René Bruno

Population Pharmacokinetics and Pharmacokinetic-Pharmacodynamic Relationships for Docetaxel

The population approach has been implemented prospectively inthe clinical development of docetaxel(Taxotere®). Overall 640 patients were evaluablefor the population PK/PD analysis. The PK analysis evidencedsignificant covariates explaining the inter-patientvariability of docetaxel clearance and the PK/PD analysisdemonstrated that the variability in clearance was asignificant predictor of several safety endpoints. In patientswith clinical chemistry suggestive of mild to moderate liverfunction impairment (SGOT and/or SGPT >1.5 × ULNconcomitant with alkaline phosphatase >2.5 × ULN),total body clearance was lowered by an average of 27%.Specific safety analyses demonstrated that these patients areat a significantly higher risk than others for the developmentof severe docetaxel-induced side effects. Population PK/PDdata were fully integrated into the regulatory dossier and inthe labeling of docetaxel worldwide. Population PK/PD modelsare being used to elaborate a simulation model to predict thesurvival of patients with non-small cell lung cancer treatedwith docetaxel.

Clinical trial simulation of docetaxel in patients with cancer as a tool for dosage optimization.

Pharmacokinetic and pharmacodynamic analyses conducted during the development of docetaxel showed that patients with non–small‐cell lung cancer with high baseline α1‐acid glycoprotein levels had shorter time to progression and time to death. To assess whether such patients might benefit from dose intensification, we initiated a series of clinical trial simulations.

Modeling and Simulation of Pivotal Clinical Trials Using Linked Models for Multiple Endpoints in Chronic Obstructive Pulmonary Disease With Roflumilast

Roflumilast is a selective phosphodiesterase 4 inhibitor (PDE4i) for the treatment of severe chronic obstructive pulmonary disease (COPD). In 2 large phase 3 trials in a broader population of COPD patients (BY217/M2‐111, ClinicalTrials.gov: NCT00076089 and BY217/M2‐112, ClinicalTrials.gov: NCT00430729), treatment with roflumilast reduced the rate of exacerbations; however, the reduction did not reach statistical significance. Two linked dose‐response models for the primary (annualized COPD exacerbation counts) and secondary (change from baseline in forced expiratory volume in 1 second [FEV1]) end points were therefore developed to characterize and quantify effect sizes and the patient characteristics influencing them. The models showed that disease severity and bronchitis, particularly the severity of bronchitis expressed in cough‐and‐sputum scores, were good predictors of exacerbation rates and differential benefit of roflumilast in exacerbation reduction. The models were used to support the rational design of 2 phase 3 randomized, placebo‐controlled clinical trials (BY217/M2‐124, ClinicalTrials.gov: NCT00297102 and BY217/M2‐125, ClinicalTrials.gov: NCT00297115) by identifying the most appropriate patient population using clinical trial simulations. Model predictions for both end points were found to be highly accurate — as confirmed by the results from these trials, which led to the approval of roflumilast as the first oral PDE4i for the treatment of COPD in patients associated with chronic bronchitis and a history of exacerbations.

Intraindividual Pharmacokinetic Variability: Focus on Small-Molecule Kinase Inhibitors

The magnitude of interindividual pharmacokinetic variability (IIV) of a drug and the factors responsible for this variability are intensively studied before—and sometimes after—registration as crucial information in anticipating and understanding variability in toxicity and efficacy. However, there has been much less attention paid to intraindividual variability, reflecting random or systematic changes in an individuals pharmacokinetics over time. We have chosen to focus on small‐molecule kinase inhibitors (SMKIs).

Model‐Based Estimates of Tumor Growth Inhibition Metrics Are Time‐Independent: A Reply to Mistry

Mistry states that “One of the key forms of bias when using covariates that are time-dependent, which TTG and in fact any model-derived metrics are, is time-dependent (immortal time) bias.” The authors contend that modelderived TGI metrics are not time-dependent and not subjected to immortal time bias. Time to growth (TTG) and other TGI metrics are estimated based on TGI profiles with a nonlinear mixed effect model and not observed (latent variables). As soon as a patient enters a clinical trial and has gotten one postbaseline tumor size measurement (sum of the longest diameters of target lesions per RECIST response evaluation criteria), i.e., typically at the end of the second cycle of treatment (6 or 8 weeks, depending on the dosing schedule), this patient is evaluable for TGI and TGI metrics can be univocally calculated from estimated individual TGI model parameters (see, e.g., supplementary data from ref. 2). Estimates of TTG can take any value independent of patient death; TTG can be estimated after death or tumor size observation time span, as previously commented. 3 This is the case when a patient dies, drops out of the clinical trial, or clinical progression due to other reasons than target lesions. In the extreme case where observations are too sparse, estimated TGI metrics are prone to parameter estimate shrinkage, which cannot create spurious correlations with overall survival. Immortal time bias occurs in observational pharmacoepidemiology studies when cohort assignment depends on a time-varying covariate (see figure 1 in ref. 5). In oncology drug development studies, cohorts are clearly assigned at the start of treatment, estimated TGI metrics are not time-varying observed values that are used to perform cohort assignments, hence they are not subject to immortal time bias.