Machine learning-enabled formulation development of implantable drug delivery systems for precision medicine: from ocular and cochlear implants to personalized therapeutics

In order to provide regulated, targeted, and prolonged therapeutic delivery while reducing systemic side effects, implantable drug delivery systems (IDDSs) have become a potential substrate. By enabling data-driven prediction of material properties, drug release kinetics, biocompatibility, and patient-specific treatment responses, recent developments in machine learning (ML) are revolutionizing the formulation and refinement of these systems. ML algorithms combine clinical, biological, and physicochemical facts to optimize implant design for a variety of biomedical applications, speed up formulation development, and lessen the burden of experiments. ML-assisted methods improve therapeutic efficacy and patient outcomes in ocular and cochlear implants by making it easier to choose biodegradable polymers, anticipate release profiles, and customize implants based on anatomical variability and disease progression. Additionally, the establishment of intelligent implantable systems with continuous monitoring and adaptive drug delivery is supported by the integration of machine learning (ML) with additive manufacturing, biosensors, and digital health technologies. This analysis thoroughly integrates machine learning-assisted formulation creation with ocular and cochlear implant technologies in the context of precision medicine, in contrast to earlier evaluations that primarily address implantable drug delivery systems (IDDSs) or machine learning separately. It also provides a thorough path for next-generation implantable drug delivery systems by highlighting recent developments in smart biomaterials, tailored therapies, predictive modeling, and future clinical translation. Despite these developments, there are still several obstacles to general use, such as a lack of high-quality datasets, model interpretability, regulatory approval, data privacy, and clinical validation. To enable clinically applicable precision healthcare solutions, future investigations should prioritize explainable AI, standardized datasets, and interdisciplinary cooperation. All things considered, machine learning has the potential to completely transform implantable drug delivery through the advancement of advanced precision healthcare, tailored therapies, and improved preparation efficiency.