Abstract: The World Health Organization has estimated that over 200 million people suffered from malaria in 2010 and that over 600,000 people died from it that year. Growing problems with resistance to existing anti-malarial drugs makes identification of new drugs a high priority. We applied a series of state-of-the-art In Silico tools to publicly available activity data from screens carried out on intact Plasmodium falciparum parasites to yield a handful of candidate molecules predicted to combine potency with good absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Here we describe the overall process used to design these molecules and report encouraging results for their activity against the parasite in culture. We also show that the ADMET properties predicted for them generally compare well to the experimentally determined values.Summary: This research aims to provide proof-in-principle that in silico tools could be applied to public data so as to efficiently identify active chemistry with good ADMET properties.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
World on Fire
Machine Learning Approach to Geometry Prediction in Cold Spray Additive Manufacturing
Dissecting the genomic profile of persistently infecting oncolytic Newcastle disease virus (NDVpi) from cancer RNA-Seq data
Ahmad U1, Chan SC2, Chau DM1, Chia SL5, Abdullah S1,3, Yusoff K5 & Veerakumarasivam A1,4*
Appropriateness of MRCP use in a DGH
A Elawad, B Billimoria
Bleeding Pseudoaneurysm in uncommon locations: interesting cases
Dr Joel James (SHO), Dr Chandni Patel (SpR) & Dr Shirish Prabhudesai (Consultant)