Creating a Roadmap to Quantitative Systems Pharmacology–Informed Rare Disease Drug Development: A Workshop Report

Abstract

One of the goals of the Accelerating Rare Disease Cures (ARC) program in the Center for Drug Evaluation and Research (CDER) at the US Food and Drug Administration (FDA) is the development and use of regulatory and scientific tools, including drug/disease modeling, dose selection, and translational medicine tools. To facilitate achieving this goal, the FDA in collaboration with the University of Maryland Center of Excellence in Regulatory Science and Innovation (M‐CERSI) hosted a virtual public workshop on May 11, 2023, entitled “Creating a Roadmap to Quantitative Systems Pharmacology–Informed Rare Disease Drug Development.” This workshop engaged scientists from pharmaceutical companies, academic institutes, and the FDA to discuss the potential utility of quantitative systems pharmacology (QSP) in rare disease drug development and identify potential challenges and solutions to facilitate its use. Here, we report the main findings from this workshop, highlight the key takeaways, and propose a roadmap to facilitate the use of QSP in rare disease drug development.

Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by quantitative systems pharmacology and machine learning

“…To tune hyperparameters, we performed a 10-fold cross validation using grid search (GridSearchCV function). Three hyperparameters were optimized: the number of trees (n_estimator, searched in [10,100,500]), the maximum depth of a single tree, (max_depth, searched in [None, 2,8,10,12]), and the maximum features of a single tree (max_features, set to either None or to the square root of the total number of features). The resulting optimal hyperparameter values were n_estimator: 500, max_depth: 12, max_features: None.…”

“…Even though GBM represents 70% of all adult primary malignant brain tumors, its incidence remains relatively low, around 3 per 100,000 individuals ( 9 ). As a result, patient recruitment for classical clinical trials may be challenging which impairs classical drug development and innovative approaches prioritizing hypothesis/therapies to test in the clinics and enabling more personalized strategies according to QSP-based biomarkers would be particularly helpful ( 10 ). The cornerstone of GBM pharmacotherapies, TMZ, usually demonstrates moderate efficacy when administered as a single agent, in combination with RT, which may be explained by the intrinsic resistance to chemotherapy of some GBM cell subpopulations or by their capacity to evolve under treatment pressure towards resistant cell phenotypes ( 11, 12 ).…”

Life scientists improve QSP model quality and impact

“…The usefulness of these tools is reduced for diseases and biological areas with limited direct data. These are the same areas where QSP models are the most helpful and a life scientist’s input the most valuable ( Bai et al, 2024 ). In the future, these tools may be more autonomous, but for now, they should be used with appropriate input from both engineering and life science team members.…”