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Updated: Jun 8, 2025

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Summary
This summary is machine-generated.

New drug modalities like PROteolysis-TArgeting Chimeras (PROTACs) require updated oral bioavailability prediction. Chromatographic assays and machine learning models help optimize these complex molecules for drug development.

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Area of Science:

  • Drug discovery and development
  • Medicinal chemistry
  • Pharmacokinetics

Background:

  • The emergence of novel drug modalities, including PROteolysis-TArgeting Chimeras (PROTACs) and peptides, presents new opportunities for targeting previously intractable biological targets.
  • The unique size and chemical properties of these advanced modalities deviate from traditional small molecule frameworks, challenging existing models for predicting oral bioavailability.
  • There is a growing need for innovative methodologies and predictive models to accommodate the complexities of larger, more intricate molecules in drug development.

Purpose of the Study:

  • To describe the implementation of high-capacity chromatographic physicochemical assays for optimizing oral bioavailability in drug discovery.
  • To explain how experimental data from these assays inform and refine machine learning models for guiding compound synthesis.
  • To identify optimal physicochemical properties, specifically log D values, for achieving oral bioavailability in PROTACs.

Main Methods:

  • High-throughput adoption of two chromatographic physicochemical assays to generate compound data.
  • Iterative compound optimization guided by experimental assay results.
  • Development and continuous refinement of internal machine learning models utilizing assay data.
  • Analysis of data from 691 PROTACs and two project case studies.

Main Results:

  • Established a workflow for iterative optimization of drug candidates using chromatographic assays.
  • Demonstrated that optimal oral bioavailability for PROTACs occurs at higher log D values compared to traditional small molecules.
  • Showcased the synergistic effect of experimental data and predictive modeling in driving efficient chemistry optimization.

Conclusions:

  • Chromatographic physicochemical assays provide valuable data for optimizing novel drug modalities.
  • Machine learning models, refined by experimental data, effectively guide the molecular design phase for improved oral bioavailability.
  • The findings establish new guidelines for oral bioavailability prediction, particularly for larger molecules like PROTACs.