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A membrane permeability database for nonpeptidic macrocycles.

Qiushi Feng1, Danjo De Chavez1, Jan Kihlberg2

  • 1Department of Chemistry-BMC, Uppsala University, SE-75123, Uppsala, Sweden.

Scientific Data
|January 3, 2025
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Summary

Developing new drugs is challenging for difficult targets. This study introduces a database and a new metric to predict macrocycle membrane permeability, aiding drug discovery.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Drug development is costly, especially for difficult-to-drug targets.
  • Macrocycles are promising for modulating challenging targets and oral administration.
  • Assessing membrane permeability for drug development is laborious and expensive.

Purpose of the Study:

  • To create a comprehensive online database of macrocycle membrane permeability data.
  • To introduce a novel descriptor, the amide ratio (AR), for classifying macrocycles.
  • To facilitate in silico prediction of membrane permeability for drug discovery.

Main Methods:

  • Curated 5638 membrane permeability data points for 4216 nonpeptidic macrocycles from literature, patents, and repositories.
  • Developed the amide ratio (AR) descriptor to quantify the peptidic nature of macrocycles.
  • Established an online database (https://swemacrocycledb.com/) for accessible data and predictions.

Main Results:

  • The database provides extensive membrane permeability data for nonpeptidic and semipeptidic macrocycles.
  • The amide ratio effectively classifies macrocycles into peptidic, semipeptidic, and nonpeptidic categories.
  • In silico predictions using the database and AR can accelerate drug discovery.

Conclusions:

  • The developed database and AR descriptor address a critical gap in macrocycle drug discovery resources.
  • This resource enables cost-effective prediction of membrane permeability, crucial for oral bioavailability and intracellular targeting.
  • Facilitates the design and selection of macrocyclic drug candidates for difficult-to-drug targets.