Tailoring epilepsy treatment: personalized micro-physiological systems illuminate individual drug responses

Shariff S., Kantawala B., Franco W. X. G., Ayele N. D., Munyangaju I., Alzain F. E., ...More

ANNALS OF MEDICINE AND SURGERY, no.6, pp.3557-3567, 2024 (ESCI) identifier identifier


Introduction:Approximately 50 million people worldwide have epilepsy, with many not achieving seizure freedom. Organ-on-chip technology, which mimics organ-level physiology, could revolutionize drug development for epilepsy by replacing animal models in preclinical studies. The authors' goal is to determine if customized micro-physiological systems can lead to tailored drug treatments for epileptic patients.Materials and methods:A comprehensive literature search was conducted utilizing various databases, including PubMed, Ebscohost, Medline, and the National Library of Medicine, using a predetermined search strategy. The authors focused on articles that addressed the role of personalized micro-physiological systems in individual drug responses and articles that discussed different types of epilepsy, diagnosis, and current treatment options. Additionally, articles that explored the components and design considerations of micro-physiological systems were reviewed to identify challenges and opportunities in drug development for challenging epilepsy cases.Results:The micro-physiological system offers a more accurate and cost-effective alternative to traditional models for assessing drug effects, toxicities, and disease mechanisms. Nevertheless, designing patient-specific models presents critical considerations, including the integration of analytical biosensors and patient-derived cells, while addressing regulatory, material, and biological complexities. Material selection, standardization, integration of vascular systems, cost efficiency, real-time monitoring, and ethical considerations are also crucial to the successful use of this technology in drug development.Conclusion:The future of organ-on-chip technology holds great promise, with the potential to integrate artificial intelligence and machine learning for personalized treatment of epileptic patients.