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. For exposure-safety analyses, incidences of grade ≥2 adverse events (AEs), grade ≥3 AEs and grade ≥2 events of clinical interest were assessed in patients with B-cell malignancies. Acalabrutinib and ACP-5862 pharmacokinetic (PK) parameter estimates were obtained from population PK modelling. Exposure calculations were based on study dosing regimens. Total active moieties were calculated to account for contributions of ACP-5862 to overall efficacy/safety. RESULTS: A total of 573 patients were included (exposure-efficacy analyses, n = 274; exposure-safety analyses, n = 573). Most patients (93%) received acalabrutinib 100 mg twice daily. Median total active area under the concentration-time curve (AUC24h,ss ) and total active maximal concentration at steady-state (Cmax,ss ) were similar for patients who received acalabrutinib as monotherapy or in combination with obinutuzumab, and for responders and nonresponders. No relationship was observed between AUC24h,ss /Cmax,ss and progression-free survival or tumour regression. Acalabrutinib AUC24h,ss and Cmax,ss were generally comparable across groups regardless of AE incidence. CONCLUSION: No clinically meaningful correlations between acalabrutinib PK exposure and efficacy and safety outcomes were observed. These data support the fixed acalabrutinib dose of 100 mg twice daily in the treatment of patients with B-cell malignancies.

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. The large variability in absorption was adequately described by transit compartment chain and first-order absorption, with between-occasion variability on the mean transit time and relative bioavailability. The PK of ACP-5862 was characterized by a 2-compartment model with first-order elimination, and the formation rate was defined as the acalabrutinib clearance multiplied by the fraction metabolized. Health status, Eastern Cooperative Oncology Group performance status, and coadministration of proton-pump inhibitors were significant covariates. However, none of the investigated covariates led to clinically meaningful changes in exposure, supporting a flat dosing of acalabrutinib.

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. In the present study, PET was used to measure brain exposure of 11C-labeled AZD1390 after intravenous (i.v.) bolus administration in healthy subjects with an intact BBB. METHODS: AZD1390 was radiolabeled with carbon-11 and a microdose (mean injected mass 1.21 µg) was injected in 8 male subjects (21-65 y). The radioactivity concentration of [11C]AZD1390 in brain was measured using a high-resolution PET system. Radioactivity in arterial blood was measured to obtain a metabolite corrected arterial input function for quantitative image analysis. Participants were monitored by laboratory examinations, vital signs, electrocardiogram, adverse events. RESULTS: The brain radioactivity concentration of [11C]AZD1390 was 0.64 SUV (standard uptake value) and reached maximum 1.00% of injected dose at Tmax[brain] of 21 min (time of maximum brain radioactivity concentration) after i.v. injection. The whole brain total distribution volume was 5.20 mL*cm-3. No adverse events related to [11C]AZD1390 were reported. CONCLUSIONS: This study demonstrates that [11C]AZD1390 crosses the intact BBB and supports development of AZD1390 for the treatment of glioblastoma multiforme or other brain malignancies. Moreover, it illustrates the potential of PET microdosing in predicting and guiding dose range and schedule for subsequent clinical studies.

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). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.

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. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. IMPLICATIONS FOR PRACTICE: The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

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. A total of 1541 serum concentrations from 290 patients were analyzed simultaneously by the population approach using NONMEM version 7.2. Covariates evaluated included demographics, renal and hepatic function markers, and disease-related parameters. RESULTS: Serum profiles were described by a one-compartment disposition model in which the absorption process was characterized by two parallel pathways following first- and zero-order kinetics. The estimated apparent volume of distribution was 18.3 L. The estimated apparent total serum clearance for a typical 74 kg patient was 513 L/day, representing a substantial difference in clearance in this population of patients with respect to healthy volunteers that could not be explained by any of the covariates tested. Body weight was the only covariate to show a statistically significant effect on the pharmacokinetic profile, but due to the overlap between the pharmacokinetic profiles of patients with lower or higher body weights the effect of body weight on clearance was not considered clinically relevant. The IPV was low for clearance (27%) and moderate to high for volume of distribution (150%) and the absorption constant (61%). CONCLUSIONS: Using two mechanisms of absorption, the pharmacokinetics of lanreotide Autogel were well-described in patients with GEP-NET. None of the patient characteristics tested were of clinical relevance to potential dose adjustment in clinical practice.

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. Cell cycle dynamics were characterized by two new parameters, FProl (the fraction of proliferative [Prol] cells that enters into the maturation chain) and kcycle (first-order rate constant governing cell cycle dynamics within the stem cell compartment). Both model parameters were identifiable as indicated by the results from a bootstrap analysis, and their estimates were supported by date from the literature. The estimates of FProl and kcycle were 0.58 and 1.94 day(-1), respectively. The new model could properly describe the neutropenic effects of diflomotecan after very different dosing scenarios, and can be used to explore the potential impact of dosing schedule dependencies on neutropenia prediction.

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. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.

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. However, the utility of RECIST is restricted due to limitations on the frequency of measurement and its categorical rather than continuous nature. We propose a population modeling framework that relates circulating biomarkers in plasma, easily obtained from patients, to tumor progression levels assessed by imaging scans (i.e., RECIST categories). We successfully applied this framework to data regarding lactate dehydrogenase (LDH) and neuron specific enolase (NSE) concentrations in patients diagnosed with small cell lung cancer (SCLC). LDH and NSE have been proposed as independent prognostic factors for SCLC. However, their prognostic and predictive value has not been demonstrated in the context of standard clinical practice. Our model incorporates an underlying latent variable ("disease level") representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment; these assumptions are in agreement with the known physiology of SCLC and these biomarkers. Our model predictions of unobserved disease level are strongly correlated with disease progression measured by RECIST criteria. In conclusion, the proposed framework enables prediction of treatment outcome based on circulating biomarkers and therefore can be a powerful tool to help clinicians monitor disease in SCLC.