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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-009-9958-3) contains supplementary material, which is available to authorized users.

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.Changes in drug disposition properties caused by liver disease complicate drug selection and therapy management. Recent advances in computational biology are providing new, broadly applicable strategies to unravel such complexities. How does cirrhosis (Hung et al., 2002a,b) cause observed changes in hepatic drug disposition? More detailed understanding of precisely how drugs behave in diseased relative to normal livers requires new modeling strategies in which events at different levels can be observed and measured in both space and time. We address this need and provide a novel means of creating and testing real, working representations of hypothesized mechanisms. We present and validate a plausible explanation for structural and functional differences in the normal and diseased rat livers from the perspective of a specific drug, diltiazem (Hung et al., 2001), a cationic compound known to interact with hepatic components at all levels. We then show how those changes may have caused the observed differences in diltiazem's disposition.The In Silico Liver (ISL) Yan et al., 2008a,c) in Fig. 1 is an abstract analog built in software using discrete, object-oriented methods. It is an advanced example of what has been referred to as executable biology (Fisher and Henzinger, 2007;Hunt et al., 2008). ISLs are physiologically based, multilevel analogs of livers undergoing perfusion. They are not intended to be accurate, precise descriptions of how we think the liver works nor are they intended to provide precise, accurate predictions. Rather, they help us reduce uncertainties about hepatic mechanisms enabling us to refine, explore, and test hypotheses about the mechanistic details of hepatic drug disposition. In depicting three-dimensional hepatic morphology at a cellular level, it uses quasiautonomous components that recognize and interact uniquely with mobile objects representing different compounds. The...

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 diseasecausing counterparts.Liver cirrhosis alters hepatic drug disposition, complicating drug therapy management (Le Couteur et al., 2005;Dourakis, 2008). The nature of alterations is dependent on both the cause and the extent of disease. Improved mechanistic insight is needed on two fronts. We need supported concrete theories for 1) how the interaction of a drug with the hepatic microarchitecture contributes to overall measures of disposition and 2) how disease progressively alters those microarchitectural features. Achieving both is complicated by the heterogeneity of hepatic microarchitectural features (for examples and a discussion, see Liu and Pang, 2006) and differences in cirrhosis. We focus on two standard rat models of cirrhosis: chronic treatment with 1) carbon tetrachloride (CCl 4 ) and 2) ethanol (Hung et al., 2002a,b). In advanced stages, CCl 4 treatment produces acute hepatocellular injury with centrilobular necrosis and stenosis. In contrast, chronic alcohol treatment produces hepatocellular injury with inflammation and perivenular macrovesicular steatosis (fatty degeneration). Both treatments cause fibrosis.We report significant progress in achieving both objectives by developing, refining, validating, and experimenting on discrete, object-and agent-based in silico livers (ISLs). ISLs are works that are in progress. Observable micromechanisms in ISLs map directly to wet-lab counterparts, which facilitates the falsification ...

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. © 2013 Wiley Periodicals, Inc.