Response-guided dosing for rheumatoid arthritis

ABSTRACT Rheumatoid arthritis (RA) is an auto-immune disease with an unknown cause. Many patients receiving traditional methotrexate treatment continue to exhibit joint damage and are then treated with biologics. Biologic treatment is difficult owing to the uncertainty in dose-response, high cost, side effects, and intravenous administration. Recent clinical trials have therefore attempted response-guided dosing (RGD), where the hope is to adapt biologic doses over the treatment course based on each patient’s observed evolution of the 28-joint disease activity score (DAS28). We provide a stochastic dynamic programming (DP) framework to facilitate RGD. We present a concrete formulation where the DAS28 response is modeled using a stochastic Michaelis-Menten formula. The goal is to balance the DAS28 attained at the end of the course with the weighted total dose administered. We perform numerical experiments using data from the OPTION trial and observe that the optimal dosing policy gives higher doses in worse DAS28 scores. We present sensitivity analyses to provide further insights into this monotone dosing policy. This basic formulation is extended to a general stochastic DP for RGD. This is also applicable to other diseases and conditions such as hepatitis C, hyperlipidemia, hypertension, and AIDS. The DP allows for an arbitrary dose-response function, and balances the disutility of doses with the disutility of the disease condition reached. We prove that, when the decision-maker is risk-averse and the dose-response is supermodular and convex, there exists an optimal policy that gives higher doses in worse disease conditions. We provide several examples where these conditions are met.