Billy Amzal

Rivastigmine exposure provided by a transdermal patch versus capsules.

ABSTRACT Objectives: The rivastigmine transdermal patch is the first transdermal treatment for Alzheimers disease (AD) and dementia associated with Parkinsons disease. The objective of this study was to evaluate the pharmacokinetics of rivastigmine following transdermal delivery by a patch versus oral delivery with conventional capsules in a population of AD patients. Methods: Both non-compartmental and compartmental analyses were performed on the same database showing relatively large inter-patient variations in pharmacokinetic parameters (up to 73% for the capsule group). The compartmental analysis provided model-based predictions of pharmacokinetic parameters, with the aim of comparing the two modes of administration when adjusting for confounding factors such as patient body weight and gender. Results: According to both non-compartmental and compartmental analyses, the patch provided significantly lower peak rivastigmine plasma concentrations (Cmax) and slower times to Cmax (tmax), compared with capsules. However, drug exposure (area under the curve; AUC) was not significantly different between the 4.6 mg/24 hour (5 cm2) patch and 3 mg BID (6 mg/day) capsule doses, or between the 9.5 mg/24 hour (10 cm2) patch and 6 mg BID (12 mg/day) capsule doses, according to both analyses. This suggests comparable exposure from these two rivastigmine delivery systems. Conclusion: The analyses were consistent with previous reports of a markedly less fluctuating, more continuous drug delivery with the rivastigmine patch. This characteristic delivery profile is associated with similar efficacy yet improved tolerability, compared with capsules.

Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates.

AbstractBackground and objective: Caffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool. Methods: In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm. Results: Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data. Conclusion: This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate.

Population effects and variability

Chemical risk assessment for human health requires a multidisciplinary approach through four steps: hazard identification and characterization, exposure assessment, and risk characterization. Hazard identification and characterization aim to identify the metabolism and elimination of the chemical (toxicokinetics) and the toxicological dose-response (toxicodynamics) and to derive a health-based guidance value for safe levels of exposure. Exposure assessment estimates human exposure as the product of the amount of the chemical in the matrix consumed and the consumption itself. Finally, risk characterization evaluates the risk of the exposure to human health by comparing the latter to with the health-based guidance value. Recently, many research efforts in computational toxicology have been put together to characterize population variability and uncertainty in each of the steps of risk assessment to move towards more quantitative and transparent risk assessment. This chapter focuses specifically on modeling population variability and effects for each step of risk assessment in order to provide an overview of the statistical and computational tools available to toxicologists and risk assessors. Three examples are given to illustrate the applicability of those tools: derivation of pathway-related uncertainty factors based on population variability, exposure to dioxins, dose-response modeling of cadmium.

The Yin–Yang of CYP3A4: a Bayesian meta-analysis to quantify inhibition and induction of CYP3A4 metabolism in humans and refine uncertainty factors for mixture risk assessment

Quantifying differences in pharmacokinetics (PK) and toxicokinetics (TK) provides a science-based approach to refine uncertainty factors (UFs) for chemical risk assessment. Cytochrome P450 (CYP) 3A4—the major hepatic and intestinal human CYP—and the P-glycoprotein (Pgp) transporter share a vast range of common substrates for which PK may be modulated through inhibition or induction in the presence of grapefruit juice (GFJ) or St. John’s wort (SJW), respectively. Here, an extensive literature search was performed on PK interactions for CYP3A4 and Pgp substrates after oral co-exposure to GFJ and SJW. Relevant data from 109 publications, extracted for both markers of acute (Cmax) and chronic [clearance and area under the plasma concentration–time curve (AUC)] exposure, were computed into a Bayesian hierarchical meta-analysis model. Bioavailability (F) and substrate fraction metabolised by CYP3A4 (Fm) were identified as the variables exhibiting the highest impact on the magnitude of interaction. The Bayesian meta-regression model developed provided good predictions for magnitudes of inhibition (maximum 5.3-fold with GFJ) and induction (maximum 2.3-fold with SJW). Integration of CYP3A4 variability, F, Fm and magnitude of interaction provided the basis to derive a range of CYP3A4 and Pgp-related UFs. Such CYP3A4 and Pgp-related UFs can be derived in the absence of human data using in vitro TK evidence for CYP3A4/Pgp inhibition or induction as conservative in silico options. The future development of quantitative in vitro–in vivo extrapolation models for mixture risk assessment is discussed with particular attention to integrating human in vitro and in vivo P/TK data on interactions with pathway-related variability.