Pharmacokinetics of the Investigational Aurora A Kinase Inhibitor Alisertib in Adult Patients With Advanced Solid Tumors or Relapsed/Refractory Lymphoma With Varying Degrees of Hepatic Dysfunction

Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring

Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.

Population Pharmacokinetics and Exposure-Safety Relationships of Alisertib in Children and Adolescents With Advanced Malignancies

Population pharmacokinetic (PK) and exposure-safety analyses of alisertib were performed in children enrolled in 2 clinical trials: NCT02444884 and NCT01154816. NCT02444884 was a dose-finding study in children with relapsed/refractory solid malignancies (phase 1) or neuroblastomas (phase 2). Patients received oral alisertib 45 to 100 mg/m2 as powder-in-capsule once daily or twice daily for 7 days in 21-day cycles. Serial blood samples were collected up to 24 hours after dosing on cycle 1, day 1. NCT01154816 was a phase 2 single-arm study evaluating efficacy in children with relapsed/refractory solid malignancies or acute leukemias. Patients received alisertib 80 mg/m2 as enteric-coated tablets once daily for 7 days in 21-day cycles. Sparse PK samples were collected up to 8 hours after dosing on cycle 1, day 1. Sources of alisertib PK variability were characterized and quantified using nonlinear mixed-effects modeling to support dosing recommendations in children and adolescents. A 2-compartment model with oral absorption described by 3 transit compartments was developed using data from 146 patients. Apparent oral clearance and central distribution volume were correlated with body surface area across the age range of 2 to 21 years, supporting the use of body surface area-based alisertib dosing in the pediatric population. The recommended dose of 80 mg/m2 once daily enteric-coated tablets provided similar alisertib exposures across pediatric age groups and comparable exposure to that in adults receiving 50 mg twice daily (recommended adult dose). Statistically significant relationships (P < .01) were observed between alisertib exposures and incidence of grade ≥2 stomatitis and febrile neutropenia, consistent with antiproliferative mechanism-related toxicities.