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An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery.

ABSTRACT: Giardia duodenalis, the most prevalent human intestinal parasite causes the disease, giardiasis. On an annual basis G. duodenalis infects ~1 billion people, of which ~280 million develop symptomatic disease. Giardiasis can be severe and chronic, causing malnutrition, stunted growth and poor cognitive development in children. Current treatment options rely on drugs with declining efficacy and side-effects. To improve the health and well-being of millions of people world-wide, new anti-Giardia drugs with different modes of action to currently used drugs are required. The Medicines for Malaria Venture's Pathogen Box, a collection of bio-active compounds specifically chosen to stimulate infectious disease drug discovery, represents an opportunity for the discovery of new anti-Giardia agents. While the anti-Giardia activity of Pathogen Box compounds has been reported, this work failed to identify known anti-Giardia controls within the compound set. It also reported the activity of compounds previously screened and shown to be inactive by others, suggesting data may be inaccurate. Given these concerns the anti-Giardia activity of Pathogen Box compounds was re-assessed in the current study. Data from this work identified thirteen compounds with anti-Giardia IC₅₀ values ?2 ?M. Five of these compounds were reference compounds (marketed drugs with known anti-microbial activity), or analogues of compounds with previously described anti-Giardia activity. However, eight, including MMV676358 and MMV028694, which demonstrated potent sub-?M IC₅₀s against assemblage A, B and metronidazole resistant parasites (0.3 ?M and 0.9 ?M respectively), may represent new leads for future drug development. Interestingly, only four of these compounds were identified in the previously reported Pathogen Box screen highlighting the importance of assay selection and design when assessing compounds for activity against infectious agents.

SUBMITTER: Tiash S

PROVIDER: S-EPMC7113605 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery.

Tiash Snigdha S Saunders Jake J Hart Christopher J S CJS Ryan John H JH Riches Andrew G AG Skinner-Adams Tina S TS

International journal for parasitology. Drugs and drug resistance 20200313

Giardia duodenalis, the most prevalent human intestinal parasite causes the disease, giardiasis. On an annual basis G. duodenalis infects ~1 billion people, of which ~280 million develop symptomatic disease. Giardiasis can be severe and chronic, causing malnutrition, stunted growth and poor cognitive development in children. Current treatment options rely on drugs with declining efficacy and side-effects. To improve the health and well-being of millions of people world-wide, new anti-Giardia dru ...[more]

PMID: 32234669

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Project description:BACKGROUND:Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes. RESULTS:A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity. CONCLUSION:In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.

Dataset Information

An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery.

Publications

An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery.

Similar Datasets

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