The Pharmacogenomics Journal

Pharmacogenomics aims to use the genetic information of an individual to personalize drug prescribing. There is evidence that pharmacogenomic testing before prescription may prevent adverse drug reactions, increase efficacy, and reduce cost of treatment. CYP2D6 is a key pharmacogene of relevance to multiple therapeutic areas. Indeed, there are prescribing guidelines available for medications based on CYP2D6 enzyme activity as deduced from CYP2D6 genetic data. The Agena MassARRAY system is a cost-effective method of detecting genetic variation that has been clinically applied to other genes. However, its clinical application to CYP2D6 has to date been limited by weaknesses such as the inability to determine which haplotype was present in more than one copy for individuals with more than two copies of the CYP2D6 gene. We report application of a new protocol for CYP2D6 haplotype phasing of data generated from the Agena MassARRAY system. For samples with more than two copies of the CYP2D6 gene for which the prior consensus data specified which one was present in more than one copy, our protocol was able to conduct CYP2D6 haplotype phasing resulting in 100% concordance with the prior data. In addition, for three reference samples known to have more than two copies of CYP2D6 but for which the exact number of CYP2D6 genes was unknown, our protocol was able to resolve the number for two out of the three of these, and estimate the likely number for the third. In addition, we demonstrate that our method is applicable to CYP2D6 hybrid tandem configurations.

BackgroundPersonalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) data provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the diagnostic applicability of exome data for pharmacogenomics remains insufficiently studied. Materials and MethodsA retrospective analysis of 6,102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from PharmGKB (levels of evidence 1A-2B), CPIC, and PharmVar. Additionally, WES limitations and the feasibility of imputing non-coding pharmacogenetic variants were evaluated. ResultsPopulation frequencies of alleles and metabolic phenotypes were determined for 13 Very Important Pharmacogenes (VIPs), along with the distribution of HLA class I and II alleles. The highest allelic and phenotypic variability was observed in CYP family genes, particularly CYP2D6, CYP2C19, and CYP2B6. A total of 663 pharmacogenomic annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), including psychotropic agents, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. At least 32 drugs require pharmacogenetic testing based on variants located in non-coding regions, as well as accurate CYP2D6 copy number determination. Linkage disequilibrium analysis demonstrated the inability to reliably impute most non-coding pharmacogenetic variants from WES data. ConclusionThese findings represent one of the largest reference assessments to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm the utility of WES for population pharmacogenomic screening while simultaneously highlighting its fundamental limitations and the need for alternative genetic diagnostic methods in selected cases.

Pharmacogenetics studies how genetic variation leads to variability in drug response. Guidelines for selecting the right drug and right dose to patients based on their genetics are clinically effective, but are still widely unused. For some drugs, the normal clinical decision making process may lead to the optimal dose of a drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may observe and adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and drug maintenance dose as well as side effect incidence. We find support for several known drug-gene associations as well as novel pharmacogenetic interactions.

IntroductionAdverse drug reactions (ADRs) remain a major public health issue, and genetic factors contribute importantly to interindividual variability in drug response. Pharmacogenetic testing helps reduce ADR risk by optimizing drug selection and dosage, particularly in monogenic disorders. Material and MethodsWhole-exome sequencing of 6,739 samples from the Russian population was performed using the MGIEasy Universal DNA Library Prep Set on the DNBSEQ-G400 platform (MGI). Variants in 48 genes were examined, focusing on inherited arrhythmias (Long QT syndrome, Short QT syndrome, Timothy syndrome, Andersen-Tawil syndrome, Brugada syndrome, Atrial fibrillation, Catecholaminergic polymorphic ventricular tachycardia), enzyme deficiencies (Glucose-6-Phosphate Dehydrogenase Deficiency [G6PDD], Porphyrias), Dravet Syndrome (DS) and Malignant Hyperthermia (MH). All identified variants had been reported at least once as pathogenic (P) or likely pathogenic (LP) in ClinVar, along with those occasionally classified as variants of uncertain significance (VUS). Each variant was manually re-evaluated according to ACMG criteria. ResultsA total of 75 unique variants in 18 genes were observed in 119 individuals (1.77%), including 21 carriers and 13 women with a G6PD mutation. Of these, 46 variants were classified as P, 21 as LP, and 8 as VUS. Missense variants accounted for the largest proportion (73.33%). The most affected genes were KCNQ1 (24/119), which exhibited the highest number of unique variants (18), G6PD (20/119), SCN1A (15/119), and RYR1 (14/119). Regarding associated conditions, mutations linked to arrhythmias were found in 51 individuals, MH in 27, G6PDD in 20, DS in 15, and Porphyrias in 6. ConclusionsIncorporating genetic information on both common and rare clinically actionable variants into therapeutic decision-making has the potential to improve medication safety, reduce preventable ADRs, and enhance the effectiveness of personalized pharmacotherapy.

The COVID-19 global pandemic has led to repurposing of drugs, with little underlying evidence for treatment safety and efficacy. This may increase complications for patients with acute viral respiratory infections. UGT1A1 and CYP2D6 enzymes are involved in the metabolism of atazanavir and fluvoxamine repurposed for COVID-19. This study aimed to elucidate the role of interethnic variation in these enzymes in the efficacy of repurposed drug therapies. A retrospective cohort of 101 Jordanian Arab samples were genotyped using Affymetrix DMET Plus Premier Package. Comprehensive global population genetic structure analyses were performed for CYP2D6 and UGT1A1 allele frequencies across multi-ethnic populations of over 131,000 global subjects from 417 published reports, revealing that Jordanian Arabs share the closest sequence homology to European and Near East populations. The East Asian populations have significantly over-representaiton of individuals with diplotype pairs for reduced atazanavir metabolism compared to the African populations and are more likely to show impaired UGT1A1 metabolism. East Asian populations are also 4.4x more likely to show impaired fluvoxamine metabolism than South Central Asian and Oceanian populations, and 8x more likely than other ancestry populations. The results here support previous findings that interethnic variation should be used for developing proper population-specific dosage guidelines.

Cytochrome P450 2D6 (CYP2D6) plays a crucial role in metabolizing approximately 20% of medications prescribed clinically. This enzyme is encoded by the CYP2D6 gene, known for its extensive polymorphism with over 170 catalogued haplotypes or star alleles, which can have a profound impact on drug efficacy and safety. Despite its importance, a gap exists in the global genomic databases, which are predominantly representative of European ancestries, thereby limiting comprehensive knowledge of CYP2D6 variation in ethnically diverse populations. In an effort to bridge this knowledge gap, we focused on elucidating the CYP2D6 variation landscape within a multi-ethnic Asian cohort, encompassing individuals of Chinese, Malay, and Indian descent. Our study comprised data analysis of 1,850 whole genomes from the SG10K_Health dataset using an in-house consensus algorithm, which integrates the capabilities of Cyrius, Aldy, and StellarPGx. This analysis unveiled distinct population-specific star-allele distribution trends, highlighting the unique genetic makeup of the Singaporean population. Significantly, 46% of our cohort harbored actionable CYP2D6 variants--those with direct implications for drug dosing and treatment strategies. Furthermore, we identified 14 potential novel CYP2D6 star-alleles, of which 7 were observed in multiple individuals, suggesting their broader relevance. Overall, our study contributes novel data on CYP2D6 genetic variations specific to the Southeast Asian context. The findings are instrumental for the advancement of pharmacogenomics and personalized medicine, not only in Southeast Asia but also in other regions with comparable genetic diversity.

Accurate pharmacogenomic genotype determination and interpretation are essential for personalized medicine, yet existing bioinformatics tools face significant limitations in detecting named alleles, maintaining current allele definitions, and providing comprehensive clinical annotations. We present VSPGx, a pharmacogenomics interpretation software solution that identifies diplotypes from next-generation sequencing data and annotates them against Clinical Pharmacogenetics Implementation Consortium (CPIC) and FDA drug recommendations using automated curation of the latest allele definitions. We benchmarked VSPGx against established tools including Aldy, PharmCAT, and Stargazer using both synthetic datasets and real-world clinical samples. In a comprehensive synthetic benchmark spanning 3,655 CYP2C9 diplotype combinations, VSPGx achieved 99.97% concordance, matching PharmCATs performance and substantially outperforming Aldy (93.08%) and Stargazer (27.06%). Clinical validation using 11 TaqMan OpenArray samples demonstrated 88.2% allele concordance and 89.1% phenotype concordance across 110 gene-sample combinations, with all discrepancies attributed to the benchmark data utilizing outdated allele definitions rather than VSPGx errors. Our automated curation process ensures continuous alignment with current CPIC guidelines, addressing a critical gap in existing pharmacogenomic analysis tools. VSPGx provides a robust, clinically-validated solution for pharmacogenomic analysis that combines high-accuracy diplotype calling with up-to-date, evidence-based drug recommendations.

CYP2D6 is a widely expressed human xenobiotic metabolizing enzyme, best known for its role in the hepatic phase I cytochrome P450 enzyme system, where it metabolizes [~]20% of medications. It is also expressed in other organs including the brain, where its potential role in physiology and mental health traits and disorders is under further investigation. Owing to the presence of homologous pseudogenes in the CYP2D locus and transposable repeat elements in the intergenic regions, the gene encoding the CYP2D6 enzyme, CYP2D6, is one of the most hypervariable known human genes - with more than 165 core haplotypes. Haplotypes include structural variants, with a subtype of these known as hybrid haplotypes or fusion genes comprising part of CYP2D6 and part of its adjacent pseudogene, CYP2D7. The fusion genes are particularly challenging to identify. High fidelity (HiFi) single molecule real-time (SMRT) long-read sequencing can cover whole CYP2D6 haplotypes in a single continuous sequence, and is therefore ideal for structural variant detection. In addition, it is highly accurate and suitable for novel haplotype identification, which is necessary as new CYP2D6 haplotypes are continuously being discovered, and many more likely remain to be identified in relatively understudied populations such as Indigenous Peoples. The aim of the present work was to develop an efficient and accurate HiFi SMRT amplicon-based method capable of detecting the full range of CYP2D6 haplotypes including fusion genes. We report proof-of-concept for 24 amplicons including three positive controls, aligned to fusion gene haplotypes, with prior cross-validation data. Amplicons with CYP2D7-D6 fusion genes, including positive controls, aligned to the *13 subhaplotypes predicted (*13F, *13A2) with 100% accuracy, with the exception of one that aligned at 99.9%. Alignment of the *68 was 100% and above 99.9% to the CYP2D6*68 partial sequences EU5300606 and JF307779, respectively. The best alignments for the remaining CYP2D6-2D7 fusion genes were [≥]99.7% (to 3 significant figures). Lower percentage alignment for CYP2D6-2D7 fusion genes may reflect imperfect PCR optimization and/or the possibility that we may have haplotypes not yet in public databases. Further work on these is in progress. Moreover, we have adapted this method for non-hybrid haplotypes. This technique could therefore suffice for the characterization of the full range of CYP2D6 haplotypes. The method that we have developed could be extended to other complex loci and to other species in a multiplexed high throughput assay.

Coronavirus disease 2019 (COVID-19), is a rapidly spreading infectious illness that causes a debilitating respiratory syndrome. While non-steroidal anti-inflammatory drugs (NSAIDs), may be prescribed for the management of pain and fever, there was early controversy on the use of ibuprofen for symptomatic treatment of COVID-19. P450 enzyme CYP2C9 are known to be involved in the metabolism of NSAIDs. Although no study has been conducted in the setting of population genetics in patients with COVID-19 yet, there are plausible mechanisms by which genetic determinants may play a role in adverse drug reactions (ADRs). In this work, we adjusted expected phenotype frequencies based on racial demographic models dependent on population ancestry in drug responses and toxicity events associated with ibuprofen treatment. A cohort of 101 Jordanian Arab samples retrospectively were selected and genotyped using Affymetrix DMET Plus Premier Package, within the context of over 100,000 global subjects in 417 published reports. European populations are 7.2x more likely to show impaired ibuprofen metabolism than Sub-Saharan populations, and 4.5x more likely than East Asian ancestry populations. Hence, a proactive assessment of the most likely gene-drug candidates will lead to more effective treatments and a better understanding of the role of pharmacogenetics for COVID-19.

BackgroundMicroarrays are a well-established and widely adopted technology capable of interrogating hundreds of thousands of loci across the human genome. Combined with imputation to cover common variants not included in the chip design, they offer a cost-effective solution for large-scale genetic studies. Beyond research applications, this technology can be applied for testing pharmacogenomics, nutrigenetics, and complex disease risk prediction. However, establishing clinical reporting workflows requires a thorough evaluation of the assays performance, which is achieved through validation studies. In this study, we performed pre-clinical validation of a genetic testing workflow based on the Illumina Global Screening Array for 25 pharmacogenomic-related genes. MethodsTo evaluate the accuracy of our workflow, we conducted multiple pre-clinical validation studies. Here, we present the results of accuracy and precision assessments, involving a total of 73 cell lines. These assessments encompass reference materials from the Genome-In-A-Bottle (GIAB), the Genetic Testing Reference Material Coordination Program (GeT-RM) projects, as well as additional samples from the 1000 Genomes project (1KGP). We conducted an accuracy assessment of genotype calls for target loci in each indication against established truth sets. ResultsIn our per-sample analysis, we observed a mean analytical sensitivity of 99.39% and specificity 99.98%. We further assessed the accuracy of star-allele calls by relying on established diplotypes in the GeT-RM catalogue or calls made based on 1KGP genotyping. On average, we detected a diplotype concordance rate of 96.47% across 14 pharmacogenomic-related genes with star allele-calls. Lastly, we evaluated the reproducibility of our findings across replicates and observed 99.48% diplotype and 100 % phenotype inter-run concordance. ConclusionOur comprehensive validation study demonstrates the robustness and reliability of the developed workflow, supporting its readiness for further development for applied testing.

NAT2 is an important pharmacogene which encodes the N-acetyltransferase 2 enzyme that is involved in the metabolism of multiple medications, and variants in this gene can affect patient response to these medications. CPIC has published a clinical guideline for prescribing hydralazine using NAT2 genotypes. Just prior to the guideline, updated NAT2 star allele numbering and definitions were released, differing somewhat from the historical nomenclature. Clinical pharmacogenomic testing panels often test for the most common star alleles, so knowledge of the most common updated NAT2 star alleles is critical for the implementation of the CPIC NAT2/hydralazine guideline. We first determine NAT2 diplotype frequencies from UK Biobank (UKBB) 200k phased genomes, then analyzed allele, diplotype, and phenotype population frequencies from the All of Us Research program, PennMedicine BioBank (PMBB) and UKBB 500k datasets. We found that analyzing NAT2 diplotypes from phased data provides critical information for algorithms designed to predict diplotypes from unphased data. We observed that NAT2*5, *6, and *4 were the most common star alleles in that order, and the top 11 most frequent NAT2 star alleles were the same across all biobanks. However, differences in star allele frequencies across biogeographical populations were observed. The largest difference led to a higher frequency of NAT2 poor metabolizer phenotypes as compared to rapid and intermediate metabolizer phenotypes in all global populations except in the EAS population, where NAT2 poor metabolizers were in the minority.

Although existing next-generation sequencing (NGS) tools, such as Aldy and Cyrius, have been applied for allele typing, they cannot achieve complete accuracy due to various genomic challenges including pseudogenes, structural variations, hybrid genes, copy number variations, and gene deletions. These complexities make accurate pharmacogene interpretation more challenging, despite the crucial role pharmacogenomics plays in precision medicine. We developed deCYPher, a tool that generates personalized pharmacogenomic reports from haplotype-resolved assemblies. The tool enables analysis of all PharmVar 1A level genes, such as CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, DPYD, NUDT15, and SLCO1B1. Applied to all HPRC haplotypes (including both release 1 and release 2 data), deCYPher demonstrated high accuracy in resolving complex gene structures. In the case of CYP2D6, release 1 identified 6% gene multiplications, 6% full gene deletions, and 4% CYP2D6/CYP2D7 hybrids. By contrast, release 2 demonstrated an increased prevalence of multiplications (14%) and hybrids (11%), while the frequency of full gene deletions remained comparable at 5%. Comparison with pb-StarPhase revealed discrepancies in 12 of 94 assemblies in the release 1 dataset. For instance, in sample HG02257, Aldy, Cyrius, and deCYPher consistently identified the genotype as *2/*35, whereas pb-StarPhase reported *2/*2. Notably, the *35-defining variants were present in the BAM and VCF files in the pb-StarPhase pipeline, but the local read depth over the *35-specific region was only 5x in HG02257-p, suggesting that the misclassification likely resulted from insufficient coverage - a known limitation of pb-StarPhase under low-depth conditions.

RationaleIntensive care units (ICU) patients are highly vulnerable to inaccurate drug dosing. Pharmacogenomics (PGx) characterizes the influence of inherited genetic variation on drug metabolism, playing an important role in the consequences of a given drug dose. ObjectivesTo assess the genetic-based risk of inaccurate drug dosing in the ICU. MethodsWe carried out whole genome sequencing (WGS) of 210 Qataris in ICU care at Hamad Medical Corporation (HMC), Doha, Qatar and assessed the WGS for predicted deleterious variants of genes that metabolize 30 drugs commonly prescribed in the ICU. Measurements and Main ResultsAnalysis of 210 Qatari ICU WGS identified 329 variants predicted deleterious associated with 85 genes known to affect metabolism of the 30 ICU drugs. Of the ICU patients that received the 5 most commonly prescribed drugs (warfarin, phenytoin, midazolam, vancomycin, levetiracetam), 93% had deleterious metabolism-related variants. Most (91%) patients carried at least one variant in a gene that that had the potential to affect the metabolism or activity of at least 1 drug that the patient received. Most patients had [≥]14 deleterious variants of genes that affect the metabolism of administered drugs. Comparison of the deleterious variants related to metabolism of ICU drugs with African/African American and European populations revealed significant population specificity in ICU related PGx variants. ConclusionsTogether, these data suggest that population specific, pharmacogenomics based on the individuals genome likely plays a significant role in effective, safe dosing in the ICU setting.

Pharmacogenomics promises improved outcomes through individualized prescribing. However, the lack of diversity in studies impedes clinical translation and equitable application of precision medicine. We evaluated the frequencies of PGx variants, predicted phenotypes, and medication exposures using whole genome sequencing and EHR data from nearly 100k diverse All of Us Research Program participants. We report 100% of participants carried at least one pharmacogenomics variant and nearly all (99.13%) had a predicted phenotype with prescribing recommendations. Clinical impact was high with over 20% having both an actionable phenotype and a prior exposure to an impacted medication with pharmacogenomic prescribing guidance. Importantly, we also report hundreds of alleles and predicted phenotypes that deviate from known frequencies and/or were previously unreported, including within admixed American and African ancestry groups.

The acceptability of antidepressant drugs partly depends on genetic factors. The list of genes involved in antidepressant response, including Adverse Drug Reactions (ADRs) is broad and contains both drug-metabolizing enzymes (pharmacogenes) and genes involved in pharmacodynamics. Variants in pharmacogenes are traditionally reported in the form of star alleles and are partially annotated with known phenotypic consequences. As it is unfeasible to analyze all genotype-phenotype pairs, computational approaches remain the practical solution. A pharmacogenetic framework to predict responses to antidepressant drug treatment would provide great benefit to patients. In this study, we present a scoring system (PharmGScore) to assess both rare and common genetic variant burden across multiple genes. The PharmGScore is constructed by normalizing and aggregating existing, well-established computational variant predictors (CADD, Fathmm-xf, PROVEAN, Mutation Assessor). We show that this score effectively distinguishes no and decreased function from normal and increased function pharmacogenetic variants reported in PharmVar (PharmGScore AUC = 0.86). PharmGScore has improved performance when compared to its component scores (AUCs: CADD = 0.79; FATHMM-XF = 0.81; PROVEAN = 0.81; Mutation Assessor = 0.75). We then apply the PharmGScore to the 200k exome sequences of the UK Biobank (UKB). We report the overrepresentation of UKB participants with high (>50) gene PharmGScore for CYP2C19 and CYP2C9 and with high (>100) compound PharmGScore from nine pharmacogenes within a group with an antidepressant toxicity diagnostic code (T43.2). We then analyze all UKB participants that received any antidepressant toxicity or ADR diagnosis (n = 602). We indicate genes for which a higher burden may be associated with antidepressant toxicity or ADRs and confirm the known roles of CYP2C19 and CYP2D6 in this process. Finally, we show that patients who experienced ADRs to antidepressants in the therapeutic process or accidental poisoning with antidepressants have a higher PharmGScore composed of nine cytochrome P450 genes. Our study proposes a novel paradigm to assess the compound genetic variant burden associated with antidepressant response from exome sequencing data. This approach can be further applied to a user-defined set of genes to investigate other pharmacological traits.

Enterally-administered nimodipine is the only approved drug formulation available in the United States for treatment of patients with aneurysmal subarachnoid hemorrhage. Intravenous nimodipine is available in other countries but it contains a high concentration of ethanol that is irritating to the vasculature, can alter the effects of other medications, impair neurological assessments and is potentially harmful to the liver. We developed a sterile aqueous solution of nimodipine solubilized in polysorbate 80 micelles (GTX-104) that circumvents these problems. GTX-104 has been administered to 168 healthy human volunteers in 2 studies. We report the second study here, a phase 1, single center, randomized, 2-period cross over study that assessed the pharmacokinetics of GTX-104 and oral nimodipine capsules, which is the reference standard, in 58 healthy human volunteers. GTX-104 was administered for 72 hours as a continuous infusion of 0.15 mg/hour with a 30 minute bolus infusion of 4 mg every 4 hours. Nimodipine capsules were administered orally at a dose of 60 mg every 4 hours for 72 hours. The maximum plasma concentrations after the first dose of each formulation were similar (GTX-104: 63 ng/mL, n=57 versus nimodipine capsules: 69 ng/mL, n=56, ratio and 90% confidence interval [CI] of geometric means: 92% [90% CI: 82-104%]). The areas under the concentration-time curves on the 3rd day at steady state also were the same (GTX-104: 497 ng*h/mL, n=55 versus nimodipine capsules: 495 ng*h/mL, n=56, ratio and 90% CI of geometric means: 106% [90% CI: 99-114%]). The secondary pharmacokinetic parameters (daily maximum concentration at steady-state and time to maximum concentration) were also similar for the 2 formulations. The variability in PK parameters was less for GTX-104 compared to oral nimodipine. The average oral bioavailability for nimodipine capsules was 7%. These results enabled a Phase 3 safety study of GTX-104 in humans with aneurysmal subarachnoid hemorrhage.

Oral nimodipine is the only drug approved in North America for treatment of patients with aneurysmal subarachnoid hemorrhage (aSAH). However, bioavailability is variable and frequently poor, leading to fluctuations in peak plasma concentrations that cause dose-limiting hypotension. Furthermore, administration is problematic in patients who cannot swallow capsules. An oral liquid formulation exists but causes gastrointestinal complications. An intravenous nimodipine formulation (GTX-104) has been developed that has bioavailability approaching 100% and is not affected by feeding or gastointestinal absorption. GTX-104 causes less hypotension and has more consistent peak plasma concentrations than oral nimodipine in healthy human volunteers. Herein we describe the protocol of a prospective, randomized, open-label safety and tolerability study of GTX-104 compared to oral nimodipine in patients with aSAH (STRIVE-ON, NCT05995405). Inclusion and exclusion criteria match the prescribing information for oral nimodipine and include adult patients with aSAH of all Hunt and Hess grades who can receive investigational product within 96 hours of aSAH. Subjects at imminent risk of death are excluded. Subjects are randomized 1:1 to GTX-104 or oral nimodipine for up to 21 days. The primary endpoint is the proportion of subjects in each group with clinically significant hypotension, defined as hypotension requiring any medical treatment, with a reasonable likelihood of being due to investigational product as determined by an independent, blinded endpoint adjudication committee. No statistical analysis of the endpoint is planned. Secondary endpoints include all episodes of hypotension, all adverse events, delayed cerebral ischemia, rescue therapy and suicidal ideation. Clinical and health economic outcomes include quality of life using the EQ-5D-3L, modified Rankin scale at 30 and 90 days after aSAH and hospital resource use. The planned sample size is 100 subjects across 25 sites in the United States and Canada. DETAILS PAGEO_LIWe confirm that manuscript complies with all instructions to authors. C_LIO_LIWe confirm that authorship requirements have been met and the final manuscript is approved by all authors. C_LIO_LIWe confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. It is planned to post it on C_LIO_LIWe confirm adherence to ethical guidelines and indicate ethical approvals and use of informed consent. C_LIO_LIAll conflicts of Interest for all authors are disclosed. C_LIO_LIWe confirm the use of the Standard Protocol Items: Recommended for Interventional Trials (SPIRIT) checklist. C_LIO_LIThe source of funding for the study is disclosed. It is Acasti Pharma. C_LI

The majority of individuals receiving treatment for major depressive disorder (MDD) do not achieve remission from the first medication they try, and over 80% subsequently discontinue pharmacotherapy or switch to a different medication. SSRI discontinuation due to side effects is common. We evaluated the effect of CYP2C19 genotype on SSRI response using self-reported data from 114,627 direct-to-consumer genetics research participants who were prescribed an SSRI primarily metabolized by CYP2C19 (citalopram, escitalopram, or sertraline). Among participants taking citalopram or escitalopram, slower metabolizers experienced side effects significantly more often than faster metabolizers (OR=1.04 per grade, from 0 for poor metabolizers to 5 for ultrarapid metabolizers, 95%CI=[1.02-1.06] and OR=1.05 per grade, 95%CI=[1.02-1.07]) and were more likely to discontinue treatment due to side effects (OR=1.05, 95%CI=[1.03-1.08], e.g. 29.7% of poor vs. 21.6% of ultrarapid metabolizers, and OR=1.07, 95%CI=[1.04-1.11], e.g. 25.7% vs. 20.2%). Slower metabolizers taking escitalopram were more likely to suffer from sleep problems and sexual problems than faster metabolizers. Slower metabolizers taking sertraline reported tremor more often than faster metabolizers. Overall, we find substantial differences in side effect risk between individuals with different CYP2C19 genotypes in a large sample, supporting the notion that individuals seeking treatment for MDD may benefit from preemptive pharmacogenetic testing and genotype-guided dosing recommendations to minimize side effects and reduce discontinuations.

Animals are used as preclinical models for human diseases in drug development. Dogs, especially, are used in preclinical research to support the clinical safety evaluations during drug development. Comparisons of patterns of regions of homozygosity (ROH) and phenotypes between dog and human are not well known. We conducted a genome-wide homozygosity analysis (GWHA) in the human and the dog genomes. We calculated ROH patterns across distinct human cohorts including the Amish, the 1000 genomes, Wellderly, Vanda 1k genomes, and Alzheimers cohort. The Amish provided a large cohort of extended kinships allowing for in depth family oriented analyses. The remaining human cohorts served as statistical references. We then calculated ROH across different dog breeds with emphasis on the beagle - the preferred breed used in drug development. Out of five studied human cohorts we reported the highest mean ROH in the Amish population. We calculated the extent of the genome covered by ROH (FROH) (human 3.2Gb, dog 2.5Gb). Overall FROH differed significantly between the Amish and the 1000 genomes, and between the human and the beagle genomes. The mean FROH per 1Mb was [~]16kb for Amish, [~]0.6kb for Vanda 1k, and [~]128kb for beagles. This result demonstrated the highest degree of inbreeding in beagles, far above that of the Amish, one of the most inbred human populations. ROH can contribute to inbreeding depression if they contain deleterious variants that are fully or partially recessive. The differences in ROH characteristics between human and dog genomes question the applicability of dog models in preclinical research, especially when the goal is to gauge the subtle effects on the organisms physiology produced by candidate therapeutic agents. Importantly, there are huge differences in a subset of ADME genes, specifically cytochrome P450 family (CYPs), constituting major enzymes involved in drug metabolism. We should hesitate to generalize from dog to human, even if human and beagle are relatively close species phylogenetically

Adverse drug reactions (ADRs) associated with drug-drug interactions (DDIs) represent a significant threat to public health. Unfortunately, most conventional methods for prediction of DDI-associated ADRs suffer from limited applicability and/or provide no mechanistic insight into DDIs. In this study, a hierarchical machine learning model was created to predict DDI-associated ADRs and pharmacological insight thereof for any drug pair. Briefly, the model takes drugs chemical structures as inputs to predict their target, enzyme, and transporter (TET) profiles, which are subsequently utilized to assess occurrences of ADRs, with an overall accuracy of ~91%. The robustness of the model for ADR classification was validated with DDIs involving three widely prescribed drugs. The model was then applied for interstitial lung disease (ILD) associated with DDIs involving atorvastatin, identifying the involvement of multiple targets, enzymes, and transporters in ILD. The model presented here is anticipated to serve as a versatile tool for enhancing drug safety.