An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research.

Chronic low back pain (LBP) is influenced by a broad spectrum of patient-specific factors as codified in domains of the biopsychosocial model (BSM). Operationalizing the BSM into research and clinical care is challenging because most investigators work in silos that concentrate on only one or two BSM domains. Furthermore, the expanding, multidisciplinary nature of BSM research creates practical limitations as to how individual investigators integrate current data into their processes of generating impactful hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for organizing knowledge, but the practical aspects for how AI may advance LBP research and clinical are beginning to be explored. The goals of the work presented here are to: (1) explore the current capabilities of knowledge integration technologies (large language models (LLM), similarity graphs (SGs), and knowledge graphs (KGs)) to synthesize biomedical literature and depict multimodal relationships reflected in the BSM, and; (2) highlight limitations, implementation details, and future areas of research to improve performance. We demonstrate preliminary evidence that LLMs, like GPT-3, may be useful in helping scientists analyze and distinguish cLBP publications across multiple BSM domains and determine the degree to which the literature supports or contradicts emergent hypotheses. We show that SG representations and KGs enable exploring LBP's literature in novel ways, possibly providing, trans-disciplinary perspectives or insights that are currently difficult, if not infeasible to achieve. The SG approach is automated, simple, and inexpensive to execute, and thereby may be useful for early-phase literature and narrative explorations beyond one's areas of expertise. Likewise, we show that KGs can be constructed using automated pipelines, queried to provide semantic information, and analyzed to explore trans-domain linkages. The examples presented support the feasibility for LBP-tailored AI protocols to organize knowledge and support developing and refining trans-domain hypotheses.

Utilizing virtual experiments to increase understanding of discrepancies involving in vitro-to-in vivo predictions of hepatic clearance.

Predictions of xenobiotic hepatic clearance in humans using in vitro-to-in vivo extrapolation methods are frequently inaccurate and problematic. Multiple strategies are being pursued to disentangle responsible mechanisms. The objective of this work is to evaluate the feasibility of using insights gained from independent virtual experiments on two model systems to begin unraveling responsible mechanisms. The virtual culture is a software analog of hepatocytes in vitro, and the virtual human maps to hepatocytes within a liver within an idealized model human. Mobile objects (virtual compounds) map to amounts of xenobiotics. Earlier versions of the two systems achieved quantitative validation targets for intrinsic clearance (virtual culture) and hepatic clearance (virtual human). The major difference between the two systems is the spatial organization of the virtual hepatocytes. For each pair of experiments (virtual culture, virtual human), hepatocytes are configured the same. Probabilistic rules govern virtual compound movements and interactions with other objects. We focus on highly permeable virtual compounds and fix their extracellular unbound fraction at one of seven values (0.05-1.0). Hepatocytes contain objects that can bind and remove compounds, analogous to metabolism. We require that, for a subset of compound properties, per-hepatocyte compound exposure and removal rates during culture experiments directly predict corresponding measures made during virtual human experiments. That requirement serves as a cross-system validation target; we identify compound properties that enable achieving it. We then change compound properties, ceteris paribus, and provide model mechanism-based explanations for when and why measures made during culture experiments under- (or over-) predict corresponding measures made during virtual human experiments. The results show that, from the perspective of compound removal, the organization of hepatocytes within virtual livers is more efficient than within cultures, and the greater the efficiency difference, the larger the underprediction. That relationship is noteworthy because most in vitro-to-in vivo extrapolation methods abstract away the structural organization of hepatocytes within a liver. More work is needed on multiple fronts, including the study of an expanded variety of virtual compound properties. Nevertheless, the results support the feasibility of the approach and plan.

Contrasting model mechanisms of alanine aminotransferase (ALT) release from damaged and necrotic hepatocytes as an example of general biomarker mechanisms.

Interpretations of elevated blood levels of alanine aminotransferase (ALT) for drug-induced liver injury often assume that the biomarker is released passively from dying cells. However, the mechanisms driving that release have not been explored experimentally. The usefulness of ALT and related biomarkers will improve by developing mechanism-based explanations of elevated levels that can be expanded and elaborated incrementally. We provide the means to challenge the ability of closely related model mechanisms to generate patterns of simulated hepatic injury and ALT release that scale (or not) to be quantitatively similar to the wet-lab validation targets, which are elevated plasma ALT values following acetaminophen (APAP) exposure in mice. We build on a published model mechanism that helps explain the generation of characteristic spatiotemporal features of APAP hepatotoxicity within hepatic lobules. Discrete event and agent-oriented software methods are most prominent. We instantiate and leverage a small constellation of concrete model mechanisms. Their details during execution help bring into focus ways in which particular sources of uncertainty become entangled with cause-effect details within and across several levels. We scale ALT amounts in virtual mice directly to target plasma ALT values in individual mice. A virtual experiment comprises a set of Monte Carlo simulations. We challenge the sufficiency of four potentially explanatory theories for ALT release. The first of the tested model theories failed to achieve the initial validation target, but each of the three others succeeded. Results for one of the three model mechanisms matched all target ALT values quantitatively. It explains how ALT externalization is the combined consequence of lobular-location-dependent drug-induced cellular damage and hepatocyte death. Falsification of one (or more) of the model mechanisms provides new knowledge and incrementally shrinks the constellation of model mechanisms. The modularity and biomimicry of our explanatory models enable seamless transition from mice to humans.

Propagation of Pericentral Necrosis During Acetaminophen-Induced Liver Injury: Evidence for Early Interhepatocyte Communication and Information Exchange.

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity.

A Model Mechanism-Based Explanation of an In Vitro-In Vivo Disconnect for Improving Extrapolation and Translation.

An improved understanding of in vivo-to-in vitro hepatocyte changes is crucial to interpreting in vitro data correctly and further improving hepatocyte-based in vitro-to-in vivo extrapolations to human targets. We demonstrate using virtual experiments as a means of helping to untangle plausible causes of inaccurate extrapolations. We start with virtual mice that use biomimetic software livers. Previously, using these mice, we discovered model mechanisms that enabled achieving quantitative validation targets while also providing plausible causal explanations for temporal characteristics of acetaminophen hepatotoxicity. We isolated virtual hepatocytes, created a virtual culture, and then conducted dose-response experiments in both culture and mice. We expected to see differences between the two dose-response curves but were somewhat surprised that they crossed because it evidenced that simulated acetaminophen metabolism and toxicity are different for virtual culture and mouse contexts even though individual hepatocyte mechanisms were unchanged. Differences in dose-response curves provide a virtual example of an in vivo-to-in vitro disconnect. We use detailed results of experiments to explain this disconnect. Individual hepatocytes contribute differently to system-level phenomena. In liver, hepatocytes are exposed to acetaminophen sequentially. Relative production of the reactive acetaminophen metabolite is largest (smallest) in pericentral (periportal) hepatocytes. Because that sequential exposure is absent in culture, hepatocytes from different lobular locations do not respond the same. A virtual culture-to-mouse translation can stand as a scientifically challengeable hypothesis explaining an in vivo-to-in vitro disconnect. It provides a framework to develop more reliable interpretations of in vitro observations, which then may be used to improve extrapolations.

Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments.

Acetaminophen-induced liver injury in mice is a model for drug-induced liver injury in humans. A precondition for improved strategies to disrupt and/or reverse the damage is a credible explanatory mechanism for how toxicity phenomena emerge and converge to cause hepatic necrosis. The Target Phenomenon in mice is that necrosis begins adjacent to the lobule's central vein (CV) and progresses outward. An explanatory mechanism remains elusive. Evidence supports that location dependent differences in NAPQI (the reactive metabolite) formation within hepatic lobules (NAPQI zonation) are necessary and sufficient prerequisites to account for that phenomenon. We call that the NZ-mechanism hypothesis. Challenging that hypothesis in mice is infeasible because 1) influential variables cannot be controlled, and 2) it would require sequential intracellular measurements at different lobular locations within the same mouse. Virtual hepatocytes use independently configured periportal-to-CV gradients to exhibit lobule-location dependent behaviors. Employing NZ-mechanism achieved quantitative validation targets for acetaminophen clearance and metabolism but failed to achieve the Target Phenomenon. We posited that, in order to do so, at least one additional feature must exhibit zonation by decreasing in the CV direction. We instantiated and explored two alternatives: 1) a glutathione depletion threshold diminishes in the CV direction; and 2) ability to repair mitochondrial damage diminishes in the CV direction. Inclusion of one or the other feature into NZ-mechanism failed to achieve the Target Phenomenon. However, inclusion of both features enabled successfully achieving the Target Phenomenon. The merged mechanism provides a multilevel, multiscale causal explanation of key temporal features of acetaminophen hepatotoxicity in mice. We discovered that variants of the merged mechanism provide plausible quantitative explanations for the considerable variation in 24-hour necrosis scores among 37 genetically diverse mouse strains following a single toxic acetaminophen dose.

Open-Ended Evolution: Perspectives from the OEE Workshop in York.

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE-it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .

Virtual Experiments Enable Exploring and Challenging Explanatory Mechanisms of Immune-Mediated P450 Down-Regulation.

Hepatic cytochrome P450 levels are down-regulated during inflammatory disease states, which can cause changes in downstream drug metabolism and hepatotoxicity. Long-term, we seek sufficient new insight into P450-regulating mechanisms to correctly anticipate how an individual's P450 expressions will respond when health and/or therapeutic interventions change. To date, improving explanatory mechanistic insight relies on knowledge gleaned from in vitro, in vivo, and clinical experiments augmented by case reports. We are working to improve that reality by developing means to undertake scientifically useful virtual experiments. So doing requires translating an accepted theory of immune system influence on P450 regulation into a computational model, and then challenging the model via in silico experiments. We build upon two existing agent-based models-an in silico hepatocyte culture and an in silico liver-capable of exploring and challenging concrete mechanistic hypotheses. We instantiate an in silico version of this hypothesis: in response to lipopolysaccharide, Kupffer cells down-regulate hepatic P450 levels via inflammatory cytokines, thus leading to a reduction in metabolic capacity. We achieve multiple in vitro and in vivo validation targets gathered from five wet-lab experiments, including a lipopolysaccharide-cytokine dose-response curve, time-course P450 down-regulation, and changes in several different measures of drug clearance spanning three drugs: acetaminophen, antipyrine, and chlorzoxazone. Along the way to achieving validation targets, various aspects of each model are falsified and subsequently refined. This iterative process of falsification-refinement-validation leads to biomimetic yet parsimonious mechanisms, which can provide explanatory insight into how, where, and when various features are generated. We argue that as models such as these are incrementally improved through multiple rounds of mechanistic falsification and validation, we will generate virtual systems that embody deeper credible, actionable, explanatory insight into immune system-drug metabolism interactions within individuals.

A cell-centered, agent-based framework that enables flexible environment granularities.

BACKGROUND: Mechanistic explanations of cell-level phenomena typically adopt an observer perspective. Explanations developed from a cell's perspective may offer new insights. Agent-based models lend themselves to model from an individual perspective, and existing agent-based models generally utilize a regular lattice-based environment. A framework which utilizes a cell's perspective in an off-lattice environment could improve the overall understanding of biological phenomena. RESULTS: We present an agent-based, discrete event framework, with a demonstrative focus on biomimetic agents. The framework was first developed in 2-dimensions and then extended, with a subset of behaviors, to 3-dimensions. The framework is expected to facilitate studies of more complex biological phenomena through exploitation of a dynamic Delaunay and Voronoi off-lattice environment. We used the framework to model biological cells and to specifically demonstrate basic biological cell behaviors in two- and three-dimensional space. Potential use cases are highlighted, suggesting the utility of the framework in various scenarios. CONCLUSIONS: The framework presented in this manuscript expands on existing cell- and agent-centered methods by offering a new perspective in an off-lattice environment. As the demand for biomimetic models grows, the demand for new methods, such as the presented Delaunay and Voronoi framework, is expected to increase.

Toward modular biological models: defining analog modules based on referent physiological mechanisms.

BACKGROUND: Currently, most biomedical models exist in isolation. It is often difficult to reuse or integrate models or their components, in part because they are not modular. Modular components allow the modeler to think more deeply about the role of the model and to more completely address a modeling project's requirements. In particular, modularity facilitates component reuse and model integration for models with different use cases, including the ability to exchange modules during or between simulations. The heterogeneous nature of biology and vast range of wet-lab experimental platforms call for modular models designed to satisfy a variety of use cases. We argue that software analogs of biological mechanisms are reasonable candidates for modularization. Biomimetic software mechanisms comprised of physiomimetic mechanism modules offer benefits that are unique or especially important to multi-scale, biomedical modeling and simulation. RESULTS: We present a general, scientific method of modularizing mechanisms into reusable software components that we call physiomimetic mechanism modules (PMMs). PMMs utilize parametric containers that partition and expose state information into physiologically meaningful groupings. To demonstrate, we modularize four pharmacodynamic response mechanisms adapted from an in silico liver (ISL). We verified the modularization process by showing that drug clearance results from in silico experiments are identical before and after modularization. The modularized ISL achieves validation targets drawn from propranolol outflow profile data. In addition, an in silico hepatocyte culture (ISHC) is created. The ISHC uses the same PMMs and required no refactoring. The ISHC achieves validation targets drawn from propranolol intrinsic clearance data exhibiting considerable between-lab variability. The data used as validation targets for PMMs originate from both in vitro to in vivo experiments exhibiting large fold differences in time scale. CONCLUSIONS: This report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases. The pharmacodynamic response module developed here is robust to changes in model context and flexible in its ability to achieve validation targets in the face of considerable experimental uncertainty. Adopting the modularization methods presented here is expected to facilitate model reuse and integration, thereby accelerating the pace of biomedical research.

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.

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.

Cloud computing and validation of expandable in silico livers.

BACKGROUND: In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. RESULTS: The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. CONCLUSIONS: The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware.

Tracing multiscale mechanisms of drug disposition in normal and diseased livers.

Hepatic drug disposition is different in normal and diseased livers. Different disease types alter disposition differently. What are the responsible micromechanistic changes and how do they influence drug movement within the liver? We provide plausible, concrete answers for two compounds, diltiazem and sucrose, in normal livers and two different types of cirrhotic rat livers: chronic pretreatment of rats with carbon tetrachloride (CCl(4)) and alcohol caused different types of cirrhosis. We started with simulated disposition data from normal, multilevel, physiologically based, object-oriented, discrete event in silico livers (normal ISLs) that validated against diltiazem and sucrose disposition data from normal livers. We searched the parameter space of the mechanism and found three parameter vectors that enabled matching the three wet-lab data sets. They specified micromechanistic transformations that enabled converting the normal ISL into two different types of diseased ISLs. Disease caused lobular changes at three of six levels. The latter provided in silico disposition data that achieved a prespecified degree of validation against wet-lab data. The in silico transformations from normal to diseased ISLs stand as concrete theories for disease progression from the disposition perspective. We also developed and implemented methods to trace objects representing diltiazem and sucrose during disposition experiments. This allowed valuable insight into plausible disposition details in normal and diseased livers. We posit that changes in ISL micromechanistic details may have disease-causing counterparts.

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 strategies unravel and trace how liver disease changes hepatic drug disposition.

Liver disease changes the disposition properties of drugs, complicating drug therapy management. We present normal and "diseased" versions of an abstract, agent-oriented In Silico Livers (ISLs), and validate their mechanisms against disposition data from perfused normal and diseased rat livers. Dynamic tracing features enabled spatiotemporal tracing of differences in dispositional events for diltiazem and sucrose across five levels, including interactions with representations of lobular microarchitectural features, cells, and intracellular factors that sequester and metabolize. Differences in attributes map to measures of histopathology. We measured disease-causing differences in local, intralobular ISL effects, obtaining until now unavailable views of how and where hepatic drug disposition may differ in normal and diseased rat livers from diltiazem's perspective. Exploration of disposition in less and more advanced stages of disease is feasible. The approach and technology represent an important step toward unraveling the complex changes from normal to disease states and their influences on drug disposition.

Essential operating principles for tumor spheroid growth.

BACKGROUND: Our objective was to discover in silico axioms that are plausible representations of the operating principles realized during characteristic growth of EMT6/Ro mouse mammary tumor spheroids in culture. To reach that objective we engineered and iteratively falsified an agent-based analogue of EMT6 spheroid growth. EMT6 spheroids display consistent and predictable growth characteristics, implying that individual cell behaviors are tightly controlled and regulated. An approach to understanding how individual cell behaviors contribute to system behaviors is to discover a set of principles that enable abstract agents to exhibit closely analogous behaviors using only information available in an agent's immediate environment. We listed key attributes of EMT6 spheroid growth, which became our behavioral targets. Included were the development of a necrotic core surrounded by quiescent and proliferating cells, and growth data at two distinct levels of nutrient. RESULTS: We then created an analogue made up of quasi-autonomous software agents and an abstract environment in which they could operate. The system was designed so that upon execution it could mimic EMT6 cells forming spheroids in culture. Each agent used an identical set of axiomatic operating principles. In sequence, we used the list of targeted attributes to falsify and revise these axioms, until the analogue exhibited behaviors and attributes that were within prespecified ranges of those targeted, thereby achieving a level of validation. CONCLUSION: The finalized analogue required nine axioms. We posit that the validated analogue's operating principles are reasonable representations of those utilized by EMT6/Ro cells during tumor spheroid development.

Predictions of hepatic disposition properties using a mechanistically realistic, physiologically based model.

Quantitative mappings were established between drug physicochemical properties (PCPs) and parameter values of a physiologically based, mechanistically realistic, in silico liver (ISL). The ISL plugs together autonomous software objects that represent hepatic components at different scales and levels of detail. Microarchitectural features are represented separately from the mechanisms that influence drug metabolism. The same ISL has been validated against liver perfusion data for sucrose and four cationic drugs: antipyrine, atenolol, labetalol, and diltiazem. Parameters sensitive to drug-specific PCPs were tuned so that ISL outflow profiles from a single ISL matched in situ perfused rat liver outflow profiles of all five compounds. Quantitative relationships were then established between the four sets of drug PCPs and the corresponding four sets of PCP-sensitive, ISL parameter values; those relationships were used to predict PCP-sensitive, ISL parameter values for prazosin and propranolol given only their PCPs. Relationships were established using three different methods: 1) a simple linear correlation method, 2) the fuzzy c-means algorithm, and 3) a simple artificial neural network. Each relationship was used separately to predict ISL parameter values for prazosin and propranolol, given their PCPs. Those values were applied in the ISL used earlier to predict the hepatic disposition details for each drug. Although we had only sparse data available, all predicted disposition profiles were judged reasonable (within a factor of 2 of referent profile data). The order of precision, based on a similarity measure, was 3 > 2 > 1.

Modeling and simulation of hepatic drug disposition using a physiologically based, multi-agent in silico liver.

PURPOSE: Validate a physiologically based, mechanistic, in silico liver (ISL) for studying the hepatic disposition and metabolism of antipyrine, atenolol, labetalol, diltiazem, and sucrose administered alone or in combination. MATERIALS AND METHODS: Autonomous software objects representing hepatic components such as metabolic enzymes, cells, and microarchitectural details were plugged together to form a functioning liver analogue. Microarchitecture features were represented separately from drug metabolizing functions. Each ISL component interacts uniquely with mobile objects. Outflow profiles were recorded and compared to wet-lab data. A single ISL structure was selected, parameterized, and held constant for all compounds. Parameters sensitive to drug-specific physicochemical properties were tuned so that ISL outflow profiles matched in situ outflow profiles. RESULTS: ISL simulations were validated separately and together against in situ data and prior physiologically based pharmacokinetic (PBPK) predictions. The consequences of ISL parameter changes on outflow profiles were explored. Selected changes altered outflow profiles in ways consistent with knowledge of hepatic anatomy and physiology and drug physicochemical properties. CONCLUSIONS: A synthetic, agent-oriented in silico liver has been developed and successfully validated, enabling us to posit that static and dynamic ISL mechanistic details, although abstract, map realistically to hepatic mechanistic details in PBPK simulations.