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Repurposing Heparin as Antimalarial: Evaluation of Multiple Modifications Toward In Vivo Application.

ABSTRACT: Heparin is a promising antimalarial drug due to its activity in inhibiting Plasmodium invasion of red blood cells and to the lack of resistance evolution by the parasite against it, but its potent anticoagulant activity is preventing the advance of heparin along the clinical pipeline. We have determined, in in vitro Plasmodium falciparum cultures, the antimalarial activity of heparin-derived structures of different origins and sizes, to obtain formulations having a good balance of in vitro safety (neither cytotoxic nor hemolytic), low anticoagulant activity (?23 IU/mL according to activated partial thromboplastin time assays), and not too low antimalarial activity (IC50 at least around 100 µg/mL). This led to the selection of five chemically modified heparins according to the parameters explored, i.e., chain length, sulfation degree and position, and glycol-split, and whose in vivo toxicity indicated their safety for mice up to an intravenous dose of 320 mg/kg. The in vivo antimalarial activity of the selected formulations was poor as a consequence of their short blood half-life. The covalent crosslinking of heparin onto the surface of polyethylene glycol-containing liposomes did not affect its antimalarial activity in vitro and provided higher initial plasma concentrations, although it did not increase mean circulation time. Finding a suitable nanocarrier to impart long blood residence times to the modified heparins described here will be the next step toward new heparin-based antimalarial strategies.

SUBMITTER: Lantero E

PROVIDER: S-EPMC7557421 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Repurposing Heparin as Antimalarial: Evaluation of Multiple Modifications Toward In Vivo Application.

Lantero Elena E Aláez-Versón Carlos Raúl CR Romero Pilar P Sierra Teresa T Fernàndez-Busquets Xavier X

Pharmaceutics 20200829 9

Heparin is a promising antimalarial drug due to its activity in inhibiting <i>Plasmodium</i> invasion of red blood cells and to the lack of resistance evolution by the parasite against it, but its potent anticoagulant activity is preventing the advance of heparin along the clinical pipeline. We have determined, in in vitro <i>Plasmodium falciparum</i> cultures, the antimalarial activity of heparin-derived structures of different origins and sizes, to obtain formulations having a good balance of ...[more]

PMID: 32872434

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Project description:BACKGROUND:Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE:The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS:Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS:Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS:The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Dataset Information

Repurposing Heparin as Antimalarial: Evaluation of Multiple Modifications Toward In Vivo Application.

Publications

Repurposing Heparin as Antimalarial: Evaluation of Multiple Modifications Toward In Vivo Application.

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