Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Hepatic Drug Metabolism, Uremic Toxins and Bacterial Composition Over Chronic Kidney Disease Progression

Uremic toxin retention and an altered gut microbiota are suspected to influence cytochrome P450s (CYPs) contributing to the unpredictable pharmacokinetics in chronic kidney disease (CKD). We aim to characterize dysbiosis and uremia to elucidate associations between CYP expression and CKD progression. Rats fed control or CKD-inducing diet were subsequently sacrificed across five time points over 42 days. CYP expression and activity were compared to alterations in the 1) plasma and liver metabolome and 2) bacterial microbiota. CYP3A2 and CYP2C11, respectively, were downregulated in CKD by ≥76% (p\u3c0.001) simultaneously or slightly premature to CKD onset defined by creatinine. Metabolite profiles were altered before the gut microbiota and gut-derived uremic toxins including indoxyl sulfate, phenyl sulfate and 4-ethylphenyl sulfate correlated with CYP3A2 or CYP2C11. Identified bacterial genera, Turicibacter and Parabacteroides, characterized CKD and require future study. In conclusion, CYP3A2 and CYP2C11 are downregulated prior to dysbiosis but correlate with select uremic toxins

Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics

2016 Fall.Includes bibliographical references.The determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data. The work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis

Individualized, discrete event, simulations provide insight into inter- and intra-subject variability of extended-release, drug products

Abstract Objective Develop and validate particular, concrete, and abstract yet plausible in silico mechanistic explanations for large intra- and interindividual variability observed for eleven bioequivalence study participants. Do so in the face of considerable uncertainty about mechanisms. Methods We constructed an object-oriented, discrete event model called subject (we use small caps to distinguish computational objects from their biological counterparts). It maps abstractly to a dissolution test system and study subject to whom product was administered orally. A subject comprises four interconnected grid spaces and event mechanisms that map to different physiological features and processes. Drugs move within and between spaces. We followed an established, Iterative Refinement Protocol. Individualized mechanisms were made sufficiently complicated to achieve prespecified Similarity Criteria, but no more so. Within subjects, the dissolution space is linked to both a product-subject Interaction Space and the GI tract. The GI tract and Interaction Space connect to plasma, from which drug is eliminated. Results We discovered parameterizations that enabled the eleven subject simulation results to achieve the most stringent Similarity Criteria. Simulated profiles closely resembled those with normal, odd, and double peaks. We observed important subject-by-formulation interactions within subjects. Conclusion We hypothesize that there were interactions within bioequivalence study participants corresponding to the subject-by-formulation interactions within subjects. Further progress requires methods to transition currently abstract subject mechanisms iteratively and parsimoniously to be more physiologically realistic. As that objective is achieved, the approach presented is expected to become beneficial to drug development (e.g., controlled release) and to a reduction in the number of subjects needed per study plus faster regulatory review

Establishing species-specific 3D liver microtissues for repeat dose toxicology and advancing in vitro to in vivo translation through computational modelling

The scientific basis of xenobiotic safety is complicated because of the variance in predictability of the primary and secondary pharmacology of foreign chemical substances, as well as variability in individual susceptibility within the population [1]. Despite a wealth of research into this field, our understanding of the mechanisms underpinning the occurrence of adverse effects from xenobiotics remains limited [2]. Adverse drug reactions (ADRs) represent a major encumbrance to the development of new therapeutics with approximately 21% of drug attrition attributed to toxicity during the development process [3]. The in vivo/ex vivo use of animals in science, and in particular drug development, is a global practice and the main purposes of animal experiments are; (i) to gain basic biological knowledge, (ii) for fundamental medical research, (iii) for the discovery and development of drugs, vaccines and medical devices, and (iv) for the toxicity testing of xenobiotics/drugs [4]. However, with there being species-species differences in mechanistic responses, it is difficult to assess results in animal trials and translate these findings in order to predict the in-vivo response in humans [5]. Current in vitro model systems developed to assess ADRs have a number of down falls including; (i) the isolating procedure of primary hepatocytes, (ii) their cost, (iii) inter-donor differences, (iv) limited availability, (v) as well as increasing ethical pressure to implement the 3R’s (Replacement, Reduction and Refinement) in research [6]. The emphasis on producing a relevant and representative in vitro model for hepatotoxicity has therefore expanded. The aim of this thesis is to characterise a novel 3D microtissue model that, in the future, aims to provide a better in vitro platform to assess liver toxicity after repeat-dose exposure to xenobiotics. This is particularly important because the processes of hepatotoxicity manifest themselves over several hours and even days, and therefore in vitro models need to be able to comprehensively assess toxic potential for repeat-dose scenarios as well as chronic exposures. Computational modelling is implemented to 14 allow translation of results and to better bridge the gap between in vitro and in vivo approaches and to exploit the knowledge gained from experimental work. Chapter 1 is a critical review of culture techniques and cell types that are used during the development stages of xenobiotic discovery. A number of in vitro models are evaluated with regards to the determination of hepatotoxic potential of compounds. This review has been previously published [7]. Chapter 1 also includes an introduction to mathematical modelling of hepatic clearance and other pharmacokinetic approaches. Chapter 2 describes the experimental characterisation of a primary rat hepatocyte (PRH) spheroid model. The application of the liquid-overlay technique (LOT) [8] with PRH results in the production of viable and reproducible microtissues, amenable for high-throughput investigations. I show that our in vitro system mimics the in vivo cellular morphology, exhibiting both structural and functional polarisation, along with active and functional transporters. Chapter 3 describes the construction of a mathematical model of oxygen diffusion for my experimental in vitro spheroid system. This model is utilised to predict oxygen profiles within the spheroids and to propose optimised operating conditions in order to recapitulate healthy sinusoidal oxygen tensions. This optimisation is based on initial cell seeding densities and experimentally derived oxygen consumption rates (OCR). Chapter 4 describes the construction of a mathematical model to predict the diffusion of xenobiotics based on their inherent physicochemical properties. The in silico system incorporates specific parameters from the experimental spheroid system including paracellular transport features, namely tortuosity and pore fraction properties. The model describes how these spatiotemporal characteristics vary over the duration of the culture period and what effect these have on the transport of xenobiotics

Mitigation of Pesticide Toxicity by Food-Grade Lactobacilli

Lactobacilli are Gram-positive bacteria used in fermented foods. Many species are commensal microbiota members that confer host benefits. This thesis investigated lactobacilli mitigation of organophosphate and neonicotinoid pesticide toxicity in mammals and insects, respectively. Lactobacillus rhamnosus GG (LGG) and GR-1 (LGR-1) were found to sequester, but not metabolize, organophosphate pesticides (parathion and chlorpyrifos) in solution. For LGG, this sequestration reduced organophosphate pesticide absorption in a Caco-2 intestinal Transwell model and promoted survival of Drosophila melanogaster lethally exposed to chlorpyrifos. Supplementation of mice with LGR-1 was found to alter host xenobiotic metabolism in the liver, and consequently chlorpyrifos metabolism following acute exposure. Drosophila supplemented with L. plantarum, a species indigenous to the fly, elicited an immune response that was correlated with increased survival following imidacloprid (neonicotinoid) exposure. Taken together, these experiments suggest that humans and honeybees could benefit from simple and affordable dietary supplementation with Lactobacillus strains to offset pesticide exposure

Caractérisation des substrats xénobiotiques et des inhibiteurs des cytochromes CYP26A1, CYP26B1 et CYP26C1 par modélisation moléculaire et études in vitro

Without crystal structures to study the CYP26 family of drug metabolizing enzymes, homology models were used to characterize CYP26A1, CYP26B1 and CYP26C1 and to identify substrates and inhibitors of the enzymes. Computational models of each isoform based on structural homology to CYP120 were validated by docking all-trans retinoic acid, an endogenous ligand of CYP26. Docking of retinoic acid receptor agonists and antagonists suggested that tazarotenic acid (TA) and adapalene may be metabolic substrates for CYP26, data which was confirmed using in vitro metabolite identification assays. Phenotyping experiments determined that CYP26s played a major role in the metabolism of these compounds in vitro. The kinetics of TA sulfoxidation by CYP26A1 and CYP26B1 were characterized and the compound was proposed as an in vitro probe of CYP26 activity in single enzyme expression systems. Structural characterization efforts identified similarities between the CYP26 homology models and the known crystal structure of CYP2C8, in agreement with previously published reports. Using TA as a probe, the IC50’s of known CYP2C8 inhibitors was measured against CYP26A1 and CYP26B1, with a statistically significant correlation observed between CYP26A1 and CYP2C8. Additional in vitro and computational experiments were used to characterize the inhibition mechanism for the most potent inhibitors. The observed in vitro inhibition was then used to predict the likelihood of CYP26 inhibition being involved in clinically relevant drug interactions. As a whole, the results presented support the role of the CYP26s in the metabolism of xenobiotic compounds as well as in potential in vivo drug interactions.En l’absence de structures tridimensionnelles expérimentales des cytochromes P450 CYP26A1, CYP26B1 et CYP26C1, la caractérisation de leur substrats et ligands s’est basée sur l’analyse des modèles structuraux obtenus par modélisation par homologie avec la structure expérimentale du cytochrome P450 CYP120. La justesse des modèles a été validée par l’amarrage de l’acide rétinoïque all-trans dans des configurations compatibles avec les métabolites attendus. L’amarrage d’agonistes et d’antagonistes des récepteurs nucléaires RARs prédirent l’acide tazaroténique (TA) et l’adapalène comme des substrats potentiels. Les expériences in vitro confirmèrent la métabolisation de ces 2 médicaments par les CYP26s. L’analyse de la cinétique de sulfoxidation du TA par CYP26A1 and CYP26B1 a permis d’établir le TA comme la référence contrôle de l’activité de ces enzymes. Puis, la comparaison des modèles des CYP26s avec la structure cristalline de CYP2C8 a permis d’identifier des similarités structurales de leurs inhibiteurs. Une corrélation entre l’inhibition de CYP26A1 et de CYP2C8 par des inhibiteurs connus de CYP2C8 a été démontrée après détermination de leurs IC50 pour CYP26A1 et CYP26B1 en utilisant le TA comme substrat de référence. La mesure de l’inhibition in vitro fut ensuite utilisée pour évaluer la possibilité que les CYP26s soient impliquées dans des interactions médicamenteuses observées pour certaines molécules. Cette thèse caractérise et appuie le rôle encore mal connu des CYP26s dans la métabolisation in vivo de certains xénobiotiques ainsi que l’effet potentiel de leur inhibition qui favoriserait la survenue d'effets indésirables

Addressing Public Health Risks of Persistent Pollutants Through Nutritional Modulation and Biomimetic Nanocomposite Remediation Platforms

Due to their relative chemical stability and ubiquity in the environment, chlorinated organic contaminants such as polychlorinated biphenyls (PCBs) pose significant health risks and enduring remediation challenges. Engineered nanoparticles (NPs) provide a novel platform for sensing/remediation of these toxicants, in addition to the growing use of NPs in many industrial and biomedical applications, but there remains concern for their potential long-term health effects. Research highlighted herein also represents a transdisciplinary approach to address human health challenges associated with exposure to PCBs and NPs. The objectives of this dissertation research are two-fold, 1) to develop effective methods for capture/sensing and remediation of environmental toxicants, and 2) to better understand associated risks and to elucidate relevant protective mechanisms, such as lifestyle-related modulators of environmental disease. Prevalent engineered nanoparticles, including aluminum oxide and titanium dioxide, have been studied to better understand effective nanoparticle dispersion methods for in vitro nanotoxicology studies. This work has served both to effectively stabilize these nanoparticles under physiological conditions and to better understand the associated mechanisms of toxicity, which links these metal nanoparticles to endothelial oxidative stress and inflammation through phosphorylation of key cellular signaling molecules and increased DNA binding of pro-inflammatory NFκB. Surface functionalization, though, is being found to limit potential toxicity and has been utilized in subsequent research. A novel polyphenol-functionalized, NP-based system has been developed which combines the biomimetic binding capabilities of nutrient polyphenols with the separation and heating capabilities of superparamagnetic iron oxide NPs for the capture/sensing of organic contaminants in polluted water sources. Magnetic nanocomposite microparticles (MNMs) incorporating the fluorescent polyphenols quercetin and curcumin exhibit high affinity for model organic pollutants followed by rapid magnetic separation, addressing the need for sustainable pollutant remediation. Further work has been performed to both better understand health concerns associated with environmental toxicants such as PCBs and to determine effective methods for modulating their toxicity. This research has shown that PCB remediation through dechlorination is a viable technique for decreasing endothelial inflammation, although complete dechlorination to biphenyl is necessary to effectively eliminate superoxide production, NFκB activation, and induction of inflammatory markers. Additionally, the nutrient polyphenol EGCG, found in green tea, has been shown to serve as a biomedical modulator of in vivo PCB toxicity by up-regulating a battery of antioxidant enzymes transcriptionally controlled by AhR and Nrf2 proteins