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P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

ABSTRACT: Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentially affected in response to drug actions. Moreover, traditional techniques do not allow to measure the speed-of-action of compounds on parasite viability, which is an essential efficacy determinant. We present here a comprehensive methodology to measure in vitro the direct effect of antimalarial compounds over the parasite viability, which is based on limiting serial dilution of treated parasites and re-growth monitoring. This methodology allows to precisely determine the killing rate of antimalarial compounds, which can be quantified by the parasite reduction ratio and parasite clearance time, which are key mode-of-action parameters. Importantly, we demonstrate that this technique readily permits to determine compound killing activities that might be otherwise missed by traditional, metabolism-based techniques. The analysis of a large set of antimalarial drugs reveals that this viability-based assay allows to discriminate compounds based on their antimalarial mode-of-action. This approach has been adapted to perform medium throughput screening, facilitating the identification of fast-acting antimalarial compounds, which are crucially needed for the control and possibly the eradication of malaria.

SUBMITTER: Sanz LM

PROVIDER: S-EPMC3285618 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

Sanz Laura M LM Crespo Benigno B De-Cózar Cristina C Ding Xavier C XC Llergo Jose L JL Burrows Jeremy N JN García-Bustos Jose F JF Gamo Francisco-Javier FJ

PloS one 20120223 2

Chemotherapy is still the cornerstone for malaria control. Developing drugs against Plasmodium parasites and monitoring their efficacy requires methods to accurately determine the parasite killing rate in response to treatment. Commonly used techniques essentially measure metabolic activity as a proxy for parasite viability. However, these approaches are susceptible to artefacts, as viability and metabolism are two parameters that are coupled during the parasite life cycle but can be differentia ...[more]

PMID: 22383983

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Project description:The increasing incidence of antimalarial drug resistance to the first-line artemisinin combination therapies underpins an urgent need for new antimalarial drugs, ideally with a novel mode of action. The recently developed 2-aminomethylphenol, JPC-3210, (MMV 892646) is an erythrocytic schizonticide with potent in vitro antimalarial activity against multidrug-resistant Plasmodium falciparum lines, low cytotoxicity, potent in vivo efficacy against murine malaria, and favorable preclinical pharmacokinetics including a lengthy plasma elimination half-life. To investigate the impact of JPC-3210 on biochemical pathways within P. falciparum-infected red blood cells, we have applied a "multi-omics" workflow based on high resolution orbitrap mass spectrometry combined with biochemical approaches. Metabolomics, peptidomics and hemoglobin fractionation analyses revealed a perturbation in hemoglobin metabolism following JPC-3210 exposure. The metabolomics data demonstrated a specific depletion of short hemoglobin-derived peptides, peptidomics analysis revealed a depletion of longer hemoglobin-derived peptides, and the hemoglobin fractionation assay demonstrated decreases in hemoglobin, heme and hemozoin levels. To further elucidate the mechanism responsible for inhibition of hemoglobin metabolism, we used in vitro β-hematin polymerization assays and showed JPC-3210 to be an intermediate inhibitor of β-hematin polymerization, about 10-fold less potent then the quinoline antimalarials, such as chloroquine and mefloquine. Further, quantitative proteomics analysis showed that JPC-3210 treatment results in a distinct proteomic signature compared with other known antimalarials. While JPC-3210 clustered closely with mefloquine in the metabolomics and proteomics analyses, a key differentiating signature for JPC-3210 was the significant enrichment of parasite proteins involved in regulation of translation. These studies revealed that the mode of action for JPC-3210 involves inhibition of the hemoglobin digestion pathway and elevation of regulators of protein translation. Importantly, JPC-3210 demonstrated rapid parasite killing kinetics compared with other quinolones, suggesting that JPC-3210 warrants further investigation as a potentially long acting partner drug for malaria treatment.

Project description:The increasing incidence of antimalarial drug resistance to the first-line artemisinins, and their combination partner drugs, underpins an urgent need for new antimalarial drugs, ideally with a novel mechanism of action. The recently developed 2-aminomethylphenol, JPC-3210, (MMV 892646) is an erythrocytic schizonticide with potent in vitro antimalarial activity against multidrug-resistant Plasmodium falciparum, low cytotoxicity, potent in vivo efficacy against murine malaria, and favourable preclinical pharmacokinetics, including a lengthy plasma elimination half-life. This study demonstrates the application of a “multi-omics” workflow based on high resolution orbitrap mass spectrometry to investigate the impact of JPC-3210 on biochemical pathways within P. falciparum infected red blood cells. Metabolomics and peptidomics analysis revealed a perturbation in hemoglobin metabolism following JPC-3210 exposure. The metabolomics data demonstrated a depletion in short hemoglobin-derived peptides, while peptidomics analysis showed a depletion in longer hemoglobin-derived peptides. In order to further elucidate the mechanism responsible for inhibition of hemoglobin metabolism, we used in vitro β-hematin polymerisation assays and showed JPC-3210 to be an intermediate inhibitor of β-hematin polymerisation, about 10-fold less potent then the quinoline antimalarials. Furthermore, quantitative proteomics analysis showed that JPC-3210 treatment results in a distinct proteomic signature in comparison to other known antimalarials. Whilst JPC-3210 clustered closely with mefloquine in the metabolomics and proteomics analyses, a key differentiating signature for JPC-3210 was the significant enrichment of parasite proteins involved in regulation of translation. In conclusion, multi-omics studies using high resolution mass spectrometry revealed JPC-3210 to possess a unique mechanism of action involving inhibition of hemoglobin digestion, depletion of DNA replication and synthesis proteins, and elevation of regulators of protein translation. Importantly, this mechanism is distinct from currently-used antimalarials, suggesting that JPC-3210 warrants further investigation as a potentially useful new antimalarial agent.

Project description: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.

Dataset Information

P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

Publications

P. falciparum in vitro killing rates allow to discriminate between different antimalarial mode-of-action.

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