**1. Introduction**

Todays' manufacturing era focuses on monitoring the process on shop-floor by utilizing various sensorial systems that are based on data collection [1–3]. The automated systems directly collect an enormous amount of performance data from the shop-floor (Figure 1), and are stored into a repository, in a raw or accumulated form [4,5]. However, automated decision making in the era of Industry 4.0 is yet to be discovered. The current work is a step towards holistic alarms management (independently of context [6]), and, in particular, automated root-cause analysis.

**Figure 1.** Key Performance Indications (KPIs) aggregation from Measured Values (MV).

#### *1.1. KPIs and Digital Twin Platforms in Manufacturing*

A lot of KPIs have been defined in literature (Manufacturing, Environmental, Design, and Customer) [7], and, despite the fact that methods have been developed for the precise acquisition of measured values [8], the interconnection of the values (Figure 1) can lead to mistaken decision making when one searches for the root cause of an alarm occurring in a manufacturing environment.

In addition, regarding manufacturing-oriented dashboard systems, they aid in the visualization of complex accumulations, trends, and directions of Key Performance Indications (KPIs) [9]. However, despite the situation awareness [10] that they o ffer, they are not able to support the right decision made at management level by elaborating automatically the performance metrics and achieving profitable production [11]. In the meantime, multiple criteria methods are widely used for decision support or the optimization of production, plant, or machine levels [1,12], based (mainly) on the four basic manufacturing attributes i.e.,: Cost, Time, Quality, and Flexibility levels [1]. This set of course can be extended. The "performance" that is collected from the shop-floor has been considered as measured values (MV) and their aggregation or accumulation into higher level Key Performance Indicators, as initially introduced by [13]. The goal of all these is the achievement of digital manufacturing [14].

On the other hand, the term digital twin can be considered as an "umbrella", and it can be implemented with various technologies beneath, such as physics [15], machine learning [16], and data/control models [17]. A digital twin deals with giving some sort of feedback back to the system and it varies from process level [15] to system level [18], and it can even handle design aspects [19].

Regarding commercial dashboard solutions, they enable either manual or automated input of measured values for the production of KPIs [20–22]. However, their functionality is limited to the reporting features of the current KPI values, typically visualized with a Graphical User Interface (GUI), usually cluttered, with gauges, charts, or tabular percentages. Additionally, there is need to incorporate various techniques from machine learning or Artificial Intelligence in general [23] and signal processing techniques [24]. The typical functionality that is found in dashboards serves as a visual display of the most important information for one or more objectives, consolidated and arranged on a single screen, so as for the information to be monitored at a glance [9]. This is extended in order to analyze the aggregated KPIs and explore potential failures in the future by utilizing monitored production performances. Finding, however, the root cause of a problem occurring, utilizing the real time data in an e fficient and fast way, is still being pursued.

#### *1.2. Similar Methods and Constrains on Applicability*

In general, categories in root-cause finding are hard to be defined, but if one borrows the terminology from Intrusion Detection Alarms [25], they can claim that there are three major categories: anomaly detection, correlation, and clustering. Specific examples of all three in decision making are given below, in the next paragraph. The alternative classifications of the Decision Support Systems that are given below permit this categorization; the first classification [26] regards: (i) Industry specific packages, (ii) Statistical or numerical algorithms, (iii) Workflow Applications, (iv) Enterprise systems, (v) Intelligence, (vi) Design, (vii) Choice, and (viii) Review. Using another criterion, the second classification [27] concerns: (a) communications driven, (b) data driven, (c) document driven, (d) knowledge driven, and (e) model driven.

There are several general purpose methods that are relevant and are mentioned here to achieve root-cause finding. Root Cause Analysis is a quite good set of techniques, however, it remains on the descriptive empirical strategy level [28,29]. Moreover, scorecards are quite descriptive and empirical, while the Analytical Hierarchical Process (AHP) requires criteria definition [30]. Finally, Factor Analysis, requires a specific kind of manipulation/modelling due to its stochastic character [31].

Regarding specific applications in manufacturing-related decision making, usually finding that the root-cause has to be addressed through identifying a defect in the production. Defects can refer to either product unwanted characteristics, or resources' unwanted behaviour. Methods that have been previously used—regardless of the application—are Case Based Reasoning [32], pattern recognition [33,34], Analysis of Variance (ANOVA) [35], neural networks [36], Hypothesis testing [37], Time Series [38], and many others. However, none of these methods is quick enough to give the results from a deterministic point of view and without using previous measurements for training. Additionally, they are quite focused in application terms, which means that they cannot be used without re-calibration to a di fferent set of KPIs. On the other hand, traditional Statistical Process Control (SPC) does not o ffer solutions without context, meaning the combination of the application and method [31].

#### *1.3. Research Gaps & Novelty*

In this work, an analysis method on KPIs has been implemented in the developed dashboard to identify, automatically and without prior knowledge, for a given KPI, the variables that are responsible for an undesired production performance. This mechanism is triggered by predicting a threshold exceedance in management level KPIs. The prediction utilizes (linear) regression, which is applied on the historical trend of each variable for the estimation of the performance values for the upcoming working period in order for the weaknesses of the production to be elicited beforehand. The dashboard in which the current methodology has been framed acts as an abstractive Digital Twin for production managers and it supports automated decision making.

In the following sections, the analysis approach is presented, followed by its implementation details within dashboard. The results from case studies, the points of importance, and the future research trends have been discussed.

#### **2. Materials and Methods**

#### *2.1. The Description of the Flow and the Calculations*

The current method consists of two stages. Initially, the formulas of each KPI are analyzed and their dependent variables are collected. Thus, the relationships between the Measured Values (MV), Intermediate Values (IV), Performance Indicators (PI), and Key Performance Indicators (KPIs) are known. This stage, in reality, constitutes the modelling of the production and it runs only once, while the data are aggregated form IoT technologies. The exact methodology can be found as IoT-Production in previous literature [39]. Regarding the second stage, Figure 2 presents its flow of the analysis for a single KPI. This stage runs continuously. In the beginning, the trend for each measure of the current period that is examined is acquired from the OLAP. For reasons of flexibility and modularity, they are not directly acquired from the data acquisition system. The measure trend is supplied as input to the tool, whereas a linear regression is applied to the estimation of the data points until the end of the next period, namely, horizon (as defined in detail of Figure 3). Once an estimated value is over the measure of the supposed goal (A1/A2), it is marked as 'out-of-goal' and the user is notified by the user interface. The user can then choose to repeat the process for lower level KPIs (turning it into investigated metric), after the automated suggestion of the analysis tool as to who is accounted for this result (B). The analysis method utilizes di fferentials; this mathematical approach that utilizes di fferentials is explained hereafter. The decisions that have to be taken (C) are beyond the scope of the current work. Nevertheless, it is noted that their generation often is straightforward, even though the existence of a knowledge base, to this end, would be extremely useful.

**Figure 2.** Schematic of the way the tool is run in real production.

**Figure 3.** Alarm that is created by prediction of KPI exceeding threshold.

Figure 4 summarizes this procedure while using a flowchart. Additionally, for easy comprehension, the algorithmic description follows, along with some notes for each step.

In the context of a specific example, it is mentioned that Figure 5 is indicative of the graphical illustration of such information. The derivatives formulas are also pre-installed, as they are used during this step.

The partial differential is the tool that is applied within the analysis function used to estimate the cause of the variation. It is a powerful tool that deterministically quantifies the effect of the variation of one KPI to the variation of another KPI. More specifically, if KPI *A(t)* is a function of PIs *A1(t)*, *A2(t)*, and *A3(t)*, the percentage that each *An*(t) contributes in the variation of *A(t)* in time is the quantity

$$\delta\_{A\_{\rm n}}^{A\*} = \varepsilon \left\{ \frac{\partial A}{\partial A\_{\rm n}} \Delta A\_{\rm n} \right\} \tag{1}$$

Given the corresponding notation, the operators of absolute value |·|, mean value ε{·}, partial derivative ∂/∂X, and difference Δ are used for the computation of the mean partial differential. This helps in pointing out the direction that the production manager should focus on.

**Figure 4.** Flow chart of the algorithmic procedure.

**Figure 5.** Relationship between Measured Values and KPI. Operations include summation, products and inversion.

#### *2.2. Implementation within a Dashboard: Services and Hardware Framework*

The dashboard is implemented in Java in an Object-Oriented (OO) paradigm as a Web Application that runs on a typical Java Servlet Container (Apache Tomcat); thus, it is accessible through a typical Internet browser. The Digital Twin is implemented in a Service-Oriented Architecture (SOA) that follows the N-Tier Architecture with multiple layers per tier. An integration layer handles the asynchronous communication of the browser with the server by enabling the user to begin the analysis at any time and without leaving the current screen. The browser sends HTTP requests to the server, whereas the JavaScript Object Notation (JSON) objects, which contain the results, are returned to the client.

Figure 6 illustrates the system's architecture comprising two individual processes. The Monitor Process records every equipment performance activity in the database, while the Actor directly interacts with the Analysis Process through the dashboard pages and requests an analysis for certain KPIs. Numbers 1 and 6 are indicative of communication between human (engineer/operator) and the dashboard, 2 and 5 denote a query request or result, and 3 and 4 are indicative of the OLAP/RDBMS communication.

**Figure 6.** Application architecture.

In particular, the Actor interacts with the AnalyticAction of the dashboard, as depicted in Figure 7, which, in turn, cooperates with the AnalysisService, employing the Facade Design Pattern [40]. This architecture allows the usability of a software library and provides a context-specific interface launching a set of services without the user being exposed to the complexity of the algorithms beneath. This performed through a variety of entities. Firstly, the AnalysisService directly uses the OLAPQueryService to acquire the values of the KPIs requested, holds them in the KPIManager along with their formula expression stored into the database—this is due to an implementation limitation of the current OLAP servers providing the calculation function for a KPI. Next, the PredictionTool applies the regression function to the data, which are consecutively fed to the FactorAnalysisTool for the analysis of the influence factor on each dependent variable of the KPI. The PredictionTool and the FactorAnalysisTool both correspond the implementations of the extrapolation tool and the tool estimating differentials that are described in the previous section.

**Figure 7.** Unified Model Language (UML) of objects implemented in dashboard and their workflow.

The dashboard also uses the RDBMS to store the information that is required for the visualization of the customized KPI views in various forms. Each KPI is defined in a KPIDefinition instance along with its formula. The Measured Values are retained in the same structure but without a formula. Formulas are lexical names that correspond to definition names. For instance, the Availability KPI is defined by the lower case name 'availability', and it is referred to by the OEE KPI as #{availability} variable, whereas the interpreter replaces it at runtime with the actual value. The exact UML of the Informational Model used is given in Figure 8. Black rhombus indicates composition, white rhombus indicates aggregation, and pure arrow indicates association.

Historical data of the production's execution are stored into the relational database management system/RDBMS (Figure 6) as records regarding the depiction of the data acquisition method. A relational table holds the machine's cycle output, whereas each record represents a single machine cycle with attributes that relate to the corresponding entities in the production system. The smallest set of these attributes can be:

**Figure 8.** UML of the Informational Model.

These are attributes and records that mostly refer to productivity. Additional attributes, even of different character (i.e., sustainability related, such as energy consumption), can be added. A graph is then formed with involved KPIs, where each KPI is represented by a vertex v and their dependency by an edge e, similar notation to precedence diagrams. The analysis method exploits the data and follows their dependencies until the root measure has been identified (among Measured Values). Finally, that measure is presented to the user as an indication of the type of measure that will negatively affect the performance of production in the near future.

The production performance is loaded from the OLAP cube [41], where the measures are formed with the help of the aggregation functions. For instance, measure 'Good Quantity' is aggregated by the count function of the 'Good Part' attribute.

#### **3. Results & Discussion**

The functionality of the methodology that is presented in the above sections is proved herein utilizing adequately complex cases. Noise is also present in the first three cases, where dummy KPIs have been used, to simulate the short-time variations of the KPIs. These first case studies have been established in order to check the numerical success of the algorithm, where the fourth case study originates from a real industrial problem and its scope is to test the applicability of the digital twin and the validity of the main algorithm in problems set in environments of higher Technology Readiness Level.

#### *3.1. Case I*

In this case, the relationship between the KPI *B(t*) and the PIs *A1(t)*, *A2(t)*, *A3(t)* is *B(t)* = *A1(t)\*A2(t)* + *A3(t).* The evolution of the PIs trends are as follows:

It is evident from the contribution diagram of Figure 9 that there is success in predicting that the contribution of the first factor should be high. The same happens if *A1(t)* decreases in time. Despite the noise existence, the algorithm worked to a satisfactory extent, which resulted in characterizing *A1(t)* as the root-cause of the variation.

**Figure 9.** Case I. Top: Evolution of KPI, Middle: Evolution of PIs, Bottom, Contribution of each PI.

#### *3.2. Case II*

In this case, the relationship between the KPI *B(t*) and the PIs *A1(t)*, *A2(t)*, *A3(t)* is *B(t)* = *A1(t)\*A2(t)* + *A3(t).* The evolution of the PIs trends is, as follows:

It is evident from the contribution diagram of Figure 10 that there is success in predicting that the contribution of the third factor should be high. The same happens if *A3(t)* decreases in time. It seems that the algorithm runs successfully, regardless of the operations that are performed among the KPIs.

**Figure 10.** Case II. Top: Evolution of KPI, Middle: Evolution of PIs, Bottom, Contribution of each PI.

#### *3.3. Case III*

In this case, the relationship between the KPI *B(t)* and the PIs *A1(t)*, *A2(t)*, and *A3(t)* is *B(t)* = *A2(t)* / *A1(t)* + *A3(t).* The evolution of the PIs trends is, as follows:

It is evident from the contribution diagram in Figure 11 that there is success in predicting that the contribution of the first signal should be higher than that of the second one. It is apparent that both of the first two PIs are causes of the variation, however, the denominator change is the one that affects more the result. Accordingly, the algorithm has pointed towards the correct correction.

**Figure 11.** Case III. Top: Evolution of KPI, Middle: Evolution of PIs, Bottom, Contribution of each PI.

#### *3.4. Case IV*

This case regards a true production system and it is used to test the validity of the algorithm in terms of the physical relationship between the PIs. This is done through forcing the Measured Values to create an KPI-level alarm and check whether the algorithm will find the root cause and indicate towards the correct Measured Value. Thus, a production system of thirty (30) identical machines that are arranged in eight work-centers has been simulated by a commercial software [42] (Figure 12). The system simulates a real-life machine-shop producing two variants of Cylinder Head parts: (a) Petrol and (b) Diesel. Different processing times and setup times have been configured.Additionally, negative exponential distribution for Mean-Time-Between-Failures (MTBF), and a real distribution on Mean-Time-To-Repair (MTTR) considered the equipment breakdown on machines.Customer demand has been set by a normal distribution. Finally, the performances have been recorded and manually fed in the informational model.

**Figure 12.** Workcenters arrangement of the production system. The number indicates the amount of machines for each workcenter.

*Appl. Sci.* **2020**, *10*, 2377

The KPIs that are utilized in this case study (top to bottom) are mentioned below and their relationship is also given in Figure 5, beginning with Overall Equipment E fficiency (OEE).

A simulation has been conducted with the machine breakdowns deliberately increased with time. Subsequently, the algorithm of finding the root-cause is run once. In Figure 13, it is clearly shown (yellow shaded rectangles and curved arrows) that the algorithm dictates the major role of E ffectiveness. This definitely helps in pointing out the direction that the production manager should focus on.

As a matter of fact, if one repeats the procedure of running the algorithm, but this time utilizing E ffectiveness instead of OEE (68% contribution), and then the Measured Value that comes up is Processing Time. This means that the engineer should focus on why the machines do not work that much, which is indeed the root cause of the OEE drop (Figure 13).

**Figure 13.** Tracking the cause of the OEE alarm back to Processing Time.

It seems that the algorithm works successfully without setting any rules or training. On the contrary, in the literature, works coming up often have to use one or the other, so a direct comparison cannot be made. For instance, in another alarm root cause detection system [43], the authors, even though they are describing the impact on case studies in brief, explicitly point out the need for rules in an internal expert system. Additionally, the use of KPIs at process level [44] is complementary to the current work. A framework that aims to detect the quality of the process, as per classifiers utilizing depth of cut and spindle rate could be integrated; corresponding indicators can be regarded as processed Measured Values. Additionally, the issue of validity of the indicators that are aggregated from IoT is also an issue that has not been raised herein, as the data have been considered to be valid. In addition to this, architectures that are based on complexity handling [45] and algorithms, such as Blockchain [46], can guarantee this goal, while, as indicated by corresponding results in literature [47], the introduction of a manipulation strategy, such as KPI-ML, will boost the performance of any algorithm, including the current. In other pieces of literature [48], the Multiple Alarms Matrix is utilized and hierarchical clustering is applied. In their results, they seem to be presenting the usability of the method towards the correlation of the alarms and extraction of the unique ones. This has not pointed out herein, as the goal has been to search for immediate causal relationships. Inferring Bayesian Networks are utilized in a di fferent work [49]; this approach can also be useful towards a causal model [50]. However, Abele et al., in their own discussion, state the use of probabilities and simulations, while expert knowledge is needed, whereas, in the current work, knowledge use is solely used in the actions generation. Furthermore, the enhancement with Max–Min Hill Climbing algorithm seems to be useful, however it is characterized as time consuming [51]. Case Based Reasoning seems to be similar to the present work, given the results in the literature [52]; however, as per the authors, it is best applied when records of previously successful solutions exist.

Moreover, the environment plays a significant role in the e fficiency of such aggregation. More specifically, regarding manufacturing, large networks that share decision making, alternative bandwidth reservation [53] can be adopted for reasons of performance. Connection to control layer, as performed in other works [54], could be potentially addressed in an extension of the current study. Along the same lines, KPIs related to CPPS issues [55] could potentially extend the applicability of the current framework. Finally, regarding innovation and technology readiness, the upscale of technologies that are integrated in shop-floor is also relevant, in terms of decision making for designing systems and process planning integrating Industry 4.0 Key Enabling Technologies. The Technology Readiness Level of the various technologies could also be described in terms of KPIs, as per the literature [56].


**1. Introduction**

Rheumatoid arthritis (RA) is an autoimmune disease that affects primarily the synovial cavity tissues on the small diarthrodial joints of the hands and feet [1], leading to persistent synovial inflammation [2] and progressive erosion of the articular structures [3,4]. The triggers for the disease susceptibility and the pathological cascade of events encompass environmental, genetic and stochastic

factors [3,4]. For example, RA has special incidence on females [5], which is related with genetic [5–7] and hormonal factors [5,8–10], but also presents a heterogeneous geographic distribution, being less common in developing countries [11,12], which confirms the involvement of environmental and socio-cultural factors [12].

The disease pathophysiology involves several interconnected mechanisms. Specific antibodies for immunoglobulin G (IgG) mediate the autoimmune process, as well as the imbalanced expression of the pro-inflammatory cytokine's profile and its functionality [1,12–14]. Consequently, this leads to inflammatory processes, autoimmunity enhancement, long-term inflammatory synovitis and joint damage [13,15].  Infiltration of B cells, CD4+ T cells and macrophages into the synovium, which in normal conditions is relatively acellular [1,14,16], leads to soft tissue oedema and stiffness [2]. Moreover, other inflammatory cells such as neutrophils, natural killer cells and mast cells play key roles in the disease's progression [13].

From the autoimmune disease (AD) subsets that entail the multiple inflammatory cascades, one of the most significant players is the TNF-α, particularly the over-expression of the TNF-α [11,13,17]. TNF-α, a 233 amino acid protein [18], is a key signaling cytokine in the immune system mainly produced by monocytes, macrophages [19], and B and T cells [2,13,17]. TNF-α stimulates the production of other inflammatory mediators, namely IL's, as well as the recruitment of immune and inflammatory cells into the joint [20,21]. As a regulatory cytokine that manages communication between immune cells and controls many of their functions when deregulated, TNF-α plays a key role in the pathogenesis of chronic inflammatory diseases, such as RA [22,23]. TNF deregulation in RA is linked to TNF-α converting enzyme (TACE or ADAM17), a metalloproteinase that cleaves trans-membrane TNF, releasing the soluble segment [18,24]; and to TNF receptors (mostly TNFr-I) [18,25,26], which are likely to be related with proinflammatory, cytotoxic and apoptotic responses [2].

The increasing knowledge on RA pathogenesis stimulated the development of different therapeutic modalities aiming to avoid joint destruction, minimize the symptomatic profile, and enhance physical function [27]. These options comprise analgesics, symptomatic management and inflammatory drugs [11,28]. Analgesics enclose the non-steroidal anti-inflammatory drugs (NSAIDs) that are commonly used to treat RA. The inflammatory suppressive drugs include glucocorticoids and disease-modifying anti-rheumatic drugs (DMARDs) both non-biologic and biologic. Due to the disease heterogeneity, therapeutic strategies must be tailored to the individual patient in order to achieve a low level of disease activity within a limited period of time [29]. Thus, the combination of two treatment modalities has been proposed to achieve such a purpose [30].

Considering the significant involvement of TNF-α in RA, as well as the rising evidences that this cytokine heads the pro-inflammatory cytokine cascade, it becomes a significant therapeutic target [31,32]. Specifically, these evidences led to the development of TNF inhibitors for the treatment of ADs [33–37], as they became the first class of biologic agents to be used in RA treatment [38]. A specific high affinity monoclonal antibody is used to recognize and neutralize selectively its antigen, i.e., TNF-α [18,27,39]. Five different types of TNF-α inhibitors are currently licensed for human clinical use in RA treatment, namely Infliximab, Entarnecept, Adalimumab, Certalizumab Pegol and Golimumab. With the exception of Infliximab, which is administrated by intravenous (IV) infusion, all other medicines are administered subcutaneously [13]. For long-term control of RA, a continuous treatment is required because of disease flares' risk when the therapy is discontinued [1]. Although these therapeutic modalities have been used for quite some time, they present many shortcomings such as lack of specificity, limited antibody half-life, high cost, response variability, or even lack of response to treatments [11,40]. Due to the systemic character of these treatments, not only target tissues but also healthy tissues are exposed to a significant dose of drug, leading to adverse side effects [41]. These effects are common to many tissues, organs and systems, such as cardiovascular, renal, dermatological, and neurological, or risk of severe infection [11,27,28,40].

Furthermore, some attention has been given to drug carriers, in order to maintain effective concentration levels in plasma for extended periods [41]. Examples of these systems are the encapsulation of Infliximab in polylactide-co-glycolide microspheres [42] or porous silicon 3D structures [43], as well as long-term release of Etanercept by polyelectrolyte complex formulated particles [44]. Despite the promising preliminary results, these systems are still poorly developed.

Considering the shortcomings of these treatments, there is a need for innovative approaches that circumvent the aforementioned adverse side effects. Therefore, the main objective of this work was to develop an implantable system capable of capturing excessive TNF-alpha present in intra-articular cavities of an RA patient. For that, we immobilized a neutralizing TNF-α antibody at the surface of a polymeric substrate (i.e., electrospun nanofibers). This strategy takes advantage of specific interactions between the antibody and the antigen, where the antibody (TNF-α antibody) binds to the antigen (TNF-α) avoiding/limiting its harmful pro-inflammatory effects. After the system´s assembly and proved ability to capture TNF-α secreted by activated monocyte-derived macrophages, the cytotoxicity of the biofunctionalized system was tested with human articular chondrocytes. Indeed, the cytocompatibility and chondrogenic differentiation potential of bare electrospun nanofibers were previously reported by us [45–47], corroborating their potential use in cartilage regeneration approaches.


*3.3.3 Phase II trials*

The first test of therapeutic efficacy where both antimicrobial and clinical effects can be measured is traditionally a phase II study (also termed a "Proof -of- Concept" or PoC) in patients with well-defined disease characteristics. The dosing regimen chosen in these studies is derived from the preclinical animal data, PK/PD modeling, and results of phase I studies. A dose close to the maximum tolerated dose seen in phase I studies may be chosen with provision for modification if significant tolerability or safety issues occur. The initial PoC study population is usually adults because only limited amount of human safety information is available. Pediatric patients are typically included later after safety has been documented in juvenile preclinical toxicology studies and in adult clinical trials.

Sample sizes in PoC studies are usually limited though efficient designs with multiple parallel arms for regimen comparison and combination therapy have become more common.

Two pairs of published phase II trials that merit further discussion illustrate some of the methodological issues for PoC and dose finding. Features common across these four studies are a multicenter, multiarm, randomized, parallel group design; enrollment of RT-PCR-positive patients with chronic (mainly indeterminate) disease; use of serial short- and long-term biomarker measurements; and PK sampling for drug exposure.

The CHAGAZOL trial [22] was an open-label, multicenter randomized trial of 79 adults conducted in a Spain comparing high and low doses of posaconazole (POZ) to 60d of standard BZN. The subject screen failure rate for study entry was 57%, and most patients (> 90%) originated from Bolivia. Sixty-five percent of subjects

had chronic indeterminate disease (CID), and the remainder had some evidence of end-organ damage, primarily cardiac (22%). Serial RT-PCR measures showed a high PCR reversion in all arms during the treatment period, but by 10 months post treatment, more than 80% of POZ-treated patients relapsed versus 38% of BZN patients. The transient effectiveness of POZ in this study prompted exploration in a combination study with BZN. The STOP-CHAGAS trial [23] was a multinational study of 120 adults with CID. This partially blinded study randomized patients across four arms comprising POZ alone, BZN plus placebo, BZN plus POZ, and placebo. The higher screen failure rate of 70% may reflect the narrower patient population and broader site participation. The primary outcome measure was RT-PCR negativity at 180d. High response rates (>90%) were seen in the three active arms during treatment but was not sustained for POZ monotherapy with only 13% response at d180 compared with >80% for the BZN arms. Notably, the treatment discontinuation rate is 32% almost halving the per protocol population in the BZN arms.

The second pair of randomized studies are the trials of fosravuconazole (E1224). The single-blind, five-arm trial from the E1224 study group [24] allocated 231 adults with CID to low, high, or short doses of E1224 or to BZN or placebo. This study was conducted in two sites in Bolivia and had screen failure rate of 59% and the antiparasitic effect was assessed through serial RT-PCR and serology. The study was powered to show superiority over placebo for a primary endpoint of sustained PCR response at 12 months after treatment start. The high initial response observed for both E1224 and BZN during treatment was only sustained in BZN-treated group. The follow-on BENDITA trial [17] was a seven-arm, double-blind, double dummy study in 210 adults with CID conducted in three regions of Bolivia. Of the 518 patients screened, 210 were eligible and randomized to one of three BZN treatment arms (2,4, or 8 weeks), one of two BZN plus E1224 combination arms, or to placebo. The primary efficacy endpoint was sustained parasite clearance at 6 months based on RT-PCR. This trial showed that 2 weeks of BZN therapy was almost as good as standard therapy in achieving sustained response with far better tolerability. Whether a 2-week regimen can translate into longer-term benefit remains to be seen. The addition of E1224 to BZN did not improve efficacy but increased the incidence severe adverse events. The low rates of protocol violation and treatment discontinuation in these two trials attest to the quality of the design and execution of the study investigators.

These well-designed and well-executed phase II designs using RT-PCR in patients with CID provided a robust evaluation of drug effect and assist dose selection. This same design approach has also been used in the studies of fexinidazole (NCT02498782, NCT03587766).

### *3.3.4 Phase III trials*

The enormous heterogeneity in published studies conducted in Chagas discussed above emphasizes the need to have robust and consistent approaches to clinical testing. Typically, data from one or more adequately conducted randomized trial would be expected by regulatory authorities to support license approval. The clinical research gold standard for generating evidence of efficacy and safety is a prospective (ideally double blind) randomized clinical trial against the current standard-ofcare (SoC) or placebo. In the majority of Chagas*'* settings, either benznidazole or nifurtimox would be the active comparative control in registration enabling clinical studies. In certain circumstances, a historical or concurrent placebo may be acceptable. The evolution of potentially shorter BZN regimens from four ongoing trials

*New Therapeutics for Chagas Disease: Charting a Course to Drug Approval DOI: http://dx.doi.org/10.5772/intechopen.102891*

(NCT03191162, NCT03981523, NCT04897516, NCT03672487) may significantly impact both a new drug TPP and the design of future comparative trials.

Beyond the fundamental requirements for minimizing bias and variability through randomization, blinding, and stratification, the numerous possibilities for comparative phase III and adaptive study designs will not be covered here as they must be tailored to answer the precise clinical research question of interest. The logistic challenges around designing and executing such trials to meet current regulatory standards are significant and a major investment in research time, finances, and resources. It behooves drug developers to have compelling phase II data before deciding to embark on such studies and to engage in early discussions with regulatory authorities.

A clinically meaningful endpoint (or an acceptable surrogate marker) should have been chosen and agreed with Health Regulatory Authorities; a dose adequately determined; and a minimum follow-up period of 12 months (or more) planned. Ideally, a trial should be prospective, multicenter, multiregion randomized, doubleblind (double dummy), and powered at a minimum for noninferiority against the SoC (BZN/NFX) and superiority against placebo (where used). Factors known to influence variability in response such as age and geographical distribution required careful balancing. Preplanned interim analyses with early stopping rules may be useful to manage risk, and an independent data safety monitoring board is strongly recommended. Adjudication committees for some endpoints may be added as needed. Centralized laboratory assessment for serology and biomarkers is recommended.

The operational challenges of mounting large phase III interventional studies in Chagas disease include effective community engagement; cooperation with local centers of expertise; engagement of government health screening and awareness programs; rapid diagnostics and facilitated access to medical facilities capable of conducting regulatory standard studies. Rigorous trial execution and patient retention are needed to minimize protocol deviations that could compromise the noninferiority designs. Post-approval requirements may include further clinical trials or extended follow-up (3+ years) of study patients.

Where more than one drug candidate exists, prioritization decisions must be made as the resource requirements to run several simultaneous phase III programs may well be prohibitive. Clinical trial designs (master protocols) to improve efficiency of testing of multiple drugs of the same therapeutic class are well accepted by Health Authorities and have been used in oncology and more recently COVID-19 settings [58, 59]. An alternative strategic option would be a combination approach with two new drugs (antiparasitic or antiparasitic plus host response modulator) in a single phase II/III program.

#### *3.3.5 Alternative development strategies*

Discussion of drug development for Chagas disease would not be complete without considering the many outstanding strategic questions concerning the ultimate goal of halting disease progression.

After four decades of use, is it realistic to expect any more gains from BZN (or NFX) alone or is the problem simply related to poor tissue targeting [60]? Is chronic indeterminate disease already too late a setting to influence the course of the disease for any drug and if so, should efforts be redirected to early detection and acute disease where the risk of cardiomyopathy is higher [52]? Finally, would combinations of antiparasitic drugs with or without anti-inflammatory/immune-modifying drugs in

both ACD and CID be more effective than monotherapy? Combination antimicrobial therapy is well established in some infectious diseases (e.g., HIV, HCV, Tuberculosis) but relatively uncommon in Chagas disease. A phase I/II trial (ICTRP REBEC-RBR-5n4htp) studying the combination of BZN and disulfiram antitrypanosomal agents is currently underway in Brazil [61]. While immunomodulatory approaches to Chagas cardiomyopathy have met with some success in preclinical models [41], there are few human trials of antiparasitic and immunomodulatory combinations. Simvastatin combined with BZN was shown to reduce cardiac inflammation in a murine model despite parasite persistence [62], and the ongoing ATOCHA trial (NCT04984616) is evaluating the effects of atorvastatin combined with BZN or NFX with chronic indeterminate disease. In a murine model, attenuated cardiac dysfunction and tissue parasite clearance were seen with the combination of fenofibrate (a peroxisome proliferator-activated receptor ligand) and BZN [63], but no human study of this combination has been published. The poor progress with monotherapy in many settings warrants preclinical and clinical exploration of combination approaches despite the potential for increased costs, more side effects, drug interactions, and increased risk of medication nonadherence.


**3. Description of the AFB-K technique**

Acetate Free Biofiltration with Potassium Profiled Dialysate (AFB-K) is the same haemodiafiltration technique free from acetate as that described above, with the addition of a potassium profiled dialysate. This is achieved with a double bag of concentrate (Santoro et al., 2002) – one bag with potassium and the other without it (Figure 2).

The dialysis machine takes concentrate from one bag or the other to form a potassium profiled dialysate: high at the start of dialysis and low at the end. The initial and final K+ values of the dialysate are established individually for each patient (e.g., 4 mmol/L at the start of dialysis and 1 mmol/L at the end), according to a series of guidelines supplied by the manufacturer, and based on the needs of each patient. The aim is to maintain a constant K+ gradient between the blood and dialysate bath, in order to prevent sudden K+ reductions (Santoro et al., 2002).

The slow decrease in K+ during dialysis is added to the advantages inherent to AFB. Dialysis with high K content in the dialysate cannot adequately reduce patient hyperkalaemia,

permeable biocompatible membranes). These advantages prove more evident in patients susceptible to suffering hypotension during dialysis, such as diabetics or elderly subjects with autonomous nervous system disorders, heart disease or liver disease, and subjects with significant comorbidity (Tsutomu et al., 2011, Sato et al., 2011). In a literature review, Santoro et al. (Santoro., 2007) described a reduction in hypotension events in AFB vs bicarbonate haemodialysis: the probability of intradialysis hypotension with AFB was reported to be close to 40% of the probability of intradialysis hypotension in the case of

**Blood entry Blood entry**

**Dialisate**

**exit**

**Dialisate Dialisat**

Na Ca K Mg

Ca K Mg

+ ++ + ++

**Dialisate without tampon**

**Dialisate without tampon**Na

Cl-

Cl -

H 2

H

CONC.

CONC.

GLUCOSE H2 O

GLUCOSE H2O

Acetate Free Biofiltration with Potassium Profiled Dialysate (AFB-K) is the same haemodiafiltration technique free from acetate as that described above, with the addition of a potassium profiled dialysate. This is achieved with a double bag of concentrate (Santoro et

The dialysis machine takes concentrate from one bag or the other to form a potassium profiled dialysate: high at the start of dialysis and low at the end. The initial and final K+ values of the dialysate are established individually for each patient (e.g., 4 mmol/L at the start of dialysis and 1 mmol/L at the end), according to a series of guidelines supplied by the manufacturer, and based on the needs of each patient. The aim is to maintain a constant K+ gradient between the blood and dialysate bath, in order to prevent sudden K+ reductions

The slow decrease in K+ during dialysis is added to the advantages inherent to AFB. Dialysis with high K content in the dialysate cannot adequately reduce patient hyperkalaemia,

al., 2002) – one bag with potassium and the other without it (Figure 2).

**Blood exit Blood exit**

conventional bicarbonate haemodialysis.

**Na + HCO 3**

**Infusion Infusion**

**-**

Fig. 1. AFB dialysis circuit.

**Patient**

**Patient**

(Santoro et al., 2002).

**3. Description of the AFB-K technique**

though symptomatically it is very well tolerated. Dialysis with low K content in the dialysate adequately reduces patient hyperkalaemia, but proves highly arrhythmogenic and is poorly tolerated by the patients. With AFB-K we are able to adequately reduce hyperkalaemia without causing arrhythmia, since blood K is gradually reduced. The patients who stand to benefit most from this dialysis technique are those with significant comorbidity and therefore with an increased risk of suffering arrhythmia.

Fig. 2. AFB-K dialysis circuit.


4.1.2. Effects on the Central Nervous and Neuromuscular Systems

Behavior alterations consistent with a neurotoxic activity of Jaburetox in *T. infestans* [84] led our group to further investigate the effects on the insect's central nervous system. Immunohistochemical techniques evidenced the labelled somata of the antennal lobe and of the suboesophageal ganglion 3 h after the injection of the peptide into the hemocoel. Co-immunoprecipitation assays followed by tandem mass spectrometry identified the enzyme UDP-*N*-acetylglucosamine pyrophosphorylase as a Jaburetox-interacting protein in the bug's brain and associated ganglia [84]. This enzyme provides the

activated precursor for the synthesis of chitin and for glycosylation pathways of glycoproteins and other derived products [90].

The central nervous system was also a target for Jaburetox in the triatominae *R. prolixus*, and the interaction between the fluorescently-labeled peptide and the organ could be observed in vitro. Furthermore, the activities of different enzymes in the central nervous system were altered after feeding the peptide to fifth instar nymphs [78].

The interaction of Jaburetox with the nervous cord tissue of *N. cinerea* was observed in vitro [62]. Injection of the peptide into the cockroaches *Phoetalia pallida* and *N. cinerea* led to the blockade of evoked contractions of coxal muscle [58,85]. In *N. cinerea*, this effect was amplified by chloral hydrate, (a drug known to reinforce effects mediated by GABA receptors), suggesting that Jaburetox could be activating the gabaergic neurotransmission [85]. As reported in the kissing bugs, the enzymatic activities of several enzymes were modulated in adult cockroaches upon injection [86].

The effect of Jaburetox was tested on *Xenopus laevis* oocytes overexpressing the Nav 1.1 channels from the cockroach *Blatella germanica* [85]. Voltage-clamp analysis showed a 50% increase in the sodium currents upon Jaburetox treatment while no alteration in the kinetics of the Nav 1.1 channel activation was noticed. Muscle and nerve action potentials recorded in the isolated leg of the locust *Locusta migratoria* decreased transiently about 20% in Jaburetox-treated preparations, returning to basal values after 20 min although by then the contraction of the tarsus has stopped. The absence of a fast decrease in the resting membrane potential during the voltage clamp studies, especially considering the membrane-disturbing effects of Jaburetox, suggested that the main component of its neurotoxicity could involve alteration of the gating properties of sodium channels [85]. These aspects were recently reviewed by Barreto et al. [91].


**Abstract**

This study describes the experiences on geriatric palliative care of 12 caregivers in Metro Manila. This study looks on the variation of experiences on motivation and social support in caregiving. This study used a qualitative-descriptive research design and involved key informant interviews of caregivers as a research method. Informants were selected through a non-probability sampling technique specifically through a purposive-convenient sampling. Caregivers cite several of motivations for caregiving. Reasons mentioned are identified to be either intrinsic motivation or extrinsic motivation. Intrinsic motivation being cited are emphatic reasons which include framing patient as family; emotional reasons such as emotional attachment; and cognitive reasons which refers to gaining knowledge in caregiving. Extrinsic motivations by caregivers are incentive reasons which include monetary gains; and negative reinforcements such as institutional punishments. In terms of social support received, caregivers mentioned different sources and functions of support they receive. Caregivers cite two types of social support structure or sources, proximal social support and institution based support. For social support function, responses are categorized into intangible and tangible support.

**Keywords:** geriatric healthcare and management, geriatric palliative caregiving, motivation, social support structure, social support function

### **1. Introduction**

In the past decade, the caregiving profession is known to continue to increase because of the fast growth of the aging population [1]. In that sense, caregivers are high in demand because of the present global situation most especially those who are formal or professional workers and volunteers under long term facilities. Caregivers are individuals who tend to express caregiving behaviors that are required in their job description. Moreover, caregiving is a phenomenon that is increasingly studied by the medical and social sciences field [2]. Caregiving is an act of tending and caring for other individual with a problematic health condition or impairment in a daily basis.

Caregivers experience different situations doing geriatric palliative care. In the past literatures, various studies found different factors that may influence doing palliative care to elderly individuals. A study by Pearlin and Schooler [3], found that personal control and mastery has been a significant factor that shapes the caregiver's experience in doing caregiving. With personal mastery, an individual has the

**156**

*Update in Geriatrics*

**References**

Epub 2018 Mar 27.

2019 Nov 30.

[2] O'Mahony D. STOPP/START criteria for potentially inappropriate medications in older people: origin and progress Review Expert Rev Clin Pharmacol. 2020 Jan;13(1):15-22. doi: 10.1080/17512433.2020.1697676. Epub

[3] Guil Sánchez J. Adequacy of Drug Prescriptions in Elderly Patients Admitted to a Convalescence Unit According Beers Criteria. Rev Calid Asist Nov-Dec 2013;28(6):391-3. doi: 10.1016/j.

cali.2013.05.004. Epub 2013

[4] WHOQOL GROUP. The World Health Organization Quality of life assessment (WHOQOL). Position Paper from the World Health Organization. Soc. Sci. Med. 1995; 41(10):1.403-1.409.

[5] Brombo G, Bianchi L, Maietti E, Malacarne F, Corsonello A, et al. Web site of WHO World Health Organization. http://www.who.int/ mediacentre/news/releases/2014/ world-health-statistics-2014/es

[6] Pendlebury ST, Lovett NG, Smith SC, Dutta N, Bendon C, Lloy-Levery A et al. Observational, longitudinal, study of delirium in consecutive unselected acute medical admissions: age specific rate and associated factors mortality and re admission. BMJ Open 2015, Nov 16;5(11):e007808 doi101136/

bmjopm-2015 007808.

[7] Brombo G, Bianchi L, Maietti E, Malacarne F, Corsonello A, et al. Association of Anticholinergic Drug

[1] Motter RF, Soder Fritzen J, Hilmer S, Vieira Paniz E, Vieira Paniz VM. Potentially inappropriate medication in the elderly: a systematic review of validated explicit criteria. Review Eur J Clin Pharmacol. 2018 Jun; 74(6):679- 700. doi: 10.1007/s00228-018-2446-0.

Burden with Cognitive and Functional Decline Over Time in Older Inpatients: Results from the CRIME Project.Drugs Aging. 2018 Oct;35(10):917-924. doi:

[8] Charlson ME, Pompei P, Ales KL. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic

[9] EuroQol group. EuroQol – a new facility for the measurement of healthrelated quality of life. Health Policy

[10] Li H, Pu S, Liu Q , Huang X, Kuang J, et al. Potentially inappropriate

[11] Achterhof AB, Rozsnyai Z, Reeve E, Jungo KT, Floriani C, Poortvliet RKE, et al. Potentially inappropriate medication and attitudes of older adults towards deprescribing. Streit S.PLoS One. 2020 Oct 26;15(10):e0240463. doi: 10.1371/ journal.pone.0240463. eCollection 2020.

[12] Todd A, Esposo A, Andrew I, Pearson SA, Lindsey L, Holmes H. Inappropriate prescription of preventive drugs in patients with life-limiting diseases: a systematic review. BMJ Support Palliat Care. 2017. 7 (2): 113-121. doi: 10.1136 /

bmjspcare-2015-000941. Epub 2016.

medications in Chinese older adults: The beers criteria compared with the screening tool of older persons'prescriptions criteria. Geriatr Gerontol Int. 2017;17(11):1951-1958. doi:

10.1007/s40266-018-0584-9.

Dis 1987; 40:373-83.

1990; 16: 199-208.

10.1111/ggi.12999.

capability to manipulate their actions hence overcomes different challenges. In the caregiver's perspective, they may be able to experience more positive experiences because they can control and manipulate their actions [4, 5].

In sociology, focuses on the global perspective of the concept of caregiving through caregivers, caregiver roles, and characteristics of caregivers as well as their motivations and social networks. Also, caregiving is studied through groups of caregivers or health care professionals tending to individuals who are in need of care. Caregiving in the field of sociology is studied through the experiences of caregivers from groups and organizations such as family, neighbors, health care institutions, as well as individuals affiliated with religious institutions [6]. All in all, sociologists focus on the influences of behaviors such as motivation as well as social networks of caregivers.

In recent studies, it is found that motivation plays a vital role among caregivers in doing palliative care towards elderly patients. Motivation is defined to be reinforcements for an individual to express a certain kind of behavior [7]. In the past, motivation is studied in the field of social psychology.  Motivations are divided into two types: intrinsic and extrinsic motivation. Intrinsic motivations are reinforcements that are internal in nature. One example of intrinsic reinforcement is having altruistic motivation. According to a study by Hyde, Harris, and Boaden [8], health care workers manifest altruism in dealing with their patients. Altruistic motivation is having a voluntary mindset intended to help and increase another's welfare because the attitude of being concerned for others [9]. Moreover, it is a motivation that seeks to overcome the feeling of sympathy towards individuals who needs help [10]. According to a study by Bhatti and Qureshi [11], health care workers are motivated by the desire to help and take on a useful activity. In the case of geriatric palliative caregiving, caregivers feel the sense of helping their elderly patients without any compensation in return. This is mostly seen among voluntary workers in nursing homes run by religious organizations.

Another theory to explain the intrinsic motivations of doing geriatric palliative caregiving is Selective Investment Theory (SIL). This theory talks about social bonds or close relationship provides motivation that makes individuals suppress their own personal goals if need to prioritize the overall well-being of the other [7]. Moreover, the theory gives emphasis on the role of having close relationships in meeting the needs of other individuals. In recent literatures, caregivers' witness their patients' life at nursing homes hence makes them attached. With attachment, caregivers tend to care more and give more importance to their patients rather than their personal life.

Emotional motivation is also found to be an effective reinforcement for individuals. In a study by Carlo and Randall [10], the feeling of empathy triggers an individual to act or behave in a certain way because of strong emotions. It evokes mostly individuals who are easily affected by people who manifest emotional behaviors. For health care workers, a feeling of empathy urges them to work hard to do pain management on their elderly patients.

Social desirability is a motivation that evokes individuals to behave in a certain way because of social pressure and norms [12]. Individuals are more likely to express caregiving behavior when being observed by their superiors. Also, caregivers feel pressure from their supervisors in order to accomplish their tasks and jobs in doing geriatric palliative caregiving. It is a norm that caregivers must do caregiving duties and nothing else. This reinforces caregivers to tend to their task and normal work. Also, health care workers are motivated to work harder when it is on the motive of desiring a gain of approval and respect from other people most especially their superiors [10].

For extrinsic kinds of motivation, the most frequently used in the health care sector is compensation and punishment. It is found that both are effective tools for reinforcing

#### *Motivation and Social Support Received in Elderly Care: The Case of Geriatric Palliative… DOI: http://dx.doi.org/10.5772/intechopen.93938*

individuals to comply with their job description. However according to Kreps [13], "providing extrinsic incentives for workers can be counterproductive, because it may destroy the workers' intrinsic motivation (p. 359)." In that sense, extrinsic motivation may not be truly effective in reinforcing individuals because it is found to be inefficient.

Access to social support is also a proximal factor that influences the experiences of caregivers in doing geriatric palliative caregiving. Social support is an available assistance from other people and it is also a coping resource used by individuals [14]. Moreover, it talks about received encouragement and assistance by individuals from other people. According to Albrecht and Adelman [15], social support is defined to be a "verbal and nonverbal communication between recipients and providers that reduces uncertainty about the situation, the self, the other, or the relationship, and functions to enhance a perception of personal control in one's life experience (p. 18)".

Social support can be a discourse between two entities. This concept is understood to be given or received. On one hand, social support is an act or behavior that an individual that is to be given to another individual. On the other hand, it is also an act that an individual receives from another individual [16]. Social support can be measured in two categories. One category is called structural support or social integration. It measures the quantity or the extent of which an individual is connected to a social network [17]. This category measures the size, frequency, or density of a social network of an individual [18]. Another category is called functional social support. This measures the quality or functional aspects of social support. It looks on the roles of the social network provides. Functions such as emotional support which focuses on the affect and emotional nurture provided by sources of social support; esteem support which focuses on bolstering an individual to handle a difficult situations; network support which focuses on the affirmation of an individual as a part of a social network; informational support which focuses on which focuses on a the communication that gives insightful information; and tangible support which focuses physical assistance and support [19].

According to Kaufman, Kosberg, Leeper, and Tang [20] having good social support causes positive behavior in doing health care management specifically in the spiritual aspect of geriatric palliative caregiving. Moreover, with good social support, the caregiving job becomes less challenging and burdensome because it is known as a coping resource used by caregivers [14]. However, the caregiving experience becomes challenging and burdensome because of factors such as lack of emotional support from colleagues and peers, social isolation, and inadequate social activities about coping [21].

Support also comes from different sources. It may come from colleagues, peers, and most especially family. Research found that lack of family support has been found to "correlate with high levels of physical as well as psychological exhaustion, with many caregivers reporting that their social support decreases over time" [14].

There may be a lot of studies about motivation and social support, however very limited studies were seen focusing on the variation of motivations and social support structure and functions of geriatric palliative caregivers in the Philippines. With that, the objective of this study is to describe various motivations and social support experiences of caregivers in geriatric palliative caregiving.

#### **2. Methodology**

This study used a qualitative-descriptive research design. This study describes the motivation and social support experiences of professional caregivers tending to elderly patients under intensive care. This study involved key informant interviews of caregivers as a research method. In addition, this involved a face-to-face interview as a research technique.

The population of this study involved professional caregivers employed in a private nursing home in Metro Manila. This study involved 12 key informants as sample. This study utilized a non-probability sampling technique. Key informants are chosen through a purposive-convenient sampling. To be eligible for the interview, the sample should fit the specific selection criteria: 1) Must have an experience doing geriatric palliative care; and 2) Has at least one to two years of experience in the caregiving profession.

The voice-recorded in-depth interviews were transcribed, and the data gathered were sorted according to the research problems they addressed. Content analysis was used as data evaluation through an evaluation of the interview transcriptions. The personal and work characteristics of the caregivers were tabulated to present the trends across these phenomena, and similar answers to the interview questions are grouped together. The information were analyzed and presented based on similarities and differences of the themes.