Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Nanomolar activity of coumarin-3-thiosemicarbazones targeting Trypanosoma cruzi cruzain and the T. brucei cathepsin L-like protease

Trypanosoma cruzi (T. cruzi) and Trypanosoma brucei (T. brucei) urgently demand innovative drug development due to their impact on public health worldwide. Their cysteine proteases, Cruzain (CRZ) and the T. brucei Cathepsin L-like protease (TbrCATL) are established drug targets for these parasites. In this study, our coumarin-3-thiosemicarbazones demonstrated nanomolar IC50 values (22-698 nM) toward these proteases. Against T. cruzi amastigotes and T. brucei trypomastigotes, LASF-01 displayed a promising result. Herein, MCG-02, the most potent TbrCATL inhibitor, underwent comprehensive analyses, including cytotoxicity assessments and in vitro tests. Molecular dynamics (MD) simulations and a multiscale Quantum Mechanics/Quantum Mechanics (QM/QM) approach were used to generate insights into their binding modes. These suggested that MCG-02 could be a reversible, competitive covalent inhibitor. Further, confirmatory assays were experimentally performed changing different parameters to prove its efficacy. Additionally, the predicted pharmacokinetic profile showed that there is no violation of the Lipinski rule of five. Notably, coumarin-3-thiosemicarbazone hybrids emerged as promising candidates for designing highly active inhibitors against CRZ and TbrCATL. Overall, the integration of in silico and experimental approaches enhanced our understanding regarding the binding modes of MCG-02, which were experimentally corroborated, providing valuable insights for future drug development.

A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer's disease (AD) and Parkinson's disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.

Oral Pyronaridine Tetraphosphate Reduces Tissue Presence of Parasites in a Mouse Model of Chagas Disease

The eukaryotic parasite Trypanosoma cruzi (T. cruzi) is responsible for Chagas disease, which results in heart failure in patients. The disease is more common in Latin America, and is an emerging infection with The Centers for Disease Control estimating that greater than 300,000 people are currently infected in the United States. This disease has also spread from South and Central America, where it is endemic to many other countries, including Australia, Japan, and Spain. Current therapy for Chagas disease is inadequate due to limited efficacy in the indeterminate and chronic phases of the disease, in addition to the adverse effects from nifurtimox and benznidazole, which are nitro-containing drugs used for therapy. There is a clear need for new therapies for the Chagas disease. Using a computational machine learning approach, we have previously shown that the antimalarial pyronaridine tetraphosphate is active against T. cruzi Brazil-luc in vitro against parasites infecting a myoblast cell line and is also active in vivo in an acute mouse model of Chagas disease when dosed i.p. We now further evaluated oral pyronaridine as a monotherapy to determine the minimum effective dose to treat acute and chronic models of Chagas disease. Our results for T. cruzi Brazil-luc demonstrated daily oral dosing with pyronaridine from 150 to 600 mg/kg resulted in statistically significant inhibition in the 7 day acute mouse model. Combination therapy with daily dosing of benznidazole and pyronaridine in the acute infection model demonstrated that 300 mg/kg pyronaridine could return statistically significant antiparasitic activity to a subtherapetic 10 mg/kg benznidazole. In contrast, pyronaridine as monotherapy or combined with benznidazole lacked efficacy in the chronic mouse model, whereas 100 mg/kg benznidazole alone demonstrated undetectable parasites in the heart of mice. Pyronaridine requires further assessment in other chronic models to identify if it can be used beyond the acute stage of T. cruzi infection.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

1-Sulfonyl-3-amino-1H-1,2,4-triazoles as Yellow Fever Virus Inhibitors: Synthesis and Structure-Activity Relationship

Yellow fever virus (YFV) transmitted by infected mosquitoes causes an acute viral disease for which there are no approved small-molecule therapeutics. Our recently developed machine learning models for YFV inhibitors led to the selection of a new pyrazolesulfonamide derivative RCB16003 with acceptable in vitro activity. We report that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class, which was recently identified as active non-nucleoside reverse transcriptase inhibitors against HIV-1, can also be repositioned as inhibitors of yellow fever virus replication. As compared to other Flaviviridae or Togaviridae family viruses tested, both compounds RCB16003 and RCB16007 demonstrate selectivity for YFV over related viruses, with only RCB16007 showing some inhibition of the West Nile virus (EC50 7.9 μM, CC50 17 μM, SI 2.2). We also describe the absorption, distribution, metabolism, and excretion (ADME) in vitro and pharmacokinetics (PK) for RCB16007 in mice. This compound had previously been shown to not inhibit hERG, and we now describe that it has good metabolic stability in mouse and human liver microsomes, low levels of CYP inhibition, high protein binding, and no indication of efflux in Caco-2 cells. A single-dose oral PK study in mice has a T1/2 of 3.4 h and Cmax of 1190 ng/mL, suggesting good availability and stability. We now propose that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class may be prioritized for in vivo efficacy testing against YFV.

Machine learning and drug discovery for neglected tropical diseases

Neglected tropical diseases affect millions of individuals and cause loss of productivity worldwide. They are common in developing countries without the financial resources for research and drug development. With increased availability of data from high throughput screening, machine learning has been introduced into the drug discovery process. Models can be trained to predict biological activities of compounds before working in the lab. In this study, we use three publicly available, high-throughput screening datasets to train machine learning models to predict biological activities related to inhibition of species that cause leishmaniasis, American trypanosomiasis (Chagas disease), and African trypanosomiasis (sleeping sickness). We compare machine learning models (tree based models, naive Bayes classifiers, and neural networks), featurizing methods (circular fingerprints, MACCS fingerprints, and RDKit descriptors), and techniques to deal with the imbalanced data (oversampling, undersampling, class weight/sample weight).

The Commoditization of AI for Molecule Design

Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become "designed by AI". AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for de novo design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Past and future of trypanosomatids high-throughput phenotypic screening

Diseases caused by trypanosomatid parasites affect millions of people mainly living in developing countries. Novel drugs are highly needed since there are no vaccines and available treatment has several limitations, such as resistance, low efficacy, and high toxicity. The drug discovery process is often analogous to finding a needle in the haystack. In the last decades a so-called rational drug design paradigm, heavily dependent on computational approaches, has promised to deliver new drugs in a more cost-effective way. Paradoxically however, the mainstay of these computational methods is data-driven, meaning they need activity data for new compounds to be generated and available in databases. Therefore, high-throughput screening (HTS) of compounds still is a much-needed exercise in drug discovery to fuel other rational approaches. In trypanosomatids, due to the scarcity of validated molecular targets and biological complexity of these parasites, phenotypic screening has become an essential tool for the discovery of new bioactive compounds. In this article we discuss the perspectives of phenotypic HTS for trypanosomatid drug discovery with emphasis on the role of image-based, high-content methods. We also propose an ideal cascade of assays for the identification of new drug candidates for clinical development using leishmaniasis as an example.

Identification of Leucinostatins from Ophiocordyceps sp. as Antiparasitic Agents against Trypanosoma cruzi

Safe and effective treatments for Chagas disease, a potentially fatal parasitic infection associated with cardiac and gastrointestinal pathology and caused by the kinetoplastid parasite Trypanosoma cruzi, have yet to be developed. Benznidazole and nifurtimox, which are currently the only available drugs against T. cruzi, are associated with severe adverse effects and questionable efficacy in the late stage of the disease. Natural products have proven to be a rich source of new chemotypes for other infectious agents. We utilized a microscopy-based high-throughput phenotypic screen to identify inhibitors of T. cruzi from a library of natural product samples obtained from fungi procured through a Citizen Science Soil Collection Program (https://whatsinyourbackyard.org/) and the Great Lakes (USA) benthic environment. We identified five leucinostatins (A, B, F, NPDG C, and NPDG D) as potent inhibitors of the intracellular amastigote form of T. cruzi. Leucinostatin B also showed in vivo suppression of T. cruzi in a mouse model of Chagas disease. Given prior reports that leucinostatins A and B have antiparasitic activity against the related kinetoplastid Trypanosoma brucei, our findings suggest a potential cross-trypanocidal compound class and provide a platform for the further chemical derivatization of a potent chemical scaffold against T. cruzi.

Drug/Lead Compound Hydroxymethylation as a Simple Approach to Enhance Pharmacodynamic and Pharmacokinetic Properties

Hydroxymethylation is a simple chemical reaction, in which the introduction of the hydroxymethyl group can lead to physical–chemical property changes and offer several therapeutic advantages, contributing to the improved biological activity of drugs. There are many examples in the literature of the pharmaceutical, pharmacokinetic, and pharmacodynamic benefits, which the hydroxymethyl group can confer to drugs, prodrugs, drug metabolites, and other therapeutic compounds. It is worth noting that this group can enhance the drug’s interaction with the active site, and it can be employed as an intermediary in synthesizing other therapeutic agents. In addition, the hydroxymethyl derivative can result in more active compounds than the parent drug as well as increase the water solubility of poorly soluble drugs. Taking this into consideration, this review aims to discuss different applications of hydroxymethyl derived from biological agents and its influence on the pharmacological effects of drugs, prodrugs, active metabolites, and compounds of natural origin. Finally, we report a successful compound synthesized by our research group and used for the treatment of neglected diseases, which is created from the hydroxymethylation of its parent drug.

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC₅₀ 3.2 μM and CC₅₀ 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Quantum Machine Learning Algorithms for Drug Discovery Applications

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.

Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs

An overview is presented of the well-established role of alkaloids in drug discovery, the application of more sustainable chemicals, and biological approaches, and the implementation of information systems to address the current challenges faced in meeting global disease needs. The necessity for a new international paradigm for natural product discovery and development for the treatment of multidrug resistant organisms, and rare and neglected tropical diseases in the era of the Fourth Industrial Revolution and the Quintuple Helix is discussed.

Gut microbiota mediates the absorption of FLZ, a new drug for Parkinson's disease treatment

The gut microbiota plays an important role in regulating the pharmacokinetics and pharmacodynamics of many drugs. FLZ, a novel squamosamide derivative, has been shown to have neuroprotective effects on experimental Parkinson's disease (PD) models. FLZ is under phase Ⅰ clinical trial now, while the underlying mechanisms contributing to the absorption of FLZ are still not fully elucidated. Due to the main metabolite of FLZ was abundant in feces but rare in urine and bile of mice, we focused on the gut microbiota to address how FLZ was metabolized and absorbed. In vitro studies revealed that FLZ could be exclusively metabolized to its major metabolite M1 by the lanosterol 14 alpha-demethylase (CYP51) in the gut microbiota, but was almost not metabolized by any other metabolism-related organs, such as liver, kidney, and small intestine. M1 was quickly absorbed into the blood and then remethylated to FLZ by catechol O-methyltransferase (COMT). Notably, dysbacteriosis reduced the therapeutic efficacy of FLZ on the PD mouse model by inhibiting its absorption. The results show that the gut microbiota mediate the absorption of FLZ through a FLZ-M1-FLZ circulation. Our research elucidates the vital role of the gut microbiota in the absorption of FLZ and provides a theoretical basis for clinical pharmacokinetic studies and clinical application of FLZ in the treatment of PD.

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

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.

Tryp-ing Up Metabolism: Role of Metabolic Adaptations in Kinetoplastid Disease Pathogenesis

Today, more than a billion people-one-sixth of the world's population-are suffering from neglected tropical diseases. Human African trypanosomiasis, Chagas disease, and leishmaniasis are neglected tropical diseases caused by protozoan parasites belonging to the genera Trypanosoma and Leishmania About half a million people living in tropical and subtropical regions of the world are at risk of contracting one of these three infections. Kinetoplastids have complex life cycles with different morphologies and unique physiological requirements at each life cycle stage. This review covers the latest findings on metabolic pathways impacting disease pathogenesis of kinetoplastids within the mammalian host. Nutrient availability is a key factor shaping in vivo parasite metabolism; thus, kinetoplastids display significant metabolic flexibility. Proteomic and transcriptomic profiles show that intracellular trypanosomatids are able to switch to an energy-efficient metabolism within the mammalian host system. Host metabolic changes can also favor parasite persistence, and contribute to symptom development, in a location-specific fashion. Ultimately, targeted and untargeted metabolomics studies have been a valuable approach to elucidate the specific biochemical pathways affected by infection within the host, leading to translational drug development and diagnostic insights.

A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules

Schistosomiasis is a chronic and painful disease of poverty caused by the flatworm parasite Schistosoma. Drug discovery for antischistosomal compounds predominantly employs in vitro whole organism (phenotypic) screens against two developmental stages of Schistosoma mansoni, post-infective larvae (somules) and adults. We generated two rule books and associated scoring systems to normalize 3898 phenotypic data points to enable machine learning. The data were used to generate eight Bayesian machine learning models with the Assay Central software according to parasite’s developmental stage and experimental time point (≤24, 48, 72, and >72 h). The models helped predict 56 active and nonactive compounds from commercial compound libraries for testing. When these were screened against S. mansoni in vitro, the prediction accuracy for active and inactives was 61% and 56% for somules and adults, respectively; also, hit rates were 48% and 34%, respectively, far exceeding the typical 1–2% hit rate for traditional high throughput screens.

In vitro anti-Trypanosoma cruzi activity enhancement of curcumin by its monoketone tetramethoxy analog diveratralacetone

Chagas disease is a tropical disease caused by the protozoan parasite Trypanosoma cruzi and currently affects millions of people worldwide. Curcumin (CUR), the major constituent of turmeric spice (dry powder of Curcuma longa L. plant rhizomes and roots), exhibits antiparasitic activity against protozoan parasites in vitro. However, because of its chemical instability, poor cellular uptake and limited bioavailability it is not suitable for clinical use. The objective of this study was to synthesize and evaluate in vitro CUR monoketone analog dibenzalacetone (DBA 1) and its non-phenolic, methoxy (2–4) and chloro (5) derivatives for better stability and bioavailability against T. cruzi. Diveratralacetone, the tetramethoxy DBA (DBA 3), was found to be the CUR analog with most enhanced activity against the amastigote forms of four strains of T. cruzi tested (Brazil, CA-I/72, Sylvio X10/4 and Sylvio X10/7) with 50% inhibitory concentration (IC₅₀) < 10 μM (1.51–9.63 μM) and selectivity index (SI) > 10 (C2C12 non-infected mammalian cells). This was supplemented by time-course assessment of its anti-T. cruzi activity. DBA 1 and its dimethoxy (DBA 2) and hexamethoxy (DBA 4) derivatives were substantially less active. The inactivity of dichloro-DBA (DBA 5) was indicative of the important role played by oxygenated groups such as methoxy in the terminal aromatic rings in the DBA molecule, particularly at para position to form reactive oxygen species essential for anti-T. cruzi activity. Although the DBAs and CUR were toxic to infected mammalian cells in vitro, in a mouse model, both DBA 3 and CUR did not exhibit acute toxicity or mortality. These results justify further optimization and in vivo anti-T. cruzi activity evaluation of the inexpensive diveratralacetone for its potential use in treating Chagas disease, a neglected parasitic disease in economically challenged tropical countries.

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First report on in vitro activity of dibenzalacetone and its methoxy derivatives against Trypanosoma cruzi.

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Diveratralacetone (tetramethoxy DBA 3) was the most active against four strains tested.

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DBA 3 showed values of IC₅₀ < 10 μM against all strains evaluated.

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DBA 3 showed SI > 10 in non-infected C2C12 cell lines.

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DBA 3 is a hit compound for further in vivo studies against T. cruzi parasites.

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

Chagas disease still has no effective treatment option for all of its phases despite being discovered more than 100 years ago. The development of commercial drugs has been stagnating since the 1960s, a fact that sheds light on the question of how drug discovery research has progressed and taken advantage of technological advances. Could it be that technological advances have not yet been sufficient to resolve this issue or is there a lack of protocol, validation and standardization of the data generated by different research teams? This work presents an overview of commercial drugs and those that have been evaluated in studies and clinical trials so far. A brief review is made of recent target-based and phenotypic studies based on the search for molecules with anti-Trypanosoma cruzi action. It also discusses how proteochemometric (PCM) modeling and microcrystal electron diffraction (MicroED) can help in the case of the lack of a 3D protein structure; more specifically, Trypanosoma cruzi carbonic anhydrase.

Chagas Disease: Current View of an Ancient and Global Chemotherapy Challenge

Chagas disease is a neglected tropical disease and a global public health issue. In terms of treatment, no progress has been made since the 1960s, when benznidazole and nifurtimox, two obsolete drugs still prescribed, were used to treat this disease. Hence, currently, there are no effective treatments available to tackle Chagas disease. Over the past 20 years, there has been an increasing interest in the disease. However, parasite genetic diversity, drug resistance, tropism, and complex life cycle, along with the limited understanding of the disease and inadequate methodologies and strategies, have resulted in the absence of new insights in drugs development and disappointing outcomes in clinical trials so far. In summary, new drugs are urgently needed. This Review considers the relevant aspects related to the lack of drugs for Chagas disease, resumes the advances in tools for drug discovery, and discusses the main features to be taken into account to develop new effective drugs.

Machine Learning for Discovery of GSK3β Inhibitors

Alzheimer's disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC50 = 97.3 nM) and GSK3α (IC50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.

Anti-HCV and anti-malaria agent, potential candidates to repurpose for coronavirus infection: Virtual screening, molecular docking, and molecular dynamics simulation study

AIMS

Coronavirus disease 2019 (COVID-19) has appeared in Wuhan, China but the fast transmission has led to its widespread prevalence in various countries, which has made it a global concern. Another concern is the lack of definitive treatment for this disease. The researchers tried different treatment options which are not specific. The current study aims to identify potential small molecule inhibitors against the main protease protein of SARS-CoV-2 by the computational approach.

MAIN METHODS

In this study, a virtual screening procedure employing docking of the two different datasets from the ZINC database, including 1615 FDA approved drugs and 4266 world approved drugs were used to identify new potential small molecule inhibitors for the newly released crystal structure of main protease protein of SARS-CoV-2. In the following to validate the docking result, molecular dynamics simulations were applied on selected ligands to identify the behavior and stability of them in the binding pocket of the main protease in 150 nanoseconds (ns). Furthermore, binding energy using the MMPBSA approach was also calculated.

KEY FINDINGS

The result indicates that simeprevir (Hepatitis C virus NS3/4A protease inhibitor) and pyronaridine (antimalarial agent) could fit well to the binding pocket of the main protease and because of some other beneficial features including broad-spectrum antiviral properties and ADME profile, they might be a promising drug candidate for repurposing to the treatment of COVID-19.

SIGNIFICANCE

Simeprevir and pyronaridine were selected by the combination of virtual screening and molecular dynamics simulation approaches as a potential candidate for treatment of COVID-19.

Pyronaridine: An update of its pharmacological activities and mechanisms of action

Pyronaridine (PYR) is an erythrocytic schizonticide with a potent antimalarial activity against multidrug-resistant Plasmodium. The drug is used in combination with artesunate for the treatment of uncomplicated P. falciparum malaria, in adults and children. The present review briefly retraces the discovery of PYR and recent antimalarial studies which has led to the approval of PYR/artesunate combination (Pyramax) by the European Medicines Agency to treat uncomplicated malaria worldwide. PYR also presents a marked antitumor activity and has revealed efficacy for the treatment of other parasitic diseases (notably Babesia and Trypanosoma infections) and to mitigate the Ebola virus propagation. On the one hand, PYR functions has an inhibitor of hemozoin (biomineral malaria pigment, by-product of hemoglobin digestion) formation, blocking the biopolymerization of β-hematin and thus facilitating the accumulation of toxic hematin into the digestive vacuole of the parasite. On the other hand, PYR is a bona fide DNA-intercalating agent and an inhibitor of DNA topoisomerase 2, leading to DNA damages and cell death. Inhibition of hematin polymerization represents the prime mechanism at the origin of the antimalarial activity, whereas anticancer effects relies essentially on the interference with DNA metabolism, as with structurally related anticancer drugs like amsacrine and quinacrine. In addition, recent studies point to an immune modulatory activity of PYR and the implication of a mitochondrial oxidative pathway. An analogy with the mechanism of action of artemisinin drugs is underlined. In brief, the biological actions of pyronaridine are recapitulated to shed light on the diverse health benefits of this unsung drug.

Current Approaches to Drug Discovery for Chagas Disease: Methodological Advances

BACKGROUND

In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays.

OBJECTIVE

Current approaches to drug discovery for Chagas disease.

METHOD

This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases.

RESULTS

Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease.

CONCLUSION

There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved kinase inhibitors already being individually evaluated for chordoma. Several published studies of compounds screened against chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor AZD2014, was the most potent against chordoma cell lines (IC50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mTOR signaling pathway in chordoma and suggest it as a promising avenue for targeted therapy. Additionally, two currently FDA approved drugs, afatinib and palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI50 = 0.43) while AZD2014 and afatanib also showed synergy (CI50 = 0.41) against a chordoma cell in vitro. These findings may be of interest clinically, and this in vitro- and in silico approach could also be applied to other rare cancers.

High-Throughput Screening of the ReFRAME Library Identifies Potential Drug Repurposing Candidates for Trypanosoma cruzi

Chagas disease, caused by the kinetoplastid parasite Trypanosoma cruzi, affects between 6 and 7 million people worldwide, with an estimated 300,000 to 1 million of these cases in the United States. In the chronic phase of infection, T. cruzi can cause severe gastrointestinal and cardiac disease, which can be fatal. Currently, only benznidazole is clinically approved by the FDA for pediatric use to treat this infection in the USA. Toxicity associated with this compound has driven the search for new anti-Chagas agents. Drug repurposing is a particularly attractive strategy for neglected diseases, as pharmacological parameters and toxicity are already known for these compounds, reducing costs and saving time in the drug development pipeline. Here, we screened 7680 compounds from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library, a collection of drugs or compounds with confirmed clinical safety, against T. cruzi. We identified seven compounds of interest with potent in vitro activity against the parasite with a therapeutic index of 10 or greater, including the previously unreported activity of the antiherpetic compound 348U87. These results provide the framework for further development of new T. cruzi leads that can potentially move quickly to the clinic.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.

Repurposing the antimalarial pyronaridine tetraphosphate to protect against Ebola virus infection

Recent outbreaks of the Ebola virus (EBOV) have focused attention on the dire need for antivirals to treat these patients. We identified pyronaridine tetraphosphate as a potential candidate as it is an approved drug in the European Union which is currently used in combination with artesunate as a treatment for malaria (EC₅₀ between 420 nM—1.14 μM against EBOV in HeLa cells). Range-finding studies in mice directed us to a single 75 mg/kg i.p. dose 1 hr after infection which resulted in 100% survival and statistically significantly reduced viremia at study day 3 from a lethal challenge with mouse-adapted EBOV (maEBOV). Further, an EBOV window study suggested we could dose pyronaridine 2 or 24 hrs post-exposure to result in similar efficacy. Analysis of cytokine and chemokine panels suggests that pyronaridine may act as an immunomodulator during an EBOV infection. Our studies with pyronaridine clearly demonstrate potential utility for its repurposing as an antiviral against EBOV and merits further study in larger animal models with the added benefit of already being used as a treatment against malaria.

To date there is no approved drug for Ebola Virus infection. Our approach has been to assess drugs that are already approved for other uses in various countries. Using computational models, we have previously identified three such drugs and demonstrated their activity against the Ebola virus in vitro. We now report on the in vitro absorption, metabolism, distribution, excretion and pharmacokinetic properties of one of these molecules, namely the antimalarial pyronaridine. We justify efficacy testing in the mouse model of ebola infection. We also demonstrate that a single dose of this drug is 100% effective against the virus. This study provides important preclinical evaluation of this already approved drug and justifies its selection for larger animal efficacy studies.

Advances in preclinical approaches to Chagas disease drug discovery

Introduction: Chagas disease affects 8-10 million people worldwide, mainly in Latin America. The current therapy for Chagas disease is limited to nifurtimox and benznidazole, which are effective in treating only the acute phase of the disease but with severe side effects. Therefore, there is an unmet need for new drugs and for the exploration of innovative approaches which may lead to the discovery of new effective and safe drugs for its treatment. Areas covered: The authors report and discuss recent approaches including structure-based design that have led to the discovery of new promising small molecule candidates for Chagas disease which affect prime targets that intervene in the sterol pathway of T. cruzi. Other trypanosome targets, phenotypic screening, the use of artificial intelligence and the challenges with Chagas disease drug discovery are also discussed. Expert opinion: The application of recent scientific innovations to the field of Chagas disease have led to the discovery of new promising drug candidates for Chagas disease. Phenotypic screening brought new hits and opportunities for drug discovery. Artificial intelligence also has the potential to accelerate drug discovery in Chagas disease and further research into this is warranted.

Design of Gallinamide A Analogs as Potent Inhibitors of the Cysteine Proteases Human Cathepsin L and Trypanosoma cruzi Cruzain

Gallinamide A, originally isolated with a modest antimalarial activity, was subsequently reisolated and characterized as a potent, selective, and irreversible inhibitor of the human cysteine protease cathepsin L. Molecular docking identified potential modifications to improve binding, which were synthesized as a suite of analogs. Resultingly, this current study produced the most potent gallinamide analog yet tested against cathepsin L (10, K_i = 0.0937 ± 0.01 nM and k_inact/K_i = 8 730 000). From a protein structure and substrate preference perspective, cruzain, an essential Trypanosoma cruzi cysteine protease, is highly homologous. Our investigations revealed that gallinamide and its analogs potently inhibit cruzain and are exquisitely toxic toward T. cruzi in the intracellular amastigote stage. The most active compound, 5, had an IC₅₀ = 5.1 ± 1.4 nM, but was relatively inactive to both the epimastigote (insect stage) and the host cell, and thus represents a new candidate for the treatment of Chagas disease.

Repurposing Approved Drugs as Inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins Syndrome

PURPOSE

Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels K_v7.1 and Na_v1.8 which triggers an increase in after-hyperpolarization and altered firing properties.

METHODS

We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (K_V7.1) or HEK293 (Na_v1.8) cells and the HTS used either ⁸⁶Rb⁺ efflux (K_V7.1) or a FLIPR assay (Na_v1.8).

RESULTS

The HTS delivered 55 inhibitors of K_v7.1 (4.2% hit rate) and 93 inhibitors of Na_v1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Na_v1.8.

CONCLUSIONS

This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Na_v1.8, as there are currently no approved treatments for this rare disorder.

Next-Generation Phenotypic Screening in Early Drug Discovery for Infectious Diseases

Cell-based phenotypic screening has proven to be valuable, notably in recapitulating relevant biological conditions, for example, the host cell/pathogen niche. However, the corresponding methodological complexity is not readily compatible with high-throughput pipelines, and fails to inform either molecular target or mechanism of action, which frustrates conventional drug-discovery roadmaps. We review the state-of-the-art and emerging technologies that suggest new strategies for harnessing value from the complexity of phenotypic screening and augmenting powerful utility for translational drug discovery. Advances in cellular, molecular, and bioinformatics technologies are converging at a cutting edge where the complexity of phenotypic screening may no longer be considered a hinderance but rather a catalyst to chemotherapeutic discovery for infectious diseases.

Exploiting machine learning for end-to-end drug discovery and development

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibition assays. Compounds from this database with ≤1 μM HIV-1 RT DNA polymerase activity inhibition and cell-based HIV-1 inhibition are correlated (Pearson r = 0.44, n = 1137, p < 0.0001). Models were trained using multiple machine learning approaches (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, support vector classification, k-Nearest Neighbors, and deep neural networks as well as consensus approaches) and then their predictive abilities were compared. Our comparison of different machine learning methods demonstrated that support vector classification, deep learning, and a consensus were generally comparable and not significantly different from each other using 5-fold cross validation and using 24 training and test set combinations. This study demonstrates findings in line with our previous studies for various targets that training and testing with multiple data sets does not demonstrate a significant difference between support vector machine and deep neural networks.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

High Throughput and Computational Repurposing for Neglected Diseases

Purpose

Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed.

Methods

We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties.

Results

Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens.

Conclusions

In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2558-3) contains supplementary material, which is available to authorized users.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

Hydroxymethylnitrofurazone treatment in indeterminate form of chronic Chagas disease: Reduced intensity of tissue parasitism and inflammation-A histopathological study

Hydroxymethylnitrofurazone (NFOH) is a nitrofurazone prodrug effective in vivo during acute infections, and it has less hepatotoxicity effect than the standard drug benznidazole (BZN) which has been used during short- and long-term treatment. In the present study, we induced the indeterminate form of Chagas disease in mice with a Y strain of Trypanosoma cruzi and analysed the histopathological data about the effects of NFOH and BZN on different tissues, including the heart, skeletal muscle, liver, kidney, colon, spleen and brain. After infection, BALB/c mice were treated with NFOH (150 mg/kg) and BZN (60 mg/kg) for 60 days and then submitted to immunosuppression using dexamethasone (5 mg/kg) for 14 days. Two trained analysts, as part of a blind evaluation, examined the results using serial sections of 3 mm diameter in two different moments. The results showed reactivation of the disease only in the infected nontreated group (POS). After treatment, amastigote nests were found in the heart, colon, liver and skeletal muscle in the POS group and in the heart and liver of the BZN group. Interestingly, amastigote nests were not found in the NFOH and NEG groups. The histopathological analysis showed fewer tissue lesions and parasite infiltrates in the NFOH group when compared with the BZN and POS groups. We have not observed any increase in the levels of hepatocellular injury biomarkers (AST/ALT) in the NFOH group. These in vivo studies show the potential for NFOH as an effective and safe compound useful as an anti-T. cruzi agent.