Two-stage model-based design of cancer phase I dose escalation trials: evaluation using the phase I program of barasertib (AZD1152)

Introduction Modeling and simulation of pharmacokinetics and pharmacodynamics has previously been shown to be potentially useful in designing Phase I programs of novel anti-cancer agents that show hematological toxicity. In this analysis, a two-stage model-based trial design was evaluated retrospectively using data from the Phase I program with the aurora kinase inhibitor barasertib. Methods Data from two Phase I trials and four regimens were used (n = 79). Using barasertib-hydroxy QPA plasma concentrations and neutrophil count data from only study 1A, a PKPD model was developed and subsequently used to predict the MTD and a safe starting dose for the other trials. Results The PKPD model based on data from the first study adequately described the time course of neutrophil count fluctuation. The two-stage model-based design provided safe starting doses for subsequent phase I trials for barasertib. Predicted safe starting dose levels were higher than those used in two subsequent trials, but lower than used in the other trial. Discussion The two-stage approach could have been applied safely to define starting doses for alternative dosing strategies with barasertib. The limited improvement in efficiency for the phase I program of barasertib may have been due to the fact that starting doses for the studied phase I trials were already nearly optimal. Conclusion Application of the two-stage model-based trial design in Phase I programs with novel anti-cancer drugs that cause haematological toxicity is feasible, safe, and may lead to a reduction in the number of patient treated at sub-therapeutic dose-levels

Two-stage model-based clinical trial design to optimize phase I development of novel anticancer agents

Background The phase I program of anticancer agents usually consists of multiple dose escalation studies to select a safe dose for various administration schedules. We hypothesized that pharmacokinetic and pharmacodynamic (PK–PD) modeling of an initial phase I study (stage 1) can be used for selection of an optimal starting dose for subsequent studies (stage 2) and that a post-hoc PK–PD analysis enhances the selection of a recommended dose for phase II evaluation. The aim of this analysis was to demonstrate that this two-stage model-based design, which does not interfere in the conduct of trials, is safe, efficient and effective. Methods PK and PD data of dose escalation studies were simulated for nine compounds and for five administration regimens (stage 1) for drugs with neutropenia as dose-limiting toxicity. PK–PD models were developed for each simulated study and were used to determine a starting dose for additional phase I studies (stage 2). The model-based design was compared to a conventional study design regarding safety (number of dose-limiting toxicities (DLTs)), efficiency (number of patients treated with a dose below the recommended dose) and effectiveness (precision of dose selection). Retrospective data of the investigational anticancer drug indisulam were used to show the applicability of the model-based design. Results The model-based design was as safe as the conventional design (median number of DLTs = 3) and resulted in a reduction of the number of patients who were treated with a dose below the recommended dose (−27%, power 89%). A post-hoc model-based determination of the recommended dose for future phase II studies was more precise than the conventional selection of the recommended dose (root mean squared error 8.3% versus 30%). Conclusions A two-stage model-based phase I design is safe for anticancer agents with dose-limiting myelosuppression and may enhance the efficiency of dose escalation studies by reducing the number of patients treated with a dose below the recommended dose and by increasing the precision of dose selection for phase II evaluation

A phase I study of ridaforolimus in adult Chinese patients with advanced solid tumors

PURPOSE: Ridaforolimus (AP23573, MK-8669 or deforolimus) is an inhibitor of mammalian target of rapamycin (mTOR), an important regulator in the cell survival pathway. This open-label, single center phase I study aimed to investigate the pharmacokinetic (PK) and safety profiles of ridaforolimus in Chinese patients with treatment-refractory advanced or relapsed solid tumors. The PK data generated from these Chinese patients were further compared with those previously reported in Caucasian and Japanese patient populations. EXPERIMENTAL DESIGN: The patients were given an oral dose of 40 mg of ridaforolimus on Day 1 of the study. On Day 8, patients were initiated on a treatment regimen that comprised a once daily dose of 40 mg of ridaforolimus for five consecutive days, followed by a 2-day off-drug interval. Patients repeated this regimen until disease progression or intolerance. Blood samples were collected at specific times pre- and post-treatment to establish the PK profile of ridaforolimus in all patients. RESULTS: Fifteen patients were given at least one dose of 40 mg of ridaforolimus. The median absorption lag-time was 2 hours, the median T(max) was 4 hours and the mean elimination half-life was 53 hours. The accumulation ratio for AUC(0-24hr) was 1.3 on day 19 (steady state)/day 1 (after a single dose). The most common drug-related adverse events (AEs) that occurred in ≥40% of patients were stomatitis, proteinuria, leukopenia, hyperglycemia, and pyrexia. Grade 3/4 drug-related AEs were anemia, stomatitis, fatigue, thrombocytopenia, constipation, gamma glutamyltransferase increase, and proteinuria. All 11 evaluable patients achieved stable disease. CONCLUSIONS: Oral ridaforolimus at a daily dose of 40 mg were generally well tolerated in Chinese patients with advanced or refractory solid tumors. Adverse events and PK profiles of ridaforolimus in this study were similar to those from Caucasian and Japanese patients reported previously

Marginal increase of sunitinib exposure by grapefruit juice

Clinical Oncolog

Medication and hemodiafiltration

The amount of drug clearance during online hemodiafiltration (HDF) is determined by (1) the pharmacokinetic properties of a drug defined by its absorption, distribution, metabolism and elimination (ADME) characteristics, (2) dialysis characteristics, including membrane properties, treatment time and blood-, dialysate-and ultrafiltration flow rates, and (3) patient factors. For several drugs, especially those within the middle molecular weight range, with low protein binding and neutrally or positively charged, clearance may be substantially higher during HDF as compared to conventional low flux hemodialysis. Based on drug characteristics, the expected additional effect of a high ultrafiltration rate, as indicated by a high convection volume, can be estimated. This is shown for anticoagulants, antibiotics and antiviral drugs. For drugs with an expected additional effect of convection and for drugs with a narrow therapeutic window, therapeutic drug monitoring may be advisable. Comparative data from clinical studies is scarce. Hence, for an individual patient it may be relevant to calculate the total amount of a drug excreted during an HDF session. This can easily be performed in routine clinical practice and may guide the clinician to estimate the dose of the drug needed for suppletion upon completion of HDF treatment. Examples are provided how to calculate drug suppletion after HDF. Collectively, this chapter is intended as a guidance to optimize pharmacotherapy in online HDF patients.</p

Optimization of anti-infective dosing regimens during online haemodiafiltration

Online haemodiafiltration (HDF) is increasingly used in clinical practice as a routine intermittent dialysis modality. It is well known that renal impairment and renal replacement therapy can substantially affect the pharmacokinetic behaviour of several drugs. However, surprisingly few data are available on the need for specific dose adjustments during HDF. Due to convection, drug clearance may be increased during HDF as compared with standard haemodialysis. This may be of particular interest in patients undergoing anti-infective therapy, since under-dosing may compromise patient outcomes and promote the emergence of bacterial resistance. Drug clearance during HDF is determined by (i) dialysis characteristics, (ii) drug characteristics and (iii) patient characteristics. In this review, we will discuss these different determinants of drug clearance during HDF and advise on how to adjust the dose of antibacterial, antimycotic and antiviral agents in patients undergoing HDF. In addition, the possible added value of therapeutic drug monitoring is discussed. The review provides guidance for optimization of anti-infective dosing regimens in HDF patients