15. Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

2020 ◽  
pp. 381-396
2020 ◽  
Author(s):  
Sanaa Bardaweel

Recently, an outbreak of fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. As the coronavirus pandemic rages, drug discovery and development become even more challenging. Drug repurposing of the antimalarial drug chloroquine and its hydroxylated form had demonstrated apparent effectiveness in the treatment of COVID-19 associated pneumonia in clinical trials. SARS-CoV-2 spike protein shares 31.9% sequence identity with the spike protein presents in the Middle East Respiratory Syndrome Corona Virus (MERS-CoV), which infects cells through the interaction of its spike protein with the DPP4 receptor found on macrophages. Sitagliptin, a DPP4 inhibitor, that is known for its antidiabetic, immunoregulatory, anti-inflammatory, and beneficial cardiometabolic effects has been shown to reverse macrophage responses in MERS-CoV infection and reduce CXCL10 chemokine production in AIDS patients. We suggest that Sitagliptin may be beneficial alternative for the treatment of COVID-19 disease especially in diabetic patients and patients with preexisting cardiovascular conditions who are already at higher risk of COVID-19 infection.


2019 ◽  
Vol 4 (7) ◽  
Author(s):  
Samuel Egieyeh ◽  
Sarel F. Malan ◽  
Alan Christoffels

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.


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