Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study.

BACKGROUND: Antimicrobial 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: Our 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.

Physicochemical properties determining drug detection in skin.

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the receiver operating characteristic curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations (SHAP) model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2561 US Food and Drug Administration (FDA)-approved drugs, we predict that therapeutic drug classes, such as nervous system drugs, are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Comparative toxicoproteogenomics of mouse and rat liver identifies TCDD-resistance genes.

The aryl hydrocarbon receptor (AHR) mediates many toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, the AHR alone does not explain the widely different outcomes among organisms. To identify the other factors involved, we evaluated three transgenic mouse lines, each expressing a different rat AHR isoform (rWT, DEL, and INS) providing widely different resistance to TCDD toxicity, as well as C57BL/6 and DBA/2 mice which exhibit a ~ tenfold divergence in TCDD sensitivity (exposures of 5-1000 µg/kg TCDD). We supplement these with whole-genome sequencing, together with transcriptomic and proteomic analyses of the corresponding rat models, Long-Evans (L-E) and Han/Wistar (H/W) rats (having a ~ 1000-fold difference in their TCDD sensitivities; 100 µg/kg TCDD), to identify genes associated with TCDD-response phenotypes. Overall, we identified up to 50% of genes with altered mRNA abundance following TCDD exposure are associated with a single AHR isoform (33.8%, 11.7%, 5.2% and 0.3% of 3076 genes altered unique to rWT, DEL, C57BL/6 and INS respectively following 1000 µg/kg TCDD). Hepatic Pxdc1 was significantly repressed in all three TCDD-sensitive animal models (C57BL/6 and rWT mice, and L-E rat) after TCDD exposure. Three genes, including Cxxc5, Sugp1 and Hgfac, demonstrated different AHRE-1 (full) motif occurrences within their promoter regions between rat strains, as well as different patterns of mRNA abundance. Several hepatic proteins showed parallel up- or downward alterations with their RNAs, with three genes (SNRK, IGTP and IMPA2) showing consistent, strain-dependent changes. These data show the value of integrating genomic, transcriptomic and proteomic evidence across multi-species models in toxicologic studies.

BPG: Seamless, automated and interactive visualization of scientific data.

BACKGROUND: We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS: This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION: BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.

Impact of Expanding ELISA Screening in DUID Investigations to Include Carisoprodol/Meprobamate and Zolpidem.

In 2013, the National Safety Council's Alcohol Drugs and Impairment Division added zolpidem and carisoprodol and its metabolite meprobamate to the list of Tier 1 drugs that should be tested for in all suspected drug impaired driving and motor vehicle fatality investigations. We describe the validation of an enzyme linked immunosorbent assays (ELISA) for both drugs in whole blood, and the utilization of the ELISA to assess their positivity in a sample of 322 suspected impaired driving cases that was retrospectively screened using the validated assays. The occurrence of carisoprodol/meprobamate was found to be 1.2%, and for zolpidem, 1.6%. In addition, we analyzed a large dataset (n = 1,672) of Driving Under the Influence of Drugs (DUID) test results from a laboratory performing high volume DUID testing to assess the frequency of detection of both drugs after implementing the expanded NSC scope. Carisoprodol or meprobamate were found positive in 5.9% (n = 99) of these samples, while zolpidem was found positive in 5.3% (n = 89) in drivers who in many cases had been found to be negative for other drugs. Carisoprodol and zolpidem are both potent CNS depressants and are appropriate additions to the recommended NSC scope of testing.