A Quantitative Systems Pharmacology (QSP) approach for developing combination immune-oncology therapies can be used to better predict effective drug combinations, especially to more accurately correlate the physiological differences between preclinical models and human patients.
This white paper addresses common PBPK myths and misconceptions and demonstrates how this approach is an industrial and regulatory necessity in modern drug development.
Biologic drug development is a rapidly evolving sector in the biopharmaceutical industry. Immunogenicity is an inherent challenge with this complex class of drugs. A quantitative systems pharmacology approach can be used to predict and better manage immunogenicity, and as a tool to guide clinical and regulatory decision-making in biologics drug development.
The impact of MIDD is especially powerful in oncology, where numerous cases demonstrate its enormous value in streamlining and accelerating the development cycle and supporting breakthrough therapy options for these fragile patients.
By combining disparate data into coherent mechanistic models, quantitative systems pharmacology is becoming a key tool for picking the right dose for first-in-human trials and other early make-or-break decisions.
Orphan drugs affect 350,000 people worldwide, including 10% of the US population and 1 in 25 Europeans. Model-informed drug development (MIDD) approaches, such as PBPK and PopPK have been embraced by sponsors and regulators, and play a key role in modernizing and accelerating orphan drug development.
Traditional toxicology methods, using in vivo animal testing, is an unrealistic approach to predict chemical risk assessment. Mechanistic modeling and simulation tools such as PBPK and QST can expedite toxicological screening, support the prioritization for testing compounds that merit greater study, and reduce unnecessary animal testing.
Precision dosing is a crucial cornerstone of precision medicine that will provide patients the most efficacious medications with minimum probability of adverse events. Model-informed precision dosing, using quantitative modeling and simulation approaches, such as PBPK and NLME, can improve precision dosing in clinical care.