Simulation-based identification of optimal combination of drug candidates for spinal muscular atrophy

Abstract

Spinal Muscular Atrophy is the second leading genetic cause of infant mortality. Homozygous absence of the Survival Motor Neuron 1 gene is the cause of Spinal Muscular Atrophy, while Spinal Muscular Atrophy severity is mainly determined by the number of SMN2 copies. It was reported that the severity of Spinal Muscular Atrophy can be essentially alleviated by an increase of SMN2 mRNA and SMN protein concentrations through inhibiting HDAC - the major molecular regulator of SMN production pathway. Resveratrol, SAHA, TSA and VPA are potential drugs that increase SMN2 mRNA and SMN protein concentrations by inhibiting HDAC. AZA is another potential drug that positively affects SMN protein production by inhibiting methylation of SMN2 gene transcription factors. According to the wet lab experiments use of these chemicals in SMA patients lead to 1.3- to 2.7-fold increase of SMN protein levels. In the present research, we create deterministic model of SMN production pathway, perform computational validation of underlying pathway by known wet lab observations, and use model checking technique to determine an optimal combination of potential drugs that results in the maximum induction of SMN protein. The simulation results show that SMN concentration can be increased up to 3.84-fold over the control. The current work is conducted in terms of hybrid Petri nets on Snoopy platform. Proposed technique can be easily adapted to other disorders as well. (c) 2018 The Authors. Published by Elsevier B.V.