Evaluation of OCT2-mediated drug-drug interactions between ulotaront and metformin in subjects with schizophrenia.

Ulotaront (SEP-363856) is a TAAR1 agonist, with 5-HT1A agonist activity, currently in clinical development for the treatment of schizophrenia. In vitro studies indicate ulotaront is an OCT2-specific inhibitor with IC50 of 1.27 µM. The primary objective of this study is to determine if a single dose of ulotaront affects the PK of metformin, an index substrate of OCT2, in subjects with schizophrenia. In a randomized, single-blind, 2-period crossover study, 25 adults with schizophrenia received a single dose of metformin-HCl 850 mg (approximately 663 mg metformin) with and without coadministration of 100 mg ulotaront. The plasma samples were analyzed by fully validated LC-MS/MS methods. The primary PK endpoints for metformin were AUCinf, AUClast, Cmax, and tmax. The highest-anticipated clinical dose of ulotaront (100 mg) had no statistically significant effect on the PK of a single dose of metformin based on Cmax and AUCinf. Geometric least squares mean ratios were 89.98% and 110.63%, respectively, with the 90% confidential interval (CI) for each parameter contained within 80%-125%. Median tmax was comparable across the treatments. Ulotaront does not act as a perpetrator of OCT2-mediated DDI against metformin. Co-administration of ulotaront is not expected to require dose adjustment of metformin or other drugs cleared by OCT2.

Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial.

Importance: Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment. Objective: To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes. Design, Setting, and Participants: A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications. Interventions: Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978). Main Outcomes and Measures: The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters. Results: Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45). Conclusions and Relevance: Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission. Trial Registration: ClinicalTrials.gov Identifier: NCT03170362.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

PBPK Analysis to Study the Impact of Genetic Polymorphism of NAT2 on Drug-Drug Interaction Potential of Isoniazid.

PURPOSE: Isoniazid (INH) is prescribed both for the prophylaxis as well as the treatment of tuberculosis. It is primarily metabolized through acetylation by a highly polymorphic enzyme, N-acetyl transferase 2 (NAT2), owing to which significant variable systemic drug levels have been reported among slow and rapid acetylators. Furthermore, many drugs, like phenytoin, diazepam, triazolam, etc., are known to show toxic manifestation when co-administered with INH and it happens prominently among slow acetylators. Additionally, it is revealed in in vitro inhibition studies that INH carries noteworthy potential to inhibit CYP2C19 and CYP3A4 enzymes. However, CYP inhibitory effect of INH gets masked by opposite enzyme-inducing effect of rifampicin, when used in combination. Thus, distinct objective of this study was to fill the knowledge gaps related to gene-drug-drug interactions (DDI) potential of INH when given alone for prophylactic purpose. METHODS: Whole body-PBPK models of INH were developed and verified for both slow and fast acetylators. The same were then utilized to carry out prospective DDI studies with CYP2C19 and CYP3A4 substrates in both acetylator types. RESULTS: The results highlighted likelihood of significant higher blood levels of CYP2C19 and CYP3A4 substrate drugs in subjects receiving INH pre-treatment. It was also re-established that interaction was more likely in slow acetylators, as compared to rapid acetylators. CONCLUSION: The novel outcome of the present study is the indication that prescribers should give careful consideration while advising CYP2C19 and CYP3A4 substrate drugs to subjects who are on prophylaxis INH therapy, and are slow to metabolic acetylation.

Utilizing Large Electronic Medical Record Data Sets to Identify Novel Drug-Gene Interactions for Commonly Used Drugs.

Real-world prescribing of drugs differs from the experimental systems, physiological-pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug-gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug-gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior-drug-stop or dose-decrease. Using this approach, we replicate 11 known drug-gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C-allele at rs4918758 in CYP2C9 was associated with a 25% (15-44%) lower odds of dose reduction of quinine, P = 1.6 × 10-5 ; the A-allele at rs9895420 in ABCC3 was associated with a 46% (24-62%) reduction in odds of dose reduction with doxazosin, P = 1.2 × 10-4 , and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18-40%) reduction in odds of stopping ramipril treatment, P = 1.01 × 10-5 , with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug-gene interactions at scale.

The impact of pharmacogenetic testing in patients exposed to polypharmacy: a scoping review.

Polypharmacy poses a significant risk for adverse reactions. While there are clinical decision support tools to assist clinicians in medication management, pharmacogenetic testing to identify potential drug-gene or drug-drug-gene interactions is not widely implemented in the clinical setting. A PRISMA-compliant scoping review was performed to determine if pharmacogenetic testing for absorption, distribution, metabolism, and excretion (ADME)-related genetic variants is associated with improved clinical outcomes in patients with polypharmacy. Six studies were reviewed. Five reported improved clinical outcomes, reduced side effects, reduction in the number of drugs used, or reduced healthcare utilization. The reviewed studies varied in methodological quality, risk of bias, and outcome measures. Age, diet, disease state, and treatment adherence also influence drug response, and may confound the relationship between genetic polymorphisms and treatment outcomes. Further studies using a randomized control design are needed. We conclude that pharmacogenetic testing represents an opportunity to improve health outcomes in patients exposed to polypharmacy, particularly in patients with psychiatric disorders and the elderly.

Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes.

Aim: Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations. Materials & methods: We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug-gene (pharmacogenetic), drug-drug and drug-drug-gene interactions associated with COVID-19 therapy in the Indian population. Results: We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics. CYP3A4, ABCB1 and ALB are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug-drug interaction candidates when used with four CYP inhibitor drugs. Conclusion: Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.

Comparison of the inhibitory effects of azole antifungals on cytochrome P450 3A4 genetic variants.

Cytochrome P450 (CYP) 3A4 is one of the major drug-metabolizing enzymes. Genetic variants of CYP3A4 with altered activity are one of the factors responsible for interindividual differences in drug metabolism. Azole antifungals inhibit CYP3A4 to cause clinically significant drug-drug interactions. In the present quantitative study, we investigated the inhibitory effects of three azole antifungals (ketoconazole, voriconazole, and fluconazole) on testosterone metabolism by recombinant CYP3A4 genetic variants (CYP3A4.1 (WT), CYP3A4.2, CYP3A4.7, CYP3A4.16, and CYP3A4.18) and compared them with those previously reported for itraconazole. The inhibition constants (Ki) of ketoconazole, voriconazole, and fluconazole for rCYP3A4.1 were 3.6 nM, 3.2 µM, and 16.1 µM, respectively. The Ki values of these azoles for rCYP3A4.16 were 13.9-, 13.6-, and 6.2-fold higher than those for rCYP3A4.1, respectively, whereas the Ki value of itraconazole for rCYP3A4.16 was 0.54-fold of that for rCYP3A4.1. The other genetic variants had similar effects on the Ki values of the three azoles, whereas a very different pattern was seen for itraconazole. In conclusion, itraconazole has unique characteristics that are distinct from those shared by the other azole anti-fungal drugs ketoconazole, voriconazole, and fluconazole with regard to the influence of genetic variations on the inhibition of CYP3A4.

Pharmacogenomics Biomarker Discovery and Validation for Translation in Clinical Practice.

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome-disease and genome-drug interactions offers the opportunity to optimize tailored drug therapy. High-throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical-grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

How precise is quantitative prediction of pharmacokinetic effects due to drug-drug interactions and genotype from in vitro data? A comprehensive analysis on the example CYP2D6 and CYP2C19 substrates.

Drug-drug interactions (DDI) and genomic variation (PG) can lead to dangerously high blood and tissue concentrations with some drugs but may be negligible with other drugs. Using a quantitative metaanalysis, we analyzed on the example of CYP2D6 and CYP2C19 substrates, how well the effects of DDI and PG can be predicted by in vitro methods. In addition, we analyzed the quantitative effect of prototypic inhibitors of the two enzymes in relation to their genetic deficiency. More than 600 published studies were screened which compared either human pharmacokinetics with and without comedication, or which compared human pharmacokinetics of deficient with extensive metabolizers, or which assessed metabolism by in vitro approaches. With human liver microsomes, the in vitro to in vivo agreement of fractional clearances was reasonably high if loss of substrate was quantified in the in vitro assays performed with and without enzyme specific inhibitors. Also a generally very high correlation between the clinical pharmacokinetic effects of inherited deficiency and inhibition by drug-drug interactions could be demonstrated. Most cases of poor correlation were explained by the lack of CYP2D6 versus CYP2C19 specificity of fluoxetine or by a poor knowledge of the quantitative contribution of the metabolic pathways if metabolite formation was quantified in the in vitro assays. The good correspondence of the in vitro data with clinical DDI and clinical PG studies may be a good basis for future application of these methods in drug development and drug therapy.

Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.

An in silico approach to analyze HCV genotype-specific binding-site variation and its effect on drug-protein interaction.

Genotype variation in viruses can affect the response of antiviral treatment. Several studies have established approaches to determine genotype-specific variations; however, analyses to determine the effect of these variations on drug-protein interactions remain unraveled. We present an in-silico approach to explore genotype-specific variations and their effect on drug-protein interaction. We have used HCV NS3 helicase and fluoroquinolones as a model for drug-protein interaction and have investigated the effect of amino acid variations in HCV NS3 of genotype 1a, 1b, 2b and 3a on NS3-fluoroquinolone interaction. We retrieved 687, 667, 101 and 248 nucleotide sequences of HCV NS3 genotypes 1a, 1b, 2b, and 3a, respectively, and translated these into amino acid sequences and used for genotype variation analysis, and also to construct 3D protein models for 2b and 3a genotypes. For 1a and 1b, crystal structures were used. Drug-protein interactions were determined using molecular docking analyses. Our results revealed that individual genotype-specific HCV NS3 showed substantial sequence heterogeneity that resulted in variations in docking interactions. We believe that our approach can be extrapolated to include other viruses to study the clinical significance of genotype-specific variations in drug-protein interactions.

How a concentration-effect analysis of data from the eliglustat thorough electrocardiographic study was used to support dosing recommendations.

Eliglustat is a first-line oral treatment for adults with Gaucher disease type 1 who have cytochrome P450 (CYP) 2D6 extensive, intermediate, or poor metabolizer phenotypes. Per International Conference on Harmonisation (ICH) E14 guidance, a Phase 1 thorough electrocardiographic (ECG) study was done during drug development to assess eliglustat's effects on cardiac repolarization by measuring ECG intervals in healthy adult subjects. Using data from the thorough ECG study, we performed pharmacokinetic/pharmacodynamic-ECG modeling to establish the relationship between eliglustat concentrations and their effects on ECG intervals. We then used that concentration-response relationship to predict the effects of eliglustat on each ECG interval for each CYP2D6 metabolizer phenotype (the main determinant of eliglustat exposure) and in different drug-drug interaction scenarios. These predictions, together with other exposure-related factors, contributed to the CYP2D6 phenotype-based dosing recommendations for eliglustat, including dose adjustments and contraindications when co-administered with drugs metabolized by the CYP2D6 and CYP3A pathways.

Drug-Drug Interactions of 257 Antineoplastic and Supportive Care Agents With 7 Anticoagulants: A Comprehensive Review of Interactions and Mechanisms.

Data on drug-drug interactions (DDI) of antineoplastic drugs with anticoagulants is scarce. We aim to evaluate factors associated with DDI of antineoplastic and supportive care drugs with anticoagulants resulting in modification of pharmacokinetics of these last mentioned. A literature review on DDI databases and summaries of products characteristics (SmPC) was done. Drug-drug interactions of 257 antineoplastic and supportive care drugs with direct oral anticoagulants (DOACs), warfarin, enoxaparin, or fondaparinux were categorized as no clinically significant expected DDI, potentially weak DDI, potentially clinically significant DDI, and recommendation against coadministration. Logistic regression models were performed to analyze the association between the dependent variable potentially clinically significant interaction/recommendation against coadministration and the mechanisms of DDI. Of the 1799 associations, 84.4% were absence of DDI, 3.6% potentially weak DDI, 10.2% potentially clinically relevant DDI, and 2.0% recommendation against coadministration. Warfarin has higher DDI potential than other anticoagulants. Enoxaparin and fondaparinux have fewer DDI than others. There was no difference between DOACs. Drug-drug interactions with apixaban and rivaroxaban was independently associated with the absence of CYP3A4 competition, P-glycoprotein inhibition, CYP3A4 induction, and drug class of tyrosine kinase inhibitors. Drug-drug interactions with dabigatran and edoxaban was associated with inhibition of P-glycoprotein and tyrosine kinase inhibitors. Warfarin, induction of CYP3A4, and inhibition of CYP2C9. Enoxaparin and fondaparinux, only tyrosine kinase inhibitors. Direct oral anticoagulants did not differ regarding DDI with antineoplastic agents. Warfarin presented more DDI than other anticoagulants. P-glycoprotein inhibition and CYP3A4 induction were independently associated with DDI of antineoplastic agents with DOACs.

Prediction of the impact of CYP2C9 and VKORC1 genotypes on warfarin-sorafenib interactions in whites and Asians.

Aim: To predict the impact of the different CYP2C9 and VKORC1 genotypes on warfarin-sorafenib interactions in whites and Asians. Materials & methods: The influences of the CYP2C9*1/*3 and VKORC1 -1639 A/A genotypes on increases in anticoagulation responses (international normalized ratio [INR]) in the presence of sorafenib were predicted using the population pharmacokinetic/pharmacodynamic (PK/PD) model in whites and Asians. Results: INRs were predicted to be 2.0-2.1- versus 1.8-1.9-times higher in the presence of sorafenib in the CYP2C9 (*1/*1 vs *1/*3) groups than those for warfarin alone in both whites and Asians. INRs were also predicted to be 2.1-2.2- versus 1.9-2.1-times higher in the VKORC1 (GG or GA vs AA) groups. Conclusion: Warfarin-sorafenib interactions might be similar irrespective of CYP2C9 and VKORC1 genotypes or ethnicity.

Cost avoidance related to a pharmacist-led pharmacogenomics service for the Program of All-inclusive Care for the Elderly.

Aim: Estimate cost avoidance of pharmacist recommendations for participants enrolled in the Program of All-inclusive Care for the Elderly. Materials & methods: Convenience sample of 200 pharmacogenomics consultations from the PHARM-GENOME-PACE study. Genetic variants, drug-gene interactions, drug-drug-gene interactions and phenoconversions were interrogated. Cost avoidance was estimated and adjusted for inflation. Results: In total, 165 participants had at least one actionable drug-gene pair totaling 429 drug-gene pairs, of which 158 (36.8%) were clinically actionable. Most (70.5%) pharmacists' recommendations were accepted. Estimated cost avoidance was $233,945 when all recommendations were included but conservatively $162,031 based on acceptance rates. Overall mean cost avoidance per actionable drug-gene pair was $1063 or $1983 per participant. Conclusion: Pharmacist-led pharmacogenomics services added to the traditional medication review can avoid substantial costs for payers. Clinical trial registration number: NCT03257605.

Severe hyperglycaemia following pazopanib treatment: The role of drug-drug-gene interactions in a patient with metastatic renal cell carcinoma-A case report.

WHAT IS KNOWN AND OBJECTIVE: Pazopanib is a tyrosine kinase inhibitor with hyperglycaemia as a known adverse event, but case reports of severe hyperglycaemia are exceptional. We report a case of severe hyperglycaemia following pazopanib administration in a patient with metastatic renal cell carcinoma. CASE SUMMARY: Severe hyperglycaemia developed in a patient one month following initiation of pazopanib therapy. As drug-drug-gene interactions may lead to hyperglycaemia, pharmacogenetic assessment was requested. The obtained findings indicated intermediate function of both OATP1B1 and P-glycoprotein transporters, which may cause prolonged pazopanib bioavailability and increased toxicity. Pazopanib was discontinued and, following patient recovery, was reintroduced at a lower dose. WHAT IS NEW AND CONCLUSION: The pharmacogenetic profiling of the patient on polypharmacy enabled better management of pazopanib therapy.

Impact of CYP2C9-Interacting Drugs on Warfarin Pharmacogenomics.

Precise dosing of warfarin is important to achieve therapeutic benefit without adverse effects. Pharmacogenomics explains some interindividual variability in warfarin response, but less attention has been paid to drug-drug interactions in the context of genetic factors. We investigated retrospectively the combined effects of cytochrome P450 (CYP)2C9 and vitamin K epoxide reductase complex (VKORC)1 genotypes and concurrent exposure to CYP2C9-interacting drugs on long-term measures of warfarin anticoagulation. Study participants predicted to be sensitive responders to warfarin based on CYP2C9 and VKORC1 genotypes, had significantly greater international normalized ratio (INR) variability over time. Participants who were concurrently taking CYP2C9-interacting drugs were found to have greater INR variability and lesser time in therapeutic range. The associations of INR variability with genotype were driven by the subgroup not exposed to interacting drugs, whereas the effect of interacting drug exposure was driven by the subgroup categorized as normal responders. Our findings emphasize the importance of considering drug interactions in pharmacogenomic studies.