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Crystallization of Membrane Proteins in Lipidic Mesophases
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Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement.

Fu Xiao1,2, Yinxiang Cheng1,3, Jian-Rong Wang1

  • 1State Key Laboratory of Drug Research and Drug Discovery and Design Center, Pharmaceutical Analytical & Solid-State Chemistry Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

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|October 27, 2022
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Summary

A new deep learning model, CocrystalGCN, successfully screened and identified novel bexarotene (BEX) cocrystals. These cocrystals significantly improve BEX solubility and bioavailability for treating cutaneous T-cell lymphoma (CTCL).

Keywords:
GCNbexarotenebioavailabilitycocrystal prediction

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Area of Science:

  • Computational chemistry and materials science.
  • Drug discovery and pharmaceutical development.
  • Artificial intelligence in drug design.

Background:

  • Bexarotene (BEX) is approved for cutaneous T-cell lymphoma (CTCL) but suffers from poor aqueous solubility and low bioavailability.
  • Limited clinical application of BEX necessitates strategies to enhance its physicochemical properties.

Purpose of the Study:

  • To develop and validate a deep learning model for in-silico screening of bexarotene cocrystals.
  • To identify novel cocrystal forms of BEX with improved solubility and bioavailability.
  • To integrate computational prediction with experimental validation for efficient cocrystal discovery.

Main Methods:

  • Development of a Graph Convolutional Network (GCN)-based deep learning model (CocrystalGCN) for virtual screening of coformer candidates.
  • In-silico scoring of 109 coformer candidates using CocrystalGCN.
  • Experimental validation of top-ranked cocrystal candidates.
  • Characterization of obtained cocrystals using single-crystal X-ray diffraction, powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis.
  • Pharmacokinetic studies to evaluate in-vivo performance.

Main Results:

  • CocrystalGCN demonstrated high performance in screening coformer candidates.
  • Successfully synthesized and characterized novel cocrystals: BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate.
  • All synthesized cocrystals exhibited superior solubility and dissolution rates compared to pure BEX.
  • Pharmacokinetic studies revealed significantly enhanced plasma exposure (AUC0-8h) for BEX-pyrazine (1.7x) and BEX-2,5-dimethylpyrazine (1.8x) compared to commercial BEX powder.

Conclusions:

  • The developed CocrystalGCN model is effective for in-silico cocrystal screening of poorly soluble drugs.
  • Novel BEX cocrystals with enhanced solubility, dissolution, and pharmacokinetic profiles were successfully discovered.
  • This integrated approach of virtual screening and experimental validation offers a promising strategy for developing new cocrystals of water-insoluble drugs.