IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information - Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj= cajand cdataj= cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj= cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon’s entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.

In silico drug repurposing for the identification of antimalarial drugs as candidate inhibitors of SARS-CoV-2

: Coronavirus disease (COVID-19) is a severe acute respiratory condition that affected millions of populations worldwide in early 2020, indicating for a global health emergency.As regards the deteriorating trends in COVID-19, none of the drugs were confirmed to have substantial efficacy in the potential treatment of COVID-19 patients in large-scale trials.The purpose of this research was to identify potential antimalarial candidate molecules for the treatment of COVID and to evaluate the possible mechanism of action by in silico screening method. Insilicoscreening study of various antimalarial compounds like Amodiaquine, Chloroquine, Hydroxychloroquine, Mefloquine, Primaquine, and Atovaquone were conducted with PyRx and AutoDoc 1.5.6 tools on ACE 2 receptor, 3CL protease, Hemagglutinin esterase, Spike protein SARS HR1 motif and Papain like protease virus proteins.Based on PyRx results, Mefloquine and Atovaquone have higher docking affinity scores against virus proteins compared to other antimalarial compounds. Screening report of Atovaquone exhibited affirmative inhibition constant on Spike protein SARS HR1 motif, 3CL and Papain like protease. In silico analysis reported Atovaquone as a promising candidate for COVID 19 therapy.

Antimalarial drug candidates in phase I and II drug development: a scoping review

The emergence and spread of parasite resistance to currently available antimalarials has highlighted the importance of developing novel antimalarials. This scoping review provides an overview of antimalarial drug candidates undergoing phase I and II studies between 1 January 2016 and 28 April 2021. PubMed, Web of Science, Embase, clinical trial registries and reference lists were searched for relevant studies. Information regarding antimalarial compound details, clinical trial characteristics, study population, drug pharmacokinetics and pharmacodynamics (PK-PD) were extracted. A total of 50 studies were included of which 24 had published their results and 26 were unpublished. New antimalarial compounds were evaluated as monotherapy (28 studies, 14 drug candidates) and combination therapy (9 studies, 10 candidates). Fourteen active compounds were identified in the current antimalarial drug development pipeline together with 11 compounds that are inactive; six due to insufficient efficacy. PK-PD data were available from 24 studies published as open-access articles. Four unpublished studies have made their results publicly available on clinical trial registries. The terminal elimination half-life of new antimalarial compounds ranged from 14.7 to 483 hours. The log 10 parasite reduction ratio over 48 hours and parasite clearance half-life for P. falciparum following a single dose monotherapy were 1.55–4.1 and 3.4–9.4 hours, respectively. The antimalarial drug development landscape has seen a number of novel compounds, with promising PK-PD properties, evaluated in phase I and II studies over the past 5 years. Timely public disclosure of PK-PD data is crucial for informative decision-making and drug development strategy.

Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action

The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds’ exact target or MoA. Whilst the latter is not initially required to progress a compound in a medicinal chemistry program, identifying the MoA early can accelerate hit prioritization, hit-to-lead optimization and preclinical combination studies in malaria research. The effects of drug treatment on a cell can be observed on systems level in changes in the transcriptome, proteome and metabolome. Machine learning (ML) algorithms are powerful tools able to deconvolute such complex chemically-induced transcriptional signatures to identify pathways on which a compound act and in this manner provide an indication of the MoA of a compound. In this study, we assessed different ML approaches for their ability to stratify antimalarial compounds based on varied chemically-induced transcriptional responses. We developed a rational gene selection approach that could identify predictive features for MoA to train and generate ML models. The best performing model could stratify compounds with similar MoA with a classification accuracy of 76.6 ± 6.4%. Moreover, only a limited set of 50 biomarkers was required to stratify compounds with similar MoA and define chemo-transcriptomic fingerprints for each compound. These fingerprints were unique for each compound and compounds with similar targets/MoA clustered together. The ML model was specific and sensitive enough to group new compounds into MoAs associated with their predicted target and was robust enough to be extended to also generate chemo-transcriptomic fingerprints for additional life cycle stages like immature gametocytes. This work therefore contributes a new strategy to rapidly, specifically and sensitively indicate the MoA of compounds based on chemo-transcriptomic fingerprints and holds promise to accelerate antimalarial drug discovery programs.

The Knockout for G Protein-Coupled Receptor-Like PfSR25 Increases the Susceptibility of Malaria Parasites to the Antimalarials Lumefantrine and Piperaquine but Not to Medicine for Malaria Venture Compounds

Previously we have reported that the G protein-coupled receptor (GPCR)-like PfSR25 in Plasmodium falciparum is a potassium (K+) sensor linked to intracellular calcium signaling and that knockout parasites (PfSR25-) are more susceptible to oxidative stress and antimalarial compounds. Here, we explore the potential role of PfSR25 in susceptibility to the antimalarial compounds atovaquone, chloroquine, dihydroartemisinin, lumefantrine, mefloquine, piperaquine, primaquine, and pyrimethamine and the Medicine for Malaria Venture (MMV) compounds previously described to act on egress/invasion (MMV006429, MMV396715, MMV019127, MMV665874, MMV665878, MMV665785, and MMV66583) through comparative assays with PfSR25- and 3D7 parasite strains, using flow cytometry assays. The IC50 and IC90 results show that lumefantrine and piperaquine have greater activity on the PfSR25- parasite strain when compared to 3D7. For MMV compounds, we found no differences between the strains except for the compound MMV665831, which we used to investigate the store-operated calcium entry (SOCE) mechanism. The results suggest that PfSR25 may be involved in the mechanism of action of the antimalarials lumefantrine and piperaquine. Our data clearly show that MMV665831 does not affect calcium entry in parasites after we depleted their internal calcium pools with thapsigargin. The results demonstrated here shed light on new possibilities on the antimalarial mechanism, bringing evidence of the involvement of the GPCR-like PfSR25.