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BackgroundOver the past 4 decades, obesity has escalated into a global epidemic, with its worldwide prevalence nearly tripling. Pharmacological treatments have evolved with the recent development of glucagon-like peptide 1 agonists, such as semaglutide. However, off-label use of drugs such as Ozempic for cosmetic weight loss has surged in popularity, raising concerns about potential misuse and the emergence of substandard and falsified products in the unregulated supply chain. ObjectiveThis study aims to conduct a multifactor investigation of product quality and patient safety risks associated with the unregulated online sale of semaglutide by examining product availability and vendor characteristics and assessing product quality through test purchases. MethodsWe used a complex risk and quality assessment methodology combining online market surveillance, search engine results page analysis, website content assessment, domain traffic analytics, conducting targeted product test purchases, visual quality inspection of product packaging, microbiological sterility and endotoxin contamination evaluation, and quantitative sample analysis using liquid chromatography coupled with mass spectrometry. ResultsWe collected and evaluated 1080 links from search engine results pages and identified 317 (29.35%) links belonging to online pharmacies, of which 183 (57.7%) led to legal pharmacies and 134 (42.3%) directed users to 59 unique illegal online pharmacy websites. Web traffic data for the period between July and September 2023 revealed that the top 30 domains directly or indirectly affiliated with illegal online pharmacies accumulated over 4.7 million visits. Test purchases were completed from 6 illegal online pharmacies with the highest number of links offering semaglutide products for sale without prescription at the lowest price range. Three injection vial purchases were delivered; none of the 3 Ozempic prefilled injection pens were received due to nondelivery e-commerce scams. All purchased vials were considered probable substandard and falsified products, as visual inspection indicated noncompliance in more than half (59%-63%) of the evaluated criteria. The semaglutide content of samples substantially exceeded labeled amounts by 28.56%-38.69%, although no peptide-like impurities were identified. The lyophilized peptide samples were devoid of viable microorganisms at the time of testing; however, endotoxin was detected in all samples with levels ranging between 2.1645 EU/mg and 8.9511 EU/mg. Furthermore, the measured semaglutide purity was significantly low, ranging between 7.7% and 14.37% and deviating from the 99% claimed on product labels by manufacturers. ConclusionsGlucagon-like peptide 1 agonist drugs promoted for weight loss, similar to erectile dysfunction medications more than 2 decades ago, are becoming the new blockbuster lifestyle medications for the illegal online pharmacy market. Protecting the pharmaceutical supply chain from substandard and falsified weight loss products and raising awareness regarding online medication safety must be a public health priority for regulators and technology platforms alike.

BackgroundThe online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. ObjectiveThe role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. MethodsWe conducted a comparative assessment of AI-generated recommendations from Google’s Search Generative Experience (SGE) and Microsoft Bing’s Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. ResultsOf the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat’s and 13.23% (18/136) of Google SGE’s recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). ConclusionsWhile the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

BackgroundAntimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. ObjectiveThe aim of this paper is to identify and characterize “no prescription” online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. MethodsWe first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. ResultsA total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. ConclusionsOur study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients.

BackgroundAmong racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. ObjectiveThis infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. MethodsThe study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention–related themes discussed by users. ResultsWe collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. ConclusionsBoth objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.

Session Description: This session will provide an overview of the “fit-for-purpose” framework that is applied to blockchain and healthcare education at UC San Diego in professional and continuing education offerings and as planned through future graduate course offerings. The presentation will outline how blockchain healthcare use cases need to be “fit-for-purpose” within the context of blockchain design principles and expected outcomes. The three primary use cases used in this course design are: (1) pharmaceutical supply chain; (2) medical device; (3) clinical trials. We will also describe how this educational content is taught in different ways through lectures, case studies, and applied group work that is then subject to presentation and judging by outside experts. This general education approach to blockchain that differs from the majority of online offerings is currently being used and expanded at UC San Diego but also has the potentially to be applied in other settings.