{"entity": "researcher", "timestamp": "2026-11-06T19:22:00.087Z", "family": "Park", "given": "Yoomi", "initials": "Y", "orcid": "0000-0003-3070-938X", "affiliations": ["Department of Physiology and Pharmacology and Center for Molecular Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden.", "Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d4b85b74a44cdd803c4d336947cf7c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d4b85b74a44cdd803c4d336947cf7c"}}, "publications": [{"entity": "publication", "iuid": "8b6a3e2b3b5c41e28df60177f0529796", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8b6a3e2b3b5c41e28df60177f0529796.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8b6a3e2b3b5c41e28df60177f0529796"}}, "title": "CYP2C19 Genotype is Associated with Citalopram Treatment Outcomes in a Real-World Setting.", "authors": [{"family": "Park", "given": "Yoomi", "initials": "Y", "orcid": "0000-0003-3070-938X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d4b85b74a44cdd803c4d336947cf7c.json"}}, {"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}], "type": "journal article", "published": "2026-08-00", "journal": {"title": "Clin. Pharmacol. Ther.", "issn": "1532-6535", "volume": "120", "issue": "2", "pages": "412-419", "issn-l": "0009-9236"}, "abstract": "CYP2C19 metabolizes various selective serotonin reuptake inhibitors (SSRIs) and genetic CYP2C19 variants are associated with SSRI tolerability and response. Yet, whether CYP2C19 variability also impacts citalopram response remained unclear. We here evaluated associations between CYP2C19 genotypes and citalopram prescription data of 11,079 patients from the UK Biobank. Importantly, CYP2C19 ultrarapid metabolizers (UMs) were more likely to experience therapeutic failure (OR 2.03, P = 0.002) and both CYP2C19 poor metabolizers (PMs) and UMs exhibited an increased likelihood of treatment resistance (PMs, OR 1.85, P = 0.06; UMs, OR 1.74, P = 0.05). Furthermore, inferred CYP2C19 metabolizer status showed significant monotonic trends with citalopram maintenance dose (from 19.7 mg/d in PMs to 24.4 mg/d in UMs; Jonckheere-Terpstra P = 0.017) and time to dose escalation (2.5 years in PMs to 1.7 years in UMs; P = 0.006). Besides CYP2C19, rare variant analyses identified BMP2K as a novel candidate locus for SSRI response. BMP2K variants were significantly associated with therapeutic failure in citalopram patients (P = 5.49 \u00d7 10-6) and this association was replicated in 220 escitalopram users (P = 0.042). Furthermore, higher BMP2K variant burden significantly correlated with lower maintenance dose. These findings provide the first report of a U-shaped association between CYP2C19 metabolizer status and citalopram therapeutic outcomes and pinpoint BMP2K as a novel candidate gene with potential contribution to interindividual differences in citalopram response. Collectively, these results demonstrate that CYP2C19 genotype is significantly associated with citalopram outcomes in a real-world setting in which patient treatment was not guided by pharmacogenetics, thus avoiding open-label awareness, eliminating expectation bias of genetic testing and highlighting the value of large-scale biobank data for advancing precision psychiatry.", "doi": "10.1002/cpt.70285", "pmid": "41979220", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [{"db": "pmc", "key": "PMC13339076"}], "notes": [], "created": "2026-11-06T09:38:19.288Z", "modified": "2026-11-06T09:38:19.330Z"}, {"entity": "publication", "iuid": "095a98be04f1467087159c2ba09b883b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/095a98be04f1467087159c2ba09b883b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/095a98be04f1467087159c2ba09b883b"}}, "title": "Feed-Forward Deep Neural Networks Predict Substrate-Specific Effects of Transporter Variants to Explain Drug Response Variability.", "authors": [{"family": "Park", "given": "Yoomi", "initials": "Y", "orcid": "0000-0003-3070-938X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d4b85b74a44cdd803c4d336947cf7c.json"}}, {"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Xiao", "given": "Ming", "initials": "M", "orcid": "0000-0002-5407-0835", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0e3f113ea31d464b9f32799aa7bab8e6.json"}}, {"family": "Nies", "given": "Anne T", "initials": "AT", "orcid": "0000-0001-6862-0730", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/45f66a44d87e4b57bde4b609bbdb86a0.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}], "type": "journal article", "published": "2026-05-00", "journal": {"title": "Clin Transl Sci", "issn": "1752-8062", "volume": "19", "issue": "5", "pages": "e70592", "issn-l": null}, "abstract": "Genetic variants in drug transporter genes shape the interindividual variability in drug response. However, their functional interpretation has remained limited due to the substrate dependence of variant effects. Existing predictors are substrate-agnostic and cannot capture how a single amino acid change differentially affects transport across drugs. Here, we present the substrate-specific effect predictor (SSEP), the first model to predict transporter variant effects in a substrate-dependent manner. SSEP integrates curated in vitro uptake assays with deep mutational scanning data, leveraging multiscale features extracted from modeled variant-substrate complexes. Based on a feed-forward deep neural network architecture, SSEP provides quantitative, substrate-specific activity scores that correlate with experimental uptake data (Spearman's \u03c1 = 0.64) across multiple transporter families (OCT1, MATE, CNT, and OATP) and substrate classes (biguanides, tetraethylammonium, selective serotonin agonists, sympathomimetics and opiates). In benchmarking analyses, SSEP showed higher concordance (Kruskal-Wallis p = 1.11 \u00d7 10-41) 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 (\u03b2 = 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.", "doi": "10.1111/cts.70592", "pmid": "42104208", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [{"db": "pmc", "key": "PMC13156069"}], "notes": [], "created": "2026-11-06T09:38:33.937Z", "modified": "2026-11-06T09:38:34.131Z"}, {"entity": "publication", "iuid": "adc7ca17f8dd46b692854c6e6d9841bb", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/adc7ca17f8dd46b692854c6e6d9841bb.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/adc7ca17f8dd46b692854c6e6d9841bb"}}, "title": "Substrate-specificity of pharmacogenomic variability - clinical relevance and emerging predictive strategies.", "authors": [{"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Park", "given": "Yoomi", "initials": "Y", "orcid": "0000-0003-3070-938X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d4b85b74a44cdd803c4d336947cf7c.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}], "type": "journal article", "published": "2026-03-00", "journal": {"title": "Expert Opin Drug Metab Toxicol", "issn": "1744-7607", "volume": "22", "issue": "3", "pages": "201-215", "issn-l": null}, "abstract": "Genetic variation in drug-metabolizing enzymes and transporters contributes to interindividual differences in drug efficacy and toxicity. While current pharmacogenomic practice typically assigns fixed functional categories to variant alleles, accumulating evidence shows that many variant alleles exert substrate-specific effects.\n\nWe here summarize the current evidence for substrate-dependent functional variability across key pharmacogenes, including CYP2D6, CYP2C8, CYP2C9 and multiple uptake and efflux transporters, based on both experimental in vitro approaches and clinical pharmacokinetic analyses. We further evaluate the performance of existing computational variant-effect predictors and outline emerging structural, sequence-based, and representation-learning strategies for the prediction of substrate-specificity. Particular focus is placed on methodological gaps that currently prevent the prediction of substrate-specific effects and on the evolving technological landscape that may enable such capabilities.\n\nSubstrate specificity of pharmacogenetic variations is clinically relevant and insufficiently captured by current functional annotations and prediction tools. We believe that new strategies based on integrative multimodal frameworks that combine structural descriptors, protein language-model embeddings, ligand features and high-quality experimental data have the potential to capture substrate-specific variant effects in the near future. These advances will provide exciting new opportunities to systematically evaluate variant function, which will benefit both drug development and precision medicine.", "doi": "10.1080/17425255.2026.2654466", "pmid": "41914879", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [], "notes": [], "created": "2026-11-06T09:37:49.255Z", "modified": "2026-11-06T09:37:49.375Z"}]}