Quantitative Structure-Activity Relationships for Structurally Diverse Chemotypes Having Anti-Trypanosoma cruzi Activity

Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models

BACKGROUND

Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology.

OBJECTIVE

Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors.

METHODS

An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library.

RESULTS

Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595.

CONCLUSIONS

Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition.

Investigative analysis of blood-brain barrier penetrating potential of electronic nicotine delivery systems (e-cigarettes) chemicals using predictive computational models

INTRODUCTION

Seizures are known potential side effects of nicotine toxicity and have been reported in electronic nicotine delivery systems (ENDS, e-cigarettes) users, with the majority involving youth or young adults.

AREAS COVERED

Using chemoinformatic computational models, chemicals (including flavors) documented to be present in ENDS were compared to known neuroactive compounds to predict the blood-brain barrier (BBB) penetration potential, central nervous system (CNS) activity, and their structural similarities. The literature search used PubMed/Google Scholar, through September 2023, to identify individual chemicals in ENDS and neuroactive compounds.The results show that ENDS chemicals in this study contain >60% structural similarity to neuroactive compounds based on chemical fingerprint similarity analyses. The majority of ENDS chemicals we studied were predicted to cross the BBB, with approximately 60% confidence, and were also predicted to have CNS activity; those not predicted to passively diffuse through the BBB may be actively transported through the BBB to elicit CNS impacts, although it is currently unknown.

EXPERT OPINION

In lieu of in vitro and in vivo testing, this study screens ENDS chemicals for potential CNS activity and predicts BBB penetration potential using computer-based models, allowing for prioritization for further study and potential early identification of CNS toxicity.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Molecular targets for Chagas disease: validation, challenges and lead compounds for widely exploited targets

INTRODUCTION

Chagas disease (CD) imposes social and economic burdens, yet the available treatments have limited efficacy in the disease's chronic phase and cause serious adverse effects. To address this challenge, target-based approaches are a possible strategy to develop new, safe, and active treatments for both phases of the disease.

AREAS COVERED

This review delves into target-based approaches applied to CD drug discovery, emphasizing the studies from the last five years. We highlight the proteins cruzain (CZ), trypanothione reductase (TR), sterol 14 α-demethylase (CPY51), iron superoxide dismutase (Fe-SOD), proteasome, cytochrome b (Cytb), and cleavage and polyadenylation specificity factor 3 (CPSF3), chosen based on their biological and chemical validation as drug targets. For each, we discuss its biological relevance and validation as a target, currently related challenges, and the status of the most promising inhibitors.

EXPERT OPINION

Target-based approaches toward developing potential CD therapeutics have yielded promising leads in recent years. We expect a significant advance in this field in the next decade, fueled by the new options for Trypanosoma cruzi genetic manipulation that arose in the past decade, combined with recent advances in computational chemistry and chemical biology.

Hierarchical Clustering and Target-Independent QSAR for Antileishmanial Oxazole and Oxadiazole Derivatives

Leishmaniasis is a neglected tropical disease that kills more than 20,000 people each year. The chemotherapy available for the treatment of the disease is limited, and novel approaches to discover novel drugs are urgently needed. Herein, 2D- and 4D-quantitative structure–activity relationship (QSAR) models were developed for a series of oxazole and oxadiazole derivatives that are active against Leishmania infantum, the causative agent of visceral leishmaniasis. A clustering strategy based on structural similarity was applied with molecular fingerprints to divide the complete set of compounds into two groups. Hierarchical clustering was followed by the development of 2D- (R2 = 0.90, R2pred = 0.82) and 4D-QSAR models (R2 = 0.80, R2pred = 0.64), which showed improved statistical robustness and predictive ability.

Novel trypanocidal thiophen-chalcone cruzain inhibitors: structure- and ligand-based studies

Background: Chagas disease is a neglected tropical disease that affects millions of people worldwide and for which no effective treatment is available. Materials & methods: 17 chalcones were synthesized, for which the inhibition of cruzain and trypanocidal activity were investigated. Results: Chalcone C8 showed the highest cruzain inhibitory (IC₅₀ = 0.536 μM) and trypanocidal activity (IC₅₀ = 0.990 μM). Molecular docking studies showed interactions involving Asp161 and the thiophen group interacting with the S2 subsite. Furthermore, quantitative structure-activity relationship (q² = 0.786; r² = 0.953) and density functional theory studies were carried out, and a correlation between the lowest unoccupied molecular orbital surface and trypanocidal activity was observed. Conclusion: These results demonstrate that these chalcones are worthwhile hits to be further optimized in Chagas disease drug discovery programs.

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.

Identification of a Quinone Derivative as a YAP/TEAD Activity Modulator from a Repurposing Library

The transcriptional regulators YAP (Yes-associated protein) and TAZ (transcriptional co-activator with PDZ-binding motif) are the major downstream effectors in the Hippo pathway and are involved in cancer progression through modulation of the activity of TEAD (transcriptional enhanced associate domain) transcription factors. To exploit the advantages of drug repurposing in the search of new drugs, we developed a similar approach for the identification of new hits interfering with TEAD target gene expression. In our study, a 27-member in-house library was assembled, characterized, and screened for its cancer cell growth inhibition effect. In a secondary luciferase-based assay, only seven compounds confirmed their specific involvement in TEAD activity. IA5 bearing a p-quinoid structure reduced the cytoplasmic level of phosphorylated YAP and the YAP–TEAD complex transcriptional activity and reduced cancer cell growth. IA5 is a promising hit compound for TEAD activity modulator development.

The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools

The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

Discovery of New Hits as Antitrypanosomal Agents by In Silico and In Vitro Assays Using Neolignan-Inspired Natural Products from Nectandra leucantha

In the present study, the phytochemical study of the n-hexane extract from flowers of Nectandra leucantha (Lauraceae) afforded six known neolignans (1–6) as well as one new metabolite (7), which were characterized by analysis of NMR, IR, UV, and ESI-HRMS data. The new compound 7 exhibited potent activity against the clinically relevant intracellular forms of T. cruzi (amastigotes), with an IC₅₀ value of 4.3 μM and no observed mammalian cytotoxicity in fibroblasts (CC₅₀ > 200 μM). Based on the results obtained and our previous antitrypanosomal data of 50 natural and semi-synthetic related neolignans, 2D and 3D molecular modeling techniques were employed to help the design of new neolignan-based compounds with higher activity. The results obtained from the models were important to understand the main structural features related to the biological response of the neolignans and to aid in the design of new neolignan-based compounds with better biological activity. Therefore, the results acquired from phytochemical, biological, and in silico studies showed that the integration of experimental and computational techniques consists of a powerful tool for the discovery of new prototypes for development of new drugs to treat CD.

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.