A novel combination of scientific investigations has shown how a drug approved for one indication can be repurposed to treat another, in an approach that could be extended to other conditions.
The findings, in The New England Journal of Medicine, demonstrate the value of blending a pioneering artificial intelligence approach with clinical research and lab work.
Researchers discovered that an inhibitor of tumor necrosis factor (TNF) could effectively treat a patient with the rare immune disorder idiopathic multicentric Castleman’s disease (iMCD) who was about to enter palliative care.
The life-threatening disorder can result in systemic inflammation and lead to multi-organ failure relating to excess interleukin-6 and other cytokines. Prognosis is poor, with 25–35% of iMCD patients dying within five years of diagnosis.
The only therapy approved by U.S. regulators is inhibition of interleukin-6 with siltuximab, but this is only effective in 40–50% of patients.
The patient in the current study had the most severe form of iMCD, which involves thrombocytopenia, anasarca, fever, reticulin fibrosis or renal dysfunction, and organomegaly (TAFRO).
The iMCD-TAFRO was highly refractory to treatment, with multiple flares and relapses despite numerous treatments including, most recently, a Bruton’s tyrosine kinase inhibitor.
“The patient in this study was entering hospice care, but now he is almost two years into remission,” said co-author David Fajgenbaum, MD, from the University of Pennsylvania, who treated the person in question and who also has iMCD himself.
“This is remarkable not just for this patient and iMCD, but for the implications it has for the use of machine learning to find treatments for even more conditions.”
He added: “There are probably a few hundred patients in the United States and few thousand patients around the world who, each year, are in the midst of a deadly flare-up like this patient had been experiencing.
“More research is needed, but I’m hopeful that many of them could benefit from this new treatment.”
Fajgenbaum and team used proteomic, transcriptomic, in vitro modeling, and computational techniques to identify new targetable pathways for treatment.
There were several signs implicating TNF signaling in iMCD pathogenesis and suggesting it might be a valuable therapeutic target.
Serum proteomic analyses comparing 26 patients with iMCD–TAFRO with 15 healthy controls identified increased TNF signaling in the former.
Single-cell RNA sequencing of peripheral blood mononuclear cells also showed increased TNF expression in CD4+ T cells, particularly naive CD4+ T cells, from patients with iMCD–TAFRO.
After stimulating cells with phorbol myristate acetate and ionomycin, the percentage of naive CD4+ T cells that produced TNF was more than double in iMCD–TAFRO patients with than in healthy control individuals, at a corresponding 43% and 17%.
At the same time, the team pioneered a novel machine-learning approach to generate predictive scores for the likelihood of every drug approved by the U.S. Food and Drug Administration to treat every disease.
The TNF inhibitor, adalimumab, which is approved for multiple autoimmune disorders, was the top predicted treatment for iMCD after interleukin-6 inhibitors.
Interestingly, the researcher point out that TNF inhibition was predicted to affect CD4+ T cells as part of the potential mechanism.
Using adalimumab in the patient about to move to hospice care resulted in the current period of sustained remission, representing their longest since diagnosis.
“We used multiple strategies to identify TNF signaling as a new, targetable mechanism in iMCD–TAFRO,” the researchers summarized.
“This study also highlights the potential of this novel translational approach—combining high-throughput ‘omics’ data with in vitro studies and machine-learning models—to rapidly identify and prioritize new treatment strategies for rare diseases. Further studies are warranted to evaluate TNF inhibition in larger cohorts of patients with iMCD.”
