Abstract
Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.
Highlights
Quantitative systems pharmacology (QSP) has been characterized as a Bquantitative analysis of the dynamic interactions between drug(s) and a biological system that aims to understand the behavior of the system as a whole [1].^ There are various existing QSP approaches and applications, and one common feature of QSP models is that they strive to incorporateElectronic supplementary material The online version of this article contains supplementary material, which is available to authorized users. 1 Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey 08543-4000, USA. 2 To whom correspondence should be addressed
4000, USA. 2 To whom correspondence should be addressed. (e-mail: brian.schmidt@bms.com) key biological pathways from the systems of interest and the pharmacology of therapeutic interventions, aiming a better holistic understanding of the biology and Boptimal and translatable pharmacological pathway interventions [2].^ QSP models are often multi-scale in that they characterize processes that occur at multiple scales of space and time and mechanistic meaning that fundamental biological processes are represented with mechanistic fidelity
Workflows to develop reasonable parameterizations, comprehensively integrate different experimental data, and investigate parameter uncertainties/variabilities are very important for QSP model deployment
Summary
QSP has been characterized as a Bquantitative analysis of the dynamic interactions between drug(s) and a biological system that aims to understand the behavior of the system as a whole [1].^ There are various existing QSP approaches and applications, and one common feature of QSP models is that they strive to incorporateElectronic supplementary material The online version of this article (doi:10.1208/s12248-017-0100-x) contains supplementary material, which is available to authorized users. 1 Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey 08543-4000, USA. 2 To whom correspondence should be addressed. (e-mail: key biological pathways from the systems of interest and the pharmacology of therapeutic interventions, aiming a better holistic understanding of the biology and Boptimal and translatable pharmacological pathway interventions [2].^ QSP models are often multi-scale in that they characterize processes that occur at multiple scales of space and time (e.g., ligand binding vs. disease progression) and mechanistic meaning that fundamental biological processes are represented with mechanistic fidelity. (e-mail: key biological pathways from the systems of interest and the pharmacology of therapeutic interventions, aiming a better holistic understanding of the biology and Boptimal and translatable pharmacological pathway interventions [2].^ QSP models are often multi-scale in that they characterize processes that occur at multiple scales of space and time (e.g., ligand binding vs disease progression) and mechanistic meaning that fundamental biological processes are represented with mechanistic fidelity This Bsystems^ approach can better inform target selection and the decision process for advancing compounds through preclinical and clinical research [3]; as such, it is becoming increasingly important in pharmaceutical research and development as a potential means of reducing attrition and improving productivity [4,5,6,7]. ODE models may be broadly applied to describe tissue, cellular, and molecular and biochemical systems, with inherent strengths and limitations that must be evaluated for a given application [8,9]
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