Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Mechanistic and predictive studies on the oxidation of furans by cytochrome P450: A DFT study

Furan-containing compounds distribute widely in food, herbal medicines, industrial synthetic products, and environmental media. These compounds can undergo oxidative metabolism catalyzed by cytochrome P450 enzymes (CYP450) within organisms, which may produce reactive products, possibly reacting with biomolecules to induce toxic effects. In this work, we performed DFT calculations to investigate the CYP450-mediated metabolic mechanism of furan-ring oxidation using 2-methylfuran as a model substrate, meanwhile, we studied the regioselective competition of another hydroxylation reaction involving methyl group of 2-methylfuran. As a result, we found the toxicological-relevant cis-enedione product can be produced from O-addition directly via a concerted manner without formation of an epoxide intermediate as traditionally believed. Moreover, our calculations demonstrate the kinetic and thermodynamic feasibility of both furan-ring oxidation and methyl hydroxylation pathways, although the former pathway is a bit more favorable. We then constructed a linear model to predict the rate-limiting activation energies (ΔE*) of O-addition with 11 diverse furan substates based on their adiabatic ionization potentials (AIPs) and condensation Fukui functions (CFFs). The results show a good predictive ability (R2=0.94, Q2CV=0.87). Therefore, AIP and CFF with clear physichem meanings relevant to the mechanism, emerge as pivotal molecular descriptors to enable the fast prediction of furan-ring oxidation reactivities for quick insight into the toxicological risk of furans, using just ground-state calculations.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

MolToxPred: small molecule toxicity prediction using machine learning approach

Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

The Role of Five-Membered Heterocycles in the Molecular Structure of Antibacterial Drugs Used in Therapy

Five-membered heterocycles are essential structural components in various antibacterial drugs; the physicochemical properties of a five-membered heterocycle can play a crucial role in determining the biological activity of an antibacterial drug. These properties can affect the drug's activity spectrum, potency, and pharmacokinetic and toxicological properties. Using scientific databases, we identified and discussed the antibacterials used in therapy, containing five-membered heterocycles in their molecular structure. The identified five-membered heterocycles used in antibacterial design contain one to four heteroatoms (nitrogen, oxygen, and sulfur). Antibacterials containing five-membered heterocycles were discussed, highlighting the biological properties imprinted by the targeted heterocycle. In some antibacterials, heterocycles with five atoms are pharmacophores responsible for their specific antibacterial activity. As pharmacophores, these heterocycles help design new medicinal molecules, improving their potency and selectivity and comprehending the structure-activity relationship of antibiotics. Unfortunately, particular heterocycles can also affect the drug's potential toxicity. The review extensively presents the most successful five-atom heterocycles used to design antibacterial essential medicines. Understanding and optimizing the intrinsic characteristics of a five-membered heterocycle can help the development of antibacterial drugs with improved activity, pharmacokinetic profile, and safety.

Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

Exploring the Effect of Halogenation in a Series of Potent and Selective A2B Adenosine Receptor Antagonists

The modulation of the A_2B adenosine receptor is a promising strategy in cancer (immuno) therapy, with A_2BAR antagonists emerging as immune checkpoint inhibitors. Herein, we report a systematic assessment of the impact of (di- and mono-)halogenation at positions 7 and/or 8 on both A_2BAR affinity and pharmacokinetic properties of a collection of A_2BAR antagonists and its study with structure-based free energy perturbation simulations. Monohalogenation at position 8 produced potent A_2BAR ligands irrespective of the nature of the halogen. In contrast, halogenation at position 7 and dihalogenation produced a halogen-size-dependent decay in affinity. Eight novel A_2BAR ligands exhibited remarkable affinity (K_i < 10 nM), exquisite subtype selectivity, and enantioselective recognition, with some eutomers eliciting sub-nanomolar affinity. The pharmacokinetic profile of representative derivatives showed enhanced solubility and microsomal stability. Finally, two compounds showed the capacity of reversing the antiproliferative effect of adenosine in activated primary human peripheral blood mononuclear cells.

BODIPY based Metal-Organic Macrocycles and Frameworks: Recent Therapeutic Developments

Boron dipyrromethene, commonly known as BODIPY, based metal-organic macrocycles (MOCs) and metal-organic frameworks (MOFs) represent an interesting part of materials due to their versatile tunability of structure and functionality as well as significant physicochemical properties, thus broadening their applications in various scientific domains, especially in biomedical sciences. With increasing concern over the efficacy of cancer drugs versus quality of patient's life dilemma, scientists have been trying to fabricate novel comprehensive therapeutic strategies along with the discovery of novel safer drugs where research with BODIPY metal complexes has shown vital advancements. In this review, we have exclusively examined the articles involving studies related to light harvesting and photophysical properties of BODIPY based MOCs and MOFs, synthesized through self-assembly process, with a special focus on biomolecular interaction and its importance in anti-cancer drug research. In the end, we also emphasized the possible practical challenges involved during the synthetic process, based on our experience on dealing with BODIPY molecules and steps to overcome them along with their future potentials. This review will significantly help our fellow research groups, especially the budding researchers, to quickly and comprehensively get the near to wholesome picture of BODIPY based MOCs and MOFs and their present status in anti-cancer drug discovery.

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 μM for inhibition of enzymatic activity and EC50 values as low as 0.8 μM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 μM, while remdesivir yields values of 0.5-2 μM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 μM, EC50 values of ca. 0.1 μM, good aqueous solubility, and no cytotoxicity.

Improved correction of F508del-CFTR biogenesis with a folding facilitator and an inhibitor of protein ubiquitination

A growing number of diseases are linked to the misfolding of integral membrane proteins, and many of these proteins are targeted for ubiquitin-proteasome-dependent degradation. One such substrate is a mutant form of the Cystic Fibrosis Transmembrane Conductance Regulator (F508del-CFTR). Protein folding "correctors" that repair the F508del-CFTR folding defect have entered the clinic, but they are unlikely to protect the entire protein from degradation. To increase the pool of F508del-CFTR protein that is available for correction by existing treatments, we determined a structure-activity relationship to improve the efficacy and reduce the toxicity of an inhibitor of the E1 ubiquitin activating enzyme that facilitates F508del-CFTR maturation. A resulting lead compound lacked measurable toxicity and improved the ability of an FDA-approved corrector to augment F508del-CFTR folding, transport the protein to the plasma membrane, and maintain its activity. These data support a proof-of-concept that modest inhibition of substrate ubiquitination improves the activity of small molecule correctors to treat CF and potentially other protein conformational disorders.

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

BACKGROUND

Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities.

METHODS

This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results.

RESULTS

In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05).

CONCLUSIONS

A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.

Evaluation of trypanocidal properties of ferrocenyl and cyrhetrenyl N-acylhydrazones with pendant 5-nitrofuryl group

Four N-acylhydrazones of general formulae [R¹-C(O)-NH-N=C(R²)(5-nitrofuryl)] with (R¹ = ferrocenyl or cyrhetrenyl and R² = H or Me) are synthesized and characterized in solution and in the solid-state. Comparative studies of their stability in solution under different experimental conditions and their electrochemical properties are reported. NMR studies reveal that the four compounds are stable in DMSO‑d₆ and complementary UV-Vis studies confirm that they also exhibit high stability in mixtures DMSO:H₂O at 37 °C. Electrochemical studies show that the half-wave potential of the nitro group of the N-acylhydrazones is smaller than that of the standard drug nifurtimox and the reduction process follows a self-protonation mechanism. In vitro studies on the antiparasitic activities of the four complexes and the nifurtimox against Trypanosoma cruzi and Trypanosoma brucei reveal that: i) the N-acylhydrazones have a potent inhibitory growth activity against both parasites [EC₅₀ in the low micromolar (in T. cruzi) or even in the nanomolar (in T. brucei) range] and ii) cyrhetrenyl derivatives are more effective than their ferrocenyl analogs. Parallel studies on the L₆ rat skeletal myoblast cell line have also been conducted, and the selectivity indexes determined. Three of the four N-acylhydrazones showed higher selectivity towards T. brucei than the standard drug nifurtimox. Additional studies suggest that the organometallic compounds are bioactivated by type I nitroreductase enzymes.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Discovery of novel immunopharmacological ligands targeting the IL-17 inflammatory pathway

Interleukin 17 (IL-17) is a proinflammatory cytokine that acts as an immune checkpoint for several autoimmune diseases. Therapeutic neutralizing antibodies that target this cytokine have demonstrated clinical efficacy in psoriasis. However, biologics have limitations such as their high cost and their lack of oral bioavailability. Thus, it is necessary to expand the therapeutic options for this IL-17A/IL-17RA pathway, applying novel drug discovery methods to find effective small molecules. In this work, we combined biophysical and cell-based assays with structure-based docking to find novel ligands that target this pathway. First, a virtual screening of our chemical library of 60000 compounds was used to identify 67 potential ligands of IL-17A and IL-17RA. We developed a biophysical label-free binding assay to determine interactions with the extracellular domain of IL-17RA. Two molecules (CBG040591 and CBG060392) with quinazolinone and pyrrolidinedione chemical scaffolds, respectively, were confirmed as ligands of IL-17RA with micromolar affinity. The anti-inflammatory activity of these ligands as cytokine-release inhibitors was evaluated in human keratinocytes. Both ligands inhibited the release of chemokines mediated by IL-17A, with an IC₅₀ of 20.9 ± 12.6 μM and 23.6 ± 11.8 μM for CCL20 and an IC₅₀ of 26.7 ± 13.1 μM and 45.3 ± 13.0 μM for CXCL8. Hence, they blocked IL-17A proinflammatory activity, which is consistent with the inhibition of the signalling of the IL-17A receptor by ligand CBG060392. Therefore, we identified two novel immunopharmacological ligands targeting the IL-17A/IL-17RA pathway with antiinflammatory efficacy that can be promising tools for a drug discovery program for psoriasis.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Discovery of substituted 3H-pyrido[2,3-d]pyrimidin-4-ones as potent, biased, and orally bioavailable sst2 agonist

The discovery of a novel 3H-pyrido[2,3-d]pyrimidin-4-one series as potent and biased sst2 agonists is described. This class of molecules exhibits excellent sst2 potency and selectivity against sst1, sst3, and sst5 receptors, and they are significantly more potent at inhibiting cAMP production than inducing internalization. The orally bioavailable 6-(3-chloro-5-methylphenyl)-3-(3-fluoro-5-hydroxyphenyl)-5-({methyl[(2S)-pyrrolidin-2-ylmethyl]amino}methyl)-3H,4H-pyrido[2,3-d]pyrimidin-4-one (36) also suppresses GH secretion in GHRH-challenged rats in a dose-dependent manner.

Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor Antagonists

A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A_2BAR antagonists has been carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates, which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identification of new ligands that combine remarkable affinity (K_i < 30 nM) and exquisite selectivity. The structure-activity relationship (SAR) trends identified were substantiated by a molecular modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity, respectively. The stereospecific interaction between hA_2BAR and the eutomer of the most attractive novel antagonist (S)-18g (K_i = 3.66 nM) was validated.

Conformational turn triggers regio-selectivity in the bioactivation of thiophene-contained compounds mediated by cytochrome P450

In the present work, we performed Density Functional Theory calculations to explore the bioactivation mechanism of thiophene-containing molecules mediated by P450s. For this purpose, relatively large size compounds, 2,5-diaminothiophene derivatives were selected particularly for this investigation. Here we found the successive regio-selectivity triggered by conformational turn played a significant role in the occurrence of bioactivation. 2,5-Diaminothiophene was oxidized to a 2,5-diimine thiophene-reactive intermediate by Compound I (Cpd I) through successive activations of two N-H bonds (H3-N11 and H1-N6). This reaction exhibited three special characteristics: (1) self-controlled regio-selectivity during the oxidation process. There was a large scale of conformational turn in the abstraction of the first H atom which triggers the selection of the second H for abstraction. (2) Proton-shuttle mechanism. In high spin (HS) state, proton-shuttle mechanism was observed for the abstraction of the second H atom. (3) Spin-selective manner. In protein environment, the energy barrier in HS state was much lower than that in low spin state. The novel proposed bioactivation mechanism of 2,5-diaminothiophene compounds can help us in rational design of thiophene-contained drugs avoiding the occurrence of bioactivation.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates. We proposed descriptors that describe topological and electrostatic characteristics of CYP substrates using Gasteiger charge. The proposed descriptors were applied to CYP3A4 data analysis as a case study. As a result of the descriptor selection, we obtained a gradient boosting decision tree-based SoM classification model that used 139 existing descriptors and the proposed 45 descriptors, and the model performed well in terms of the Matthews correlation coefficient. We also developed a structure converter to predict CYP products. This converter correctly generated 51 structural formulas of experimentally observed CYP3A4 products according to a manual evaluation.

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

BACKGROUND

Available toxicity data can be optimally interpreted if they are integrated using computational approaches such as systems biology modeling. Such approaches are particularly warranted in cases where regulatory decisions have to be made rapidly.

OBJECTIVES

The study aims at developing and applying a new integrative computational strategy to identify associations between bisphenol S (BPS), a substitute for bisphenol A (BPA), and components of adverse outcome pathways (AOPs).

METHODS

The proposed approach combines a text mining (TM) procedure and integrative systems biology to comprehensively analyze the scientific literature to enrich AOPs related to environmental stressors. First, to identify relevant associations between BPS and different AOP components, a list of abstracts was screened using the developed text-mining tool AOP-helpFinder, which calculates scores based on the graph theory to prioritize the findings. Then, to fill gaps between BPS, biological events, and adverse outcomes (AOs), a systems biology approach was used to integrate information from the AOP-Wiki and ToxCast databases, followed by manual curation of the relevant publications.

RESULTS

Links between BPS and 48 AOP key events (KEs) were identified and scored via 31 references. The main outcomes were related to reproductive health, endocrine disruption, impairments of metabolism, and obesity. We then explicitly analyzed co-mention of the terms BPS and obesity by data integration and manual curation of the full text of the publications. Several molecular and cellular pathways were identified, which allowed the proposal of a biological explanation for the association between BPS and obesity.

CONCLUSIONS

By analyzing dispersed information from the literature and databases, our novel approach can identify links between stressors and AOP KEs. The findings associating BPS and obesity illustrate the use of computational tools in predictive toxicology and highlight the relevance of the approach to decision makers assessing substituents to toxic chemicals. https://doi.org/10.1289/EHP4200.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation

Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

Self-Assembly of Novel Thiophene-Based BODIPY RuII Rectangles: Potential Antiproliferative Agents Selective Against Cancer Cells

Novel Ru (2+2) rectangles were designed and synthesized by self-assembly of a new thiophene-functionalized dipyridyl BODIPY ligand, BDPS, and ruthenium(II) precursors. The complexes exhibited dose-dependent antiproliferative activities against cancer cells, in which some compounds selectively kill cancer cells. The net fluorescence due to BODIPY allowed us to visualize their location inside cancer cells. Moreover, the metalla-rectangles displayed substantial propensity to bind with biomolecules.

Not all boronic acids with a five-membered cycle induce tremor, neuronal damage and decreased dopamine

Several striatal toxins can be used to induce motor disruption. One example is MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), whose toxicity is accepted as a murine model of parkinsonism. Recently, 3-Thienylboronic acid (3TB) was found to produce motor disruption and biased neuronal damage to basal ganglia in mice. The aim of this study was to examine the toxic effects of four boronic acids with a close structural relationship to 3TB (all having a five-membered cycle), as well as boric acid and 3TB. These boron-containing compounds were compared to MPTP regarding brain access, morphological disruption of the CNS, and behavioral manifestations of such disruption. Data was collected through acute toxicity evaluations, motor behavior tests, necropsies, determination of neuronal survival by immunohistochemistry, Raman spectroscopic analysis of brain tissue, and HPLC measurement of dopamine in substantia nigra and striatum tissue. Each compound showed a distinct profile for motor disruption. For example, motor activity was not disrupted by boric acid, but was decreased by two boronic acids (caused by a sedative effect). 3TB, 2-Thienyl and 2-furanyl boronic acid gave rise to shaking behavior. The various manifestations generated by these compounds can be linked, in part, to different levels of dopamine (measured by HPLC) and degrees of neuronal damage in the basal ganglia and cerebellum. Clearly, motor disruption is not induced by all boronic acids with a five-membered cycle as substituent. Possible explanations are given for the diverse chemico-morphological changes and degrees of disruption of the motor system, considering the role of boron and the structure-toxicity relationship.