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A Drug Repositioning Approach Identifies a Combination of Compounds as a Potential Regimen for Chronic Lymphocytic Leukemia Treatment.

ABSTRACT: Drug repositioning is a promising and powerful innovative strategy in the field of drug discovery. In this study, we screened a compound-library containing 800 Food and Drug Administration approved drugs for their anti-leukemic effect. All screening activities made use of human peripheral blood mononuclear cells (PBMCs), isolated from healthy or leukemic donors. Compounds with confirmed cytotoxicity were selected and classified in three groups: i) anti-neoplastic compounds which are drugs used in leukemia treatment, ii) compounds known to have an anti-cancer effect and iii) compounds demonstrating an anti-leukemic potential for the first time. The latter group was the most interesting from a drug repositioning perspective and yielded a single compound, namely Isoprenaline which is a non-selective β-adrenergic agonist. Analysis of the cytotoxic effect of this drug indicated that it induces sustainable intracellular ATP depletion leading, over time, to necrotic cell death. We exploited the Isoprenaline-induced intracellular ATP depletion to sensitize primary leukemic cells to fludarabine (purine analogue) and Ibrutinib (Bruton's tyrosine kinase inhibitor) treatment. In-vitro treatment of primary leukemic cells with a combination of Isoprenaline/fludarabine or Isoprenaline/Ibrutinib showed a very high synergistic effect. These combinations could constitute a new efficient regimen for CLL treatment following successful evaluation in animal models and clinical trials.

SUBMITTER: Nehdi A

PROVIDER: S-EPMC8195271 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDrug repositioning, also known as drug repurposing, defines new indications for existing drugs and can be used as an alternative to drug development. In recent years, the accumulation of large volumes of information related to drugs and diseases has led to the development of various computational approaches for drug repositioning. Although herbal medicines have had a great impact on current drug discovery, there are still a large number of herbal compounds that have no definite indications.ResultsIn the present study, we constructed a computational model to predict the unknown pharmacological effects of herbal compounds using machine learning techniques. Based on the assumption that similar diseases can be treated with similar drugs, we used four categories of drug-drug similarity (e.g., chemical structure, side-effects, gene ontology, and targets) and three categories of disease-disease similarity (e.g., phenotypes, human phenotype ontology, and gene ontology). Then, associations between drug and disease were predicted using the employed similarity features. The prediction models were constructed using classification algorithms, including logistic regression, random forest and support vector machine algorithms. Upon cross-validation, the random forest approach showed the best performance (AUC = 0.948) and also performed well in an external validation assessment using an unseen independent dataset (AUC = 0.828). Finally, the constructed model was applied to predict potential indications for existing drugs and herbal compounds. As a result, new indications for 20 existing drugs and 31 herbal compounds were predicted and validated using clinical trial data.ConclusionsThe predicted results were validated manually confirming the performance and underlying mechanisms - for example, irinotecan as a treatment for neuroblastoma. From the prediction, herbal compounds were considered to be drug candidates for related diseases which is important to be further developed. The proposed prediction model can contribute to drug discovery by suggesting drug candidates from herbal compounds which have potentials but few were studied.

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A Drug Repositioning Approach Identifies a Combination of Compounds as a Potential Regimen for Chronic Lymphocytic Leukemia Treatment.

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