Evaluating Low-Cost Optical Spectrometers for the Detection of Simulated Substandard and Falsified Medicines.

Distribution of substandard and falsified (SF) medicines is on the rise, and its impact on public health, particularly in low-resource countries, is becoming increasingly significant. Portable, nondestructive screening devices can support regulatory authorities in their defense against the spread of SF medicines. Vibrational spectroscopy is an ideal candidate due to its sampling ease and speed. In this work, five portable, among which four are considered low-cost, spectroscopic devices based on near-infrared (NIR), Raman, and mid-infrared (MIR) were evaluated to quantify active pharmaceutical ingredients (APIs) and formulation accuracy within simulated authentic, falsified, and substandard medicines. Binary sample mixtures containing a typical API in antimalarial, antiretroviral, or anti-tuberculosis medicines were assessed. In both univariate and multivariate analyses, the API quantification performance of the digital light processing (DLP) NIR spectrometer and a handheld Raman device consistently matched or exceeded that of the other NIR spectrometers and a scientific grade MIR spectrometer. In the formulation accuracy tests, data from all devices, other than the silicon photodiode array NIR spectrometer, were able to create regression models with less than 6% error. From this exploratory study, we conclude that certain portable NIR devices hold significant promise as cost-effective screening tools for falsified and potentially substandard medicines, and they warrant further investigation and development.

Development of a spatially offset Raman spectroscopy probe for breast tumor surgical margin evaluation.

The risk of local recurrence for breast cancers is strongly correlated with the presence of a tumor within 1 to 2 mm of the surgical margin on the excised specimen. Previous experimental and theoretical results suggest that spatially offset Raman spectroscopy (SORS) holds much promise for intraoperative margin analysis. Based on simulation predictions for signal-to-noise ratio differences among varying spatial offsets, a SORS probe with multiple source-detector offsets was designed and tested. It was then employed to acquire spectra from 35 frozen-thawed breast tissue samples in vitro. Spectra from each detector ring were averaged to create a composite spectrum with biochemical information covering the entire range from the tissue surface to ∼2 mm below the surface, and a probabilistic classification scheme was used to classify these composite spectra as "negative" or "positive" margins. This discrimination was performed with 95% sensitivity and 100% specificity, or with 100% positive predictive value and 94% negative predictive value.

Monte Carlo model of spatially offset Raman spectroscopy for breast tumor margin analysis.

We have previously demonstrated the discrimination of two layers of soft tissue, specifically normal breast tissue overlying breast tumor, using spatially offset Raman spectroscopy (SORS). In this report, a Monte Carlo code for evaluating SORS in soft tissues has been developed and compared to experimental results. The model was employed to investigate the effects of tissue and probe geometry on SORS measurements and therefore to develop the design strategies of applying SORS for breast tumor surgical margin evaluation. The model was used to predict SORS signals for different tissue geometries difficult to precisely control experimentally, such as varying normal and tumor layer sizes and the addition of a third layer. The results from the model suggest that, using source-detector separations of up to 3.75 mm, SORS can detect sub-millimeter-thick tumors under a 1 mm normal layer, and tumors at least 1 mm thick can be detected under a 2 mm normal layer.