Synthesis and Characterization of Process and Product Impurities of Lidocaine with Machine Learning-Based Solubility Prediction: Ensuring Safety and Efficacy

Introduction: Lidocaine (lignocaine) is a widely used local anesthetic and an anti-arrhythmic drug. However, conventional synthetic routes involve toxic reagents and environmentally hazardous conditions. This study aims to develop a cost-effective, eco-friendly, and scalable synthetic methodology for lidocaine and its process- and product-related impurities. Methods: An environmentally benign synthetic route was developed using palladium on carbon (Pd/C) as a reducing agent, methanol as a safer solvent, and potassium carbonate and potassium iodide as mild bases. Thirteen lidocaine analogs and impurities were synthesized under optimized conditions. The compounds were purified by column chromatography and characterized using TLC, IR, ^1H NMR, and mass spectrometry. Reaction parameters, such as yield, temperature, and stoichiometry, were systematically optimized. In silico solubility prediction of lidocaine hydrochloride was performed using a machine learning-based model. Results: The method achieved high yields (80–97%) across most analogs, demonstrating improved efficiency compared to conventional approaches. Spectroscopic characterization confirmed the structures of the synthesized compounds. The process reduced the use of hazardous reagents while maintaining reproducibility and scalability. The synthesized impurities were suitable as reference standards for pharmaceutical quality control. Solubility prediction indicated favorable aqueous solubility at physiological pH. Discussion: The developed approach addresses limitations of traditional methods by improving environmental safety, process efficiency, and product purity. The integration of computational solubility prediction enhances the applicability in formulation development, although reliance on predictive models remains a limitation. Conclusion: This study presents a sustainable and efficient synthetic protocol (3336+) for lidocaine and its impurities, offering significant advantages for large-scale pharmaceutical manufacturing and impurity profiling.