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Primary sclerosing cholangitis (PSC) is a rare disease wherein altered bileacid metabolism contributes to sustained liver injury. This paper introducesREMEDI, a framework that captures bile acid dynamics and the body's adaptiveresponse during PSC progression that can assist in exploring treatments. REMEDImerges a differential equation (DE)-based mechanistic model that describes bileacid metabolism with reinforcement learning (RL) to emulate the body'sadaptations to PSC continuously. An objective of adaptation is to maintainhomeostasis by regulating enzymes involved in bile acid metabolism. Theseenzymes correspond to the parameters of the DEs. REMEDI leverages RL toapproximate adaptations in PSC, treating homeostasis as a reward signal and theadjustment of the DE parameters as the corresponding actions. On real-worlddata, REMEDI generated bile acid dynamics and parameter adjustments consistentwith published findings. Also, our results support discussions in theliterature that early administration of drugs that suppress bile acid synthesismay be effective in PSC treatment.

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