![]() Furthermore, given mathematical similarities in QSP models, computational implementations of QSP workflows are also generalizable for many applications. ![]() The similarities in intended applications for many QSP models suggest common conceptual workflows for how to develop and apply QSP models. 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). Ordinary differential equation (ODE) modeling frameworks are commonly, but not exclusively, employed in QSP models. QSP modeling approaches have been categorized into statistical data-driven, logic-based, differential equations, cellular automata and agent-based, and hybrid and integrated models ( 8). QSP models often are developed to impact drug discovery and development, and often enable the investigation of relationships between biological pathways and observed biomarkers, efficacious dose projections, and population variability ( 1). This “systems” 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– 7). disease progression) and mechanistic meaning that fundamental biological processes are represented with mechanistic fidelity. ![]() QSP has been characterized as a “quantitative 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 incorporate key biological pathways from the systems of interest and the pharmacology of therapeutic interventions, aiming not only a better holistic understanding of the biology but also “optimal 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. 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. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. 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. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. 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. ![]() Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. ![]() 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. 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. ![]()
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