Safety Prediction for Pre 1988 Permitted Fixed Dose Combinations Using Artificial Intelligence - In Silico Approach

Background: Since the Drug Controller General of India (DCGI) approved more than 130 FDCs, the usage of fixed dose combinations (FDCs) has become more popular. FDCs are medications that contain more than one active component, aiming to enhance adherence, reduce adverse effects, and improve effectiveness. In a recent development, the Central Drug Standard Control Organisation (CDSCO) issued a notice on January 11, 2024, calling for the evaluation of pre-1988 permitted FDCs. Objective: This study aimed to determine whether AI algorithms could predict the safety of FDCs by examining their pharmacokinetic properties. Methods: In order to assess pharmacokinetic properties and verify the safety of pre-1988 permitted fixed dose combinations that were approved by The Central Drug Standard Control Organisation, India (CDSCO), this study used a range of AI tools, including ADMET.AI, SwissADME, vNN-ADMET, and AERSMine. Results: The results made by these tools were giving dissimilar properties in the same drug combinations. So, these tools are presently needs to be developed to have more accurate and precise results. Conclusion: This research contributes to the ongoing discourse surrounding FDCs, pharmaceutical innovation, and regulatory practices, providing valuable insights that can shape future approaches in drug development and patient care with the help of artificial intelligence and data-based analysis tools. Additionally, this work helps to strengthen the credibility of the current A.I. development for in-silico pharmacokinetic predictions.