In Vitro Toxicological Insights from the Biomedical Applications of Iron Carbide Nanoparticles in Tumor Theranostics: A Systematic Review and Meta-Analysis.

(1) Background: Despite the encouraging indications regarding the suitability (biocompatibility) of iron carbide nanoparticles (ICNPs) in various biomedical applications, the published evidence of their biosafety is dispersed and relatively sparse. The present review synthesizes the existing nanotoxicological data from in vitro studies relevant to the diagnosis and treatment of cancer. (2) Methods: A systematic review was performed in electronic databases (PubMed, Scopus, and Wiley Online Library) on December 2023, searching for toxicity assessments of ICNPs of different sizes, coatings, and surface modifications investigated in immortalized human and murine cell lines. The risk of bias in the studies was assessed using the ToxRTool for in vitro studies. (3) Results: Among the selected studies (n = 22), cell viability emerged as the most frequently assessed cellular-level toxicity endpoint. The results of the meta-analysis showed that cell models treated with ICNPs had a reduced cell viability (SMD = -2.531; 95% CI: -2.959 to -2.109) compared to untreated samples. A subgroup analysis was performed due to the high magnitude of heterogeneity (I2 = 77.1%), revealing that ICNP concentration and conjugated ligands are the factors that largely influence toxicity (p < 0.001). (4) Conclusions: A dose-dependent cytotoxicity of ICNP exposure was observed, regardless of the health status of the cell, tested organism, and NP size. Inconsistent reporting of ICNP physicochemical properties was noted, which hinders comparability among the studies. A comprehensive exploration of the available in vivo studies is required in future research to assess the safety of ICNPs' use in bioimaging and cancer treatment.

"Hit" to lead optimization and chemoinformatic studies for a new series of Autotaxin inhibitors.

Robust experimental evidence has highlighted the role of Autotaxin (ATX)/Lysophosphatidic acid (LPA) axis not only in the pathogenesis of chronic inflammatory conditions and especially in fibroproliferative diseases but also in several types of cancer. As a result, different series of substrate-, lipid-based and small-molecule ATX inhibitors have been identified thus far by both academia and pharma. The "crowning achievement" of these drug discovery campaigns was the development and entry of the first-in-class ATX inhibitor (ziritaxestat, GLPG-1690) in advanced clinical trials against idiopathic pulmonary fibrosis. Herein, the potency optimization efforts of a new series of Autotaxin inhibitors, namely 2-substituted-2,6-dihydro-4H-thieno[3,4-c]pyrazol-1-substituted amide, is described using a previously identified novel chemical scaffold as a "hit". The mode of inhibition of the most promising ATX inhibitors was investigated, while their cellular activity, aqueous solubility and cytotoxicity were evaluated. Our pharmacological results were corroborated by chemoinformatic tools (molecular docking and molecular dynamics simulations) deployed, to provide insight into the binding mechanism of the synthesized inhibitors to ATX.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes.

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients' progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints' synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors' drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Structure-Based Discovery of Novel Chemical Classes of Autotaxin Inhibitors.

Autotaxin (ATX) is a secreted glycoprotein, widely present in biological fluids, largely responsible for extracellular lysophosphatidic acid (LPA) production. LPA is a bioactive growth-factor-like lysophospholipid that exerts pleiotropic effects in almost all cell types, exerted through at least six G-protein-coupled receptors (LPAR1-6). Increased ATX expression has been detected in different chronic inflammatory diseases, while genetic or pharmacological studies have established ATX as a promising therapeutic target, exemplified by the ongoing phase III clinical trial for idiopathic pulmonary fibrosis. In this report, we employed an in silico drug discovery workflow, aiming at the identification of structurally novel series of ATX inhibitors that would be amenable to further optimization. Towards this end, a virtual screening protocol was applied involving the search into molecular databases for new small molecules potentially binding to ATX. The crystal structure of ATX in complex with a known inhibitor (HA-155) was used as a molecular model docking reference, yielding a priority list of 30 small molecule ATX inhibitors, validated by a well-established enzymatic assay of ATX activity. The two most potent, novel and structurally different compounds were further structurally optimized by deploying further in silico tools, resulting to the overall identification of six new ATX inhibitors that belong to distinct chemical classes than existing inhibitors, expanding the arsenal of chemical scaffolds and allowing further rational design.

MS-275 Chemical Analogues Promote Hemoglobin Production and Erythroid Differentiation of K562 Cells.

ß-Thalassemia (ß-thal) is a hemoglobinopathy characterized by reduced or absent ß-globin production. Pharmacological reactivation of the γ-globin gene for the production of fetal hemoglobin (Hb F) presents an attractive treatment strategy. In an effort to identify promising therapeutic agents, we evaluated 80 analogues of the histone deacetylase inhibitor MS-275, a known Hb F inducer. The chemical analogues were identified via molecular modeling and targeted chemical modifications. Nine novel agents exhibited significant hemoglobin (Hb)-inducing and erythroid differentiation activities in the human K562 erythroleukemia cell line. Five of them appeared to be stronger inducers than the lead compound, MS-275, demonstrating the effectiveness of our method.

In Silico Discovery of Plant-Origin Natural Product Inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).

An in silico drug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently tested in vitro against TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC50 values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

Current Status and Future Prospects of Small-molecule Protein-protein Interaction (PPI) Inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).

The overexpression of Tumor Necrosis Factor (TNF) is directly related to the development of several autoimmune diseases, such as rheumatoid and psoriatic arthritis, inflammatory bowel disease, Crohn's disease, refractory asthma, and multiple sclerosis. Receptor Activator of Nuclear Factor Kappa- B Ligand (RANKL) belongs to the TNF family and is the primary mediator of osteoclast-induced bone resorption through interaction with its receptor RANK. The function of RANKL is physiologically inhibited by the action of osteoprotegerin (OPG), which is a decoy receptor that binds to RANKL and prevents the process of osteoclastogenesis. Malfunction among RANK/RANKL/OPG can also result in bone loss diseases, including postmenopausal osteoporosis, rheumatoid arthritis, bone metastasis and multiple myeloma. To disrupt the unwanted functions of TNF and RANKL, current attempts focus on blocking TNF and RANKL binding to their receptors. In this review, we present the research efforts toward the development of low-molecular-weight pharmaceuticals that directly block the detrimental actions of TNF and RANKL.

Aqueous Solubility Enhancement for Bioassays of Insoluble Inhibitors and QSPR Analysis: A TNF-α Study.

The aim of this study is to improve the aqueous solubility of a group of compounds without interfering with their bioassay as well as to create a relevant prediction model. A series of 55 potential small-molecule inhibitors of tumor necrosis factor-alpha (TNF-α; SPD304 and 54 analogues), many of which cannot be bioassayed because of their poor solubility, was used for this purpose. The solubility of many of the compounds was sufficiently improved to allow measurement of their respective dissociation constants (Kd). Parameters such as dissolution time, initial state of the solute (solid/liquid), co-solvent addition (DMSO and PEG3350), and sample filtration were evaluated. Except for filtration, the remaining parameters affected aqueous solubility, and a solubilization protocol was established according to these. The aqueous solubility of the 55 compounds in 5% DMSO was measured with this protocol, and a predictive quantitative structure property relationship model was developed and fully validated based on these data. This classification model separates the insoluble from the soluble compounds and predicts the solubility of potential small-molecule inhibitors of TNF-α in aqueous solution (containing 5% DMSO as co-solvent) with an accuracy of 81.2%. The domain of applicability of the model indicates the type of compounds for which estimation of aqueous solubility can be confidently predicted.

Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).

We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure-based with ligand-based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

Development of a Predictive Pharmacophore Model and a 3D-QSAR Study for an in silico Screening of New Potent Bcr-Abl Kinase Inhibitors.

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder, characterized, in most cases, by the presence of the Bcr-Abl fusion oncogene. Bcr-Abl is a constitutively active tyrosine kinase that is responsible for the malignant transformation. Targeting the Bcr-Abl kinase is an attractive treatment strategy for CML. First and second generation Bcr-Abl inhibitors have focused on targeting the ATP-binding domain of the kinase. Mutations in that region are relatively resistant to drug manipulation. Therefore, non-ATP-competitive agents have been recently developed and tested. In the present study, in an attempt to aid the design of new chemotypes with enhanced cytotoxicity against K562 cells, 3D pharmacophore models were generated and 3D-QSAR CoMFA and CoMSIA studies were carried out on the 33 novel Abl kinase inhibitors (E)-α-benzylthio chalcones synthesized by Reddy et al. A five-point pharmacophore with a hydrogen bond acceptor, two hydrophobic groups and two aromatic rings as pharmacophore features, and a statistically significant 3D-QSAR model with excellent predictive power were developed. The pharmacophore model was also used for alignment of the 33 compounds in a CoMFA/CoMSIA analysis. The contour maps of the fields of CoMFA and CoMSIA models were utilized to provide structural insight into how these molecules promote their toxicity. The possibility of using this model for the design of drugs for the treatment of β-thalassemia and sickle cell disease (SCD), since several Bcr-Abl inhibitors are able to promote erythroid differentiation and γ-globin expression in CML cell lines and primary erythroid cells is discussed.

Searching for Novel Janus Kinase-2 Inhibitors Using a Combination of Pharmacophore Modeling, 3D-QSAR Studies and Virtual Screening.

The Janus kinases (JAKs) play a pivotal role in cytokine receptor signaling pathways via activation of downstream signal transducers and activators of transcription (STAT) pathway. Intracellular pathways that include JAKs are critical to immune cell activation and pro-inflammatory cytokine production. Selective inhibitors of JAKs are potentially disease-modifying anti-inflammatory drugs for the treatment of rheumatoid arthritis (RA). Each of the four members of the JAK family plays an individual role in the oncogenesis of the immune system, and therefore, the development of potent and specific inhibitors for each member is needed. Although there is a high sequence homology and structural identity of JAK1 and JAK2, such as a very similar binding mode of inhibitors at the ATPbinding site of enzymes, obvious differences surrounding the JAK1 and JAK2 ATP-binding sites provide a platform for the rational design of JAK2- and JAK1-specific inhibitors. In the present study, a dataset of 33 compounds characterized by a common scaffold of 2-amino-[1,2,4]triazolo[1,5-α]pyridine with well-defined in vitro activity values was computationally explored. Most of the compounds included in the dataset had higher ligand efficiency against JAK2 than JAK1. To improve further the selectivity of these triazolopyridines, Common Pharmacophore Hypotheses (CPHs) were generated and 3D-QSAR studies were carried out on them, in order to comprehend on the molecular features responsible for their selectivity. The proposed computational approach was applied in order to perform an in silico database virtual screening study with the aim to discover novel potent and selective JAK2 inhibitors.

Antiproliferative novel isoxazoles: modeling, virtual screening, synthesis, and bioactivity evaluation.

A series of novel isoxazole derivatives were efficiently synthesized through the adaptation/modification of an in situ synthetic procedure for pyrazoles. All novel compounds were tested against four different cell lines to evaluate their antiproliferative activity. Based on the Hela cells results of this study and previous work, a classification model to predict the anti-proliferative activity of isoxazole and pyrazole derivatives was developed. Random Forest modeling was used in view of the development of an accurate and reliable model that was subsequently validated. A virtual screening study was then proposed for the design of novel active derivatives. Compounds 9 and 11 demonstrated significant cytostatic activity; the fused isoxazole derivative 18 and the virtually proposed compound 2v, were proved at least 10 times more potent as compared to compound 9, with IC50 values near and below 1 µM. In conclusion, a new series of isoxazoles was exploited with some of them exhibiting promising cytostatic activities. Further studies on the substitution pattern of the isoxazole core can potentially provide compounds with cytostatic action at the nM scale. In this direction the in silico approach described herein can also be used to screen existing databases to identify derivatives with anticipated activity.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERß ligands was assembled (546 for ERα and 137 for ERß). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure-activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERß. High predictive accuracy was achieved for ERα binding affinity (MTL R(2)=0.71, STL R(2)=0.73). For ERß binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERß, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERß agonist (PDB ID: 2NV7), and ERß antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA.

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.

A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.

A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.