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

/pdf/qsp-toolbox-computational-implementation-of-integrated-237nt28tnj.pdf

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

Since its origins, Chemical Engineering has undergone huge transformations and most importantly led to the birth of Biological Engineering, a Chemical Engineering branch devoted to studying the biomedical problems by means of the mass, energy, and momentum balances. Thus, Biological Engineering can represent the Chemical Engineering contribution to Quantitative Systems Pharmacology, an emerging discipline aimed at the application of systems biology modeling to drug discovery. In the wide frame of Biological Engineering, this chapter deals with drug delivery systems based on polymeric matrices as they are very versatile and reliable. In particular, attention is focused on the description of polymeric matrices and their characterization by means of different experimental techniques such as rheology, low-field NMR, crioporosimetry, small-angle neutron scattering, release, and swelling tests. Then, the release mechanisms are presented and discussed and, finally, a mathematical model explaining matrix viscoelasticity is described in detail.

Drug delivery from polymeric matrices

Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Various computational methods such as ADME/Tevaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.

/pdf/computational-methods-and-applications-for-quantitative-2jczj5pf84.pdf

Computational methods and applications for quantitative systems pharmacology

Interest in quantitative systems pharmacology (QSP) as an approach to mechanistically inform drug development has exploded in recent years. Additionally, both drug development and regulatory scientists have increasingly expressed interest in the potential for QSP to guide regulatory decisions. The extent to which QSP can routinely do either is an open question, and there have been examples of QSP facilitating key decisions and transitions in drug discovery and development. This article is protected by copyright. All rights reserved.

https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12403

Quantitative Systems Pharmacology: A Regulatory Perspective on Translation.

Abstract Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP’s pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP’s quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor–ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a “learn and confirm paradigm,” integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP’s utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency. 

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

Quantitative Systems Pharmacology (QSP) is experiencing increased application in the drug discovery and development process. Like its older sibling, systems biology, the QSP field is comprised of a mix of established disciplines and methods, from molecular biology to engineering to pharmacometrics. As a result, there exist critical segments of the discipline that differ dramatically in approach and a need to bring these groups together toward a common goal.

GPS for QSP: A Summary of the ACoP6 Symposium on Quantitative Systems Pharmacology and a Stage for Near-Term Efforts in the Field

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GPS for QSP: A Summary of the ACoP6 Symposium on Quantitative Systems Pharmacology and a Stage for Near-Term Efforts in the Field