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Closing the Design-Make-Test-Analyze Loop: Interplay between Experiments and Predictions Drives PROTACs Bioavailability
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AI
DOI：10.1021/acs.jmedchem.4c01642.png)
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Abstract
En 中文
The drug development landscape is expanding to include drug modalities such as PROteolysis-TArgeting Chimeras (PROTACs) and peptides, offering possibilities for previously intractable biological targets. However, with their size and chemical nature, they diverge from established frameworks for the prediction of oral bioavailability. This evolution to larger and more complex molecules necessitates new methodologies and prediction models to continuously expand on bioavailability guidelines. We describe the high-capacity adoption of two chromatographic physicochemical assays and their application for iterative compound optimization to achieve oral bioavailability. We further describe how these data underpin the continuous refinement of internal machine learning models, which guide compound synthesis decisions in the molecular design phase. Based on data for a set of 691 PROTACs, and two project examples, we confirm a sweet spot for oral bioavailability at log D values higher than the norm for small molecules and show how experimental data and prediction models synergize to effectively drive chemistry optimization.
Keywords:
PHYSICOCHEMICAL PROPERTIES
DRUG DISCOVERY
PERMEABILITY
SOLUBILITY
SPACE
LIPOPHILICITY
ABSORPTION
CANDIDATES
HPLC