Stochastic formal model of PI3K/mTOR pathway in Alzheimer's disease for drug repurposing: An evaluation of rapamycin, LY294002, and NVP-BEZ235

Alzheimer's disease is the most common form of dementia characterized by the gradual loss of memory and cognition of patients. There is no drug capable of curing or preventing the disease, being the discovery of an efficient treatment for the disease is of vital importance. An approach that can contribute to this purpose of reducing time and costs of new discoveries is drug repositioning and in silico techniques. Statistical Model Checking is a formal verification technique that can aid in analyzing protein and drugs interactions and test pharmacological strategies, contributing to drug discovery and repurposing. In this work, we present a stochastic formal model that allows us to test different drugs, or combination of drugs, that target the PI3K/mTOR pathway and to evaluate the effect on tau protein and A beta peptide, which are two important components that contribute to the progression of Alzheimer's disease. We have analyzed the effect of rapamycin, LY294002, and NVP-BEZ235 on those proteins. Our results have shown that rapamycin has the potential to slow down one of the biological processes that cause neuronal death. Moreover, we have identified the ideal dose of rapamycin to obtain such results. However, our findings have unveiled that LY294002 and NVP-BEZ235 can increase tau phosphorylation in all scenarios tested. This is an alert to the scientific community, as this deleterious effect can be caused by other PI3K inhibitor drugs used to treat different diseases. These results suggest the inhibition of some proteins of the pathway can trigger an unexpected pathology. Besides that, rapamycin has not reduced this side effect when administered together with LY294002. The proposed methodology has been able to model both the disease and drug interactions. We have analyzed three drugs and our model is flexible to test other drugs and pharmacological strategies and efficient to analyze the properties of the model generating new insights.(c) 2023 Elsevier B.V. All rights reserved.