Personalized insulin therapy for individuals with Type 1 Diabetes via closed‑loop artificial pancreas systems requires rapid adaptation of dosing strategies to each patient's unique insulin response. However, learning patient‑specific policies from scratch demands extensive exploration, which is often impractical. In this work, we study a framework that integrates insulin-response-informed transfer learning with model-based reinforcement learning for insulin dosing. We first train an LSTM‑based insulin responsiveness predictor on virtual patients, using their glucose, insulin, and meal history to forecast future glucose levels. Analysis of insulin responsiveness of in-silico patients uncovers natural insulin‑response groups characterized by similar sensitivity and dynamics profiles. For a new patient, we identify a representative model from their response group and use it to generate synthetic trajectories. These trajectories are integrated into an enhanced H-step Deep Dyna-Q algorithm, enabling accelerated policy optimization through model-based planning. The dynamics model trained entirely in simulation achieves 91.31\% accuracy in predicting blood glucose ranges on the Ohio Type 1 Diabetes dataset, indicating strong zero-shot generalization. Additionally, we find that bootstrapping a new patient with a physiologically-matched reference model accelerates convergence of effective dosing policies across in-silico cohorts of children, adolescents, and adults. These findings suggest that leveraging response-group-specific synthetic experience can expedite personalized insulin therapy, offering a promising pathway towards clinical validation.