Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features

Ramesh, Priyanka; Shanthi, V.

doi:10.1007/s12010-021-03720-8

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The dataset was then preprocessed using statistical parameters like variance and correlation to eliminate irrelevant features. The preprocessing of the generated dataset was executed according to the procedure reported in our recent publication [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

2022

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Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC 50 values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC. Supplementary Information The online version contains supplementary material available at 10.1007/s12032-022-01924-4.

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Section: Methodsmentioning

confidence: 99%

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

2022

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“…The analysis was performed using k-fold and cross_val_ score subpackages of sklearn.model_selection package in python. 24…”

Section: K-fold Cross Validationmentioning

confidence: 99%

Exploring Machine Learning Models for Recurrence Prediction in Lung Cancer Patients

Ramesh¹,

Jain²,

Karuppasamy³

et al. 2022

Ind. J. Pharm. Edu. Res

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Background: A proper assessment for the probability of recurrence in lung cancer is mandatory for a clinician to make an effective treatment-decision. Materials and Methods: Here, we employed machine learning algorithms to predict the lung cancer recurrence rate using the Caribbean and few white ethnicities populations. A 100 metastatic record with 15 predictor variables and 1 dependent variable was considered for model development. These models were evaluated using seven performance metrics, including accuracy and F1 score. Results: Our study results show that the decision tree outperformed the other models with the highest accuracy and F1 score of about 0.95 and 0.90, respectively. Of note, the p-value and correlation matrix show that the most significant features accounting for the tumor recurrence are cancer stage, ethnicity, tumor size, genome doubled and time to recurrence. Conclusion: Thus, our study provides insights into implementing machine learning algorithms to evaluate cancer outcomes in a clinical setting.

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Section: Descriptor Generation and Dataset Pruningmentioning

confidence: 99%

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Ramesh

Ramanathan

Shanthi

2022

Preprint

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Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC50 values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC.

show abstract

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features

Cited by 3 publications

References 35 publications

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Exploring Machine Learning Models for Recurrence Prediction in Lung Cancer Patients

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

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