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In the liver and bile canaliculi, influx and efflux transporters modification can influence intrinsic clearance. Transporters play a significant role in moving drugs within liver cells. Elaborate models, such as the Biopharmaceutical Classification System (BCS), are essential to relate transporters to drug disposition. This system categorizes drugs into four classes based on solubility and permeability, providing insights into elimination routes and the effects of transporters following oral...
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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Refined ADME Profiles for ATC Drug Classes.

Luca Menestrina1, Raquel Parrondo-Pizarro1,2, Ismael Gómez1

  • 1Chemotargets SL, Parc Cientific de Barcelona, Baldiri Reixac 4 (TR-03), 08028 Barcelona, Catalonia, Spain.

Pharmaceutics
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning models to define distinct drug property profiles for specific therapeutic indications, refining absorption, distribution, metabolism, and excretion (ADME) profiles for better drug design.

Keywords:
ADMEAI drug discoveryAI/ML modelsATC classificationdrug classesgenerative chemistrypharmacokineticsphysicochemical properties

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Generative chemistry aims to create novel, potent, and selective drug candidates with favorable pharmacokinetic properties.
  • Established absorption, distribution, metabolism, and excretion (ADME) property ranges are broadly applied in drug discovery.
  • Specific therapeutic indications and administration routes necessitate tailored drug property profiles.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting physicochemical and ADME properties of small molecules.
  • To analyze ADME property distributions across different drug classes.
  • To establish refined ADME profiles for guiding *de novo* drug design.

Main Methods:

  • Development of a methodological pipeline for building and validating ML models.
  • Utilizing publicly available datasets for physicochemical and ADME properties.
  • Comparative analysis of predicted versus experimental ADME data across fourteen drug classes.

Main Results:

  • Significant variations in ADME property distributions were observed across Anatomical, Therapeutic, and Chemical (ATC) drug classifications.
  • ML model predictions showed good agreement with experimental data for most ADME properties.
  • Distinct ADME profiles were identified for various drug classes.

Conclusions:

  • Refined ADME profiles specific to ATC drug classes can guide the *de novo* generation of lead structures.
  • This approach supports the design of advanced drug candidates tailored for specific therapeutic targets.
  • The study provides a framework for data-driven optimization of drug properties in discovery programs.