Swapping drugs could reduce interactions
The overall prevalence of potential drug-drug interactions among the patients included in the study was 81.2%.
The researchers then determined the proportion of patients who would be at risk of additional drug-drug interaction if they switched from one DMT to another, or to a Bruton tyrosine kinase inhibitor, given all their other medications.
They found, for example, that more than 40% of patients who switched to the immunomodulator fingolimod (Gilenya) would be at increased risk for bradycardia.
Just under 40% of patients who changed their DMT to the purine analogue cladribine (Mavenclad) would have an increased risk, or worsening, of bleeding, as would approximately 25% of those who switched to the anthracenedione antineoplastic agent mitoxantrone (Novantrone).
Dr. Hecker also showed the deep neural network could make suggestions as to how critical drug-drug interactions could be avoided by replacing interacting drugs with alternatives that have similar pharmacological effects.
For example, carbamazepine (Tegretol, Equetro) could be replaced with topiramate (several brand names) to avoid hepatotoxicity in patients also taking acetaminophen, while liothyronine (Cytomel, Triostat) could replace levothyroxine in patients also taking teriflunomide (Aubagio).
Finally, Dr. Hecker reported there was a subset of 6,860 potential drug-food interactions with the patients’ medications, resulting in reduced or increased concentrations of the drugs, particularly with fish or mushroom consumption.
He conceded, however, there were several limitations to their study, including that it included only small-molecule drugs, and that they did not ask patients about their diet or if they had observed any undesired drug effects.
Furthermore, “only a small number” of the potential drug-drug or drug-food interactions they identified would be “clinically relevant.”
Dr. Hecker also pointed out that each drug has one record, but it is used for different indications, with different dosages, and has different side effects, depending how it is used. “The model does not distinguish this,” he said, and so some of the interactions it highlights could be related to other doses than the one used in MS, for example.
Promise for the future
Pavan Bhargava, MBBS, MD, associate professor of neurology, Johns Hopkins Precision Medicine Center of Excellence for Multiple Sclerosis, Baltimore, commented that, as with all AI tools, “it’s only as good as what we’re putting into it.”
Dr. Bhargava, who cochaired the session, said that “there’s limitations on the information in the databases” that are being fed into the deep neural network.
He also highlighted that, “at this point, it didn’t seem like it was coming up with much clinically useful information,” but noted that, “we may get to that point.”
“Right now, there’s promise,” Dr. Bhargava said, but “it’s still not quite there.”
No funding was declared. Dr. Hecker declares relationships with Bayer HealthCare, Biogen, Merck Healthcare, Novartis, and Teva. Several other coauthors also declared financial relationships with industry.
A version of this article first appeared on Medscape.com.