New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Autor: Mitra A; Clinical Pharmacology, Kura Oncology Inc., Boston, Massachusetts, USA., Tania N; Translational Clinical Sciences, Pfizer Research and Development, Cambridge, Massachusetts, USA., Ahmed MA; Quantitative Clinical Pharmacology, Takeda Development Center, Cambridge, Massachusetts, USA., Rayad N; Clinical Pharmacology, Modeling and Simulation, Parexel International (Canada) LTD, Mississauga, Ontario, Canada., Krishna R; Certara Drug Development Solutions, Certara USA, Inc., Princeton, New Jersey, USA., Albusaysi S; Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia., Bakhaidar R; Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia., Shang E; Global Regulatory Affairs and Clinical Safety, Merck &Co., Inc., Rahway, New Jersey, USA., Burian M; Clinical Science, UCB Biopharma SRL, Braine-l'Alleud, Belgium., Martin-Pozo M; Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee, USA., Younis IR; Quantitative Pharmacology and Pharmacometrics, Merck &Co., Inc., Rahway, New Jersey, USA.
Jazyk: angličtina
Zdroj: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2024 Dec; Vol. 116 (6), pp. 1398-1411. Date of Electronic Publication: 2024 Jul 11.
DOI: 10.1002/cpt.3366
Abstrakt: Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.
(© 2024 Kura Oncology Inc. Clinical Pharmacology & Therapeutics © 2024 American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
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