QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Abstract

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.

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors

“…Importantly, ongoing clinical trials may provide insights on the effect of entinostat and ipilimumab on the immune system and resistance mechanism in breast cancer development, which would allow us to make step-bystep modification of the model and its parameters and improve its predictive power (Pitt et al, 2016;Darvin et al, 2018;Eladdadi et al, 2018;Mahlbacher et al, 2019). Our goal is to understand the dynamic interactions between drugs and the immune system in cancer as a whole, to update our assumptions on drug/tumor-immune dynamics through comparison between model predictions and clinical observations, and thereby to guide drug development and clinical trial design (Cheng et al, 2017;Nijsen et al, 2018;Bai et al, 2019;Bradshaw et al, 2019).…”

“…It is built with a detailed MDSC module and pharmacokinetics and pharmacodynamics of entinostat, to investigate the effect of entinostat and its combination with nivolumab and ipilimumab by conducting an in silico virtual clinical trial. Virtual clinical trials aim to generate virtual patient cohorts with physiologically plausible parameters and predict efficacies of treatments of interest using in silico simulations with a QSP model (Allen et al, 2016;Cheng et al, 2017;Rieger et al, 2018). Due to the heterogeneity of patient cohorts enrolled in clinical trials and wide range of treatment strategies, in silico simulations using a virtual patient cohort that resembles the desired clinical population can provide insights into the potential therapeutic outcome even before the therapy begins.…”

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

“…Several software tools (e.g., PhysioLab by Entelos, Aegis by Immunetrics, PKSim, and MoBi by Bayer Technologies) were developed specifically for QSP modeling as a primary objective. There have been additional QSP software initiatives, including DBSolve, ViSP, and Open Systems Pharmacology, as well as specialized MATLAB and R packages including KroneckerBio, the QSP Toolbox, MatVPC, and mrgsolve . However, these tools have yet to gain a large following in the QSP community, as they are used primarily by end users within or in collaboration with their originating institutions.…”

Review of quantitative systems pharmacological modeling in thrombosis

“…QSP) is in aiding in the process of hypothesis generation and testing in drug discovery and clinical development (Figure 4). 32,55,56 There are numerous published examples of the use of mechanistic models of thrombosis being used to aid in drug discovery and development. As mentioned above, a mechanistic model of the coagulation cascade was used to predict the doseresponse of rivaroxaban for VTE and bleeding in orthopedic surgery patients; as well as to estimate the optimal timing of switching from warfarin to rivaroxaban.…”