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Moving beyond in silico tools to in silico science in support of drug development research
Author(s) -
Hunt C. Anthony,
Ropella Glen E.P.
Publication year - 2011
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.20412
Subject(s) - in silico , computer science , computational biology , process (computing) , focus (optics) , drug discovery , class (philosophy) , function (biology) , action (physics) , drug development , biochemical engineering , computational model , machine learning , artificial intelligence , biological system , bioinformatics , chemistry , drug , biology , pharmacology , engineering , biochemistry , physics , quantum mechanics , evolutionary biology , gene , optics , operating system
Exploitation of concretized mechanistic models and simulation methods enables the acquisition of a competitive advantage through deeper, easily shared, mechanistic insight into the disease and/or health phenomena that are the focus of the research and development (R&D) organization. The models are analogues of the biological wet‐lab models used to support that R&D. An analogue is an explanatory and evolving hypothesis about the mechanistic consequences of xenobiotic or biologic interventions. As such, it is fundamentally different from the familiar inductive, equation‐based, pharmacokinetic, pharmacodynamic, and related models. Analogues are designed for experimentation and to be useful in the face of incomplete data and multiple uncertainties. These models use interchangeable components and require iterative refinement. They enable linking coarse‐grained systemic phenomena to fine‐grained molecular details, including molecular targets. To simplify and focus this discussion, we describe one example of the new class of models, in silico livers (ISLs). We present a vision of how the biological wet‐lab side of the R&D process might function when these models and methods are fully implemented within a common computational framework. Accumulated mechanistic knowledge is easily measured and visualized in action; thus, it can be easily challenged. Components within analogues that have been validated for many compounds can use programmed “intelligence” to automatically parameterize for, and respond to, a new, not previously seen compound based on its physicochemical properties. Each analogue can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete, while moving closer to personalized medicine. Drug Dev Res 72: 153–161, 2011. © 2010 Wiley‐Liss, Inc.