Identification of the Structural Requirements for Mutagencitiy, by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals. II. General Ames Mutagenicity Model
Abstract:The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship… Show more
“…4 . The eight molecular properties studied here, including molecular weight (MW), H-bond acceptor count (a_acc), flexible rotatable bond count (b_rotN), octanol–water partitioning coefficient (SlogP), intrinsic solubility (logS), topological polar surface area (TPSA), van der Waals volume (vdw_vol), molecular flexibility index (KierFlex), have been widely used in the prediction of ADME and toxicity [ 58 – 65 ]. Fig.…”
Background
Determination of acute toxicity, expressed as median lethal dose (LD50), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.
ResultsIn this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD50 values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by qext2 for the test set containing 2376 molecules, ranged from 0.572 to 0.659.ConclusionConsidering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (qext2 = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity.
Graphical abstractWorkflow of combinatorial QSAR modelling to predict rat oral acute toxicity
“…4 . The eight molecular properties studied here, including molecular weight (MW), H-bond acceptor count (a_acc), flexible rotatable bond count (b_rotN), octanol–water partitioning coefficient (SlogP), intrinsic solubility (logS), topological polar surface area (TPSA), van der Waals volume (vdw_vol), molecular flexibility index (KierFlex), have been widely used in the prediction of ADME and toxicity [ 58 – 65 ]. Fig.…”
Background
Determination of acute toxicity, expressed as median lethal dose (LD50), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.
ResultsIn this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD50 values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by qext2 for the test set containing 2376 molecules, ranged from 0.572 to 0.659.ConclusionConsidering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (qext2 = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity.
Graphical abstractWorkflow of combinatorial QSAR modelling to predict rat oral acute toxicity
“…The scope of this profile is to examine the occurrence of structural alerts within the target chemical compound liable for interaction with DNA related to a genetic mutation. The profiler consists of 85 structural alerts disjointed into eight mechanistic domains, and each domain alienated into mechanistic alerts [45,46].…”
Section: Mutagenic Potential Of Dichlorvos (Ddvp) Based On Dna Alertsmentioning
Dichlorvos (DDVP) has been abused in Nigeria for suicide attempts, topical applications to treat an ectoparasitic infestation, and indiscriminate use on farm produce. Exposure to this compound in subacute concentration can cause toxicity in different tissues by alteration of the cellular antioxidative defence mechanism. This analysis is aimed at the systematic profiling of DDVP to assess its cytotoxic and mutagenic potential for human vulnerability using an
in silico
classification model. DDVP was grouped into categories of analogue chemical compounds generated from inventories based on structural alerts that measure the biological effects on cell lines and animal models using the quantitative structure-activity relationship (QSAR) model. The cytotoxic and mutagenic potential of DDVP was assessed by analyzing target endpoints like skin sensitization, oral/inhalation toxicity, neurotoxicity and mutagenicity. DDVP shows moderate sensitization potential that can induce skin irritation during prolonged exposure because of the presence of dichlorovenyl side-chain that interacts with cellular proteins and causes degradation. 50% lethal dose (LD
50
) of DDVP per body weight was determined to be 26.2 mg/kg in a rat model at 95% confidence range for acute oral toxicity, and 14.4 mmol/L was estimated as 50% lethal concentration (LC
50
) in the atmosphere due to acute inhalation toxicity. DDVP can also inhibit acetylcholinesterase in the nervous system to produce nicotinic and muscarinic symptoms like nausea, vomiting, lacrimation, salivation, bradycardia, and respiratory failure may cause death. The widely used pesticide causes weak DNA methylation which can repress gene transcription on promoter sites. DDVP is volatile so it can cause oral and inhalation toxicity coupled with neurotoxicity during prolonged exposure. Serum cholinesterase blood tests should be encouraged in federal and state hospitals to investigate related health challenges as DDVP is still used in Nigeria.
“…A new upgrade of the mathematical formalism of the COREPA method [22] is now used to account for non-orthogonality of the conformer distributions. Currently developed models for estrogen binding affinity [22] and genotoxic effects (AMES mutagenicity and chromosomal aberrations) [23,24] demonstrate significantly improved performance of the model. A significant step in assessing the model performance is its external validation.…”
Section: Sar and Qsar In Environmental Research 267mentioning
The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.
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