Lata Vadlamudi

Personalising Epilepsy Regimes with Stem cells and artificial Intelligence models for Superior Treatment outcomes (PERSIST)

PERSIST, funded by the Medical Research Future Fund, is a new collaboration between the University of Queensland (QLD) and Monash University (VIC). We are now offering unique opportunities for trans-disciplinary honours/master/PhD research to students with background in neuroscience, cell biology, electrophysiology, machine learning, and clinical medicine. Students may be enrolled through either university, with opportunities to visit the other institution (depending on travel restrictions). Scholarships will be available for suitable students.

Epilepsy affects 1 in 26 people. Patients with recurrent seizures that may cause injuries or even death. Despite the development of many new medications over the last 20 years, more than 30% of patients do not have their seizure controlled. Currently it is not possible to predict which medications, either singly or in combination, will be effective for an individual patient, and no patient can trial all possible combinations within their lifetime. Under the current paradigm, the patient is sequentially trialled on different medications, doses and combinations in the hope of eventually finding an effective regime. For the patient this protracted (often years long) journey results in substantive co-morbidity, loss of productivity and greater risk of sudden death.

Instead of trial-and-error the PERSIST project will test a more personalised treatment strategy. The project aims to; a) use patient-specific induced pluripotent stem cell derived brain organoids to identify drugs that are able to modulate hyperactive neural activity, b) create an integrated predictive model for drug selection via artificial intelligence (AI) analysis of in vitro, clinical, and genomics data sets, c) validate treatment predictions in vitro and in real-world clinical care settings. The first part of this project will involve establishment of induced pluripotent stem cell lines from drug-resistant epilepsy patient blood samples and identify anti-seizure medications (ASMs) and drug combinations that suppress hyperactive neural activity in brain organoids derived from these cell lines. The second part of the project will involve integration of novel artificial intelligence approaches to further enhance the accuracy of these personalized drug efficacy profiles by incorporating single cell gene expression data sets, clinical information, and patient genomics data.