Explore the vast range of titles available.

GNPS is an open-access community-curated analysis platform for sharing natural product mass spectrometry data that enables continuous, automatic reanalysis of deposited 'living' data sets. The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu ), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. models built using existing data facilitate rapid acute toxicity predictions without using animals. The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ ( )], and nontoxic chemicals ( ). An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with results. CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Photoacoustic Tomography (PAT) is a deep-tissue imaging modality, with potential clinical applications in the diagnosis of arthritis, cancer and other disease conditions. Here, we identified Clofazimine (CFZ), a red-pigmented dye and anti-inflammatory FDA-approved drug, as a macrophage-targeting photoacoustic (PA) imaging agent. Spectroscopic experiments revealed that CFZ and its various protonated forms yielded optimal PAT signals at wavelengths −450 to 540 nm. CFZ’s macrophage-targeting chemical and structural forms were detected with PA microscopy at a high c ontrast-to- n oise r atio (CNR > 22 dB) as well as with macroscopic imaging using synthetic gelatin phantoms. In vivo , natural and synthetic CFZ formulations also demonstrated significant anti-inflammatory activity. Finally, the injection of CFZ was monitored via a real-time ultrasound-photoacoustic (US-PA) dual imaging system in a live animal and clinically relevant human hand model. These results demonstrate an anti-inflammatory drug repurposing strategy, while identifying a new PA contrast agent with potential applications in the diagnosis and treatment of arthritis.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

Dietary Supplements and Natural Products have minor oversight of their safety and efficacy. We assembled a collection of Dietary Supplements and Natural Products (DSNP) as well as Traditional Chinese Medicinal (TCM) Plant extracts, which were screened against an in vitro panel of assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-gp transporter assays, to assess their activity. This pipeline facilitated the interrogation of Natural Product-Drug Interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection. Many of the approved drugs have well-annotated mechanisms of action (MOA) while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NCATS Pharmaceutical Collection to predict the MOAs of the DSNP/TCM substances. Overall, we highlight four significant bioactivity profiles (measured by p-values) as examples of this prediction. These results can be used as a starting point for further exploration on the toxicity potential and clinical relevance of these substances. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://pubchem.ncbi.nlm.nih.gov/source/NCGC

Measures of similarity between diseases have been used for applications from discovering drug-target interactions to identifying disease-gene relationships. It is challenging to quantitatively compare diseases because much of what we know about them is captured in free text descriptions. Here we present an application of Latent Dirichlet Allocation as a way to measure similarity between diseases using textual descriptions. We learn latent topic representations of text from Online Mendelian Inheritance in Man records and use them to compute similarity. We assess the performance of this approach by comparing our results to manually curated relationships from the Disease Ontology. Despite being unsupervised, our model recovers a record's curated Disease Ontology relations with a mean Receiver Operating Characteristic Area Under the Curve of 0.80. With low dimensional models, topics tend to represent higher level information about affected organ systems, while higher dimensional models capture more granular genetic and phenotypic information. We examine topic representations of diseases for mapping concepts between ontologies and for tagging existing text with concepts. We conclude topic modeling on disease text leads to a robust approach to computing similarity that does not depend on keywords or ontology.

The first-line treatments for uncomplicated Plasmodium falciparum malaria are artemisinin-based combination therapies (ACTs), consisting of an artemisinin derivative combined with a longer acting partner drug. However, the spread of P. falciparum with decreased susceptibility to artemisinin and partner drugs presents a significant challenge to malaria control efforts. To stem the spread of drug resistant parasites, novel chemotherapeutic strategies are being evaluated, including the implementation of triple artemisinin-based combination therapies (TACTs). Currently, there is limited knowledge on the pharmacodynamics and pharmacogenetic interactions of proposed TACT drug combinations. To evaluate these interactions, we established an in vitro high-throughput process for measuring the drug dose-response to three distinct antimalarial drugs present in a TACT. Sixteen different TACT combinations were screened against fifteen parasite lines from Cambodia, with a focus on parasites with differential susceptibilities to piperaquine and artemisinins. Analysis revealed drug-drug interactions unique to specific genetic backgrounds, including antagonism between piperaquine and pyronaridine associated with gene amplification of plasmepsin II/III, two aspartic proteases that localize to the parasite digestive vacuole. From this initial study, we identified parasite genotypes with decreased susceptibility to specific TACTs, as well as potential TACTs that display antagonism in a genotype-dependent manner. Our assay and analysis platform can be further leveraged to inform drug implementation decisions and evaluate next-generation TACTs. In vitro process to evaluate triple-drug combinations for prioritizing antimalarial combinations for in vivo evaluation.

Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant populations from randomized, controlled trials. These factors increase risk for adverse drug outcomes and reduce quality of care for pregnant populations. Herein, we propose the application of artificial intelligence to systematically predict the teratogenicity of a prescriptible small molecule from information inherent to the drug. Using unsupervised and supervised machine learning, our model probes all small molecules with known structure and teratogenicity data published in research-amenable formats to identify patterns among structural, meta-structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this workflow, we discovered three chemical functionalities that predispose a drug towards increased teratogenicity and two moieties with potentially protective effects. Our models predict three clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive accuracy of a blind control for the same task, suggesting successful modeling. We also present extensive barriers to translational research that restrict data-driven studies in pregnancy and therapeutically 'orphan' pregnant populations. Collectively, this work represents a first-in-kind platform for the application of computing to study and predict teratogenicity. Footnotes * https://github.com/apchalla/teratogenicity-qsar.