Abstract
It is estimated that 10.5% of medicines in low- and middle-income countries are substandard or falsified (SF), causing approximately 1 million deaths annually. Over the past two decades, there have been significant technological advancements in low-cost, portable screening devices to detect poor-quality medicines, which could be especially beneficial in these countries. The pharmaceutical market in Nigeria is valued at USD 4.5 billion and is growing at over 9% annually. However, SF medicines remain a major public health concern. We compared a novel Near-Infrared (NIR) Spectrometer with high-performance liquid chromatography (HPLC) by analyzing 246 drug samples purchased from retail pharmacies across the six geopolitical regions of Nigeria. We measured the sensitivity and specificity of a patented and Artificial Intelligence (AI) - powered handheld NIR spectrometer, which uses a proprietary machine-learning algorithm as well as hardware and software, across four categories of medicines: analgesics, antimalarials, antibiotics, and antihypertensives. Our findings reveal that the prevalence of SF medicines remains high, with 25% of samples failing the HPLC test. When tested with the NIR spectrometer, only a smaller subset of medicines-specifically analgesics-failed the test. Sensitivity and specificity for all medicines were 11% and 74%, respectively. For analgesics, the sensitivity was 37%, and the specificity was 47%. While these devices hold great potential, regulators should require more independent evaluations of various drug formulations before implementing them in real-world settings. Improving the sensitivity of these devices should be prioritized to ensure that no SF medicines reach patients.