The ability to identify factors that predict efficacy of anti–tumor necrosis factor (TNF) agents is crucial if clinicians are to optimize treatment and minimize side effects and costs.
Over the past 20 years, the use of anti—tumor necrosis factor (TNF) agents has revolutionized the treatment of patients with inflammatory bowel disease (IBD) by allowing them to avoid the use of steroids, promoting mucosal healing, reducing hospitalizations and surgeries, and dramatically improving quality of life. However, primary nonresponse to these drugs or loss of response (LOR) over time due to immunogenicity or treatment-related side-effects are a concern.
Nonresponse to anti-TNF therapy occurs in 20% to 40% of patients in clinical trials and in 10% to 20% of patients in real-world scenarios; the incidence of secondary LOR ranges from 23% to 46% a year after treatment initiation. Thus, the abililty to identify factors that predict efficacy of these drugs is crucial if clinicians are to optimize treatment and minimize side effects and costs.
To this point, studies investigating predictive factors have produced controversial results, according to a review by Loris Riccardo Lopetuso, MD, PhD, and colleagues, published in International Journal of Molecular Sciences. The authors undertake this goal through a literature search on the predictive factors of the short- and long-term benefits of anti-TNF therapy in IBD patients, evaluating multiple patient-, disease-, and treatment-related factors for infliximab, adalimumab, golimumab, and certolizumab pegol in both controlled clinical trials and real-world studies.
The authors found the following factors to be predictive of response to anti-TNF treatment in clinical practice:
The review also lists factors that could be predictive of response to anti-TNF therapy, but that are not yet available in clinical practice. These factors, which could be seen in controlled trials, include genetic variants of particular genes in patients with CD.
“Clearly, in the future, the creation of specific algorithms with the combination of multiple variables could deeply improve their predictive baseline strength before starting [anti-TNF] agents,” the researchers note. “At the same time, in the era of precision medicine, newly diagnosed IBD patients will need to have their genetic, microbiome, and immune characteristics measured at time 0, then matched to the most appropriate biological or immunosuppressive treatment based on likelihood of response/adverse effects.”