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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. 

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. 

ABSTRACT Introduction Materials and methods Results Discussion Conclusion Acknowledgements Disclosure statement Additional information References ABSTRACT Global health researchers often discount mutual learning and benefit to address shared health challenges across high and low- and middle-income settings. Drawing from a 30-year partnership called AMPATH that started between Indiana University in the US and Moi University in Kenya, we describe an innovative approach and program for mutual learning and benefit coined ‘reciprocal innovation.’ Reciprocal innovation harnesses a bidirectional, co-constituted, and iterative exchange of ideas, resources, and innovations to address shared health challenges across diverse global settings. The success of AMPATH in Kenya, particularly in HIV/AIDS and community health, resulted in several innovations being ‘brought back’ to the US. To promote the bidirectional flow of learning and innovations, the Indiana CTSI reciprocal innovation program hosts annual meetings of multinational researchers and practitioners to identify shared health challenges, supports pilot grants for projects with reciprocal exchange and benefit, and produces educational and training materials for investigators. The transformative power of global health to address systemic health inequities embraces equitable and reciprocal partnerships with mutual benefit across countries and communities of academics, practitioners, and policymakers. Leveraging a long-standing partnership, the Indiana CTSI has built a reciprocal innovation program with promise to redefine global health for shared wellbeing at a global scale. 

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. 

Background: Over 10% of antibiotics in low- and middle-income countries (LMICs) are substandard or falsified. Detection of poor-quality antibiotics via the gold standard method, high-performance liquid chromatography (HPLC), is slow and costly. Paper analytical devices (PADs) and antibiotic paper analytical devices (aPADs) have been developed as an inexpensive way to estimate antibiotic quality in LMICs. Aim: To model the impact of using a rapid screening tools, PADs/aPADs, to improve the quality of amoxicillin used for treatment of childhood pneumonia in Kenya. Methods: We developed an agent-based model, ESTEEM (Examining Screening Technologies with Economic Evaluations for Medicines), to estimate the effectiveness and cost savings of incorporating PADs and aPADs in amoxicillin quality surveillance in Kenya. We compared the current testing scenario (batches of entire samples tested by HPLC) with an expedited HPLC scenario (testing smaller batches at a time), as well as a screening scenario using PADs/aPADs to identify poor-quality amoxicillin followed by confirmatory analysis with HPLC. Results: Scenarios using PADs/aPADs or expedited HPLC yielded greater incremental benefits than the current testing scenario by annually averting 586 (90% uncertainty range (UR) 364–874) and 221 (90% UR 126–332) child pneumonia deaths, respectively. The PADs/aPADs screening scenario identified and removed poor-quality antibiotics faster than the expedited or regular HPLC scenarios, and reduced costs significantly. The PADs/aPADs scenario resulted in an incremental return of $14.9 million annually compared with the reference scenario of only using HPLC. Conclusion: This analysis shows the significant value of PADs/aPADs as a medicine quality screening and testing tool in LMICs with limited resources. 

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. 

Lactose is commonly used as a cutting agent in illicit drugs. Currently, presumptive field color test kits for illicit drugs do not test for the presence of lactose or other cutting agents. A method was developed to detect lactose on a paper-based test card. A three-enzyme system comprised of lactase, glucose oxidase, and peroxidase was used to break down lactose into peroxide, which was then detected with a redox indicator. The test can detect lactose concentrations as low as 5% in solid samples and shows no interference when lactose is mixed with illicit drugs or commercial pharmaceuticals. Prepared test cards were stable on the shelf for up to five months. In a blinded study of samples composed of mixtures of heroin, methamphetamine, cocaine HCl, crack cocaine, fillers, and lactose, the sensitivity for detection of lactose across three readers was 100% and specificity was 96.4% ( n = 96). When this test was incorporated into a 12-lane test card for the detection of illicit drugs, readers were correctly able to identify the illicit drug and the presence of lactose with 99.3% sensitivity and 100% specificity ( n = 54). This test is a robust and affordable way to detect lactose in illicit drug samples.