**3.3.4 VOBOX**

The VOBOX [42] is a standard WLCG service developed in 2006 in order to provide the LHC experiments with a place where they can run their own specific agents and services. In addition, it provides the file system access to the experiment software area. This area is shared between VOBOX and the Worker Nodes at the given site. In the case of ALICE, the VOBOX is installed at the WLCG sites on dedicated machines and its installation is mandatory for sites

to enter the grid production (it is an "entry door" for a site to the WLCG environment). The access to the VOBOX is restricted to the Software Group Manager (SGM) of the given Virtual Organization. Since 2008, this WLCG service has been VOMS-aware [42]. In the following section, we will describe the services running on the VOBOX machines reserved at a site for the ALICE computing.


**1. Introduction**

The economic burden of drug-related morbidity and mortality as a result of non-optimized medication therapy is estimated to be more than 16% of total US health care annual expenditures [1]. Overlooking major pharmacokinetic characteristics of a drug is one of the key players in inappropriate pharmaceutical dosing, which can lead to reduced efficacy and an increased rate of adverse drug reactions (ADRs) requiring medical intervention [2]. Pharmacokinetic drug-drug interactions (DDIs) can be responsible for about half of all DDIs depending on the patient group [3,4]. Furthermore, these DDIs have the potential to cause very pronounced (several hundred-fold) and abrupt changes in concentration and effect of the victim drug, depending on the start and stop of the causative (perpetrator) comedication and on fluctuations of its concentration during therapy [2,5,6].

Different components in absorption, distribution, metabolism and excretion can affect the overall pharmacokinetic profile of drugs. For agents that primarily undergo hepatic elimination, transport-mediated mechanisms of hepatocellular uptake can have a particularly significant clinical impact on pharmacotherapy; thus, this field of research has gained increased attention in recent years [7]. The organic anion transporting polypeptides OATP1B1 and OATP1B3 are examples of such transporters that can facilitate the uptake of a diverse array of xenobiotics, including many anticancer drugs, into the liver in advance of metabolism, and that are sensitive to inhibition by other medicines given concurrently.

Two of the most commonly acknowledged risk factors of DDIs are polypharmacy and advanced age [2,8–10]. Consistent with this notion, cancer patients are particularly at high risk for the occurrence of potentially harmful DDIs, since they often take a large number of medications concomitantly, which tends to increase as their disease progresses, and because the majority of cancer diagnoses happens in older ages [10,11]. Indeed, prior investigations have demonstrated that as many as 30% of cancer patients receiving chemotherapeutic treatment are at a risk for DDIs [12,13]. As the number of new treatment options in oncology continues to grow, DDIs are increasingly recognized as significant health hazards that can negatively influence treatment outcomes. These issues are particularly concerning given the increasing use orally-administered chemotherapeutic agents. While such drugs o ffer advantages in terms of patient preference, the convenience of use, reduced healthcare resource utilization, the possibility to achieve sustained drug exposure associated with the need for chronic use without requiring prolonged drug infusions, and may improve the overall quality of life, recent studies have suggested that the use of such agents increases the risk of potentially serious DDIs with commonly used outpatient medications [14]. In addition, unsupervised administration of other medications as well as their possibly prolonged use has been advanced as concerns with oral chemotherapy drugs, which could potentiate DDIs that may remain unanticipated. Although recent studies have suggested that the prevalence of DDIs with oral chemotherapy drugs is as high as 50% with nearly 20% potentially increasing toxicity, the clinical impact of DDIs involving oral chemotherapy remains largely unstudied [10].

In this article, we provide an overview of this field of research in relation to tyrosine kinase inhibitors (TKIs), a rapidly expanding group of orally-administered drugs commonly used in the treatment of solid tumors and hematological malignancies, with particular emphasis on OATP1B1- and OATP1B3-related mechanisms. In addition to reviewing existing published data, we aimed to identify potential knowledge gaps that could help improve our understanding of the clinical impact of DDIs mediated through this mechanism.

#### **2. Tyrosine Kinase Inhibitors (TKIs)**

Since the US Food and Drug Administration (FDA) approval of the first TKI, imatinib, in 2001 for the treatment of chronic myeloid leukemia (CML), almost 50 additional TKIs have been approved for the treatment of various cancers, and many more are currently being developed and evaluated [15,16]. Protein tyrosine kinases (PTKs) are enzymes that catalyze the transfer of a gamma phosphate group from adenosine triphosphate (ATP) to a tyrosine residue on a protein. The phosphorylation of PTKs leads to the downstream activation of signal transduction pathways that are important in the regulation of cell growth, di fferentiation, and a series of other physiological and biochemical processes involved in cell survival and migration. Dysregulation of PTK function results in proliferation disorders, with those most notably being cancers [17–19]. Because of their importance in signal transduction, many PTKs have been the target of therapeutic intervention with the use of small-molecule TKIs. As a result, TKIs function by competing with ATP for the ATP-binding pocket of PTKs, thus reducing the downstream signaling cascade and provide useful targeted strategies in oncogenic treatment [20,21].

While TKIs have revolutionized anticancer therapy, some challenges have also risen in the use of these agents. Unlike conventional cytotoxic agents that are given intravenously, TKIs are administered orally and daily for prolonged periods [22]. As mentioned before, while this is more convenient, this also increases their susceptibility to unpredictable patterns of oral absorption and causes both wide inter-individual pharmacokinetic variability and potential for DDIs with co-administered agents [23–25]. Most TKIs are highly prone to cause DDIs [26], as patients receiving these agents are often subsequently treated for concomitant diseases, and because polypharmacy is highly prevalent [25]. Comorbid conditions such as hypertension, chronic obstructive pulmonary disease, diabetes, cardiovascular disease, congestive heart failure, and peripheral vascular disease are frequently reported in the population of cancer patients [27], and this further increases the risk for potential DDIs. Indeed, a recent study indicated that 97.1% of patients receiving treatment with TKIs were using at least one other drug simultaneously, with a median of 4 concurrent medications, and 47.4% experienced at least one potential TKI-mediated DDI [28]. In another study, 44.7% of the potential DDIs identified involving TKIs were considered severe [29]. Interestingly, most available data in this field have investigated TKIs as victims in DDIs [30–33], and conclusive information on their role as perpetrators in DDIs is generally lacking.

#### **3. Organic Anion Transporting Polypeptides (OATPs)**

The vast majority of orally-administered TKIs are eliminated from the body by enzyme-mediated metabolism, which occurs predominantly in the liver, followed by biliary or urinary excretion of the metabolites. These processes require drugs to cross the selectively permeable biological membrane of hepatocytes and are dependent, at least in part, on interaction with membrane transporters. These include the organic anion transporting polypeptides (OATPs), a family of influx transporters expressed in various tissues, including the liver [34–36]. Experimental studies with TKIs have predominantly evaluated transport by the liver-specific transporters OATP1B1 and OATP1B3, which are encoded by the *SLCO1B1* and *SLCO1B3* genes [37], respectively. Moreover, it has also been shown that some TKIs can additionally act as inhibitors of the transporters for which they are substrates [38]. Inhibition of OATPs can lead to defective elimination, result in sudden increases in plasma concentration and area under the curve (AUC) for drugs that are substrates of these transporters [36], and ultimately increase the risk of therapy-related side effects. Known substrates of OATP1B1 and OATP1B3 include statins, repaglinide, olmesartan, enalapril, valsartan, several xenobiotic glucuronide metabolites, as well as a host of cytotoxic chemotherapeutic agents, including the taxanes paclitaxel and docetaxel, the platinum-based drug cisplatin, and methotrexate. As hypertension and diabetes are among the prevalent comorbidities in cancer patients, many xenobiotic OATP1B1 and OATP1B3 substrate drugs are likely to be co-administrated with OATP-inhibitory TKIs, and therefore, clinically significant toxicities such as rhabdomyolysis, hyperkalemia, and hypoglycemia can be anticipated [39–41].

#### **4. Regulatory Guidance Documents**

As more and more DDIs involving uptake transporters have been reported in recent years, so have regulatory agencies such as the FDA and the European Medicines Agency (EMA) put increasing emphasis on investigating each new drug entity for their potential to induce/inhibit such transporters. It should be noted that both the "EMA Guideline on the Investigation of Drug Interactions" and "FDA guidance for In Vitro Drug Interaction Studies—Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions" recognize the fact that the field of transporter interaction assessments is still rapidly evolving and therefore the recommendations offered are relatively flexible and advocate the use of a variety of methods. However, some specifications have been proposed as a means to ensure that the in vitro models have optimal prediction potential for transporter-mediated interactions:

calculation of R values with the formula R = 1 + ((fu,p × Iin,max)/ IC50) where fu,p is the unbound fraction in plasma, IC50 is the half-maximal inhibitory concentration and Iin, max is the estimated maximum plasma inhibitor concentration at the inlet to the liver. An R-value ≥ 1.1 suggests that the drug has the potential to inhibit OATP1B1 and/or OATP1B3 in vivo.

#### **5. Identification and Retrieval of Relevant Data**

Acquisition of the data for this article was compiled independently up to and including June 2020 by various members of the Division of Pharmaceutics and Pharmacology at the Ohio State University with specific expertise in drug transporters (D.A.G.), pharmacy (Z.T.), and cancer pharmacology (E.D.E.), and subsequently reviewed by members with expertise in pharmacokinetics (A.S.) and TKIs (S.D.B.). Data on FDA-approved TKIs was extracted from the full prescribing information as provided by the respective drug manufacturers. A search was subsequently conducted using publicly-available, unpublished databases from the FDA and EMA guidance documents for industry to further collect information on OATP1B1 and OATP1B3 inhibition studies previously conducted for each of the TKIs (Figure 1). It should be noted that although published studies have indicated that certain TKIs such as erlotinib are inhibitors of 2B1 and can cause DDIs, this was considered beyond the scope of the present article since regulatory guidance documents lack information on this transporter [42].

**Figure 1.** Applied methods for the acquisition of relevant data on TKI-related interactions with OATP1B1 and OATP1B3.

All DDI data included for consideration focused exclusively on the TKIs as inhibitors of the transporter (the perpetrator) of interest. The selection of relevant literature articles for inclusion was performed based on predefined inclusion/exclusion criteria, where eligible articles included either peer-reviewed publications, meeting abstracts, and previously published reviews. As a primary search module, PubMed (National Library of Medicine) was utilized to identify potentially relevant publications using the following MeSH terms in the search strategy: ["TKI of interest"] AND [OATP1B1] or ["TKI of interest"] AND [OATP1B3]. Google Scholar was consecutively consulted to ensure no published article of relevance to this literature review was omitted. Three authors (D.A.G., Z.T., and E.D.E.) independently reviewed the collected data for eligibility and accuracy. In our analysis, concordant outcomes were defined as those for which the prescribing information, documentation from the FDA and/or EMA, and all the retrieved published literature on a specific TKI were in agreement that the TKI was either an inhibitor or not an inhibitor of OATP1B1 and/or OATP1B3. Outcomes were considered discordant outcomes if the identified reports on a particular TKI regarding its inhibitory properties towards OATP1B1 and/or OATP1B3 were conflicting. All data of relevance was tabulated to highlight such discrepancies (see below).

#### **6. E** ff**ects of TKIs on the Function of OATP1B1 and OATP1B3**

A descriptive summary of the main findings resulting from surveying the available prescribing information (PIs), and FDA and EMA guidance documents are shown in Table 1. The PIs showed that of the 48 FDA-approved TKIs evaluated, 7 (15%) are claimed to be inhibitors of OATP1B1 and 5 (10%) are inhibitors of OATP1B3. In addition, it is reported that of those 48 TKIs, 22 (48%) and 21 (44%) are reported in the PIs to not be inhibitors of OATP1B1 or OATP1B3, respectively. However, it is of note that the PIs for 19 (40%) of the TKIs do not mention whether or not drug interactions with OATP1B1 are of concern, and 22 (46%) do not mention that information for OATP1B3. As shown in Table 1, some inconsistencies were observed for some TKIs between what is reported in the regulatory guidance. Many of the di fferences can be accounted for by di fferences in cuto ff for IC50 values (shown in Supplementary Materials Tables S1–S9).

**Table 1.** Comparison of regulatory guidance documents on OATP1B inhibition by FDA-approved TKIs.

"Yes" indicates a TKI as an OATP1B1/3 inhibitor provided by the prescribing information, FDA documents, or EMA documents. "No" indicates a TKI is not an inhibitor of OATP1B1/3 inhibitor provided by the prescribing information, FDA documents, or EMA documents. Sources: PI, FDA, EMA documents provided on public databases, details of the links can be found in the Supplementary Materials. Access date: May 2020.

Next, we conducted a literature search on published data addressing OATP1B1 or OATP1B3 inhibition by di fferent TKIs. In vitro, in vivo, and clinical data were extracted. The details of the articles were inserted into tables (shown in Supplementary Materials Tables S1–S9) [43–47] For alectinib, avapritinib, baracitinib, binimetinib, brigatinib, cobimetinib, dacomitinib, encorafenib, erdafitnib, fedratinib, gilteritinib, ibrutinib, laroctrectinib, lorlatinib, midostaurin, pexidartinib, ponatinib, trametinib, and zanbrutinib no published reports were found. In data collected for 17 TKIs, the results of the published data were largely inconsistent in that some of the published results for a given TKI identified the TKI as an inhibitor of OATP1B1 or OATP1B3, while other sources identified it expressly as a non-inhibitor. It should be noted that di fferent transfected cell lines (Flp-In T-Rex293, HEK293, MDCK-II, CHO, SF9, or HepaRG) and di fferent substrates were used in the various studies. The latter included estradiol-17b-d-glucuronide (E2G), 8-(2-(fluoresceinyl)-aminoethylthio)-adenosine-3,5-cyclic

monophosphate (8FcA), fluorescein (FL), 2,7-dichlorofluorescein (DCF), valsartan, atorvastatin, SN-38, Na-Fluo, fluvastatin, estrone-3-sulfate (E1S) for OATP1B1 and taurocholic acid (TCA), cholecystokinin octapeptide (CCK-8) for OATP1B3. Furthermore, the preincubation time, the method of detection, the data analysis metric (percent inhibition or IC50), and even the concentration of the TKI were found to vary among the published reports. The details of these methodological di fferences are summarized in Table 2.

Data from clinical and in vivo studies were also collected and reviewed for this article, the results of which can be seen in the supplements. Very few studies have directly investigated the role of OATPs in TKI pharmacokinetics with di fferent methodologies, however the results from available studies seem to be consistent with regulatory data. Since the main scope of this review is to focus on discrepancies between published data and FDA and EMA guidelines, their results were not further explored here. Moreover, as OATP1B1 and OATP1B3 substrates used in the retrieved data have complex pharmacokinetic profiles involving drug-metabolizing enzymes and other transporters, the results of such case reports should be carefully analyzed to decide on the importance of each part of the pathway [48–59].


4.3.1. Satisfaction with Local Dispute Resolution Mechanisms

Given that respondents in both study areas expressed a high preference for traditional over statutory mechanisms of land dispute resolution, it was instructive to further ascertain the extent to which respondents perceived customary mechanisms to be effective in land dispute resolution. Respondents were therefore asked about whether they were satisfied with the local dispute mechanisms in their respective communities, the results of which are shown in Tables 4 and 5.

**Table 4.** Satisfaction with local dispute resolution mechanisms by ethnicity.

\*\* significant at *p* ≤ 0.05;

**Table 5.** Satisfaction with local dispute resolution mechanisms by gender.

From Table 4, it is quite clear that respondents were generally satisfied with the customary dispute resolution mechanisms in the study areas. In Ankasa, however, a comparatively lower proportion of migrants (57%) as against indigenes (83%) expressed satisfaction with traditional adjudicatory mechanisms (*p* < 0.05; *χ*2 = 8.01), which perhaps suggests their waning confidence in traditional authorities as impartial arbiters in land dispute resolution. Corroborating this assertion is a typical remark by a migrant farmer in Ankasa:

*'If you are a settler farmer here and you have an issue over land with a Nzema [indigene], it is likely that the decision by the traditional authorities would favour your opponent due to his ethnicity. In the eyes of the chiefs, you, the stranger, cannot profess stronger ties to the land than the indigene as you only came to make a living* ... *'.(Interview 1, Ankasa, June 2015)*

Elsewhere in south-western Ghana, Boone and Duku [59] and Boni [60] reported the incidence of landlord–migrant disputes ostensibly aggravated by local adjudicatory mechanisms operating to favour native landlords at the expense of migrant–tenant farmers.


**Author details**

Dinesh G. Harkut Prof. Ram Meghe College of Engineering and Management, Sant Gadge Baba Amravati University, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**13**

**Chapter 2**

**Abstract**

cloud infrastructures.

**1. Introduction**

Cloud Computing Security

*Akashdeep Bhardwaj and Sam Goundar*

firewall, security model, monitor traffic, authorization

service categories are identified and illustrated as follows:

• Identity access management

• Data loss prevention

• Web security

Services to Mitigate DDoS Attacks

This chapter focuses on the challenges and risks faced in cloud security services in the areas which include identity access management, web security, email security, network security, encryption, information security, intrusion management, and disaster management while implementing a cloud service infrastructure. This chapter endorses the best practices in successfully deploying a secure private cloud infrastructure with security measures and mitigation and proposed a unique three-tier infrastructure design to mitigate distributed denial of service attacks on

**Keywords:** threats/vulnerabilities, security policies, data protection/security,

Cloud computing is vastly increasing in demand for its popularity. Cloud services deliver up to its expectations if properly maintained. Users choose cloud because the cost is affordable, is easy to access, and has a positive uptime. Unfortunately, a high number of cloud users face difficulties when issues arise such as the frightening news about data confidentiality and integrity which gets posted online all the time, and they are in darkness when such situations occur [1]. In this modern age, cloud computing has been progressively popular within the IT organizations, and we notice many institutes are moving most of their IT services towards the cloud services here in Fiji to improve their information communication technology (ICT) service delivery to the clients or stakeholders. It is important for any organization to do an appropriate background research before making decisions of upgrading for which type of cloud services they are acquiring, depending on the organization's requirements. Many organizations prefer a private cloud infrastructure which has many advantages compared to the public cloud and hybrid cloud services; however, administrators tend to overlook that private cloud infrastructure comes with an exceptional set of challenges and risks. Cloud computing security


**Joanna Wicher-Dysarz**

Department of Hydraulic and Sanitary Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-637 Poznan, Poland<EMAIL_ADDRESS>Tel.: +48 61 848 7730

Received: 8 March 2019; Accepted: 1 April 2019; Published: 4 April 2019

**Abstract:** In this paper an analysis of the lowland reservoir operation in atypical conditions is presented. The chosen study object is the Stare Miasto reservoir in the Powa river (Poland), which has been in operation since 2006. It is a two-stage reservoir, consisting of an upper sedimentation part and a lower main reservoir. The upper part is separated from the main part by an internal dam with a sluice gate. Such a construction enabled better control of sediment deposits and their removal. The atypical conditions were caused by flood wave propagation in the Powa river and the reservoir in 2014. In the research, three reservoir bathymetries are analyzed—from 2006, 2013, and 2018. Two-dimensional (2D) hydrodynamic modeling is applied to analyze spatial variability of investigated hydraulic parameters. Such an approach enabled better recognition of the changes observed in the reservoir during 2006–2018. In the research, the spatial distributions of the velocities, the shear stresses, and the stream power are the basis for the analyses and comparisons. The simulations enabled identification of the main elements prone to collapse during flood wave propagation. The presented results and approach may be applied for improvement of reservoir design, with special emphasis on specific structures located in a reservoir basin.

**Keywords:** 2D model flow; two-stage reservoir; shear stress; stream power; operational problem