Subtle structural differences of nucleotide analogs may impact SARS-CoV-2 RNA-dependent RNA polymerase and exoribonuclease activity.

The rapid spread and public health impact of the novel SARS-CoV-2 variants that cause COVID-19 continue to produce major global impacts and social distress. Several vaccines were developed in record time to prevent and limit the spread of the infection, thus playing a pivotal role in controlling the pandemic. Although the repurposing of available drugs attempts to provide therapies of immediate access against COVID-19, there is still a need for developing specific treatments for this disease. Remdesivir, molnupiravir and Paxlovid remain the only evidence-supported antiviral drugs to treat COVID-19 patients, and only in severe cases. To contribute on the search of potential Covid-19 therapeutic agents, we targeted the viral RNA-dependent RNA polymerase (RdRp) and the exoribonuclease (ExoN) following two strategies. First, we modeled and analyzed nucleoside analogs sofosbuvir, remdesivir, favipiravir, ribavirin, and molnupiravir at three key binding sites on the RdRp-ExoN complex. Second, we curated and virtually screened a database containing 517 nucleotide analogs in the same binding sites. Finally, we characterized key interactions and pharmacophoric features presumably involved in viral replication halting at multiple sites. Our results highlight structural modifications that might lead to more potent SARS-CoV-2 inhibitors against an expansive range of variants and provide a collection of nucleotide analogs useful for screening campaigns.

A Longitudinal 1H NMR-Based Metabolic Profile Analysis of Urine from Hospitalized Premature Newborns Receiving Enteral and Parenteral Nutrition.

Preterm newborns are extremely vulnerable to morbidities, complications, and death. Preterm birth is a global public health problem due to its socioeconomic burden. Nurturing preterm newborns is a critical medical issue because they have limited nutrient stores and it is difficult to establish enteral feeding, which leads to inadequate growth frequently associated with poor neurodevelopmental outcomes. Parenteral nutrition (PN) provides nutrients to preterm newborns, but its biochemical effects are not completely known. To study the effect of PN treatment on preterm newborns, an untargeted metabolomic 1H nuclear magnetic resonance (NMR) assay was performed on 107 urine samples from 34 hospitalized patients. Multivariate data (Principal Component Analysis, PCA, Orthogonal partial least squares discriminant analysis OPLS-DA, parallel factor analysis PARAFAC-2) and univariate analyses were used to identify the association of specific spectral data with different nutritional types (NTs) and gestational ages. Our results revealed changes in the metabolic profile related to the NT, with the tricarboxylic acid cycle and galactose metabolic pathways being the most impacted pathways. Low citrate and succinate levels, despite higher glucose relative urinary concentrations, seem to constitute the metabolic profile found in the studied critically ill preterm newborns who received PN, indicating an energetic dysfunction that must be taken into account for better nutritional management.

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets.

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well-validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high-throughput and medium-throughput screening of epigenetic-focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications.

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants.

Natural products are an invaluable source of molecules with a large variety of biological activities. Interest in natural products in drug discovery is documented in an increasing number of publications of bioactive secondary metabolites. Among those, medicinal plants are one of the most studied for this endeavor. An ever thriving area of opportunity within the field concerns the discovery of antidiabetic natural products. As a result, a vast amount of secondary metabolites are isolated from medicinal plants used against diabetes mellitus but whose information has not been organized systematically yet. Several research articles enumerate antidiabetic compounds, but the lack of a chemical database for antidiabetic metabolites limits their application in drug development. In this work, we present DiaNat-DB, a comprehensive collection of 336 molecules from medicinal plants reported to have in vitro or in vivo antidiabetic activity. We also discuss a chemoinformatic analysis of DiaNat-DB to compare antidiabetic drugs and natural product databases. To further explore the antidiabetic chemical space based on DiaNat compounds, we searched for analogs in ZINC15, an extensive database listing commercially available compounds. This work will help future analyses, design, and development of new antidiabetic drugs. DiaNat-DB and its ZINC15 analogs are freely available at http://rdu.iquimica.unam.mx/handle/20.500.12214/1186.