Naïve Bayesian Models for Vero Cell Cytotoxicity

Antimicrobial and anti-biofilm activity of a thiazolidinone derivative against Staphylococcus aureus in vitro and in vivo

Staphylococcus aureus (S. aureus) causes many infections with significant morbidity and mortality. S. aureus can form biofilms, which can cause biofilm-associated diseases and increase resistance to many conventional antibiotics, resulting in chronic infection. It is critical to develop novel antibiotics against staphylococcal infections, particularly those that can kill cells embedded in biofilms. This study aimed to investigate the bacteriocidal and anti-biofilm activities of thiazolidinone derivative (TD-H2-A) against S. aureus. A total of 40 non-duplicate strains were collected, and the minimum inhibitory concentrations (MICs) of TD-H2-A were determined. The effect of TD-H2-A on established S. aureus mature biofilms was examined using a confocal laser scanning microscope (CLSM). The antibacterial effects of the compound on planktonic bacteria and bacteria in mature biofilms were investigated. Other characteristics, such as cytotoxicity and hemolytic activity, were researched. A mouse skin infection model was used, and a routine hematoxylin and eosin (H&E) staining was used for histological examination. The MIC values of TD-H2-A against the different S. aureus strains were 6.3-25.0 µg/mL. The 5 × MIC TD-H2-A killed almost all planktonic S. aureus USA300. The derivative was found to have strong bacteriocidal activity against cells in mature biofilms meanwhile having low cytotoxicity and hemolytic activity against Vero cells and human erythrocytes. TD-H2-A had a good bacteriocidal effect on S. aureus SA113-infected mice. In conclusion, TD-H2-A demonstrated good bacteriocidal and anti-biofilm activities against S. aureus, paving the way for the development of novel agents to combat biofilm infections and multidrug-resistant staphylococcal infections.IMPORTANCEStaphylococcus aureus, a notorious pathogen, can form a stubborn biofilm and develop drug resistance. It is crucial to develop new anti-infective therapies against biofilm-associated infections. The manuscript describes the new antibiotic to effectively combat multidrug-resistant and biofilm-associated diseases.

The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Antileishmanial activity of 2-amino-thiophene derivative SB-200

Leishmaniasis, presenting the highest number of cases worldwide is one of the most serious Neglected Tropical Diseases (NTDs). Clinical manifestations are intrinsically related to the host's immune response making immunomodulatory substances the target of numerous studies on antileishmanial activity. The currently available drugs used for treatment present various problems including high toxicity, low efficacy, and associated drug resistance. The search for therapeutic alternatives is urgent, and in this context, thiophene derivatives appear to be a promising therapeutic alternative (many have shown promising anti-leishmanial activity). The objective of this study was to investigate the antileishmanial activity of the 2-amino-thiophenic derivative SB-200. The thiophenic derivative was effective in inhibiting the growth of Leishmania braziliensis, Leishmania major, and Leishmania infantum promastigotes, obtaining respective IC50 values of 4.25 μM, 4.65 μM, and 3.96 μM. For L. infantum, it was demonstrated that the antipromastigote effect of SB-200 is associated with cell membrane integrity losses, and with morphological changes observed during scanning and transmission electron microscopy. Cytotoxicity was performed for J774.A1 macrophages and VERO cells, to obtain a CC50 of 42.52 μM and a SI of 10.74 for macrophages and a CC50 of 39.2 μM and an SI of 9.89 for VERO cells. The anti-amastigote activity of SB-200 revealed an IC50 of 2.85 μM and an SI of 14.97 against macrophages and SI of 13.8 for VERO cells. The anti-amastigote activity of SB-200 is associated with in vitro immunomodulation. For acute toxicity, SB-200 against Zophobas morio larvae permitted 100% survival. We conclude that the 2-amino-thiophenic derivative SB-200 is a promising candidate for in vivo anti-leishmania drug tests to evaluate its activity, efficacy, and safety.

Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents

The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.

Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection

Rickettsia is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, and concerns over drug resistance is driving a need to identify novel treatments for these obligate intracellular bacteria. Utilizing an uvGFP plasmid reporter, we developed a screen for identifying anti-rickettsial small molecule inhibitors using Rickettsia canadensis as a model organism. The screening data were utilized to train a Bayesian model to predict growth inhibition in this assay. This two-pronged methodology identified anti-rickettsial compounds, including duartin and JSF-3204 as highly specific, efficacious, and noncytotoxic compounds. Both molecules exhibited in vitro growth inhibition of R. prowazekii, the causative agent of epidemic typhus. These small molecules and the workflow, featuring a high-throughput phenotypic screen for growth inhibitors of intracellular Rickettsia spp. and machine learning models for the prediction of growth inhibition of an obligate intracellular Gram-negative bacterium, should prove useful in the search for new therapeutic strategies to treat infections from Rickettsia spp. and other obligate intracellular bacteria.

Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins

Zika virus (ZIKV) is a dangerous human pathogen and no antiviral drugs have been approved to date. The chalcones are a group of small molecules that are found in a number of different plants, including Angelica keiskei Koidzumi, also known as ashitaba. To examine chalcone anti-ZIKV activity, three chalcones, 4-hydroxyderricin (4HD), xanthoangelol (XA), and xanthoangelol-E (XA-E), were purified from a methanol-ethyl acetate extract from A. keiskei. Molecular and ensemble docking predicted that these chalcones would establish multiple interactions with residues in the catalytic and allosteric sites of ZIKV NS2B-NS3 protease, and in the allosteric site of the NS5 RNA-dependent RNA-polymerase (RdRp). Machine learning models also predicted 4HD, XA and XA-E as potential anti-ZIKV inhibitors. Enzymatic and kinetic assays confirmed chalcone inhibition of the ZIKV NS2B-NS3 protease allosteric site with IC₅₀s from 18 to 50 µM. Activity assays also revealed that XA, but not 4HD or XA-E, inhibited the allosteric site of the RdRp, with an IC₅₀ of 6.9 µM. Finally, we tested these chalcones for their anti-viral activity in vitro with Vero cells. 4HD and XA-E displayed anti-ZIKV activity with EC₅₀ values of 6.6 and 22.0 µM, respectively, while XA displayed relatively weak anti-ZIKV activity with whole cells. With their simple structures and relative ease of modification, the chalcones represent attractive candidates for hit-to-lead optimization in the search of new anti-ZIKV therapeutics.

Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus

We present the application of Bayesian modeling to identify chemical tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151 was predicted by modeling and then empirically demonstrated to be active against in vitro cultured clinical methicillin- and vancomycin-resistant strains while also exhibiting efficacy in a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism (IBDM) approach to probe the mechanism by which JSF-3151 is transformed within the bacteria. We also identify and then validate two mechanisms of resistance in S. aureus: one mechanism involves increased expression of a lipocalin protein, and the other arises from the loss of function of an azoreductase. The computational and experimental approaches, discovery of an antibacterial agent, and elucidated resistance mechanisms collectively hold promise to advance our understanding of therapeutic regimens for drug-resistant S. aureus.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Targeting Mycobacterium tuberculosis response to environmental cues for the development of effective antitubercular drugs

Sensing and response to environmental cues, such as pH and chloride (Cl⁻), is critical in enabling Mycobacterium tuberculosis (Mtb) colonization of its host. Utilizing a fluorescent reporter Mtb strain in a chemical screen, we have identified compounds that dysregulate Mtb response to high Cl⁻ levels, with a subset of the hits also inhibiting Mtb growth in host macrophages. Structure–activity relationship studies on the hit compound “C6,” or 2-(4-((2-(ethylthio)pyrimidin-5-yl)methyl)piperazin-1-yl)benzo[d]oxazole, demonstrated a correlation between compound perturbation of Mtb Cl⁻ response and inhibition of bacterial growth in macrophages. C6 accumulated in both bacterial and host cells, and inhibited Mtb growth in cholesterol media, but not in rich media. Subsequent examination of the Cl⁻ response of Mtb revealed an intriguing link with bacterial growth in cholesterol, with increased transcription of several Cl⁻-responsive genes in the simultaneous presence of cholesterol and high external Cl⁻ concentration, versus transcript levels observed during exposure to high external Cl⁻ concentration alone. Strikingly, oral administration of C6 was able to inhibit Mtb growth in vivo in a C3HeB/FeJ murine infection model. Our work illustrates how Mtb response to environmental cues can intersect with its metabolism and be exploited in antitubercular drug discovery.

Responding to environmental cues such as pH and chloride is critical in enabling Mycobacterium tuberculosis to colonize its host. A chemical screen using an M. tuberculosis strain bearing a fluorescent reporter identifies a compound that perturbs the bacterial response to chloride and inhibits its growth in a murine infection model.

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.

Random Forest Model Prediction of Compound Oral Exposure in the Mouse

An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K _d 2.15 ± 1.02 μM) and mexenone (K _d 8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.

Pruned Machine Learning Models to Predict Aqueous Solubility

Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae

PURPOSE

To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.

METHODS

Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >10⁵ drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits.

RESULTS

Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae.

CONCLUSIONS

This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.

Magnetite Nanoparticles Functionalized with RNases against Intracellular Infection of Pseudomonas aeruginosa

Current treatments against bacterial infections have severe limitations, mainly due to the emergence of resistance to conventional antibiotics. In the specific case of Pseudomonas aeruginosa strains, they have shown a number of resistance mechanisms to counter most antibiotics. Human secretory RNases from the RNase A superfamily are proteins involved in a wide variety of biological functions, including antimicrobial activity. The objective of this work was to explore the intracellular antimicrobial action of an RNase 3/1 hybrid protein that combines RNase 1 high catalytic and RNase 3 bactericidal activities. To achieve this, we immobilized the RNase 3/1 hybrid on Polyetheramine (PEA)-modified magnetite nanoparticles (MNPs). The obtained nanobioconjugates were tested in macrophage-derived THP-1 cells infected with Pseudomonas aeruginosa PAO1. The obtained results show high antimicrobial activity of the functionalized hybrid protein (MNP-RNase 3/1) against the intracellular growth of P. aeruginosa of the functionalized hybrid protein. Moreover, the immobilization of RNase 3/1 enhances its antimicrobial and cell-penetrating activities without generating any significant cell damage. Considering the observed antibacterial activity, the immobilization of the RNase A superfamily and derived proteins represents an innovative approach for the development of new strategies using nanoparticles to deliver antimicrobials that counteract P. aeruginosa intracellular infection.

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.