A comparative study between Near-Infrared (NIR) spectrometer and High-Performance Liquid Chromatography (HPLC) on the...

Area of Science:

• Pharmaceutical Sciences
• Analytical Chemistry
• Public Health

Background:

• Substandard and falsified (SF) medicines are a critical global health issue, particularly in low- and middle-income countries, contributing to an estimated 1 million deaths annually.
• Nigeria's pharmaceutical market, valued at USD 4.5 billion, faces a significant challenge with SF medicines, necessitating effective detection methods.

Purpose of the Study:

• To evaluate the performance of a novel, Artificial Intelligence (AI)-powered handheld Near-Infrared (NIR) spectrometer for detecting SF medicines in Nigeria.
• To compare the diagnostic accuracy (sensitivity and specificity) of the NIR spectrometer against high-performance liquid chromatography (HPLC) across various drug categories.

Main Methods:

• A total of 246 drug samples were analyzed, sourced from retail pharmacies across Nigeria's six geopolitical regions.
• The study employed high-performance liquid chromatography (HPLC) as the gold standard and a patented handheld NIR spectrometer utilizing a proprietary AI-driven machine-learning algorithm.

Main Results:

• A high prevalence of SF medicines was confirmed, with 25% of samples failing the HPLC test.
• The NIR spectrometer demonstrated a sensitivity of 11% and a specificity of 74% for all tested medicines. For analgesics, sensitivity was 37% and specificity was 47%.

Conclusions:

• While portable NIR spectrometers offer promise for combating SF medicines, their current sensitivity and specificity necessitate further improvement and independent validation.
• Regulators should mandate rigorous evaluations of diverse drug formulations before widespread adoption to ensure patient safety and prevent SF medicines from reaching the market.