Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions

Development of an extensive workflow for comprehensive clinical pharmacogenomic profiling: lessons from a pilot study on 100 whole exome sequencing data

This pilot study is aimed at implementing an approach for comprehensive clinical pharmacogenomics (PGx) profiling. Fifty patients with cardiovascular diseases and 50 healthy individuals underwent whole-exome sequencing. Data on 1800 PGx genes were extracted and analyzed through deep filtration separately. Theoretical drug induced phenoconversion was assessed for the patients, using sequence2script. In total, 4539 rare variants (including 115 damaging non-synonymous) were identified. Four publicly available PGx bioinformatics algorithms to assign PGx haplotypes were applied to nine selected very important pharmacogenes (VIP) and revealed a 45-70% concordance rate. To ensure availability of the results at point-of-care, actionable variants were stored in a web-hosted database and PGx-cards were developed for quick access and handed to the study subjects. While a comprehensive clinical PGx profile could be successfully extracted from WES data, available tools to interpret these data demonstrated inconsistencies that complicate clinical application.

Pharmacogenes that demonstrate high association evidence according to CPIC, DPWG, and PharmGKB

BACKGROUND

Different levels of evidence related to the variable responses of individuals to drug treatment have been reported in various pharmacogenomic (PGx) databases. Identification of gene-drug pairs with strong association evidence can be helpful in prioritizing the implementation of PGx guidelines and focusing on a gene panel. This study aimed to determine the pharmacogenes with the highest evidence-based association and to indicate their involvement in drug-gene interactions.

METHODOLOGY

The publicly available datasets CPIC, DPWG, and PharmGKB were selected to determine the pharmacogenes with the highest drug outcome associations. The upper two levels of evidence rated by the three scoring methods were specified (levels A-B in CPIC, 3-4 in DPWG, or 1-2 levels in PharmGKB). The identified pharmacogenes were further ranked in this study based on the number of medications they interacted with.

RESULTS

Fifty pharmacogenes, with high to moderately high evidence of associations with drug response alterations, with potential influence on the therapeutic and/or toxicity outcomes of 152 drugs were identified. CYP2D6, CYP2C9, CYP2C19, G6PD, HLA-B, SLCO1B1, CACNA1S, RYR1, MT-RNR1, and IFNL4 are the top 10 pharmacogenes, where each is predicted to impact patients' responses to ≥5 drugs.

CONCLUSION

This study identified the most important pharmacogenes based on the highest-ranked association evidence and their frequency of involvement in affecting multiple drugs. The obtained data is useful for customizing a gene panel for PGx testing. Identifying the strength of scientific evidence supporting drug-gene interactions aids drug prescribers in making the best clinical decision.

Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients

Background

Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc.

Purpose

This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level.

Patients and methods

A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support.

Results

The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically.

Conclusion

With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered (“personalized medication landscape “) and to identify critical patients even before problems are reported (“medication alert”). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine.

Genetics of Severe Cutaneous Adverse Reactions

Severe cutaneous adverse reactions (SCARs) including Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug rash with eosinophilia and systemic symptoms (DRESS) are T cells-mediated life-threatening immune reactions, most commonly induced by drug. The last decade has seen significant progress in SCARs research. Recent studies have unveiled the pathogenesis of SCARs involved in susceptible genes, including human leukocyte antigens (HLA) and drugs-T cell receptor (TCR) interaction that may trigger T cell activation with downstream immune signaling of cytokines/chemokines and specific cytotoxic proteins releases. Advances in identification of multiple genetic alleles associated with specific drugs related SCARS in different populations is an important breakthrough in recent years for prevention of SCARs. This article summarized the findings on genetic factors related to SJS/TEN, especially for HLA.

Genetic Determinants in HLA and Cytochrome P450 Genes in the Risk of Aromatic Antiepileptic-Induced Severe Cutaneous Adverse Reactions

Adverse drug reaction (ADR) is a pressing health problem, and one of the main reasons for treatment failure with antiepileptic drugs. This has become apparent in the event of severe cutaneous adverse reactions (SCARs), which can be life-threatening. In this review, four hypotheses were identified to describe how the immune system is triggered in the development of SCARs, which predominantly involve the human leukocyte antigen (HLA) proteins. Several genetic variations in HLA genes have been shown to be strongly associated with the susceptibility to developing SCARs when prescribed carbamazepine or phenytoin. These genetic variations were also shown to be prevalent in certain populations. Apart from the HLA genes, other genes proposed to affect the risk of SCARs are genes encoding for CYP450 drug-metabolising enzymes, which are involved in the pharmacokinetics of offending drugs. Genetic variants in CYP2C9 and CYPC19 enzymes were also suggested to modulate the risk of SCARs in some populations. This review summarizes the literature on the manifestation and aetiology of antiepileptic-induced SCARs, updates on pharmacogenetic markers associated with this reaction and the implementation of pre-emptive testing as a preventive strategy for SCARs.