A dual-absorption pharmacokinetic model best described the complex pharmacokinetics of paliperidone after intramuscular administration of its palmitate ester. These results suggest that the pharmacokinetics of paliperidone palmitate are mostly influenced by BMI, CL(CR), INJS, IVOL and NDLL.

The objective of this analysis was to develop a semi-mechanistic nonlinear disease progression model using an expanded set of covariates that captures the longitudinal change of Alzheimer's Disease Assessment Scale (ADAS-cog) scores from the Alzheimer's Disease Neuroimaging Initiative study that consisted of 191 Alzheimer disease patients who were followed for 2 years. The model describes the rate of progression and baseline disease severity as a function of influential covariates. The covariates that were tested fell into 4 categories: (1) imaging volumetric measures, (2) serum biomarkers, (3) demographic and genetic factors, and (4) baseline cognitive tests. Covariates found to affect baseline disease status were years since disease onset, hippocampal volume, and ventricular volume. Disease progression rate in the model was influenced by age, total cholesterol, APOE ε4 genotype, Trail Making Test (part B) score, and current levels of impairment as measured by ADAS-cog. Rate of progression was slower for mild and severe Alzheimer patients compared with moderate Alzheimer patients who exhibited faster rates of deterioration. In conclusion, this model describes disease progression in Alzheimer patients using novel covariates that are important for understanding the worsening of ADAS-cog scores over time and may be useful in the future for optimizing study designs through clinical trial simulations.

A population pharmacokinetic model of doripenem was constructed using data pooled from phase 1, 2, and 3 studies utilizing nonlinear mixed effects modeling. A 2-compartment model with zero-order input and first-order elimination best described the log-transformed concentration-versus-time profile of doripenem. The model was parameterized in terms of total clearance (CL), central volume of distribution (V c ), peripheral volume of distribution (V p ), and distribution clearance between the central and peripheral compartments (Q). The final model was described by the following equations (for jth subject): CL j (liters/h) ‫؍‬ 13. interindividual variability (percent coefficient of variation [% CV]) for CL (liters/h), V c (liters), V p (liters), and Q (liters/h) were 13.6 (19%), 11.6 (19%), 6.0 (25%), and 4.7 (42%), respectively. Residual variability, estimated using three separate additive residual error models, was 0.17 standard deviation (SD), 0.55 SD, and 0.92 SD for phase 1, 2, and 3 data, respectively. Creatinine clearance was the most significant predictor of doripenem clearance. Mean Bayesian clearance was approximately 33%, 55%, and 76% lower for individuals with mild, moderate, or severe renal impairment, respectively, than for those with normal renal function. The population pharmacokinetic model based on healthy volunteer data and patient data informs us of doripenem disposition in a more general population as well as of the important measurable intrinsic and extrinsic factors that significantly influence interindividual pharmacokinetic differences.

Background The Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) has been used widely as a cognitive end point in Alzheimer’s Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS-Cog, but failure in these trials can be difficult to interpret because the scale has well-known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS-Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials. Methods Using Alzheimer’s Disease Neuroimaging Initiative study data, we identified the most informative cognitive measures from the ADAS-Cog and other available scales. We used cross-sectional analyses to characterize trajectories of ADAS-Cog and its individual subscales, as well as other cognitive, functional, or global measures across disease stages. Informative measures were identified based on standardized mean of 2-year change from baseline and were combined into novel composite endpoints. We assessed performance of the novel endpoints based on sample size requirements for a 2-year clinical trial. A bootstrap validation procedure was also undertaken to assess the reproducibility of the standardized mean changes of the selected measures and the corresponding composites. Results All proposed novel endpoints have improved standardized mean changes and thus improved statistical power compared with the ADAS-Cog 11. Further improvements were achieved by using cognitive–functional composites. Combining the novel composites with an enrichment strategy based on cerebral spinal fluid beta-amyloid (Aβ1-42) in a 2-year trial yielded gains in power of 20% to 40% over ADAS-Cog 11, regardless of the novel measure considered. Conclusion An empirical, data-driven approach with e xisting instruments was used to derive novel composite scales based on ADAS-Cog 11 with improved performance characteristics for MCI and early AD clinical trials. Together with patient enrichment based on Aβ1-42 pathology, these modified endpoints may allow more efficient clinical trials in these populations and can be assessed without modifying current test administration procedures in ongoing trials.