Pharmacogenomics Research Research Articles

New and Emerging Research on Solute Carrier and ATP Binding Cassette Transporters in Drug Discovery and Development: Outlook From the International Transporter Consortium.

Enabled by a plethora of new technologies, research in membrane transporters has exploded in the past decade. The goal of this state‐of‐the‐art article is to describe recent advances in research on membrane transporters that are particularly relevant to drug discovery and development. This review covers advances in basic, translational, and clinical research that has led to an increased understanding of membrane transporters at all levels. At the basic level, we describe the available crystal structures of membrane transporters in both the solute carrier (SLC) and ATP binding cassette superfamilies, which has been enabled by the development of cryogenic electron microscopy methods. Next, we describe new research on lysosomal and mitochondrial transporters as well as recently deorphaned transporters in the SLC superfamily. The translational section includes a summary of proteomic research, which has led to a quantitative understanding of transporter levels in various cell types and tissues and new methods to modulate transporter function, such as allosteric modulators and targeted protein degraders of transporters. The section ends with a review of the effect of the gut microbiome on modulation of transporter function followed by a presentation of 3D cell cultures, which may enable in vivo predictions of transporter function. In the clinical section, we describe new genomic and pharmacogenomic research, highlighting important polymorphisms in transporters that are clinically relevant to many drugs. Finally, we describe new clinical tools, which are becoming increasingly available to enable precision medicine, with the application of tissue‐derived small extracellular vesicles and real‐world biomarkers.

Different responses to risperidone treatment in Schizophrenia: a multicenter genome-wide association and whole exome sequencing joint study

Risperidone is routinely used in the clinical management of schizophrenia, but the treatment response is highly variable among different patients. The genetic underpinnings of the treatment response are not well understood. We performed a pharmacogenomic study of the treatment response to risperidone in patients with schizophrenia by using a SNP microarray -based genome-wide association study (GWAS) and whole exome sequencing (WES)-based GWAS. DNA samples were collected from 189 patients for the GWAS and from 222 patients for the WES after quality control in multiple centers of China. Antipsychotic response phenotypes of patients who received eight weeks of risperidone treatment were quantified with percentage change on the Positive and Negative Syndrome Scale (PANSS). The GWAS revealed a significant association between several SNPs and treatment response, such as three GRM7 SNPs (rs141134664, rs57521140, and rs73809055). Gene-based analysis in WES revealed 13 genes that were associated with antipsychotic response, such as GPR12 and MAP2K3. We did not identify shared loci or genes between GWAS and WES, but association signals tended to cluster into the GPCR gene family and GPCR signaling pathway, which may play an important role in the treatment response etiology. This study may provide a research paradigm for pharmacogenomic research, and these data provide a promising illustration of our potential to identify genetic variants underlying antipsychotic responses and may ultimately facilitate precision medicine in schizophrenia.

“It’s what makes us unique”: Indigenous Australian perspectives on genetics research to improve comorbid mental and chronic disease outcomes

Background The role of personalized treatment approaches, including those based on genetic testing, are increasingly enabling informed decision-making to improve health outcomes. Research involving Indigenous Australians has been lagging behind, although this population experiences a higher prevalence of chronic disease and mental health disorders. Methods Using community-based participatory research principles, this study purposefully interviewed participants with a diagnosed common mental disorder and a comorbid chronic disease condition. This was an inductive thematic analysis on semi-structured interviews with consenting participants (n = 48). Common themes and analytical domains were identified that provided a semantic understanding shared by participants. Results Five emerging themes were identified, primarily focusing on: (1) The perceptions and understanding of genetics research; (2) culturally appropriate conduct of genetics research; (3) the role of indigenous-led genetics research; (4) future prospects of genetics research; and (5) the importance of genetics research for patients with mental and physical health comorbidities. Conclusion Indigenous Australians are under-represented in pharmacogenomics research despite well-documented epidemiological research demonstrating that Indigenous people globally experience greater risk of developing certain chronic diseases and more severe disease progression. Positive outcomes from this study highlight the importance of not only involving Indigenous participants, but providing leadership and governance opportunities for future genetics research.

Sub-Analysis of CYP-GUIDES Data: Assessing the Prevalence and Impact of Drug-Gene Interactions in an Ethnically Diverse Cohort of Depressed Individuals.

Introduction: Minority groups are underrepresented in pharmacogenomics (PGx) research. Recent sub-analysis of CYP-GUIDES showed reduced length of stay (LOS) in depressed patients with CYP2D6 sub-functional status. Our primary objective was to determine whether PGx guided (G) versus standard treatment (S) influenced LOS among different race/ethnic groups. Secondary objectives included prevalence of drug-gene interactions (DGIs) and readmission rates (RAR). Methods: Retrospective sub-analysis of CYP-GUIDES data comprising CYP2D6 phenotypes was reclassified using standardized CYP2D6 genotype to phenotype recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG). The Mann-Whitney test was used to determine differences in LOS between groups G and S and Kruskal Wallis test to compare LOS among different race/ethnic groups. Logistic regression was used to determine covariates associated with RAR. Results: This study included 1,459 patients with 67.3% in G group (n = 982). The majority of patients were White (57.5%), followed by Latinos (25.6%) and Blacks (12.3%). Although there were no differences in LOS between G and S groups, Latinos had significant shorter LOS than Whites (p = 0.002). LOS was significantly reduced by 5.6 days in poor metabolizers in group G compared to S (p = 0.002). The proportion of supra functional and ultra-rapid metabolizers (UMs) were 6 and 20.3% using CYP-GUIDES and CPIC/DPWG definitions, respectively. Prevalence of DGIs was 40% with significantly fewer DGIs in Blacks (p < 0.001). Race/ethnicity was significantly associated with RAR (aOR 1.30; p = 0.003). Conclusion: A greater number of patients were classified as CYP2D6 UMs using CPIC/DPWG definitions as compared to CYP-GUIDES definitions. This finding may have clinical implications for using psychotropics metabolized by CYP2D6.

Pharmacists Leading the Way to Precision Medicine: Updates to the Core Pharmacist Competencies in Genomics

Genomics is becoming an increasingly important part of health care, and pharmacists are well-positioned to be practice-based leaders in pharmacogenomics and precision medicine. Competencies available through the Genetics/Genomics Competency Center provide a framework for pharmacogenomics instruction in both pharmacy school curricula and continuing education programs. Given the significant advancements in pharmacogenomics over the past decade, the 2019-2020 American Association of Colleges of Pharmacy Pharmacogenomics Special Interest Group updated the pharmacist competencies. The process used a systematic approach which included mapping pharmacogenomics-specific competencies to the entrustable professional activities for pharmacists and seeking consensus from key stakeholders. The result is an expansion to 30 competencies that reflect the contemporary roles pharmacists play in the application of pharmacogenomics in clinical practice. When implemented into curricula, these competencies will ensure that learners are "practice ready" to integrate pharmacogenomics into patient care. Additional postgraduate training is needed for advanced roles in pharmacogenomics implementation, education, and research.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Genetic Epidemiology of Medication Safety and Efficacy Related Variants in the Central Han Chinese Population With Whole Genome Sequencing.

Medication safety and efficacy-related pharmacogenomic research play a critical role in precision medicine. This study comprehensively analyzed the pharmacogenomic profiles of the central Han Chinese population in the context of medication safety and efficacy and compared them with other global populations. The ultimate goal is to improve medical treatment guidelines. We performed whole-genome sequencing in 487 Han Chinese individuals and investigated the allele frequencies of pharmacogenetic variants in 1,731 drug response-related genes. We identified 2,139 (81.18%) previously reported variants in our population with annotations in the PharmGKB database. The allele frequencies of these 2,139 clinical-related variants were similar to those in other East Asian populations but different from those in other global populations. We predicted the functional effects of nonsynonymous variants in the 1,731 pharmacogenes and identified 1,281 novel and 4,442 previously reported deleterious variants. Of the 1,281 novel deleterious variants, five are common variants with an allele frequency >5%, and the rest are rare variants with an allele frequency <5%. Of the 4,442 known deleterious variants, the allele frequencies were found to differ from those in other populations, of which 146 are common variants. In addition, we found many variants in non-coding regions, the functions of which require further investigation. This study compiled a large amount of data on pharmacogenomic variants in the central Han Chinese population. At the same time, it provides insight into the role of pharmacogenomic variants in clinical medication safety and efficacy.

Use of a Baculovirus-Mammalian Cell Expression-System for Expression of Drug-Metabolizing Enzymes: Optimization of Infection With a Focus on Cytochrome P450 3A4.

Heterologous expression systems are important for analyzing the effects of genetic factors including single nucleotide polymorphisms on the functions of drug-metabolizing enzymes. In this study, we focused on a baculovirus-mammalian cell (Bac-Mam) expression system as a safer and more efficient approach for this purpose. The baculovirus-insect cell expression system is widely utilized in large-scale protein expression. Baculovirus has been shown to also infect certain mammalian cells, although the virus only replicates in insect cells. With this knowledge, baculovirus is now being applied in a mammalian expression system called the Bac-Mam system wherein a gene-modified baculovirus is used whose promotor is replaced with one that can function in mammalian cells. We subcloned open-reading frames of cytochrome P450 3A4 (CYP3A4), UDP-glucuronosyltransferase (UGT) 1A1, and UGT2B7 into a transfer plasmid for the Bac-Mam system, and prepared recombinant Bac-Mam virus. The obtained virus was amplified in insect Sf9 cells and used to infect mammalian COS-1 cells. Expression of CYP3A4, UGT1A1, and UGT2B7 in COS-1 cell homogenates were confirmed by immunoblotting. Optimum infection conditions including the amount of Bac-Mam virus, culture days before collection, and concentration of sodium butyrate, an enhancer of viral-transduction were determined by monitoring CYP3A4 expression. Expressed CYP3A4 showed appropriate activity without supplying hemin/5-aminolevulinic acid or co-expressing with NADPH-cytochrome P450 reductase. Further, we compared gene transfer efficiency between the Bac-Mam system and an established method using recombinant plasmid and transfection reagent. Our results indicate that the Bac-Mam system can be applied to introduce drug-metabolizing enzyme genes into mammalian cells that are widely used in drug metabolism research. The expressed enzymes are expected to undergo appropriate post-translational modification as they are in mammalian bodies. The Bac-Mam system may thus accelerate pharmacogenetics and pharmacogenomics research.

Genetic Risk Factors for Adverse Drug Reactions

The use of medicines may in some cases be associated with the development of drug-induced diseases (DIDs) аnd other adverse drug reactions (ADRs), which leads to an increase in morbidity/mortality rates, and/or symptoms forcing a patient to seek medical attention or resulting in hospitalisation. ADRs may develop due to changes in a patient’s genotype, which entail an inadequate pharmacological response. The aim of the study was to analyse and summarise literature data on genetic risk factors that cause DIDs аnd other ADRs. It was shown that the polymorphism of genes encoding enzymes of drug metabolism (CYP, UGT, NAT, TPMT, EPHX, GST, etc.) or carriers (transporters) of drugs (P-gp, BCRP, MRP, OATP, OCT, etc.) can change the pharmacokinetics of drugs, affecting their activity. Polymorphism of RYR1, CACNA1S, MT-RNR1, VKORC1, and other genes encoding receptors targeted by drugs, and human leukocyte antigen (HLA) gene, may affect drug pharmacodynamics by modifying drug targets or changing the sensitivity of biological pathways to pharmacological effects of medicines. Changes in drug pharmacokinetics and pharmacodynamics may cause DIDs аnd other ADRs. The use of pharmacogenetic tests will allow a personalised approach to patients’ treatment and prevention or timely detection of potential ADRs during therapy. Before prescribing some medicines, clinicians should use recommendations on their dosing based on pharmacogenetic tests, which are posted on the official websites of Pharmacogenomics Research Network (PGRN), Pharmacogenomics Knowledgebase (PharmGKB), and Clinical Pharmacogenetics Implementation Consortium (CPIC). The results of ongoing clinical studies on the effect of gene polymorphism on drug safety will soon allow for higher personalisation of the choice of pharmacotherapy and prevention of many ADRs, including DIDs.

Effect of Genetic Variants of Gonadotropins and Their Receptors on Ovarian Stimulation Outcomes: A Delphi Consensus.

BackgroundA Delphi consensus was conducted to evaluate the influence of single nucleotide polymorphisms (SNPs) in genes encoding gonadotropin and gonadotropin receptors on clinical ovarian stimulation outcomes following assisted reproductive technology (ART) treatment.MethodsNine experts plus two Scientific Coordinators discussed and amended statements plus supporting references proposed by the Scientific Coordinators. The statements were distributed via an online survey to 36 experts, who voted on their level of agreement or disagreement with each statement. Consensus was reached if the proportion of participants agreeing or disagreeing with a statement was >66%.ResultsEleven statements were developed, of which two statements were merged. Overall, eight statements achieved consensus and two statements did not achieve consensus. The statements reaching consensus are summarized here. (1) SNP in the follicle stimulating hormone receptor (FSHR), rs6166 (c.2039A>G, p.Asn680Ser) (N=5 statements): Ser/Ser carriers have higher basal FSH levels than Asn/Asn carriers. Ser/Ser carriers require higher amounts of gonadotropin during ovarian stimulation than Asn/Asn carriers. Ser/Ser carriers produce fewer oocytes during ovarian stimulation than Asn/Asn or Asn/Ser carriers. There is mixed evidence supporting an association between this variant and ovarian hyperstimulation syndrome. (2) SNP of FSHR, rs6165 (c.919G>A, p.Thr307Ala) (N=1 statement): Few studies suggest Thr/Thr carriers require a shorter duration of gonadotropin stimulation than Thr/Ala or Ala/Ala carriers. (3) SNP of FSHR, rs1394205 (−29G>A) (N=1 statement): Limited data in specific ethnic groups suggest that A/A allele carriers may require higher amounts of gonadotropin during ovarian stimulation and produce fewer oocytes than G/G carriers. (4) SNP of FSH β-chain (FSHB), rs10835638 (−211G>T) (N=1 statement): There is contradictory evidence supporting an association between this variant and basal FSH levels or oocyte number. (5) SNPs of luteinizing hormone β-chain (LHB) and LH/choriogonadotropin receptor (LHCGR) genes (N=1 statement): these may influence ovarian stimulation outcomes and could represent potential future targets for pharmacogenomic research in ART, although data are still very limited.ConclusionsThis Delphi consensus provides clinical perspectives from a diverse international group of experts. The consensus supports a link between some variants in gonadotropin/gonadotropin receptor genes and ovarian stimulation outcomes; however, further research is needed to clarify these findings.

Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function.

The discovery of haplotypes with unknown or uncertain function in the CYP2D6pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanningand phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.

A Systematic Review and Analysis of the Use of Polygenic Scores in Pharmacogenomics.

Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGSs for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGSs were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g., schizophrenia-derived PGSs for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGSs, with between 3 and 6.6 million variants included in the PGSs. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGSs deposited on the Polygenic Score Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Pharmacogenomic analysis of a genetically distinct Indigenous population.

Indigenous Australians face a disproportionately severe burden of chronic disease relative to other Australians, with elevated rates of morbidity and mortality. While genomics technologies are slowly gaining momentum in personalised treatments for many, a lack of pharmacogenomic research in Indigenous peoples could delay adoption. Appropriately implementing pharmacogenomics in clinical care necessitates an understanding of the frequencies of pharmacologically relevant genetic variants within Indigenous populations. We analysed whole-genome sequence data from 187 individuals from the Tiwi Islands and characterised the pharmacogenomic landscape of this population. Specifically, we compared variant profiles and allelic distributions of previously described pharmacologically significant genes and variants with other population groups. We identified 22 translationally relevant pharmacogenomic variants and 18 clinically actionable guidelines with implications for drug dosing and treatment of conditions including heart disease, diabetes and cancer. We specifically observed increased poor and intermediate metabolizer phenotypes in the CYP2C9 (PM:19%, IM:44%) and CYP2C19 (PM:18%, IM:44%) genes.

Genetic Polymorphism of Drug Metabolic Gene CYPs, VKORC1, NAT2, DPYD and CHST3 of Five Ethnic Minorities in Heilongjiang Province, Northeast China.

IntroductionGenetic variability in genes encoding drug-metabolizing enzymes may contribute to the heterogeneity of drug responses in different populations. Extensive research in pharmacogenomics in major populations around the world provides us with a great deal of information about drug-related genetic polymorphisms.ObjectiveThe purpose of this study was to detect the genetic variation of drug-metabolism-related genes in the five ethnic minorities Daur, Hezhen, Ewenki, Mongolian and Manchu in China, and to analyze the distribution differences among ethnic groups.MethodsWe genotyped 32 SNPs of drug metabolism genes in 882 healthy Chinese volunteers from five ethnic groups. The genotype frequency and allele frequency of the five ethnic groups were calculated, and the different variants among the five ethnic groups were compared by chi-square test. Genetic parameters were analyzed using Popgene software. The genetic structure of five ethnic minorities was analyzed by principal component analysis, and compared with 26 populations.ResultsWe found that SNPs of genes related to drug metabolism existed diversity in different populations. Among them, rs8192766 and rs9419082 in CYP2E1 showed statistical differences between Daur and Manchu, and NAT2 rs1801280 showed statistical differences between Hezhen and Mongolian. In addition, the five populations we studied had the smallest differences with EAS populations. There was haplotype diversity in CHST3, VKORC1, CYP1A2 and CYP2E1 genes in the five ethnic minorities, and these haplotype polymorphisms were related to the use of corresponding drug doses. Cluster analysis shows that the five ethnic minorities in Heilongjiang Province are clustered together with the EAS populations.ConclusionThese results suggest that understanding the diversity of drug-related genetic markers is critical for individualized drug gene therapy programs in ethnic minorities in China as well as in populations highly mixed with these ethnic groups.

Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS’ escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

Genetic landscape of 125 pharmacogenes in Chinese from the Chinese Millionome Database

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.

Assessment of HLA-B genetic variation with an HLA-B leader tool and implications in clinical transplantation

AbstractSequence variation in the HLA-B gene is critically linked to differential immune responses. A dimorphism at -21 of HLA-B exon 1 gives rise to leader peptides that are markers for risk of acute graft-versus-host disease, relapse, and mortality after unrelated donor and cord blood transplantation. To optimize the selection of stem cell transplant sources based on the HLA-B leader, an HLA-BLeader Assessment Tool (BLEAT) was developed to automate the assignment of leader genotypes, define HLA-B leader match statuses, and rank order candidate stem cell sources according to clinical risk. The base cohort consisted of 9 417 614 registered donors from the Be The Match Registry with HLA-B typing. Among these donors, the performance of BLEAT was assessed in 1 098 358 donors with sequence data for HLA-B exon 1 (2 196 716 haplotypes). The accuracy of leader assignment was then assessed in a second cohort of 1259 patients and their unrelated transplant donors. We furthermore established the frequencies of HLA-B leader genotype (MM, MT, TT) representations in broad racial categories in the 9.42 million donors. BLEAT has direct applications for the selection of optimal stem cell sources for transplantation and broad utility in basic and clinical research in pharmacogenomics, vaccine development, and cancer and infectious disease studies of human populations.

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42–53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm’s capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm’s citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p’s < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

Genotyping HLA alleles to predict the development of Severe cutaneous adverse drug reactions (SCARs): state-of-the-art

ABSTRACT Introduction: Pharmacogenomics has great potential in reducing drug-induced severe cutaneous adverse drug reactions (SCARs). Pharmacogenomic studies have revealed an association between HLA genes and SCARs including acute generalized exanthematous pustulosis (AGEP), drug reaction with eosinophilia and systemic symptoms (DRESS), Stevens-Johnson syndrome (SJS), and toxic epidermal necrolysis (TEN). Areas covered: Pharmacogenomics-guided therapy could prevent severe drug hypersensitivity reactions. The US Food and Drug Administration (FDA), Clinical Pharmacogenetics Implementation Consortium (CPIC), and Dutch Pharmacogenetics Working Group (DPWG) provided guidelines in the translation of clinically relevant and evidence-based SCARs pharmacogenomics research into clinical practice. In this review, we intended to summarize the significant HLA alleles associated with SCARs induced by different drugs in different populations. We also summarize the SCARs associated with genetic and non-genetic factors and the cost-effectiveness of screening tests. Expert opinion: The effectiveness of HLA screening on a wider scale in clinical practice requires significant resources, including state-of-the-art laboratory; multidisciplinary team approach and health care provider education and engagement; clinical decision support alert system via electronic medical record (EMR); laboratory standards and quality assurance; evidence of cost-effectiveness; and cost of pharmacogenomics tests and reimbursement.