Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D

One of the promises of precision medicine is to understand and act on inter-individual genetic differences in drug responses. SNPdrug3D contains the complete genomic landscape of missense single nucleotide variants (SNV) across the human proteome and at a population-wide level that could affect drug binding. Here, we map SNVs in over 80,000 individuals from the Singapore SG10K Health and gnomAD cohorts to identify ~1.17 million variants mapped to residues near ~6000 bound drugs in protein-drug complexes and experimentally verify effects of selected SNVs, including previously uncharacterized variants, on drug binding in relevant proteins ranging from kinases to cytochrome P450s (CYPs). The latter led to a specific predictor for interpreting variants in the CYP family that outperforms existing tools in the prediction of pharmacogenetic effects based on database-annotated (AUROC = 0.9) or assay-based (AUROC = 0.8) test sets. By placing variants and drugs in structural contexts, SNPdrug3D aids drug development by pre-emptively flagging potential resistance sites based on population-specific variability. Precision medicine exploits genetic differences to tailor drug treatments. Here the authors mapped ~1.17 M variants near drug-binding sites, built SNPdrug3D, and showed such variants often disrupt binding, improving prediction tools and enabling population-specific drug response insights.

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Journal
Nature Communications
Published
2026-06-09
DOI
https://doi.org/10.1038/s41467-026-73816-4
Primary Topic
Pharmacogenetics and Drug Metabolism
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article
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article

Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D

Paul Eillot, Miao Ling Chee, Xueling Sim, Claire Bellis et al.
Nature Communications
Pharmacogenetics and Drug Metabolism
article

Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D

Paul Eillot, Miao Ling Chee, Xueling Sim, Claire Bellis, Pi Kuang Tsai, Tat Hung Koh, Khung Keong Yeo, Vachiranee Limviphuvadh, Sebastian Maurer‐Stroh, Rinkoo Dalan, Li-xian Grace Toh, John C. Chambers, Wen Jie Chew, Shyam Prabhakar, Chee Jian Pua, T WONG, Can Can Xue, Marie Chiew Shia Loh, Wey Ching Sim, Woon Loong Calvin Chin, Mert B. Ozturk, Guo-Liang Chew, Chandra Shekhar Verma, E Shyong Tai, Chris Soon Heng Tan, Maxime Hebrard, Cheng‐Shoong Chong, Yee Yen Sia, Eng Sing Lee, Liuh Ling Goh, Jin Fang Chai, Erwin Tantoso, Ying Swan Ho, Hengtong Li, Jhoann M.T. Miyajima, Nilanjana Sadhu, Joanna Tan, Stuart A. Cook, Weng Khong Lim, Dimitar Kenanov, Neerja Karnani, Chiea Chuen Khor, Ashar J. Malik, Jack Ling Ow, Chengxi Yang, Jianjun Liu, Zhihui Li, Zhenxun Wang, Rodrigo Toro Jimenez, Jimmy Lee, Hong Kiat Ng, Tock Han Lim, Yik Ying Teo, Qingsheng Peng, Pritesh Rajesh Jain, Nicolas Bertin, Fabian Yap, Johan Gunnar Eriksson, Y F C Chong, Dorrain Low, Theresia Mina, Xiaoyan Wang, Johan G. Eriksson, Rob M. van Dam, Srinivasaraghavan Kannan, Chandra Verma, Judice Koh, Elio Riboli, Ching-Yu Cheng, Charumathi Sabanayagam, Bitong Clarabelle Alexandrine Lin, Peter D. Gluckman, Hong-Yun Chang, Shiqi Mok, Khai Pang Leong, Saumya S. Jamuar, Patrick Tan, Tin Aung, Qiqi Wang, Roberto Tirado Magallanes, Darwin Tay, Joanne Ngeow, Sonia Davila, Yih Chung Tham, Chia Wei Lim, Justin Jeyakani, Roberto-Tirado Magallanes, Paul Chung Pui Cheng, Yung Seng Lee
article en

Abstract

One of the promises of precision medicine is to understand and act on inter-individual genetic differences in drug responses. SNPdrug3D contains the complete genomic landscape of missense single nucleotide variants (SNV) across the human proteome and at a population-wide level that could affect drug binding. Here, we map SNVs in over 80,000 individuals from the Singapore SG10K Health and gnomAD cohorts to identify ~1.17 million variants mapped to residues near ~6000 bound drugs in protein-drug complexes and experimentally verify effects of selected SNVs, including previously uncharacterized variants, on drug binding in relevant proteins ranging from kinases to cytochrome P450s (CYPs). The latter led to a specific predictor for interpreting variants in the CYP family that outperforms existing tools in the prediction of pharmacogenetic effects based on database-annotated (AUROC = 0.9) or assay-based (AUROC = 0.8) test sets. By placing variants and drugs in structural contexts, SNPdrug3D aids drug development by pre-emptively flagging potential resistance sites based on population-specific variability. Precision medicine exploits genetic differences to tailor drug treatments. Here the authors mapped ~1.17 M variants near drug-binding sites, built SNPdrug3D, and showed such variants often disrupt binding, improving prediction tools and enabling population-specific drug response insights.

Nature Communications
Agency for Science, Technology and Research (SG), Milken Institute (US), University of Helsinki (FI), Garvan Institute of Medical Research (AU), Baker Heart and Diabetes Institute (AU), University of Auckland (NZ), SingHealth (SG), Queensland University of Technology (AU), The University of Queensland (AU), National University of Singapore (SG), Nanyang Technological University (SG), Illumina (United States) (US), George Washington University (US), Singapore National Eye Center (SG), Tan Tock Seng Hospital (SG), Khoo Teck Puat Hospital (SG), Southern University of Science and Technology (CN), SingHealth Duke-NUS Academic Medical Centre (SG), National University Cancer Institute, Singapore (SG), KK Women's and Children's Hospital (SG), Singapore Eye Research Institute (SG), Duke-NUS Medical School (SG), Experimental Drug Development Centre (SG), National Cancer Centre Singapore (SG), Bioprocessing Technology Institute (SG), Institute of Mental Health (SG), National University Hospital (SG), National Heart Centre Singapore (SG), Bioinformatics Institute (SG), National University Health System (SG), Beijing Tsinghua Chang Gung Hospital (CN), Folkhälsans Forskningscentrum (FI), Imperial College London (GB), Genome Institute of Singapore (SG)
Lee Foundation, Tanoto Foundation, National Research Foundation, Agency for Science, Technology and Research, National University of Singapore, National Research Foundation Singapore, Ministerio de Economía y Competitividad, National University Health System, Medical Research Council, National Medical Research Council, Lee Kong Chian School of Medicine, Nanyang Technological University, Biomedical Research Council
Good health and well-being
Openalex Percentile: Top 7%
Pharmacogenetics and Drug Metabolism
DOIOpen Access PDFOpenAlex
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