1
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Ashayeri H, Sobhi N, Pławiak P, Pedrammehr S, Alizadehsani R, Jafarizadeh A. Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition. Cancers (Basel) 2024; 16:2138. [PMID: 38893257 PMCID: PMC11171544 DOI: 10.3390/cancers16112138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.
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Affiliation(s)
- Hamidreza Ashayeri
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran;
| | - Navid Sobhi
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran; (N.S.); (A.J.)
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Siamak Pedrammehr
- Faculty of Design, Tabriz Islamic Art University, Tabriz 5164736931, Iran;
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Burwood, VIC 3216, Australia;
| | - Ali Jafarizadeh
- Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran; (N.S.); (A.J.)
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665811, Iran
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2
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Zhou Y, Pirmann S, Lauschke VM. APF2: an improved ensemble method for pharmacogenomic variant effect prediction. THE PHARMACOGENOMICS JOURNAL 2024; 24:17. [PMID: 38802404 PMCID: PMC11129946 DOI: 10.1038/s41397-024-00338-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/26/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024]
Abstract
Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
- Center for Molecular Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tübingen, Tübingen, Germany.
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3
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Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
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Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
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4
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Tremmel R, Hofmann U, Haag M, Schaeffeler E, Schwab M. Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes. Annu Rev Pharmacol Toxicol 2024; 64:65-87. [PMID: 37585662 DOI: 10.1146/annurev-pharmtox-032023-121106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
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Affiliation(s)
- Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg (DKFZ), Partner Site, Tübingen, Germany
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5
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Deserranno K, Tilleman L, Rubben K, Deforce D, Van Nieuwerburgh F. Targeted haplotyping in pharmacogenomics using Oxford Nanopore Technologies' adaptive sampling. Front Pharmacol 2023; 14:1286764. [PMID: 38026945 PMCID: PMC10679755 DOI: 10.3389/fphar.2023.1286764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Pharmacogenomics (PGx) studies the impact of interindividual genomic variation on drug response, allowing the opportunity to tailor the dosing regimen for each patient. Current targeted PGx testing platforms are mainly based on microarray, polymerase chain reaction, or short-read sequencing. Despite demonstrating great value for the identification of single nucleotide variants (SNVs) and insertion/deletions (INDELs), these assays do not permit identification of large structural variants, nor do they allow unambiguous haplotype phasing for star-allele assignment. Here, we used Oxford Nanopore Technologies' adaptive sampling to enrich a panel of 1,036 genes with well-documented PGx relevance extracted from the Pharmacogenomics Knowledge Base (PharmGKB). By evaluating concordance with existing truth sets, we demonstrate accurate variant and star-allele calling for five Genome in a Bottle reference samples. We show that up to three samples can be multiplexed on one PromethION flow cell without a significant drop in variant calling performance, resulting in 99.35% and 99.84% recall and precision for the targeted variants, respectively. This work advances the use of nanopore sequencing in clinical PGx settings.
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Affiliation(s)
| | | | | | | | - Filip Van Nieuwerburgh
- Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
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6
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Tremmel R, Pirmann S, Zhou Y, Lauschke VM. Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance. Br J Clin Pharmacol 2023. [PMID: 37759374 DOI: 10.1111/bcp.15915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
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.
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Affiliation(s)
- Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sebastian Pirmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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7
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Graansma LJ, Zhai Q, Busscher L, Menafra R, van den Berg RR, Kloet SL, van der Lee M. From gene to dose: Long-read sequencing and *-allele tools to refine phenotype predictions of CYP2C19. Front Pharmacol 2023; 14:1076574. [PMID: 36937863 PMCID: PMC10014917 DOI: 10.3389/fphar.2023.1076574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/31/2023] [Indexed: 03/06/2023] Open
Abstract
Background: Inter-individual differences in drug response based on genetic variations can lead to drug toxicity and treatment inefficacy. A large part of this variability is caused by genetic variants in pharmacogenes. Unfortunately, the Single Nucleotide Variant arrays currently used in clinical pharmacogenomic (PGx) testing are unable to detect all genetic variability in these genes. Long-read sequencing, on the other hand, has been shown to be able to resolve complex (pharmaco) genes. In this study we aimed to assess the value of long-read sequencing for research and clinical PGx focusing on the important and highly polymorphic CYP2C19 gene. Methods and Results: With a capture-based long-read sequencing panel we were able to characterize the entire region and assign variants to their allele of origin (phasing), resulting in the identification of 813 unique variants in 37 samples. To assess the clinical utility of this data we have compared the performance of three different *-allele tools (Aldy, PharmCat and PharmaKU) which are specifically designed to assign haplotypes to pharmacogenes based on all input variants. Conclusion: We conclude that long-read sequencing can improve our ability to characterize the CYP2C19 locus, help to identify novel haplotypes and that *-allele tools are a useful asset in phenotype prediction. Ultimately, this approach could help to better predict an individual's drug response and improve therapy outcomes. However, the added value in clinical PGx might currently be limited.
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Affiliation(s)
- Lonneke J. Graansma
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Qinglian Zhai
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Loes Busscher
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Roberta Menafra
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Redmar R. van den Berg
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Susan L. Kloet
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Maaike van der Lee
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- *Correspondence: Maaike van der Lee,
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8
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Zhou Y, Koutsilieri S, Eliasson E, Lauschke VM. A paradigm shift in pharmacogenomics: From candidate polymorphisms to comprehensive sequencing. Basic Clin Pharmacol Toxicol 2022; 131:452-464. [PMID: 35971800 PMCID: PMC9805052 DOI: 10.1111/bcpt.13779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/28/2022] [Accepted: 08/09/2022] [Indexed: 01/09/2023]
Abstract
Genetic factors have long been recognized as important determinants of interindividual variability in drug efficacy and toxicity. However, despite the increasing number of established gene-drug associations, candidate polymorphisms can only explain a fraction of the genetically encoded functional variability in drug disposition. Advancements in genetic profiling methods now allow to analyse the landscape of human pharmacogenetic variations comprehensively, which opens new opportunities to identify novel factors that could explain the "missing heritability." Here, we provide an updated overview of the landscape of pharmacogenomic variability based on recent analyses of population-scale sequencing projects. We then summarize the current state-of-the-art how the functional consequences of variants with unknown effects can be quantitatively estimated while discussing challenges and peculiarities that are specific to pharmacogenes. In the last sections, we discuss the importance of considering ethnogeographic diversity to provide equitable benefits of pharmacogenomics and summarize current roadblocks for the implementation of sequencing-based guidance of clinical decision-making. Based on the current state of the field, we conclude that testing is likely to gradually shift from the interrogation of selected candidate polymorphisms to comprehensive sequencing, which allows to consider the full spectrum of pharmacogenomic variations for a true personalization of genomic prescribing.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden,Department of Laboratory MedicineKarolinska InstitutetStockholmSweden
| | | | - Erik Eliasson
- Department of Laboratory MedicineKarolinska InstitutetStockholmSweden
| | - Volker M. Lauschke
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden,Dr Margarete Fischer‐Bosch Institute of Clinical PharmacologyStuttgartGermany,University of TübingenTübingenGermany
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9
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Chen S, Li T, Yang L, Zhai F, Jiang X, Xiang R, Ling G. Artificial intelligence-driven prediction of multiple drug interactions. Brief Bioinform 2022; 23:6720429. [PMID: 36168896 DOI: 10.1093/bib/bbac427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 12/14/2022] Open
Abstract
When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.
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Affiliation(s)
- Siqi Chen
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Tiancheng Li
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Luna Yang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Fei Zhai
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Xiwei Jiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Rongwu Xiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China.,Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center, Shenyang 110016, China
| | - Guixia Ling
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
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10
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van der Lee M, Swen JJ. Artificial intelligence in pharmacology research and practice. Clin Transl Sci 2022; 16:31-36. [PMID: 36181380 PMCID: PMC9841296 DOI: 10.1111/cts.13431] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/23/2022] [Accepted: 09/24/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.
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Affiliation(s)
- Maaike van der Lee
- Department of Clinical Pharmacy and ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | - Jesse J. Swen
- Department of Clinical Pharmacy and ToxicologyLeiden University Medical CenterLeidenThe Netherlands
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11
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Zhou Y, Tremmel R, Schaeffeler E, Schwab M, Lauschke VM. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol Sci 2022; 43:852-865. [PMID: 36008164 DOI: 10.1016/j.tips.2022.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 12/26/2022]
Abstract
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany; Department of Clinical Pharmacology, and Department of Biochemistry and Pharmacy, University of Tübingen, Tübingen, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden; Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany.
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12
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ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation. PLoS One 2022; 17:e0272129. [PMID: 35901010 PMCID: PMC9333201 DOI: 10.1371/journal.pone.0272129] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
The accurate identification of genetic variants contributing to therapeutic drug response or adverse effects is the first step in implementation of precision drug therapy. Targeted sequencing has recently become a common methodology for large-scale studies of genetic variation thanks to its favorable balance between low cost, high throughput, and deep coverage. Here, we present ClinPharmSeq, a targeted sequencing panel of 59 genes with associations to pharmacogenetic (PGx) phenotypes, as a platform to explore the relationship between drug response and genetic variation, both common and rare. For validation, we sequenced DNA from 64 ethnically diverse Coriell samples with ClinPharmSeq to call star alleles (haplotype patterns) in 27 genes using the bioinformatics tool PyPGx. These reference samples were extensively characterized by multiple laboratories using PGx testing assays and, more recently, whole genome sequencing. We found that ClinPharmSeq can consistently generate deep-coverage data (mean = 274x) with high uniformity (30x or above = 94.8%). Our genotype analysis identified a total of 185 unique star alleles from sequencing data, and showed that diplotype calls from ClinPharmSeq are highly concordant with that from previous publications (97.6%) and whole genome sequencing (97.9%). Notably, all 19 star alleles with complex structural variation including gene deletions, duplications, and hybrids were recalled with 100% accuracy. Altogether, these results demonstrate that the ClinPharmSeq platform offers a feasible path for broad implementation of PGx testing and optimization of individual drug treatments.
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Zhai Q, van der Lee M, van Gelder T, Swen JJ. Why We Need to Take a Closer Look at Genetic Contributions to CYP3A Activity. Front Pharmacol 2022; 13:912618. [PMID: 35784699 PMCID: PMC9243486 DOI: 10.3389/fphar.2022.912618] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Cytochrome P450 3A (CYP3A) subfamily enzymes are involved in the metabolism of 40% of drugs in clinical use. Twin studies have indicated that 66% of the variability in CYP3A4 activity is hereditary. Yet, the complexity of the CYP3A locus and the lack of distinct drug metabolizer phenotypes has limited the identification and clinical application of CYP3A genetic variants compared to other Cytochrome P450 enzymes. In recent years evidence has emerged indicating that a substantial part of the missing heritability is caused by low frequency genetic variation. In this review, we outline the current pharmacogenomics knowledge of CYP3A activity and discuss potential future directions to improve our genetic knowledge and ability to explain CYP3A variability.
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14
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From pharmacogenetics to pharmaco-omics: Milestones and future directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Marie C. Sadler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
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15
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Chen L, Wang Y, Zhao F. Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence. Bioinformatics 2022; 38:3164-3172. [PMID: 35389435 PMCID: PMC9890318 DOI: 10.1093/bioinformatics/btac214] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Though genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants. RESULTS We will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets. AVAILABILITY AND IMPLEMENTATION https://github.com/lichen-lab/TLVar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Li Chen
- To whom correspondence should be addressed.
| | | | - Fengdi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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16
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Brown KE, Staples JW, Woodahl EL. Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function. Pharmacogenomics 2022; 23:255-262. [PMID: 35083931 PMCID: PMC8890136 DOI: 10.2217/pgs-2021-0149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.
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Affiliation(s)
- Karen E Brown
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Jack W Staples
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Erica L Woodahl
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA,Author for correspondence: Tel.: +1 406 243 4129;
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17
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Sankara Narayanan P, Runthala A. Accurate computational evolution of proteins and its dependence on deep learning and machine learning strategies. BIOCATAL BIOTRANSFOR 2022. [DOI: 10.1080/10242422.2022.2030317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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18
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Mroz P, Michel S, Allen JD, Meyer T, McGonagle EJ, Carpentier R, Vecchia A, Schlichte A, Bishop JR, Dunnenberger HM, Yohe S, Thyagarajan B, Jacobson PA, Johnson SG. Development and Implementation of In-House Pharmacogenomic Testing Program at a Major Academic Health System. Front Genet 2021; 12:712602. [PMID: 34745204 PMCID: PMC8564018 DOI: 10.3389/fgene.2021.712602] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/16/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacogenomics (PGx) studies how a person's genes affect the response to medications and is quickly becoming a significant part of precision medicine. The clinical application of PGx principles has consistently been cited as a major opportunity for improving therapeutic outcomes. Several recent studies have demonstrated that most individuals (> 90%) harbor PGx variants that would be clinically actionable if prescribed a medication relevant to that gene. In multiple well-conducted studies, the results of PGx testing have been shown to guide therapy choice and dosing modifications which improve treatment efficacy and reduce the incidence of adverse drug reactions (ADRs). Although the value of PGx testing is evident, its successful implementation in a clinical setting presents a number of challenges to molecular diagnostic laboratories, healthcare systems, providers and patients. Different molecular methods can be applied to identify PGx variants and the design of the assay is therefore extremely important. Once the genotyping results are available the biggest technical challenge lies in turning this complex genetic information into phenotypes and actionable recommendations that a busy clinician can effectively utilize to provide better medical care, in a cost-effective, efficient and reliable manner. In this paper we describe a successful and highly collaborative implementation of the PGx testing program at the University of Minnesota and MHealth Fairview Molecular Diagnostic Laboratory and selected Pharmacies and Clinics. We offer detailed descriptions of the necessary components of the pharmacogenomic testing implementation, the development and technical validation of the in-house SNP based multiplex PCR based assay targeting 20 genes and 48 SNPs as well as a separate CYP2D6 copy number assay along with the process of PGx report design, results of the provider and pharmacists usability studies, and the development of the software tool for genotype-phenotype translation and gene-phenotype-drug CPIC-based recommendations. Finally, we outline the process of developing the clinical workflow that connects the providers with the PGx experts within the Molecular Diagnostic Laboratory and the Pharmacy.
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Affiliation(s)
- Pawel Mroz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Stephen Michel
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Josiah D Allen
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Tim Meyer
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
| | - Erin J McGonagle
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | | | | | | | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States.,Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Henry M Dunnenberger
- Mark R Neaman Center for Personalized Medicine Center, NorthShore University HealthSystem, Evanston, IL, United States
| | - Sophia Yohe
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Pamala A Jacobson
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
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19
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McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide Association Studies in Pharmacogenomics. Clin Pharmacol Ther 2021; 110:637-648. [PMID: 34185318 PMCID: PMC8376796 DOI: 10.1002/cpt.2349] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California, USA
| | - Yash Pershad
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Departments of Genetics, Medicine, Biomedical Data Science, Stanford, California, USA
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20
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Chong CS, Limviphuvadh V, Maurer-Stroh S. Global spectrum of population-specific common missense variation in cytochrome P450 pharmacogenes. Hum Mutat 2021; 42:1107-1123. [PMID: 34153149 DOI: 10.1002/humu.24243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 04/12/2021] [Accepted: 06/08/2021] [Indexed: 11/06/2022]
Abstract
Next-generation sequencing technology has afforded the discovery of many novel variants that are of significance to inheritable pharmacogenomics (PGx) traits but a large proportion of them have unknown consequences. These include missense variants resulting in single amino acid substitutions in cytochrome P450 (CYP) proteins that can impair enzyme function, leading to altered drug efficacy and toxicity. While most unknown variants are rare, an overlooked minority are variants that are collectively rare but enriched in specific populations. Here, we analyzed sequence variation data in 141,456 individuals from across eight study populations in gnomAD for 38 CYP genes to identify such variants in addition to common variants. By further comparison with data from two PGx-specific databases (PharmVar and PharmGKB) and ClinVar, we identified 234 missense variants in 35 CYP genes, of which 107 were unknown to these databases. Most unknown variants (n = 83) were population-specific common variants and several (n = 7) were found in important CYP pharmacogenes (CYP2D6, CYP4F2, and CYP2C19). Overall, 29% (n = 31) of 107 unknown variants were predicted to affect CYP enzyme function although further biochemical characterization is necessary. These variants may elucidate part of the unexplained interpopulation differences observed in drug response.
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Affiliation(s)
- Cheng-Shoong Chong
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.,Innovations in Food and Chemical Safety Programme (IFCS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.,National University of Singapore Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore, Singapore
| | - Vachiranee Limviphuvadh
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.,Innovations in Food and Chemical Safety Programme (IFCS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Sebastian Maurer-Stroh
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.,Innovations in Food and Chemical Safety Programme (IFCS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.,National University of Singapore Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore, Singapore
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21
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Zhou Y, Lauschke VM. Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability. Clin Pharmacol Ther 2021; 110:626-636. [PMID: 33998671 DOI: 10.1002/cpt.2289] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/07/2021] [Indexed: 12/19/2022]
Abstract
Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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22
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Luo S, Jiang R, Grzymski JJ, Lee W, Lu JT, Washington NL. Comprehensive Allele Genotyping in Critical Pharmacogenes Reduces Residual Clinical Risk in Diverse Populations. Clin Pharmacol Ther 2021; 110:759-767. [PMID: 33930192 PMCID: PMC8453755 DOI: 10.1002/cpt.2279] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/19/2021] [Indexed: 11/25/2022]
Abstract
Genomic‐guided pharmaceutical prescribing is increasingly recognized as an important clinical application of genetics. Accurate genotyping of pharmacogenomic (PGx) genes can be difficult, owing to their complex genetic architecture involving combinations of single‐nucleotide polymorphisms and structural variation. Here, we introduce the Helix PGx database, an open‐source star allele, genotype, and resulting metabolic phenotype frequency database for CYP2C9, CYP2C19, CYP2D6, and CYP4F2, based on short‐read sequencing of >86,000 unrelated individuals enrolled in the Helix DNA Discovery Project. The database is annotated using a pipeline that is clinically validated against a broad range of alleles and designed to call CYP2D6 structural variants with high (98%) accuracy. We find that CYP2D6 has greater allelic diversity than the other genes, manifest in both a long tail of low‐frequency star alleles, as well as a disproportionate fraction (36%) of all novel predicted loss‐of‐function variants identified. Across genes, we observe that many rare alleles (<0.1% frequency) in the overall cohort have 10 times higher frequency in one or more subgroups with non‐European genetic ancestry. Extending these PGx genotypes to predicted metabolic phenotypes, we demonstrate that >90% of the cohort harbors a high‐risk variant in one of the four pharmacogenes. Based on the recorded prescriptions for >30,000 individuals in the Healthy Nevada Project, combined with predicted PGx metabolic phenotypes, we anticipate that standard‐of‐care screening of these 4 pharmacogenes could impact nearly half of the general population.
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Affiliation(s)
| | | | - Joseph J Grzymski
- Desert Research Institute, Reno, Nevada, USA.,Renown Institute of Health Innovation, Reno, Nevada, USA
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23
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McInnes G, Sharo AG, Koleske ML, Brown JEH, Norstad M, Adhikari AN, Wang S, Brenner SE, Halpern J, Koenig BA, Magnus DC, Gallagher RC, Giacomini KM, Altman RB. Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. Am J Hum Genet 2021; 108:535-548. [PMID: 33798442 PMCID: PMC8059338 DOI: 10.1016/j.ajhg.2021.03.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA
| | - Andrew G Sharo
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Megan L Koleske
- Department of Bioengineering and Therapeutics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Julia E H Brown
- Program in Bioethics, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Health & Aging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew Norstad
- Program in Bioethics, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Health & Aging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Aashish N Adhikari
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Illumina, Inc., Foster City, CA 94404, USA
| | - Sheng Wang
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Steven E Brenner
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jodi Halpern
- UCSF-UCB Joint Medical Program, School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Barbara A Koenig
- Program in Bioethics, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Health & Aging, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Social & Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Humanities & Social Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David C Magnus
- Stanford Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Renata C Gallagher
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Russ B Altman
- Departments of Bioengineering & Genetics, Stanford University, Stanford, CA 94305, USA.
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24
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Caspar SM, Schneider T, Stoll P, Meienberg J, Matyas G. Potential of whole-genome sequencing-based pharmacogenetic profiling. Pharmacogenomics 2021; 22:177-190. [PMID: 33517770 DOI: 10.2217/pgs-2020-0155] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Pharmacogenetics represents a major driver of precision medicine, promising individualized drug selection and dosing. Traditionally, pharmacogenetic profiling has been performed using targeted genotyping that focuses on common/known variants. Recently, whole-genome sequencing (WGS) is emerging as a more comprehensive short-read next-generation sequencing approach, enabling both gene diagnostics and pharmacogenetic profiling, including rare/novel variants, in a single assay. Using the example of the pharmacogene CYP2D6, we demonstrate the potential of WGS-based pharmacogenetic profiling as well as emphasize the limitations of short-read next-generation sequencing. In the near future, we envision a shift toward long-read sequencing as the predominant method for gene diagnostics and pharmacogenetic profiling, providing unprecedented data quality and improving patient care.
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Affiliation(s)
- Sylvan Manuel Caspar
- Center for Cardiovascular Genetics & Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich 8952, Switzerland.,Department of Health Sciences & Technology, Laboratory of Translational Nutrition Biology, ETH Zurich, Schwerzenbach 8603, Switzerland
| | - Timo Schneider
- Center for Cardiovascular Genetics & Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich 8952, Switzerland
| | - Patricia Stoll
- Center for Cardiovascular Genetics & Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich 8952, Switzerland
| | - Janine Meienberg
- Center for Cardiovascular Genetics & Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich 8952, Switzerland
| | - Gabor Matyas
- Center for Cardiovascular Genetics & Gene Diagnostics, Foundation for People with Rare Diseases, Schlieren-Zurich 8952, Switzerland.,Zurich Center for Integrative Human Physiology, University of Zurich, Zurich 8057, Switzerland
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25
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Tong H, Phan NVT, Nguyen TT, Nguyen DV, Vo NS, Le L. Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2021; 14:61-75. [PMID: 33469342 PMCID: PMC7812041 DOI: 10.2147/pgpm.s290781] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/26/2020] [Indexed: 12/27/2022]
Abstract
Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.
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Affiliation(s)
- Hang Tong
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Nga V T Phan
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Thanh T Nguyen
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Dinh V Nguyen
- Department of Respiratory, Allergy and Clinical Immunology, Vinmec International Hospital, Hanoi, Vietnam.,College of Health Sciences, VinUniversity, Hanoi, Vietnam
| | - Nam S Vo
- Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | - Ly Le
- School of Biotechnology, International University, Ho Chi Minh City, Vietnam.,Vietnam National University, Ho Chi Minh City, Vietnam.,Department of Translational Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
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26
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McInnes G, Lavertu A, Sangkuhl K, Klein TE, Whirl-Carrillo M, Altman RB. Pharmacogenetics at Scale: An Analysis of the UK Biobank. Clin Pharmacol Ther 2020; 109:1528-1537. [PMID: 33237584 DOI: 10.1002/cpt.2122] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/22/2020] [Indexed: 01/06/2023]
Abstract
Pharmacogenetics (PGx) studies the influence of genetic variation on drug response. Clinically actionable associations inform guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), but the broad impact of genetic variation on entire populations is not well understood. We analyzed PGx allele and phenotype frequencies for 487,409 participants in the UK Biobank, the largest PGx study to date. For 14 CPIC pharmacogenes known to influence human drug response, we find that 99.5% of individuals may have an atypical response to at least 1 drug; on average they may have an atypical response to 10.3 drugs. Nearly 24% of participants have been prescribed a drug for which they are predicted to have an atypical response. Non-European populations carry a greater frequency of variants that are predicted to be functionally deleterious; many of these are not captured by current PGx allele definitions. Strategies for detecting and interpreting rare variation will be critical for enabling broad application of pharmacogenetics.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informfatics Training Program, Stanford University, Stanford, California, USA
| | - Adam Lavertu
- Biomedical Informfatics Training Program, Stanford University, Stanford, California, USA
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Department of Medicine, Stanford University, Stanford, California, USA
| | | | - Russ B Altman
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Departments of Bioengineering, Genetics, and Medicine, Stanford University, Stanford, California, USA
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