In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids

Abstract

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.

Multi-Anti-Parasitic Activity of Arylidene Ketones and Thiazolidene Hydrazines against Trypanosoma cruzi and Leishmania spp.

“…In addition, arylideneketones were recently reported as good antiparasitic agents. These compounds have antimalarial activity, and some are trypanosomicidal agents [ 16 , 17 ]. Moreover, the induction of oxidative stress was demonstrated for the arylideneketones in T. cruzi , T. brucei and Leishmania spp.…”

AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis

“…In addition to cross‐validation, the maps are exposed to the validation of their competence to predict the compounds that are active against the Plasmodium parasite. Previously, we have built a SAR‐dedicated map specifically for this purpose, and it has shown robust classification performances, on par with classical SVM models . For the four current maps, this truly ‘external’ validation is more challenging, because, unlike in previous work, they are specialized in the separation of compounds by their respective target and were never presented with anti‐ Plasmodium SAR information at their construction stage.…”

“…The dataset was further enriched by merging the subsets by compounds from ChEMBL database with dose‐response activity values obtained in similar conditions. The considered experimental details and the merging strategy are explained in detail elsewhere, the schematic representation of the merging workflow is given in Figure . This resulted in 17 distinct datasets describing in total 2093 compounds (molecules may be shared between several sets).…”

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

“…In silico methods are engaged in many steps of drug development (Figure ) and the significant role of computer‐aided drug design is highlighted in the steps of hit identification and lead optimization of many therapeutic agents for the treatment of cancers, infections, metabolic diseases, and diseases of the CNS . Amongst the available computational approaches, quantitative structure‐activity relationship (QSAR) is a robust tool that plays an important role in drug development and provide useful information for guiding design and synthesis of pharmacologically active molecules and are thus highly effective in terms of saving time and cost as well as in increasing the success rate of development .…”

“…231 As such, information technologies (ie, database, applications, and web services) and computational approaches (ie, data mining, bioinformatic, and cheminformatic techniques) are effective tools facilitating data analysis as well as providing useful information for decision-making in the drug development pipeline. 232 In silico methods are engaged in many steps of drug development (Figure 20) and the significant role of computer-aided drug design is highlighted in the steps of hit identification and lead optimization of many therapeutic agents for the treatment of cancers, [233][234][235] infections, [236][237][238][239] metabolic diseases, 237,240 and diseases of the CNS. [241][242][243] Amongst the available computational approaches, quantitative structure-activity relationship (QSAR) is a robust tool that plays an important role in drug development and provide useful information for guiding design and synthesis of pharmacologically active molecules and are thus highly effective in terms of saving time and cost as well as in increasing the success rate of development.…”