DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development—Validation through paracetamol and vanillin as a case study

The compatibility of drugs with excipients plays a crucial role in the prospective stability of pharmaceutical formulations. Apart from real-time stability studies, conventional analytical tools like DSC, FTIR, NMR, and chromatography help identify the possibilities of drug-excipient interactions. Machine learning can assist in developing a predictive tool for drug-excipient incompatibility. In the present work, PubChem Fingerprint is employed as the descriptor of compounds that thoroughly represents the drug's and excipient's chemistry. The 881-bit binary fingerprints of each drug and excipient make 1762 inputs, and one categorical output makes an instance in the dataset. A dataset of more than 3500 instances of drugs and excipients is carefully selected from peer-reviewed research papers. Rigorous training of the Artificial Neural Network (ANN) model was performed with maximum validation accuracy, minimum validation loss, and maximum validation precision as the checkpoints. The machine learning model (DE-Interact) was trained, achieving training and validation accuracies of 0.9930 and 0.9161, respectively. The performance of the DE-Interact model was evaluated by confirming three incompatible predictions by conventional analytical tools. Paracetamol with vanillin, paracetamol with methylparaben, and brinzolamide with polyethyleneglycol are these instances which are predicted as incompatible by the DE-Interact. DSC, FTIR, HPTLC, and HPLC analysis confirm the prediction. The present work offers a reliable DE-Interact tool for quick referencing while selecting excipients in formulation design.