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

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics. 

/pdf/virtual-populations-for-quantitative-systems-pharmacology-2fqbgf1r.pdf

Virtual Populations for Quantitative Systems Pharmacology Models.

Project Optimus is a U.S. Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. While there is much promise in this initiative, there are several challenges that need to be addressed, including multi-dimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence (ToE) and with the mindset of model-informed drug development (MIDD), we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Realizing the Promise of Project Optimus: Challenges and Emerging Opportunities for Dose Optimization in Oncology Drug Development.

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

It is probably not well known that 4000 years BC. Sumerians were able to prepare many medicaments and illness was considered a sort of divine punishment while healing was the consequent purification, a viewpoint that has heavily affected the Western world up to the modern age. The scientific darkness permeating Europe after the end of the Roman Empire favored the survival of this concept while the golden age of the Arab Science (9th–13th centuries) and Renaissance represented the seeds of a paramount change. However, it is only at the end of the 19th century that, under a rigorous experimental Galilean approach, the real origins of many diseases were discovered. As a matter of fact, the modern age of drug delivery started after the World War II and the first example of mathematical modeling in this field dates back to 1961. The clear affirmation of mathematical modeling in the biopharmaceutical field took place in the last 20 years of the 20th century thanks to valuable researchers such as Peppas and Langer. The third millennium opened with new important challenges such as the simultaneous modeling of drug release, adsorption, distribution metabolism and elimination (ADME), processes that rule drug fate in vivo. Thus, this chapter aims to describe and comment on the most recent advances in the mathematical modeling of drug release and ADME processes in the light of Paracelsus’ belief of the human body as a chemical plant. 

Drug delivery and in vivo absorption

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.

https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12109

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

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.

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