Screening of potential targets in Plasmodium falciparum using stage-specific metabolic network analysis

Deep Learning Methods in Predicting Gene Expression Levels for the Malaria Parasite

Malaria is a mosquito-borne disease caused by single-celled blood parasites of the genus Plasmodium. The most severe cases of this disease are caused by the Plasmodium species, Falciparum. Once infected, a human host experiences symptoms of recurrent and intermittent fevers occurring over a time-frame of 48 hours, attributed to the synchronized developmental cycle of the parasite during the blood stage. To understand the regulated periodicity of Plasmodium falciparum transcription, this paper forecast and predict the P. falciparum gene transcription during its blood stage life cycle implementing a well-tuned recurrent neural network with gated recurrent units. Additionally, we also employ a spiking neural network to predict the expression levels of the P. falciparum gene. We provide results of this prediction on multiple genes including potential genes that express possible drug target enzymes. Our results show a high level of accuracy in being able to predict and forecast the expression levels of the different genes.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Antimalarials with Benzothiophene Moieties as Aminoquinoline Partners

Malaria is a severe and life-threatening disease caused by Plasmodium parasites that are spread to humans through bites of infected Anopheles mosquitoes. Here, we report on the efficacy of aminoquinolines coupled to benzothiophene and thiophene rings in inhibiting Plasmodium falciparum parasite growth. Synthesized compounds were evaluated for their antimalarial activity and toxicity, in vitro and in mice. Benzothiophenes presented in this paper showed improved activities against a chloroquine susceptible (CQS) strain, with potencies of IC₅₀ = 6 nM, and cured 5/5 Plasmodium berghei infected mice when dosed orally at 160 mg/kg/day × 3 days. In the benzothiophene series, the examined antiplasmodials were more active against the CQS strain D6, than against strains chloroquine resistant (CQR) W2 and multidrug-resistant (MDR) TM91C235. For the thiophene series, a very interesting feature was revealed: hypersensitivity to the CQR strains, resistance index (RI) of <1. This is in sharp contrast to chloroquine, indicating that further development of the series would provide us with more potent antimalarials against CQR strains.

In silico multiple-targets identification for heme detoxification in the human malaria parasite Plasmodium falciparum

Detoxification of hemoglobin byproducts or free heme is an essential step and considered potential targets for anti-malaria drug development. However, most of anti-malaria drugs are no longer effective due to the emergence and spread of the drug resistant malaria parasites. Therefore, it is an urgent need to identify potential new targets and even for target combinations for effective malaria drug design. In this work, we reconstructed the metabolic networks of Plasmodium falciparum and human red blood cells for the simulation of steady mass and flux flows of the parasite's metabolites under the blood environment by flux balance analysis (FBA). The integrated model, namely iPF-RBC-713, was then adjusted into two stage-specific metabolic models, which first was for the pathological stage metabolic model of the parasite when invaded the red blood cell without any treatment and second was for the treatment stage of the parasite when a drug acted by inhibiting the hemozoin formation and caused high production rate of heme toxicity. The process of identifying target combinations consisted of two main steps. Firstly, the optimal fluxes of reactions in both the pathological and treatment stages were computed and compared to determine the change of fluxes. Corresponding enzymes of the reactions with zero fluxes in the treatment stage but non-zero fluxes in the pathological stage were predicted as a preliminary list of potential targets in inhibiting heme detoxification. Secondly, the combinations of all possible targets listed in the first step were examined to search for the best promising target combinations resulting in more effective inhibition of the detoxification to kill the malaria parasites. Finally, twenty-three enzymes were identified as a preliminary list of candidate targets which mostly were in pyruvate metabolism and citrate cycle. The optimal set of multiple targets for blocking the detoxification was a set of heme ligase, adenosine transporter, myo-inositol 1-phosphate synthase, ferrodoxim reductase-like protein and guanine transporter. In conclusion, the method has shown an effective and efficient way to identify target combinations which are obviously useful in the development of novel antimalarial drug combinations.