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WIREs Syst Biol Med
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Parsing interindividual drug variability: an emerging role for systems pharmacology

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There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype‐drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug‐specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically‐orientated systems biology and pharmacometrics. Pharmacologically‐orientated systems biology utilizes high throughput omics technologies, including next‐generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data‐derived models of sufficient detail will be required to help realize the goal of precision medicine. WIREs Syst Biol Med 2015, 7:221–241. doi: 10.1002/wsbm.1302 This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine
The interrelated processes for systems pharmacology multiscale model development. This figure provides a nonexhaustive overview of the processes and interconnections relevant to multiscale modelling. First, from a clinical observation and/or new research finding, a new research question is generated. Three major empirical resources can be harnessed to address the question: clinical, in vitro/animal and publically available empirically derived databases. Multi‐omics approaches coupled with bioinformatics can uncover new associations. Network description and analysis can glean further information from existing databases (e.g., of high throughput data), predicting new targets and defining molecular sub‐networks associated with drug response phenotypes of interest (e.g., adverse drug reactions). Conventional biological investigations can validate these new associations and predictions, derive mechanistic insight, and perform detailed biochemical kinetics analyses. This empirical data can be incorporated into quantitative pharmacometric models. Population pharmacokinetics (POP PK) top down modelling is tightly fitted to empirical data. However physiologically based PK (PBPK) coupled with in vitro–in vivo extrapolation (IVIVE) and enhanced pharmacodynamics (ePD) modelling are more bottom up, using empirical data where available and assumptions when necessary. Model simulations and assumptions will drive further empirical experimentation, leading to an iterative process of model development and refinement. Through combining detailed PBPK‐IVIVE and ePD models that are adequately fitted to empirical data, systems pharmacology multiscale models with adequate predictive power to facilitate precision medicine will hopefully be developed.
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The rationale for a multiscale network‐based understanding of drug action. The human body can be parsed into a hierarchy of biological levels; interactions within and between levels form networks that are interconnected to other networks, resulting in the complex human body system. The founder constituents of this dynamic complex system are the genome and exposome; the latter represents environmental exposures (e.g., smoking) that interact with and influence all biological levels of the human body. Many drugs have more than one protein target and therefore a network‐based understanding is more informative than a single target perspective. In this figure, the genomic, proteomic and tissue/organ levels have been expanded, although all levels can inform an individual's response to drug therapy. Genetic polymorphisms can, e.g., alter the structure and/or abundance of a drug target and important proteins mediating the drug‐induced proteomic network response. This network response influences other levels in different times and spaces, for example altering gene transcription and tissue function. Intra‐ and inter‐level interactions ultimately lead to the emergence of an individual patient's clinical drug response. Through investigating and modelling these interactions using empirical and pharmacometric methods, illustrated further in Figure , the aim is to develop multiscale models to facilitate dose‐ and drug‐adjusted precision medicine. ADR: adverse drug reaction
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Overview of the main pharmacokinetic modelling methods. (a) Noncompartmental analysis is the preferred method to determine overall drug exposure (i.e., AUC), using the trapezoidal rule, and other pharmacokinetic parameters (e.g., Cmax, clearance, elimination half‐life, etc.) as it involves few assumptions. (b) Compartmental and (c) physiologically based pharmacokinetic models (PBPK) are constructed from compartments that are interconnected using differential equations that describe drug flow between model constituents. Conventional compartmental models are constructed from one or more compartments that are descriptive, rather than mechanistically representative; the final model is parsimonious and compartments are only included if they noticeably improve the final model fit to the empirical data. PBPK models include multiple compartments that represent actual physiology (i.e., organs and blood), incorporate data from more diverse sources, and if properly validated can be used to make PK predictions and extrapolations for circumstances (e.g., different doses or routes of administration) beyond those used to construct the model. (Reprinted with permission from Ref. ; Copyright 2013.
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