Numerical description of chemical structures is necessary for development of machine learning and deep learning models for predicting the potential toxicity of chemicals. Mold2 is a software tool developed in C++ for fast calculating molecular descriptors from two-dimensional structures. Mold2 descriptors contain rich information and can be used to build high-performance models in computational toxicology. Multiple studies have compared Mold2 descriptors with other descriptors and fingerprints in machine learning and deep learning models for predicting the toxicity of chemicals. These studies have demonstrated that models built with Mold2 descriptors outperform models developed with other descriptors and fingerprints.

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

The research topic “Novel Therapeutic Interventions Against Infectious Diseases: COVID-19” intends to examine, at the molecular level, the mechanisms of SARS-CoV-2 infection and their potential inhibition through computational or experimental approaches. Drug targets for SARSCoV-2 infections and macromolecules responsible for the virion’s binding to the host receptor protein are described in detail. The 15 research articles in this issue, each focusing on a different aspect of the fight against SARS-CoV-2, use a variety of interdisciplinary approaches, including computational chemistry, biochemical analyses, and biological activity testing. Contributing authors have searched for novel leads from the available natural substances, new chemical entities, and FDA-approved drugs to target SARS-CoV-2. Indari et al. present a comprehensive update on FDA-approved drugs for repurposing, namely chloroquine, hydroxychloroquine, remdesivir, lopinavir-ritonavir, favipiravir, ribavirin, azithromycin, umifenovir, and oseltamivir as well as convalescent plasma therapy used as antiviral therapy against SARS-CoV-2. Preclinical and clinical findings, treatment regimens, pharmacokinetics, and drug–drug interactions are discussed in this review. Some clinically approved medications have been proposed as potential anti-SARS-CoV-2 options as a result of this repurposing strategy. Yadalam et al. performed a computational study to identify the essential oil components as SARSCoV-2 antivirals, especially in the pre-procedural mouth rinses for dental settings. Pre-procedural mouth rinses are helpful in decreasing viral particles in the oral cavity, since most of COVID-19 dissemination occurs due to the virus’ presence in the mouth. Through the molecular docking and conceptual density functional theory (DFT) approach, the antiviral efficacy of essential oil components are studied against the receptor binding domain (RBD) of the spike protein. The compounds cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active by showing strong interactions with the RBD and shown to be active based on the correlation between the structure and the activity of the compounds. They recommend these components to be included as pre-procedural mouth rinses for dental procedures. Cai et al. has come up with a rehabilitation strategy by applying intermittent hypoxic preconditioning (IHP) and also showed how IHP can be a beneficial treatment strategy in the management of COVID-19. IHP, a non-drug alternative therapy for COVID-19 management, showed beneficial effects related to the impact of oxidative stress, inflammation, and the immune response. Li and Peng have reported the strategy and challenges and described recent progress in identifying broad-spectrum antivirals through drug repurposing, by classifying them into direct-acting repurposed antivirals (DARA) and hosttargeting repurposed antivirals (HTRA). In addition, they have summarized and examined the Edited and reviewed by: Salvatore Salomone, University of Catania, Italy

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Since growing evidence has manifested that bisphenol A (BPA) may adversely affect human health, numerous BPA replacement compounds have been gradually introduced into the industry. Although BPA replacement compounds have been detected in various environmental media, foodstuffs, and human biological samples, investigations on their health effects and the underlying mechanisms are scarce. The present perspective outlines the current status of knowledge on the occurrence of BPA replacement compounds and also the associations between their exposure and adverse health outcomes, including endocrine disruption, reproduction problems, development abnormity, metabolic diseases, and other health effects. The mechanisms underlying the toxicity of BPA replacement compounds may be explained by their chemical properties, such as induction of oxidative stress, interactions with estrogen receptors, and regulations of gene expression. Nevertheless, further analysis on these novel BPA replacement compounds regarding human health is still required in order to fill the knowledge gaps and promote their better applications.

Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). As of October 21, 2020, more than 41.4 million confirmed cases and 1.1 million deaths have been reported. Thus, it is immensely important to develop drugs and vaccines to combat COVID-19. The spike protein present on the outer surface of the virion plays a major role in viral infection by binding to receptor proteins present on the outer membrane of host cells, triggering membrane fusion and internalization, which enables release of viral ssRNA into the host cell. Understanding the interactions between the SARS-CoV-2 trimeric spike protein and its host cell receptor protein, angiotensin converting enzyme 2 (ACE2), is important for developing drugs and vaccines to prevent and treat COVID-19. Several crystal structures of partial and mutant SARS-CoV-2 spike proteins have been reported; however, an atomistic structure of the wild-type SARS-CoV-2 trimeric spike protein complexed with ACE2 is not yet available. Therefore, in our study, homology modeling was used to build the trimeric form of the spike protein complexed with human ACE2, followed by all-atom molecular dynamics simulations to elucidate interactions at the interface between the spike protein and ACE2. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) and in silico alanine scanning were employed to characterize the interacting residues at the interface. Twenty interacting residues in the spike protein were identified that are likely to be responsible for tightly binding to ACE2, of which five residues (Val445, Thr478, Gly485, Phe490, and Ser494) were not reported in the crystal structure of the truncated spike protein receptor binding domain (RBD) complexed with ACE2. These data indicate that the interactions between ACE2 and the tertiary structure of the full-length spike protein trimer are different from those between ACE2 and the truncated monomer of the spike protein RBD. These findings could facilitate the development of drugs and vaccines to prevent SARS-CoV-2 infection and combat COVID-19.

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and there is an urgent need for safe and effective drugs for COVID-19 treatment. Since developing a new drug is time consuming, many approved or investigational drugs have been repurposed for COVID-19 treatment in clinical trials. Therefore, selection of safe drugs for COVID-19 patients is vital for combating this pandemic. Our goal was to evaluate the safety concerns of drugs by analyzing adverse events reported in post-market surveillance. We collected 296 drugs that have been evaluated in clinical trials for COVID-19 and identified 28,597,464 associated adverse events at the system organ classes (SOCs) level in the FDA adverse events report systems (FAERS). We calculated Z-scores of SOCs that statistically quantify the relative frequency of adverse events of drugs in FAERS to quantitatively measure safety concerns for the drugs. Analyzing the Z-scores revealed that these drugs are associated with different significantly frequent adverse events. Our results suggest that this safety concern metric may serve as a tool to inform selection of drugs with favorable safety profiles for COVID-19 patients in clinical practices. Caution is advised when administering drugs with high Z-scores to patients who are vulnerable to associated adverse events.

Severe acute respiratory syndrome coronavirus 2 (SARS−CoV−2) has caused the ongoing global COVID-19 pandemic that began in late December 2019. The rapid spread of SARS−CoV−2 is primarily due to person-to-person transmission. To understand the epidemiological traits of SARS−CoV−2 transmission, we conducted phylogenetic analysis on genome sequences from >54K SARS−CoV−2 cases obtained from two public databases. Hierarchical clustering analysis on geographic patterns in the resulting phylogenetic trees revealed a co-expansion tendency of the virus among neighboring countries with diverse sources and transmission routes for SARS−CoV−2. Pairwise sequence similarity analysis demonstrated that SARS−CoV−2 is transmitted locally and evolves during transmission. However, no significant differences were seen among SARS−CoV−2 genomes grouped by host age or sex. Here, our identified epidemiological traits provide information to better prevent transmission of SARS−CoV−2 and to facilitate the development of effective vaccines and therapeutics against the virus.

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR α7 receptor subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR α7 would contribute to methods assessing the addictive potential of tobacco constituents. Among the receptor-ligand complexes obtained from the Protein Data Bank (PDB), we found that there were two distinct clusters of binding-pocket conformations for nAChR α7. One template was selected from each cluster to construct the 3D structures of human nAChR α7 using homology modeling. The competitive docking model was developed for determination of the structure of nAChR α7 bound with a chemical. To develop a prediction model of human α7 binding activity, we used a training data set of extracted 930 chemicals assessed for human α7 binding from the two databases, PubChem and ChEMBL. We also extracted 1448 chemicals evaluated for rat α7 binding from the same databases as the external testing data set. Based on the competitive docking results, the winning docking scores were partitioned to identify the key residues that play important roles in the receptor-ligand binding for each chemical. Our previously published algorithm, Decision Forest (DF), which combines multiple Decision Tree models was used to train the human α7 binding activity prediction model based on the partitioned docking scores. Five-fold cross validations were conducted to estimate the performance of the DF models. The DF model was tested using the external testing data set and then was used to predict the potential human α7 binding activity for the 5275 tobacco constituents of unknown activity. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm the prediction results. The overall prediction accuracy, sensitivity and specificity were 77.4%, 57.7% and 86.2%, respectively, demonstrating that the developed predictive model of human α7 binding activity could be a useful tool for high-throughput screening of potential addictive tobacco constituents. Disclaimer: The findings and conclusions in this abstract are not a formal dissemination of information by the US Food and Drug Administration (FDA) and do not represent Agency position or policy.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. Objectives: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). Methods: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. Results: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. Discussion: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program’s Integrated Chemical Environment. https://doi.org/10.1289/EHP5580

Persistent organic pollutants (POPs) present in foods have been a major concern for food safety due to their persistence and toxic effects. To ensure food safety and protect human health from POPs, it is critical to achieve a better understanding of POP pathways into food and develop strategies to reduce human exposure. POPs could present in food in the raw stages, transferred from the environment or artificially introduced during food preparation steps. Exposure to these pollutants may cause various health problems such as endocrine disruption, cardiovascular diseases, cancers, diabetes, birth defects, and dysfunctional immune and reproductive systems. This review describes potential sources of POP food contamination, analytical approaches to measure POP levels in food and efforts to control food contamination with POPs.

Computational toxicology is a discipline seeking to computationally model and predict toxicity of chemicals including drugs, food additives, and other environmental chemicals. Risk assessment of chemicals using current in vitro or in vivo experimental methods is at best time-consuming and expensive. Computational toxicology seeks to accelerate this process and decrease the cost by predicting the risk of chemicals to humans and animals. Molecular dynamics (MD) simulation, an emerging computational toxicology technique, characterizes the interactions of chemicals with biomolecules such as proteins and nucleic acids. This chapter will give a brief review both of available software tools for MD simulations and also how to apply these software tools to computational toxicology challenges. We also summarize key protocols to run MD simulations.

Background: Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed. Methods: We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs. Results: The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected). Conclusion: A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

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Dopamine is important in physiological processes associated with the rewards and pleasure system. Its function is facilitated by five G Protein – Coupled Receptors (GPCRs). The coupling that occurs between the dopamine receptor and G protein dictates the signaling cascade. The dopamine receptors D1 and D5 couple to the Gs protein while the dopamine receptors D2, D3, and D4 couple to the Gi protein. Dysregulation of the dopamine receptors can lead to neuropsychiatric disorders such as schizophrenia. These disorders are difficult to treat due to the side‐effects that transpire. Understanding the coupling that occurs between the dopamine receptors and the G proteins can further assist with the development of pharmaceutical drugs by helping to alleviate some of the side‐effects. Using computational biophysical methods, the coupling between the dopamine receptors and G protein is being studied to understand the structural mechanisms observed in G protein selectivity of these receptors. The dopamine receptor structures for D1 and D2 in complex with the two G proteins (Gs and Gi) have been relaxed in the membrane environment. A structural analysis of those complexes in combination with structural bioinformatics methods has elucidated receptor residues playing a role in the G protein coupling, which has led to the design of receptor mutants with altered G protein selectivities. These mutants are now being characterized by computational biophysical methods before they will be tested in biochemical assays for altered G protein preferences.Support or Funding InformationNational Institutes of Health (NIH) Building Infrastructure Leading to Diversity (BUILD) #5TL4GM118977This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.