Moving beyond in silico tools to in silico science in support of drug development research

An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein

Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.

Agent-Based Modeling in Systems Pharmacology

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. WIREs Syst Biol Med 2014, 6:225–245. doi:10.1002/wsbm.1270

How to cite this article:WIREs Syst Biol Med 2014, 6:289–309. doi:10.1002/wsbm.1270

Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT)

The mucosa of the intestinal tract represents a finely tuned system where tissue structure strongly influences, and is turn influenced by, its function as both an absorptive surface and a defensive barrier. Mucosal architecture and histology plays a key role in the diagnosis, characterization and pathophysiology of a host of gastrointestinal diseases. Inflammation is a significant factor in the pathogenesis in many gastrointestinal diseases, and is perhaps the most clinically significant control factor governing the maintenance of the mucosal architecture by morphogenic pathways. We propose that appropriate characterization of the role of inflammation as a controller of enteric mucosal tissue patterning requires understanding the underlying cellular and molecular dynamics that determine the epithelial crypt-villus architecture across a range of conditions from health to disease. Towards this end we have developed the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) to dynamically represent existing knowledge of the behavior of enteric epithelial tissue as influenced by inflammation with the ability to generate a variety of pathophysiological processes within a common platform and from a common knowledge base. In addition to reproducing healthy ileal mucosal dynamics as well as a series of morphogen knock-out/inhibition experiments, SEGMEnT provides insight into a range of clinically relevant cellular-molecular mechanisms, such as a putative role for Phosphotase and tensin homolog/phosphoinositide 3-kinase (PTEN/PI3K) as a key point of crosstalk between inflammation and morphogenesis, the protective role of enterocyte sloughing in enteric ischemia-reperfusion and chronic low level inflammation as a driver for colonic metaplasia. These results suggest that SEGMEnT can serve as an integrating platform for the study of inflammation in gastrointestinal disease.

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. © 2013 Wiley Periodicals, Inc.

Enabling clearance predictions to emerge from in silico actions of quasi-autonomous hepatocyte components

We demonstrate the feasibility of using in silico hepatocyte cultures (ISHCs) to provide predictions of the intrinsic clearance (CL) of compounds in hepatocyte cultures. We compare results with predictions obtained using a multiple linear regression method. Our expectation is that the method can be extended to predict in vivo clearance of new compounds in humans. Within ISHCs, mobile "compounds" carry information describing referent compound properties. We used an iterative refinement protocol for ISHC refinement and development of parameterization methods. Quasi-autonomous "hepatocytes" and their components (including "transporters" and "enzymes") use a small, event-specific subset of compound properties to decide how to interact with encountered compounds each simulation cycle. The probability of occurrence for each event is specified by a rule that uses a subset of compound properties known to influence that event in vitro. ISHC experiments mimic in vitro counterparts. In silico clearance is measured the same as in vitro clearance and is used to predict a corresponding CL value. For 39 of 73 compounds having calculated CL S.D.s, 79% of ISHC predictions and 23% of regression predictions were within CL ± 2 S.D. For all 73 compounds, 38% of ISHC predictions and 32% of regression predictions were within a factor of 2 of the referent CL values. ISHC details during simulations stand as a mechanistic hypothesis of how clearance phenomena emerge during in vitro experiments.