A multi-institutional group of researchers led by Harvard Medical School and the Novartis Institutes for BioMedical Research has created an open-source machine learning tool that identifies proteins associated with drug side effects.
The work, published June 18 in the Lancet journal EBioMedicine, offers a new method for developing safer medicines by identifying potential adverse reactions before drug candidates reach human clinical trials or enter the market as approved medicines. The findings also offer insights into how the human body responds to drug compounds at the molecular level in both desired and unintended ways.
“Machine learning is not a silver bullet for drug discovery, but I do believe it can accelerate many different aspects in the difficult and long process of developing new medicines,” said paper co-first author Robert Ietswaart, a research fellow in genetics in the Blavatnik Institute at Harvard. “Although it cannot predict all possible adverse effects, we hope that our work will help researchers spot potential trouble early on and develop safer drugs in the future.”
Drug side effects, technically known as adverse drug reactions, range from mild to fatal. They may occur either when taking a drug as prescribed or as a result of incorrect dosages, interaction of multiple medicines, or off-label use (taking a drug for something other than what it was approved for). Adverse drug reactions are responsible for 2 million U.S. hospitalizations each year, according to the Department of Health and Human Services, and occur during 10% to 20% of hospitalizations, according to the Merck Manuals.
Researchers and healthcare providers have applied many tactics over the decades to avoid or at least minimize adverse drug reactions. However, because a single drug often interacts with multiple proteins in the body—not always limited to the intended targets—it can be hard to predict what, if any, side effects a medicine may generate. Further, if a drug does end up causing an adverse reaction, it can be hard to identify which of its protein targets could be responsible.
In the new study, researchers took one existing database of reported adverse drug reactions and another database of 184 proteins that specific drugs are known to often interact with. Then they constructed a computer algorithm to connect the dots.
“Learning” from the data, the algorithm unearthed 221 associations between individual proteins and specific adverse drug reactions. Some were known and some were new. The associations indicated which proteins likely represent drug targets that contribute to particular side effects and which others may be innocent bystanders.
Based on what it has already “learned,” and strengthened by any new data that researchers feed it, the program may help doctors and scientists predict whether a new drug candidate is likely to cause a certain side effect on its own or when combined with particular medicines. The algorithm can help with these predictions before a drug is tested in humans, based on lab experiments that reveal which proteins the drug interacts with. The hope is to raise the likelihood that a drug candidate will prove safe for patients before and after it reaches the market.
“This could reduce the risks that study participants face during the first-in-human clinical trials and minimize risks for patients if a drug gains [U.S. Food and Drug Administration] approval and enters clinical use,” said Ietswaart.
Edited by Gary Cramer
