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<p>Many pharmaceutical targets partition into biomolecular condensates, whose microenvironments can significantly influence drug distribution. Nevertheless, it is unclear how drug design principles should adjust for these targets to optimize target engagement. To address this question, we systematically investigated how condensate microenvironments influence drug-targeting efficiency. We found that condensates highlight a notable heterogeneity, with nonpolar-residue-enriched condensates being more hydrophobic and housing more hydrophobic drugs. Furthermore, L1000 dataset analysis revealed a strong positive correlation between inhibitor hydrophobicity and targeting efficiency for phase-separated proteins, represented by estrogen receptor 1 (ESR1) enriched with nonpolar residues. We developed random forest models to predict inhibitor targeting efficiency from molecular properties, with hydrophobicity identified as a key determinant. In cellulo experiments with ESR1 condensates confirmed that both binding affinity and hydrophobicity of inhibitors contribute significantly to potency. These results suggest a new drug design principle for phase-separated proteins by considering condensate micropolarity, potentially leading to drugs with optimal target engagement.</p><p></p>

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Navigating condensate micropolarity to enhance small-molecule drug targeting

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Jian Ouyang,
  • Junlin Chen,
  • Zhili Wu,
  • Kaiqiang You,
  • Taoyu Chen,
  • Yi Qin Gao,
  • Pilong Li,
  • Xin Zhang,
  • Tingting Li

摘要

Many pharmaceutical targets partition into biomolecular condensates, whose microenvironments can significantly influence drug distribution. Nevertheless, it is unclear how drug design principles should adjust for these targets to optimize target engagement. To address this question, we systematically investigated how condensate microenvironments influence drug-targeting efficiency. We found that condensates highlight a notable heterogeneity, with nonpolar-residue-enriched condensates being more hydrophobic and housing more hydrophobic drugs. Furthermore, L1000 dataset analysis revealed a strong positive correlation between inhibitor hydrophobicity and targeting efficiency for phase-separated proteins, represented by estrogen receptor 1 (ESR1) enriched with nonpolar residues. We developed random forest models to predict inhibitor targeting efficiency from molecular properties, with hydrophobicity identified as a key determinant. In cellulo experiments with ESR1 condensates confirmed that both binding affinity and hydrophobicity of inhibitors contribute significantly to potency. These results suggest a new drug design principle for phase-separated proteins by considering condensate micropolarity, potentially leading to drugs with optimal target engagement.