The investigators noted that their results can be replicated independently in a real-world cohort, they may have clinical implications for daily practice.
Data compiled from 2 independent multiple sclerosis (MS) datasets, a randomized controlled trial and an observational cohort, suggest that a criterion such as Treatment Response Score to help choose between glatiramer acetate and interferon beta-1a (IFN-ß1a) as a treatment for patients based on their characteristics is possible to develop.
The investigators, including Maria Pia Sormani, PhD, MS, faculty, Department of Health Sciences, University of Genoa, and colleagues noted that if their results can be replicated independently in a real-world cohort, they may have clinical implications for daily practice. The study findings were published in Neurology.
“The methodology presented here can be applied to all the comparisons involving currently available therapies and it should be a stimulus for pharmaceutical companies to allow the re-analysis of their clinical trial data to try to define the drug responders’ profiles,” Sormani et al. wrote. “It will be essential to develop more extended scores which integrate imaging, biological and genetic predictive markers to tailor treatment for each individual patient.”
The group used data from the CombiRx trial (NCT00211887) and a cohort from the MSbase registry. Overall, the primary outcome of annualized relapse rate (ARR) ratio of glatiramer acetate compared to IFN-ß1a in the CombiRx trial was 0.72 (95%CI, 0.55–0.95; P = .018), a difference of 28%. Data were available for 98.8% (n = 503) of patients of the 2 arms (IFNß-1a and GA).
Sormani and colleagues then used Treatment Response Scores (consisting of a linear combination of age, sex, relapses in the previous year, disease duration and Expanded Disability Status Scale [EDSS] score) to detect differential response for glatiramer acetate vs. IFN-ß1a. They found that in the trial those with the largest benefits from glatiramer acetate vs IFN-ß1a—the lowest score quartile—had an ARR ratio of 0.40 (95% CI, 0.25–0.63). Those in the middle quartiles had an ARR ratio of 0.90 (95% CI, 0.61–1.34), and those in the upper quartile had an ARR ratio of 1.14 (95% CI, 0.59–2.18; heterogeneity P = .012).
“The treatment by group interaction indicates that the relative effectiveness of the 2 compared therapies on ARR are associated with the Treatment Response Score,” Sormani et al. wrote. Using the MSbase cohort to validate the results, Sormani et al. reported corresponding ARR ratios of 0.58 (95% CI, 0.46–0.72) for the lower quartile, 0.92 (95% CI, 0.77–1.09) for the middle quartiles, and 1.29 (95% CI, 0.97–1.71; heterogeneity P <.0001) for the upper quartile.
The investigators conducted a sensitivity analysis including the number of gadolinium-enhancing (Gd+) lesions at baseline, which improved the performance of the Treatment Response Score. The ARR ratios for those with large benefit from glatiramer acetate over IFN-ß1a, those with small benefit, and those with small benefit from IFN-ß1a over glatiramer acetate were 0.38, to 0.81, to 1.33 in the training CombiRx dataset, and 0.64, 1.13, and 1.60 in the validation MSBase dataset, respectively.
To elucidate the practical implications of these data, Sormani et al. used a case of a 25-year-old man with MS, with a disease duration of 2 years, 1 relapse in the previous year, and an EDSS score of 1. His Treatment Response Score would be -0.63, classifying him as receiving large benefit from glatiramer acetate over IFN-ß1a. The estimated ARR ratio of GA vs IFNbeta-1a for MS (0.58) indicates a 42% reduction of ARR of GA vs IFNbeta-1a.
“The aim of personalized medicine is the tailoring of medical treatment to the individual characteristics of each patient in order to optimize individuals’ outcomes,” Sormani et al. wrote. "The key issue for personalized medicine is finding the criteria for an early identification of patients who can be responders or non-responders to each therapy. This is now an important topic in MS, due to the availability of many effective drugs, making informed treatment decisions complex.”
A calculator of the Treatment Response Score was made available by the authors, which can be found by clicking here.