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The top priority of today’s healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.

The fight against counterfeit pharmaceuticals is a global issue of utmost importance, as failed medication results in millions of deaths every year. Particularly affected are antimalarial tablets. A very important issue is the identification of substandard tablets that do not contain the nominal amounts of the active pharmaceutical ingredient (API), and the differentiation between genuine products and products without any active ingredient or with a false active ingredient. This work presents a novel approach based on fiber-array based Raman hyperspectral imaging to qualify and quantify the antimalarial APIs lumefantrine and artemether directly and non-invasively in a tablet in a time-efficient way. The investigations were carried out with the antimalarial tablet Riamet® and self-made model tablets, which were used as examples of counterfeits and substandard. Partial least-squares regression modeling and density functional theory calculations were carried out for quantification of lumefantrine and artemether and for spectral band assignment. The most prominent differentiating vibrational signatures of the APIs were presented.

Substandard and falsified medical products present a serious threat to public health, especially in low- and middle-income countries. Their identification using pharmacopeial analysis is expensive and requires sophisticated equipment and highly trained personnel. Simple, low-cost technologies are required in addition to full pharmacopeial analysis in order to accomplish widespread routine surveillance for poor-quality medicines in low- and middle-income countries. Ten faith-based drug supply organizations in seven countries of Africa and Asia were each equipped with a Minilab of the Global Pharma Health Fund (GPHF, Frankfurt, Germany), suitable for the analysis of about 85 different essential medicines by thin-layer chromatography. Each organization was asked to collect approximately 100 medicine samples from private local medicine outlets, especially from the informal sector. The medicine samples were tested locally according to the Minilab protocols. Medicines which failed Minilab testing were subjected to confirmatory analysis in a WHO-prequalified medicine quality control laboratory in Kenya. Out of 869 medicine samples, 21 were confirmed to be substandard or falsified medical products. Twelve did not contain the stated active pharmaceutical ingredient (API), six contained insufficient amounts of the API, and three showed insufficient dissolution of the API. The highest proportion of substandard and falsified medicines was found in Cameroon (7.1%), followed by the Democratic Republic of Congo (2.7%) and Nigeria (1.1%). Antimalarial medicines were most frequently found to be substandard or falsified (9.5% of all antimalarials). Thin-layer chromatography according to the Minilab protocols was found to be specific and reproducible in the identification of medicines which did not contain the stated API. Since only samples which failed Minilab testing were subjected to confirmatory testing using pharmacopeial methods, this study did not assess the sensitivity of the Minilab methodology in the detection of substandard medicines, and may underestimate the prevalence of poor-quality medicines. Surveillance for poor-quality medicines can be carried out by local organizations in low- and middle-income countries using a simple, low-cost technology. Such surveillance can identify an important subgroup of the circulating substandard and falsified medical products and can help to prevent them from causing harm in patients. A collaboration of the national drug regulatory authorities with faith-based organizations and other NGOs may therefore represent a promising strategy towards the Sustainable Development Goal of \"ensuring access to quality medicines\".

The criminal trafficking of falsified medical products is a worldwide, yet still largely overlooked, public health problem. A falsified medicine fraudulently misrepresents its identity, composition and/or source, often being ineffective or toxic for patients. Although techniques have been developed to detect falsified medicines, it remains a challenge to trace where- and by whom- the products are manufactured. We aim to discuss plausible biological and physico-chemical analytical techniques that could reveal information about the origin of medical falsifications. We first provide a brief overview on the prevalence, criminal activities, health impacts and (bio)chemical features of falsified medical products. We then explore diverse laboratory approaches, that are used in food fraud, illicit drug and wildlife trafficking investigations, and discuss how they could be combined and redirected towards tracing falsified medicine origin and hence empowering enforcement to counter this pernicious but neglected global health problem. •The lucrative trading of falsified medicines is a persistent global health threat.•Exposing the links and origins of falsified medicines is key for their eradication.•Chemo-isotopic and eDNA approaches can inform falsified medicines’ origin and links.•Databases and multi-analytical frameworks for products’ origin tracing are needed.

Substandard and falsified medicines threaten global health and require reliable data and screening technologies to combat their spread. This study examined the quality of 241 samples containing azithromycin, cefixime, esomeprazole and losartan collected from licenced private vendors in the Saptari (121 samples; convenience sampling) and Kathmandu (120 samples; randomised sampling) districts of Nepal. Nearly 10% (24 samples; 95% CI 6.5–14.5) of samples failed pharmacopoeial quality analysis and were classified as ‘substandard’ or ‘probably substandard’. No falsified medicines were identified. Small-scale dissolution acceptance criteria were applied to all 20 three-unit combinations of 213 samples tested in the first stage of the United States Pharmacopoeia dissolution test. Approximately 1% of these results were false positives when compared with the final United States Pharmacopoeia dissolution test results, suggesting the test’s usefulness in encouraging dissolution testing in resource-limited contexts. In the narrow sense of presence/absence, two portable Raman spectrometers reliably detected azithromycin, cefixime and losartan in most samples based on effective methods for detecting falsified medicines; however, none of the substandard samples were identified. The findings suggest that falsified medicines are less prevalent in Nepal and the surrounding region than suggested by regional concerns about Nepal and global concerns about low- and middle-income countries. Nevertheless, the Nepalese government should continue to ensure the quality of all distributed medicines.

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.

Background Over the past years, there has been a growing concern that a considerable amount of anti-malarial supply in the underdeveloped world particularly in the private sector, is of poor quality. The World Health Organization (WHO) has received about 1500 reports that mentions instances of substandard and falsified products since 2013. The majority of the reports concerned antibiotics and anti-malarials. The majority of reports (42%) originate from the WHO African region. Objective This study intends to assess the quality of the most widely used anti-malarial medications [artemether-lumefantrine tablets, chloroquine phosphate tablets, primaquine phosphate tablets, artesunate, and artemether injections] in Gambella, South-West, Ethiopia. Methods A total of 52 samples were collected on June 2022 from Gambella National Regional State, Ethiopia. Half of the districts (six) located in the four zones of the region were chosen using simple random sampling technique. All drug retail outlets available in the selected districts (locally known as woredas) were included. The samples were subjected to visual inspection with a tool adopted from the joint WHO/FIP/ USP checklist. The pharmacopeial tests for identification, uniformity of dosage forms, assay, thickness, diameter, hardness, friability, disintegration test, dissolution, and sterility tests were carried out according to the USP 44-NF 39 and International Pharmacopoeia 11th edition, 2022 monographs. Results and Discussion Only 25% of the samples were registered on the Ethiopian Food and Drug Authority (EFDA’s) electronic regulatory/ registration system (ERIS). Besides, 88.8% of artemether injection products were presented in clear glass ampoules. This might expose the products to photochemical degradation that leads to in loss of anti-plasmodial activity. In addition, 50% of the artemether products assessed were not bioequivalent with the comparator product in the in vitro dissolution comparison tests. Overall, the study findings reveal a high prevalence (58.3%) of substandard anti-malarial drugs in the region. The stated percent of the samples had failed in one or more of the quality test parameters assessed in this study. Conclusion The study findings reveal a high prevalence (58.3%) of substandard anti-malarial drugs in the region. Only a quarter were registered and 38% of the unregistered products failed the quality tests. Hence, the national, regional medicine regulatory bodies and other stake holders should perform the required roles to circumvent presence of Substandard and Falsified (SF) anti-malarial drugs in the study sites.

The Chinese cordyceps, a complex of the fungus Ophiocordyceps sinensis and its species-specific host insects, is also called “DongChongXiaCao” in Chinese. Habitat degradation in recent decades and excessive harvesting by humans has intensified its scarcity and increased the prices of natural populations. Some counterfeits are traded as natural Chinese cordyceps for profit, causing confusion in the marketplace. To promote the safe use of Chinese cordyceps and related products, a duplex PCR method for specifically identifying raw Chinese cordyceps and its primary products was successfully established. Chinese cordyceps could be precisely identified by detecting an internal transcribed spacer amplicon from O. sinensis and a cytochrome oxidase c subunit 1 amplicon from the host species, at a limit of detection as low as 32 pg. Eleven commercial samples were purchased and successfully tested to further verify that the developed duplex PCR method could be reliably used to identify Chinese cordyceps. It provides a new simple way to discern true commercial Chinese cordyceps from counterfeits in the marketplace. This is an important step toward achieving an authentication method for this Chinese medicine. The methodology and the developmental strategy can be used to authenticate other traditional Chinese medicinal materials.

Post-market surveillance is a key regulatory function to prevent substandard and falsified (SF) medicines from being consumed by patients. Field deployable technologies offer the potential for rapid objective screening for SF medicines. We evaluated twelve devices: three near infrared spectrometers (MicroPHAZIR RX, NIR-S-G1, Neospectra 2.5), two Raman spectrometers (Progeny, TruScan RM), one mid-infrared spectrometer (4500a), one disposable colorimetric assay (Paper Analytical Devices, PAD), one disposable immunoassay (Rapid Diagnostic Test, RDT), one portable liquid chromatograph (C-Vue), one microfluidic system (PharmaChk), one mass spectrometer (QDa), and one thin layer chromatography kit (GPHF-Minilab). Each device was tested with a series of field collected medicines (FCM) along with simulated medicines (SIM) formulated in a laboratory. The FCM and SIM ranged from samples with good quality active pharmaceutical ingredient (API) concentrations, reduced concentrations of API (80% and 50% of the API), no API, and the wrong API. All the devices had high sensitivities (91.5 to 100.0%) detecting medicines with no API or the wrong API. However, the sensitivities of each device towards samples with 50% and 80% API varied greatly, from 0% to 100%. The infrared and Raman spectrometers had variable sensitivities for detecting samples with 50% and 80% API (from 5.6% to 50.0%). The devices with the ability to quantitate API (C-Vue, PharmaChk, QDa) had sensitivities ranging from 91.7% to 100% to detect all poor quality samples. The specificity was lower for the quantitative C-Vue, PharmaChk, & QDa (50.0% to 91.7%) than for all the other devices in this study (95.5% to 100%). The twelve devices evaluated could detect medicines with the wrong or none of the APIs, consistent with falsified medicines, with high accuracy. However, API quantitation to detect formulations similar to those commonly found in substandards proved more difficult, requiring further technological innovation.

The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of 840 individual authentic (modeled) versus counterfeit (non-modeled) ID verification assessments performed. Counterfeit detection is performed on a fingerprint-by-fingerprint basis with best case per-device Counterfeit Detection Rate (%CDR) estimates including 87.6% < %CDR < 99.9% and yielding an average cross-device %CDR ≈ 92.5%. This full-dimensional feature set performance was echoed by dimensionally reduced feature set performance that included per-device 87.0% < %CDR < 99.7% and average cross-device %CDR ≈ 91.4% using only 18-of-291 features—the demonstrated %CDR > 90% with an approximate 92% reduction in the number of fingerprint features is sufficiently promising for small-scale network applications and warrants further consideration.