Quantitative pharmacology approach in Alzheimer's disease: efficacy modeling of early clinical data to predict clinical outcome of tesofensine

Model-Based Approach for Optimizing Study Design and Clinical Drug Performances of Extended-Release Formulations of Methylphenidate for the Treatment of ADHD

Methylphenidate (MPH) is currently used to treat children with attention deficit hyperactivity disorder (ADHD). Several extended-release (ER) formulations characterized by a dual release process were developed to improve efficacy over an extended duration. In this study, a model-based approach using literature data was developed to: 1) evaluate the most efficient pharmacokinetic (PK) model to characterize the complex PK profile of MPH ER formulations; 2) provide PK endpoint metrics for comparing ER formulations; 3) define criteria for optimizing development of ER formulations using a convolution-based model linking in vitro release, in vivo release, and hour-by-hour behavioral ratings of ADHD symptoms; and 4) define an optimized trial design for assessing the activity of MPH in pediatric populations. The convolution-based model accurately described the complex PK profiles of a variety of ER MPH products, providing a natural framework for establishing an in vitro/in vivo correlation and for defining criteria for assessing comparative bioequivalence of MPH ER products.

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with ADHD

Guanfacine extended-release (GXR) is a selective α2A-adrenergic receptor agonist approved in the United States for once-daily administration for the treatment of attention-deficit hyperactivity disorder (ADHD) in children and adolescents ages 6-17 years old either as monotherapy or adjunctive to stimulant medications. This analysis integrates exposure-response, placebo, and dropout data from 10 clinical trials that used GXR in adolescents and children with ADHD. In these trials, the ADHD Rating Scale-IV (ADHD RS-IV) score was collected longitudinally within patients over the course of 6-13 weeks. Non-linear mixed effects models were developed and used to describe the exposure-response of the GXR and placebo time course. The OpenBUGS program was utilized to describe the dropout time course across the trials. Placebo time course was best described by an inverse Bateman function with a 3-group mixture model that allowed for the onset and offset of the placebo response. Dropout time modeling indicated a missing at random mechanism for dropouts which was best described by a Weibull distribution with an estimated percentage of non-dropout patients. A linear exposure-response model with an adolescent effect on maximum slope (SLPmax), and a time delay for reaching SLPmax, provided the best description of the GXR exposure-response time course. The GXR exposure-response model indicated that the typical (95 % confidence interval) decrease in ADHD RS-IV score from the placebo-response trajectory would be 37.1 % (32.2, 42.0 %) per 0.1 mg/kg of GXR exposure. There was little noticeable difference between the exposure-response in adolescents and children or across ADHD subtypes.

A semi-physiological model of amyloid-β biosynthesis and clearance in human cerebrospinal fluid: a tool for alzheimer's disease research and drug development

Stable isotope labeling kinetics (SILK) was successfully applied to quantify endogenous amyloid-β (Aβ) metabolism in human cerebrospinal fluid (CSF). A semi-physiological model describing Aβ biosynthesis and degradation in human CSF and the impact of the γ-secretase inhibitor semagacestat should be developed and validated based on digitized data from three published SILK studies. Aβ biosynthesis was adequately characterized by six transit compartments. At each transition step, a first-order degradation process was implemented. A two-compartment model best described semagacestat CSF concentration-time profiles. Semagacestat concentrations were linked to the Aβ production by an inhibitory Emax model. For model validation, three individual Aβ profiles from literature were successfully predicted. Model application demonstrated a 35% decreased Aβ elimination rate constant in Alzheimer's disease (AD) patients. Study design optimization revealed that SILK studies could be conducted with significant less sampling points compared to the standard protocol without losing information about the Aβ metabolism, if analyzed by the presented model. In conclusion, the analysis outlined the advantages and opportunities of integrating all available data and knowledge into a semi-physiological model. The model can serve as valuable tool for researchers and clinicians interested in the pathology of AD as well as in the development of new therapeutics for AD.

Simultaneous pharmacokinetic model for rolofylline and both M1-trans and M1-cis metabolites

Rolofylline is a potent, selective adenosine A1 receptor antagonist that was under development for the treatment of patients with acute congestive heart failure and renal impairment. Rolofylline is metabolized primarily to the pharmacologically active M1-trans and M1-cis metabolites (metabolites) by cytochrome P450 (CYP) 3A4. The aim of this investigation was to provide a pharmacokinetic (PK) model for rolofylline and metabolites following intravenous administration to healthy volunteers. Data included for this investigation came from a randomized, double-blind, dose-escalation trial in four groups of healthy volunteers (N=36) where single doses of rolofylline, spanning 1 to 60 mg ,were infused over 1-2 h. The rolofylline and metabolite data were analyzed simultaneously using NONMEM. The simultaneous PK model comprised, in part, a two-compartment linear PK model for rolofylline, with estimates of clearance and volume of distribution at steady-state of 24.4 L/h and 239 L, respectively. In addition, the final PK model contained provisions for both conversion of rolofylline to metabolites and stereochemical conversion of M1-trans to M1-cis. Accordingly, the final model captured known aspects of rolofylline metabolism and was capable of simultaneously describing the PK of rolofylline and metabolites in healthy volunteers.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.