{"entity": "journal", "iuid": "1a26df598ae24cd5be1f33d0be67b06a", "timestamp": "2026-11-06T19:23:42.194Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Expert%20Opin%20Drug%20Metab%20Toxicol.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Expert%20Opin%20Drug%20Metab%20Toxicol"}}, "title": "Expert Opin Drug Metab Toxicol", "issn": "1744-7607", "issn-l": null, "publications_count": 2, "publications": [{"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"}, {"entity": "publication", "iuid": "38c9c8560f404b6da6e8cce401b8312d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/38c9c8560f404b6da6e8cce401b8312d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/38c9c8560f404b6da6e8cce401b8312d"}}, "title": "Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury.", "authors": [{"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Zhong", "given": "Yi", "initials": "Y"}, {"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": "2025-05-00", "journal": {"title": "Expert Opin Drug Metab Toxicol", "issn": "1744-7607", "volume": "21", "issue": "5", "pages": "563-577", "issn-l": null}, "abstract": "Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug development projects.\n\nWe provide an overview of available human liver models for DILI research and discuss how they have been used to aid in early risk assessments and to mitigate the risk of project closures due to DILI in clinical stages. We summarize the different data that can be provided by such models and illustrate how these diverse data types can be interfaced with machine learning strategies to improve predictions of liver safety liabilities.\n\nAdvanced human liver models closely mimic human liver phenotypes and functions for many weeks, allowing for the recapitulation of hepatotoxicity events in vitro. Integration of the biochemical, histological, and toxicogenomic output data from these models with physicochemical compound properties using different machine learning architectures holds promise to enhance preclinical DILI predictions. However, to realize this aim, it is important to benchmark the available liver models on test sets of DILI positive and negative compounds and to carefully annotate and share the resulting data.", "doi": "10.1080/17425255.2025.2461484", "pmid": "39893552", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [], "notes": [], "created": "2026-11-06T09:36:07.841Z", "modified": "2026-11-06T09:36:07.898Z"}], "created": "2026-11-06T09:36:07.879Z", "modified": "2026-11-06T09:36:07.879Z"}