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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/m 2 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/m 2 , while the LSs mean change from baseline in QTcF was -1.7 milliseconds 1 hour postdose at 50 mg/m 2 . The maximum 2-sided 90% upper confidence bound was 6.7 and 2.9 milliseconds for pevonedistat at 25 and 50 mg/m 2 , 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/m 2 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)., (© 2022 Takeda Pharmaceutical and The Authors. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

A population pharmacokinetic (PK) analysis was conducted to characterize sources of interpatient variability on the PK of TAK-931, a cell division cycle 7 kinase inhibitor, in adult patients with advanced solid tumors using data from 198 patients who received oral TAK-931 over the range of 30 to 150 mg once daily in multiple dosing schedules in 2 phase 1 and 1 phase 2 clinical studies. A 2-compartment model with 2 transit compartments describing the absorption and first-order linear elimination adequately described the PK of TAK-931. The apparent oral clearance (CL/F) of TAK-931 was estimated to be 38 L/h, and the terminal half-life was estimated to be approximately 6 hours. Creatinine clearance (CrCL) was identified as a covariate on CL/F, and body weight as a covariate on CL/F, apparent central volume of distribution, and apparent intercompartmental clearance. Simulations using the final model indicated that the effect of CrCL (≥35 mL/min) or body weight (29.8-127 kg) on TAK-931 systemic exposures was not considered clinically meaningful, suggesting that no dose adjustments were necessary to account for body weight or renal function (CrCL ≥35 mL/min). Sex, age (36-88 years), race, and mild hepatic impairment had no impact on the CL/F of TAK-931. Taken together, the population PK analysis supports the same starting dose of TAK-931 in Asian and Western cancer patients in a global setting., (© 2021 Millennium Pharmaceuticals, Inc. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI-OS modeling methods. Historical dataset from a phase III non-small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets., (© 2020 Genentech Inc. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

We report population pharmacokinetic, pharmacodynamic, and pharmacokinetic-safety analyses to support phase II/III dose/regimen selection of alisertib, a selective Aurora A kinase (AAK) inhibitor. Phase I studies in adult cancer patients evaluated dosing on Days 1-7 in 21-day cycles or Days 1-21 in 35-day cycles, with corresponding maximum tolerated doses of 50 mg twice daily (BID) and 50 mg QD, respectively. Population pharmacokinetic analyses supported dose- and time-linear pharmacokinetics without identification of clinically meaningful covariates. Exposure-related increases in skin mitotic index and decreases in chromosomal alignment/spindle bipolarity in tumor mitotic cells confirmed AAK inhibition. Exposures in the 7-day schedule at or near 50 mg BID are expected to result in tumor AAK inhibition based on pharmacodynamic assessment in patient tumors. Exposure-safety analyses of data from patients receiving doses of 5-200 mg/day in the 7-day schedule support a low (∼7%) predicted incidence of dose-limiting toxicity at 50 mg BID. Taken together, these analyses support a pharmacologically active and acceptably tolerated dose range of alisertib for future clinical development., (© 2014, The American College of Clinical Pharmacology.)