No abstract

Pharmacokinetic/pharmacodynamic modelling is most often performed using non-linear mixed-effects models based on ordinary differential equations with uncorrelated intra-individual residuals. More sophisticated residual error models as e.g. stochastic differential equations (SDEs) with measurement noise can in many cases provide a better description of the variations, which could be useful in various aspects of modelling. This general approach enables a decomposition of the intra-individual residual variation epsilon into system noise w and measurement noise e. The present work describes implementation of SDEs in a non-linear mixed-effects model, where parameter estimation was performed by a novel approximation of the likelihood function. This approximation is constructed by combining the First-Order Conditional Estimation (FOCE) method used in non-linear mixed-effects modelling with the Extended Kalman Filter used in models with SDEs. Fundamental issues concerning the proposed model and estimation algorithm are addressed by simulation studies, concluding that system noise can successfully be separated from measurement noise and inter-individual variability.

The aims, data, models, results and value-to-drug development process across four stages of model development are described: (i) the validation of the pharmacokinetic assay; (ii) the development and application of an empirical patient pharmacokinetic/pharmacodynamic model; (iii) the development of a mechanistic-based model to bridge between patients and healthy volunteers; and (iv) propagation of the stage III model to a large efficacy study. The analyses utilised available concentration measurements (stages I-IV), CD11b receptor occupancy data (stages I-III) and neutrophil count data (stages III-IV) from three healthy volunteers (study 1, n=51; study 2, n=31; study 4, n=15) and two patient studies (study 3, n=169; study 5, n=992). In studies 1-4, subjects received placebo or between three and six doses of UK-279,276 covering a range of 0.006 and 1.5 mg/kg as a single 15-minute intravenous infusion. In study 5, subjects received placebo or one of 15 possible doses of UK-279,276 (10--20mg) assigned through adaptive design and administered as a single 15-minute intravenous infusion. All model building was conducted using NONMEM version VI (beta). The empirical pharmacokinetic/pharmacodynamic model developed during stage I was used to demonstrate that the pharmacokinetic assay was measuring biologically active drug. Simulations from the stage II model, developed from study 3, were used in the design of study 5. The model supported the switch to a fixed-dose regimen and the selection of the maximum dose and dosage increments. The common mechanistic-based model developed during stage III was used to support the 'comparability strategy' for UK-279,276 and provided insight into the underlying clearance mechanisms. At stage 4, the prior functionality available with NONMEM was used to successfully propagate the model from stage III in order to analyse the pharmacokinetic data from study 5. The analysis indicated that the exposure in study 5 was consistent with prior data. The role of empirical-based models in providing the learning for future mechanistic model development was highlighted. Similarly, the qualitative and quantitative aspects to knowledge propagation and the ultimate benefits from the development of the mechanistic-based model were demonstrated. While the empirical-based models were used to guide some early drug development decisions for UK-279,276, the development of the mechanistic-based model was valuable in linking the complex pharmacokinetics/pharmacodynamics of UK-279,276 across the phases of drug development.

In most of the patients with acute stroke receiving AR-R15896AR the intended high plasma levels were reached within a short time period. However, active treatment produced more side effects than placebo, thus indicating safety concerns and tolerability issues for use in high doses in an acute stroke population.

The objectives of this study were to develop a population pharmacodynamic model describing the in vitro drug sensitivity of tumor cells and to relate in vitro parameters to clinical outcome. Cell samples from 179 patients with acute myelocytic leukemia were exposed to cytosine arabinoside and daunorubicin, and cytotoxicity was analyzed using the fluorometric microculture cytotoxicity assay. A sigmoid E(max)-model for daunorubicin and an E(max)-model for cytosine arabinoside described the data. The model predicted drug potency (EC(50)) adequately from 1 concentration measurement. A logistic regression on individual in vitro parameters of 46 patients treated with the daunorubicin plus cytosine arabinoside regimen showed that the probability of complete response was significantly (P < .05) related to the product of the E(max)/EC(50) ratio of the two drugs. The findings demonstrate the value of population pharmacodynamic modeling of in vitro drug sensitivity data and a significant relationship between the in vitro parameters and clinical outcome.