Ph share feature type and number (except for the additional HBA and the differentiation of one HYD to aliphatic HYD). Reported distances from the PI group to HYD features are similar, but not so much as it regards to their disposition and angles. Using the MOE Pharmacophore Elucidation routine, Wünsch's group aligned a training set of 66 spirocyclic derivatives to generate an additional pharmacophore model (Oberdorf–Ph) with four annotation points: aromatic, HYD, PI and HBA (Oberdorf et al., 2010). In 2012, another σ1R 5-features model for a series of 32 N-substituted azahexacyclododecanols was developed using the Phase program provided in Maestro (Banister et al., 2012). Its composition of HYD, PI and HBA features is in accordance with previous published models, but again with particular pairwise distances and angles. In summary, all the available pharmacophoric models share the presence of a PI and several HYD features with variations in distances and angles, and all of them, except Glenon's and Langer's ones include the presence of a polar group.

The first σ1R homology model was published in 2011 by Pricl's group (Laurini et al., 2011). It was built taking as reference non-overlapping segments of four crystalized proteins with ≥30% sequence identities to the σ1R. The N-terminal domain (residues 1–16) was built de novo and the four fragments were joined, generating and ranking alternative models for the loop portions in each junction zone. This initial 3D model was then subjected to refinement by molecular dynamics and a putative binding site was identified. The refined σ1R homology model was then used for docking and binding affinity determination of a series of bioactive ligands and reference σ1R ligands via the MM/PBSA methodology, as well as for the design of new ligands and their ranking for receptor affinity (Laurini et al., 2012, 2013). Later on, another σ1R homology model was published, with results based on only the cold-active aminopeptidase, (PDB code 3CIA), also used by Pricl's group among template structures, wherein two distinct but closely proximal binding sites were suggested from docking studies of pentacycloundecylamines using MOE (Geldenhuys et al., 2013).

In 2016, the first crystal structure of the human σ1R was published in complex with two ligands, PD144418 and 4- IBP (Schmidt et al., 2016). More recently the same group reported co-crystallization with additional compounds (Schmidt et al., 2018). The crystal structure shows an overall trimeric receptor arrangement, with a single transmembrane helix in each protomer, and each protomer binding a single ligand molecule. The single-pass transmembrane architecture was surprising in view of the widely accepted two-pass transmembrane architecture, compatible with or suggested from fluorescent tags and immunocytochemistry (Aydar et al., 2002; Hayashi and Su, 2007), radioiodinated photoprobe (Fontanilla et al., 2008) or solution circular dichroism-nuclear magnetic resonance (Ortega-Roldan et al., 2015) studies, although a single transmembrane segment close to the N-terminus and coded by exon 2 had already been suggested from the very beginning by hydropathy analysis of the amino acid sequence (Hanner et al., 1996; Kekuda et al., 1996; Seth et al., 1997; Prasad et al., 1998).

Taking advantage of the information and resolution provided by the X-ray crystallographic structure, we explored its contribution to the prediction of binding affinities in virtual screening conditions compared to previous pharmacophore models. To this aim we developed two new σ1R pharmacophore models using the structural information revealed by the crystal structure, which was also used for docking studies in several conditions. Additionally we reproduced most of the published σ1R pharmacophore models and compared their performance in front of a fraction of our chemical database, experimentally assayed for σ1R affinity, containing more than 25,000 unique structures. To the best of our knowledge this is the first time that such a large compound dataset is used for establishing the predictive value of σ1R models.

### MATERIALS AND METHODS

#### Protein Preparation

The recently crystalized σ1R structure (PDB = 5HK1) was prepared using Discovery Studio 16 (Dassault Systèmes BIOVIA, 2016a). Sulfate ions and oleoyl-glycerol molecules were removed, as well as all waters, since no key water molecules were observed within the binding site. Incomplete side chains were added, the structure was typed with the CHARMm forcefield and atoms were ionized according to the predicted pK at pH = 7.4, using the 'calculate protein ionization and residue pK' protocol. The charge of Asp126 was set to zero, allowing a hydrogen bond with the charged Glu172 as previously hypothesized (Schmidt et al., 2016). Subunit B of the trimeric structure was selected for further calculation as it shows the lowest average isotropic displacement. However, very similar results should be obtained using any of the other two subunits, as the RMSD of the 3 subunits superimposed by C-alpha pairs of residues within 5 Å distance to the ligand has a value of 0.25 between chains A and B and of 0.18 between chains B and C (RMSD superimposed using the whole chains is a bit higher due to the different bending of the helices).

#### Ligand Databases Collection and Preparation

All in-house characterized compounds for σ1R binding together with their data were retrieved from ESTEVE's internal Activity Base database (IDBS, 2016). This made up a total of 25,676 unique structures. Compounds were obtained in the neutral form, as salts had been already striped in the registration process. Then a 3D multiconformational database was built with Catalyst as implemented in Discovery Studio 2016, using the BEST methodology (Smellie et al., 1995; Kirchmair et al., 2006). 3D conformational generation was launched from Pipeline Pilot 2016 (Dassault Systèmes BIOVIA, 2016b). Special attention was given to correctly retain the stereochemical information of the compounds. Both chirality options were included in the conformation generation process for racemic mixtures. In the case of enantiomeric mixtures with grouped stereocenters, Catalyst is not able to take the stereochemistryrelated information into account for conformer generation. Hence, different 3D entries for each of those compounds with the stereochemical combination defined by the stereo-groups were created, generating conformations specifically for each of them and joining them afterward with the same compound identifier. Compounds with fused cyclopropyl groups as well as some substituted cyclobutyl derivatives cannot be treated by the BEST algorithm. In this case conformations were built by systematic search using a default torsion increment of 60 for sp3-sp3 and sp3-sp2 bonds and of 180 for sp2-sp2, followed by minimization using the MMFF force field. The final database generated, consisting of 3,707,672 conformers, was used as input for pharmacophoric screening.

Additionally a second multiconformational database of the same compounds, but ionized, was built. To do so, basic pKa constants were calculated for all compounds using both ACDclassic and ACD-Galas (ACD/Labs, 2014). A Pipeline Pilot protocol was designed to generate a pool of different ionization states for each compound, by protonating basic points with pKa values above 5 or unprotonating acidic points below 5 successively on the previous state, and adding the resulting ionized structure to the pool. The protocol was run for the

ACD-classic and ACD-Galas generated values, and both output structures were merged and duplicate ionization states removed. Finally, the same procedure described above was followed, obtaining a new database with 7,573,004 conformers.

For the purpose of this work, structures were classified as actives when their Ki value was equal or under 1 µM (18.6% of the samples; 4766 structures) or as inactives in the contrary cases or when Ki values had not been determined because their percentage inhibition at 1 µM was under 50% (81.4% of the samples; 20,910 structures).

#### Pharmacophore Generation

The receptor-ligand pharmacophore generator job implemented in Discovery Studio 16 was run on the prepared subunit B of the σ1R with the co-crystallized ligand PD144418 to obtain **5HK1–Ph.A**. The algorithm (Sutter et al., 2011) generates pharmacophore models from the features that correspond to the receptor-ligand interactions, identifying in a first step all ligand features and pruning then those features that do not match the protein-ligand interactions. It additionally places as well excluded volumes to represent the steric aspect of the protein. **5HK1–Ph.B** was built modifying **5HK1–Ph.A** in the Discovery Studio interface using the available pharmacophore edition functionalities, specifically the averaging, the tolerance edition tool, and the feature customization functionality which was used to exclude certain substructures from the amidine and guanidine default mapping definition of PI that did not show basicity following the prediction of both ACD-classic and ACD-Galas (ACD/Labs, 2014). Langer–Ph, developed in Catalyst, now included in the Discovery Studio platform, was reproduced thanks to the definitions, coordinates, tolerances and weights included in its publication (Laggner et al., 2005). Zampieri–Ph and Banister–Ph were reproduced deriving the feature positions that fulfill the published distances and angles and setting a default constrain radius of 1.6Å for the features. In the case of Zampieri–Ph, the angle of the projection point of the HBA feature was not reported, thus no location constrain was set for that projection point to avoid filtering out any hits of the original Zampieri–Ph. Regarding Banister–Ph, although it was built with the Phase program, Catalyst equivalent features were set for the different pharmacophoric points. In the case of the HBA and the Aromatic Ring (AR) features, as no directionality information was described, again the projection points of those features were left without location constrains. Gund–Ph, originally built using the Sybyl package, was reproduced in Discovery Studio using the given coordinate points (Gund et al., 2004). To be as accurate as possible in replicating the original features, the default tail definition of the Catalyst HBA feature was modified, accepting only the mapping to nitrogen atoms. Thus, the new HBA feature could be used to map the nitrogen location and the provided projection point of the hydrogen bond between the nitrogen and the receptor. To solve the issue of two normal vectors defining the AR, and understanding them as an attempt to map a pi-pi stacking from both sites of the ring, two Catalyst pharmacophores were built: one with the projection point on one side and the other with the projection on the other, requiring the fitting of both pharmacophores at the same time. Again a default constrain radius of 1.6 Å was set for all features except for the HBA projection point where the default radius is 2.2 Å. Oberdorf–Ph could not be reproduced, as no distances, angles or feature coordinates were provided by the authors.

#### Screening Methods

The generated multiconformational database with 3,707,672 conformers was screened with the Ligand Pharmacophore Mapping protocol launched from the Pipeline Pilot 2016 interface (Dassault Systèmes BIOVIA, 2016b), were each conformation was mapped separately and only the best mapping solution was returned for each of them, keeping finally only the mapped conformation with the best FitValue for each compound. Further, typical virtual screening conditions were used in the calculation: the omission of any feature was not allowed, and both rigid fit between each ligand conformation and the pharmacophore, as well as flexible fit, where slight conformational modifications are allowed to better fit the pharmacophore, were applied. In the case of the Langer–Ph, the published affinity prediction conditions were also used for screening, using the published weights and setting in this case the maximum number of omitted features to any. In the case of Gund–Ph to achieve the double directionality of the aromatic feature, we screened compounds first with a pharmacophore having the AR pointing to the direction of Tyr103, as determined after the pharamacophore-receptor alignment: Gund-up–Ph, and then we filtered the resulting conformations in place and without fitting, with a second pharmacophore equal to Gund-up–Ph but with the inverted projection point of AR.

For the docking studies, the LibDock program (Diller and Merz, 2001; Rao et al., 2007) implemented in Discovery Studio 16 was used, taking the prepared subunit B of the 5HK1 structure and the generated multi-conformational database of ionized compounds. A Site Sphere of 10 Å centered on the crystallized PD144418 ligand was defined and the docking grid was calculated using 1000 hotspots. No minimum cut-off value was set for the LibDockScore and up to 100 ligand poses could be saved for each ligand, but a filter requiring a charge interaction of the output poses with Glu172 was established to lower the number of possible solutions, as this interaction is the strongest interaction found in the crystallized structure (Schmidt et al., 2016) and mutation of Glu172 has been proven to abolish binding (Seth et al., 2001). Additionally, to ensure a proper orientation of the ligand, a hydrogen bond as part of the electrostatic saltbridge interaction was also required (Bissantz et al., 2010). Finally poses with unfavorable interactions were filtered out. The remaining LibDock settings were left to their default values, and to score the resulting poses, the following seven scoring functions as implemented in Discovery Studio 16 were used: LigScore1, LigScore2 (Krammer et al., 2005), PLP1, PLP2 (Gehlhaar et al., 1995), Jain (Jain, 1996), PMF (Muegge and Martin, 1999), and PMF04 (Muegge, 2006).

#### Human Sigma-1 Receptor Radioligand Assay

The binding properties of the 25,676 compounds to human σ1R were studied in transfected HEK-293 membranes using [3H](+)-pentazocine (Perkin Elmer, NET-1056) as the

radioligand. The assay was carried out with 7 µg of membrane suspension, [3H]-(+)-pentazocine (5 nM) in either absence or presence of either buffer or 10 µM haloperidol for total and non-specific binding, respectively. Binding buffer contained Tris-HCl (50 mM, at pH 8). Plates were incubated at 37◦C for 120 min. After the incubation period, the reaction mix was transferred to MultiScreen HTS, FC plates (Millipore), filtered and plates were washed (3 times) with ice-cold Tris–HCl (10 mM, pH 7.4). Filters were dried and counted at approximately 40% efficiency in a MicroBeta scintillation counter (Perkin-Elmer) using EcoScint liquid scintillation cocktail. The distribution of activities obtained is indicated in **Table 1**.

#### Evaluation of Screening Performance

For evaluating the effectiveness of the different models, well-known metrics were used. The Enrichment Factor (EFx%) measures the density of active compounds that can be found at a given fraction of the model-ordered database in comparison to a random selection. It is calculated by Equation (1), where Activesx% Selected is the number of active compounds found at top x% of the database screened, following the model ranking; N x% Selected is the number of compounds at top x% of the database; ActivesTotal is the number of active ligands in entire database; and Ntotal is the number of compounds in the entire database. A major drawback of the Enrichment Factor, that turns it unsuitable for comparison of screening performance among different databases, is its dependency on the ratio between active and inactive compounds. However,

TABLE 1 | Experimentally determined σ1R affinity range distribution of compounds in the dataset of 25,676 unique structures used for virtual screening and validation of the different models.

it allows a ranking of different models for the same database (Truchon and Bayly, 2007).

$$EF^{\mathfrak{N}} = \frac{Acti\nu \text{es}\_{\text{Seletted}}^{\mathfrak{N}} / N\_{\text{Seletted}}^{\mathfrak{N}}}{Acti\nu \text{es}\_{Total} / N\_{\text{total}}} \tag{1}$$

The Hit Rate (HRx%) corresponds to the ratio of known hits found within the top x% and it is defined as the quotient of the real EF and the ideal EF (Hamza et al., 2012).

Sensitivity (TPR) is the fraction of correctly identified active compounds within the selected top x%.

Specificity (TNR) is the fraction of correctly identified inactive compounds within that x%.

The Receiver Operator Characteristic (ROC) curve plots sensitivity (true positive rate) versus specificity at all possible selection thresholds (Fawcett, 2006). The area under its curve (ROC-AUC) is a practical and objective way of measuring the performance of screening models, being independent of the balance of active and inactive compounds present in the database. ROC-AUC values range from 0.0 to 1.0, with 0.5 meaning random selection.

#### Similarity Calculations

Extended-Connectivity Fingerprints (Rogers and Hahn, 2010), Functional-Class Fingerprints and MDL public keys (Durant et al., 2002) as implemented in Pipeline Pilot were used as structural descriptors. All pairwise Tanimoto distances among compounds of each set were calculated and statistical values and histogram frequencies were obtained with implemented protocols.

#### RESULTS

As a first step, a new σ1R pharmacophore model based on the receptor-ligand interactions observed in the 5HK1 crystal structure was automatically built. Only four out of the ten pharmacophoric features present in PD144418 were chosen by the algorithm as being the most characteristic and selective ones. Those were one PI feature and three HYD features, two on one side of the PI with distances from 7 to 13 Å and one on the other side at 3.7 Å ± 0.8 Å. The PI feature stands for the ionic interaction between the amine of PD144418 and Glu172 and Asp126; the HYD on one side for the hydrophobic interaction of the propyl chain with Ile124 and His154; and the two other HYD features for the interactions of the phenyl ring and the methyl with Leu182, Tyr206, and Ile178. These features together with the excluded volumes constituted the new σ1R pharmacophore model **5HK1–Ph.A** (**Figure 2**).

Comparing **5HK1–Ph.A** with previously described models, we found that it perfectly matched the distances of Glennon–Ph. Langer–Ph just differed by having one additional HYD feature, while distances and angles were almost in perfect overlap with the new model (an RMS displacement of 1.1 Å if disregarding the additional HYD1 feature, **Figure 1**). This supports the feasibility of building ligand-based global models that account for receptor interactions, as well as HypoGen's model building power when a

proper diverse training set with a wide activity range is selected. The fact that the additional HYD feature present in Langer–Ph (HYD1) was not necessary for σ1R binding, and could be replaced by other non-hydrophobic chemical groups, had already been observed for some of our σ1R ligand families. For example, in a series of 4-aminotriazole derivatives (Díaz et al., 2015), the HYD1 feature was reported not to be covered by high affinity ligands; instead, triazole nitrogen atoms were present in that region.

To further determine whether HYD1 and its position may be dispensable (although it can account for the interaction of particular compound families), Langer–Ph was displaced and positioned into the σ1R active site in two different ways. As a first option the within Discovery Studio available pharmacophore alignment algorithm was used. The second strategy entailed a rigid fitting of PD144418 into the pharmacophore, allowing the omission of one feature, followed by the displacement of the fitted structure to its crystallographic position, displacing at the same time the pharmacophore itself. In both cases, HYD1 turned out to be located directly over Tyr103. This implies that the conformation of a ligand that fulfills the geometrical disposition of the five features that make up the Langer–Ph would be positioned in a way that would at least initially clash with the crystallized σ1R (**Figure 3**).