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The rise of genomics and personalized medicine
Published in Priya Hays, Advancing Healthcare Through Personalized Medicine, 2017
Pharmacogenetics testing is based on information derived from in vivo human studies and therefore is limited. There is one publicly available research tool for pharmacokinetics; it is a reliable resource for current genetic variations contributing to drug metabolism. PharmGKB is updated by the NIH Pharmacogenomics Research Network (PGRN). For example, dosing guidelines for 20 antidepressant drugs, based on CYP2D6 and CYP2C19 variants, can vary from 20% of the usual dose for a poor metabolizer (PM) to 300% of the usual dose for an ultrarapid metabolizer (UM). Drug companies are now developing companion diagnostic and pharmacogenetics testing, alongside developing new drugs.
Melanoma Genomics—Techniques and Implications for Therapy
Published in Sanjiv S. Agarwala, Vernon K. Sondak, Melanoma, 2008
Adil I. Daud, Vernon K. Sondak, Ashani Weeraratna
Studies have shown that patients’ immune and drug responses may have a genetic basis and that identifying these hereditary genetic variations between patients could help to optimize therapy as well as identify molecular targets for therapy (54). These variations take the form of single nucleotide changes or SNPs within the genome and are quite abundant, occurring once every 1000 bps in the human genome (which has a total of 3 billion bps), resulting in an estimated total of over 10 million SNPs (55). The mapping of the human genome has also allowed for genome-wide association studies, giving researchers the ability to link genetic traits to drug response, without the necessity of an a priori knowledge of the SNPs that need to be included. Public databases such as http://www.ncbi.nlm.nih.gov/SNP, HapMap (56), jSNp (57) exist, and over six million SNP sequences have been deposited into these types of databases (58). In silico mapping of SNPs is also being performed by private groups such as The SNP Consortium (TSC) (a private, not-for-profit alliance of 13 major multinational companies and the Wellcome Trust; http://snp.cshl.org) (59). The National Institutes of Health have established a Pharmacogenetics Research Network group (60), and this group has developed a research tool known as PharmGKB that allows for the researching and comparison of existing SNP databases. Other tools such as PolyMAPr also exist (61). Such cataloguing and identification of SNPs allow for their production and synthesis, and currently, the most commonly used technique is to analyze SNPs using a microarray-based platform (62). Unlike microarray analysis, SNP analysis yields a binary answer; so data analysis is much simpler, which allows for the analysis of a greater number of sequences. Recent studies have demonstrated that the higher the density of SNPs used (over half a million SNPs on 1 chip), the more precisely a locus can be identified (63). In addition, SNP analysis appears to be even more sensitive than array CGH (64). These types of studies may herald the advent of individualized therapy and improved diagnostics for patients suffering from various diseases, including melanoma.
UGT1A1 Polymorphisms and Mutations Affect Anticancer Drug Therapy
Published in Sherry X. Yang, Janet E. Dancey, Handbook of Therapeutic Biomarkers in Cancer, 2021
Tristan M. Sissung, Roberto Barbier, Lisa M. Cordes, William D. Figg
Several professional organizations publish recommendations surrounding the use of pharmacogenomics in the clinical care of patients with cancer: the American Society of Clinical Oncology (ASCO), the Clinical Pharmacogenomics Implementation Committee (CPIC), the Dutch Pharmacogenomics Working Group (DPWG), the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group, the European Medicines Agency (EMA), the United States FDA, and the National Comprehensive Cancer Network (NCCN). The PharmGKB website compiles data taken from CPIC, DPWG, FDA, and EMA [137]. Guidelines for UGT1A1 genotyping prior to irinotecan therapy are published by the DPWG, EGAPP, FDA, and NCCN, although such guidelines are not available from the other pharmacogenomics implementation committees. Each organization often provides different recommendations and does not recommend all patients be tested for UGT1A1 [137]. For instance, the FDA-approved prescribing information recommends the irinotecan starting dose be lowered by at least one dose level (25 mg/m2 for weekly or 50 mg/m2 for every 3 week dosing) prior to irinotecan initiation in UGT1A1*28/*28 genotype carriers. (https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/020571s048lbl.pdf) However, it is acknowledged that the precise dose reduction is not known and that dose modifications should be considered based on patient tolerance. The DPWG suggests that, while evidence is not consistent, a reduction of the initial irinotecan dose by 30% is required for regimens >250 mg/m2 for patients carrying the UGT1A1*28/*28 genotype [138]. According to CPIC, UGT1A1 genotyping prior to irinotecan dosing does not meet the evidence level necessary for implementation; yet CPIC also acknowledges the clinical utility of UGT1A1 testing (https://cpicpgx.org/prioritizationof-cpic-guidelines/). The EGAPP similarly found that evidence was insufficient to provide dosing guidelines for neutropenia risk mitigation using UGT1A1 genotypes since reducing the dose may result in patient harm due to lower efficacy of dose reductions in UGT1A1*28 [139]. Such discordance in recommendations results in a significant lack of clarity in various clinical scenarios, although the complexity of irinotecan dosing recommendations is acknowledged. Clinical recommendations for other cancer therapies that interact with UGT1A1 are similarly unclear.
Current pharmacogenomic recommendations in chronic respiratory diseases: Is there a biomarker ready for clinical implementation?
Published in Expert Review of Respiratory Medicine, 2022
Ingrid Fricke-Galindo, Ramcés Falfán-Valencia
PharmGKB is a comprehensive resource for clinicians and researchers to present selected pharmacogenetic information. PharmGKB collects, curates, and disseminates knowledge about clinically actionable gene-drug associations and genotype–phenotype relationships. There are available different levels of clinical annotations according to the source of information: a) variant annotations from peer-reviewed published literature in PubMed; b) PGx guideline annotations, guidelines from CPIC and/or Royal Dutch Association for the Advancement of Pharmacy; and c) Drug Label Annotations, taken from Agencies’ Recommendations [41]. Clinical annotations summarize all PharmGKB’s annotations of published evidence for the relationship between a particular genetic variant and a medication. PharmGKB gives them a rating depending on how much published proof there is for a connection found in PharmGKB and the quality of that evidence. We found 54 results when searching for pulmonary diseases (search term: pulmonary) and filtering by annotation types (Clinical Guideline Annotation, Drug Label Annotation, Clinical Annotation, and Variant Annotation). Several annotations can be found, including negative and positive associations with different levels of evidence. The pharmacogenetic variants significantly associated with the variability in response to drugs employed for the treatment of chronic respiratory diseases are included in Table 2.
Exploring the Kinh Vietnamese genomic database for the polymorphisms of the P450 genes towards precision public health
Published in Annals of Human Biology, 2022
Diep Thi Hoang, Tran Van Hiep, Thao Thi Phuong Nguyen, Hoang Thi My Nhung, Kien Trung Tran, Le Sy Vinh
The well-known Pharmacogenomics Knowledge Base (PharmGKB; http://www.pharmgkb.org) maintains a list of human genes significantly involved in drug metabolism or response (Whirl-Carrillo et al. 2012). Of which, the six CYP genes having the strongest evidence to support their importance include CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, and CYP4F2. According to the Clinical Pharmacogenetic Implementation Consortium (CPIC) guidelines, about 30% of common drugs are metabolised by these genes (Relling and Klein 2011). CYP genes are well-known as being highly polymorphic or greatly varying inter-individually and across populations (Lakiotaki et al. 2017). Therefore, population-specific studies on the genetic diversity of CYP genes are necessary to explain the variability in drug responses in different populations (Sivadas and Scaria 2019).
Clinical pharmacogenomics testing in the era of next generation sequencing: challenges and opportunities for precision medicine
Published in Expert Review of Molecular Diagnostics, 2018
Yuan Ji, Yue Si, Gwendolyn A. McMillin, Elaine Lyon
Interpretation of rare and novel PGx variants can be especially challenging compared to testing of genetic diseases in which a disease phenotype helps guides variant analysis. Current knowledge of genotype–phenotype correlations that affect PK or PD is related to common variants, with minor allele frequencies of at least 1% in a specific population. Therefore, we do not know the impact of rare SNVs or the combination of several variants on the predicted drug response phenotype if the PGx test is performed preemptively. Further, even if we can predict the effect of the combination of several PGx variants on certain category of medications (such as opioids), caution should be taken about individual predictions since many other factors may alter the ultimate drug response phenotypes such as drug–drug interactions; variation associated with age, gender, physical condition including kidney and liver functions; etc. PharmGKB is a central database with aggregated PGx information including VIP genes with curated variants, key literature, PK and PD pathways, leveled evidence for drug–gene associations, and dosing guidelines [74].