Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico.

BACKGROUND: The SARS-CoV-2 outbreak poses a challenge to health care systems due to its high complication rates in patients with cardiometabolic diseases. Here, we identify risk factors and propose a clinical score to predict COVID-19 lethality, including specific factors for diabetes and obesity, and its role in improving risk prediction. METHODS: We obtained data of confirmed and negative COVID-19 cases and their demographic and health characteristics from the General Directorate of Epidemiology of the Mexican Ministry of Health. We investigated specific risk factors associated to COVID-19 positivity and mortality and explored the impact of diabetes and obesity on modifying COVID-19-related lethality. Finally, we built a clinical score to predict COVID-19 lethality. RESULTS: Among the 177 133 subjects at the time of writing this report (May 18, 2020), we observed 51 633 subjects with SARS-CoV-2 and 5,332 deaths. Risk factors for lethality in COVID-19 include early-onset diabetes, obesity, chronic obstructive pulmonary disease, advanced age, hypertension, immunosuppression, and chronic kidney disease (CKD); we observed that obesity mediates 49.5% of the effect of diabetes on COVID-19 lethality. Early-onset diabetes conferred an increased risk of hospitalization and obesity conferred an increased risk for intensive care unit admission and intubation. Our predictive score for COVID-19 lethality included age ≥ 65 years, diabetes, early-onset diabetes, obesity, age < 40 years, CKD, hypertension, and immunosuppression and significantly discriminates lethal from non-lethal COVID-19 cases (C-statistic = 0.823). CONCLUSIONS: Here, we propose a mechanistic approach to evaluate the risk for complications and lethality attributable to COVID-19, considering the effect of obesity and diabetes in Mexico. Our score offers a clinical tool for quick determination of high-risk susceptibility patients in a first-contact scenario.

Consistent Cell-selective Analog Series as Constellation Luminaries in Chemical Space.

High-throughput screening data of compounds consistently tested against the same panel of cell lines is a rich source of information for interrogating cell-selectivity of a compound. Nevertheless, there is a high risk of false positives for these rapid-testing strategies. Then, a single cell-inactive compound can be mistakenly labeled as highly cell-selective if a false positive occurs in any of the cell assays. More interesting would be the case of a series of analogs, which are structurally related compounds, that have a trend to be active only against a small number of cells. To this end, it is herein proposed a proof-of-concept of a method for finding consistent cell-selective analog series of chemical compounds through analysis of high-throughput cell-compound screening data systematically obtained. Furthermore, statistics for quantifying cell-promiscuity and consistency of an analog series are presented.

Specialty choice determinants among Mexican medical students: a cross-sectional study.

BACKGROUND: The choice of medical specialty is related to multiple factors, students' values, and specialty perceptions. Research in this area is needed in low- and middle-income countries, where the alignment of specialty training with national healthcare needs has a complex local interdependency. The study aimed to identify factors that influence specialty choice among medical students. METHODS: Senior students at the National Autonomous University of Mexico (UNAM) Faculty of Medicine answered a questionnaire covering demographics, personal experiences, vocational features, and other factors related to specialty choice. Chi-square tests and factor analyses were performed. RESULTS: The questionnaire was applied to 714 fifth-year students, and 697 provided complete responses (response rate 81%). The instrument Cronbach's alpha was 0.8. The mean age was 24 ± 1 years; 65% were women. Eighty percent of the students wanted to specialize, and 60% had participated in congresses related to the specialty of interest. Only 5% wanted to remain as general practitioners. The majority (80%) wanted to enter a core specialty: internal medicine (29%), general surgery (24%), pediatrics (11%), gynecology and obstetrics (11%) and family medicine (4%). The relevant variables for specialty choice were grouped in three dimensions: personal values that develop and change during undergraduate training, career needs to be satisfied, and perception of specialty characteristics. CONCLUSIONS: Specialty choice of medical students in a middle-income country public university is influenced by the undergraduate experience, the desire to study a subspecialty and other factors (including having skills related to the specialty and type of patients).

Reaching for the bright StARs in chemical space.

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.

Systematic Extraction of Analogue Series from Large Compound Collections Using a New Computational Compound-Core Relationship Method.

Chemical optimization of organic compounds produces a series of analogues. In addition to considering an analogue series (AS) or multiple series on a case-by-case basis, which is often done in the practice of chemistry, the extraction of analogues from compound repositories is of high interest in organic and medicinal chemistry. In organic chemistry, ASs are a source of alternative synthetic routes and also aid in exploring relationships between compounds from different sources including synthetic vs. naturally occurring molecules. In medicinal chemistry, ASs are the major source of structure-activity relationship information and of hits or leads for drug development. ASs might be identified in different ways. For a given reference compound, a substructure search can be carried out using its scaffold. Alternatively, matched molecular pairs can be calculated to retrieve analogues from a compound repository. However, if no query compounds are used, the identification of ASs in databases is a difficult task. Herein, we introduce a computational approach to systematically identify ASs in collections of organic compounds. The approach involves compound decomposition on the basis of well-established retrosynthetic rules, organization of compound-core relationships, and identification of analogues sharing the same core. The method was applied on a large scale to extract ASs from the ChEMBL database, yielding more than 30 000 distinct series.

Finding Constellations in Chemical Space Through Core Analysis.

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

WIP1 Contributes to the Adaptation of Fanconi Anemia Cells to DNA Damage as Determined by the Regulatory Network of the Fanconi Anemia and Checkpoint Recovery Pathways.

DNA damage adaptation (DDA) allows the division of cells with unrepaired DNA damage. DNA repair deficient cells might take advantage of DDA to survive. The Fanconi anemia (FA) pathway repairs DNA interstrand crosslinks (ICLs), and deficiencies in this pathway cause a fraction of breast and ovarian cancers as well as FA, a chromosome instability syndrome characterized by bone marrow failure and cancer predisposition. FA cells are hypersensitive to ICLs; however, DDA might promote their survival. We present the FA-CHKREC Boolean Network Model, which explores how FA cells might use DDA. The model integrates the FA pathway with the G2 checkpoint and the checkpoint recovery (CHKREC) processes. The G2 checkpoint mediates cell-cycle arrest (CCA) and the CHKREC activates cell-cycle progression (CCP) after resolution of DNA damage. Analysis of the FA-CHKREC network indicates that CHKREC drives DDA in FA cells, ignoring the presence of unrepaired DNA damage and allowing their division. Experimental inhibition of WIP1, a CHKREC component, in FA lymphoblast and cancer cell lines prevented division of FA cells, in agreement with the prediction of the model.

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis.

Computational approaches have previously been introduced to predict compounds with activity against multiple targets or compound combinations with synergistic functional effects. By contrast, there are no computational studies available that explore combinations of targets that might act synergistically upon small molecule treatment. Herein, we introduce an approach designed to identify synergistic target pairs on the basis of cell-based screening data and compounds with known target annotations. The targets involved in forming synergistic pairs were analyzed through a novel network propagation algorithm for rationalizing possible common synergy mechanisms. This algorithm enabled further analysis of each synergistic target pair and the identification of "interactors", i.e., proteins with higher propagation scores than would be expected by adding the individual contributions of each target in the synergistic pair. We detected 137 synergistic target pairs including 51 unique targets. A global network analysis of these 51 targets made it possible to derive a subnetwork of proteins with significant synergy. Furthermore, interactors were identified for 87 synergistic target pairs upon individual analysis of the network propagation of each pair. These interactors were associated with pathways related to cancer and apoptosis, membrane transport, and steroid metabolism and provided possible explanations of synergistic effects.

DataWarrior: an evaluation of the open-source drug discovery tool.

INTRODUCTION: DataWarrior is open and interactive software for data analysis and visualization that integrates well-established and novel chemoinformatics algorithms in a single environment. Since its public release in 2014, DataWarrior has been used by research groups in universities, government, and industry. Areas covered: Herein, the authors discuss, in a critical manner, the tools and distinct technical features of DataWarrior and analyze the areas of opportunity. Authors also present the most common applications as well as emerging uses in research areas beyond drug discovery with an emphasis on multidisciplinary projects. Expert opinion: In the era of big data and data-driven science, DataWarrior stands out as a technology that combines prediction of physicochemical properties of pharmaceutical interest, cheminformatics calculations, multivariate data analysis, and interactive visualization with dynamic plots. The well-established chemoinformatics tools implemented in DataWarrior, as well as the innovative algorithms, make the technology useful and attractive as revealed by the increasing number of documented applications.

A general approach for retrosynthetic molecular core analysis.

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure-activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied "single molecule-single scaffold" correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure-property relationships, and identification of analog series and ASBS. The molecule-core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure-property relationships analyses.

HitPickV2: a web server to predict targets of chemical compounds.

MOTIVATION: The identification of protein targets of novel compounds is essential to understand compounds' mechanisms of action leading to biological effects. Experimental methods to determine these protein targets are usually slow, costly and time consuming. Computational tools have recently emerged as cheaper and faster alternatives that allow the prediction of targets for a large number of compounds. RESULTS: Here, we present HitPickV2, a novel ligand-based approach for the prediction of human druggable protein targets of multiple compounds. For each query compound, HitPickV2 predicts up to 10 targets out of 2739 human druggable proteins. To that aim, HitPickV2 identifies the closest, structurally similar compounds in a restricted space within a vast chemical-protein interaction area, until 10 distinct protein targets are found. Then, HitPickV2 scores these 10 targets based on three parameters of the targets in such space: the Tanimoto coefficient (Tc) between the query and the most similar compound interacting with the target, a target rank that considers Tc and Laplacian-modified naïve Bayesian target models scores and a novel parameter introduced in HitPickV2, the number of compounds interacting with each target (occur). We present the performance results of HitPickV2 in cross-validation as well as in an external dataset. AVAILABILITY AND IMPLEMENTATION: HitPickV2 is available in www.hitpickv2.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of a large food chemical database: chemical space, diversity, and complexity.

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FoodDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of "Food Informatics". Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections.

Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling.

Epigenetic drug discovery is an emerging strategy against several chronic and complex diseases. The increased interest in epigenetics has boosted the development and maintenance of large information on structure-epigenetic activity relationships for several epigenetic targets. In turn, such large databases-many in the public domain-are a rich source of information to explore their structure-activity relationships (SARs). Herein, we conducted a large-scale analysis of the SAR of epigenetic targets using the concept of activity landscape modeling. A comprehensive quantitative analysis and a novel visual representation of the epigenetic activity landscape enabled the rapid identification of regions of targets with continuous and discontinuous SAR. This information led to the identification of epigenetic targets for which it is anticipated an easier or a more difficult drug-discovery program using conventional hit-to-lead approaches. The insights of this work also enabled the identification of specific structural changes associated with a large shift in biological activity. To the best of our knowledge, this work represents the largest comprehensive SAR analysis of several epigenetic targets and contributes to the better understanding of the epigenetic activity landscape.

Protein-Protein Interaction Modulators for Epigenetic Therapies.

Targeting protein-protein interactions (PPIs) is becoming an attractive approach for drug discovery. This is particularly true for difficult or emerging targets, such as epitargets that may be elusive to drugs that fall into the traditional chemical space. The chemical nature of the PPIs makes attractive the use of peptides or peptidomimetics to selectively modulate such interactions. Despite the fact peptide-based drug discovery has been challenging, the use of peptides as leads compounds for drug discovery is still a valid strategy. This chapter discusses the current status of PPIs in epigenetic drug discovery. A special emphasis is made on peptides and peptide-like compounds as potential drug candidates.

Chemoinformatics: a perspective from an academic setting in Latin America.

This perspective discusses the current progress of a chemoinformatics group in a major university in Latin America. Three major aspects are discussed in a critical manner: research, education, and collaboration with industry and other public research networks. It is also presented an overview of the progress in applied research and development of research concepts. Efforts to teach chemoinformatics at the undergraduate and graduate levels are discussed. It is addressed how the partnership with industry and other not-for-profit research institutions not only brings additional sources of funding but, more importantly, increases the impact of the multidisciplinary work and offers the students to be exposed to other research environments. We also discuss the main perspectives and challenges that remain to be addressed in these settings.

Insights from pharmacological similarity of epigenetic targets in epipolypharmacology.

As the number of compounds tested against epigenetic targets grows, exploration of the possible associations in chemical space among these targets could lead to the identification of new drugs or new designs of epipolypharmacological molecules. Thus, here we review compound-epitarget associations of public databases. Specifically, we explore the structure-multitarget activity relationships and diversity of over 7000 compounds tested against 52 epigenetic-related targets. We found that, whereas inhibitors of histone deacetylases and other epigenetic targets are clustered in the chemical space, the chemical space of inhibitors of different DNA methyltransferases (DNMTs) did not overlap, indicating DNMT selectivity. These and other compound-epitarget relationships discussed here could be useful for both drug repurposing and the rational design of epipolypharmacological compounds.

Chemical space, diversity and activity landscape analysis of estrogen receptor binders.

Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.

ChemMaps: Towards an approach for visualizing the chemical space based on adaptive satellite compounds.

We present a novel approach called ChemMaps for visualizing chemical space based on the similarity matrix of compound datasets generated with molecular fingerprints' similarity. The method uses a 'satellites' approach, where satellites are, in principle, molecules whose similarity to the rest of the molecules in the database provides sufficient information for generating a visualization of the chemical space. Such an approach could help make chemical space visualizations more efficient. We hereby describe a proof-of-principle application of the method to various databases that have different diversity measures. Unsurprisingly, we found the method works better with databases that have low 2D diversity. 3D diversity played a secondary role, although it seems to be more relevant as 2D diversity increases. For less diverse datasets, taking as few as 25% satellites seems to be sufficient for a fair depiction of the chemical space. We propose to iteratively increase the satellites number by a factor of 5% relative to the whole database, and stop when the new and the prior chemical space correlate highly. This Research Note represents a first exploratory step, prior to the full application of this method for several datasets.

Chemoinformatic expedition of the chemical space of fungal products.

AIM: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. RESULTS: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. CONCLUSION: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

Activity landscape analysis of novel 5α-reductase inhibitors.

Inhibitors of the enzyme 5[Formula: see text]-reductase (5aR) are promising therapeutic agents for the treatment of benign prostatic hyperplasia (BPH) and prostate cancer. The lack of structural data of the enzyme 5aR prompts the application of ligand-based approaches to systematically explore the activity landscape of 5aR inhibitors. As part of an effort to develop inhibitors of this enzyme for the treatment of BPH, herein we discuss a chemoinformatic-based analysis of the activity landscape of a novel set of 53 novel pregnane and androstene compounds. It was found that, in general, for each pair of compounds in the set, as the structure similarity of the compounds increases the corresponding potency difference decreases. These results are in agreement with an overall smooth activity landscape. However, two potent activity cliff generators were identified pointing to specific small structural changes that have a large impact on the inhibition of 5aR.