REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression

TL;DR

REMEDI is a novel framework combining mechanistic modeling and reinforcement learning to simulate bile acid metabolism and adaptive responses in primary sclerosing cholangitis, aiding treatment exploration.

Contribution

It introduces a reinforcement learning-driven approach to model disease progression and adaptive responses in PSC, integrating mechanistic bile acid metabolism models.

Findings

Generated bile acid dynamics consistent with published data.

Supported early bile acid synthesis suppression as a potential treatment.

Demonstrated the framework's ability to emulate biological adaptation.

Abstract

Primary sclerosing cholangitis (PSC) is a rare disease wherein altered bile acid metabolism contributes to sustained liver injury. This paper introduces REMEDI, a framework that captures bile acid dynamics and the body's adaptive response during PSC progression that can assist in exploring treatments. REMEDI merges a differential equation (DE)-based mechanistic model that describes bile acid metabolism with reinforcement learning (RL) to emulate the body's adaptations to PSC continuously. An objective of adaptation is to maintain homeostasis by regulating enzymes involved in bile acid metabolism. These enzymes correspond to the parameters of the DEs. REMEDI leverages RL to approximate adaptations in PSC, treating homeostasis as a reward signal and the adjustment of the DE parameters as the corresponding actions. On real-world data, REMEDI generated bile acid dynamics and parameter adjustments consistent with published findings. Also, our results support discussions in the literature that early administration of drugs that suppress bile acid synthesis may be effective in PSC treatment.