Artificial intelligence-driven prediction and validation of blood-brain barrier permeability and absorption, distribution, metabolism, excretion profiles in Natural Product Research Laboratory compounds

Authors

Jai-Sing Yang, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, TaiwanFollow
Eddie TC Huang, NVIDIA AI Technology Center, NVIDIA Corporation, USA
Ken YK Liao, NVIDIA AI Technology Center, NVIDIA Corporation, USA
Da-Tian Bau, Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
Shih-Chang Tsai, Department of Biological Science and Technology, China Medical University, Taichung, Taiwan
Chao-Jung Chen, Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
Kuan-Wen Chen, GGA, Corp. Molecular science and digital innovation center, Taipei, Taiwan
Ting-Yuan Liu, Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
Yu-Jen Chiu, Division of Plastic and Reconstructive Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
Fuu-Jen Tsai, School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan

Abstract

Introduction: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.

Objectives: The study aims to investigate the pharmacokinetic ADME properties and BBB permeability coefficients of NPRL compounds.

Materials and Methods: A combined model using a transformer-based MegaMolBART encoder and XGBoost classifier was employed to predict BBB permeability. Machine learning (ML) tools from Discovery Studio were used to assess the ADME characteristics of the NPRL compounds. The CCK-8 assay was conducted to evaluate the cytotoxic effects of NPRL compounds on bEnd.3 brain endothelial cells after exposure to 10 μg/mL of the compounds. We assessed the permeability coefficient by subjecting bEnd.3 cell monolayers to the test compounds and measuring the permeability of FITC-dextran.

Results: There were 4,956 compounds that could cross the blood-brain barrier

(BBB+) and 2,851 that could not (BBB−) in the B3DB dataset that was utilized for training. A total of 2,461 BBB+ and 2,184 BBB− compounds were used in the

NPRL-CMUH dataset for testing. The permeability coefficient of temozolomide (TMZ) and 21 other BBB+ compounds exceeded 10×10^-7 cm/s. Computational analysis revealed that NPRL compounds exhibited a variety of ADME characteristics.

Conclusion: Computer-based predictions for the NPRL of CMUH compounds regarding their capacity to traverse the BBB are verified by the findings. Artificial intelligence (AI) prediction models have effectively identified the potential ADME characteristics of various compounds.

Recommended Citation

Yang, Jai-Sing; Huang, Eddie TC; Liao, Ken YK; Bau, Da-Tian; Tsai, Shih-Chang; Chen, Chao-Jung; Chen, Kuan-Wen; Liu, Ting-Yuan; Chiu, Yu-Jen; and Tsai, Fuu-Jen (2024) "Artificial intelligence-driven prediction and validation of blood-brain barrier permeability and absorption, distribution, metabolism, excretion profiles in Natural Product Research Laboratory compounds," BioMedicine: Vol. 14 : Iss. 4 , Article 7.
DOI: 10.37796/2211-8039.1474

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