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Summary: Purpose Galunisertib, a TGF-β inhibitor, has demonstrated antitumor effects in preclinical and radiographic responses in some patients with malignant glioma. This Phase 1b/2a trial investigated the clinical benefit of combining galunisertib with temozolomide-based radiochemotherapy (TMZ/RTX) in patients with newly diagnosed malignant glioma (NCT01220271). Methods This is an open-label, 2-arm Phase 1b/2a study (N = 56) of galunisertib (intermittent dosing: 14 days on/14 days off per cycle of 28 days) in combination with TMZ/RTX (n = 40), versus a control arm (TMZ/RTX, n = 16). The primary objective of Phase 1b was to determine the safe and tolerable Phase 2 dose of galunisertib. The primary objective of Phase 2a was to confirm the tolerability and pharmacodynamic profile of galunisertib with TMZ/RTX, and the secondary objectives included determining the efficacy and pharmacokinetic (PK) profile of galunisertib with TMZ/RTX in patients with glioblastoma. This study also characterized the changes in the major T-cell subsets during TMZ/RTX plus galunisertib treatment. Results In the Phase 2a study, efficacy results for patients treated with galunisertib plus TMZ/RTX or TMZ/RTX were: median overall survival (18.2 vs 17.9 months), median progression-free survival (7.6 vs 11.5 months), and disease control rate (80% [32/40] vs 56% [9/16] patients) respectively. PK profile of galunisertib plus TMZ/RTX regimen was consistent with previously published PK data of galunisertib. The overall safety profile across treatment arms was comparable. Conclusion No differences in efficacy, safety or pharmacokinetic variables were observed between the two treatment arms. [ABSTRACT FROM AUTHOR]

This paper presents a method for optimal design of multiresponse population pharmacokinetic experiments taking into account correlations between interindividual variances. Expressions for the population Fisher information matrix have been defined for uniresponse and multiresponse pharmacokinetic experiments. A major assumption often made is that the variance-covariance matrix of the interindividual variance components has only diagonal elements so that whenever intersubject covariance elements are present, they are ignored during the design of the experiment. Recently expressions that accounted for these off diagonal elements were developed for uniresponse population pharmacokinetic experiments. The work presented here extends these expressions to multiresponse population pharmacokinetic experiments. These were applied to a population pharmacokinetic model, a population pharmacokinetic-pharmacodynamic model, and a parent-metabolite pharmacokinetic model example. The results obtained showed that optimal designs are different with diagonal omega matrix and full omega matrix and ignoring the off diagonal elements can lead to a design that produces more biased and less precise parameter estimates compared to a design that includes the off diagonal elements. The results also showed correlation between residual components of the responses has an effect on the optimal design. [ABSTRACT FROM AUTHOR]

The design of pharmacokinetic and pharmacodynamic experiments concerns a number of issues, among which are the number of observations and the times when they are taken. Often a model is used to describe these data and the pharmacokinetic–pharmacodynamic behavior of a drug. Knowledge of the data analysis model at the design stage is beneficial for collecting patient data for parameter estimation. A number of criteria for model-oriented experiments, which maximize the information content of the data, are available. In this paper we present a program, Popdes, to investigate the D-optimal design of individual and population multivariate response models, such as pharmacokinetic–pharmacodynamic, physiologically based pharmacokinetic, and parent drug and metabolites models. A pre-clinical and clinical pharmacokinetic–pharmacodynamic model describing the concentration–time profile and effect of an oncology compound in development is used for illustration. [Copyright &y& Elsevier]

Summary: Planning any experiment includes issues such as how many samples are to be taken and their location given some predictor variable. Often a model is used to explain these data; hence including this formally in the design will be beneficial for any subsequent parameter estimation and modelling. A number of criteria for model oriented experiments, which maximise the information content of the collected data are available. In this paper we present a program, Optdes, to investigate the optimal design of pharmacokinetic, pharmacodynamic, drug metabolism and drug–drug interaction models. Using the developed software the location of either a predetermined number of design points (exact designs) or together with the proportion of samples at each point (continuous designs) can be determined. Local as well as Bayesian designs can be optimised by either D- or A-optimality criteria. Although often the optimal design cannot be applied for practical reasons, alternative designs can be readily evaluated. [Copyright &y& Elsevier]

In the last update of the RECIST criteria in 2009, it was proposed that the number of target lesions to be followed over time for response-to-treatment assessment be reduced from 10 to 5 lesions maximum, with up to 2 per organ. We explored the impact of reducing the number of target lesion on the assessment of drug effect in a randomised phase III clinical trial using a tumour growth inhibition (TGI) model. Tumour size measurements from 441 (out of 456) patients were used to build two datasets for which observations were the sum of longest diameters of all measurable lesions (ALL dataset) or following the RECIST 1.1 recommendations (R1.1 dataset). TGI models incorporating a categorical covariate for treatment group or a pharmacokinetic metric (i.e. dose; simulated area under the curve) were used to describe the longitudinal tumour size kinetics. Drug exposure was not superior to treatment group at describing drug effect. ALL and R1.1 individual estimates of drug effect appeared to be strongly correlated (r2=0.88). Including pharmacokinetic metrics in TGI models should be conducted carefully when no pharmacokinetic samples are available. Reducing the number of target lesion did not seem to compromise the determination of drug effect using TGI models. [ABSTRACT FROM AUTHOR]