In-Silico Mining of the Toxins Database (T3DB) towards Hunting Prospective Candidates as ABCB1 Inhibitors: Integrated Molecular Docking and Lipid Bilayer-Enhanced Molecular Dynamics Study

Abdeljawaad, Khlood A. A.; Abdelrahman, Alaa H. M.; Sidhom, Peter A.; Tawfeek, Ahmed M.; Mekhemer, Gamal A. H.; El-Rahman, Mohamed K. Abd; Dabbish, Eslam; Shoeib, Tamer

doi:10.3390/ph16071019

Cited by 2 publications

(2 citation statements)

References 56 publications

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“…T3DB is a toxic exposure database, encompassing various categories such as pollutants, toxic drugs, and carcinogens, as well as naturally occurring or chronically toxic compounds . The T3DB database has been utilized for supplementing drug development with hepatotoxicity libraries, predicting acute oral toxicity, and even screening anticancer drugs based on the target protein . In this study, toxic small molecules from T3DB have global toxicity and are regarded as positive samples.…”

Section: Discussionmentioning

confidence: 99%

“… 28 The T3DB database has been utilized for supplementing drug development with hepatotoxicity libraries, 37 predicting acute oral toxicity, 38 and even screening anticancer drugs based on the target protein. 39 In this study, toxic small molecules from T3DB have global toxicity and are regarded as positive samples. Several classifiers based on machine learning have been constructed to distinguish explicitly toxic small molecules from exogenous small molecules and to predict the global toxicity of molecules.…”

Section: Discussionmentioning

confidence: 99%

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Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Zhang,

Zhao,

Cui

2024

ACS Omega

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Molecular toxicity is a critical feature of drug development. It is thus very important to develop computational models to evaluate the toxicity of small molecules. The accuracy of toxicity prediction largely depends on the quality of molecular representation; however, current methods for this purpose do not address this issue well. Here, we introduce a new metric, gap-Δenergy, which is designed to quantify the intermolecular bond energy difference with atom distance. We next find significant variations in the gap-Δenergy distribution among different types of molecules. Moreover, we show that this metric is able to distinguish the toxic small molecules. We collected data sets of toxic and exogenous small molecules and presented a novel index, namely, global toxicity, to evaluate the overall toxicity of molecules. Based on molecular descriptors and the proposed gap-Δenergy metric, we further constructed machine learning models that were trained with 7816 small molecules. The XGBoost-based model achieved the best performance with an AUC score of 0.965 and an F1 score of 0.849 on the test set (1954 small molecules), which outperformed the model that did not use gap-Δenergy features, with a sensitivity score increase of 3.2%.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Zhang,

Zhao,

Cui

2024

ACS Omega

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Computationally accelerated identification of P-glycoprotein inhibitors

McCormick,

Park

et al. 2024

Preprint

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Overexpression of the polyspecific efflux transporter, P-glycoprotein (P-gp, MDR1, ABCB1), is a major mechanism by which cancer cells acquire multidrug resistance (MDR), the resistance to diverse chemotherapeutic drugs. Inhibiting drug transport by P-gp can resensitize cancer cells to chemotherapy. However, there are no P-gp inhibitors available to patients. Clinically unsuccessful P-gp inhibitors tend to bind at the pump's transmembrane drug binding domains and are often P-gp transport substrates, resulting in lowered intracellular concentration of the drug and altered pharmacokinetics. In prior work, we used virtual-assisted drug discovery to identify novel P-gp inhibitors that target the pump's cytoplasmic nucleotide binding domains and showed that these domains are viable drug discovery targets. Here we develop an enhanced virtual-assisted pipeline that expands upon prior work by iteratively screening compounds against multiple regions and conformations of P-gp. This increased computational complexity is offset by custom Tanimoto chemical datasets which compress the initial docking library while maximizing chemical diversity. Molecules that were similar to resultant hits were subsequently retrieved and screened. We identified nine novel P-gp inhibitors that reverse MDR in two types of P-gp overexpressing human cancer cell lines, reflecting a 14% hit rate. Of these inhibitors, all were non-toxic to non-cancerous human cells, and six were not transport substrates of P-gp. Our novel P-gp inhibitors are chemically diverse and are good candidates for lead optimization. Our results demonstrate that the nucleotide binding domains of P-gp are an underused target in the effort to reverse P-gp-mediated multidrug resistance.

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In-Silico Mining of the Toxins Database (T3DB) towards Hunting Prospective Candidates as ABCB1 Inhibitors: Integrated Molecular Docking and Lipid Bilayer-Enhanced Molecular Dynamics Study

Cited by 2 publications

References 56 publications

Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Computationally accelerated identification of P-glycoprotein inhibitors

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