ArticlePharmaceutical research2025
Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach.
Article in Pharmaceutical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Hybrid mechanistic-machine learning PK/PD models with digital biomarkers: from cage to clinic.Frontiers in pharmacology · 2026Review
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4 authors.
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Abstract
purposeAccurate prediction of human clearance (CL) is essential in early drug development. Single Species Scaling (SSS) using rat pharmacokinetic (PK) data, particularly with unbound plasma fraction (f
methodsWe developed a new approach, called Fraction-based Linear EXtrapolation SSS (FLEX-SSS fu Rat), which switches between SSS fu Rat and SSS Rat formulas based on an optimized fu threshold. The threshold and scaling coefficients were derived using a training set of 200 compounds. Additionally, a random forest (RF) machine learning model was built using molecular descriptors. Both models were validated using an external dataset of 62 compounds.
resultsAll five predictive models showed comparable performance; among them, the consensus model combining FLEX-SSS fu Rat and RF yielded the best result: 40.3% within 2-fold error, only 16.1% above 5-fold, and GMFE of 2.7.
conclusionThis study is the first to systematically validate SSS fu Rat using an independent dataset. The integration of threshold-based allometry and machine learning enabled more accurate human CL prediction, supporting informed decisions in first-in-human dose selection.
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