Pharmacogenomics at the tipping point: challenges and opportunities

Genetic polymorphisms study of pharmacogenomic VIP variants in Han ethnic of China's Shaanxi province

BACKGROUND

Multiple factors include genetic and non-genetic interactions induce to different drug response among different individuals. Lots of researches proved that different frequencies of genetic variants exists different ethnic groups. The aim of this study was to screen Han volunteers in Shaanxi for VIP gene polymorphisms.

MATERIALS AND METHODS

We genotyped 80 Very Important Pharmacogenes (VIP) (selected from the PharmGKB database) in 192 unrelated, healthy Han ethnic adults from Shaanxi, the northwest of China, and then analyzed genotyping data wtih Structure and F-statistics (Fst) analysis.

RESULTS

We compared our data with 15 other populations (Deng, Kyrgyz, Tajik, Uygur and 11 HapMap populations), and found the frequency distribution of Han population in Shaanxi is most similar with CHB. Also, Structure and Fst showed that Shaanxi Han has a closest genetic background with CHB.

CONCLUSIONS

Our study have supplemented the Han Chinese data related to pharmacogenomics and illustrated differences in genotypic frequencies of selected VIP variants' among the Han population and 15 other populations.

Genetic polymorphisms of pharmacogenomic VIP variants in the Mongol of Northwestern China

Background

Within a population, the differences of pharmacogenomic variant frequencies may produce diversities in drug efficacy, safety, and the risk associated with adverse drug reactions. With the development of pharmacogenomics, widespread genetic research on drug metabolism has been conducted on major populations, but less is known about minorities.

Results

In this study, we recruited 100 unrelated, healthy Mongol adults from Xinjiang and genotyped 85 VIP variants from the PharmGKB database. We compared our data with eleven populations listed in 1000 genomes project and HapMap database. We used χ² tests to identify significantly different loci between these populations. We downloaded SNP allele frequencies from the ALlele FREquency Database to observe the global genetic variation distribution for these specific loci. And then we used Structure software to perform the genetic structure analysis of 12 populations.

Conclusions

Our results demonstrated that different polymorphic allele frequencies exist between different nationalities,and indicated Mongol is most similar to Chinese populations, followed by JPT. This information on the Mongol population complements the existing pharmacogenomic data and provides a theoretical basis for screening and therapy in the different ethnic groups within Xinjiang.

Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study

Up to 50% of multiple sclerosis (MS) patients do not respond to interferon-beta (IFN-β) treatment and determination of response requires lengthy clinical follow-up of up to 2 years. Response predictive genetic markers would significantly improve disease management. We aimed to identify IFN-β treatment response genetic marker(s) by performing a two-stage genome-wide association study (GWAS). The GWAS was carried out using data from 151 Australian MS patients from the ANZgene/WTCCC2 MS susceptibility GWAS (responder (R)=51, intermediate responders=24 and non-responders (NR)=76). Of the single-nucleotide polymorphisms (SNP) that were validated in an independent group of 479 IFN-β-treated MS patients from Australia, Spain and Italy (R=273 and NR=206), eight showed evidence of association with treatment response. Among the replicated associations, the strongest was observed for FHIT (Fragile Histidine Triad; combined P-value 6.74 × 10^-6) and followed by variants in GAPVD1 (GTPase activating protein and VPS9 domains 1; combined P-value 5.83 × 10^-5) and near ZNF697 (combined P-value 8.15 × 10^-5).

Genetic polymorphisms of pharmacogenomic VIP variants in the Kyrgyz population from northwest China

Pharmacogenomic variant information is well known for major human populations; however, this information is less commonly studied in minorities. In the present study, we genotyped 85 very important pharmacogenetic (VIP) variants (selected from the PharmGKB database) in the Kyrgyz population and compared our data with other four major human populations including Han Chinese in Beijing, China (CHB), the Japanese in Tokyo, Japan (JPT), a northern and western Europe population (CEU), and the Yoruba in Ibadan, Nigeria (YRI). There were 13, 12 and 16 of the selected VIP variant genotype frequencies in the Kyrgyz which differed from those of the CHB, JPT and CEU, respectively (p<0.005). In the YRI, there were 32 different variants, compared to the Kyrgyz (p<0.005). Genotype frequencies of ADH1B, AHR, CYP3A5, PTGS2, VDR, and VKORC1 in the Kyrgyz differed widely from those in the four populations. Haplotype analyses also showed differences among the Kyrgyz and the other four populations. Our results complement the information provided by the database of pharmacogenomics on Kyrgyz. We provide a theoretical basis for safer drug administration and individualized treatment plans for the Kyrgyz. We also provide a template for the study of pharmacogenomics in various ethnic minority groups in China.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine

Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.

Assessment of healthcare students' views on pharmacogenomics at the University of Minnesota

AIM

The aim was to determine if the University of Minnesota (MN, USA) healthcare students' perceived value of pharmacogenomics matches their self-observed comfort and education in pharmacogenomics.

MATERIALS & METHODS

A 24-question, anonymous, online survey was distributed to all pharmacy, nursing and medical students enrolled at the University of Minnesota.

RESULTS

Among healthcare students, 70.6% agreed or strongly agreed that pharmacogenomics should be an important part of their curriculum; however, only 11.1% agreed or strongly agreed that it actually is. Only 29.7% of students reported taking a genetics course that specifically addressed the applications of genetics in pharmacy, and those students were more likely to feel comfortable interpreting information from a pharmacogenetics test, answering questions on pharmacogenomics, educating patients on risks and benefits of testing, and were comfortable that they knew which medications required pharmacogenomics testing.

CONCLUSION

Healthcare students consider pharmacogenomics to be an important area of clinical practice; yet generally express it has not been an important part of their curriculum. Education emphasizing medical applications of pharmacogenomics can increase student comfort level in pharmacogenomics practice.

Insights from genome-wide association studies of drug response

Early genome-wide association studies (GWAS) using relatively small samples have identified both rare and common genetic variants with large impact on severe adverse drug reactions, dosing, and efficacy. Here we outline the challenges and recent successes of the GWAS approach in disease genetics and the ways in which these can be applied to pharmacogenomics for biological discovery, determination of heritability, and personalized treatment. We highlight that the genetic architecture of drug efficacy reflects a complex trait yet that of adverse drug reactions more closely mirrors the architecture of Mendelian diseases and how this difference affects future study design. Given that multiple layers of biological data are increasingly available on large samples from biorepositories linked to electronic medical records, GWAS will remain a key component of the systems biology approach to uncovering small to moderate genetic determinants of drug response; these discoveries should move us closer to a personalized approach to health care.

Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping

Routine integration of genotype data into drug decision making could improve patient safety, particularly if many relevant genetic variants can be assayed simultaneously before prescribing the target drug. The frequency of opportunities for pharmacogenetic prescribing and the potential adverse events (AEs) mitigated are unknown. We examined the frequency with which 56 medications with known outcomes influenced by variant alleles were prescribed in a cohort of 52,942 medical home patients at Vanderbilt University Medical Center (VUMC). Within a 5-year window, we estimated that 64.8% (95% confidence interval (CI): 64.4-65.2%) of individuals were exposed to at least one medication with an established pharmacogenetic association. Using previously published results for six medications with severe, well-characterized, genetically linked AEs, we estimated that 383 events (95% CI, 212-552) could have been prevented with an effective preemptive genotyping program. Our results suggest that multiplexed, preemptive genotyping may represent an efficient alternative approach to current single-use ("reactive") methods and may also improve safety.

A common 5'-UTR variant in MATE2-K is associated with poor response to metformin

Multidrug and toxin extrusion 2 (MATE2-K (SLC47A2)), a polyspecific organic cation exporter, facilitates the renal elimination of the antidiabetes drug metformin. In this study, we characterized genetic variants of MATE2-K, determined their association with metformin response, and elucidated their impact by means of a comparative protein structure model. Four nonsynonymous variants and four variants in the MATE2-K basal promoter region were identified from ethnically diverse populations. Two nonsynonymous variants-c.485C>T and c.1177G>A-were shown to be associated with significantly lower metformin uptake and reduction in protein expression levels. MATE2-K basal promoter haplotypes containing the most common variant, g.-130G>A (>26% allele frequency), were associated with a significant increase in luciferase activities and reduced binding to the transcriptional repressor myeloid zinc finger 1 (MZF-1). Patients with diabetes who were homozygous for g.-130A had a significantly poorer response to metformin treatment, assessed as relative change in glycated hemoglobin (HbA1c) (-0.027 (-0.076, 0.033)), as compared with carriers of the reference allele, g.-130G (-0.15 (-0.17, -0.13)) (P=0.002). Our study showed that MATE2-K plays a role in the antidiabetes response to metformin.

Pharmacogenetics: past, present and future

The subject area of pharmacogenetics, also known as pharmacogenomics, has a long history. Research in this area has led to fundamental discoveries, which have helped our understanding of the reasons why individuals differ in the way they handle drugs, and ultimately in the way they respond to drugs, either in terms of efficacy or toxicity. However, not much of this knowledge has been translated into clinical practice, most drug-gene associations that have some evidence of clinical validity have not progressed to clinical settings. Advances in genomics since 2000, including the ready availability of data on the variability of the human genome, have provided us with unprecedented opportunities to understand variability in drug responses, and the opportunity to incorporate this into patient care. This is only likely to occur with a systematic approach that evaluates and overcomes the different translational gaps in taking a biomarker from discovery to clinical practice. In this article, I explore the history of pharmacogenetics, appraise the current state of research in this area, and finish off with suggestions for progressing in the field in the future.