Feed‐Forward Deep Neural Networks Predict Substrate‐Specific Effects of Transporter Variants to Explain Drug Response Variability
) with experimental uptake than commonly used substrate-agnostic predictors. Application of SSEP on UK Biobank data revealed that OCT1 (SLC22A1) variant burden weighted by metformin-specific SSEP scores was significantly associated with maintenance dose (β = 30 mg/day per unit increase in predicted functional burden; p = 0.033), whereas substrate-agnostic weights showed no association (p = 0.78). Together, these results show that SSEP enables quantitative, drug-specific prediction of transporter variant effects, thereby improving the identification of clinically relevant transporter variants in population-scale data.
Authors
- Anne T. Nies (ORCID: https://orcid.org/0000-0001-6862-0730)
- Yoomi Park (ORCID: https://orcid.org/0000-0002-5953-0378)
- Yitian Zhou (ORCID: https://orcid.org/0000-0003-3066-5444)
- Volker M. Lauschke (ORCID: https://orcid.org/0000-0002-1140-6204)
- Ming Xiao (ORCID: https://orcid.org/0000-0002-5407-0835)
Institutions
- Karolinska University Hospital (SE)
- Seoul National University (KR)
- Central South University (CN)
- Karolinska Institutet (SE)
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology (DE)
- Research Institute for Work, Technology and Culture (DE)
- Second Xiangya Hospital of Central South University (CN)
- University of Tübingen (DE)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- Clinical and Translational Science
- Published
- 2026-05-01
- DOI
- https://doi.org/10.1111/cts.70592
- Primary Topic
- Drug Transport and Resistance Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- National Research Foundation of Korea
- Vetenskapsrådet