Developing AI-Optimized Drug Formulations for Rare Diseases

DOI: https://doi.org/10.63345/ijrmp.v12.i11.4

Rohan Nair

Independent Researcher

Kerala, India

Abstract

Rare diseases, though affecting a small proportion of the population individually, collectively impose significant burdens on global health. Traditional drug development processes are time-consuming and resource intensive, making the discovery of effective formulations for rare conditions especially challenging. This study proposes an innovative framework for AI-optimized drug formulation, integrating machine learning algorithms with high-throughput experimental methods to expedite and refine the development process. We describe an end-to-end approach, including data preprocessing, feature selection, model training, and validation using retrospective datasets, followed by prospective in vitro and in vivo assays. Our findings suggest that AI models can reliably predict optimal formulation parameters, significantly reducing the experimental workload and enhancing the probability of clinical success. This research sets the stage for future work aimed at translating AI-derived formulations into clinical practice, ultimately improving therapeutic options for patients with rare diseases.

Keywords

Artificial Intelligence; Drug Formulation; Rare Diseases; Machine Learning; High-Throughput Screening; Predictive Modeling

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