Hi Tim, this place of discussion is great! I am wondering if mathematical models have brought any light to the understanding of DA. Thanks and warm regards from Brazil. Mauricio.
Thank you for the question, Mauricio. There certainly are a number of groups attempting to use mathematical modeling to gain some insight into AD pathology progression (some recent examples include: J Math Biol. 2014 Nov;69(5):1207-35, Alzheimers Dement. 2011 Mar;7(2):151-60, PLoS One. 2010 Dec 14;5(12):e15176, and J Math Biol. 2013 Dec;67(6-7):1369-92) and others seeking to predict the potential of various therapeutic approaches (e.g. Bull Math Biol. 2002 Sep;64(5):1011-31, J Biomol Struct Dyn. 2015 Oct 15:1-10, and PLoS One. 2014 Dec 4;9(12):e114339). However, I am not aware (and perhaps some of the panelists or other users of this site may correct me) of any of these modeling strategies leading directly to new insights into AD pathogenic mechanisms or approaches for clinical intervention. This may be due more to the apparent lack of communication between the computational modeling labs and their experimental counterparts than to any deficiency in the models proposed, though. It will be interesting to see whether such modeling can provide novel predictions about AD pathogenesis or progression that can be confirmed in pre-clinical models and, hopefully, AD patients in the future.
In terms of mathematical modeling in general and Quantitative Systems Pharmacology (QSP) in particular, we have developed a mechanism-based computer platform, based on the neurophysiology and neuropharmacology of biophysically realistic neuronal circuit networks and implemented with an Alzheimer pathology. This model for symptomatic cognitive effects is calibrated with ADAS-Cog data on 28 different drug-dose-trial duration scenarios (Roberts et al. 2012 Simulations of Symptomatic Treatments for Alzheimer's Disease: Computational Analysis of Pathology and Mechanisms of Drug Action, Alzheimer Research & Therapy, 4:50). Interestingly, this QSP platform was able to predict prospectively an unexpected clinical outcome for a 5-HT4 partial agonist in a Ph1 scopolamine trial. (Nicholas et al 2013 Systems Pharmacology Modeling in Neuroscience: Prediction and Outcome of PF-04995274, a 5HT4 Partial Agonist, in a Clinical Scopolamine Impairment Trial , Advances in Alzheimer’s Disease, Vol.2, No.3, 83-98). We are now applying this model to simulate the impact of different beta-amyloid peptide dynamics on cognitive outcome by implementing insights from human SILK data biochemical aggregation kinetics and interaction of both Ab40 and Ab42 on glutamate and nicotinic AchR (Geerts et al 2014 Using in silico mechanistic disease modeling to address the effect of amyloid beta manipulation on cognitive clinical readouts Alzheimer’s & Dementia, Vol. 7, Issue 4, S774–S775). This is yielding interesting insights into the pharmacodynamic effect of passive beta-amyloid vaccination strategies. Because the platform also incorporates the neuropharmacology of different G-protein coupled receptors it allows to simulate the pharmacodynamic impact of comedications and a number of common genotypes, such as APOE, COMTV1l136Met, 5-HTTLPR s/l and CACNA1C.