Tanja Obradovic: Path to Delivering Optimal Dose of the New Drug to Cancer Patients is Complex
Tanja Obradovic, Oncology Medical Strategy Advisor at Mercurial AI, shared on LinkedIn:
“Path to delivering optimal dose of the new drug to cancer patients is complex and involves strategic clinical development during end of preclinical selection and into early clinical phases with utilization of improvements in tools available including simulation and AI.
For several years both FDA and ASCO committed many workshops and comments to help disseminate useful information to the field. Latest comes as excellent review just published in Journal of Clinical Oncology
Article outlines the factors contributing to the need for a paradigm shift, identifies remaining gaps in dosage development, and proposes a set of scientific principles that are critical to the path ahead. Five major principles are listed that should guide development tailored for specific drug case:
- Collection and use of more expansive patient safety and tolerability data
- Tailoring of the drug development plans to the characteristics of the drug
- Use of the modern trial designs to generate systematic data on dosage
- Use expanded eligibility criteria in dosage optimization trials to foster the generalizability of results to postmarket settings
- Expanding and refining clinical pharmacology analyses and observations (exposure-toxicity, exposure-response) and only incorporate biopsies where scientifically justified
Adapting to the new paradigm by all stakeholders is a process. Building successful oncology development strategy, decision making rigor within company structure, adoption of advanced tools and navigation of resources toward most optimal use by Pharma and Biotech is a challenge posed by FDA and larger ASCO community with ultimate goal of improved outcomes for the patients at the bed side.”
Title: Totality of the Evidence: Optimizing Dosage Selection Strategies in Oncology
Authors: Laura A. Levit, Mirat Shah, Mark J. Ratain, Elizabeth Garrett-Mayer, Atiqur Rahman, Marc Theoret, R. Donald Harvey
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OncoThon 2024, Online
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Dec. 14-16, 2023