Abstract Details

Poster 15: A Novel Drug for Uncomplicated Malaria: Chemoinformatic Compound Selection and QSAR Towards an Optimised Lead

Neil G. Berry1, Raman Sharma1, Alexandre S. Lawrenson1, Paul M. O'Neill1, Steve A. Ward2, Giancarlo A. Biagini2, Nick Fisher2, Serge Parel3
1Department of Chemistry, Crown Street, University of Liverpool, L69 7ZD, UK
2Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
3Biofocus DPI, a Galapagos Company,Gewerbestrasse, 16, CH-4123, Allschwil, Switzerland
Death and morbidity from malaria are increasing. Consequently, there are more people dying of malaria now than there were 20 years ago. The solution to this problem is the discovery and development of drugs with novel mechanisms of action. We have recently identified an enzyme in the electron transport chain (ETC) of P. falciparum mitochondria as a potentially outstanding chemotherapeutic target, “alternative Complex I” or PfNDH2. The primary objective of this project is to produce a candidate drug, targeting PfNDH2, suitable for clinical development as an antimalarial drug.

Initially we knew of only one active molecule, thus we sought to identify novel chemical scaffolds that inhibit PfNDH2 which would subsequently enter a medicinal chemistry phase. To this end, an enzyme based assay has been developed by us and successfully transferred to a high throughput screening facility. A variety of chemoinformatics methods were employed in library selection to identify the “best” ~17000 compounds for screening from the Biofocus DPI compound library of ~750,000. The compounds selected were chosen to have “lead” like or “drug” like properties. The selected compounds have been subjected to the high throughput screening cascade and several new chemotypes have been identified as being active against PfNDH2.

A large number of analogues of the new chemotypes have been synthesised and tested for whole cell parasite growth inhibition and PfNDH2 enzyme inhibition at the Liverpool School of Tropical Medicine. To aid the hit to lead and lead optimisation phases of the project quantitative structure activity relationships (QSAR) have been developed. QSAR models were generated using a range of machine learning techniques and the models were shown to be highly predictive and robust through a process of rigorous statistical validation. These QSAR models are being used to inform and drive the synthetic medicinal chemistry efforts towards a quality lead molecule. The lead compounds are currently being developed in with Medicines for Malaria Venture.

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