Big data method could speed up the hunt for new drugs | Digital Science

Researchers have discovered a new anti-epileptic drug target and a whole new approach that promises to speed up the discovery of future drugs to treat debilitating diseases, including epilepsy.

The researchers developed an advanced computational approach to predict new drug targets. As a proof-of-concept, the investigators applied the computational approach to epilepsy and discovered a new drug target, which they then experimentally confirmed by showing that the pharmacological blockade of the target had anti-seizure effects.

“The identification of drug targets is highly challenging, particularly for diseases of the brain,” explains Michael Johnson, a professor in the division of brain sciences at Imperial College London. “With our approach—which we named the ‘Causal Reasoning Analytical Framework for Target discovery,’ or CRAFT—we discovered and validated a potential new anti-epileptic drug in less than two years.”

Getting CRAFTy

CRAFT draws on genomic “big data” and applies a systems-level computational framework to drug target discovery that combines gene regulatory information with causal reasoning. Starting from gene expression data from the target tissue, CRAFT’s predictive framework identifies cell membrane receptors that play a regulatory role in disease-related gene expression. This enables researchers to understand the mechanism of action of a disease, and computationally predict the effectiveness of a potential drug target.

Epilepsy is a debilitating brain disease. Approximately one in three epilepsy patients are resistant to all currently available anti-epileptic drugs and none of the current drugs are disease modifying or curative. Conventional drug development methods, particularly for diseases of the central nervous system, suffer from a high rate of attrition due to inadequate drug target validation in the early stages of discovery, researchers say.

“In contrast with traditional drug discovery pipelines, CRAFT provides an efficient data-driven approach based on a systems genetics framework that permits the identification of gene networks driving disease and their master control points in record time—a strategy that we implemented here for predicting membrane receptors as effective drug targets that can then be experimentally validated at the earliest stage of the drug discovery process,” says Enrico Petretto, an assistant professor at the Centre for Computational Biology at Duke-NUS Medical School who also leads the Systems Genetics group there.

A faster way

CRAFT’s systems genetics approach replaced the traditional approach of examining only one component of a complex system at a time.

“We first describe the disease in terms of its gene expression signature and then using knowledge of how genes are controlled, CRAFT identifies membrane receptors predicted to exert a regulatory effect over the disease state,” Johnson explains.

“We specifically chose to develop a method connecting disease states to druggable membrane receptors because over half of existing drugs already target membrane receptors, and so CRAFT allows the maximum opportunity for drug repurposing and rapid experimental medicine proofs of concept, as well as new drug development,” he says. “In the case of epilepsy, this led to the identification of the microglial membrane receptor Csf1R as a potential new therapeutic target for epilepsy.”

“We moved away from traditional drug screening approaches into computational identification of key disease drivers in order to match them with already existing drugs that have the desired mode of action. This new strategy has the potential to dramatically accelerate the drug development process and bring new treatments to patients much faster,” says Rafal Kaminski from the pharmaceutical company UCB in Belgium.

The study appears in Nature Communications.

Source: National University of Singapore