Murine cancer cachexia models replicate elevated catabolic pembrolizumab clearance in humans.

BACKGROUND: Monoclonal antibody (mAb) immune checkpoint inhibitor (ICI) therapies have dramatically impacted oncology this past decade. However, only about one-third of patients respond to treatment, and biomarkers to predict responders are lacking. Recent ICI clinical pharmacology data demonstrate high baseline drug clearance (CL0) significantly associates with shorter overall survival, independent of ICI exposure, in patients receiving ICI mAb therapies. This suggests CL0 may predict outcomes from ICI therapy, and cachectic signalling may link elevated CL0 and poor response. Our aim was to determine if mouse models of cancer cachexia will be useful for studying these phenomena and their underlying mechanisms. METHODS: We evaluated pembrolizumab CL in the C26 and Lewis lung carcinoma mouse models of cancer cachexia. A single treatment of vehicle or pembrolizumab, at a dose of 2 or 10 mg/kg, was administered intravenously by tail vein injection. Pembrolizumab was quantified by an ELISA in serial plasma samples, and FcRn gene (Fcgrt) expression was assessed in liver using real-time quantitative reverse transcription PCR. Non-compartmental and mixed-effects pharmacokinetics analyses were performed. RESULTS: We observed higher pembrolizumab CL0 and decreased Fcgrt expression in whole liver tissue from tumour-bearing vs. tumour-free mice. In multivariate analysis, presence of tumour, total murine IgG, muscle weight and Fcgrt expression were significant covariates on CL, and total murine IgG was a significant covariate on V1 and Q. CONCLUSIONS: These data demonstrate increases in catabolic clearance of monoclonal antibodies observed in humans can be replicated in cachectic mice, in which Fcgrt expression is also reduced. Notably, FcRn activity is essential for proper antigen presentation and antitumour immunity, which may permit the study of cachexia's impact on FcRn-mediated clearance and efficacy of ICI therapies.

Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics.

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data.

Pharmacokinetic-Pharmacodynamic Model of Neutropenia in Patients With Myeloma Receiving High-Dose Melphalan for Autologous Stem Cell Transplant.

High-dose melphalan (HDM) is part of the conditioning regimen in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT). However, individual sensitivity to melphalan varies, and many patients experience severe toxicities. Prolonged severe neutropenia is one of the most severe toxicities and contributes to potentially life-threatening infections and failure of ASCT. Granulocyte-colony stimulating factor (G-CSF) is given to stimulate neutrophil proliferation after melphalan administration. The aim of this study was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model capable of predicting neutrophil kinetics in individual patients with MM undergoing ASCT with high-dose melphalan and G-CSF administration. The extended PK/PD model incorporated several covariates, including G-CSF regimen, stem cell dose, hematocrit, sex, creatinine clearance, p53 fold change, and race. The resulting model explained portions of interindividual variability in melphalan exposure, therapeutic effect, and feedback regulation of G-CSF on neutrophils, thus enabling simulation of various doses and prediction of neutropenia duration.