Rene Bruno

Clinical pharmacokinetics of erlotinib in patients with solid tumors and exposure‐safety relationship in patients with non–small cell lung cancer

Our objective was to assess the pharmacokinetics of erlotinib in a large patient population with solid tumors, identify covariates, and explore relationships between exposure and safety outcomes (rash and diarrhea) in patients with non‐small cell lung cancer receiving single‐agent erlotinib.

Population Pharmacokinetics of Rituximab (Anti‐CD20 Monoclonal Antibody) in Rheumatoid Arthritis Patients During a Phase II Clinical Trial

Rituximab is a B cell‐depleting anti‐CD20 chimeric IgGK monoclonal antibody being investigated for the treatment of rheumatoid arthritis. The purpose of this study was to develop a population pharmacokinetic model in rheumatoid arthritis patients. In addition, the final pharmacokinetic model was used to assess the variability in drug exposure (AUC0‐∞) for fixed versus body surface area‐based dosing. A total of 102 patients were included in this population pharmacokinetic analysis. A 2‐compartment pharmacokinetic model described the data reasonably well. Body surface area and gender were the most significant covariates for both CL and Vc. Body surface area alone only explained about 19.7% of the total interindividual variability of CL. In a simulation study, body surface area‐based dosing normalized drug exposure over a wide range of body surface area but did not seem to improve the predictability of rituximab AUC0‐∞ in rheumatoid arthritis patients. Therefore, no rationale for body surface area‐based dosing for rituximab in rheumatoid arthritis patients was found.

Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics

PURPOSE We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. METHODS We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. RESULTS The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. CONCLUSION The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.

Phase I trial of docetaxel and cisplatin in previously untreated patients with advanced non-small-cell lung cancer.

PURPOSE To determine the maximum-tolerated doses (MTDs), principal toxicities, and pharmacokinetics of the combination of docetaxel and cisplatin administered every 3 weeks to patients with advanced non-small-cell lung cancer (NSCLC) who have not received prior chemotherapy and to recommend a dose for phase II studies. PATIENTS AND METHODS Patients with advanced NSCLC and performance status 0 to 2 who had not received prior chemotherapy received docetaxel over 1 hour followed by cisplatin over 1 hour with hydration. Dose levels studied were (docetaxel/cisplatin) 50/75, 75/75, 75/100, and 100/75 mg/m2 repeated every 3 weeks. Colony-stimulating factor (CSF) support was not used. Pharmacokinetics of docetaxel and cisplatin were studied in the first cycle of therapy. Most patients (79%) had metastatic disease or intrathoracic recurrence after prior radiation and/or surgery. RESULTS Of 24 patients entered, all were assessable for toxicity and 18 for response. The MTD schedules were docetaxel 75 mg/m2 with cisplatin 100 mg/m2 (dose-limiting toxicities [DLTs] in five of six patients), and docetaxel 100 mg/m2 with cisplatin 75 mg/m2 (DLTs in two of two patients, including one fatal toxicity). Limiting toxicities were febrile neutropenia and nonhematologic, principally diarrhea and renal. Two patients had neutropenic enterocolitis. Pharmacokinetics of both drugs were consistent with results from single-agent studies, which suggests no major pharmacokinetic interaction. Neutropenia was related to docetaxel area under the plasma concentration-versus-time curve (AUC). An alternative schedule was investigated, with cisplatin being administered over 3 hours commencing 3 hours after docetaxel, but toxicity did not appear to be less. Independently reviewed responses occurred in eight of 18 patients (44%; 95% confidence interval, 22% to 69%), most following 75 mg/m2 of both drugs. CONCLUSION Docetaxel 75 mg/m2 over 1 hour followed by cisplatin 75 mg/m2 over 1 hour is recommended for phase II studies. The responses seen in this phase I study suggest a high degree of activity of this combination in previously untreated advanced NSCLC.

Population Pharmacokinetics of Efalizumab (Humanized Monoclonal Anti‐CD11a Antibody) Following Long‐Term Subcutaneous Weekly Dosing in Psoriasis Subjects

The population pharmacokinetics of efalizumab was characterized in patients with moderate to severe plaque psoriasis. The study included 1088 subjects who received 1 or 2 mg/kg/wk subcutaneous efalizumab for 12 weeks from a phase I (64 subjects) and 3 phase III studies with day 42 and/or day 84 trough levels (1024 patients). Due to the limitation of the data, a 1‐compartment model with first‐order absorption and elimination was used to fit the data. The population means for V/F, Ka, and CL/F were 9.13 L, 0.191 day−1, and 1.29 L/d, respectively, for a typical subject receiving a 1‐mg/kg dose. Interindividual variability in CL/F was 48.2%. Body weight has the largest influence on CL/F. Other covariates (obesity, baseline lymphocyte counts, Psoriasis Area and Severity Index score, and age) had only modest effects. Subjects in the 2‐mg/kg dose group had a 24.0% lower CL/F, consistent with nonlinear pharmacokinetics of efalizumab. The results of this analysis support the current body weight‐adjusted dosing strategy.

Phase I and Pharmacokinetic Study of Docetaxel and Irinotecan in Patients With Advanced Solid Tumors

PURPOSE We conducted a phase I and pharmacokinetic study of docetaxel in combination with irinotecan to determine the dose-limiting toxicity (DLT), the maximum-tolerated dose (MTD), and the dose at which at least 50% of the patients experienced a DLT during the first cycle, and to evaluate the safety and pharmacokinetic profiles in patients with advanced solid tumors. PATIENTS AND METHODS Patients with only one prior chemotherapy treatment (without taxanes or topoisomerase I inhibitors) for advanced disease were included in the study. Docetaxel was administered as a 1-hour IV infusion after premedication with corticosteroids followed immediately by irinotecan as a 90-minute IV infusion, every 3 weeks. No hematologic growth factors were allowed. RESULTS Forty patients were entered through the following seven dose levels (docetaxel/irinotecan): 40/140 mg/m(2), 50/175 mg/m(2), 60/210 mg/m(2), 60/250 mg/m(2), 60/275 mg/m(2), 60/300 mg/m(2), and 70/250 mg/m(2). Two hundred cycles were administered. Two MTDs were determined, 70/250 mg/m(2) and 60/300 mg/m(2); the DLTs were febrile neutropenia and diarrhea. Neutropenia was the main hematologic toxicity, with 85% of patients experiencing grade 4 neutropenia. Grade 3/4 nonhematologic toxicities in patients included late diarrhea (7.5%), asthenia (15.0%), febrile neutropenia (22.5%), infection (7.5%), and nausea (5.0%). Pharmacokinetics of both docetaxel and irinotecan were not modified with the administration schedule of this study. CONCLUSION The recommended dose of docetaxel in combination with irinotecan is 60/275 mg/m(2), respectively. At this dose level, the safety profile is manageable. The activity of this combination should be evaluated in phase II studies in different tumor types.

Evaluation of Tumor Size Response Metrics to Predict Survival in Oncology Clinical Trials

Model‐based drug development in oncology is still lagging despite a good momentum in the clinical pharmacology and pharmacometry community in the past few years. The failure rate of late‐stage oncology studies is one of the highest across therapeutic areas. The modeling of the relationship between longitudinal tumor size and overall survival has been proposed to enhance learning in early clinical studies, to predict overall survival, and to simulate clinical trials. This approach has the potential to support proof of concept, early clinical decisions, and design of late‐stage trials, but it is not yet widely integrated into the oncology drug development process. In this article, we review the state of these modeling efforts and discuss several key applications of these models. We conclude by suggesting a few paths forward.

Pharmacokinetics/Pharmacodynamics in Drug Development: An Industrial Perspective

In a health care environment dominated by the growth of managed care organizations, generic competition, therapeutic substitution and drug utilization review, drug development is an extremely risky proposition. Consequently, it is imperative to incorporate a mechanistic approach to drug development that combines a thorough understanding of a drug at the molecular/cellular level with a rigorous preclinical, and clinical pharmacology program. This should enable the sponsor to evaluate multiple hypotheses during the early “learning” phases of clinical development (Phases I and IIA) and to eliminate nonpromising candidates early on while drug development costs are low. Clinical research done properly in the early stages of drug development will also set the stage for designing and conducting optimal “confirming” registrational Phase IIB/III studies for promising drug candidates. Pharmacokinetics (PK) and pharmacodynamics (PD) modeling and simulation are crucial components of a mechanistic approach to optimal drug development and their application has significant impact in both early and late development efforts. This communication describes several applications of pharmacokinetics and pharmacodynamics modeling and simulation that were important in guiding, optimizing and ensuring the success of development efforts for drug candidates in the therapeutic areas of cardiology and oncology. These examples are used to illustrate and discuss the use of the current state‐of‐the‐art in pharmacokinetics and pharmacodynamics modeling and simulation at numerous stages in the development cycle and to postulate on future directions in this area.

Simulations Using a Drug–Disease Modeling Framework and Phase II Data Predict Phase III Survival Outcome in First‐Line Non–Small‐Cell Lung Cancer

Simulations were performed for carboplatin/paclitaxel (C/P) plus motesanib or bevacizumab vs. C/P as first‐line treatment for advanced non–small‐cell lung cancer (NSCLC) using a published drug–disease model. With 700 patients in each arm, simulated hazard ratios for motesanib (0.87; 95% confidence interval [CI], 0.71–1.1) and bevacizumab (0.89; 95% CI, 0.73–1.1) agreed with results from the respective phase III studies but did not discriminate between failed and successful studies. The current model may require further enhancement to improve its utility for predicting phase III outcomes.

Model‐Based Drug Development in Oncology: What's Next?

Model‐based estimates of tumor growth inhibition (TGI) metrics have the potential to enhance learning in early (phase II) clinical studies. They can be used as end points and biomarkers to predict treatment effect on clinical outcome measures—e.g., overall survival (OS)—and support phase II study design, end‐of‐phase II decisions, and phase III planning and execution. Efforts should be made to assess models in simulating independent studies with treatments of varying mechanisms of action.