ADMETboost: a web server for accurate ADMET prediction

Metabolism characterization and toxicity of N-hydap, a marine candidate drug for lung cancer therapy by LC-MS method

N-Hydroxyapiosporamide (N-hydap), a marine product derived from a sponge-associated fungus, has shown promising inhibitory effects on small cell lung cancer (SCLC). However, there is limited understanding of its metabolic pathways and characteristics. This study explored the in vitro metabolic profiles of N-hydap in human recombinant cytochrome P450s (CYPs) and UDP-glucuronosyltransferases (UGTs), as well as human/rat/mice microsomes, and also the pharmacokinetic properties by HPLC-MS/MS. Additionally, the cocktail probe method was used to investigate the potential to create drug-drug interactions (DDIs). N-Hydap was metabolically unstable in various microsomes after 1 h, with about 50% and 70% of it being eliminated by CYPs and UGTs, respectively. UGT1A3 was the main enzyme involved in glucuronidation (over 80%), making glucuronide the primary metabolite. Despite low bioavailability (0.024%), N-hydap exhibited a higher distribution in the lungs (26.26%), accounting for its efficacy against SCLC. Administering N-hydap to mice at normal doses via gavage did not result in significant toxicity. Furthermore, N-hydap was found to affect the catalytic activity of drug metabolic enzymes (DMEs), particularly increasing the activity of UGT1A3, suggesting potential for DDIs. Understanding the metabolic pathways and properties of N-hydap should improve our knowledge of its drug efficacy, toxicity, and potential for DDIs.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges.Communicated by Ramaswamy H. Sarma.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Exploring azomethine ylides reactivity with acrolein through cycloaddition reaction and computational antiviral activity assessment against hepatitis C virus

CONTEXT

The regioselectivity and diastereoselectivity of the 1,3-dipolar cycloaddition reaction between azomethine ylides and acrolein were investigated. The DFT studies revealed that the favored pathway leads to the formation of cis-cycloadduct pyrrolidine and these computational findings align with experimental observations. The cis-cycloadduct pyrrolidine product serves as an advanced intermediate in the synthesis of a hepatitis C virus inhibitor. For this, the antiviral activity of cis-cycloadduct pyrrolidine against cyclophilin A, the co-factor responsible for hepatitis C virus, was also evaluated through molecular docking simulations which revealed intriguing interactions and a high C-score, which were further confirmed by molecular dynamics simulations, demonstrating stability over a 100-ns simulation period. Furthermore, the cis-cycloadduct pyrrolidine exhibits favorable drug-like properties and a better ADMET profile compared to hepatitis C virus inhibitor.

METHODS

Chemical reactivity studies were performed using DFT method by the functional B3LYP at 6-31G (d, p) computational level by GAUSSIAN 16 program. Frontal molecular orbitals theory used to investigate HOMO/LUMO interactions between azomethine ylides and acrolein. Findings of this approach were confirmed by global reactivity indices and electron displacement was investigated based on Fukui functions. Furthermore, the activation energies were determined after frequency calculations using TS Berny algorithm and transition states were confirmed by the presence of a single imaginary frequency. Moreover, antiviral activity of cis-cycloadduct was explored through molecular docking using Surflex-Dock suite SYBYL X 2.0, and molecular dynamics simulation using GROMACS program. Finally, drug-like properties were investigated with SwissADME and ADMETlab 2.0.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

Summary

The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours.

Availability and Implementation

The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930 ). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418 .

Naturally Occurring Isorhamnetin Glycosides as Potential Agents Against Influenza Viruses: Antiviral and Molecular Docking Studies

Influenza remains one of the most widespread infections, causing an annual illness in adults and children. Therefore, the search for new antiviral drugs is one of the priorities of practical health care. Eight isorhamnetin glycosides were purified from Persicaria species, characterized by nuclear magnetic resonance spectroscopy and mass spectrometry and then evaluated as potential agents against influenza virus. A comprehensive in vitro and in vivo assessment of the compounds revealed that compound 5 displayed the most potent inhibitory activity with an EC50 value of 1.2-1.3 μM, better than standard drugs (isorhamnetin 28.0-56.0 μM and oseltamivir 1.3-9.1 μM). Molecular docking results also revealed that compound 5 has the lowest binding energy (-10.7 kcal/mol) among the tested compounds and isorhamnetin (-8.1 kcal/mol). The ability of the isorhamnetin glycosides to suppress the reproduction of the influenza virus was studied on a model of a cell culture and chicken embryos. The ability of active compounds to influence the structure of the virion, as well as the activity of hemagglutinin and neuraminidase, has been demonstrated. Compound 1, 5, and 6 demonstrated the most effective inhibition of virus replication for all tested viruses. Molecular dynamics simulation techniques were run for 100 ns for compound 5 with two protein receptors Hem (1RUY) and Neu (3BEQ). These results revealed that the Hem-complex system acquired a relatively more stable conformation and even better descriptors than the other Neu-complex studied systems, suggesting that it can be an effective inhibiting drug toward hemagglutinin than neuraminidase inhibition. Based on the reported results, compound 5 can be a good candidate to be evaluated for effectiveness in preclinical testing.

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC50 values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management.

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.

Natural compound targeting BDNF V66M variant: insights from in silico docking and molecular analysis

Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin gene family gene that encodes proteins vital for the growth, maintenance, and survival of neurons in the nervous system. The study aimed to screen natural compounds against BDNF variant (V66M), which affects memory, cognition, and mood regulation. BDNF variant (V66M) as a target structure was selected, and Vitamin D, Curcumin, Vitamin C, and Quercetin as ligands structures were taken from PubChem database. Multiple tools like AUTODOCK VINA, BIOVIA discovery studio, PyMOL, CB-dock, IMOD server, Swiss ADEMT, and Swiss predict ligands target were used to analyze binding energy, interaction, stability, toxicity, and visualize BDNF-ligand complexes. Compounds Vitamin D3, Curcumin, Vitamin C, and Quercetin with binding energies values of - 5.5, - 6.1, - 4.5, and - 6.7 kj/mol, respectively, were selected. The ligands bind to the active sites of the BDNF variant (V66M) via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analysis of the ligands revealed they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD simulation study showed that the most active ligand bound favorably and dynamically to the target protein, and protein-ligand complex stability was determined. The finding of this research could provide an excellent platform for discovering and rationalizing novel drugs against stress related to BDNF (V66M). Docking, preclinical drug testing and MD simulation results suggest Quercetin as a more potent BDNF variant (V66M) inhibitor and forming a more structurally stable complex.

Study on the Antioxidant Effect of Shikonin-Loaded β-Cyclodextrin Forming Host-Guest Complexes That Prevent Skin from Photoaging

When the skin is overexposed to ultraviolet rays, free radicals will accumulate in the skin, causing lipid damage and even inducing photoaging of the skin. Combination therapy with antioxidant drugs is a good choice for topical treatment of skin photoaging due to its special physiological structure. In this paper, shikonin was encapsulated in β-cyclodextrin (SH-β-CD) by the precipitation crystallization method, which delayed the release of the drug and increased drug solubility. The average diameter of SH-β-CD was 203.0 ± 21.27 nm with a zeta potential of -14.4 ± 0.5 mV. The encapsulation efficiency (EE%) was 65.9 ± 7.13%. The results of the in vitro permeation across the dialysis membrane and ex vivo transdermal release rates were 52.98 ± 1.21% and 88.25 ± 3.26%, respectively. In vitro antioxidant and antilipid peroxidation model assay revealed the antioxidant potential of SH and SH-β-CD. In the mice model of skin photoaging, SH and SH-β-CD had a recovery effect on the skin damage of mice, which could significantly increase the superoxide dismutase (SOD) activity in the skin. Briefly, SH-β-CD had an obvious therapeutic effect on the skin photoaging of mice caused by UV, and it is promising in skin disease treatment and skin care.

In Silico Neuroprotective Effects of Specific Rheum palmatum Metabolites on Parkinson's Disease Targets

Parkinson's disease (PD) is one of the large-scale health issues detrimental to human quality of life, and current treatments are only focused on neuroprotection and easing symptoms. This study evaluated in silico binding activity and estimated the stability of major metabolites in the roots of R. palmatum (RP) with main protein targets in Parkinson's disease and their ADMET properties. The major metabolites of RP were subjected to molecular docking and QSAR with α-synuclein, monoamine oxidase isoform B, catechol o-methyltransferase, and A2A adenosine receptor. From this, emodin had the greatest binding activity with Parkinson's disease targets. The chemical stability of the selected compounds was estimated using density functional theory analyses. The docked compounds showed good stability for inhibitory action compared to dopamine and levodopa. According to their structure-activity relationship, aloe-emodin, chrysophanol, emodin, and rhein exhibited good inhibitory activity to specific targets. Finally, mediocre pharmacokinetic properties were observed due to unexceptional blood-brain barrier penetration and safety profile. It was revealed that the major metabolites of RP may have good neuroprotective activity as an additional hit for PD drug development. Also, an association between redox-mediating and activities with PD-relevant protein targets was observed, potentially opening discussion on electrochemical mechanisms with biological functions.

In silico design and identification of new peptides for mitigating hIAPP aggregation in type 2 diabetes

The aberrant misfolding and self-aggregation of human islet amyloid polypeptide (hIAPP or amylin) into cytotoxic aggregates are implicated in the pathogenesis of type 2 diabetes (T2D). Among various inhibitors, short peptides derived from the amyloidogenic regions of hIAPP have been employed as hIAPP aggregation inhibitors due to their low immunogenicity, biocompatibility, and high chemical diversity. Recently, hIAPP fragment HSSNN18-22 was identified as an amyloidogenic sequence and displayed higher antiproliferative activity to RIN-5F cells. Various hIAPP aggregation inhibitors have been designed by chemical modifications of the highly amyloidogenic sequence (NFGAIL) of hIAPP. In this work, a library of pentapeptides based on fragment HSSNN18-22 was designed and assessed for their efficacy in blocking hIAPP aggregation using an integrated computational screening approach. The binding free energy calculations by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method identified HSSQN and HSSNQ that bind to hIAPP monomer with a binding affinity of -21.25 ± 4.90 and -19.73 ± 3.10 kcal/mol, respectively, which is notably higher as compared to HSSNN (-11.90 ± 4.12 kcal/mol). The sampling of the non aggregation-prone helical conformation was notably increased from 23.5 ± 3.0 in the hIAPP monomer to 38.1 ± 3.6, and 33.8 ± 3.0% on the incorporation of HSSQN, and HSSNQ, respectively, which indicate reduced aggregation propensity of hIAPP monomer. The pentapeptides, HSSQN and HSSNQ, identified as hIAPP aggregation inhibitors in this work can be further conjugated with various metal chelating peptides to yield more efficacious and clinically relevant multifunctional modulators for targeting various pathological hallmarks of T2D.

pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules

Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.

Open-Source Machine Learning in Computational Chemistry

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction

With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this study, five graph models (message-passing neural network, graph convolution network, graph attention network, path-augmented graph transformer network, and Attentive FP) were applied on four toxicity tasks (fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri). With the lowest prediction error, Attentive FP was reported to have the best performance in all four tasks. Moreover, the attention weights of the Attentive FP model helped to construct atomic heatmaps and provide good explainability.

Molecular docking analysis of acetylcholinesterase inhibitors for Alzheimer's disease management

Alzheimer's disease (AD) is a neurological disease that is related to aging and is the leading cause of dementia globally. AD has a significant influence on cognitive functions, particularly memory, resulting in a variety of functional deficits. Given the increasing prevalence of AD, there is an urgent need for the development of effective therapeutic therapies. In a quest to uncover a holistic remedy for AD, a total of 41 bioactive compounds derived from three distinct medicinal plant sources were screened to evaluate their potential to inhibit the active sites of acetylcholinesterase (AChE). The insilico screening protocol included 24 licorice-derived compounds, 5 ginkgo biloba-derived compounds, and 11 ginseng-derived compounds. Two compounds (Ginkgolide A and Licorice glycoside D2) were observed to display greater binding energy (BE) relative to the control by interacting with crucial residues in the active site of AChE. Ginkgolide A and Licorice glycoside D2 exhibited BEs of -11.3 and -11.2 kcal/mol, respectively, whereas the control, Donepezil, demonstrated a BE of -10.8 kcal/mol. Further, these compounds exhibit favorable drug-likeness properties. This study suggests that further experimental investigations can be conducted on Ginkgolide A and Licorice glycoside D2 to explore their potential therapeutic applications for AD.

Molecular docking analysis of KRAS inhibitors for cancer management

The majority of human tumors are characterized by abnormal signaling caused by oncogenic RAS proteins. KRAS is a member of the RAS family and is currently one of the most thoroughly researched targets for cancer treatment due to its prevalence in a variety of deadly malignancies. Targeting the KRAS protein, which plays a crucial role in regulating cell growth, differentiation, and apoptosis, shows great potential as a strategy for fighting cancer. Herein, in silico screening of 530 natural compounds against KRAS protein was performed. The top-scoring hits, namely ZINC32502206, ZINC98363763, ZINC85645815, and ZINC98364259 displayed a robust affinity towards KRAS as evidenced by their respective binding affinity values of -10.50, -10.01, -9.80, and -9.70 kcal/mol, respectively which were notably higher than that of the control compound AMG 510 (-9.10 kcal/mol). Through virtual screening and visual inspection, it was observed that these hits effectively interacted with the essential residues located within the active site of KRAS. Based on the findings of this study, it can be inferred that these compounds may have the potential to be employed in the treatment of cancer by targeting KRAS.

Antileishmanial activity of 5-nitroindazole derivatives

Background

Currently, there is no safe and effective vaccine against leishmaniasis and existing therapies are inadequate due to high toxicity, cost and decreased efficacy caused by the emergence of resistant parasite strains. Some indazole derivatives have shown in vitro and in vivo activity against Trichomonas vaginalis and Trypanosoma cruzi. On that basis, 20 indazole derivatives were tested in vitro against Leishmania amazonensis.

Objective

To evaluate the in vitro activity of twenty 2-benzyl-5-nitroindazolin-3-one derivatives against L. amazonensis.

Design

For the selection of promising compounds, it is necessary to evaluate the indicators for in vitro activity. For this aim, a battery of studies for antileishmanial activity and cytotoxicity were implemented. These results enabled the determination of the substituents in the indazole derivatives responsible for activity and selectivity, through the analysis of the structure-activity relationship (SAR).

Methods

In vitro cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for 20 compounds. Compounds that showed adequate selectivity were tested against intracellular amastigotes. The SAR from the results in promastigotes was represented using the SARANEA software.

Results

Eight compounds showed selectivity index >10% and 50% inhibitory concentration <1 µM against the promastigote stage. Against intracellular amastigotes, four were as active as Amphotericin B. The best results were obtained for 2-(benzyl-2,3-dihydro-5-nitro-3-oxoindazol-1-yl) ethyl acetate, with 50% inhibitory concentration of 0.46 ± 0.01 µM against amastigotes and a selectivity index of 875. The SAR study showed the positive effect on the selectivity of the hydrophilic fragments substituted in position 1 of 2-benzyl-5- nitroindazolin-3-one, which played a key role in improving the selectivity profile of this series of compounds.

Conclusion

2-bencyl-5-nitroindazolin-3-one derivatives showed selective and potent in vitro activity, supporting further investigations on this family of compounds as potential antileishmanial hits.