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. 2019 Feb;8(2):62-76.
doi: 10.1002/psp4.12373. Epub 2019 Jan 30.

A Survey of Software Tool Utilization and Capabilities for Quantitative Systems Pharmacology: What We Have and What We Need

Sergey Ermakov  1 , Brian J Schmidt  2 , Cynthia J Musante  3 , Craig J Thalhauser  2
Affiliations

A Survey of Software Tool Utilization and Capabilities for Quantitative Systems Pharmacology: What We Have and What We Need

Sergey Ermakov et al. CPT Pharmacometrics Syst Pharmacol. 2019 Feb.

Abstract

Quantitative systems pharmacology (QSP) is a rapidly emerging discipline with application across a spectrum of challenges facing the pharmaceutical industry, including mechanistically informed prioritization of target pathways and combinations in discovery, target population, and dose expansion decisions early in clinical development, and analyses for regulatory authorities late in clinical development. QSP's development has influences from physiologic modeling, systems biology, physiologically-based pharmacokinetic modeling, and pharmacometrics. Given a varied scientific heritage, a variety of tools to accomplish the demands of model development, application, and model-based analysis of available data have been developed. We report the outcome from a community survey and resulting analysis of how modelers view the impact and growth of QSP, how they utilize existing tools, and capabilities they need improved to further accelerate their impact on drug development. These results serve as a benchmark and roadmap for advancements to the QSP tool set.

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Conflict of interest statement

The authors declared no competing interests for this work.

Figures

Figure 1
Figure 1
Composition of survey participants by (a) affiliation and (b) experience. DMPK, drug metabolism and pharmacokinetics; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic; PKPD, pharmacokinetic/pharmacodynamic; QSP, quantitative systems pharmacology.
Figure 2
Figure 2
(a) Effect of quantitative systems pharmacology (QSP) modeling in drug discovery and development, (b) expected near term QSP modeling goals and deliverables, and (c) major obstacles to further progress in QSP modeling. MID3, model‐informed drug discovery and development; MOA, mechanism of action.
Figure 3
Figure 3
Upper part shows the software tools selected by survey participants for their evaluation; 102 responses total. Lower plot indicates the feature(s) in which the selected software tool excels, as perceived by its user; responses combined for all tools evaluated.
Figure 4
Figure 4
Quantitative systems pharmacology models most frequently developed by users. ODE, ordinary differential equation; PDE, partial differential equation.
Figure 5
Figure 5
Quantitative systems pharmacology software features and their importance as evaluated by users. Survey questions are given on the right side of the plot, answer options are presented on the left against corresponding bar plots. Percent values show numbers calculated with respect to the total number of answers (survey questions 2.12–2.18).
Figure 6
Figure 6
Prevalence of parameter estimation algorithms used for quantitative systems pharmacology modeling.
See this image and copyright information in PMC

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