Translating pharmacogenomic sequencing data into drug response predictions

• Verfasst von: Tremmel, Roman [VerfasserIn]
• Verfasst von: Pirmann, Sebastian [VerfasserIn]
• Verfasst von: Zhou, Yitian [VerfasserIn]
• Verfasst von: Lauschke, Volker Martin [VerfasserIn]
• Titel: Translating pharmacogenomic sequencing data into drug response predictions
• Titelzusatz: how to interpret variants of unknown significance
• Verf.angabe: Roman Tremmel, Sebastian Pirmann, Yitian Zhou, Volker M. Lauschke
• E-Jahr: 2023
• Jahr: 27 September 2023
• Umfang: 12 S.
• Fussnoten: Erstveröffentlichung: 27. September 2023 ; Gesehen am 14.02.2024
• Titel Quelle: Enthalten in: British journal of clinical pharmacology
• Ort Quelle: Oxford : Wiley-Blackwell, 1974
• Jahr Quelle: 2023
• Band/Heft Quelle: (2023), early view, Seite 1-12
• ISSN Quelle: 1365-2125
• DOI: doi:10.1111/bcp.15915
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: artificial intelligence
• Sach-SW: electronic health records
• Sach-SW: population-scale sequencing
• Sach-SW: precision medicine
• Sach-SW: variant effect predictions
• K10plus-PPN: 1880723999

Abstract

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.