1
|
Vaidhya A, Ghildiyal K, Rajawat D, Nayak SS, Parida S, Panigrahi M. Relevance of pharmacogenetics and pharmacogenomics in veterinary clinical practice: A review. Anim Genet 2024; 55:3-19. [PMID: 37990577 DOI: 10.1111/age.13376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/03/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023]
Abstract
The recent advances in high-throughput next-generation sequencing technologies have heralded the arrival of the Big Data era. As a result, the use of pharmacogenetics in drug discovery and individualized drug therapy has transformed the field of precision medicine. This paradigm shift in drug development programs has effectively reshaped the old drug development practices, which were primarily concerned with the physiological status of patients for drug development. Pharmacogenomics bridges the gap between pharmacodynamics and pharmacokinetics, advancing current diagnostic and treatment strategies and enabling personalized and targeted drug therapy. The primary goals of pharmacogenetic studies are to improve drug efficacy and minimize toxicities, to identify novel drug targets, to estimate drug dosage for personalized medicine, and to incorporate it as a routine diagnostic for disease susceptibility. Although pharmacogenetics has numerous applications in individualized drug therapy and drug development, it is in its infancy in veterinary medicine. The objective of this review is to present an overview of historical landmarks, current developments in various animal species, challenges and future perspectives of genomics in drug development and dosage optimization for individualized medicine in veterinary subjects.
Collapse
Affiliation(s)
- Ayushi Vaidhya
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India
| |
Collapse
|
2
|
Park Y, Lauschke V. Towards more accurate pharmacogenomic variant effect predictions. Pharmacogenomics 2023; 24:841-844. [PMID: 37846582 DOI: 10.2217/pgs-2023-0187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023] Open
Abstract
Tweetable abstract Accurate variant interpretation has become a key bottleneck for the translation of an individual's pharmacogenome into actionable recommendations. We recommend an integrated use of multiplexed assays, structure-based predictions and biobank data to develop more accurate effect predictors.
Collapse
Affiliation(s)
- Yoomi Park
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, South Korea
- Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Volker Lauschke
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Department of Physiology & Pharmacology, Karolinska Institutet, Stockholm, Sweden
- University of Tübingen, Tübingen, Germany
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Koido M. Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data. Br J Clin Pharmacol 2023. [PMID: 37743713 DOI: 10.1111/bcp.15913] [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: 07/31/2023] [Revised: 09/05/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury. Experimental modelling using multiple donors' human primary hepatocytes and human liver organoids demonstrated the polygenic architecture of drug-induced liver injury susceptibility and found biological vulnerability in genetically high-risk tissue donors. Second, we discuss the challenges of interpreting the roles of variants in noncoding regions. Beyond methods involving expression quantitative trait locus analysis and massively parallel reporter assays, we suggest the use of in silico mutagenesis through machine learning methods to understand the roles of variants in transcriptional regulation. This review underscores the importance of these post-GWAS methods in providing critical insights into PGx, potentially facilitating drug development and personalized treatment.
Collapse
Affiliation(s)
- Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
5
|
Tafazoli A, Mikros J, Khaghani F, Alimardani M, Rafigh M, Hemmati M, Siamoglou S, Golińska AK, Kamiński KA, Niemira M, Miltyk W, Patrinos GP. Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics. Hum Genomics 2023; 17:62. [PMID: 37452347 PMCID: PMC10347842 DOI: 10.1186/s40246-023-00508-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.
Collapse
Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology Polish Academy of Sciences, Kraków, Poland
| | - John Mikros
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Faeze Khaghani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran
| | - Maliheh Alimardani
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Rafigh
- Medical Genetics Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Hemmati
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Stavroula Siamoglou
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | | | - Karol A Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
- Department of Cardiology, Medical University of Bialystok, Białystok, Poland
| | - Magdalena Niemira
- Clinical Research Centre, Medical University of Bialystok, Białystok, Poland
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland.
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.
- Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
- Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
| |
Collapse
|
6
|
Landau J, Tsaban L, Yaacov A, Ben Cohen G, Rosenberg S. Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants. Cancer Res 2023; 83:74-88. [PMID: 36264175 DOI: 10.1158/0008-5472.can-22-1038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/02/2022] [Accepted: 10/18/2022] [Indexed: 02/03/2023]
Abstract
Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations. SIGNIFICANCE A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.
Collapse
Affiliation(s)
- Jakob Landau
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Linoy Tsaban
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Adar Yaacov
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gil Ben Cohen
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shai Rosenberg
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.,The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
7
|
Parada-Márquez JF, Maldonado-Rodriguez ND, Triana-Fonseca P, Contreras-Bravo NC, Calderón-Ospina CA, Restrepo CM, Morel A, Ortega-Recalde OJ, Silgado-Guzmán DF, Angulo-Aguado M, Fonseca-Mendoza DJ. Pharmacogenomic profile of actionable molecular variants related to drugs commonly used in anesthesia: WES analysis reveals new mutations. Front Pharmacol 2023; 14:1047854. [PMID: 37021041 PMCID: PMC10069477 DOI: 10.3389/fphar.2023.1047854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 03/06/2023] [Indexed: 04/07/2023] Open
Abstract
Background: Genetic interindividual variability is associated with adverse drug reactions (ADRs) and affects the response to common drugs used in anesthesia. Despite their importance, these variants remain largely underexplored in Latin-American countries. This study describes rare and common variants found in genes related to metabolism of analgesic and anaesthetic drug in the Colombian population. Methods: We conducted a study that included 625 Colombian healthy individuals. We generated a subset of 14 genes implicated in metabolic pathways of common medications used in anesthesia and assessed them by whole-exome sequencing (WES). Variants were filtered using two pipelines: A) novel or rare (minor allele frequency-MAF <1%) variants including missense, loss-of-function (LoF, e.g., frameshift, nonsense), and splice site variants with potential deleterious effect and B) clinically validated variants described in the PharmGKB (categories 1, 2 and 3) and/or ClinVar databases. For rare and novel missense variants, we applied an optimized prediction framework (OPF) to assess the functional impact of pharmacogenetic variants. Allelic, genotypic frequencies and Hardy-Weinberg equilibrium were calculated. We compare our allelic frequencies with these from populations described in the gnomAD database. Results: Our study identified 148 molecular variants potentially related to variability in the therapeutic response to 14 drugs commonly used in anesthesiology. 83.1% of them correspond to rare and novel missense variants classified as pathogenic according to the pharmacogenetic optimized prediction framework, 5.4% were loss-of-function (LoF), 2.7% led to potential splicing alterations and 8.8% were assigned as actionable or informative pharmacogenetic variants. Novel variants were confirmed by Sanger sequencing. Allelic frequency comparison showed that the Colombian population has a unique pharmacogenomic profile for anesthesia drugs with some allele frequencies different from other populations. Conclusion: Our results demonstrated high allelic heterogeneity among the analyzed sampled, enriched by rare (91.2%) variants in pharmacogenes related to common drugs used in anesthesia. The clinical implications of these results highlight the importance of implementation of next-generation sequencing data into pharmacogenomic approaches and personalized medicine.
Collapse
Affiliation(s)
| | | | - Paula Triana-Fonseca
- Department of Molecular Diagnosis, Genética Molecular de Colombia SAS, Bogotá, Colombia
| | - Nora Constanza Contreras-Bravo
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
| | - Carlos Alberto Calderón-Ospina
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
| | - Carlos M. Restrepo
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
| | - Adrien Morel
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
| | - Oscar Javier Ortega-Recalde
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
| | | | - Mariana Angulo-Aguado
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
- *Correspondence: Mariana Angulo-Aguado, ; Dora Janeth Fonseca-Mendoza,
| | - Dora Janeth Fonseca-Mendoza
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, Colombia
- *Correspondence: Mariana Angulo-Aguado, ; Dora Janeth Fonseca-Mendoza,
| |
Collapse
|
8
|
Zhou Y, Lauschke VM. Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy. Handb Exp Pharmacol 2023; 280:237-260. [PMID: 35792943 DOI: 10.1007/164_2022_596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.
Collapse
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.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tuebingen, Tuebingen, Germany.
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Silva-Alarcon S, Valencia C, Newball L, Saldarriaga W, Castillo A. Molecular Variants in Genes related to the Response to Ocular Hypotensive Drugs in an Afro-Colombian Population. Open Ophthalmol J 2022. [DOI: 10.2174/18743641-v16-e2205250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Aims:
This study aimed to conduct an exploratory analysis of the pharmacogenomic variants involved in ocular hypotensive drugs to understand the individual differential response in an Afro-descendant population.
Background:
Glaucoma is the leading cause of irreversible blindness worldwide. The pharmacologic treatment available consists of lowering intraocular pressure by administering topical drugs. In Asian and Caucasian people, pharmacogenomic variants associated with the efficacy of these treatments have been identified. However, in Afro-descendant populations, there is a profound gap in this knowledge.
Objective:
This study identified the pharmacogenomic variants related to ocular hypotensive efficacy treatment in Afro-descendant individuals from the Archipelago of San Andres and Providence, Colombia.
Methods:
An analysis of whole-exome sequencings (WES), functional annotation, and clinical significance was performed for pharmacogenomic variants reported in PharmGKB databases; in turn, an in silico available prediction analysis was carried out for the novel variants.
Results:
We identified six out of 18 non-synonymous variants with a clinical annotation in PharmGKB. Five were classified as level three evidence for the hypotensive drugs; rs1801252 and rs1801253 in the ADRB1 gene and rs1042714 in the ADRB2 gene. These pharmacogenomic variants have been involved in a lack of efficacy of topical beta-blockers and higher systolic and diastolic pressure under treatment with ophthalmic timolol drug. The rs1045642 in the ABCB1 gene was associated with greater efficacy of treatments with latanoprost drug. Also, we found the haplotypes *17 for CYP2D6 and *10 for CYP2C19; both related to reducing the enzyme activity to timolol drug metabolization. In addition, we observed 50 novel potentially actionable variants; 36 synonymous, two insertion variants that caused frameshift mutations, and 12 non-synonymous, where five were predicted to be pathogenic based on several pathogenicity predictions.
Conclusion:
Our results suggested that the pharmacogenomic variants were found to decrease the ocular hypotensive efficacy treatment in a Colombian Afro-descendant population and revealed a significant proportion of novel variants with a potential to influence drug response.
Collapse
|
12
|
Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
Collapse
Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| |
Collapse
|
13
|
Lanillos J, Carcajona M, Maietta P, Alvarez S, Rodriguez-Antona C. Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data. NPJ Genom Med 2022; 7:12. [PMID: 35181665 PMCID: PMC8857256 DOI: 10.1038/s41525-022-00283-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 01/21/2022] [Indexed: 11/22/2022] Open
Abstract
Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.
Collapse
Affiliation(s)
- Javier Lanillos
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Madrid, Spain
| | | | | | | | - Cristina Rodriguez-Antona
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Madrid, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain.
| |
Collapse
|
14
|
Siamoglou S, Koromina M, Hishinuma E, Yamazaki S, Tsermpini EE, Kordou Z, Fukunaga K, Chantratita W, Zhou Y, Lauschke V, Mushiroda T, Hiratsuka M, Patrinos GP. Identification and functional validation of novel pharmacogenomic variants using a next-generation sequencing-based approach for clinical pharmacogenomics. Pharmacol Res 2022; 176:106087. [PMID: 35033648 DOI: 10.1016/j.phrs.2022.106087] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 01/10/2023]
Abstract
Inter-individual variability in pharmacokinetics and drug response is heavily influenced by single-nucleotide variants (SNVs) and copy-number variations (CNVs) in genes with importance for drug disposition. Nowadays, a plethora of studies implement next generation sequencing to capture rare and novel pharmacogenomic (PGx) variants that influence drug response. To address these issues, we present a comprehensive end-to-end analysis workflow, beginning from targeted PGx panel re-sequencing to in silico analysis pipelines and in vitro validation assays. Specifically, we show that novel pharmacogenetic missense variants that are predicted or putatively predicted to be functionally deleterious, significantly alter protein activity levels of CYP2D6 and CYP2C19 proteins. We further demonstrate that variant priorization pipelines tailored with functional in vitro validation assays provide supporting evidence for the deleterious effect of novel PGx variants. The proposed workflow could provide the basis for integrating next-generation sequencing for PGx testing into routine clinical practice.
Collapse
Affiliation(s)
- Stavroula Siamoglou
- University of Patras School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece
| | - Maria Koromina
- University of Patras School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece
| | - Eiji Hishinuma
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Japan; Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Shuki Yamazaki
- Laboratory of Pharmacotherapy of Life-Style Related Diseases, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Evangelia-Eirini Tsermpini
- University of Patras School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece
| | - Zoe Kordou
- University of Patras School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece
| | - Koya Fukunaga
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Wasun Chantratita
- Center for Medical Genomics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden; Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tuebingen, Tuebingen, Germany
| | - Taisei Mushiroda
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masahiro Hiratsuka
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Japan; Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Laboratory of Pharmacotherapy of Life-Style Related Diseases, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan; Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - George P Patrinos
- University of Patras School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece; United Arab Emirates University, College of Medicine and Health Sciences, Department of Pathology, Al-Ain, United Arab Emirates; United Arab Emirates University, Zayed Center for Health Sciences, Al-Ain, United Arab Emirates.
| |
Collapse
|
15
|
Genetic landscape of 125 pharmacogenes in Chinese from the Chinese Millionome Database. Sci Rep 2021; 11:19222. [PMID: 34584183 PMCID: PMC8478937 DOI: 10.1038/s41598-021-98877-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/16/2021] [Indexed: 12/13/2022] Open
Abstract
Inter-individual differences of drug responses could be attributed to genetic variants of pharmacogenes such as cytochrome P450 (CYP), phase 2 enzymes, and transporters. In contrast to extensive studies on the genetic polymorphisms of CYP gene, genetic mutation spectrum of other pharmacogenes was under-representative in the pharmacogenetics investigations. Here we studied the genetic variations of 125 pharmacogenes including drug transporters, non-CYP phase 1 enzymes, phase 2 enzymes, nuclear receptors and others in Chinese from the Chinese Millionome Database (CMDB), of which 38,188 variants were identified. Computational analyses of the 2554 exonic variants found 617 deleterious missense variants, 91.1% of which were rare, and of the 54 loss-of-function (splice acceptor, splice donor, start lost, and stop gained) variants, 53 (98.1%) were rare. These results suggested an enrichment of rare variants in functional ones for pharmacogenes. Certain common functional variants including NUDT15 13:48611934 G/A (rs186364861), UGT1A1 2:234676872 C/T (rs34946978), and ALDH2 12:112241766 G/A (rs671) were population-specific for CMDB Chinese because they were absent (with a zero of variant allele frequency) or very rare in other gnomAD populations. These findings might be useful for the further pharmacogenomics research and clinical application in Chinese.
Collapse
|
16
|
Geck RC, Boyle G, Amorosi CJ, Fowler DM, Dunham MJ. Measuring Pharmacogene Variant Function at Scale Using Multiplexed Assays. Annu Rev Pharmacol Toxicol 2021; 62:531-550. [PMID: 34516287 DOI: 10.1146/annurev-pharmtox-032221-085807] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As costs of next-generation sequencing decrease, identification of genetic variants has far outpaced our ability to understand their functional consequences. This lack of understanding is a central challenge to a key promise of pharmacogenomics: using genetic information to guide drug selection and dosing. Recently developed multiplexed assays of variant effect enable experimental measurement of the function of thousands of variants simultaneously. Here, we describe multiplexed assays that have been performed on nearly 25,000 variants in eight key pharmacogenes (ADRB2, CYP2C9, CYP2C19, NUDT15, SLCO1B1, TMPT, VKORC1, and the LDLR promoter), discuss advances in experimental design, and explore key challenges that must be overcome to maximize the utility of multiplexed functional data. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Renee C Geck
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Gabriel Boyle
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Clara J Amorosi
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; , .,Department of Bioengineering, University of Washington, Seattle, Washington 98195, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; ,
| |
Collapse
|
17
|
Zhou Y, Arribas GH, Turku A, Jürgenson T, Mkrtchian S, Krebs K, Wang Y, Svobodova B, Milani L, Schulte G, Korabecny J, Gastaldello S, Lauschke VM. Rare genetic variability in human drug target genes modulates drug response and can guide precision medicine. SCIENCE ADVANCES 2021; 7:eabi6856. [PMID: 34516913 PMCID: PMC8442892 DOI: 10.1126/sciadv.abi6856] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Interindividual variability in drug response constitutes a major concern in pharmacotherapy. While polymorphisms in genes involved in drug disposition have been extensively studied, drug target variability remains underappreciated. By mapping the genomic variability of all human drug target genes onto high-resolution crystal structures of drug target complexes, we identified 1094 variants localized within 6 Å of drug-binding pockets and directly affecting their geometry, topology, or physicochemical properties. We experimentally show that binding site variants affect pharmacodynamics with marked drug- and variant-specific differences. In addition, we demonstrate that a common BCHE variant confers resistance to tacrine and rivastigmine, which can be overcome by the use of derivatives based on squaric acid scaffolds or tryptophan conjugation. These findings underscore the importance of genetic drug target variability and demonstrate that integration of genomic data and structural information can inform personalized drug selection and genetically guided drug development to overcome resistance.
Collapse
Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Gabriel Herras Arribas
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Ainoleena Turku
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
- Orion Pharma R&D, P.O. Box 65 (Orionintie 1), FI-02101 Espoo, Finland
| | - Tuuli Jürgenson
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Souren Mkrtchian
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Kristi Krebs
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 310058 Hangzhou, China
| | - Barbora Svobodova
- Biomedical Research Centre, University Hospital Hradec Králové, Sokolská 581, 500 05 Hradec Králové, Czech Republic
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Gunnar Schulte
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Jan Korabecny
- Biomedical Research Centre, University Hospital Hradec Králové, Sokolská 581, 500 05 Hradec Králové, Czech Republic
| | - Stefano Gastaldello
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Volker M. Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Corresponding author.
| |
Collapse
|
18
|
Tafazoli A, Guchelaar HJ, Miltyk W, Kretowski AJ, Swen JJ. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021; 12:693453. [PMID: 34512329 PMCID: PMC8424415 DOI: 10.3389/fphar.2021.693453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.
Collapse
Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Adam J. Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jesse J. Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| |
Collapse
|
19
|
Pharmacogenomics of statins: lipid response and other outcomes in Brazilian cohorts. Pharmacol Rep 2021; 74:47-66. [PMID: 34403130 DOI: 10.1007/s43440-021-00319-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/21/2021] [Accepted: 07/30/2021] [Indexed: 01/20/2023]
Abstract
Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase, a key enzyme in cholesterol biosynthesis, that are highly effective in reducing plasma low-density lipoprotein (LDL) cholesterol and decreasing the risk of cardiovascular events. In recent years, a multitude of variants in genes involved in pharmacokinetics (PK) and pharmacodynamics (PD) have been suggested to influence the cholesterol-lowering response. However, the vast majority of studies have analyzed the pharmacogenetic associations in populations in Europe and the USA, whereas data in other populations, including Brazil, are mostly lacking. This narrative review provides an update of clinical studies on statin pharmacogenomics in Brazilian cohorts exploring lipid-lowering response, adverse events and pleiotropic effects. We find that variants in drug transporter genes (SLCO1B1 and ABCB1) positively impacted atorvastatin and simvastatin response, whereas variants in genes of drug metabolizing enzymes (CYP3A5) decreased response. Furthermore, multiple associations of variants in PD genes (HMGCR, LDLR and APOB) with statin response were identified. Few studies have explored statin-related adverse events, and only ABCB1 but not SLCO1B1 variants were robustly associated with increased risk in Brazil. Statin-related pleiotropic effects were shown to be influenced by variants in PD (LDLR, NR1H2) and antioxidant enzyme (NOS3, SOD2, MTHFR, SELENOP) genes. The findings of these studies indicate that statin pharmacogenomic associations are distinctly different in Brazil compared to other populations. This review also discusses the clinical implications of pharmacogenetic studies and the rising importance of investigating rare variants to explore their association with statin response.
Collapse
|
20
|
McConnell H, Andrews TD, Field MA. Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants. PeerJ 2021; 9:e11774. [PMID: 34316407 PMCID: PMC8286708 DOI: 10.7717/peerj.11774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 06/23/2021] [Indexed: 01/04/2023] Open
Abstract
Background Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools-these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. Methods Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. Results As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as 'benign'. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as 'benign'. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. Conclusion In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.
Collapse
Affiliation(s)
- Hannah McConnell
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - T Daniel Andrews
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Matt A Field
- Australian Institute of Tropical Health and Medicine, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Smithfield, Australia.,Immunogenomics Lab, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
Russell LE, Zhou Y, Almousa AA, Sodhi JK, Nwabufo CK, Lauschke VM. Pharmacogenomics in the era of next generation sequencing - from byte to bedside. Drug Metab Rev 2021; 53:253-278. [PMID: 33820459 DOI: 10.1080/03602532.2021.1909613] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.
Collapse
Affiliation(s)
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Ahmed A Almousa
- Department of Pharmacy, London Health Sciences Center, Victoria Hospital, London, ON, Canada
| | - Jasleen K Sodhi
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, CA, USA.,Department of Drug Metabolism and Pharmacokinetics, Plexxikon, Inc., Berkeley, CA, USA
| | | | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
24
|
Borczyk M, Piechota M, Rodriguez Parkitna J, Korostynski M. Prospects for personalization of depression treatment with genome sequencing. Br J Pharmacol 2021; 179:4220-4232. [PMID: 33786859 DOI: 10.1111/bph.15470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/22/2021] [Accepted: 03/24/2021] [Indexed: 12/20/2022] Open
Abstract
The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient's condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.
Collapse
Affiliation(s)
- Malgorzata Borczyk
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Marcin Piechota
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Jan Rodriguez Parkitna
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Michal Korostynski
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| |
Collapse
|
25
|
Blake S, Hemming I, Heng JIT, Agostino M. Structure-Based Approaches to Classify the Functional Impact of ZBTB18 Missense Variants in Health and Disease. ACS Chem Neurosci 2021; 12:979-989. [PMID: 33621064 DOI: 10.1021/acschemneuro.0c00758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The Cys2His2 type zinc finger is a motif found in many eukaryotic transcription factor proteins that facilitates binding to genomic DNA so as to influence cellular gene expression. One such transcription factor is ZBTB18, characterized as a repressor that orchestrates the development of mammalian tissues including skeletal muscle and brain during embryogenesis. In humans, it has been recognized that disease-associated ZBTB18 missense variants mapping to the coding sequence of the zinc finger domain influence sequence-specific DNA binding, disrupt transcriptional regulation, and impair neural circuit formation in the brain. Furthermore, general population ZBTB18 missense variants that influence DNA binding and transcriptional regulation have also been documented within this domain; however, the molecular traits that explain why some variants cause disease while others do not are poorly understood. Here, we have applied five structure-based approaches to evaluate their ability to discriminate between disease-associated and general population ZBTB18 missense variants. We found that thermodynamic integration and Residue Scanning in the Schrodinger Biologics Suite were the best approaches for distinguishing disease-associated variants from general population variants. Our results demonstrate the effectiveness of structure-based approaches for the functional characterization of missense alleles to DNA binding, zinc finger transcription factor protein-coding genes that underlie human health and disease.
Collapse
Affiliation(s)
- Steven Blake
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
| | - Isabel Hemming
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- The Faculty of Health and Medical Sciences, Medical School, The University of Western Australia, Crawley, Western Australia 6009, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
| | - Mark Agostino
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
- Curtin Institute for Computation, Curtin University, Bentley, Western Australia, Australia
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Pharmacogenomic Biomarkers of Follicle-Stimulating Hormone Receptor Malfunction in Females with Impaired Ovarian Response-A Genetic Survey. J Clin Med 2021; 10:jcm10020170. [PMID: 33561079 PMCID: PMC7825139 DOI: 10.3390/jcm10020170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 02/07/2023] Open
Abstract
Follicle-stimulating hormone receptor (FSHR) plays an essential role as one of the most important molecules in response to some of infertility related medications. Impaired ovarian reserve and poor response to such treatments are partially dependent on the FSHR molecule itself. However, the function and drug sensitivity for this receptor may change due to various allele and polymorphisms in the FSHR gene. Studies indicated some of the FSHR-mediated treatments utilized in clinical centers display different outcomes in specific populations, which may arise from FSHR altered genotypes in certain patients. To support the increased demands for reaching the personalized drug and hormone therapy in clinics, focusing on actionable variants through Pharmacogenomic analysis of this receptor may be necessary. The current study tries to display a perspective view on genetic assessments for Pharmacogenomic profiling of the FSHR gene via providing a systematic and critical overview on the genetics of FSHR and its diverse responses to ligands for infertility treatment in females with impaired ovarian responses and show the potential effects of the patient genetic make-up on related binding substances efficacy. All identified functional drug-related alleles were selected through a comprehensive literature search and analyzed. Advanced technologies for the genetic evaluation of them are also discussed properly.
Collapse
|
28
|
Al-Mahayri ZN, Patrinos GP, Wattanapokayakit S, Iemwimangsa N, Fukunaga K, Mushiroda T, Chantratita W, Ali BR. Variation in 100 relevant pharmacogenes among emiratis with insights from understudied populations. Sci Rep 2020; 10:21310. [PMID: 33277594 PMCID: PMC7718919 DOI: 10.1038/s41598-020-78231-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/17/2020] [Indexed: 02/08/2023] Open
Abstract
Genetic variations have an established impact on the pharmacological response. Investigating this variation resulted in a compilation of variants in "pharmacogenes". The emergence of next-generation sequencing facilitated large-scale pharmacogenomic studies and exhibited the extensive variability of pharmacogenes. Some rare and population-specific variants proved to be actionable, suggesting the significance of population pharmacogenomic research. A profound gap exists in the knowledge of pharmacogenomic variants enriched in some populations, including the United Arab Emirates (UAE). The current study aims to explore the landscape of variations in relevant pharmacogenes among healthy Emiratis. Through the resequencing of 100 pharmacogenes for 100 healthy Emiratis, we identified 1243 variants, of which 63% are rare (minor allele frequency ≤ 0.01), and 30% were unique. Filtering the variants according to Pharmacogenomics Knowledge Base (PharmGKB) annotations identified 27 diplotypes and 26 variants with an evident clinical relevance. Comparison with global data illustrated a significant deviation of allele frequencies in the UAE population. Understudied populations display a distinct allelic architecture and various rare and unique variants. We underscored pharmacogenes with the highest variation frequencies and provided investigators with a list of candidate genes for future studies. Population pharmacogenomic studies are imperative during the pursuit of global pharmacogenomics implementation.
Collapse
Affiliation(s)
- Zeina N Al-Mahayri
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 17666, Al-Ain, United Arab Emirates
| | - George P Patrinos
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 17666, Al-Ain, United Arab Emirates.,Department of Pharmacy, School of Health Sciences, University of Patras, University Campus, Rion, Patras, Greece.,Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Sukanya Wattanapokayakit
- Division of Genomic Medicine and Innovation Support, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | - Nareenart Iemwimangsa
- Center for Medical Genomics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Koya Fukunaga
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Taisei Mushiroda
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Wasun Chantratita
- Center for Medical Genomics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Bassam R Ali
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 17666, Al-Ain, United Arab Emirates. .,Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates. .,Department of Genetics and Genomics, College of Medicine and Heath Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
| |
Collapse
|
29
|
Simões AR, Fernández-Rozadilla C, Maroñas O, Carracedo Á. The Road so Far in Colorectal Cancer Pharmacogenomics: Are We Closer to Individualised Treatment? J Pers Med 2020; 10:E237. [PMID: 33228198 PMCID: PMC7711884 DOI: 10.3390/jpm10040237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 12/12/2022] Open
Abstract
In recent decades, survival rates in colorectal cancer have improved greatly due to pharmacological treatment. However, many patients end up developing adverse drug reactions that can be severe or even life threatening, and that affect their quality of life. These remain a limitation, as they may force dose reduction or treatment discontinuation, diminishing treatment efficacy. From candidate gene approaches to genome-wide analysis, pharmacogenomic knowledge has advanced greatly, yet there is still huge and unexploited potential in the use of novel technologies such as next-generation sequencing strategies. This review summarises the road of colorectal cancer pharmacogenomics so far, presents considerations and directions to be taken for further works and discusses the path towards implementation into clinical practice.
Collapse
Affiliation(s)
- Ana Rita Simões
- Grupo de Medicina Xenómica, Universidade de Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain; (A.R.S.); (O.M.); (Á.C.)
- Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain
| | - Ceres Fernández-Rozadilla
- Grupo de Medicina Xenómica, Universidade de Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain; (A.R.S.); (O.M.); (Á.C.)
- Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain
| | - Olalla Maroñas
- Grupo de Medicina Xenómica, Universidade de Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain; (A.R.S.); (O.M.); (Á.C.)
| | - Ángel Carracedo
- Grupo de Medicina Xenómica, Universidade de Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain; (A.R.S.); (O.M.); (Á.C.)
- Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain
- Fundación Pública Galega de Medicina Xenómica; SERGAS, 15706 Santiago de Compostela, Spain
- Consorcio Centro de Investigación Biomédica en Red de Enfermedades Raras—CIBERER, 28029 Madrid, Spain
| |
Collapse
|
30
|
Abstract
Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.
Collapse
Affiliation(s)
- Mohammed A Alaidarous
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Kingdom of Saudi Arabia. E-mail.
| |
Collapse
|
31
|
McInnes G, Dalton R, Sangkuhl K, Whirl-Carrillo M, Lee SB, Tsao PS, Gaedigk A, Altman RB, Woodahl EL. Transfer learning enables prediction of CYP2D6 haplotype function. PLoS Comput Biol 2020; 16:e1008399. [PMID: 33137098 PMCID: PMC7660895 DOI: 10.1371/journal.pcbi.1008399] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 11/12/2020] [Accepted: 09/24/2020] [Indexed: 12/31/2022] Open
Abstract
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.
Collapse
Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, United States of America
| | - Rachel Dalton
- Department of Biomedical and Pharmaceutical Sciences, University of Montana, Missoula, Montana, United States of America
- Department of Biomedical and Translational Research, University of Florida, Gainesville, Florida, United States of America
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Michelle Whirl-Carrillo
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Seung-been Lee
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Philip S. Tsao
- VA Palo Alto Epidemiology Research and Information Center for Genomics, VAPAHCS, Palo Alto, California, United States of America
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children’s Mercy Kansas City, Kansas City, Missouri, United States of America
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Departments of Bioengineering, Genetics, and Medicine, Stanford University, Stanford, California, United States of America
| | - Erica L. Woodahl
- Department of Biomedical and Pharmaceutical Sciences, University of Montana, Missoula, Montana, United States of America
| |
Collapse
|
32
|
Zhou Y, Dagli Hernandez C, Lauschke VM. Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier. Br J Cancer 2020; 123:1782-1789. [PMID: 32973300 PMCID: PMC7722893 DOI: 10.1038/s41416-020-01084-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/26/2020] [Accepted: 09/02/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Inter-individual differences in dihydropyrimidine dehydrogenase (DPYD encoding DPD) and thiopurine S-methyltransferase (TPMT) activity are important predictors for fluoropyrimidine and thiopurine toxicity. While several variants in these genes are known to decrease enzyme activities, many additional genetic variations with unclear functional consequences have been identified, complicating informed clinical decision-making in the respective carriers. METHODS We used a novel pharmacogenetically trained ensemble classifier to analyse DPYD and TPMT genetic variability based on sequencing data from 138,842 individuals across eight populations. RESULTS The algorithm accurately predicted in vivo consequences of DPYD and TPMT variants (accuracy 91.4% compared to 95.3% in vitro). Further analysis showed high genetic complexity of DPD deficiency, advocating for sequencing-based DPYD profiling, whereas genotyping of four variants in TPMT was sufficient to explain >95% of phenotypic TPMT variability. Lastly, we provided population-scale profiles of ethnogeographic variability in DPD and TPMT phenotypes, and revealed striking interethnic differences in frequency and genetic constitution of DPD and TPMT deficiency. CONCLUSION These results provide the most comprehensive data set of DPYD and TPMT variability published to date with important implications for population-adjusted genetic profiling strategies of fluoropyrimidine and thiopurine risk factors and precision public health.
Collapse
Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Carolina Dagli Hernandez
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden.,Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, 05508-000, Sao Paulo, Brazil
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177, Stockholm, Sweden.
| |
Collapse
|
33
|
Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions. J Pers Med 2020; 10:jpm10030084. [PMID: 32796505 PMCID: PMC7563167 DOI: 10.3390/jpm10030084] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 08/01/2020] [Accepted: 08/06/2020] [Indexed: 12/16/2022] Open
Abstract
Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.
Collapse
|
34
|
Sanavia T, Birolo G, Montanucci L, Turina P, Capriotti E, Fariselli P. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput Struct Biotechnol J 2020; 18:1968-1979. [PMID: 32774791 PMCID: PMC7397395 DOI: 10.1016/j.csbj.2020.07.011] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 12/13/2022] Open
Abstract
Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.
Collapse
Affiliation(s)
- Tiziana Sanavia
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
| | - Ludovica Montanucci
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
| |
Collapse
|
35
|
Russell LE, Schwarz UI. Variant discovery using next-generation sequencing and its future role in pharmacogenetics. Pharmacogenomics 2020; 21:471-486. [DOI: 10.2217/pgs-2019-0190] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.
Collapse
Affiliation(s)
- Laura E Russell
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, London, ON, N6A 5C1, Canada
| | - Ute I Schwarz
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, London, ON, N6A 5C1, Canada
- Division of Clinical Pharmacology, Department of Medicine, Western University, London Health Sciences Centre – University Hospital, 339 Windermere Road, London, ON, N6A 5A5, Canada
| |
Collapse
|
36
|
Wang L, Zhang L. Circulating Exosomal miRNA as Diagnostic Biomarkers of Neurodegenerative Diseases. Front Mol Neurosci 2020; 13:53. [PMID: 32351363 PMCID: PMC7174585 DOI: 10.3389/fnmol.2020.00053] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 03/17/2020] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases (NDDs) are a group of diseases caused by chronic and progressive degeneration of neural tissue. The main pathological manifestations are neuronal degeneration and loss in the brain and/or spinal cord. Common NDDs include Alzheimer disease (AD), Parkinson disease (PD), Huntington disease (HD), and amyotrophic lateral sclerosis (ALS). The complicated pathological characteristics and different clinical manifestations of NDDs result in a lack of sensitive and efficient diagnostic methods. In addition, no sensitive biomarkers are available to monitor the course of NDDs, predict their prognosis, and monitor the therapeutic response. Despite extensive research in recent years, analysis of amyloid β (Aβ) and α-synuclein has failed to effectively improve NDD diagnosis. Although recent studies have indicated circulating miRNAs as promising diagnostic biomarkers of NDDs, the miRNA in the peripheral circulation is susceptible to interference by other components, making circulating miRNA results less consistent. Exosomes are small membrane vesicles with a diameter of approximately 30-100 nm that transport proteins, lipids, mRNA, and miRNA. Because recent studies have shown that exosomes have a double-membrane structure that can resist ribonuclease in the blood, giving exosomal miRNA high stability and making them resistant to degradation, they may become an ideal biomarker of circulating fluids. In this review, we discuss the applicability of circulating exosomal miRNAs as biomarkers, highlight the technical aspects of exosomal miRNA analysis, and review studies that have used circulating exosomal miRNAs as candidate diagnostic biomarkers of NDDs.
Collapse
Affiliation(s)
- Lin Wang
- Department of Emergency Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lijuan Zhang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
37
|
Lauschke VM, Ingelman-Sundberg M. Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation. NPJ Genom Med 2020; 5:9. [PMID: 32194983 PMCID: PMC7057970 DOI: 10.1038/s41525-020-0119-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/15/2020] [Indexed: 12/13/2022] Open
Abstract
The genomic inter-individual heterogeneity remains a significant challenge for both clinical decision-making and the design of clinical trials. Although next-generation sequencing (NGS) is increasingly implemented in drug development and clinical trials, translation of the obtained genomic information into actionable clinical advice lags behind. Major reasons are the paucity of sufficiently powered trials that can quantify the added value of pharmacogenetic testing, and the considerable pharmacogenetic complexity with millions of rare variants with unclear functional consequences. The resulting uncertainty is reflected in inconsistencies of pharmacogenomic drug labels in Europe and the United States. In this review, we discuss how the knowledge gap for bridging pharmacogenomics into the clinics can be reduced. First, emerging methods that allow the high-throughput experimental characterization of pharmacogenomic variants combined with novel computational tools hold promise to improve the accuracy of drug response predictions. Second, tapping of large biobanks of therapeutic drug monitoring data allows to conduct high-powered retrospective studies that can validate the clinical importance of genetic variants, which are currently incompletely characterized. Combined, we are confident that these methods will improve the accuracy of drug response predictions and will narrow the gap between variant identification and its utilization for clinical decision-support.
Collapse
Affiliation(s)
- Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | | |
Collapse
|
38
|
Russell LE, Zhou Y, Lauschke VM, Kim RB. In Vitro Functional Characterization and in Silico Prediction of Rare Genetic Variation in the Bile Acid and Drug Transporter, Na+-Taurocholate Cotransporting Polypeptide (NTCP, SLC10A1). Mol Pharm 2020; 17:1170-1181. [DOI: 10.1021/acs.molpharmaceut.9b01200] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Laura E. Russell
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, Rm 216, N6A 5C1 London, Ontario, Canada
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Volker M. Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Richard B. Kim
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, Rm 216, N6A 5C1 London, Ontario, Canada
- Division of Clinical Pharmacology, Department of Medicine, Western University, 339 Windermere Rd, N6A 5A5 London, Ontario, Canada
| |
Collapse
|
39
|
Nofziger C, Turner AJ, Sangkuhl K, Whirl-Carrillo M, Agúndez JAG, Black JL, Dunnenberger HM, Ruano G, Kennedy MA, Phillips MS, Hachad H, Klein TE, Gaedigk A. PharmVar GeneFocus: CYP2D6. Clin Pharmacol Ther 2019; 107:154-170. [PMID: 31544239 DOI: 10.1002/cpt.1643] [Citation(s) in RCA: 133] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 08/29/2019] [Indexed: 01/13/2023]
Abstract
The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus. CYP2D6 genetic variation impacts the metabolism of numerous drugs and, thus, can impact drug efficacy and safety. This GeneFocus provides a comprehensive overview and summary of CYP2D6 genetic variation and describes how the information provided by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).
Collapse
Affiliation(s)
| | - Amy J Turner
- Section of Genomic Pediatrics, Department of Pediatrics, Children's Research Institute, The Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,RPRD Diagnostics LLC, Wauwatosa, Wisconsin, USA
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | | | - José A G Agúndez
- University Institute of Molecular Pathology Biomarkers, UEx, Cáceres, Spain.,ARADyAL Instituto de Salud Carlos III, Madrid, Spain
| | - John L Black
- Division of Laboratory Genetics and Genomics, Personalized Genomics Laboratory, Mayo Clinic Laboratories, Mayo Clinic, Rochester, Minnesota, USA
| | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanton, Illinois, USA
| | - Gualberto Ruano
- Institute of Living at Hartford Hospital, Genomas Laboratory of Personalized Health, Hartford, Connecticut, USA
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University Otago, Christchurch, New Zealand
| | | | - Houda Hachad
- Translational Software, Bellevue, Washington, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology, & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA.,School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
| |
Collapse
|
40
|
The genetic landscape of the human solute carrier (SLC) transporter superfamily. Hum Genet 2019; 138:1359-1377. [PMID: 31679053 PMCID: PMC6874521 DOI: 10.1007/s00439-019-02081-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 10/26/2019] [Indexed: 12/22/2022]
Abstract
The human solute carrier (SLC) superfamily of transporters is comprised of over 400 membrane-bound proteins, and plays essential roles in a multitude of physiological and pharmacological processes. In addition, perturbation of SLC transporter function underlies numerous human diseases, which renders SLC transporters attractive drug targets. Common genetic polymorphisms in SLC genes have been associated with inter-individual differences in drug efficacy and toxicity. However, despite their tremendous clinical relevance, epidemiological data of these variants are mostly derived from heterogeneous cohorts of small sample size and the genetic SLC landscape beyond these common variants has not been comprehensively assessed. In this study, we analyzed Next-Generation Sequencing data from 141,456 individuals from seven major human populations to evaluate genetic variability, its functional consequences, and ethnogeographic patterns across the entire SLC superfamily of transporters. Importantly, of the 204,287 exonic single-nucleotide variants (SNVs) which we identified, 99.8% were present in less than 1% of analyzed alleles. Comprehensive computational analyses using 13 partially orthogonal algorithms that predict the functional impact of genetic variations based on sequence information, evolutionary conservation, structural considerations, and functional genomics data revealed that each individual genome harbors 29.7 variants with putative functional effects, of which rare variants account for 18%. Inter-ethnic variability was found to be extensive, and 83% of deleterious SLC variants were only identified in a single population. Interestingly, population-specific carrier frequencies of loss-of-function variants in SLC genes associated with recessive Mendelian disease recapitulated the ethnogeographic variation of the corresponding disorders, including cystinuria in Jewish individuals, type II citrullinemia in East Asians, and lysinuric protein intolerance in Finns, thus providing a powerful resource for clinical geneticists to inform about population-specific prevalence and allelic composition of Mendelian SLC diseases. In summary, we present the most comprehensive data set of SLC variability published to date, which can provide insights into inter-individual differences in SLC transporter function and guide the optimization of population-specific genotyping strategies in the bourgeoning fields of personalized medicine and precision public health.
Collapse
|
41
|
Pharmacogenes (PGx-genes): Current understanding and future directions. Gene 2019; 718:144050. [DOI: 10.1016/j.gene.2019.144050] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 12/14/2022]
|
42
|
Qi G, Han C, Sun Y, Zhou Y. Genetic insight into cytochrome P450 in Chinese from the Chinese Millionome Database. Basic Clin Pharmacol Toxicol 2019; 126:341-352. [PMID: 31661191 DOI: 10.1111/bcpt.13356] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 10/15/2019] [Indexed: 12/21/2022]
Abstract
Genetic variations of cytochrome P450 (CYP) influence the inter-individual differences in drug response. Here, we collected 8682 variants of 57 CYP genes and cytochrome P450 oxidoreductase (POR) from a large-scale sequencing project in Chinese, Chinese Millionome Database (CMDB). In addition, 52 294 variants from the Genome Aggregation Database (gnomAD) had been simultaneously identified and analysed. Rare variants with a variant allele frequency (VAF) < 0.01 comprised 41.4% (3594/8682) of identified variations in the CMDB, while 98.1% (51 320/52 294) in the gnomAD were rare. Out of 8682 variants in the CMDB, 66.9% (5808/8682) were in introns and only 4.3% (377/8682) were missense variants. In contrast, 36.2% (18 929/52 294) variants in the gnomAD were missense. The common alleles with a VAF over 0.1 were found in CYP1A2*1C, CYP1A2*1F, CYP2C19*2, CYP2D6*2, CYP2D6*10, CYP3A5*3 and CYP4F2*3, with a VAF of 0.161, 0.6, 0.27, 0.274, 0.678, 0.92 and 0.233, respectively. The growing number of genetic variations in CYP genes as more genomes are sequenced would increase the power to predict drug metabolism and response based on the genotype of the particular individual.
Collapse
Affiliation(s)
- Guangzhao Qi
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chao Han
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ya Sun
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yubing Zhou
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
43
|
Gonzalez-Covarrubias V, Morales-Franco M, Cruz-Correa OF, Martínez-Hernández A, García-Ortíz H, Barajas-Olmos F, Genis-Mendoza AD, Martínez-Magaña JJ, Nicolini H, Orozco L, Soberón X. Variation in Actionable Pharmacogenetic Markers in Natives and Mestizos From Mexico. Front Pharmacol 2019; 10:1169. [PMID: 31649539 PMCID: PMC6796793 DOI: 10.3389/fphar.2019.01169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022] Open
Abstract
The identification and characterization of pharmacogenetic variants in Latin American populations is still an ongoing endeavor. Here, we investigated SNVs on genes listed by the Pharmacogenomics Knowledge Base in 1284 Mestizos and 94 Natives from Mexico. Five institutional cohorts with NGS data were retrieved from different research projects at INMEGEN, sequencing files were filtered for 55 pharmacogenes present in all cohorts to identify novel and known variation. Bioinformatic tools VEP, PROVEAN, and FATHMM were used to assess, in silico, the functional impact of this variation. Next, we focused on 17 genes with actionable variants that have been clinically implemented. Allele frequencies were compared with major continental groups and differences discussed in the scope of a pharmacogenomic impact. We observed a wide genetic variability for known and novel SNVs, the largest variation was on UGT1A > ACE > COMT > ABCB1 and the lowest on APOE and NAT2. Although with allele frequencies around 1%, novel variation was observed in 16 of 17 PGKB genes. In Natives we identified 59 variants and 58 in Mestizos. Several genes did not show novel variation, on CYP2B6, CYP2D6, and CYP3A4 in Natives; and APOE, UGT1A, and VKORC1 in Mestizos. Similarities in allele frequency, comparing major continental groups for VIP pharmacogenes, hint towards a comparable PGx for drugs metabolized by UGT1A1, DPYD, ABCB1, CBR3, COMT, and TPMT; in contrast to variants on CYP3A5 and CYP2B6 for which significant MAF differences were identified. Our observations offer some discernment into the extent of pharmacogenetic variation registered up-to-date in Mexicans and contribute to quantitatively dissect actionable pharmacogenetic variants in Natives and Mestizos.
Collapse
Affiliation(s)
| | | | | | | | - Humberto García-Ortíz
- Immunogenomics and Metabolic Diseases Laboratory, INMEGEN, CDMX, Mexico City, Mexico
| | | | | | | | - Humberto Nicolini
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, INMEGEN, Mexico City, Mexico
| | - Lorena Orozco
- Immunogenomics and Metabolic Diseases Laboratory, INMEGEN, CDMX, Mexico City, Mexico
| | - Xavier Soberón
- Pharmacogenomics Laboratory, INMEGEN, CDMX, Mexico City, Mexico
| |
Collapse
|
44
|
Abou Diwan E, Zeitoun RI, Abou Haidar L, Cascorbi I, Khoueiry Zgheib N. Implementation and obstacles of pharmacogenetics in clinical practice: An international survey. Br J Clin Pharmacol 2019; 85:2076-2088. [PMID: 31141189 DOI: 10.1111/bcp.13999] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/07/2019] [Accepted: 05/19/2019] [Indexed: 12/17/2022] Open
Abstract
AIMS Eight years ago, a paper-based survey was administered during the World Pharma 2010 meeting, asking about the challenges of implementing pharmacogenetics (PGx) in clinical practice. The data collected at the time gave an idea about the progress of this implementation and what still needs to be done. Since then, although there have been major initiatives to push PGx forward, PGx clinical implementation may still be facing different challenges in different parts of the world. Our aim was therefore to distribute a follow-up international survey in electronic format to elucidate an overview on the current stage of implementation, acceptance and challenges of PGx in academic institutions around the world. METHODS This is an online anonymous LimeSurvey-based study launched on 11 November 2018. Survey questions were adapted from the initially published manuscript in 2010. An extensive web search for worldwide scientists potentially involved in PGx research resulted in a total of 1973 names. Countries were grouped based on the Human Development Index. RESULTS There were 204 respondents from 43 countries. Despite the wide availability of PGx tests, the consistently positive attitude towards their applications and advances in the field, progress of the clinical implementation of PGx still faces many challenges all around the globe. CONCLUSIONS Clinical implementation of PGx started over a decade ago but there is a gap in progress around the globe and discrepancies between the challenges reported by different countries, despite some challenges being universal. Further studies on ways to overcome these challenges are warranted.
Collapse
Affiliation(s)
| | - Ralph I Zeitoun
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Lea Abou Haidar
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Ingolf Cascorbi
- Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Nathalie Khoueiry Zgheib
- Department of Pharmacology and Toxicology, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| |
Collapse
|
45
|
Lauschke VM, Zhou Y, Ingelman-Sundberg M. Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity. Pharmacol Ther 2019; 197:122-152. [PMID: 30677473 PMCID: PMC6527860 DOI: 10.1016/j.pharmthera.2019.01.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.
Collapse
Key Words
- 5cac, 5- carboxylcytosine
- 5fc, 5- formylcytosine
- 5hmc, 5-hydroxymethylcytosine
- abc-hss, abacavir hypersensitivity syndrome.
- all, acute lymphoblastic leukemia
- cat, catalase
- cftr, cystic fibrosis transmembrane conductance regulator
- chip, chromatin immunoprecipitation
- cnvs, copy number variations
- cpic, clinical pharmacogenetics implementation consortium
- dhr, drug hypersensitivity reactions
- dihs, drug-induced hypersensitivity syndrome.
- dili, drug-induced liver injury
- dnmts, dna methyltransferases
- dpwg, dutch pharmacogenetics working group
- dress, drug rash with eosinophilia and systemic symptoms
- eqtl, quantitative trait locus
- gpcr, g-protein coupled receptor
- gst, glutathione-s-transferase
- hdacs, histone deacetylases
- maf, minor allele frequencies
- mpe, maculopapular exanthema
- ms, multiple sclerosis
- pm, poor metabolism
- oxbs-seq, oxidative bisulfite sequencing
- prc2, polycomb repressive complex 2
- ptms, posttranslational modifications
- ra, retinoic acid
- scar, severe cutaneous adverse reaction
- sjs, stevens-johnson syndrome
- snvs, single nucleotide variations
- tab-seq, tet-assisted bisulfite sequencing
- ten, toxic epidermal necrolysis
- um, ultrarapid metabolism
Collapse
Affiliation(s)
- Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Magnus Ingelman-Sundberg
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Biomedicum 5B, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| |
Collapse
|
46
|
Affiliation(s)
- Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), Vourles, France.
| | - Julian L Griffin
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, Tennis Court Road, Cambridge, CB2 1GA, UK
- Computational and Systems Medicine, Department of Surgery and Oncology, Imperial College London, London, SW7 2AZ, UK
| | - Muin J Khoury
- Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA, 30329, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Baylor Plaza, Houston, TX, 77030, USA
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstraße, 70376, Stuttgart, Germany
- Department of Clinical Pharmacology, University Hospital Tübingen, Auf der Morgenstelle, 72076, Tübingen, Germany
| |
Collapse
|
47
|
Klein K, Tremmel R, Winter S, Fehr S, Battke F, Scheurenbrand T, Schaeffeler E, Biskup S, Schwab M, Zanger UM. A New Panel-Based Next-Generation Sequencing Method for ADME Genes Reveals Novel Associations of Common and Rare Variants With Expression in a Human Liver Cohort. Front Genet 2019; 10:7. [PMID: 30766545 PMCID: PMC6365429 DOI: 10.3389/fgene.2019.00007] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/09/2019] [Indexed: 01/10/2023] Open
Abstract
We developed a panel-based NGS pipeline for comprehensive analysis of 340 genes involved in absorption, distribution, metabolism and excretion (ADME) of drugs, other xenobiotics, and endogenous substances. The 340 genes comprised phase I and II enzymes, drug transporters and regulator/modifier genes within their entire coding regions, adjacent intron regions and 5′ and 3′UTR regions, resulting in a total panel size of 1,382 kbp. We applied the ADME NGS panel to sequence genomic DNA from 150 Caucasian liver donors with available comprehensive gene expression data. This revealed an average read-depth of 343 (range 27–811), while 99% of the 340 genes were covered on average at least 100-fold. Direct comparison of variant annotation with 363 available genotypes determined independently by other methods revealed an overall accuracy of >99%. Of 15,727 SNV and small INDEL variants, 12,022 had a minor allele frequency (MAF) below 2%, including 8,937 singletons. In total we found 7,273 novel variants. Functional predictions were computed for coding variants (n = 4,017) by three algorithms (Polyphen 2, Provean, and SIFT), resulting in 1,466 variants (36.5%) concordantly predicted to be damaging, while 1,019 variants (25.4%) were predicted to be tolerable. In agreement with other studies we found that less common variants were enriched for deleterious variants. Cis-eQTL analysis of variants with (MAF ≥ 2%) revealed significant associations for 90 variants in 31 genes after Bonferroni correction, most of which were located in non-coding regions. For less common variants (MAF < 2%), we applied the SKAT-O test and identified significant associations to gene expression for ADH1C and GSTO1. Moreover, our data allow comparison of functional predictions with additional phenotypic data to prioritize variants for further analysis.
Collapse
Affiliation(s)
- Kathrin Klein
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany
| | - Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany
| | - Sarah Fehr
- CeGaT GmbH, Tübingen, Germany.,Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Florian Battke
- CeGaT GmbH, Tübingen, Germany.,Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Tim Scheurenbrand
- CeGaT GmbH, Tübingen, Germany.,Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany
| | - Saskia Biskup
- CeGaT GmbH, Tübingen, Germany.,Praxis für Humangenetik Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany.,Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany.,Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Ulrich M Zanger
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.,Medical School, University of Tübingen, Tübingen, Germany
| |
Collapse
|
48
|
Lauschke VM, Ingelman-Sundberg M. Prediction of drug response and adverse drug reactions: From twin studies to Next Generation Sequencing. Eur J Pharm Sci 2019; 130:65-77. [PMID: 30684656 DOI: 10.1016/j.ejps.2019.01.024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 01/12/2023]
Abstract
Understanding and predicting inter-individual differences related to the success of drug therapy is of tremendous importance, both during drug development and for clinical applications. Importantly, while seminal twin studies indicate that the majority of inter-individual differences in drug disposition are driven by hereditary factors, common genetic polymorphisms explain only less than half of this genetically encoded variability. Recent progress in Next Generation Sequencing (NGS) technologies has for the first time allowed to comprehensively map the genetic landscape of human pharmacogenes. Importantly, these projects have unveiled vast numbers of rare genetic variants, which are estimated to contribute substantially to the missing heritability of drug metabolism phenotypes. However, functional interpretation of these rare variants remains challenging and constitutes one of the important frontiers of contemporary pharmacogenomics. Furthermore, NGS technologies face challenges in the interrogation of genes residing in complex genomic regions, such as CYP2D6 and HLA genes. We here provide an update of the implementation of pharmacogenomic variations in the clinical setting and present emerging strategies that facilitate the translation of NGS data into clinically useful information. Importantly, we anticipate that these developments will soon result in a paradigm shift of pre-emptive genotyping away from the interrogation to candidate variants and towards the comprehensive profiling of an individuals genotype, thus allowing for a true individualization of patient drug treatment regimens.
Collapse
Affiliation(s)
- Volker M Lauschke
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Magnus Ingelman-Sundberg
- Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| |
Collapse
|