Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease.

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications.

Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.

VEGFA GENE variation influences hallucinations and frontotemporal morphology in psychotic disorders: a B-SNIP study.

Vascular endothelial growth factor A (VEGFA) dysfunction may contribute to a number of pathological processes that characterize psychotic disorders. However, the influence of VEGFA gene variants on clinical and neuroimaging phenotypes in psychotic disorders has yet to be shown. In the present study, we examined whether different VEGFA gene variants influence psychosis risk, symptom severity, cognition, and brain volume. The study group included 480 probands (Bipolar I disorder with psychosis, n = 205; Schizoaffective disorder, n = 112; Schizophrenia, n = 163) and 126 healthy controls that were recruited across six sites in the B-SNIP consortium. VEGFA variants identified for analysis (rs699947, rs833070, and rs2146323) were quantified via SNP chip array. We assessed symptoms and cognition using standardized clinical and neuropsychological batteries. The dorsolateral prefrontal cortex (DLPFC), medial temporal lobe, and hippocampal volumes were quantified using FreeSurfer. In our sample, VEGFA rs2146323 A- carriers showed reduced odds of being a proband (p = 0.037, OR = 0.65, 95% CI = 0.43-0.98) compared to noncarriers, but not for rs699947 or rs833070. In probands, rs2146323 A- carriers demonstrated fewer hallucinations (p = 0.035, Cohen's d = 0.194), as well as significantly greater DLPFC (p < 0.05, Cohen's d = -0.21) and parahippocampal volumes (p < 0.01, Cohen's d = -0.27). No clinical or neuroimaging associations were identified for rs699947 or rs833070. In general, we found that the three SNPs exhibited several significant negative relationships between psychosis symptoms and brain structure. In the probands and control groups, positive relationships were identified between several cognitive and brain volume measures. The findings suggest VEGFA effects in the DLPFC and hippocampus found in animals may also extend to humans. VEGFA variations may have important implications in identifying dimensional moderators of function that could be targeted through VEGFA-mediated interventions.

Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site.

Cystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations have yet to be analyzed in aggregate to assess their distribution across the tertiary structure of the CFTR protein, an approach that could provide valuable insights into the structure-function relationship of CFTR. In addition, the binding site of Class I correctors (VX-809, VX-661, and C18) is not well understood. In this study, exonic CFTR mutations and mutant allele frequencies described in 3 curated databases (ABCMdb, CFTR1, and CFTR2, comprising >130 000 data points) were mapped to 2 different structural models: a homology model of full-length CFTR protein in the open-channel state, and a cryo-electron microscopy core-structure of CFTR in the closed-channel state. Accordingly, residue positions of 6 high-frequency mutant CFTR alleles were found to spatially co-localize in CFTR protein, and a significant cluster was identified at the NBD1:ICL4 interdomain interface. In addition, immunoblotting confirmed the approximate binding site of Class I correctors, demonstrating that these small molecules act via a similar mechanism in vitro, and in silico molecular docking generated binding poses for their complex with the cryo-electron microscopy structure to suggest the putative corrector binding site is a multi-domain pocket near residues F374-L375. These results confirm the significance of interdomain interfaces as susceptible to disruptive mutation, and identify a putative corrector binding site. The structural pharmacogenomics approach of mapping mutation databases to protein models shows promise for facilitating drug discovery and personalized medicine for monogenetic diseases.

Structural coverage of the proteome for pharmaceutical applications.

Structure-based computational drug discovery efforts have traditionally focused on the structure of a single, well-known drug target. Important applications, such as target deconvolution and the analysis of polypharmacology, require proteome-scale molecular docking and have been inaccessible to structure-based in silico approaches. One important reason for this inaccessibility was that the structure of most proteins was not known. Lately, this 'structure gap' has been closing rapidly, and proteome-scale molecular docking seems within reach. Here, we survey the current state of structural coverage of the human genome and find that coverage is truly proteome-wide, both overall and in most pharmaceutically relevant categories of proteins. The time is right for structure-based approaches to target deconvolution and polypharmacology.

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474.

The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study's proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses.

BACKGROUND: Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown. METHODS: We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands. RESULTS: 482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity. CONCLUSIONS: Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

Laboratory Medicine in the Clinical Decision Support for Treatment of Hypercholesterolemia: Pharmacogenetics of Statins.

Statin responsiveness is an area of great research interest given the success of the drug class in the treatment of hypercholesterolemia and in primary and secondary prevention of cardiovascular disease. Interrogation of the patient's genome for gene variants will eventually guide anti-hyperlipidemic intervention. In this review, we discuss methodological approaches to discover genetic markers predictive of class-wide and drug-specific statin efficacy and safety. Notable pharmacogenetic findings are summarized from hypothesis-free genome wide and hypothesis-led candidate gene association studies. Physiogenomic models and clinical decision support systems will be required for DNA-guided statin therapy to reach practical use in medicine.

Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study.

OBJECTIVE: The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored. METHODS: We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools. RESULTS: Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP. CONCLUSIONS: Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis.

Genetic Sources of Subcomponents of Event-Related Potential in the Dimension of Psychosis Analyzed From the B-SNIP Study.

OBJECTIVE: Biological risk factors underlying psychosis are poorly understood. Biological underpinnings of the dimension of psychosis can be derived using genetic associations with intermediate phenotypes such as subcomponents of auditory event-related potentials (ERPs). Various ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder are heritable and are expressed in unaffected relatives, although studies investigating genetic contributions to ERP abnormalities are limited. The authors used a novel parallel independent component analysis (para-ICA) to determine which empirically derived gene clusters are associated with data-driven ERP subcomponents, assuming a complex etiology underlying psychosis. METHOD: The authors examined the multivariate polygenic association of ERP subcomponents from 64-channel auditory oddball data in 144 individuals with schizophrenia, 210 psychotic bipolar disorder probands, and 95 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Data were reduced by principal components analysis to two target and one standard ERP waveforms. Multivariate association of compressed ERP waveforms with a set of 20,329 single-nucleotide polymorphisms (SNPs) (reduced from a 1-million-SNP array) was examined using para-ICA. Genes associated with SNPs were further examined using pathway analysis tools. RESULTS: Para-ICA identified four ERP components that were significantly correlated with three genetic components. Enrichment analysis revealed complement immune response pathway and multiple processes that significantly mediate ERP abnormalities in psychosis, including synaptic cell adhesion, axon guidance, and neurogenesis. CONCLUSIONS: This study identified three genetic components comprising multiple genes mediating ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder. The data suggest a possible polygenic structure comprising genes influencing key neurodevelopmental processes, neural circuitry, and brain function mediating biological pathways plausibly associated with psychosis.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia.

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

Validation of candidate genes associated with cardiovascular risk factors in psychiatric patients.

The purpose of this study was to identify genetic variants predictive of cardiovascular risk factors in a psychiatric population treated with second generation antipsychotics (SGA). 924 patients undergoing treatment for severe mental illness at four US hospitals were genotyped at 1.2 million single nucleotide polymorphisms. Patients were assessed for fasting serum lipid (low density lipoprotein cholesterol [LDLc], high density lipoprotein cholesterol [HDLc], and triglycerides) and obesity phenotypes (body mass index, BMI). Thirteen candidate genes from previous studies of the same phenotypes in non-psychiatric populations were tested for association. We confirmed 8 of the 13 candidate genes at the 95% confidence level. An increased genetic effect size was observed for triglycerides in the psychiatric population compared to that in the cardiovascular population.

Guidance of pharmacotherapy in a complex psychiatric case by CYP450 DNA typing.

PURPOSE: To illustrate the utility of CYP450 genotyping to guide clinical psychopharmacological treatment decisions and minimize or avoid harmful and costly adverse drug reactions (ADRs). DATA SOURCES: DNA was extracted from a whole blood sample from the case study subject and tested for CYP450 gene polymorphisms in the CLIA certified Laboratory of Personalized Health at Genomas, Inc. Clinical data were obtained from patient records and clinician observations. CONCLUSIONS: We present a case in which the ascertainment of multiple CYP450 isoenzyme deficiencies resulted in a dramatic change in psychotropic treatment approach. Shortly after making these adjustments, the patient saw a significant improvement in most of her debilitating psychiatric symptoms. IMPLICATIONS FOR PRACTICE: In complex cases, CYP450 DNA testing can guide pharmacotherapy by exposing innate hepatic metabolic deficiencies as a result of DNA polymorphism. In such cases, clinicians can favor treatments that target functional isoenzyme pathways rather than deficient or null pathways thus leading to decreased risk of ADRs and improved patient response.

Novel drug metabolism indices for pharmacogenetic functional status based on combinatory genotyping of CYP2C9, CYP2C19 and CYP2D6 genes.

AIMS: We aim to demonstrate clinical relevance and utility of four novel drug-metabolism indices derived from a combinatory (multigene) approach to CYP2C9, CYP2C19 and CYP2D6 allele scoring. Each index considers all three genes as complementary components of a liver enzyme drug metabolism system and uniquely benchmarks innate hepatic drug metabolism reserve or alteration through CYP450 combinatory genotype scores. METHODS: A total of 1199 psychiatric referrals were genotyped for polymorphisms in the CYP2C9, CYP2C19 and CYP2D6 gene loci and were scored on each of the four indices. The data were used to create distributions and rankings of innate drug metabolism capacity to which individuals can be compared. Drug-specific indices are a combination of the drug metabolism indices with substrate-specific coefficients. RESULTS: The combinatory drug metabolism indices proved useful in positioning individuals relative to a population with regard to innate drug metabolism capacity prior to pharmacotherapy. Drug-specific indices generate pharmacogenetic guidance of immediate clinical relevance, and can be further modified to incorporate covariates in particular clinical cases. CONCLUSIONS: We believe that this combinatory approach represents an improvement over the current gene-by-gene reporting by providing greater scope while still allowing for the resolution of a single-gene index when needed. This method will result in novel clinical and research applications, facilitating the translation from pharmacogenomics to personalized medicine, particularly in psychiatry where many drugs are metabolized or activated by multiple CYP450 isoenzymes.

Physiogenomic analysis of CYP450 drug metabolism correlates dyslipidemia with pharmacogenetic functional status in psychiatric patients.

AIMS: To investigate associations between novel human cytochrome P450 (CYP450) combinatory (multigene) and substrate-specific drug metabolism indices, and elements of metabolic syndrome, such as low density lipoprotein cholesterol (LDLc), high density lipoprotein cholesterol (HDLc), triglycerides and BMI, using physiogenomic analysis. METHODS: CYP2C9, CYP2C19 and CYP2D6 genotypes and clinical data were obtained for 150 consecutive, consenting hospital admissions with a diagnosis of major depressive disorder and who were treated with psychotropic medications. Data analysis compared clinical measures of LDLc, HDLc, triglyceride and BMI with novel combinatory and substrate-specific CYP450 drug metabolism indices. RESULTS: We found that a greater metabolic reserve index score is related to lower LDLc and higher HDLc, and that a greater metabolic alteration index score corresponds with higher LDLc and lower HLDc values. We also discovered that the sertraline drug-specific indices correlated with cholesterol and triglyceride values. CONCLUSIONS: Overall, we demonstrated how a multigene approach to CYP450 genotype analysis yields more accurate and significant results than single-gene analyses. Ranking the individual with respect to the population represents a potential tool for assessing risk of dyslipidemia in major depressive disorder patients who are being treated with psychotropics. In addition, the drug-specific indices appear useful for modeling a variable of potential relevance to an individual's risk of drug-related dyslipidemia.

Mechanisms of statin-induced myalgia assessed by physiogenomic associations.

OBJECTIVE: We investigated genetic variants predictive of muscular side effects in patients treated with statins. We utilized a physiogenomic approach to prototype a multi-gene panel correlated with statin-induced myalgia. BACKGROUND: Statin-induced myalgia occurs in ∼10% of lipid clinic outpatients. Its clinical manifestation may depend in part upon gene variation from patient to patient. METHODS: We genotyped 793 patients (377 with myalgia and 416 without) undergoing statin therapy at four U.S. outpatient clinic sites to evaluate 31 candidate genes from the literature for their association with statin-induced common myalgia. RESULTS: Three previously hypothesized candidate genes were validated: COQ2 (rs4693570) encoding para-hydroxybenzoate-polyprenyltransferase, which participates in the biosynthesis of coenzyme Q10 (p<0.000041); ATP2B1 (rs17381194) which encodes a calcium transporting ATPase involved in calcium homeostasis (p<0.00079); and DMPK (rs672348) which encodes a protein kinase implicated in myotonic dystrophy (p<0.0016). CONCLUSIONS: The candidate genes COQ2, ATP2B1, and DMPK, representing pathways involved in myocellular energy transfer, calcium homeostasis, and myotonic dystonia, respectively, were validated as markers for the common myalgia observed in patients receiving statin therapy. The three genes integrated into a physiogenomic predictive system could be relevant to myalgia diagnosis and prognosis in clinical practice.

Genetic associations of brain structural networks in schizophrenia: a preliminary study.

BACKGROUND: Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenia's pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays. METHOD: We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes. RESULTS: We identified an sMRI component that significantly correlated with a genetic component (r = -.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A. CONCLUSIONS: Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

Physiogenomic analysis of statin-treated patients: domain-specific counter effects within the ACACB gene on low-density lipoprotein cholesterol?

AIM: Administered at maximal dosages, the most common statins--atorvastatin, simvastatin and rosuvastatin--lower low-density lipoprotein cholesterol (LDLC) by an average of 37-57% in patients with primary hypercholesterolemia. We hypothesized novel genetic underpinnings for variation in LDLC levels in the context of statin therapy. MATERIALS & METHODS: Genotyping of 384 SNPs in 202 volunteers from a lipid outpatient clinic was accomplished and LDLC levels obtained from chart records. The SNPs were distributed across 222 genes representing physiological pathways such as general metabolism, cholesterol biochemistry, cardiovascular function, inflammation, neurobiology and cell proliferation. We discovered significant associations with LDLC levels for the rs34274 SNP (p < 0.0002) and for rs2241220 (p < 0.008) in the acetyl-coenzyme A carboxylase beta (ACACB) gene. When corrected for multiple testing, the false-discovery rate associated with rs34274 was 0.076 (significance threshold: 0.10) and for rs2241220 the false-discovery rate was 0.93 (not significant). The acetyl coenzyme A carboxylase beta enzyme synthesizes malonyl coenzyme A, an essential substrate for hepatic fatty acid synthesis and an inhibitor of fatty acid oxidation. RESULTS: The SNPs were in weak linkage disequilibrium (D = 0.302). Minor alleles at these sites demonstrate opposing influences on LDLC; the C>T substitution at rs34724 is a risk marker and the C>T substitution at rs2241220 a protective marker for LDLC levels. These SNPs hypothetically influence enzymatic activity through different mechanisms, rs34274 through the PII promoter and rs2241220 via alteration of the protein's responsiveness to allosteric influence. CONCLUSION: Physiogenomic evidence suggests a novel link between LDLC levels and the regulation of fatty acid metabolism. The findings complement previously discovered novel SNP relationships to myalgia (pain) and myositis (serum creatine kinase activity). By genotyping for myositis, myalgia and LDLC levels, a physiogenomic model may be developed to help clinicians maximize effectiveness and minimize side effects in prescribing statins.

CYP2C9 and VKORC1 genotypes in Puerto Ricans: A case for admixture-matching in clinical pharmacogenetic studies.

BACKGROUNDS: Admixture is of great relevance to the clinical application of pharmacogenetics and personalized medicine, but unfortunately these studies have been scarce in Puerto Ricans. Besides, allele frequencies for clinically relevant genetic markers in warfarin response (i.e., CYP2C9 and VKORC1) have not yet been fully characterized in this population. Accordingly, this study is aimed at investigating whether a correlation between overall genetic similarity and CYP2C9 and/or VKORC1 genotypes could be established. METHODS: 98 DNA samples from Puerto Ricans were genotyped for major CYP2C9 and VKORC1 polymorphisms and tested on a physiogenomic (PG)-array to infer population structure and admixture pattern. RESULTS: Analysis affirmed that Puerto Ricans are broadly admixed. A genetic distance dendrogram was constructed by clustering those subjects with similar genetic profiles. Individual VKORC1 and CYP2C9 genotypes were visually overlaid atop the three dendrogram sectors. Sector-1, representing Amerindian ancestry, showed higher VKORC1 -1639G>A variant frequency than the rest of the population (p=0.051). Although CYP2C9*3 allele frequencies matched the expected HapMap values, admixture may explain deviations from published findings regarding VKORC1 -1639G>A and CYP2C9*2 allele frequencies in sector-3. CONCLUSIONS: Results suggest that the observed inter-individual variations in ancestral contributions have significant implications for the way each Puerto Rican responds to warfarin therapy. Our findings provide valuable evidence on the importance of controlling for admixture in pharmacogenetic studies of Puerto Rican Hispanics.

Exposure to non-therapeutic INR in a high risk cardiovascular patient: potential hazard reduction with genotype-guided warfarin (Coumadin) dosing.

A case to illustrate the utility of genetic screening in warfarin (Coumadin) management is reported. A 45 year-old woman of Puerto Rican ancestry was admitted to the emergency room twice within one month with chest pain. She was diagnosed with congestive heart failure, which was stabilized both times. At her second release, warfarin therapy was initiated at 5 mg/ day to prevent thrombus formation and was lowered to 3.75 mg/day at day 7 by her primary physician. International Normalized Ratio (INR) test results in the follow-up period at days 1, 7, and 10 of warfarin therapy were 4.5, 6.5, and 7.3, respectively-far in excess of the therapeutic range, despite the lower dosage in effect from day 7 onward. The patient achieved target INR over the next 43 days after downward adjustment of the dose to a dose of 1.5 mg/day by trial and error. DNA-typing specific for the CYP2C9*2,*3,*4,*5,*6 alleles and seven variants in the VKORC1 gene, including the VKORC1-1639 G > A polymorphism, revealed the presence of combinatorial CYP2C9*2/*3 and VKORC1-1639 G/A genotypes in this patient. Entering the patient's demographic and genotype status data into independent algorithms available in the public domain to predict effective warfarin dose yielded predicted doses which ranged from 1.5 to 1.8 mg/day. Notably, the prediction of 1.5 mg/day, which was generated by the online resource www.warfarindosing.org, coincided with the patient's actual effective warfarin dose. We conclude that the rapid rise in INR observed upon the initiation of warfarin therapy and the final effective warfarin dose of 1.5 mg/day, are attributable in some part to the presence of two minor alleles in CYP2C9, which together significantly reduce warfarin metabolism. Warfarin genotyping can therefore inform the clinician of the predicted effective warfarin dose. The results highlight the potential for warfarin genetic testing to improve patient care.