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Machine Learning Exploration of the Relationship Between Drugs and the Blood–Brain Barrier: Guiding Molecular Modification

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Abstract

Objective

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB).

Methods

We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model.

Results

The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone.

Conclusions

The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

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Data Availability

This study utilized a database of 307 compounds with quantitative BBB permeability descriptions, which is available in Supplementary Material (1). Additionally, a database of 494 CNS drugs was used, which can be found in Supplementary Material (2). Furthermore, the substructures obtained by fragmenting acyclovir using the Morgan algorithm, as well as the predicted results for the B3DB database, are also provided in Supplementary Material (3) and (4), respectively. The training script used in this study can be obtained by contacting the corresponding author.

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Acknowledgements

This work was supposed by Operation Subsidy Project of Guangxi Key Laboratory of TCM Efficacy Research in 2020 (Grant No: 20-065-38).

Funding

Guangxi Key Laboratory of TCM Efficacy Research in 2020,20-065-38, Jiagang Deng

Author information

Authors and Affiliations

  1. School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, 530200, China

    Qi Yang & Lili Fan

  2. Guangxi Key Laboratory of Efficacy Study On Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, 530200, China

    Erwei Hao, Xiaotao Hou, Jiagang Deng, Zhongshang Xia & Zhengcai Du

  3. Guangxi Collaborative Innovation Center for Research On Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, 530200, China

    Erwei Hao, Xiaotao Hou, Jiagang Deng, Zhongshang Xia & Zhengcai Du

  4. Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, 530200, China

    Erwei Hao, Xiaotao Hou, Jiagang Deng, Zhongshang Xia & Zhengcai Du

  5. Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, 530200, China

    Zhengcai Du

  6. Guangxi University of Chinese Medicine (Xianhu Campus), No.13 Wuhe Avenue, Qingxiu District, Nanning, Guangxi, China

    Lili Fan, Erwei Hao & Zhongshang Xia

Authors
  1. Qi Yang
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  2. Lili Fan
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Contributions

Qi Yang proposed innovative ideas and wrote the first draft and code of this article. Lili Fan was responsible for review and revision, Erwei Hao, Xiaotao Hou, Zhengcai Du, and Zhongshang Xia were responsible for collecting data and providing resources, and Jiagang Deng was responsible for providing project support. All authors finally reviewed this article and agreed to submit it.

Corresponding authors

Correspondence to Lili Fan, Erwei Hao or Zhongshang Xia.

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Yang, Q., Fan, L., Hao, E. et al. Machine Learning Exploration of the Relationship Between Drugs and the Blood–Brain Barrier: Guiding Molecular Modification. Pharm Res (2024). https://doi.org/10.1007/s11095-024-03686-2

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