The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus and a comprehensive summary of structural variation. CYP2D6 contributes to the metabolism of numerous drugs and thus, genetic variation in its gene impacts drug efficacy and safety. To accurately predict a patient's CYP2D6 phenotype, testing must include structural variants including gene deletions, duplications, hybrid genes, and combinations thereof. This tutorial offers a comprehensive overview of CYP2D6 structural variation, terms and definitions, a review of methods suitable for their detection and characterization, and practical examples to address the lack of standards to describe CYP2D6 structural variants or any other pharmacogene. This PharmVar tutorial offers practical guidance on how to detect the many, often complex, structural variants, as well as recommends terms and definitions for clinical and research reporting. Uniform reporting is not only essential for electronic health record-keeping but also for accurate translation of a patient's genotype into phenotype which is typically utilized to guide drug therapy.

Oral extended-release (ER) dosage forms have been used to sustain blood drug levels, reduce adverse events, and improve patient compliance. We investigated potential effects of comedication on pharmacokinetic exposure of nifedipine ER products with different formulation designs and manufacturing processes. A clinical study compared a generic version of nifedipine ER tablet with pH-dependent dissolution behavior with an osmotic pump product with pH independent drug release under fasting condition. In this study, two nifedipine tablet products were tested with or without short-term omeprazole comedication in healthy subjects. Seven-day administration of omeprazole before nifedipine dosing significantly increased the gastric pH, and subsequently increased the geometric least square (LS) means of area under the concentration-time curve from time zero to the last measurable timepoint (AUC0-t ) and maximum plasma concentration (Cmax ) of nifedipine to 132.6% (90% confidence interval (CI): 120.6-145.7%) and 112.8% (90% CI: 100.8-126.3%) for pH-dependent ER tablets, and 120.6% (90% CI: 109.7-132.5%) and 122.5% (90% CI: 113.7-131.9%) for the pH-independent ER tablets, respectively. Similar extent of increase in AUC0-t and Cmax was confirmed in the subpopulations whose gastric pH was ≥ 4 or ≤ 3 in subjects with or without omeprazole administration. Given that similar increases in drug exposures were observed for both pH-dependent and pH-independent nifedipine formulations and the geometric LS mean ratios were between 112% and 133% with and without short-term omeprazole comedication, the gastric pH may have limited effects on omeprazole-induced nifedipine PK changes on the tested formulations. The inhibition of cytochrome P450 3A4 activity may play a significant role causing nifedipine exposure changes for both formulations, which would warrant additional assessment.

Coincidental with the intensified regulatory and industry focus on the design and conduct of Human Absorption, Metabolism and Excretion (hAME) studies in the past 12 months, we have recently completed our 500th cohort involving radiolabelled test item administration to humans. Here we build upon a recent industry white paper in this journal (1) and share some of our own experiences as a Contract Research Organisation (CRO) based upon collaborations with numerous pharma companies and their differing approaches to design and timing, to add further context to the discussion regarding hAME studies and the pivotal role that Drug Metabolism and Pharmacokinetics (DMPK) plays. In this article, we explore how both changing relationships within the industry and shifting regulatory guidelines are impacting strategies, and compare EU and US pre-study approval requirements, before evaluating the trends from over 500 studies conducted at our global facilities conducted over more than 30 years. We conclude with a review of how improved technical capabilities and strategies are influencing the design and conduct of hAME studies, before speculating on some of the driving factors which may shape the direction they take in the future.

The majority of deaths from malaria are in young African children. Parenteral artesunate (ARS) is the first-line treatment for severe falciparum malaria. Since 2015, the World Health Organization has recommended individual doses of 3 mg/kg for children weighing < 20 kg. Recently, the US Food and Drug Administration (FDA) has challenged this recommendation, based on a simulated pediatric population, and argued for a lower dose in younger children (2.4 mg/kg). In this study, we performed population pharmacokinetic (PK) modeling of plasma concentration data from 80 children with severe falciparum malaria in the Democratic Republic of Congo who were given 2.4 mg/kg of ARS intravenously. Bayesian hierarchical modeling and a two-compartment parent drug-metabolite PK model for ARS were used to describe the population PKs of ARS and its main biologically active metabolite dihydroartemisinin. We then generated a virtual population representative of the target population in which the drug is used and simulated the total first-dose exposures. Our study shows that the majority of younger children given the lower 2.4 mg/kg dose of intravenous ARS do not reach the same drug exposures as older children above 20 kg. This finding supports withdrawal of the FDA's recent lower ARS dose recommendation as parenteral ARS is an extremely safe and well-tolerated drug and there is potential for harm from underdosing in this rapidly lethal infection.

Pegozafermin is a long-acting glycoPEGylated analog of fibroblast growth factor 21 (FGF21) in development for the treatment of nonalcoholic steatohepatitis (NASH) and severe hypertriglyceridemia. In a phase Ib/IIa placebo-controlled, double-blind, multiple-ascending-dose study in patients with NASH (NCT04048135), administration of pegozafermin resulted in clinically meaningful reductions in hepatic fat fraction (HFF), with a favorable safety and tolerability profile. We aimed to characterize the relationship between pegozafermin dosing, exposure and effects on HFF reduction. We used pharmacokinetic (PK) and pharmacodynamic (PD) modeling of data from the phase Ib/IIa study to identify model parameters and covariates affecting the exposure-response relationship. Clinical simulations were performed to help support dose selection for larger studies. Pegozafermin exposure was adequately described by a one compartment PK model, with one additional transit absorption compartment. PK/PD modeling demonstrated that HFF reduction was significantly related to pegozafermin exposure. HFF outcomes were correlated with average pegozafermin concentrations regardless of weekly dosing (QW) or dosing every 2 weeks (Q2W). The significant PK/PD model covariates included baseline body weight, alanine aminotransferase level and liver volume. Simulations showed that the 30 mg QW dose approximated the full PD effect; almost all patients would benefit from a ≥30% HFF reduction, suggesting fibrosis regression. Furthermore, 44 mg Q2W dosing (~22 mg QW) appeared to be an effective regimen for HFF reduction. Our modeling supports the feasibility of QW and Q2W dosing for achieving favorable treatment outcomes in patients with NASH, and provides the rationale for dose selection for the phase 2b ENLIVEN study (NCT04929483).

Evaluating cancer treatments in real-world data (RWD) requires informative endpoints. This study replicated the atezolizumab and docetaxel arms of the OAK trial using RWD and compared progression-free survival (PFS) outcomes derived from abstracted physician's notes in RWD (rwPFS) against PFS outcomes derived from the clinical trial (ctPFS). Atezolizumab and docetaxel arms of the phase III OAK RCT (NCT02008227) were replicated in a US nationwide real-world database using selected OAK inclusion/exclusion criteria and propensity score-based adjustment for baseline prognostic variables. Concordance of outcomes was assessed using Kaplan-Meier (KM) medians and hazard ratios (HRs). The RWD cohorts comprised 133 atezolizumab and 479 docetaxel patients. After adjustment, prognostic variables were balanced between RCT arms and corresponding RWD cohorts. rwPFS and ctPFS outcomes showed better concordance for docetaxel (2.99 vs 3.52 months; HR, 0.99, 95% CI, 0.85-1.15) than for atezolizumab (3.71 vs 2.76 months; HR, 0.8, 95% CI 0.61-1.02). Excluding events labeled "pseudo-progression" from both RWD and RCT improved concordance for atezolizumab (4.24 vs 4.14 months; HR, 0.95, 95% CI, 0.70-1.25). These findings were robust across sensitivity analyses. Replicating RCTs using RWD and comparing outcomes can help characterize RWD endpoints. Similarity of results between rwPFS and ctPFS at the cohort level may depend on drug category, highlighting the need for further studies to verify and understand when the corresponding outcomes can be compared, including within the same patient.

Environmental health science seeks to predict how environmental toxins, chemical toxicants, and prescription drugs accumulate and interact within the body. Xenobiotic transporters of the ATP-binding cassette (ABC) and solute carrier (SLC) superfamilies are major determinants of the uptake and disposition of xenobiotics across the kingdoms of life. The goal of this study was to integrate drug and environmental chemical interactions of mammalian ABC and SLC proteins in a centralized, integrative database. We built upon an existing publicly accessible platform-the "TransPortal"-which was updated with novel data and searchable features on transporter-interfering chemicals from manually curated literature data. The integrated resource TransPortal-TICBase (https://transportal.compbio.ucsf.edu) now contains information on 46 different mammalian xenobiotic transporters of the ABC- and SLC-type superfamilies, including 13 newly added rodent and 2 additional human drug transporters, 126 clinical drug-drug interactions, and a more than quadrupled expansion of the initial in vitro chemical interaction data from 1,402 to 6,296 total interactions. Based on our updated database, environmental interference with major human and rodent drug transporters occurs across the ABC- and SLC-type superfamilies, with kinetics indicating that some chemicals, such as the ionic liquid 1-hexylpyridinium chloride and the antiseptic chlorhexidine, can act as strong inhibitors with potencies similar or even higher than pharmacological model inhibitors. The new integrated web portal serves as a central repository of current and emerging data for interactions of prescription drugs and environmental chemicals with human drug transporters. This archive has important implications for predicting adverse drug-drug and drug-environmental chemical interactions and can serve as a reference website for the broader scientific community of clinicians and researchers.

BAY1128688 is a selective inhibitor of AKR1C3, investigated recently in a trial that was prematurely terminated due to drug-induced liver injury. These unexpected observations prompted use of the quantitative systems toxicology model, DILIsym, to determine possible mechanisms of hepatotoxicity. Using mechanistic in vitro toxicity data as well as clinical exposure data, DILIsym predicted the potential for BAY1128688 to cause liver toxicity (elevations in serum alanine aminotransferase (ALT)) and elevations in serum bilirubin. Initial simulations overpredicted hepatotoxicity and bilirubin elevations, so the BAY1128688 representation within DILIsym underwent optimization. The liver partition coefficient Kp was altered to align simulated bilirubin elevations with those observed clinically. Altering the mode of bile acid canalicular and basolateral efflux inhibition was necessary to accurately predict ALT elevations. Optimization results support that bilirubin elevations observed early during treatment are due to altered bilirubin metabolism and transporter inhibition, which is independent of liver injury. The modeling further supports that on-treatment ALT elevations result from inhibition of bile acid transporters, particularly the bile salt excretory pump, leading to accumulation of toxic bile acids. The predicted dose-dependent intrinsic hepatotoxicity may increase patient susceptibility to an adaptive immune response, accounting for ALT elevations observed after completion of treatment. These BAY1128688 simulations provide insight into the mechanisms behind hepatotoxicity and bilirubin elevations and may inform the potential risk posed by future compounds.

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. The platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi-prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi-prospective validation dataset. The first prospective predictions were made and placed on date-stamped preprint servers in 2016. To validate our model in a real-world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9-month return on investment.

Infants with neonatal opioid withdrawal syndrome commonly receive morphine treatment to manage their withdrawal signs. However, the effectiveness of this pharmacotherapy in managing the infants' withdrawal signs vary widely. We sought to understand how information available early in infant monitoring can anticipate this treatment response, focusing on early modified Finnegan Neonatal Abstinence Scoring System (FNASS) scores, polygenic risk for opioid dependence (polygenic risk score (PRS)), and drug exposure. Using k-means clustering, we divided the 213 infants in our cohort into 3 groups based on their FNASS scores in the 12 hours before and after the initiation of pharmacotherapy. We found that these groups were pairwise significantly different for risk factors, including methadone exposure, and for in-hospital outcomes, including total morphine received, length of stay, and highest FNASS score. Whereas PRS was not predictive of receipt of treatment, PRS was pairwise significantly different between a subset of the groups. Using tree-based machine learning methods, we then constructed network graphs of the relationships among these groups, FNASS scores, PRS, drug exposures, and in-hospital outcomes. The resulting networks also showed meaningful connection between early FNASS scores and PRS, as well as between both of those and later in-hospital outcomes. These analyses present clinicians with the opportunity to better anticipate infant withdrawal progression and prepare accordingly, whether with expedited morphine treatment or non-pharmacotherapeutic alternative treatments.

In clinical trials of cannabidiol (CBD) for the treatment of seizures in patients with Dravet syndrome, Lennox-Gastaut syndrome, and tuberous sclerosis complex, elevations in serum alanine aminotransferase (ALT) > 3× the upper limit of normal were observed in some patents, but the incidence was much greater in patients who were receiving treatment with valproate (VPA) before starting CBD. To explore potential mechanisms underlying this interaction, we used DILIsym, a quantitative systems toxicology model, to predict ALT elevations in a simulated human population treated with CBD alone, VPA alone, and when CBD dosing was starting during treatment with VPA. We gathered in vitro data assessing the potential for CBD, the two major CBD metabolites, and VPA to cause hepatotoxicity via inhibition of bile acid transporters, mitochondrial dysfunction, and production of reactive oxygen species (ROS). Physiologically-based pharmacokinetic models for CBD and VPA were used to predict liver exposure. DILIsym simulations predicted dose-dependent ALT elevations from CBD treatment and this was predominantly driven by ROS production from the parent molecule. DILIsym also predicted VPA treatment to cause ALT elevations which were transient when mitochondrial biogenesis was incorporated into the model. Contrary to the clinical experience, simulation of 2 weeks treatment with VPA prior to introduction of CBD treatment did not predict an increase of the incidence of ALT elevations relative to CBD treatment alone. We conclude that the marked increased incidence of CBD-associated ALT elevations in patients already receiving VPA is unlikely to involve the three major mechanisms of direct hepatotoxicity.

Tenofovir diphosphate (TFVdp; an active metabolite of oral HIV pre-exposure prophylaxis (PrEP)) is measured in dried blood spots (DBS) to estimate adherence. However, TFVdp's long half-life in whole blood may lead to misclassification following a recent change in adherence. PrEP's other metabolite, emtricitabine triphosphate (FTCtp), has a shorter half-life in whole blood but adherence thresholds are undefined. We characterized DBS TFVdp and FTCtp concentrations across many dosing scenarios. Population pharmacokinetic models were fit to TFVdp and FTCtp DBS concentrations from a directly observed therapy study (NCT03218592). Concentrations were simulated for 90 days of daily dosing followed by 90 days of 1 to 7 doses/week and for event-driven PrEP (edPrEP) scenarios. Thresholds of 1,000 and 200 fmol/punch, for TFVdp and FTCtp, respectively, were reflective of taking 4 doses/week (a minimum target for effective PrEP in men). TFVdp was < 1,000 fmol/punch for 17 days after initiating daily PrEP and > 1,000 fmol/punch for 62 days after decreasing to 3 doses/week. Respectively, FTCtp was < 200 fmol/punch for 4 days and > 200 fmol/punch for 6 days. Accuracy of edPrEP adherence classification depended on duration between last sex act and DBS sampling for both measures with misclassification ranging from 9-100%. These data demonstrate adherence misclassification by DBS TFVdp for 2 months following a decline in adherence, elucidating the need for FTCtp to estimate recent adherence. We provide proof of principle that individualized interpretation is needed to support edPrEP adherence monitoring. Our collective approach facilitates clinicians' ability to interpret DBS results and administer patient-centric interventions.

Prediction of treatment responses is essential to move forward translational science. Our question was to identify patient-based variables that predicted responses to treatments. We conducted secondary analyses on pooled data from two randomized phase III clinical trials (NCT02697773 and NCT02709486) conducted in participants with moderate to severe osteoarthritis randomized to subcutaneous placebo (n = 514) or tanezumab 2.5 mg (n = 514). We used gradient boosted regression trees to identify variables that predicted Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain subscale scores at Week 16 and marginal plots to determine the directional relationship between each variable category and responses to placebo or tanezumab within the models. We also used Virtual Twins models to identify potential subgroups of response to the active treatment vs. placebo. We found that responses to placebo were predicted by baseline WOMAC Physical Function, baseline WOMAC Pain, the radiographic classification of the index joint, and the standard deviation of diary pain scores at baseline. In contrast, baseline WOMAC Pain along with failure of prior medications, duration of disease, and standard deviation of diary pain scores at baseline were predictive of tanezumab responses as expressed by the WOMAC Pain scores at Week 16. Those who responded to tanezumab vs. placebo were identified based on the radiographic classification of the index joint and either age or smoking status. These secondary-data analyses identified distinct and common patient-based variables to predict response to placebo or tanezumab. These findings will inform the design of future clinical trials, helping to move forward clinical pharmacology and translational science.

Self-perceived statin-associated muscle symptoms (SAMS) are prevalent, but only a minority is drug-dependent. Diagnostic biomarkers are not yet identified. The local statin exposure in skeletal muscle tissue may correlate to the adverse effects. We aimed to determine whether atorvastatin metabolites in blood reflect the corresponding metabolite levels in skeletal muscle, and whether genetic variants of statin transporters modulate this relationship. We also addressed atorvastatin metabolites as potential objective biomarkers of SAMS. Muscle symptoms were examined in patients with coronary disease and self-perceived SAMS during 7 weeks of double-blinded treatment with atorvastatin 40 mg/day and placebo in randomized order. A subset of 12 patients individually identified with more muscle symptoms on atorvastatin than placebo (confirmed SAMS) and 15 patients with no difference in muscle symptom intensity (non-SAMS) attended the present follow-up study. All received 7 weeks of treatment with atorvastatin 40 mg/day followed by 8 weeks without statins. Biopsies from the quadriceps muscle and blood plasma were collected after each treatment period. Strong correlations (rho > 0.7) between muscle and blood plasma concentrations were found for most atorvastatin metabolites. The impact of the SLCO1B1 c.521T>C (rs4149056) gene variant on atorvastatin's systemic pharmacokinetics was translated into muscle tissue. The SLCO2B1 c.395G>A (rs12422149) variant did not modulate the accumulation of atorvastatin metabolites in muscle tissue. Atorvastatin pharmacokinetics in patients with confirmed SAMS were not different from patients with non-SAMS. In conclusion, atorvastatin metabolite levels in skeletal muscle and plasma are strongly correlated, implying that plasma measurements are suitable proxies of atorvastatin exposure in muscle tissue. The relationship between atorvastatin metabolites in plasma and SAMS deserves further investigation.

This proof-of-concept study retrospectively assessed the feasibility of applying a hybrid control arm design to a completed phase III randomized controlled trial (RCT; CheckMate-057) in advanced non-small cell lung cancer using a real-world data (RWD) source. The emulated trial consists of an experimental arm (patients from the RCT experimental cohort) and a hybrid control arm (patients from the RCT and RWD control cohorts). For the RWD control cohort, this study used a nationwide electronic health record-derived de-identified database. Three frequentist statistical borrowing methods were evaluated: a two-step Cox model, a fixed Cox model, and propensity score-integrated composite likelihood ("Methods 1-3"). The experimental treatment effect for hybrid control designs were evaluated using hazard ratios (HRs) with 95% confidence interval (CI) estimated from the Cox models accounting for covariate differences. The reduction in study duration compared to the RCT was also evaluated. All three statistical borrowing methods achieved comparable experimental treatment effects to that observed in the CheckMate-057 clinical trial, with HRs of 0.73 (95% CI: 0.59, 0.92), 0.74 (95% CI: 0.61, 0.91), 0.72 (95% CI: 0.59, 0.88) for Methods 1-3, respectively. Reduction in study duration time was 99-115 days when borrowing 30-38 events for Methods 1-3, respectively. This study demonstrated that it is feasible to emulate an RCT using a hybrid control arm design using three frequentist propensity-score based statistical borrowing methods. Selection of an appropriate, fit-for-use RWD cohort is critical to minimizing bias in experimental treatment effect.

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression.

Selective safety data collection may simplify late-stage clinical trials and improve their feasibility. However, the impact on increasing overall drug safety knowledge is unknown. The aim of this study is to evaluate how much safety information is added to the drug label based on large trials after initial authorization. Changes made to the "undesirable effects" section of the drug label of cardiometabolic agents approved between 2000 and 2020 based on the results of large (> 1,000 patient) clinical trials submitted to the European Medicines Agency (EMA) were evaluated. The study focused on glucose lowering, antithrombotic, and lipid-modifying agents. The primary outcome was the number of changes in adverse drug reactions in the drug label. The EMA reviewed 55 large trials concerning 25 cardiometabolic agents after the initial marketing authorization, which included 402,444 patients. Ultimately, 38 trials (69%) resulted in a safety section update, whereas 17 trials (31%) did not. Changes in listed adverse drug reactions were made following 19 trials (35%) for 12 agents: 77 adverse drug reactions were added, 11 were deleted, and the frequencies of 43 were changed. Most changes in adverse drug reactions arose from trials with antithrombotic agents (88%) and trials performed in a new population (92%). Large trials for cardiometabolic agents reported after authorization add limited new safety information on adverse drug reactions, especially when performed in the population studied prior to approval. This suggests that selective safety data collection does not reduce learnings from late stage cardiometabolic trials in populations comprehensively studied before.

Prioritization of diversity, equity, and inclusion in all facets of our work is long overdue for the clinical pharmacology community. Increasing diversity in clinical research will deepen our understanding of nuanced patient populations and help improve all patient outcomes. Fostering an inclusive and diverse workforce will lead to broader perspectives that can better inform critical decisions and create work environments where everyone can thrive. In this call to action, we invite you to join us.

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into sub-questions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and non-clinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that helps better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritise traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigour in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory scepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterisation. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision-making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (a) a repository connecting questions, data, and methods; (b) the development and validation of high-quality standards for data and methods; (c) credibility assessment, The ecosystem is applied to four use-cases for illustration. The need for training and regulatory guidance is also discussed.

Hepatotoxicity is a serious adverse drug reaction related to methotrexate (MTX). However, the cause of drug-induced liver injury (DILI) is still unclear and unpredictable. Genetic risk factors may predispose for MTX-DILI. Therefore, we conducted a nested case control genome-wide association study to explore genetic risk factors associated with MTX DILI. Seven international groups contributed blood samples and data of rheumatoid arthritis patients who used MTX. MTX-induced DILI was defined as an ALT level of at least 3X the upper limit of normal (ULN), to increase contrast controls ALT levels did not raise above 2X ULN. Per study site, control and MTX DILI patients (ratio 3:1) were matched for age, gender, and duration of MTX use. Patients were genotyped using Illumina GSA MD-24v1-0 and data were imputed using the 1000Genomes reference panel. SNPs were analyzed using an additive genetic model, corrected for sex, country, and age. A p-value of ≤5x10-8 was considered significant, while a p-value of ≤5x10-6 was considered suggestive. A total of 104 MTX-induced DILI cases and 315 controls were included for association analysis. None of the SNPs were significantly associated with MTX DILI. However, we found seven suggestive genetic variants associated with MTX DILI (p-value 7.43*10-8 to 4.86*10-6 ). Of those, five SNPs were in the intronic protein-coding regions of FTCDNL1, BCOR, FGF14, RBMS3, and PFDN4/DOK5. Investigation of candidates SPATA9 (rs72783407), PLCG2 (rs60427389), RAVER2 (rs72675408), JAK1 (rs72675451), PTPN2 (rs2476601), MTHFR C677T (rs1801133) and into the HLA region did not show significant findings. No genetic variants associated with MTX-DILI were found, while suggestive SNPs need further investigation.