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Probing with Pharmacophore Modeling the Chloroquine Resistance and Designing Novel Antimalarials

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Biophysical and Computational Tools in Drug Discovery

Part of the book series: Topics in Medicinal Chemistry ((TMC,volume 37))

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Abstract

Needless to say that cure from malaria disease is a longstanding goal and helping the present worldwide effort to reduce the malarial disease burden by many folds. Chloroquine (CQ) and Artemisinin (ART) antimalarial drugs are among the most successful in treating malaria; however, CQ-resistant Plasmodium species are now at every corner of the malarial epidemic regions, while ART resistance is upcoming. Pharmacophore features derived from both drugs can be implemented to overcome the growing resistance as well as designing multi-targeted novel non-Chloroquine and non-Artemisinin scaffolds containing inhibitors. In the present study, two different pharmacophore modeling approaches are used to understand the pharmacophore feature distribution between the CQ-Sensitive and CQ-Resistance analogs. To exclude the possible resistance-causing features for CQ, a subtractive pharmacophore modeling protocol has been illustrated and further optimized to a CQ model which is used to screen novel antimalarial inhibitors. Along with CQ, Artemisinin analogs are also used for pharmacophore modeling and additive pharmacophore modeling has been implemented for novel antimalarials designing. We have discussed and used these models for searching novel inhibitors. The subtractive model is used to identify non-CQ scaffolds containing inhibitors while the additive model is used to design the non-ART & non-CQ scaffold containing inhibitors by chemical mining approach to overcome malarial resistance.

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Abbreviations

ART:

Artemisinin

CQ:

Chloroquine

CQR:

Chloroquine resistant

CQS:

Chloroquine sensitive

DHA:

dihydroartemisinin

PC:

Physicochemical properties

PH:

Pharmacophore

QSAR:

Quantitative structure-activity relationship

SBDD:

Structure-based drug design

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Acknowledgments

PK would like to acknowledge the Indian Council of medical Research (ICMR) for ICMR-SRF fellowship, and IG wishes to thank the Department of Biotechnology CCPM projects and the DST purse, Government of India, for support. We are also thankful to Dassault Systems and Schrodinger for providing the Discovery Studio and Phase software, without which a part of this work would have not been possible.

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  1. School of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi, India

    Pawan Kumar & Indira Ghosh

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  1. GIPER, Kashipur, India

    Anil Kumar Saxena

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Kumar, P., Ghosh, I. (2021). Probing with Pharmacophore Modeling the Chloroquine Resistance and Designing Novel Antimalarials. In: Saxena, A.K. (eds) Biophysical and Computational Tools in Drug Discovery. Topics in Medicinal Chemistry, vol 37. Springer, Cham. https://doi.org/10.1007/7355_2021_131

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