期刊
PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY
卷 139, 期 -, 页码 15-22出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.pbiomolbio.2018.06.002
关键词
Global optimization; Acceptance rejection sampling; Mathematical modeling; Ordinary differential equations; Genetic algorithm; Metropolis-Hastings
资金
- National Institute on Alcohol Abuse and Alcoholism [1R01AA022714-01A1]
- Air Force Office of Scientific Research [AFOSR FA9550-15-1-0298]
- US Department of Education Graduate Assistance in Areas of National Need (GAANN) [P200A120047]
- Clarendon fund
- EPSRC [EP/G036861/1]
- Pfizer Inc.
Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs' pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen et al., 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop. (C) 2018 Elsevier Ltd. All rights reserved.
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