Computer-Assisted Methods in Chemical Toxicity Prediction

Mohan, C. Gopi; Gandhi, Tamanna; Garg, Divita; Shinde, Ranajit Nivrutti

doi:10.2174/138955707780619554

Cited by 65 publications

(13 citation statements)

References 31 publications

(37 reference statements)

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“…Poor pharmacokinetic properties and toxicity of the compounds are one of the main reasons for terminating the development of drug candidates [40]. In this context, we have computed Lipinski's rule of five [41] and other pharmacokinetic properties of the compounds, which includes distribution coefficient (Log D ), computed aqueous solubility (Log S ), polar surface area (PSA), percent human oral absorption, BBB penetration, CNS activity using ADME module of DS2.5, and QikProp module of Schrödinger software.…”

Section: Materials and Methodsmentioning

confidence: 99%

Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening

Gupta

Mohan

2014

BioMed Research International

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In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

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Section: Materials and Methodsmentioning

confidence: 99%

Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening

Gupta

Mohan

2014

BioMed Research International

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“…Derek ® (Nexus v2.0) for Windows is an expert knowledge base system, containing descriptions of molecular substructures which have been associated with toxic endpoints (structural alerts), that predicts whether a chemical is toxic in humans, other mammals and bacteria. The programme applies structure-activity relationships ((Q)SARs) and other expert knowledge rules to derive a reasoned conclusion about the potential toxicity of the query chemical [25,26]. Toxtree ® is an open source application, which is able to estimate toxic hazards by applying a decision tree approach and making structure-based predictions for a number of toxicological endpoints using different modules.…”

Section: Methodsmentioning

confidence: 99%

Stability-indicating HPLC-DAD/UV-ESI/MS impurity profiling of the anti-malarial drug lumefantrine

Verbeken

Suleman

Baert

et al. 2011

Malar J

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BackgroundLumefantrine (benflumetol) is a fluorene derivative belonging to the aryl amino alcohol class of anti-malarial drugs and is commercially available in fixed combination products with β-artemether. Impurity characterization of such drugs, which are widely consumed in tropical countries for malaria control programmes, is of paramount importance. However, until now, no exhaustive impurity profile of lumefantrine has been established, encompassing process-related and degradation impurities in active pharmaceutical ingredients (APIs) and finished pharmaceutical products (FPPs).MethodsUsing HPLC-DAD/UV-ESI/ion trap/MS, a comprehensive impurity profile was established based upon analysis of market samples as well as stress, accelerated and long-term stability results. In-silico toxicological predictions for these lumefantrine related impurities were made using Toxtree® and Derek®.ResultsSeveral new impurities are identified, of which the desbenzylketo derivative (DBK) is proposed as a new specified degradant. DBK and the remaining unspecified lumefantrine related impurities are predicted, using Toxtree® and Derek®, to have a toxicity risk comparable to the toxicity risk of the API lumefantrine itself.ConclusionsFrom unstressed, stressed and accelerated stability samples of lumefantrine API and FPPs, nine compounds were detected and characterized to be lumefantrine related impurities. One new lumefantrine related compound, DBK, was identified and characterized as a specified degradation impurity of lumefantrine in real market samples (FPPs). The in-silico toxicological investigation (Toxtree® and Derek®) indicated overall a toxicity risk for lumefantrine related impurities comparable to that of the API lumefantrine itself.

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“…Until about 2009, Descriptor-based quantitative structure-activity relationships (QSAR) models [64] were the predictor model of choice for toxicology [65]. However, the quality of those models depended on the quality of data used to develop the model and the limitation of the nature of the compounds used to develop the model [66].…”

Section: How the Bayesian Mindset Work With Different Biological mentioning

confidence: 99%

Modeling to Optimize Terminal Stem Cell Differentiation

Gallicano

2013

Scientifica

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Embryonic stem cell (ESC), iPCs, and adult stem cells (ASCs) all are among the most promising potential treatments for heart failure, spinal cord injury, neurodegenerative diseases, and diabetes. However, considerable uncertainty in the production of ESC-derived terminally differentiated cell types has limited the efficiency of their development. To address this uncertainty, we and other investigators have begun to employ a comprehensive statistical model of ESC differentiation for determining the role of intracellular pathways (e.g., STAT3) in ESC differentiation and determination of germ layer fate. The approach discussed here applies the Baysian statistical model to cell/developmental biology combining traditional flow cytometry methodology and specific morphological observations with advanced statistical and probabilistic modeling and experimental design. The final result of this study is a unique tool and model that enhances the understanding of how and when specific cell fates are determined during differentiation. This model provides a guideline for increasing the production efficiency of therapeutically viable ESCs/iPSCs/ASC derived neurons or any other cell type and will eventually lead to advances in stem cell therapy.

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Computer-Assisted Methods in Chemical Toxicity Prediction

Cited by 65 publications

References 31 publications

Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening

Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening

Stability-indicating HPLC-DAD/UV-ESI/MS impurity profiling of the anti-malarial drug lumefantrine

Modeling to Optimize Terminal Stem Cell Differentiation

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