Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

ABSTRACT

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

KEYWORDS:

• Breast cancer resistance protein (BCRP)
• ABCG2
• QSAR
• Monte Carlo optimization
• Bayesian classification model
• machine learning

Acknowledgements

Tarun Jha is also thankful for the financial support from RUSA 2.0 of UGC, New Delhi, India to Jadavpur University, Kolkata, India. We are very much thankful to the Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India and Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India for providing the research facilities.

Disclosure statement

The authors have no conflict of interests.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2020.1771769.

Additional information

Funding

Sk. Abdul Amin sincerely acknowledges Council of Scientific and Industrial Research (CSIR), New Delhi, India for awarding the Senior Research Fellowship (SRF) [FILE NO.: 09/096(0967)/2019-EMR-I, Dated: 01-04-2019].