3D Printed Personalized Pharmaceutical Dosage Forms

Abstract

Three-dimensional (3D) printing has transformed pharmaceutical manufacturing by enabling the production of individualized dosage forms tailored to patient-specific therapeutic needs. Despite remarkable progress, current formulation workflows still depend heavily on empirical optimization, making personalized drug production time-consuming and resource-intensive. This study identifies a critical research gap: the absence of an integrated artificial intelligence (AI) framework capable of simultaneously predicting formulation composition, printability, and patient-specific therapeutic performance before manufacturing. To address this limitation, the paper proposes an explainable machine learning approach that combines patient characteristics, physicochemical drug properties, and printable material attributes for intelligent formulation recommendation. Recent developments in AI, additive manufacturing, and digital health are synthesized to establish the conceptual foundation of the framework. The manuscript also reviews contemporary advances in pharmaceutical 3D printing between 2021 and 2026 while highlighting unresolved technical and regulatory challenges. The proposed research aims to improve formulation efficiency, reduce experimental iterations, and support precision medicine through intelligent pharmaceutical manufacturing