Cesar Pichardo-Almarza (1), Diana Clausznitzer (2), Neil Benson (1)
(1) Certara QSP. Unit 43, Innovation centre, University road, Canterbury. CT2 7FG, UK, (2) AbbVie Deutschland GmbH & Co. KG, Drug Metabolism and Pharmacokinetics, Knollstraße, 67061, Ludwigshafen, Germany
Introduction:
Physiological modelling and simulation including brain physiology (and/or pharmacology) have been typically based on the assumption that brain is a constant volume. However, several studies have shown that the volume of the brain is not constant during the life of a person, being fully developed around 20 years and starting to decrease after this age [1]. When studying neurodegenerative chronic diseases, e.g. Alzheimer’s disease, changes in brain volume, either growth during brain development or decrease during adulthood and later ages, becomes quite important in the estimation of the concentration of specific biomarkers in brain, e.g. amyloid beta, cholesterol, etc.
Objectives:
- Developing a new Quantitative Systems Pharmacology (QSP) modelling methodology considering brain volume changes for the estimation of biomarker concentrations.
- Comparing the results of a model with variable brain volume with a model using constant volume.
- Evaluating the effect of the variability of brain volume changes in a given population on the estimation of biomarker concentrations.
Methods:
The methodology proposed includes the implementation of a Quantitative Systems Pharmacology (QSP) model describing the main mechanisms related to Amyloid Precursor Protein (APP) processing and amyloid-beta production and accumulation in brain [2,3] in combination with an “ageing” model considering the effects of ageing on the variations (i.e. decrease) in the brain volume. The model was implemented in Matlab/Simbiology version 2017b (The Mathworks Inc., Natick, USA) as the flexibility of this modelling software allows the modeller adding the additional feature of variable volume in an easy and user-friendly way. The model was simulated using a stiff ODE (ordinary differential equation) solver (ode15s) given the different time scales needed to be considered and the time span being simulated (several decades of a patient live). To evaluate the variability of brain volume changes in a given population, the model was evaluated for three different virtual populations, i.e. only male, only female and mixed (50% males and 50% females) populations, simulating the dynamic biomarker changes for each of them.
Results:
The integrated Quantitative Systems Pharmacology (QSP) model shows sensible simulation results when comparing with clinical data published in the literature. When studying the accumulation of specific biomarkers (e.g. amyloid-beta), using a dynamic model which considers the lifespan of a patient and his brain volume change because of ageing, allows seeing how the decrease in brain volume can be related to the increasing concentration levels observed in the clinic.
Conclusions:
Simulation results from the integrated Quantitative Systems Pharmacology (QSP) model proposed suggest that the inclusion of a variable brain volume can help to have a better dynamic description of neurodegenerative chronic diseases in ageing populations and also to have a better estimation of biomarker concentration levels in this specific tissue.
References:
[1] Dekaban, A. S. and Sadowsky, D. (1978), Changes in brain weights during the span of human life: Relation of brain weights to body heights and body weights. Ann Neurol., 4: 345–356
[2] Potter R, Patterson BW, Elbert DL, Ovod V, Kasten T, Sigurdson W, et al. Increased in vivo Amyloid-β42 production, exchange, and irreversible loss in Presenilin Mutations Carriers. Science Translational Medicine. 2013
[3] Elbert DL, Patterson BW, Bateman RJ. Analysis of a compartmental model of amyloid beta production, irreversible loss and exchange in humans. Mathematical biosciences. 2015;261:48-61. doi:10.1016/j.mbs.2014.11.004.
Reference: PAGE 27 (2018) Abstr 8504 [www.page-meeting.org/?abstract=8504]
Poster: Methodology - New Modelling Approaches