Predictive model of response to tafamidis in hereditary ATTR polyneuropathy
Cecília Monteiro, … , Teresa Coelho, Jeffery W. Kelly
Cecília Monteiro, … , Teresa Coelho, Jeffery W. Kelly
Published June 20, 2019
Citation Information: JCI Insight. 2019;4(12):e126526. https://doi.org/10.1172/jci.insight.126526.
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Clinical Research and Public Health Neuroscience Therapeutics

Predictive model of response to tafamidis in hereditary ATTR polyneuropathy

Abstract

BACKGROUND The hereditary transthyretin (TTR) amyloidoses are a group of diseases for which several disease-modifying treatments are now available. Long-term effectiveness of these therapies is not yet fully known. Moreover, the existence of alternative therapies has resulted in an urgent need to identify patient characteristics that predict response to each therapy.METHODS We carried out a retrospective cohort study of 210 patients with hereditary TTR amyloidosis treated with the kinetic stabilizer tafamidis (20 mg qd). These patients were followed for a period of 18–66 months, after which they were classified by an expert as responders, partial responders, or nonresponders. Correlations between baseline demographic and clinical characteristics, as well as plasma biomarkers and response to therapy, were investigated.RESULTS 34% of patients exhibited an almost complete arrest of disease progression (classified by an expert as responders); 36% had a partial to complete arrest in progression of some but not all disease components (partial responders); whereas the remaining 30% continued progressing despite therapy (nonresponders). We determined that disease severity, sex, and native TTR concentration at the outset of treatment were the most relevant predictors of response to tafamidis. Plasma tafamidis concentration after 12 months of therapy was also a predictor of response for male patients. Using these variables, we built a model to predict responsiveness to tafamidis.CONCLUSION Our study indicates long-term effectiveness for tafamidis, a kinetic stabilizer approved for the treatment of hereditary TTR amyloidosis. Moreover, we created a predictive model that can be potentially used in the clinical setting to inform patients and clinicians in their therapeutic decisions.

Authors

Cecília Monteiro, Jaleh S. Mesgazardeh, João Anselmo, Joana Fernandes, Marta Novais, Carla Rodrigues, Gabriel J. Brighty, David L. Powers, Evan T. Powers, Teresa Coelho, Jeffery W. Kelly

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Figure 1

Patients can be classified in 3 groups according to expert opinion and selected outcome measures.

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Patients can be classified in 3 groups according to expert opinion and s...
(A) Study flowchart. (B) NIS change from baseline and (C) change in weight (in kg) from baseline according to expert opinion response classification in 3 groups. x axis in B and C represent number of follow-up months (m); numbers below B and C represent number of patients evaluated at each time point (NR, nonresponders; PR, partial responders; R, responders). Data are shown as median, with error bars representing interquartile range; P values were calculated using Kruskal-Wallis test with Dunn’s multiple-comparisons correction. *P < 0.05, **P < 0.01; P values are only shown when differences exist between the 3 groups using multiple-comparisons correction.