Integrating molecular pathogenesis and host response into a digital twin framework for predicting therapeutic outcomes in Balamuthia mandrillaris encephalitis

Balamuthia mandrillaris is a free-living amoeba that causes granulomatous amoebic encephalitis, a rare but devastating central nervous system infection with mortality exceeding 95%. Treatment relies on empirical, multidrug regimens lasting several months, yet prognostic indicators and optimal dosing strategies remain undefined. Advances in computational biology now permit the creation of digital twins, data-driven and patient-specific virtual replicas that integrate clinical, imaging, molecular, and pharmacological data to simulate disease dynamics and therapeutic response. By incorporating molecular mechanisms of Balamuthia pathogenesis and host susceptibility into such a model, it becomes possible to forecast treatment trajectories, personalize drug dosing, and predict toxicity in real time. This paper outlines the molecular and immunological underpinnings of Balamuthia infection and proposes a digital twin framework that bridges mechanistic biology with predictive analytics to improve management and survival in this neglected infection.