Medicinal chemistry meets nanotechnology: machine learning assisted colorimetric sensing platform for oxalic acid based on drug mediated copper oxide nanoparticles.

Increased levels of oxalic acid are associated with an increased risk of kidney stone formation, which can lead to renal failure. In addition, its high concentration in the blood can lead to cardiovascular diseases. Therefore, it is vital to detect and quantify oxalic acid economically and rapidly. Copper oxide nanoparticles (CuONPs) are gaining importance as colorimetric nanosensors due to their intrinsic color change, cost-effectiveness, and easy synthesis. Paracetamol-mediated CuO NPs were synthesized through a new approach and characterized through various spectroscopic and morphological techniques. UV-visible spectroscopy confirmed the synthesis of CuO NPs through surface plasmon resonance at 225 nm. The peak at 850 cm-1 corresponds to the stretching vibration of CuO NPs. The XRD and SEM characterization techniques confirmed the particle size of 27.51 nm with a spherical morphology. A machine learning-assisted strategy was developed with four prediction models: Random Forest, Linear Regression, XGBoost, and Decision Tree Regression. The intrinsic colorimetric features of CuO NPs were observed through the naked eye and quantified through spectroscopy with the addition of oxalate. The developed platform selectively detected oxalate levels in concentrations ranging from 1 to 120 µM, with a limit of detection (LOD) of 0.23 µM and a limit of quantification (LOQ) of 0.78 µM. The developed biosensor successfully quantified oxalate, crucial for diagnosing hyperoxaluria and preventing calcium oxalate stone formation in the kidneys. The machine learning complementary tools further bolster the accuracy of colorimetric concentration prediction.