An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

Abstract

<p>The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against <i>Plasmodium falciparum</i> <i>in vitro</i> and <i>in vivo</i> and that have been suggested to act through the inhibition of <i>Pf</i>ATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na<sup>+</sup> concentration. The structure of <i>Pf</i>ATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-<i>Pf</i>ATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.</p>In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

Optimisation-based modelling for drug discovery in malaria

“…The piecewise equations in Table 1 suggest that the Series 3 compounds are likely to be active, as the maximum activity values calculated from the two equations are either pIC 50 = 5.47 (when Bit 0484 = 1) or pIC 50 = 4.60 (when Bit 0484 = 0 and Bit 0179 = 1), which is lower than our defined threshold of pIC 50 ≥ 5.80. 19 This is also supported by the distribution of the true pIC 50 values of Series 3 compounds. As shown in Figure S3c, the median and mode value of Series 3 are both less than 5, and only Modules related to OSM Series 4: Module m02, Module m03 and Module m05 describe the binding activity of Series 2 and Series 4, as the piecewise equations demonstrated in Table 1.…”

“…28 A correlation has been found between molecular potency and parasite ion-regulated assay. 32 This study focused first on building a predictive model for Series 4 analogues, 19 but as our method can inherently distinguish structurally heterogeneous chemical sets, all series and assays were then considered for a more comprehensive analysis. A raw dataset containing all OSM compounds from Series 1 to 4 and their respective assay data was downloaded from the Master List of chemicals provided by OSM.…”

“…On the other hand, if a compound includes this fragment (Bit 0350 = 1), the model predicts that its bioactivity will be pIC 50 = 4.30, thus inactive against Plasmodium Falciparum, if an activity threshold pIC 50 ≥ 5.80 is assumed. 19 By exploring m01 (Region 01) equation, we can also see which of the remaining fragments selected by the algorithm make positive or negative contributions to the bioactivity of these compounds.…”

“…activity (pIC 50 ) of compounds from the OSM dataset against Plasmodium falciparum. We have previously applied modSAR to an earlier version of the OSM data using pre-defined molecular descriptors, 19 and here we develop this work further in order to offer a better understanding of these antimalarial candidates as well as paving the way for future SAR explorations, lead optimisation and new de novo drug design efforts for malaria.…”

“…Recently, a competition was launched to develop and evaluate strategies for accurate prediction of anti-PfATP4 activity among Series 4 compounds from the OSM master chemical list database, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds. 19 Figure 1: The pipeline of data processing and analysis via modSAR. (A) The OSM dataset was pre-processed following the steps specified in https://github.com/ OpenSourceMalaria/Series4_PredictiveModel/issues/1, and in the end the ECFP of each OSM compound was obtained.…”