Population pharmacokinetics of riociguat in a pediatric population (aged ≥ 6 years) with pulmonary arterial hypertension.

BACKGROUND: The PATENT-CHILD study investigated riociguat in children aged ≥ 6 to <18 years with pulmonary arterial hypertension (PAH) treated with tablets or an oral pediatric suspension based on bodyweight-adjusted dosing of up to 2.5 mg three times daily. PATENT-CHILD demonstrated an acceptable riociguat safety profile and individual plasma concentrations in pediatric patients were consistent with those in adult patients. METHODS: Using the data set from PATENT-CHILD and building on existing population pharmacokinetic (PK) models for riociguat and its major metabolite (M1) in adults with PAH, a coupled riociguat-M1 PK model was developed. The final model developed incorporated a one-compartment model for riociguat, coupled to a one-compartment model for M1, allowing for presystemic formation of M1. It included allometric scaling exponents for bodyweight. RESULTS: Apparent clearance of riociguat was similar in children and adult patients with PAH (median [interquartile range] 2.20 [1.75-3.44] and 2.08 L/h [1.55-2.97]). Factors contributing to lower PK exposure were lower riociguat maintenance dose in PATENT-CHILD, and a higher riociguat clearance in some adolescent patients, compared with adult patients. No effects of formulation, sex, or age on riociguat PK were observed. An exploratory PK/pharmacodynamics analysis found the increase in 6-min walking distance in pediatric patients treated with riociguat was not related to riociguat PK. CONCLUSIONS: Body size is the main determinant of PK in growing children, and the model supports clinical data that, for children weighing < 50 kg, a bodyweight-adjusted dose of riociguat should be used to achieve a similar exposure to that observed in adults with PAH.

A 3D brain unit model to further improve prediction of local drug distribution within the brain.

The development of drugs targeting the brain still faces a high failure rate. One of the reasons is a lack of quantitative understanding of the complex processes that govern the pharmacokinetics (PK) of a drug within the brain. While a number of models on drug distribution into and within the brain is available, none of these addresses the combination of factors that affect local drug concentrations in brain extracellular fluid (brain ECF). Here, we develop a 3D brain unit model, which builds on our previous proof-of-concept 2D brain unit model, to understand the factors that govern local unbound and bound drug PK within the brain. The 3D brain unit is a cube, in which the brain capillaries surround the brain ECF. Drug concentration-time profiles are described in both a blood-plasma-domain and a brain-ECF-domain by a set of differential equations. The model includes descriptions of blood plasma PK, transport through the blood-brain barrier (BBB), by passive transport via paracellular and transcellular routes, and by active transport, and drug binding kinetics. The impact of all these factors on ultimate local brain ECF unbound and bound drug concentrations is assessed. In this article we show that all the above mentioned factors affect brain ECF PK in an interdependent manner. This indicates that for a quantitative understanding of local drug concentrations within the brain ECF, interdependencies of all transport and binding processes should be understood. To that end, the 3D brain unit model is an excellent tool, and can be used to build a larger network of 3D brain units, in which the properties for each unit can be defined independently to reflect local differences in characteristics of the brain.

The 3D Brain Unit Network Model to Study Spatial Brain Drug Exposure under Healthy and Pathological Conditions.

PURPOSE: We have developed a 3D brain unit network model to understand the spatial-temporal distribution of a drug within the brain under different (normal and disease) conditions. Our main aim is to study the impact of disease-induced changes in drug transport processes on spatial drug distribution within the brain extracellular fluid (ECF). METHODS: The 3D brain unit network consists of multiple connected single 3D brain units in which the brain capillaries surround the brain ECF. The model includes the distribution of unbound drug within blood plasma, coupled with the distribution of drug within brain ECF and incorporates brain capillaryblood flow, passive paracellular and transcellular BBB transport, active BBB transport, brain ECF diffusion, brain ECF bulk flow, and specific and nonspecific brain tissue binding. All of these processes may change under disease conditions. RESULTS: We show that the simulated disease-induced changes in brain tissue characteristics significantly affect drug concentrations within the brain ECF. CONCLUSIONS: We demonstrate that the 3D brain unit network model is an excellent tool to gain understanding in the interdependencies of the factors governing spatial-temporal drug concentrations within the brain ECF. Additionally, the model helps in predicting the spatial-temporal brain ECF concentrations of existing drugs, under both normal and disease conditions.

The need for mathematical modelling of spatial drug distribution within the brain.

The blood brain barrier (BBB) is the main barrier that separates the blood from the brain. Because of the BBB, the drug concentration-time profile in the brain may be substantially different from that in the blood. Within the brain, the drug is subject to distributional and elimination processes: diffusion, bulk flow of the brain extracellular fluid (ECF), extra-intracellular exchange, bulk flow of the cerebrospinal fluid (CSF), binding and metabolism. Drug effects are driven by the concentration of a drug at the site of its target and by drug-target interactions. Therefore, a quantitative understanding is needed of the distribution of a drug within the brain in order to predict its effect. Mathematical models can help in the understanding of drug distribution within the brain. The aim of this review is to provide a comprehensive overview of system-specific and drug-specific properties that affect the local distribution of drugs in the brain and of currently existing mathematical models that describe local drug distribution within the brain. Furthermore, we provide an overview on which processes have been addressed in these models and which have not. Altogether, we conclude that there is a need for a more comprehensive and integrated model that fills the current gaps in predicting the local drug distribution within the brain.

Functions of the CB1 and CB 2 receptors in neuroprotection at the level of the blood-brain barrier.

The cannabinoid (CB) receptors are the main targets of the cannabinoids, which include plant cannabinoids, endocannabinoids and synthetic cannabinoids. Over the last few years, accumulated evidence has suggested a role of the CB receptors in neuroprotection. The blood-brain barrier (BBB) is an important brain structure that is essential for neuroprotection. A link between the CB receptors and the BBB is thus likely, but this possible connection has only recently gained attention. Cannabinoids and the BBB share the same mechanisms of neuroprotection and both protect against excitotoxicity (CB1), cell death (CB1), inflammation (CB2) and oxidative stress (possibly CB independent)-all processes that also damage the BBB. Several examples of CB-mediated protection of the BBB have been found, such as inhibition of leukocyte influx and induction of amyloid beta efflux across the BBB. Moreover, the CB receptors were shown to improve BBB integrity, particularly by restoring the tightness of the tight junctions. This review demonstrated that both CB receptors are able to restore the BBB and neuroprotection, but much uncertainty about the underlying signaling cascades still exists and further investigation is needed.