Exposure-response analysis of acalabrutinib and its active metabolite, ACP-5862, in patients with B-cell malignancies.

Aims: Examine relationships between the systemic exposure of acalabrutinib, a highly selective, next-generation Bruton tyrosine kinase inhibitor, and its active metabolite (ACP-5862) vs. efficacy and safety responses in patients with B-cell malignancies who received acalabrutinib as monotherapy or in combination with obinutuzumab.

Methods: For exposure-efficacy analyses, patients with untreated chronic lymphocytic leukaemia were assessed for best overall response, progression-free survival and tumour regression.

Improved characterization of the pharmacokinetics of acalabrutinib and its pharmacologically active metabolite, ACP-5862, in patients with B-cell malignancies and in healthy subjects using a population pharmacokinetic approach.

This analysis aimed to describe the pharmacokinetics (PK) of acalabrutinib and its active metabolite, ACP-5862. A total of 8935 acalabrutinib samples from 712 subjects and 2394 ACP-5862 samples from 304 subjects from 12 clinical studies in patients with B-cell malignancies and healthy subjects were analysed by nonlinear mixed-effects modelling. Acalabrutinib PK was characterized by a 2-compartment model with first-order elimination.

Brain exposure of the ATM inhibitor AZD1390 in humans-a positron emission tomography study.

Background: The protein kinase ataxia telangiectasia mutated (ATM) mediates cellular response to DNA damage induced by radiation. ATM inhibition decreases DNA damage repair in tumor cells and affects tumor growth. AZD1390 is a novel, highly potent, selective ATM inhibitor designed to cross the blood-brain barrier (BBB) and currently evaluated with radiotherapy in a phase I study in patients with brain malignancies.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors.

The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN).

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications.

Unlabelled: Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce.

Population Pharmacokinetic Analysis of Lanreotide Autogel/Depot in the Treatment of Neuroendocrine Tumors: Pooled Analysis of Four Clinical Trials.

Background And Objectives: Lanreotide Autogel (lanreotide Depot in the USA) has demonstrated anti-tumor activity and control of the symptoms associated with hormone hypersecretion in patients with neuroendocrine tumors. The objectives of this study were to describe the pharmacokinetics of lanreotide Autogel administered 4-weekly by deep subcutaneous injections of 60, 90, or 120 mg in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs), to quantify the magnitude of inter-patient variability (IPV), and to identify those patient characteristics that impact on pharmacokinetics.

Methods: Analyses were based on pooled data from clinical trials.

Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules.

The current work integrates cell-cycle dynamics occurring in the bone marrow compartment as a key element in the structure of a semimechanistic pharmacokinetic/pharmacodynamic model for neutropenic effects, aiming to describe, with the same set of system- and drug-related parameters, longitudinal data of neutropenia gathered after the administration of the anticancer drug diflomotecan (9,10-difluoro-homocamptothecin) under different dosing schedules to patients (n = 111) with advanced solid tumors. To achieve such an objective, the general framework of the neutropenia models was expanded, including one additional physiologic process resembling cell cycle dynamics. The main assumptions of the proposed model are as follows: within the stem cell compartment, proliferative and quiescent cells coexist, and only cells in the proliferative condition are sensitive to drug effects and capable of following the maturation chain.

Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology.

Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification.

A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients.

The development of individualized therapies poses a major challenge in oncology. Significant hurdles to overcome include better disease monitoring and early prediction of clinical outcome. Current clinical practice consists of using Response Evaluation Criteria in Solid Tumors (RECIST) to categorize response to treatment.