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Falsified antimalarial pharmaceuticals are a worldwide problem with negative public health implications. Here, we develop a surface-enhanced Raman scattering (SERS) protocol to recognize substandard and falsified antimalarial drugs present in commercially available tablets. After recording SERS spectra for pure chloroquine, primaquine, and doxycycline, SERS is used to measure these drugs formulated as active pharmaceutical ingredients (APIs) in the presence of common pharmaceutical caplet excipients. To demonstrate the viability of our approach, a red team study was also performed where low-quality and falsified formulations of all three drugs presented as unknowns were identified. These data in conjunction with promising results from a portable Raman spectrometer suggest that SERS is a viable technique for on-site analysis of drug quality.
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Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning
ABSTRACT: 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 supportvector 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.
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- PAR ID:
- 10356203
- Date Published:
- Journal Name:
- Analytical Chemistry
- ISSN:
- 0003-2700
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Emerging portable near infrared (NIR) spectroscopic approaches coupled with data analysis and chemometric techniques provide opportunities for the rapid characterization of spray-dried products and process optimization. This study aimed to enhance the understanding of applying NIR spectroscopy in spray-dried samples by comparing two sample preparation strategies and two spectrometers. Two sets of whey protein–maltodextrin matrixes, one with a protein content gradient and one with a consistent protein content, were spray-dried, and the effect of the two preparation strategies on NIR calibration model development was studied. Secondly, a portable NIR spectrometer (PEAK) was compared with a benchtop NIR spectrometer (CARY) for the moisture analysis of prepared samples. When validating models with the samples with focused protein contents, the best PLS protein models established from the two sample sets had similar performances. When comparing two spectrometers, although CARY outperformed PEAK, PEAK still demonstrated reliable performance for moisture analysis, indicating that it is capable as an inline sensor.more » « less
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Substandard and falsified pharmaceuticals, prevalent in low- and middle-income countries, substantially increase levels of morbidity, mortality and drug resistance. Regulatory agencies combat this problem using post-market surveillance by collecting and testing samples where consumers purchase products. Existing analysis tools for post-market surveillance data focus attention on the locations of positive samples. This article looks to expand such analysis through underutilized supply-chain information to provide inference on sources of substandard and falsified products. We first establish the presence of unidentifiability issues when integrating this supply-chain information with surveillance data. We then develop a Bayesian methodology for evaluating substandard and falsified sources that extracts utility from supply-chain information and mitigates unidentifiability while accounting for multiple sources of uncertainty. Using de-identified surveillance data, we show the proposed methodology to be effective in providing valuable inference.more » « less
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PURPOSE A postmarket evaluation of chemotherapy dosage forms in Ethiopia was conducted to test the accuracy of the chemoPAD, a paper analytical device for drug quality screening. MATERIALS AND METHODS In September of 2018 in Addis Ababa, Ethiopia, 41 anticancer drug dosage forms (representing 4 active ingredients, 5 brands, and 7 lot numbers) were collected and were rapidly screened for quality using a chemotherapy paper analytical device (chemoPAD). Confirmatory analysis via high performance liquid chromatography was conducted. RESULTS The chemoPAD showed that the correct active pharmaceutical ingredient was present in doxorubicin, methotrexate, and oxaliplatin injectable dosage forms. However, 11 of 20 cisplatin samples failed the screening test. Confirmatory assay by high-performance liquid chromatography showed that all 20 cisplatin samples—comprising three lot numbers of a product stated to be Cisteen—were substandard, containing on average 54% ± 6% of the stated cisplatin content. Inductively coupled plasma optical emission spectroscopy analysis of five representative samples found 57% to 71% of the platinum that should have been present. The sensitivity of the chemoPAD for detection of falsified products could not be measured (as none were present in these samples), but the selectivity was 100% (no false positives). The sensitivity for detection of substandard products was 55%, and the selectivity was 100% (no false positives). CONCLUSION Although instrumental analysis by pharmacopeia methods must remain the gold standard for assessing overall drug quality, these methods are time consuming and patients could be exposed to a bad-quality drug while clinical workers wait for testing to be performed. The chemoPAD technology could allow clinicians to check at the point of use for serious problems in the quality of chemotherapy drugs on a weekly or monthly schedule.more » « less
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Substandard and falsified (SF) pharmaceuticals account for an estimated 10% of the pharmaceutical supply chain in low- and middle-income countries (LMICs), where a lack of regulatory and laboratory resources limits the ability to conduct effective post-market surveillance and allows SF products to penetrate the supply chain. The Distributed Pharmaceutical Analysis Laboratory (DPAL) was established in 2014 to expand testing of pharmaceutical dosage forms sourced from LMICs; DPAL is an alliance of academic institutions throughout the United States and abroad that provides high quality, validated chemical analysis of pharmaceutical dosage forms sourced from partners in LMICs. Results from analysis are reported to relevant regulatory agencies and are used to inform purchasing decisions made by in-country stakeholders. As the DPAL program has expanded to testing more than 1000 pharmaceutical dosage forms annually, challenges have surfaced regarding data management and sample tracking. Here, we describe a pilot project between DPAL and ARTiFACTs that applies blockchain to organize and manage key data generated during the DPAL workflow, including a sample’s progress through the workflow, its physical location, provenance of metadata, and lab reputability. Recording time and date stamps with this data will create a permanent and verifiable chain-of-custody for samples. This secure, distributed ledger will be linked to an easy-to-use dashboard, allowing stakeholders to view results and experimental details for each sample in real time and verify the integrity of DPAL analysis data. Introducing this blockchain-based system as a pilot will allow us to test the technology with real users analyzing real samples. Feedback from users will be recorded and necessary adjustments will be made to the system before the implementation of blockchain across all DPAL sites. Anticipated benefits of implementing blockchain for managing DPAL data include efficient management for routing work, increasing throughput, creating a chain of custody for samples and their data in alignment with the distributed nature of DPAL, and using the analysis results to detect patterns of quality within and across brands of products and develop enhanced sampling techniques and best practices.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.analchem.2c00998
