Xinhua News Agency, Hong Kong, March 18 (Reporter Zhang Yashi) City University of Hong Kong (CityU) told reporters on the 18th that the scientific research team led by the university successfully developed a technology platform that can more quickly and accurately predict the efficacy of drugs for nervous system diseases, which will help reduce the cost of developing new drugs.
CityU's Department of Biomedical Engineering and Biomedical Sciences, together with Harvard Medical School, spent five years conducting research aimed at providing a technology platform to predict whether compounds have the potential to develop new drugs for the treatment of brain diseases.
The research team used zebrafish as the subject of the study, applied a clinical central nervous system drug to thousands of zebrafish one by one, and made a map of brain activity. These atlases show how different regions of the zebrafish's brain respond to these drugs.
Shi Peng, an associate professor in the Department of Biomedical Engineering at CityU who led the research, said that the research team used robots, microfluidics and hydrodynamics to automatically catch and fix an unesthetized zebrafish within 20 seconds, so multiple zebrafish could be scanned at the same time without the need to use anesthetics to reduce the motor function of zebrafish and avoid the interference of anesthetics.
The team used machine learning algorithms to predict the potential clinical therapeutic efficacy of 121 novel compounds, predicted that 30 of them had anti-epileptic properties, and then randomly selected 14 of these 30 compounds to conduct behavioral tests in animal models of epilepsy in zebrafish.
The results showed that 7 of the 14 compounds reduced zebrafish seizures without any calming side effects.
Shi Peng said that this study, combined with a rapid in vivo drug screening system and machine learning, provides a shortcut to assist in screening out new compounds with more therapeutic medicinal potential, which can accelerate drug development and reduce the failure rate throughout the process.
The results of the study have been published in the scientific journal Nature Communications.