Structure-activity Relationship Approach Research Articles

Optimizing Linezolid: Transforming It into a Selective MAO-B Inhibitor via a Toxicity-to-Activity Optimization Approach.

Linezolid, a widely used oxazolidinone antibiotic, exhibits potent activity against resistant bacterial infections but is associated with serotonergic toxicity, primarily due to its inhibition of monoamine oxidase (MAO). MAOs, consisting of MAO-A and MAO-B isoforms, play crucial roles in neurotransmitter metabolism, with implications for neurodegenerative disorders like Parkinson's and Alzheimer's diseases. This study aims to optimize Linezolid's structure to transform it into a selective MAO-B inhibitor. Utilizing structure-activity and structure-toxicity relationship approaches, novel analogues of Linezolid were synthesized by replacing its oxazolidinone ring with a thiadiazole scaffold. Among the synthesized compounds, 6b emerged as a lead candidate, displaying a remarkable MAO-B inhibitory activity (IC50 = 0.03 μM) and 464-fold selectivity over MAO-A, compared to the standard drugs Pargyline (IC50 = 0.14 μM) and Clorgyline (IC50 = 1.85 μM). Furthermore, docking and molecular dynamics simulations corroborated the high affinity and stability of compound 6b in the MAO-B enzyme's binding pocket. These findings suggest that optimized Linezolid analogues, particularly compound 6b, hold promise as selective MAO-B inhibitors, offering therapeutic potential for treating neurodegenerative diseases while avoiding the risks associated with serotonergic toxicity.

Design of Novel Acat Inhibitors as Potent Anti-Hyperlipidemic Agents Using Chemometric Approaches

Aim: A 2D QSAR study of acyl-coenzyme A (CoA): cholesterol acyltransferase (ACAT) inhibitors revealed that electronic, topological, and steric properties are important structural features required for activity against ACAT. Background: In order to interpret the evidence encrypted by the molecular structure of the compounds, a standard physicochemical descriptors-centered, and Quantitative Structure-Activity Relationship (QSAR) approach was implemented on a data set of Indoline derivatives were reported to be acyl-coenzyme A (CoA): cholesterol acyltransferase ACAT inhibitors. Objective: The ACAT enzyme plays an important role in the absorption of dietary cholesterol. Therefore, the inhibition of ACAT is a key strategy or primary objective for the treatment of hypercholesterolemia and atherosclerosis. Method: Chemo metric models were designed by inserting a battery of statistical techniques in the current study that demonstrate the linear approaches of analysis, including multiple linear regression (MLR), partial least square PLS, and non-linear methods such as artificial neural networks (ANN). Result: The activity contributions of these molecules were analyzed through regression equation, and the best QSAR model was created with an excellent correlative and predictive ability. Significant statistical values S = 0.35, F = 60.30, r = 0.92, r² = 0.85, r² (CV) = 0.82 of the designed models were obtained using stepwise MLR and a comparable PLS and FFNN model with r² (CV) = 0.82, 0.88 and 0.86 respectively and the relevant descriptors like inertia moment 1 size, Kier Chiv4 (cluster) index, Kier Chiv6(ring) index offered important information regarding this model. Conclusion: The model reveals that inertia moment 1 size, Kier Chiv4 (cluster) index, and Kier Chiv6 (ring) index are prerequisite descriptors to determine other promising ACAT antagonists with high and liable potency against the target. Therefore, these characteristics may be used efficiently for the design and evaluation of active compounds as new ACAT inhibitors thanks to their utilization.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors.

Machine Learning-Based In Silico Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid in silico tool. However, there are few in silico models focusing on the species differences between rat and human in the P450s inhibition. This study aimed to establish in silico models to classify chemical substances as inhibitors or non-inhibitors of various rat and human P450s, using only molecular descriptors. Using the in-house test results from our in vitro experiments, we used 326 substances for model construction and internal validation data. Apart from the 326 substances, 60 substances were used as external validation data set. We focused on seven rat P450s (CYP1A1, CYP1A2, CYP2B1, CYP2C6, CYP2D1, CYP2E1, and CYP3A2) and 11 human P450s (CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4). Most of the models established using XGBoost showed an area under the receiver operating characteristic curve (ROC-AUC) of 0.8 or more in the internal validation. When we set an applicability domain for the models and confirmed their generalization performance through external validation, most of the models showed an ROC-AUC of 0.7 or more. Interestingly, for CYP1A1 and CYP1A2, we discovered that a human P450 inhibitory activity model can predict rat P450 inhibitory activity and vice versa. These models are the first attempts to predict inhibitory activity against a wide variety of P450s in both rats and humans using chemical structure information. Our experimental results and in silico models would be helpful to support information for species similarities and differences in chemical-induced toxicity.

Development of Macrocyclic Neurotensin Receptor Type 2 (NTS2) Opioid-Free Analgesics.

The opioid crisis has highlighted the urgent need to develop non-opioid alternatives for managing pain, with an effective, safe, and non-addictive pharmacotherapeutic profile. Using an extensive structure-activity relationship approach, here we have identified a new series of highly selective neurotensin receptor type 2 (NTS2) macrocyclic compounds that exert potent, opioid-independent analgesia in various experimental pain models. To our knowledge, the constrained macrocycle in which the Ile12 residue of NT(7-12) was substituted by cyclopentylalanine, Pro7 and Pro10 were replaced by allyl-glycine followed by side-chain to side-chain cyclization is the most selective analog targeting NTS2 identified to date (Ki 2.9 nM), showing 30,000-fold selectivity over NTS1. Of particular importance, this macrocyclic analog is also able to potentiate the analgesic effects of morphine in a dose- and time-dependent manner. Exerting complementary analgesic actions via distinct mechanisms of nociceptive transmission, NTS2-selective macrocycles can therefore be exploited as opioid-free analgesics or as opioid-sparing therapeutics, offering superior pain relief with reduced adverse effects to pain patients.

Tolfenpyrad Derivatives Exhibit Potent Francisella-Specific Antibacterial Activity without Toxicity to Mammalian Cells In Vitro.

Tularemia is a deadly disease caused by Francisella tularensis, an emerging intracellular bacterial pathogen that can be disseminated rapidly through aerosols and vector-borne transmission. Recent surveillance data demonstrate an increasing incidence in several countries. Although clinical isolates of Francisella strains are sensitive to currently used antibiotics, engineered or horizontal acquisition of antibiotic resistance is a constant threat to public health. Therefore, the identification of antibiotics that target previously undrugged pathways is required to safeguard human health. An environmental pesticide that is registered for use in multiple countries, tolfenpyrad, shows promising activity to block Francisella growth; however, it is not a suitable antimicrobial candidate for use in vivo due to potential toxicity in humans and other animals. In this study, we applied a structure-activity relationship approach to tolfenpyrad to generate compounds with improved antibacterial activity and reduced toxicity. Through screening of a library of derivatives, we identified analogs with improved therapeutic windows compared with tolfenpyrad. Although structural diversity exists among these analogs, they inhibit the growth of Francisella species but not other Gram-negative or Gram-positive species. These compounds block intramacrophage growth of F. novicida and pathogenesis in an in vivo arthropod model of infection. Although the biochemical activity of these drugs is unknown, they appear to target the same pathway as the parent molecule because F. novicida mutants that are resistant to tolfenpyrad are also resistant to its analogs. Taken together, these findings suggest that these tolfenpyrad-derived compounds comprise a new class of Francisella-targeted antimicrobials and merit further evaluation and development.

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2LOO=0.68, mean absolute error [MAE]training=0.54). The model was further validated using the test set (Q2F1=0.77, Q2F2=0.75, MAEtest=0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2F1 values exceeding 0.7, Q2LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm.

Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.

Potency-Enhanced Peptidomimetic VHL Ligands with Improved Oral Bioavailability.

The von Hippel-Lindau (VHL) protein plays a pivotal role in regulating the hypoxic stress response and has been extensively studied and utilized in the targeted protein degradation field, particularly in the context of bivalent degraders. In this study, we present a comprehensive peptidomimetic structure-activity relationship (SAR) approach, combined with cellular NanoBRET target engagement assays to enhance the existing VHL ligands. Through systematic modifications of the molecule, we identified the 1,2,3-triazole group as an optimal substitute of the left-hand side amide bond that yields 10-fold higher binding activity. Moreover, incorporating conformationally constrained alterations on the methylthiazole benzylamine moiety led to the development of highly potent VHL ligands with picomolar binding affinity and significantly improved oral bioavailability. We anticipate that our optimized VHL ligand, GNE7599, will serve as a valuable tool compound for investigating the VHL pathway and advancing the field of targeted protein degradation.

Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D–2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D–2D descriptors. Then, the combined pool of RASAR and the identified 0D–2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to Treat Metabolic Syndrome.

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism. To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome. In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor. The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 μM and 0.0027 μM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed. The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis.

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

Machine Learning-Based Enhanced Drug Delivery System and Its Applications – A Systematic Review

Machine Learning (ML) methods offer advanced algorithms and tools to enhance drug development. Quantitative Structure-Activity Relationship (QSAR) approaches have successfully predicted several physicochemical aspects of pharmaceuticals, including toxicity, intake, drug-drug interactions, carcinogenesis, and dispersion. ML, a branch of artificial intelligence, has demonstrated significant promise in drug development. The methods presented in this study can model non-linear databases and handle big data that is becoming more extensive and intricate. Diverse ML methodologies are currently employed for making forecasts of drug targets, modeling the framework of drug targets, forecasting binding sites, conducting ligand-based similarity searches, designing novel ligands with specific properties, creating scoring algorithms for molecular docking, constructing QSAR models for predicting biological reactions, and forecasting the pharmacokinetic and pharmacodynamic characteristics of ligands. The findings of this study illustrate the widespread utilization of ML techniques in drug discovery, suggesting a favorable outlook for these advances. These findings have the potential to facilitate further exploration of ML in connection with drug discovery and growth by researchers, learners, and pharmaceutical companies.

Development of UTX-143, a selective sodium-hydrogen exchange subtype 5 inhibitor, using amiloride as a lead compound

NHE5, an isoform of the Na+/H+ exchanger (NHE) protein, is an ion-transporting membrane protein that regulates intracellular pH and is highly expressed in colorectal adenocarcinoma. Therefore, we hypothesized that NHE5 inhibitors can be used as anticancer drugs. However, because NHE1 is ubiquitously expressed in all cells, it is extremely important to demonstrate its selective inhibitory activity against NHE5. We used amiloride, an NHE non-selective inhibitor, as a lead compound and created UTX-143, which has NHE5-selective inhibitory activity, using a structure–activity relationship approach. UTX-143 showed selective cytotoxic effects on cancer cells and reduced the migratory and invasive abilities of cancer cells. These results suggest a new concept wherein drugs exhibit cancer-specific cytotoxic effects through selective inhibition of NHE5 and the possibility of UTX-143 as a lead NHE5-selective inhibitor.

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.

Efficient removal of organic pollutants by activation of peroxydisulfate with the magnetic CoFe2O4/carbon nanotube composite.

A magnetic composite of CoFe2O4 and carbon nanotube (CNT) was prepared using the solvothermal approach and then employed for the activation of peroxydisulfate (PDS) to degrade reactive black 5 (RB5) and other organic pollutants. Characterization results of the composite catalyst revealed the successful loading of spherical CoFe2O4 particles on CNTs, possessing abundant porosity as well as magnetic separation capability. Under the degradation conditions of 0.2g/L CoFe2O4-CNT dosage and 4mM PDS dosage, the removal efficiencies of 10mg/L RB5 and other pollutants were in the range of 94.5 to ~ 100%. The effects of pH, co-existing ions/humic acid, and water matrices as well as the reusability of the catalyst were also investigated in detail. Furthermore, the degradation mechanism and pathway were proposed based on quenching experiments, LC-MS analysis, and density functional theory (DFT) calculations, and the toxicity of the degradation products was evaluated in the quantitative structure-activity relationship approach.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Effect of the Electronic Structure of Para-Substituted Benzaldehyde Benzohydrazone on Its Antimicrobial Activity: A DFT Analysis

Bacillus subtilis is a bacterium that has demonstrated its efficacy across various domains, including industry, agriculture, and commerce, owing to its protective, inhibitory, and biological mechanisms against specific microbes. However, at high concentrations, it can lead to food poisoning and severe infections, resulting in symptoms such as diarrhea and vomiting. Bacterial spores produced by Bacillus subtilis can induce conditions like gas gangrene and tetanus. In this context, benzohydrazones are recognized for their antimicrobial activity, particularly against Bacillus subtilis. This study aims to elucidate the relationship between the electronic structure of para-substituted benzaldehyde benzohydrazone derivatives and their antimicrobial activity. This leads to the proposal of a 2D pharmacophore for predicting the antibacterial activity of these derivatives. The quantitative structure-activity relationship (QSAR) approach employed is the KPG method. The electronic structures were optimized using the density functional theory (DFT) method with the B3LYP functional and the 6-31G (d,p) basis set. Charge and local molecular orbitals were considered in the optimization process. The resulting prediction equation (R=98.95%, R²=97.91%, Adjusted R²=96.76%, F(5,9)=84.52) derived from multiple linear regression provides the basis for the proposed 2D pharmacophore. This equation shows that antimicrobial activity of benzohydrazone derivative is on charge and orbital controlled. This pharmacophore holds potential utility in designing new molecular structures with enhanced activity against Bacillus subtilis.

Mechanistic insights into the selectivity of bicyclophosphorothionate antagonists for housefly versus rat GABA receptors.

γ-Aminobutyric acid (GABA) receptors (GABARs) are validated targets of insecticides. Bicyclophosphorothionates are a group of insecticidal compounds that act as noncompetitive antagonists of GABARs. We previously reported that the analogs exhibit various degrees of selectivity for housefly versus rat GABARs, depending on substitutions at the 3- and 4-positions. We here sought to elucidate the unsolved mechanisms of the receptor selectivity using quantitative structure-activity relationship (QSAR), molecular docking, and molecular dynamics approaches. Three-dimensional (3D)-QSAR studies using Topomer comparative molecular field analysis quantitatively demonstrated how the introduction of a small alkyl group at the 3-position of bicyclophosphorothionates contributes to the housefly versus rat GABAR selectivity. To investigate the molecular mechanisms of the selective action, bicyclophosphorothionates were docked into housefly Resistance to dieldrin (RDL) GABAR and rat α1β2γ2 GABAR homology models built using the published 3D-structures of human GABARs as templates. The results of molecular docking and molecular dynamics simulations revealed that the 2'Ala and 6'Thr residues of the RDL subunit within the channel are the key amino acids for binding to the housefly GABARs, whereas the 2'Ser residue of γ2 subunit plays a crucial role in binding to rat GABARs. We revealed the molecular mechanisms underlying the selective antagonistic action of bicyclophosphorothionates on housefly versus rat GABARs. The information presented should help design and develop novel, safe GABAR-targeting insecticides. © 2023 Society of Chemical Industry.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.