Computational identification of potential multitarget treatments for ameliorating the adverse effects of amyloid-β on synaptic plasticity.
The leading hypothesis on Alzheimer Disease (AD) is that it is caused by buildup of the peptide amyloid-β (Aβ), which initially causes dysregulation of synaptic plasticity and eventually causes destruction of synapses and neurons. Pharmacological efforts to limit Aβ buildup have proven ineffective, and this raises the twin challenges of understanding the adverse effects of Aβ on synapses and of suggesting pharmacological means to prevent them. The purpose of this paper is to initiate a computational approach to understanding the dysregulation by Aβ of synaptic plasticity and to offer suggestions whereby combinations of various chemical compounds could be arrayed against it. This data-driven approach confronts the complexity of synaptic plasticity by representing findings from the literature in a course-grained manner, and focuses on understanding the aggregate behavior of many molecular interactions. The same set of interactions is modeled by two different computer programs, each written using a different programming modality: one imperative, the other declarative. Both programs compute the same results over an extensive test battery, providing an essential crosscheck. Then the imperative program is used for the computationally intensive purpose of determining the effects on the model of every combination of ten different compounds, while the declarative program is used to analyze model behavior using temporal logic. Together these two model implementations offer new insights into the mechanisms by which Aβ dysregulates synaptic plasticity and suggest many drug combinations that potentially may reduce or prevent it.
['Anastasio|Thomas J|TJ|']
Alzheimer disease computational model drug targets formal methods multidrug therapy