Understanding the degradation behaviour of Rilpivirine through combined DFT calculations, bond dissociation energy analysis, and experimental degradation studies: An AI/ML approach

Rilpivirine is a second-generation non-nucleoside reverse transcriptase inhibitor widely used in the management of human immunodeficiency virus (HIV) infections. Although its pharmaceutical stability is important for ensuring product quality and therapeutic efficacy, limited information is available regarding the molecular mechanisms governing its degradation under different pH conditions. The present study aimed to investigate the degradation behaviour of Rilpivirine by integrating computational methods consisting of density function theory (DFT) based calculations and Bond Dissociation Energy (BDE) calculations which predicts the most susceptible structural sites for cleavage under the acidic, basic and neutral conditions. The computationally predicted fragments were subsequently evaluated experimentally using stress degradation studies followed by liquid chromatography-mass spectrometry (LC-MS) analysis, The LC-MS results confirmed the formation of major degradation products under the investigated stress conditions, and their proposed structures showed a good agreement with the fragments predicted by the computational studies. This correlation provided a mechanistic insight into the degradation behaviour of Rilpivirine under the provided stress conditions. The findings demonstrates that integration of the computational approaches in the stress degradation studies of drug can ease the understanding of the degradation pattern of the drug and also provide an aid in identification and structural elucidation of the formed degradation products.