Optimizing Performance of Endogenous Neural Stem Cell Therapy for Ischemic Stroke: A Neuroinformatics and Neuroimaging Approach to Translational Medicine
Suhela Kapoor (National Brain Research Centre), VPS Rallabandi (National Brain Research Centre), Prasun Roy (National Brain Research Centre)
Material & Methods: A predictive mathematical model was designed to discretize the steps involved in neural stem cell proliferation, migration and differentiation leading to neurorestorative recovery. MATLAB algorithms were run to compute the optimal dosage and time-point of drug administration. To verify the accuracy of the design, a robust rodent ischemia model using the Middle Cerebral Arterial Occlusion (MCAO) technique was established. The effect of multiple combinations such as erythropoeitin derivatives, brain-derived neurotrophic factor and insulin-like growth factor (IGF-1) versus control is checked. MRI & Diffusion-weighted imaging is done to ensure similar ischemic lesions across patients and also reduction of hypoxic volume post-therapy. Behavioral monitoring using a battery of sensory-motor tests is done to correlate with biochemical and cellular changes.
Results: On analysis of the effect of applying the computed dose of therapeutic agent at an optimal time point, on neural progenitor dynamics, we observed a strong peak of synaptic recovery. Findings based on animal experiments, MRI and histopathology provide empirical corroboration, thus establishing this approach to be useful for optimizing recovery in ischemic stroke.
Conclusions: Given that the ischemic brain has evolved an incisive way to partly recoup itself by increasing the production of endogenous stem cell niches, the proposed approach can enable maximal/optimal recovery. Our efforts can be seen as the 1st endeavour of incorporating endogenous stem-cell processing influenced by neuro-modulators as a robust neuroinformatics template that allows for incorporation of patient specific parameters, thereby enabling one to optimize recovery using image-guided drug-scheduling.