Toward Dynamic Prescribing Information: Codevelopment of Companion Model-Informed Precision Dosing Tools in Drug
Thomas M Polasek1,2, Craig R Rayner1,2, Richard W Peck3
1Certara, Princeton, NJ, USA.
Clinical Pharmacology in Drug Development
|December 1, 2018
Summary
Model-informed precision dosing (MIPD) uses biosimulation to personalize drug doses for better outcomes. Codeveloping MIPD tools during drug development can generate needed evidence for wider clinical use.
Area of Science:
- Pharmacometrics
- Clinical Pharmacology
- Translational Science
Background:
- Model-informed precision dosing (MIPD) leverages biosimulation to optimize drug dosage for individual patients, aiming to enhance efficacy and reduce toxicity.
- While biosimulation is common in drug development, MIPD adoption is limited in clinical practice due to insufficient evidence presented in a clinically relevant format.
- Bridging the gap between academic research and clinical application requires translating complex data into actionable, evidence-based medicine for healthcare providers.
Purpose of the Study:
- To advocate for the integrated development of MIPD tools alongside new drug development.
- To propose a strategy for accelerating the generation of robust evidence supporting MIPD implementation in routine clinical settings.
- To highlight the potential of MIPD tools to become dynamic prescribing resources for complex patient populations.
Main Methods:
- This commentary proposes a strategic approach to MIPD tool development.
- It emphasizes the co-development of MIPD tools concurrent with the drug development lifecycle.
- The strategy focuses on generating evidence that aligns with evidence-based medicine principles.
Main Results:
- Codevelopment of MIPD tools during drug development can expedite the creation of essential supporting evidence.
- This approach facilitates the translation of biosimulation insights into practical clinical decision-making.
- Companion MIPD tools can evolve into dynamic prescribing information, aiding dose selection for challenging cases.
Conclusions:
- Integrating MIPD tool development into the drug development process is crucial for broader clinical adoption.
- Generating robust, clinician-friendly evidence is key to overcoming current barriers to MIPD implementation.
- Dynamic prescribing information derived from MIPD tools holds promise for improving patient care and outcomes.
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