Repurposing of known drugs for leishmaniasis treatment using bioinformatic predictions, in vitro validations and pharmacokinetic simulations

Bustamante, Christian; Ochoa, Rodrigo; Asela, Claudia; Muskus, Carlos

doi:10.1007/s10822-019-00230-y

Cited by 21 publications

(13 citation statements)

References 33 publications

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“…We sorted the molecules predicted as potentially Leishmanicidal according to their probability of being active and selected the top 10 candidates for each model ( Table 1 ). For RF, literature review demonstrated that 5 out of the top 10 molecules were indeed described as anti- Leishmania agents, namely, albendazole, 11 pyrazinamide (Mendez et al, 2009) 19 , domperidone, 12 - 14 dibucaine 15 , and lidocaine. 16 , 17 For SVM, three molecules out of the 10 were described as anti- Leishmania effectors, namely, phenelzine, 18 domperidone, 12 - 14 and rifabutin.…”

Section: Resultsmentioning

confidence: 99%

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Harigua‐Souiai

Oualha

Souiai

et al. 2022

Bioinform Biol Insights

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Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti- Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti- Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.

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Section: Resultsmentioning

confidence: 99%

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Harigua‐Souiai

Oualha

Souiai

et al. 2022

Bioinform Biol Insights

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“…These computational techniques involve systematic analysis of any data type such as bioinformatics targeting gene expression, chemical structure and genotype or proteomic data. In this sense, molecular docking is a computational strategy to predict binding sites between the ligands (drugs, for example) and the target (a receptor) [107]. In conventional docking, one receptor/protein target is chosen so that multiple drugs could be tested against that target.…”

Section: Drug Repurposingmentioning

confidence: 99%

“…parasites. In this study, 33 drugs were identified with potential target prediction with in vitro action (rifabutin and perphenazine) [107]. Metallodrugs have been identified with important antitumor, anti-inflammatory, and antimicrobial actions.…”

Section: Drug Repurposingmentioning

confidence: 99%

Current Strategies and Recent Advances in Nanostructured Delivery Systems for Managing Leishmaniasis-a Review

Chaurasia

Jain

2022

JASR

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Neglected tropical diseases (NTDs) comprise of a group of 17th infectious conditions endemic in many developing countries. Among these diseases, deadly, prevalent parasitic infection such as leishmaniasis, presents a significant global burden which is responsible for high morbidity/mortality rate especially in low-and middle-income/developed countries transmitted by phlebotomines sandflies. As per the WHO report, a total of 0.7-1.0 million new leishmaniasis cases, which are spread by 23 leishmania species in more than 98 countries are estimated with an alarming 26,000-65,000 death toll every year. The disease can be characterized by at least four syndromes: cutaneous leishmaniasis (CL), muco-cutaneous leishmaniasis (MCL), visceral leishmaniasis (VL) also known as kala-azar in the Indian sub-continent or black fever, which is the most firm form of the disease being lethal if untreated and post-kala-azar dermal leishmaniasis (PKDL). The lack of cheap, portable, easy to use diagnostic tools exhibiting high efficiency and specificity impede the early diagnosis of the disease. Its intracellular nature and disseminated locations of parasite, limited number of chemotherapeutic agents, increasing incidences of resistance to first line drugs, along with the cost, toxicities, low patient compliance and require long-term regimen and usually hospitalization, pose a great challenge to formulation scientists that have necessitated effective management of leishmaniasis infection by modulating the delivery of existing drugs. Over the past decade, research on development of alternative treatments such as nanotechnology-based drug delivery systems (nanoparticles, nanotubes, nanosuspensions, liposomes, nanovaccines, etc.), use of natural products as well as development of antileishmanial vaccine has been extensively investigated due to their unique properties, such as bioavailability, lowered toxicity, targeted drug delivery, high biocomatibilty and biodegradability. To identify all relevant literature, we searched Web of Sciences, Scopus, PubMed, NCBI, Scielo, Google Scholar, and profiled studies published between 1986 and 2020. The aim of this review is to summarize new advances and new strategies used on leishmaniasis therapy addressing alternative and innovative treatment paths such as combination or multi-drug uses, immunomodulation, drug repurposing and the nanotechnology-based drug delivery systems have been used to improve the therapeutic aspects of existing antileishmanial drugs, presenting a critical review and some suggestions for improving drug delivery.

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“…The rationale and principles for repurposing drugs for new diseases have been reviewed ( Pushpakom et al, 2019 ). Very recent reviews have summarized HT and computational repurposing for NTDs ( Hernandez et al, 2018 ; Andrade et al, 2019 ; Bustamante et al, 2019 ).…”

Section: Perspectivementioning

confidence: 99%

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Winkler

2021

Front. Chem.

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Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

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Repurposing of known drugs for leishmaniasis treatment using bioinformatic predictions, in vitro validations and pharmacokinetic simulations

Cited by 21 publications

References 33 publications

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Current Strategies and Recent Advances in Nanostructured Delivery Systems for Managing Leishmaniasis-a Review

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

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