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Quantifying and Communicating Uncertainty in Preclinical Human Dose-Prediction.
M Sundqvist1, A Lundahl1, M B Någård1
1CVMD iMed DMPK, AstraZeneca R&D SE-431, 83, Mölndal, Sweden.
CPT: Pharmacometrics & Systems Pharmacology
|July 31, 2015
Summary
Quantify and communicate uncertainty in human dose predictions for drug discovery. This method integrates uncertain inputs into a single plot, aiding compound ranking and clinical trial design.
Area of Science:
- Drug discovery and development
- Pharmacokinetics and pharmacodynamics
- Quantitative systems pharmacology
Background:
- Accurate human dose prediction is crucial for advancing drug candidates from discovery to clinical trials.
- Quantifying uncertainty in these predictions is essential for informed decision-making in pharmaceutical research.
- Current methods for uncertainty communication can be complex and hinder efficient interpretation.
Purpose of the Study:
- To present a tutorial on quantifying and communicating uncertainty in human dose predictions.
- To demonstrate an uncomplicated method for integrating diverse uncertain input data.
- To facilitate better decision-making in drug discovery and early clinical trial design.
Main Methods:
- Utilizing three distinct drug-discovery case studies.
- Integrating multiple uncertain input parameters into a unified predictive framework.
- Developing a single, uncomplicated plot to visualize key predictions and their associated uncertainties.
Main Results:
- Demonstrated successful integration of various uncertain input data into a single visualization.
- Showcased the ability to present predictions and uncertainties for multiple compounds or scenarios simultaneously.
- Provided a clear and efficient method for communicating complex predictive information.
Conclusions:
- The presented method offers an effective approach to quantify and communicate uncertainty in human dose predictions.
- This facilitates improved compound ranking and more robust early clinical trial design.
- The uncomplicated plotting method enhances the utility of dose-prediction models in drug discovery.
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