Minimizing Drug Side Effects with HPC

By Tiffany Trader

October 20, 2014

It’s an unfortunate side effect of the drug development process that unsafe drugs sometimes slip through the extensive vetting period. So far no magic bullet drug has been developed that is completely without risk, but scientists are working hard to root out dangerous side effects, which according to the journal Nature, kill at least 100,000 patients a year.

A team of researchers from Lawrence Livermore National Laboratory is studying the connection between protein binding and adverse drug reactions (ADR) or side effects. Using the lab’s supercomputers, the researchers identified proteins that cause medications to have certain adverse drug reactions. They devised a high-tech method of processing proteins and drug compounds that produces reliable data outside of a laboratory setting.

The team presented its findings in the journal PLOS ONE under the title “Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computer Machines.” An article on the lab’s website has additional information.

“We need to do something to identify these side effects earlier in the drug development cycle to save lives and reduce costs,” said Monte LaBute, a researcher from LLNL’s Computational Engineering Division and the paper’s lead author.

On average, it takes pharmaceutical companies about 15 years and $2 billion to bring a drug to market. Out of 100 drugs that undergo early state testing, just eight make it to market.

The discovery process usually starts by establishing the relevant proteins for a given disease. The binding phase tests candidate drug compounds with potential target proteins, yielding data about the drug’s effectiveness (efficacy) and its harmful side effects (toxicity).

This approach provides information about some potential or likely side effects, but there are additional so-called “off-target” proteins that could bind to a candidate drug causing unanticipated side effects.

Testing drug candidates against a complete set of off-target proteins is time-consuming and costly, so pharmaceutical companies employ sampling methods. This is how sometimes serious side effects (caused by off-target proteins) can elude the safety net of the testing process.

For the study, the lab researchers took 906 FDA-approved small molecule compounds and 409 protein targets from public databases and fed these through an LLNL software program called VinaLC to determine docking scores in order to assess binding. The binding scores were fed into another computer program and compared with 560 FDA-approved drugs with known side effects. The analysis revealed significant information about the likelihood of off-target effects.

The study showed that for two categories of disorders — vascular disorders and neoplasms — the computational approach was more predictive than current statistical methods that do not use binding scores.

“We have discovered a very viable way to find off-target proteins that are important for side effects,” LaBute said. “This approach using HPC and molecular docking to find ADRs never really existed before.”

The team plans to scale its model to include more off-target proteins until it can screen every protein in the body. It’s a goal that could take a decade to complete, but LaBute says it will only be possible with cooperation from pharmaceutical companies, health care providers and the FDA.

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