28/06/2026
Substandard and falsified medicines are a global health threat, especially in resource-limited areas. While traditional testing methods like HPLC work fine, they are expensive, time-consuming, and require heavy laboratory infrastructure.
A new proof of concept method developed by researchers at Kathmandu Institute of Applied Sciences (KIAS) in collaboration with researchers from Florida A&M University in USA proposes to verify the quality of eye drops just by looking at how they dry. 💧📉
The research work published as a pre-print study (not peer reviewed yet) introduces a game-changing, low-cost solution by combining Evaporation-Induced Deposit Imaging combined with Machine Learning. Researchers took hundreds of images, looked at their pattern through 46 image features and trained few machine learning models to predict the identity and concentrations of the ophthalmic antibiotics.
Why This Matters 🌍
This serves as a powerful proof-of-concept proving that macroscopic images can unlock complex chemical information. By leveraging simple imaging and smart machine learning models, this framework opens the door to rapid, accessible, and affordable medicine quality screening right where it is needed most.
Congratulations to the research team—Alisha Bogati, Sanam Pudasaini, Beni B. Dangi, and Basant Giri—on this innovative work! 👏
Pre-print link: https://doi.org/10.26434/chemrxiv.15005197/v1
Substandard and falsified medicines remain a significant global health challenge, while conventional analytical quality control techniques such as high-performance liquid chromatography and X-ray powder diffraction are often costly, infrastructure-...