The more medications a patient takes, the greater the likelihood that interactions between those drugs could trigger negative side effects, including long-term organ damage and even death. Now, researchers at Penn State have developed a machine learning system that may be able to warn doctors and patients about possible negative side effects that might occur when drugs are mixed.
In a study, researchers designed an algorithm that analyzes data on drug-drug interactions listed in reports — compiled by the Food and Drug Administration and other organizations — for use in a possible alert system that would let patients know when a drug combination could prompt dangerous side effects.
“Let’s say I’m taking a popular over-the-counter pain reliever and then I’m put on blood pressure medicine, and these medications have an interaction with each other that, in turn, affects my liver,” said Soundar Kumara, the Allen E. Pearce and Allen M. Pearce Professor of Industrial Engineering, Penn State. “Essentially, what we have done, in this study, is to collect all of the data on all the diseases related to the liver and see what drugs interact with each other to affect the liver.”
Drug-drug interaction problems are significant because patients are frequently prescribed multiple drugs and they take over-the-counter medicine on their own, added Kumara, who also is an affiliate of the Institute for CyberScience, which provides supercomputing resources for Penn State researchers.
“This study is of very high importance,” said Kumara. “Most patients are not on one single drug. They’re on multiple drugs. A study like this is of immense use to these people.”
To create the alert system, the researchers relied on an autoencoder model, which is a type of artificial neural network that is loosely designed on how the human brain processes information. Traditionally, computers require labeled data, which means people need to describe the data for the system, to produce results. For drug-drug interactions, it might require programmers to label data from thousands of drugs and millions of different combinations of possible interactions. The autoencoder model, however, is suited for semi-supervised algorithms, which means it can use both data that is labeled by people, and unlabeled data.
The high number of possible adverse drug-drug interactions, which can range from minor to severe, may inadvertently cause doctors and patients to ignore alerts, which the researchers call “alert fatigue.” In order to avoid alert fatigue, the researchers identified only interactions that would be considered high priority, such as life-threatening, disability, hospitalization and required intervention.
Kumara said that analyzing how drugs interact is the first step. Further development and refinement of the technology could lead to more precise — and even more personalized — drug interaction alerts.
“The reactions are not independent of these chemicals interacting with each other — that’s the second level,” said Kumara. “The third level of this is the chemical-to-chemical interactions with the genomic data of the individual patient.”
The researchers, who released their findings in a recent issue of Biomedical and Health Informatics, used self-reported data from the FDA Adverse Event Reporting System and information on potentially severe drug-drug interactions from the Office of the National Coordinator for Health Information Technology. They also used information from online databases at DrugBank and Drugs.com. Duplicate reports and reports about non-serious interactions were removed.
The list included about 2,891 drugs, or approximately 110,495 drug combinations. The researchers found a total of 1,740,770 reports on serious health outcomes from drug-drug interactions.
Learn more: AI could offer warnings about serious side effects of drug-drug interactions
The Latest on: Drug-drug interactions
[google_news title=”” keyword=”drug-drug interactions” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Drug-drug interactions
- Revealing Protein-Ligand Interactions Using AI Enables Drug Discoveryon April 26, 2024 at 7:34 am
Researchers developed a method to assess the ability of small molecules to bind to hundreds of human proteins using AI and ML.
- AI-Driven Drug Discovery by Pfizer and Austrian Institute Set to Transform Healthcareon April 26, 2024 at 5:38 am
Pfizer, the top pharmaceutical firm, and the Research Center for Molecular Medicine of the Austrian Academy of Sciences (CeMM) have developed an AI-driven drug discovery method The novel approach, ...
- A Closed-Loop Drug-Delivery System Could Improve Chemotherapyon April 25, 2024 at 3:19 pm
New CLAUDIA system could continuously monitor patients during an infusion, adjust dosage to maintain optimal drug levels ...
- Unplanned ‘Ozempic Babies’ Are on the Rise — but the Drug Can Cause ‘Pregnancy Complications'on April 25, 2024 at 8:39 am
Ozempic — the diabetes drug popularly used off-label for weight-loss — may increase fertility, but getting pregnant while taking the medication comes with a risk.
- The Difference Is the Data: Drug Discovery’s AI Revolutionon April 25, 2024 at 4:00 am
New data-intensive platforms continue to leverage artificial intelligence tools for improved speed and failure rate reductions for drug discovery.
- Whose guns, drugs are they? Man faces 28 years in prison, but now other man says some were hison April 24, 2024 at 9:54 am
Syracuse, N.Y. -- Anthony Braswell took a plea deal that would send him to prison for 28 years for pleading guilty to drugs and guns found in a Syracuse apartment. Now, another man has come forward to ...
- Madison firefighter faces felony drug charges, accused of selling cocaine out of fire stationon April 23, 2024 at 2:25 pm
Trevor Wiggins, 47, was charged April 12 with four felony drug trafficking charges after he allegedly sold cocaine out of the fire station he worked at.
- Responsible drug use? Jersey City council to vote on program that provides test strips, Narcan for userson April 23, 2024 at 1:53 pm
The city council is expected to vote Wednesday on a resolution to seek out quotes from companies to establish and operate an automated kiosk outside of the City Hall Annex, on 1 Jackson Square. The ...
- Oncologists' meetings with drug reps don't help cancer patients live longeron April 22, 2024 at 7:01 am
Drug company reps commonly visit doctors to talk about new medications. A team of economists wanted to know if that helps patients live longer. They found that for cancer patients, the answer is no.
- A Close Look at Denial of Pain Meds to People Who Use Drugson April 18, 2024 at 7:05 am
Researchers investigated factors associated with denial. These included self-management of pain prior to seeking formal pain treatment.
via Bing News