A comparative field evaluation of six medicine quality screening devices in Laos

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Usefulness of medicine screening tools in the frame of pharmaceutical post-marketing surveillance

The negative consequences of Substandard and falsified (SF) medicines are widely documented nowadays and there is still an urgent need to find them in more efficient ways. Several screening tools have been developed for this purpose recently. In this study, three screening tools were used on 292 samples of ciprofloxacin and metronidazole collected in Cameroon. Each sample was then analyzed by HPLC and disintegration tests. Seven additional samples from the nitro-imidazole (secnidazole, ornidazole, tinidazole) and the fluoroquinolone (levofloxacin, ofloxacin, norfloxacin, moxifloxacin) families were analyzed to mimic falsified medicines. Placebo samples that contained only inert excipients were also tested to mimic falsified samples without active pharmaceutical ingredient (API). The three screening tools implemented were: a simplified visual inspection checklist, a low-cost handheld near infrared (NIR) spectrophotometer and paper analytical devices (PADs). Overall, 61.1% of the samples that failed disintegration and assay tests also failed the visual inspection checklist test. For the handheld NIR, one-class classifier models were built to detect the presence of ciprofloxacin and metronidazole, respectively. The APIs were correctly identified in all the samples with sensitivities and specificities of 100%. However, the importance of a representative and up-to-date spectral database was underlined by comparing models built with different calibration set spanning different variability spaces. The PADs were used only on ciprofloxacin samples and detected the API in all samples in which the presence of ciprofloxacin was confirmed by HPLC. However, these PADs were not specific to ciprofloxacin since they reacted like ciprofloxacin to other fluoroquinolone compounds. The advantages and drawbacks of each screening tool were highlighted. They are promising means in the frame of early detection of SF medicines and they can increase the speed of decision about SF medicines in the context of pharmaceutical post-marketing surveillance.

Strategies for the content determination of capsaicin and the identification of adulterated pepper powder using a hand-held near-infrared spectrometer

To achieve the goals of rapid content determination of capsaicin and adulteration detection of pepper powder. The method based on the hand-held near-infrared spectrometer combined with ensemble preprocessing was proposed. DoE-based ensemble preprocessing technique was utilized to develop the partial least squares regression models of red pepper [Capsicum annuum L. var. conoides (Mill.) Irish] powders. The performance of final models was evaluated using coefficient of determination (R²), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Model development using selective ensemble preprocessing gave the best prediction of capsaicin in Yanjiao pepper powder (R² = 0.9800, RPD = 7.090, RMSEP = 0.00689) and Tianying pepper powder (R² = 0.8935, RPD = 3.017, RMSEP = 0.06154). Moreover, the potential of grey wolf optimizer-support vector machine (GWO-SVM) to detect adulterated pepper powder was investigated. The samples were composed of two authentic products and three different adulterants with different adulteration levels. The results showed that the classification accuracy of GWO-SVM model for Yanjiao peppers was over 90 %, which realized the adulteration detection of Yanjiao pepper. And GWO-SVM showed better performance in detecting adulterated Tianying pepper compared to hierarchical cluster analysis, orthogonal partial least squares discriminant analysis and random forest. In summary, the quality control strategy established in this paper can provide a solution for the adulteration detection and quality evaluation of pepper powder in a rapid and on-site way.

Comparing the return on investment of technologies to detect substandard and falsified amoxicillin: A Kenya case study

The prevalence of substandard and falsified medicines in low- and middle-income countries (LMICs) is a major global public health concern. Multiple screening technologies for post-market surveillance of medicine quality have been developed but there exists no clear guidance on which technology is optimal for LMICs. This study examined the return on investment (ROI) of implementing a select number of screening technologies for post-market surveillance of amoxicillin quality in a case study of Kenya. An agent-based model, Examining Screening Technologies using Economic Evaluations for Medicines (ESTEEM), was developed to estimate the costs, benefits, and ROI of implementing screening technologies for post-market surveillance of substandard and falsified amoxicillin for treatment of pediatric pneumonia in Kenya. The model simulated sampling, testing, and removal of substandard and falsified amoxicillin from the Kenyan market using five screening technologies: (1) Global Pharma Health Fund's GPHF-Minilab, (2) high-performance liquid chromatography (HPLC), (3) near-infrared spectroscopy (NIR), (4) paper analytical devices / antibiotic paper analytical devices (PADs/aPADs), and (5) Raman spectroscopy. The study team analyzed the population impact of utilizing amoxicillin for the treatment of pneumonia in children under age five in Kenya. We found that the GPHF-Minilab, NIR, and PADs/aPADs were similar in their abilities to rapidly screen for and remove substandard and falsified amoxicillin from the Kenyan market resulting in a higher ROI compared to HPLC. NIR and PADs/aPADs yielded the highest ROI at $21 (90% Uncertainty Range (UR) $5-$51) each, followed by GPHF-Minilab ($16, 90%UR $4 - $38), Raman ($9, 90%UR $2 - $21), and HPLC ($3, 90%UR $0 - $7). This study highlights screening technologies that can be used to reduce costs, speed up the removal of poor-quality medicines, and consequently improve health and economic outcomes in LMICs. National medicine regulatory authorities should adopt these fast, reliable, and low-cost screening technologies to better detect substandard and falsified medicines, reserving HPLC for confirmatory tests.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

An open-source smartphone app for the quantitative evaluation of thin-layer chromatographic analyses in medicine quality screening

Substandard and falsified medicines present a serious threat to public health. Simple, low-cost screening tools are important in the identification of such products in low- and middle-income countries. In the present study, a smartphone-based imaging software was developed for the quantification of thin-layer chromatographic (TLC) analyses. A performance evaluation of this tool in the TLC analysis of 14 active pharmaceutical ingredients according to the procedures of the Global Pharma Health Fund (GPHF) Minilab was carried out, following international guidelines and assessing accuracy, repeatability, intermediate precision, specificity, linearity, range and robustness of the method. Relative standard deviations of 2.79% and 4.46% between individual measurements were observed in the assessments of repeatability and intermediate precision, respectively. Small deliberate variations of the conditions hardly affected the results. A locally producible wooden box was designed which ensures TLC photography under standardized conditions and shielding from ambient light. Photography and image analysis were carried out with a low-cost Android-based smartphone. The app allows to share TLC photos and quantification results using messaging apps, e-mail, cable or Bluetooth connections, or to upload them to a cloud. The app is available free of charge as General Public License (GPL) open-source software, and interested individuals or organizations are welcome to use and/or to further improve this software.