Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning.

ABSTRACT

WHAT IS KNOWN AND OBJECTIVES: The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE. METHODS: The Emax model was selected for machine learning, where the evaluation indicator was the change rate of systemic lupus erythematosus disease activity index from baseline value. RESULTS: A total 103 patients with SLE were included for modelling, where the Emax , ET50 were -53.9%, 1.53 months in the final model respectively, and the evaluation of the final model was good. Further simulation found that the follow-up time to achieve 25%, 50%, 75% and 80% (plateau) Emax of sirolimus effecting on disease activity in patients with SLE were 0.51, 1.53, 4.59 and 6.12 months, respectively. In addition, the sirolimus dosage was flexible and adjusted according to drug concentration, where the intersection of sirolimus concentration range included in this study was about 8-10 ng/ml. WHAT IS NEW AND CONCLUSIONS: This study was the first time to predict the effect of sirolimus on disease activity in patients with SLE and in order to achieve better therapeutic effect maintaining a concentration of 8-10 ng/ml sirolimus for at least 6.12 months was necessary.