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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‐1β 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‐1β 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.

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, ξ 0 and ξ * , optimised for prior and true population parameters, Ψ 0 and Ψ*, and several ADs (two-, three- and five-stage). All designs had 50 patients. For ADs, the first cohort design was ξ 0 . 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.

MOTIVATION Models are now used not just for data analysis, but for knowledge representation integrating across a wide range of data sources and model types. MDL provides the means to describe models in a clear and consistent manner for modelers and those using the models, and, together with the other DDMoRe exchange standards—Pharmacometrics Markup Language (PharmML), which defines the XML‐based software interchange standard; probability distribution ontology and knowledge‐base (ProbOnto), which provides a consistent basis for definition of probability distributions across MDL and PharmML and how these distributions are encoded in various target software tools; and the Standard Output (SO) definition, which defines a consistent XML representation of output from target software tools—provides standards for model definition, software input, output and interoperability, and knowledge management through metadata annotation using suitable ontologies. Estimation X Initial Values X MLX task properties Bayesian estimation X X X BUGS task properties Visual Predictive Check X Estimated Parameters X NONMEM task properties Prediction/simulation (X) X Estimated Parameters X simulx task properties Optimal design/evaluation X Estimated Parameters X PFIM or PopED task properties 1The Task Properties Object contains settings for the specific modeling and simulation task relevant to the target software tool for that task. 2For prediction or simulation, a Data Object can be used as an alternative to the Design Object. MDL AS A COMMUNICATION TOOL Without clear communication of all aspects of the model, including structural form, model hierarchy, distributional properties of random variables, covariate relationships, mathematical and statistical aspects there is little hope of accurately conveying knowledge imbued within the model.