Inhibitory effects on bovine babesial infection by iron chelator, 1-(N-acetyl-6-aminohexyl)- 3-hydroxy-2-methylpyridin-4-one (CM1), and antimalarial drugs

BACKGROUND

Babesiosis is an infectious disease caused by protozoa of the apicomplexan phylum, genus Babesia. It is a malaria-like parasitic disease that can be transmitted via tick bites. The apicomplexan phylum of eukaryotic microbial parasites has had detrimental impacts on human and veterinary medicine. There are only a few drugs currently available to treat this disease; however, parasitic strains that are resistant to these commercial drugs are increasing in numbers. Plasmodium and Babesia are closely related as they share similar biological features including mechanisms for host cell invasion and metabolism. Therefore, antimalarial drugs may be useful in the treatment of Babesia infections. In addition to antimalarials, iron chelators also inhibit parasite growth. In this study, we aimed to evaluate the in vitro inhibitory efficacy of iron chelator and different antimalarials in the treatment of Babesia bovis.

METHODS

Cytotoxicity of antimalarial drugs; pyrimethamine, artefenomel, chloroquine, primaquine, dihydroarthemisinine, and the iron chelator, 1-(N-acetyl-6-aminohexyl)- 3-hydroxy-2 methylpyridin-4-one (CM1), were evaluated against Madin Darby Bovine Kidney (MDBK) cells and compared to diminazene aceturate, which is the currently available drug for animal babesiosis using an MTT solution. Afterwards, an evaluation of the in vitro growth-inhibitory effects of antimalarial drug concentrations was performed and monitored using a flow cytometer. Half maximal inhibitory concentrations (IC50) of each antimalarial and iron chelator were determined and compared to the antibabesial drug, diminazine aceturate, by interpolation using a curve-fitting technique. Subsequently, the effect of the drug combination was assessed by constructing an isobologram. Values of the sum of fractional inhibitions at 50% inhibition were then estimated.

RESULTS

Results indicate that all drugs tested could safely inhibit babesia parasite growth, as high as 2500 μM were non-toxic to mammalian cells. Although no drugs inhibited B. bovis more effectively than diminazine aceturate in this experiment, in vitro growth inhibition results with IC50 values of pyrimethamine 6.25 ± 2.59 μM, artefenomel 2.56 ± 0.67 μM, chloroquine 2.14 ± 0.76 μM, primaquine 22.61 ± 6.72 μM, dihydroarthemisinine 4.65 ± 0.22 μM, 1-(N-acetyl-6-aminohexyl)- 3-hydroxy-2 methylpyridin-4-one (CM1) 9.73 ± 1.90 μM, and diminazine aceturate 0.42 ± 0.01 μM, confirm that all drugs could inhibit B. bovis and could be used as alternative treatments for bovine babesial infection. Furthermore, the efficacy of a combination of the iron chelator, CM1, in combination with artefenomel dihydroarthemisinin or chloroquine, and artefenomel in combination with the iron chelator, CM1, dihydroarthemisinin or chloroquine, exhibited synergism against B. bovis in vitro.

CONCLUSION

Our evaluation of the inhibitory efficacy of the iron chelator CM1, antimalarial drugs, and a combination of these drugs against B. bovis could be potentially useful in the development and discovery of a novel drug for the treatment of B. bovis in the future.

Prediction of synergistic drug combinations using PCA-initialized deep learning

Background

Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy.

Results

We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction.

Conclusions

Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.