Virtual Pharmacist: A Platform for Pharmacogenomics

Pharmacogenomics Tools for Precision Public Health and Lessons for Low- and Middle-Income Countries: A Scoping Review

Pharmacogenomics is the integration of genomics and pharmacology to optimize drug response and reduce side effects. In terms of personalized or individualized medicine, PGx is defined as the identification and analysis of specific genetic variants associated with particular drug treatments for each patient. Under a precision public health (PPH) approach, population-level data are analyzed to generate public health strategies. The objective of this study was to conduct a scoping review of technological tools, examining their evolution, the predominance of high-income countries in their development, and the gaps and needs for genomic data and advances in low- and middle-income countries (LMICs). This review was conducted in accordance with the ScPRISMA guidelines. A search was conducted in PubMed, Web of Science and Embase until January 2024. A total of 40 documents were selected, which revealed the continuous evolution and progressive development of pharmacogenomic tools. The technological tools developed come from high-income countries, particularly the United States, Canada, China, and several European nations, where international collaboration has been essential to maintain and expand these tools, which have evolved to keep pace with the rapid generation of genomic data. This trend shows a scarce development of technological tools for public health precision in LMICs, which evidences the need to increase investment in genomic research infrastructure in this aspect and in the development of capacities to guarantee global accessibility and boost PPH for all populations.

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks

Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.

Systematic Review and Meta-Analysis of the Influence of Genetic Variation on Ototoxicity in Platinum-Based Chemotherapy

OBJECTIVE

The objective of this meta-analysis is to evaluate the impact of genetic polymorphisms on platinum-based chemotherapy (PBC)-induced ototoxicity.

DATA SOURCES

Systematic searches of PubMed, Embase, Cochrane, and Web of Science were conducted from the inception of the databases to May 31, 2022. Abstracts and presentations from conferences were also reviewed.

REVIEW METHODS

Four investigators independently extracted data in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Differences in the prevalence of PBC-induced ototoxicity between reference and variant (i) genotypes and (ii) alleles were analyzed. The overall effect size was presented using the random-effects model as an odds ratio (OR) with a 95% confidence interval (CI).

RESULTS

From 32 included articles, 59 single nucleotide polymorphisms on 28 genes were identified, with 4406 total unique participants. For allele frequency analysis, the A allele in ACYP2 rs1872328 was positively associated with ototoxicity (OR: 2.61; 95% CI: 1.06-6.43; n = 2518). Upon limiting to cisplatin use only, the T allele of COMT rs4646316 and COMT rs9332377 revealed significant results. For genotype frequency analysis, the CT/TT genotype in ERCC2 rs1799793 demonstrated an otoprotective effect (OR: 0.50; 95% CI: 0.27-0.94; n = 176). Excluding studies using carboplatin or concomitant radiotherapy revealed significant effects with COMT rs4646316, GSTP1 rs1965, and XPC rs2228001. Major sources of variations between studies include differences in patient demographics, ototoxicity grading systems, and treatment protocols.

CONCLUSION

Our meta-analysis presents polymorphisms that exert ototoxic or otoprotective effects in patients undergoing PBC. Importantly, several of these alleles are observed at high frequencies globally, highlighting the potential for polygenic screening and cumulative risk evaluation for personalized care.

PGxKnow: a pharmacogenomics educational HoloLens application of augmented reality and artificial intelligence

Aim: To develop and assess an augmented reality tool for pharmacogenomics (PGx) education based on artificial intelligence. Materials & methods: A HoloLens application was developed using feedback from three clinical PGx-trained pharmacists. 15 Participants independently reviewed the application and assessed usability using the system usability scale (SUS). Results & conclusion: Eighteen different frames were developed. Each video module was 2-3 min for the education. The application included textual information and 3D structures of PGx concepts. The mean SUS score for 15 participants (11 pharmacy students and four pharmacists) was 83, with a standard deviation of 6.6. Results suggest that PGxKnow has the potential to bridge the gap in PGx education, further widespread utilization of PGx and boost its impact on precision medicine.

PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation

The increasing availability of next generation sequencing (NGS) for personal genomics could promote pharmacogenomics (PGx) discovery and application. However, current tools for analysis and interpretation of pharmacogenomic variants from NGS data are inadequate, as none offer comprehensive analytic functions in a simple, web-based platform. In addition, no tools exist to analyze human leukocyte antigen (HLA) genes for determining potential risks of immune-mediated adverse drug reaction (IM-ADR). We describe PharmVIP, a web-based PGx tool, for one-stop comprehensive analysis and interpretation of genome-wide variants obtained from NGS platforms. PharmVIP comprises three main interpretation modules covering analyses of pharmacogenes involved in pharmacokinetics, pharmacodynamics and IM-ADR. The Guideline module provides Clinical Pharmacogenetics Implementation Consortium (CPIC) drug guideline recommendations based on the translation of genotypic data in genes having guidelines. The HLA module reports HLA genotypes, potential adverse drug reactions, and the relevant drug guidelines. The Pharmacogenes module is employed for prioritizing variants according to variant effect on gene function. Detailed, customizable reports are provided as exportable files and as an interactive web version. PharmVIP is a new integrated NGS workflow for the PGx community to facilitate discovery and clinical application.

g-Nomic: a new pharmacogenetics interpretation software

We present g-Nomic, a pharmacogenetics interpretation software that analyzes globally a prescribed medication taking into account the personal background genetics, drug–drug interactions, lifestyle, nutritional supplements, inhibitors, inducers, and other risks to analyze primary or secondary metabolism pathways. G-Nomic provides a set of recommendations describing the suitability of a given combination of drugs for each patient according to their genes and polymedication. G-Nomic is updated monthly including data from the new drugs to be included, their known interactions, and the relevant pharmacokinetic biomarkers. For the interactions, the list is curated manually, only keeping those with clinical relevance. For each drug, their FDA and EMA drug labels are accessed, to check for relevant enzymes and transport proteins that influence its pharmacokinetics, and for their ability to induce or inhibit other enzymes, particularly the CYP-450 system. When this information is not available, a PubMed search is made to look for these characteristics. In addition, a distinction is made between drugs and prodrugs. A query on the g-Nomic software begins with entering the medication by either their common or commercial name. Non-pharmacological substances can be also added or selected under “lifestyle habits”. The lifestyle list is dynamic, showing only the substances known to interact with the drugs that are currently selected, and includes herb compounds, such as St. John’s wort, as well as proper lifestyle substances such as grapefruit or cigarette smoking. The software provides a list of the genes classified as primary biomarkers as candidates for genetic testing, and a list of the interactions that have been detected. If genetic information is available then, or is made available at a later point, these results can also be entered and the software returns pharmacogenetics recommendations regarding specific genotypes. g-Nomic takes all the above-mentioned parameters in an easy and user-friendly tool making prescription safer.

Population-scale genomics-Enabling precision public health

The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.

Further Investigation of the Role of ACYP2 and WFS1 Pharmacogenomic Variants in the Development of Cisplatin-Induced Ototoxicity in Testicular Cancer Patients

Purpose: Adverse drug reactions such as ototoxicity, which occurs in approximately one-fifth of adult patients who receive cisplatin treatment, can incur large socioeconomic burdens on patients with testicular cancer who develop this cancer during early adulthood. Recent genome-wide association studies have identified genetic variants in ACYP2 and WFS1 that are associated with cisplatin-induced ototoxicity. We sought to explore the role of these genetic susceptibility factors to cisplatin-induced ototoxicity in patients with testicular cancer.Experimental Design: Extensive clinical and demographic data were collected for 229 patients with testicular cancer treated with cisplatin. Patients were genotyped for two variants, ACYP2 rs1872328 and WFS1 rs62283056, that have previously been associated with hearing loss in cisplatin-treated patients. Analyses were performed to investigate the association of these variants with ototoxicity in this cohort of adult patients with testicular cancer.Results: Pharmacogenomic analyses revealed that ACYP2 rs1872328 was significantly associated with cisplatin-induced ototoxicity [P = 2.83 × 10^-3, OR (95% CI):14.7 (2.6-84.2)]. WFS1 rs62283056 was not significantly associated with ototoxicity caused by cisplatin (P = 0.39); however, this variant was associated with hearing loss attributable to any cause [P = 5.67 × 10^-3, OR (95% CI): 3.2 (1.4-7.7)].Conclusions: This study has provided the first evidence for the role of ACYP2 rs1872328 in cisplatin-induced ototoxicity in patients with testicular cancer. These results support the use of this information to guide the development of strategies to prevent cisplatin-induced ototoxicity across cancers. Further, this study has highlighted the importance of phenotypic differences in replication studies and has provided further evidence for the role of WFS1 rs62283056 in susceptibility to hearing loss, which may be worsened by cisplatin treatment. Clin Cancer Res; 24(8); 1866-71. ©2018 AACR.

Pharmacogenetic Variation in Over 100 Genes in Patients Receiving Acenocumarol

Coumarins are widely prescribed worldwide, and in Mexico acenocumarol is the preferred form. It is well known that despite its efficacy, coumarins show a high variability for dose requirements. We investigated the pharmacogenetic variation of 110 genes in patients receiving acenocumarol using a targeted NGS approach. We report relevant population differentiation for variants on CYP2C8, CYP2C19, CYP4F11, CYP4F2, PROS, and GGCX, VKORC1, CYP2C18, NQO1. A higher proportion of novel-to-known variants for 10 genes was identified on 41 core pharmacogenomics genes related to the PK (29), PD (3), of coumarins, and coagulation proteins (9) including, CYP1A1, CYP3A4, CYP3A5, and F8, and a low proportion of novel-to-known variants on CYP2E1, VKORC1, and SULT1A1/2. Using a Bayesian approach, we identified variants influencing acenocumarol dosing on, VKORC1 (2), SULT1A1 (1), and CYP2D8P (1) explaining 40–55% of dose variability. A collection of pharmacogenetic variation on 110 genes related to the PK/PD of coumarins is also presented. Our results offer an initial insight into the use of a targeted NGS approach in the pharmacogenomics of coumarins in Mexican Mestizos.

ePGA: A Web-Based Information System for Translational Pharmacogenomics

One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project.