Asia-Inclusive Global Development of Enpatoran: Results of an Ethno-Bridging Study, Intrinsic/Extrinsic Factor Assessments and Disease Trajectory Modeling to Inform Design of a Phase II Multiregional Clinical Trial.

Enpatoran is a novel, highly selective, and potent dual toll-like receptor (TLR)7 and TLR8 inhibitor currently under development for the treatment of autoimmune disorders including systemic lupus erythematosus (SLE), cutaneous lupus erythematosus (CLE), and myositis. The ongoing phase II study (WILLOW; NCT05162586) is evaluating enpatoran for 24 weeks in patients with active SLE or CLE and is currently recruiting. To support development of WILLOW as an Asia-inclusive multiregional clinical trial (MRCT) according to International Conference on Harmonisation E5 and E17 principles, we have evaluated ethnic sensitivity to enpatoran based on clinical pharmacokinetic (PK), pharmacodynamic (PD), and safety data from an ethno-bridging study (NCT04880213), supplemented by relevant quantitative PK, PD, and disease trajectory modeling (DTM) results, and drug metabolism/disease knowledge. A single-center, open-label, sequential dose group study in White and Japanese subjects matched by body weight, height, and sex demonstrated comparable PK and PD properties for enpatoran in Asian vs. non-Asian (White and other) subjects across single 100, 200, and 300 mg orally administered doses. DTM suggested no significant differences in SLE disease trajectory for Asian vs. non-Asian individuals. Aldehyde oxidase (AOX) is considered to be a key contributor to enpatoran metabolism, and a literature review indicated no relevant ethnic differences in AOX function based on in vitro and clinical PK data from marketed drugs metabolized by AOX, supporting the conclusion of low ethnic sensitivity for enpatoran. Taken together, the inclusion of Asian patients in MRCTs including WILLOW was informed based on a Totality of Evidence approach.

Immunogenicity of avelumab in patients with metastatic Merkel cell carcinoma or advanced urothelial carcinoma.

Like other monoclonal antibodies, immune checkpoint inhibitors may be immunogenic in some patients, potentially affecting pharmacokinetics (PKs) and clinical outcomes. In post hoc analyses, we characterized antidrug antibody (ADA) development with avelumab monotherapy in patients with metastatic Merkel cell carcinoma (mMCC) from the JAVELIN Merkel 200 trial (first-line [1L; N = 116] and second-line or later [≥2L; N = 88] cohorts) or with advanced urothelial carcinoma (aUC) from the JAVELIN Bladder 100 (1L maintenance [N = 350]) and JAVELIN Solid Tumor (≥2L [N = 249]) trials. Treatment-emergent ADAs developed in a numerically higher proportion of patients with aUC (1L maintenance, 19.1%; ≥2L, 18.1%) versus mMCC (1L, 8.2%; ≥2L, 8.9%); incidences within tumor types were similar by line of therapy. In PK analyses, numerically lower avelumab trough concentration and higher baseline clearance were observed in treatment-emergent ADA+ versus ADA- subgroups; however, differences were not clinically relevant. Numerical differences in overall survival, progression-free survival, or objective response rate by ADA status were observed; however, no clinically meaningful trends were identified. Proportions of patients with treatment-emergent adverse events (TEAEs; any grade or grade 3/4), serious TEAEs, TEAEs leading to treatment discontinuation, or infusion-related reactions were similar, with overlapping 80% confidence intervals between ADA subgroups. Efficacy and safety observations were similar in subgroups defined by early development of ADA+ status during treatment. In conclusion, no meaningful differences in PKs, efficacy, and safety were observed between subgroups of avelumab-treated patients with different ADA status. Overall, these data suggest that ADAs are not relevant for treatment decisions with avelumab.

Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach.

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Model-Informed Selection of the Recommended Phase III Dose of the Inhibitor of Apoptosis Protein Inhibitor, Xevinapant, in Combination with Cisplatin and Concurrent Radiotherapy in Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck.

Xevinapant, an oral inhibitor of apoptosis protein (IAP) inhibitor, demonstrated efficacy in combination with chemoradiotherapy in a randomized phase II study (NCT02022098) in patients with locally advanced squamous cell carcinoma of the head and neck at 200 mg/day on days 1-14 of a 3-week cycle. To confirm 200 mg/day as the recommended phase III dose (RP3D), we integrated preclinical, clinical, pharmacokinetic/pharmacodynamic (PK/PD), and exposure-response modeling results. Population PK/PD modeling of IAP inhibition in peripheral blood mononuclear cells in 21 patients suggested the pharmacologically active dose range was 100-200 mg/day, with a trend for more robust inhibition at the end of the dosing interval at 200 mg/day based on an indirect response model. Additionally, the unbound average plasma concentration at 200 mg/day was similar to that associated with efficacy in preclinical xenograft models. Logistic regression exposure-response analyses of data from 62 patients in the phase II study showed exposure-related increases in probabilities of locoregional control at 18 months (primary end point), overall response, complete response, and the radiosensitization mechanism-related composite safety end point "mucositis and/or dysphagia" (P < 0.05). Exposure-response relationships were not discernible for 12 of 13 evaluated safety end points, incidence of dose reductions, and time to first dose reduction. Quantitative integration of all available data, including model-derived target inhibition profiles, positive exposure-efficacy relationships, and lack of discernible exposure-safety relationships for most safety end points, supports selection of xevinapant 200 mg/day on days 1-14 of a 3-week cycle as the RP3D, allowing for successive dose reductions to 150 and 100 mg/day to manage adverse events.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti et al. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model-Informed Precision Medicine.

The increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as model-informed precision medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing end point qualification and biomarker discovery, and informing patient enrichment for proof-of-concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real-world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data-driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug-disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno-oncology.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Challenges and Opportunities for In Vitro-In Vivo Extrapolation of Aldehyde Oxidase-Mediated Clearance: Toward a Roadmap for Quantitative Translation.

Underestimation of aldehyde oxidase (AO)-mediated clearance by current in vitro assays leads to uncertainty in human dose projections, thereby reducing the likelihood of success in drug development. In the present study we first evaluated the current drug development practices for AO substrates. Next, the overall predictive performance of in vitro-in vivo extrapolation of unbound hepatic intrinsic clearance (CLint,u) and unbound hepatic intrinsic clearance by AO (CLint,u,AO) was assessed using a comprehensive literature database of in vitro (human cytosol/S9/hepatocytes) and in vivo (intravenous/oral) data collated for 22 AO substrates (total of 100 datapoints from multiple studies). Correction for unbound fraction in the incubation was done by experimental data or in silico predictions. The fraction metabolized by AO (fmAO) determined via in vitro/in vivo approaches was found to be highly variable. The geometric mean fold errors (gmfe) for scaled CLint,u (mL/min/kg) were 10.4 for human hepatocytes, 5.6 for human liver cytosols, and 5.0 for human liver S9, respectively. Application of these gmfe's as empirical scaling factors improved predictions (45%-57% within twofold of observed) compared with no correction (11%-27% within twofold), with the scaling factors qualified by leave-one-out cross-validation. A road map for quantitative translation was then proposed following a critical evaluation on the in vitro and clinical methodology to estimate in vivo fmAO In conclusion, the study provides the most robust system-specific empirical scaling factors to date as a pragmatic approach for the prediction of in vivo CLint,u,AO in the early stages of drug development. SIGNIFICANCE STATEMENT: Confidence remains low when predicting in vivo clearance of AO substrates using in vitro systems, leading to de-prioritization of AO substrates from the drug development pipeline to mitigate risk of unexpected and costly in vivo impact. The current study establishes a set of empirical scaling factors as a pragmatic tool to improve predictability of in vivo AO clearance. Developing clinical pharmacology strategies for AO substrates by utilizing mass balance/clinical drug-drug interaction data will help build confidence in fmAO.

Clinical Pharmacology of Brigatinib: A Next-Generation Anaplastic Lymphoma Kinase Inhibitor.

Brigatinib, a next-generation anaplastic lymphoma kinase (ALK) inhibitor designed to overcome mechanisms of resistance associated with crizotinib, is approved for the treatment of ALK-positive advanced or metastatic non-small cell lung cancer. After oral administration of single doses of brigatinib 30-240 mg, the median time to reach maximum plasma concentration ranged from 1 to 4 h. In patients with advanced malignancies, brigatinib showed dose linearity over the dose range of 60-240 mg once daily. A high-fat meal had no clinically meaningful effect on systemic exposures of brigatinib (area under the plasma concentration-time curve); thus, brigatinib can be administered with or without food. In a population pharmacokinetic analysis, a three-compartment pharmacokinetic model with transit absorption compartments was found to adequately describe brigatinib pharmacokinetics. In addition, the population pharmacokinetic analyses showed that no dose adjustment is required based on body weight, age, race, sex, total bilirubin (< 1.5× upper limit of normal), and mild-to-moderate renal impairment. Data from dedicated phase I trials have indicated that no dose adjustment is required for patients with mild or moderate hepatic impairment, while a dose reduction of approximately 40% (e.g., from 180 to 120 mg) is recommended for patients with severe hepatic impairment, and a reduction of approximately 50% (e.g., from 180 to 90 mg) is recommended when administering brigatinib to patients with severe renal impairment. Brigatinib is primarily metabolized by cytochrome P450 (CYP) 3A, and results of clinical drug-drug interaction studies and physiologically based pharmacokinetic analyses have demonstrated that coadministration of strong or moderate CYP3A inhibitors or inducers with brigatinib should be avoided. If coadministration with a strong or moderate CYP3A inhibitor cannot be avoided, the dose of brigatinib should be reduced by approximately 50% (strong CYP3A inhibitor) or approximately 40% (moderate CYP3A inhibitor), respectively. Brigatinib is a weak inducer of CYP3A in vivo; data from a phase I drug-drug interaction study showed that coadministration of brigatinib 180 mg once daily reduced the oral midazolam area under the plasma concentration-time curve from time zero to infinity by approximately 26%. Brigatinib did not inhibit CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, or CYP2D6 at clinically relevant concentrations in vitro. Exposure-response analyses based on data from the ALTA (ALK in Lung Cancer Trial of AP26113) and ALTA-1L pivotal trials of brigatinib confirm the favorable benefit versus risk profile of the approved titration dosing regimen of 180 mg once daily (after a 7-day lead-in at 90 mg once daily).

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer.

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-ß and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Response-Based Dosing for Ponatinib: Model-Based Analyses of the Dose-Ranging OPTIC Study.

Optimizing Ponatinib Treatment in CP-CML (OPTIC) was a randomized, phase II dose-optimization trial of ponatinib in chronic phase-chronic myeloid leukemia (CP-CML) resistant to ≥ 2 tyrosine kinase inhibitors or with T315I mutation. Patients were randomized to starting doses of 45-, 30-, or 15-mg ponatinib once daily. Patients receiving 45- or 30-mg reduced to 15-mg upon achievement of ≤ 1% BCR::ABL1IS (≥ molecular response with 2-log reduction (MR2)). The exposure-molecular response relationship was described using a four-state, discrete-time Markov model. Time-to-event models were used to characterize the relationship between exposure and arterial occlusive events (AOEs), grade ≥ 3 neutropenia, and thrombocytopenia. Increasing systemic exposures were associated with increasing probability of transitioning from no response to ≥ MR1, and from MR1 to ≥ MR1, with odds ratios of 1.63 (95% confidence interval (CI), 1.06-2.73) and 2.05 (95% CI, 1.53-2.89) for a 15-mg dose increase, respectively. Ponatinib exposure was a significant predictor of AOEs (hazard ratio (HR) 2.05, 95% CI, 1.43-2.93, for a 15-mg dose increase). In the exposure-safety models for neutropenia and thrombocytopenia, exposure was a significant predictor of grade ≥ 3 thrombocytopenia (HR 1.31, 95% CI, 1.05-1.64, for a 15-mg dose increase). Model-based simulations predicted a clinically meaningful higher rate of ≥ MR2 response at 12 months for the 45-mg starting dose (40.4%) vs. 30-mg (34%) and 15-mg (25.2%). The exposure-response analyses supported a ponatinib starting dose of 45 mg with reduction to 15 mg at response for patients with CP-CML.

Brigatinib pharmacokinetics in patients with chronic hepatic impairment.

Brigatinib is an anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive non-small cell lung cancer. This open-label, parallel-group study investigated the effect of chronic hepatic impairment on the pharmacokinetics (PK) of brigatinib to inform dosing recommendations for these patients. Participants with hepatic impairment classified according to Child-Pugh categories of mild (A), moderate (B), or severe (C) and matched-healthy participants with normal hepatic function received a single oral dose of 90-mg brigatinib. Plasma samples were collected for the determination of brigatinib plasma protein binding and estimation of plasma PK parameters. Twenty-seven participants were enrolled (Child-Pugh A-C, n = 6 each; matched-healthy participants, n = 9). The mean fraction of free plasma brigatinib was comparable for the Child-Pugh A (11.1%), Child-Pugh B (10.8%), and healthy participant groups (8.5%); free brigatinib was higher in the Child-Pugh C group (23.1%). There were no clinically meaningful effects of mild or moderate hepatic impairment on unbound systemic exposures (area under the plasma concentration-time curve [AUC]) of brigatinib (geometric least-squares mean ratios [90% CI] of 89.32% [69.79%-114.31%] and 99.55% [77.78%-127.41%], respectively). In the severe hepatic impairment group, brigatinib unbound AUC was approximately 37% higher (geometric least-squares mean ratio [90% CI] of 137.41% [107.37%-175.86%]) compared with healthy participants with normal hepatic function. Brigatinib was well tolerated in healthy participants and in participants with hepatic impairment. No dose adjustment is required for patients with mild or moderate hepatic impairment. The brigatinib dose should be reduced by approximately 40% for patients with severe hepatic impairment.

Variable or variate? A conundrum in pharmacometrics exposure-response models.

Key elements of scientific writing-consistency and clarity-can be compromised in case of inaccurate use of methodological terms, especially in complex and multidisciplinary scientific fields. Such is the case in reports of pharmacometrics exposure-response analyses with the use of the terms univariate/multivariate and univariable/multivariable. This perspective outlines the issues in the use of these terms, clarifies their definitions, provides examples, and makes recommendations for authors, reviewers, and journals in the fields of clinical pharmacology and pharmacometrics.

Effect of Pevonedistat, an Investigational NEDD8-Activating Enzyme Inhibitor, on the QTc Interval in Patients With Advanced Solid Tumors.

The purpose of this study was to assess the effect of pevonedistat, a neural precursor cell expressed, developmentally down-regulated protein 8 (NEDD8)-activating enzyme inhibitor, on the heart rate-corrected QT (QTc) interval in cancer patients. Patients were randomized 1:1 to receive pevonedistat 25 or 50 mg/m2 on day 1 and the alternate dose on day 8. Triplicate electrocardiograms were collected at intervals over 0-11 hours and at 24 hours via Holter recorders on days -1 (baseline), 1, and 8. Changes from time-matched baseline values were calculated for QTc by Fridericia (QTcF), PR, and QRS intervals. Serial time-matched blood samples for analysis of pevonedistat plasma pharmacokinetics were collected and a concentration-QTc analysis conducted. Safety was assessed by monitoring vital signs, physical examinations, and clinical laboratory tests. Forty-four patients were included in the QTc analysis. Maximum least square (LS) mean increase from time-matched baseline in QTcF was 3.2 milliseconds at 1 hour postdose for pevonedistat at 25 mg/m2 , while the LSs mean change from baseline in QTcF was -1.7 milliseconds 1 hour postdose at 50 mg/m2 . The maximum 2-sided 90% upper confidence bound was 6.7 and 2.9 milliseconds for pevonedistat at 25 and 50 mg/m2 , respectively. Pevonedistat did not result in clinically relevant effects on heart rate, nor on PR or QRS intervals. Results from pevonedistat concentration-QTc analysis were consistent with these findings. Administration of pevonedistat to cancer patients at a dose of up to 50 mg/m2 showed no evidence of QT prolongation, indicative of the lack of clinically meaningful effects on cardiac repolarization. ClinicalTrials.gov identifier: NCT03330106 (first registered on November 6, 2017).