In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach

A novel procedure for selection of molecular descriptors: QSAR model for mutagenicity of nitroaromatic compounds

Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity.

Advances in Activity/Property Prediction from Chemical Structures

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

Virtual screening of FOXO3a activators from natural product-like compound library

FOXO3a is an inevitable transcription factor, which is involved in the regulation of biological processes such as proliferation, DNA damage repair, cell cycle arrest and cell death. Previous studies confirmed that FOXO3a is an excellent tumor suppressor and in cancer cells, it gets phosphorylated followed by proteasomal degradation. FOXO3a is found to be inactivated in cancer cells, whereas in normal cells it gets activated and upregulates its downstream targets, which induces apoptotic pathways. Hence, activation of FOXO3a can be implicated in cancer prevention and treatment. A variety of commercially available FOXO3a activators such as doxorubicin and metformin possess undesirable adverse effects to normal cells and tissues, which are their major limitations. Natural bioactive compounds, eliminating the limitations of such compounds, become an excellent choice for the treatment and prevention of cancer. In this study, a library of natural product-like compounds was screened for their FOXO3a activation potential through in silico approach, which included the use of several bioinformatics tools and processes. Other molecular interaction studies as well as binding and specificity studies were carried out with the help of molecular dynamics simulation. Virtual screening of 7700 small molecules from the Natural Products-like Compound Library revealed the top three FOXO3a activators F3385-6269, F2183-0033 and F3351-0330. Further validation studies are warranted to confirm these findings.

What is the role of nitrate/nitrite in trace organic contaminants degradation and transformation during UV-based advanced oxidation processes?

The effectiveness of UV-based advanced oxidation processes (UV-AOPs) in degrading trace organic contaminants (TrOCs) can be significantly influenced by the ubiquitous presence of nitrate (NO3-) and nitrite (NO2-) in water and wastewater. Indeed, NO3-/NO2- can play multiple roles of NO3-/NO2- in UV-AOPs, leading to complexities and conflicting results observed in existing research. They can inhibit the degradation of TrOCs by scavenging reactive species and/or competitively absorbing UV light. Conversely, they can also enhance the elimination of TrOCs by generating additional •OH and reactive nitrogen species (RNS). Furthermore, the presence of NO3-/NO2- during UV-AOP treatment can affect the transformation pathways of TrOCs, potentially resulting in the nitration/nitrosation of TrOCs. The resulting nitro(so)-products are generally more toxic than the parent TrOCs and may become precursors of nitrogenous disinfection byproducts (N-DBPs) upon chlorination. Particularly, since the impact of NO3-/NO2- in UV-AOPs is largely due to the generation of RNS from NO3-/NO2- including NO•, NO2•, and peroxynitrite (ONOO-/ONOOH), this review covers the generation, properties, and detection methods of these RNS. From kinetic, mechanistic, and toxicologic perspectives, future research needs are proposed to advance the understanding of how NO3-/NO2- can be exploited to improve the performance of UV-AOPs treating TrOCs. This critical review provides a comprehensive framework outlining the multifaceted impact of NO3-/NO2- in UV-AOPs, contributing insights for basic research and practical applications of UV-AOPs containing NO3-/NO2-.

Scaffold Hopping and Screening for Potent Small Molecule Agonists for GRP94: Implications to Alleviate ER Stress-Associated Pathogenesis

Disparity in the activity of Endoplasmic reticulum (ER) leads to degenerative diseases, mainly associated with protein misfolding and aggregation leading to cellular dysfunction and damage, ultimately contributing to ER stress. ER stress activates the complex network of Unfolded Protein Response (UPR) signaling pathways mediated by transmembrane proteins IRE1, ATF6, and PERK. In addition to UPR, many ER chaperones have evolved to optimize the output of properly folded secretory and membrane proteins. Glucose-regulated protein 94 (GRP94), an ER chaperone of heat shock protein HSP90 family, directs protein folding through interaction with other components of the ER protein folding machinery and assists in ER-associated degradation (ERAD). Activation of GRP94 would increase the efficacy of protein folding machinery and regulate the UPR pathway toward homeostasis. The present study aims to screen for novel agonists for GRP94 based on Core hopping, pharmacophore hypothesis, 3D-QSAR, and virtual screening with small-molecule compound libraries in order to improve the efficiency of native protein folding by enhancing GRP94 chaperone activity, therefore to reduce protein misfolding and aggregation. In this study, we have employed the strategy of small molecule-dependent ER programming to enhance the chaperone activity of GRP94 through scaffold hopping-based screening approach to identify specific GRP94 agonists. New scaffolds generated by altering the cores of NECA, the known GRP94 agonist, were validated by employing pharmacophore hypothesis testing, 3D-QSAR modeling, and molecular dynamics simulations. This facilitated the identification of small molecules to improve the efficiency of native protein folding by enhancing GRP94 activity. High-throughput virtual screening of the selected pharmacophore hypothesis against Selleckchem and ZINC databases retrieved a total of 2,27,081 compounds. Further analysis on docking and ADMET properties revealed Epimedin A, Narcissoside, Eriocitrin 1,2,3,4,6-O-Pentagalloylglucose, Secoisolariciresinol diglucoside, ZINC92952357, ZINC67650204, and ZINC72457930 as potential lead molecules. The stability and interaction of these small molecules were far better than the known agonist, NECA indicating their efficacy in selectively alleviating ER stress-associated pathogenesis. These results substantiate the fact that small molecule-dependent ER reprogramming would activate the ER chaperones and therefore reduce the protein misfolding as well as aggregation associated with ER stress in order to restore cellular homeostasis.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry

Most quantitative structure-property/activity relationships (QSPRs/QSARs) techniques involve using different programs separately for generating molecular descriptors and separately for building models based on available descriptors. Here, the capabilities of the CORAL program are evaluated. A user of the program should apply as the basis for models the representation of the molecular structure by means of the simplified molecular input-line entry system (SMILES) as well as experimental data on the endpoint of interest. The local symmetry of SMILES is a novel composition of symmetrically represented symbols, which are three 'xyx', four 'xyyx', or five symbols 'xyzyx'. We updated our CORAL software using this optimal, new flexible descriptor, sensitive to the symmetric composition of a specific part of the molecule. Computational experiments have shown that taking account of these attributes of SMILES can improve the predictive potential of models for the mutagenicity of nitroaromatic compounds. In addition, the above computational experiments have confirmed the advantage of using the index of ideality of correlation (IIC) and the correlation intensity index (CII) for Monte Carlo optimization of the correlation weights for various attributes of SMILES, including the local symmetry. The average value of the coefficient of determination for the validation set (five different models) without fragments of local symmetry is 0.8589 ± 0.025, whereas using fragments of local symmetry improves this criterion of the predictive potential up to 0.9055 ± 0.010.

Potent FOXO3a Activators from Biologically Active Compound Library for Cancer Therapeutics: An in silico Approach

The forkhead transcription factor FOXO3a is a member of the FOXO subfamily, which controls a number of cellular processes including apoptosis, proliferation, cell cycle progression, DNA damage, and carcinogenesis. In addition, it reacts to a number of biological stressors such as oxidative stress and UV radiation. FOXO3a has been predominantly associated with many diseases including cancer. Recent research suggests that FOXO3a suppresses tumor growth in cancer. By cytoplasmic sequestration of the FOXO3a protein or mutation of the FOXO3a gene, FOXO3a is commonly rendered inactive in cancer cells. Furthermore, the onset and development of cancer are linked to its inactivation. In order to reduce and prevent tumorigenesis, FOXO3a needs to be activated. So, it is critical to develop new strategies to enhance FOXO3a expression for cancer therapy. Hence, the present study has been aimed to screen small molecules targeting FOXO3a using bioinformatics tools. Molecular docking and molecular dynamic simulation studies reveal the potent FOXO3a activating small molecules such as F3385-2463, F0856-0033, and F3139-0724. These top three compounds will be subjected to further wet experiments. The findings of this study will lead us to explore the potent FOXO3a activating small molecules for cancer therapeutics.

The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity

Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average value of the determination coefficient for the validation set is 0.8737 ± 0.0312.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

A Comprehensive Review on Fused Heterocyclic as DNA Intercalators: Promising Anticancer Agents

Since the discovery of DNA intercalating agents (by Lerman, 1961), a growing number of organic, inorganic, and metallic compounds have been developed to treat life-threatening microbial infections and cancers. Fused-heterocycles are amongst the most important group of compounds that have the ability to interact with DNA. DNA intercalators possess a planar aromatic ring structure that inserts itself between the base pairs of nucleic acids. Once inserted, the aromatic structure makes van der Waals interactions and hydrogen-bonding interactions with the base pairs. The DNA intercalator may also contain an ionizable group that can form ionic interactions with the negatively charged phosphate backbone. After the intercalation, other cellular processes could take place, leading ultimately to cell death. The heterocyclic nucleus present in the DNA intercalators can be considered as a pharmacophore that plays an instrumental role in dictating the affinity and selectivity exhibited by these compounds. In this work, we have carried out a revision of small organic molecules that bind to the DNA molecule via intercalation and cleaving and exert their antitumor activity. A general overview of the most recent results in this area, paying particular attention to compounds that are currently under clinical trials, is provided. Advancement in spectroscopic techniques studying DNA interaction can be examined in-depth, yielding important information on structure-activity relationships. In this comprehensive review, we have focused on the introduction to fused heterocyclic agents with DNA interacting features, from medicinal point of view. The structure-activity relationships points, cytotoxicity data, and binding data and future perspectives of medicinal compounds have been discussed in detail.

A deep neural network-based approach for prediction of mutagenicity of compounds

We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species

In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R², cross-validation coefficient Q²_LOO (leave one out) while the test set predictions were analyzed using Q²F₁ (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE_95%train, average r_m(LOO)² and Δr_m(LOO)² (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE_95%test, average r_m² and Δr_m² (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity.

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

Mechanism-based structure-activity relationship (SAR) analysis of aromatic amines and nitroaromatics carcinogenicity via statistical analyses based on CPDB

Cancer is a leading cause of human mortality around the globe. In this study, mechanism-based SAR (Structure-Activity Relationship) was employed to investigate the carcinogenicity of aromatic amines and nitroaromatics based on CPDB. Principal component analysis and cluster analysis were used to construct the SAR model. Principle component analysis generated three principal components from 12 mechanism-based descriptors. The extracted principal components were later used for cluster analysis, which divided the selected 55 chemicals into six clusters. The three principal components were proposed to describe the "transport", "reactivity" and "electrophilicity" properties of the chemicals. Cluster analysis indicated that the relevant "transport" properties positively correlated with the carcinogenic potential and were contributing factors in determining the carcinogenicity of the studied chemicals. The mechanism-based SAR analysis suggested the electron donating groups, electron withdrawing groups and planarity are significant factors in determining the carcinogenic potency for studied aromatic compounds. The present study may provide insights into the relationship between the three proposed properties and the carcinogenesis of aromatic amines and nitroaromatics.

Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood⁻brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure⁻activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80⁻0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats

Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD₅₀) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were discussed.

Systems Biology Approach to Bioremediation of Nitroaromatics: Constraint-Based Analysis of 2,4,6-Trinitrotoluene Biotransformation by Escherichia coli

Microbial remediation of nitroaromatic compounds (NACs) is a promising environmentally friendly and cost-effective approach to the removal of these life-threating agents. Escherichia coli (E. coli) has shown remarkable capability for the biotransformation of 2,4,6-trinitro-toluene (TNT). Efforts to develop E. coli as an efficient TNT degrading biocatalyst will benefit from holistic flux-level description of interactions between multiple TNT transforming pathways operating in the strain. To gain such an insight, we extended the genome-scale constraint-based model of E. coli to account for a curated version of major TNT transformation pathways known or evidently hypothesized to be active in E. coli in present of TNT. Using constraint-based analysis (CBA) methods, we then performed several series of in silico experiments to elucidate the contribution of these pathways individually or in combination to the E. coli TNT transformation capacity. Results of our analyses were validated by replicating several experimentally observed TNT degradation phenotypes in E. coli cultures. We further used the extended model to explore the influence of process parameters, including aeration regime, TNT concentration, cell density, and carbon source on TNT degradation efficiency. We also conducted an in silico metabolic engineering study to design a series of E. coli mutants capable of degrading TNT at higher yield compared with the wild-type strain. Our study, therefore, extends the application of CBA to bioremediation of nitroaromatics and demonstrates the usefulness of this approach to inform bioremediation research.