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Modeling suggests combined-drug treatments for disorders impairing synaptic plasticity via shared signaling pathways

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Abstract

Genetic disorders such as Rubinstein-Taybi syndrome (RTS) and Coffin-Lowry syndrome (CLS) cause lifelong cognitive disability, including deficits in learning and memory. Can pharmacological therapies be suggested that improve learning and memory in these disorders? To address this question, we simulated drug effects within a computational model describing induction of late long-term potentiation (L-LTP). Biochemical pathways impaired in these and other disorders converge on a common target, histone acetylation by acetyltransferases such as CREB binding protein (CBP), which facilitates gene induction necessary for L-LTP. We focused on four drug classes: tropomyosin receptor kinase B (TrkB) agonists, cAMP phosphodiesterase inhibitors, histone deacetylase inhibitors, and ampakines. Simulations suggested each drug type alone may rescue deficits in L-LTP. A potential disadvantage, however, was the necessity of simulating strong drug effects (high doses), which could produce adverse side effects. Thus, we investigated the effects of six drug pairs among the four classes described above. These combination treatments normalized impaired L-LTP with substantially smaller individual drug ‘doses’. In addition three of these combinations, a TrkB agonist paired with an ampakine and a cAMP phosphodiesterase inhibitor paired with a TrkB agonist or an ampakine, exhibited strong synergism in L-LTP rescue. Therefore, we suggest these drug combinations are promising candidates for further empirical studies in animal models of genetic disorders that impair histone acetylation, L-LTP, and learning.

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Acknowledgments

We thank Y. Zhang for comments on an earlier draft of the manuscript. Supported by NIH grants NS102490 (to JHB) and AG051807 and AG057558 (to MAW).

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Smolen, P., Wood, M.A., Baxter, D.A. et al. Modeling suggests combined-drug treatments for disorders impairing synaptic plasticity via shared signaling pathways. J Comput Neurosci 49, 37–56 (2021). https://doi.org/10.1007/s10827-020-00771-4

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