Introduction:Obesity is associated with many physiological changes. We review available evidence regarding five commonly accepted assumptions to a priori predict the impact of obesity on drug pharmacokinetics (PK). Areas covered: The investigated assumptions are: 1) lean body weight is the preferred descriptor of clearance and dose adjustments; 2) volume of distribution increases for lipophilic, but not for hydrophilic drugs; 3) CYP-3A4 activity is suppressed and UGT activity is increased, implying decreased and increased dose requirements for substrates of these enzyme systems, respectively; 4) glomerular filtration rate is enhanced, necessitating higher doses for drugs cleared through glomerular filtration; 5) drug dosing information from obese adults can be extrapolated to obese adolescents. Expert opinion: Available literature contradicts, or at least limits the generalizability, of all five assumptions. Clinical studies should focus on quantifying the impact of duration and severity of obesity on drug PK in adults and adolescents, and also include oral bioavailability and pharmacodynamics in these studies. Physiologically based PK approaches can be used to predict PK changes for individual drugs but can also be used to define in general terms based on patient characteristics and drug properties, when certain assumptions can or cannot be expected to be systematically accurate.

Edoxaban disposition and the variability in this disposition, including influence of covariates, after oral administration were adequately characterized in patients with NVAF. The 50 % dose reduction in patients with low WT (≤60 kg), moderate renal impairment (CLCR ≤50 mL/min), or concomitant P-gp inhibitors led to 30 % lower exposure than in the other patients.

ABSTRACT.In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.