Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology.

PURPOSE: In this study we aimed to evaluate adaptive designs (ADs) by clinical trial simulation for a pharmacokinetic-pharmacodynamic model in oncology and to compare them with one-stage designs, i.e., when no adaptation is performed, using wrong prior parameters. METHODS: We evaluated two one-stage designs, ξ0 and ξ*, optimised for prior and true population parameters, Ψ0 and Ψ*, and several ADs (two-, three- and five-stage). All designs had 50 patients. For ADs, the first cohort design was ξ0. The next cohort design was optimised using prior information updated from the previous cohort. Optimal design was based on the determinant of the Fisher information matrix using PFIM. Design evaluation was performed by clinical trial simulations using data simulated from Ψ*. RESULTS: Estimation results of two-stage ADs and ξ * were close and much better than those obtained with ξ 0. The balanced two-stage AD performed better than two-stage ADs with different cohort sizes. Three- and five-stage ADs were better than two-stage with small first cohort, but not better than the balanced two-stage design. CONCLUSIONS: Two-stage ADs are useful when prior parameters are unreliable. In case of small first cohort, more adaptations are needed but these designs are complex to implement.

First-in-human safety, tolerability, and pharmacokinetic results of DFV890, an oral low-molecular-weight NLRP3 inhibitor.

This first-in-human study evaluated the safety, tolerability, single- and multiple-dose pharmacokinetic profiles with dietary influence, and pharmacodynamics (PD) of DFV890, an oral NLRP3 inhibitor, in healthy participants. In total, 122 participants were enrolled into a three-part trial including single and 2-week multiple ascending oral doses (SAD and MAD, respectively) of DFV890, and were randomized (3:1) to DFV890 or placebo (SAD [3-600 mg] and MAD [fasted: 10-200 mg, once-daily or fed: 25 and 50 mg, twice-daily]). DFV890 was generally well-tolerated. Neither deaths nor serious adverse events were reported. A less than dose-proportional increase in exposure was observed with the initially used crystalline suspension (3-300 mg); however, an adjusted suspension formulation using spray-dried dispersion (SDD; 100-600 mg) confirmed dose-proportional increase in exposure. Relative bioavailability between crystalline suspension and tablets, and food effect were evaluated at 100 mg. Under fasting conditions, Cmax of the tablet yielded 78% compared with the crystalline suspension, and both formulations showed comparable AUC. The fed condition led to a 2.05- and 1.49-fold increase in Cmax and AUC0-last compared with the fasting condition. The median IC50 and IC90 for ex-vivo lipopolysaccharide-stimulated interleukin IL-1ß release inhibition (PD) were 61 (90% CI: 50, 70) and 1340 ng/mL (90% CI: 1190, 1490). Crystalline tablets of 100 mg once-daily or 25 mg twice-daily were sufficient to maintain ~90% of the IL-1ß release inhibition over 24 h at steady state. Data support dose and formulation selection for further development in diseases, in which an overactivated NLRP3 represents the underlying pathophysiology.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

BACKGROUND AND OBJECTIVE: Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. METHODS: Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. RESULTS: The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. CONCLUSION: PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.

Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice.

Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were (i) to evaluate the importance of including measurements during tumor regrowth and (ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules, and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e., control versus treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e., "short" and "long" studies, respectively. In long studies, measurements could be taken up to 6 g of tumor weight, whereas in short studies the experiment was stopped 3 days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected.