Many of the screening techniques for trying to find falsified medicines in the field and back at the lab rely on testing for the amount of active pharmaceutical ingredient (API).
The problem with that approach is that as criminals react to increased scrutiny of the trade in fake drugs, some falsified medicines actually contain the correct API - in the right amount - to try to fool the typical battery of analytical tests.
These 'high-quality' fakes can be spotted using a technique developed by scientists in Russia, which they claim also copes with the variations that can occur with different batches of genuine product as well as legitimate generic versions of a drug.
The chemometric modelling technique - called Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) - was applied to sample tablets of the cardiovascular drug amlodipine and pancreatin for digestive problems that were tested using a widely-used technique call near infrared (NIR) spectroscopy.
NIR spectra were used to reveal the principal components of the samples - including the API and excipients - which were sourced from multiple suppliers. The data was used to develop a 'decision rule' that can be applied to identify falsified medicines and avoid misclassification of genuine products.
"The real world examples demonstrate that the proposed technique is flexible enough [and] is capable to recognize 'high quality' fakes successfully, write the researchers.
The work is published in the Journal of Pharmaceutical and Biomedical Analysis (September 2014 edition).