OptiRanker: a simulation and optimization framework for efficient in vivo validation of drug prioritization...

Area of Science:

• Computational biology
• Bioinformatics
• Pharmacogenomics

Background:

• Personalized medicine requires optimal drug selection based on individual patient profiles.
• Drug prioritization algorithms using omics data are increasing, but lack rigorous in vivo validation.
• Efficient frameworks are needed to determine minimal experimental cohort sizes for robust results.

Purpose of the Study:

• To introduce OptiRanker, a statistical simulation framework for optimizing in vivo validation of drug prioritization algorithms.
• To enable the determination of the smallest experimental cohort size that yields statistically robust outcomes.
• To reduce the scale of experimental validation for computational drug discovery tools.

Main Methods:

• OptiRanker perturbs algorithmic predictions with controlled noise to assess performance ranking preservation.
• It uses weighted mean squared error (WMSE) and Spearman correlation against baseline rankings.
• In silico validation involved 36 drug IC50s, 798 cell-line models, and 3 published algorithms.

Main Results:

• Accurate predictor rankings were recovered across simulated conditions, substantially reducing experimental scale.
• The full predictor ranking was achieved with as few as six individuals and one drug in silico.
• OptiRanker demonstrated the ability to identify minimal cohorts for statistically robust results.

Conclusions:

• OptiRanker provides a reproducible and exploratory approach to optimize in vivo validation trials.
• It addresses a key bottleneck in translating computational models into clinical applications.
• The framework facilitates the development of clinically actionable tools for personalized medicine by optimizing experimental design.