Advanced computational methods accelerate the development of drug-targeted therapy

Imperial College London, Duke National University School of Medicine in Singapore and Belgian pharmaceutical company UB have teamed up to discover a new anti-epileptic drug target and a new approach that promises to accelerate the treatment and drug development of debilitating diseases such as epilepsy.
the authors and others have developed an advanced computational method for predicting new drug targets. As a proof of concept, the researchers applied their calculations to epilepsy targets and drug discovery studies. Preclinical model test results verified the effectiveness of the target and the anti-epileptic effect after drug blocking.
"the identification of drug targets is very challenging, especially for brain diseases," the authors explain. "Through our approach - which we have named 'Target Discovery's Causal Reasoning Analysis Framework' or CRAFT - we have discovered and validated a potential new anti-epileptic drug in less than two years. Not only have we discovered a new drug candidate, but we have also created an innovation that promises to change medicine and improve lives by accelerating the identification of new drug molecules in human diseases. Using
"big data", CRAFT applied a system-level computing framework to drug target discovery, combining gene regulation information with causal reasoning. Starting with gene expression data from the target tissue, CRAFT's predictive framework identifies cell membrane subjects that regulate disease-related gene expression. This allows researchers to understand the mechanisms of disease and calculate the effectiveness of predicting potential drug targets.
epilepsy is a debilitating brain disease, with about one-third of people with epilepsy worldwide resistant to all anti-epileptic drugs currently available. Traditional drug development methods, especially for diseases of the central nervous system, suffer from a higher rate of elimination due to inadequate validation of drug targets in the early stages of detection.
"Compared to traditional drug discovery pipelines, CRAFT provides an efficient data-driven approach based on a systematic genetic framework that allows the identification of the gene network that drives the disease and its main control point in a record time - the strategic subjects we implement here to predict membranes as effective drug targets that can then be experimentally validated at the earliest stages of the drug discovery process," the authors explain.
system genetics approach to CRAFT replaces the traditional method of examining only one component of a complex system at a time. "We first described the disease based on its gene expression characteristics, and then used knowledge of gene control methods to identify membrane subjects that predict the regulation of disease status," the authors said. In particular, we chose to develop a way to link disease status to drugable membranes. Because more than half of existing drugs are known to target membranes, CRAFT ensures maximum drug reuse and quick proof of the concept of experimental medicine and the development of new drugs.
between academia and industry is key to this finding. "Our collaboration paves the way for drug discovery for epilepsy," said Dr Kaminski from UCCB. "We moved from traditional drug screening methods to computational identification of key disease drivers to match them to drugs that already exist in the desired mode of action. This new strategy has the potential to dramatically speed up the drug development process and bring new treatments to patients much faster. (Bio Valley)