Quinoline-based thiazolyl-hydrazones target cancer cells through autophagy inhibition.

Heterocyclic pharmacophores such as thiazole and quinoline rings have a significant role in medicinal chemistry. They are considered privileged structures since they constitute several Food and Drug Administration (FDA)-approved drugs for cancer treatment. Herein, we report the synthesis, in silico evaluation of the ADMET profiles, and in vitro investigation of the anticancer activity of a series of novel thiazolyl-hydrazones based on the 8-quinoline (1a-c), 2-quinoline (2a-c), and 8-hydroxy-2-quinolyl moiety (3a-c). The panel of several human cancer cell lines and the nontumorigenic human embryonic kidney cell line HEK-293 were used to evaluate the compound-mediated in vitro anticancer activities, leading to [2-(2-(quinolyl-8-ol-2-ylmethylene)hydrazinyl)]-4-(4-methoxyphenyl)-1,3-thiazole (3c) as the most promising compound. The study revealed that 3c blocks the cell-cycle progression of a human colon cancer cell line (HCT-116) in the S phase and induces DNA double-strand breaks. Also, our findings demonstrate that 3c accumulates in lysosomes, ultimately leading to the cell death of the hepatocellular carcinoma cell line (Hep-G2) and HCT-116 cells, by the mechanism of autophagy inhibition.

BRCT Domains: Structure, Functions, and Implications in Disease-New Therapeutic Targets for Innovative Drug Discovery against Infections.

The search for new therapeutic targets and their implications in drug development remains an emerging scientific topic. BRCT-bearing proteins are found in Archaea, Bacteria, Eukarya, and viruses. They are traditionally involved in DNA repair, recombination, and cell cycle control. To carry out these functions, BRCT domains are able to interact with DNA and proteins. Moreover, such domains are also implicated in several pathogenic processes and malignancies including breast, ovarian, and lung cancer. Although these domains exhibit moderately conserved folding, their sequences show very low conservation. Interestingly, sequence variations among species are considered positive traits in the search for suitable therapeutic targets, since non-specific drug interactions might be reduced. These main characteristics of BRCT, as well as its critical implications in key biological processes in the cell, have prompted the study of these domains as therapeutic targets. This review explores the possible roles of BRCT domains as therapeutic targets for drug discovery. We describe their common structural features and relevant interactions and pathways, as well as their implications in pathologic processes. Drugs commonly used to target these domains are also presented. Finally, based on their structures, we describe new drug design possibilities using modern and innovative techniques.

N-1,2,3-Triazole-isatin derivatives: anti-proliferation effects and target identification in solid tumour cell lines.

Molecular hybridization approaches have become an important strategy in medicinal chemistry, and to this end, we have developed a series of novel N-1,2,3-triazole-isatin hybrids that are promising as tumour anti-proliferative agents. Our isatin hybrids presented high cytotoxic activity against colon cancer cell line SW480, lung adenocarcinoma cell line A549, as well as breast cancer cell lines MCF7 and MDA-MB-231. All tested compounds demonstrated better anti-proliferation (to 1-order of magnitude) than the cis-platin (CDDP) benchmark. In order to explore potential biological targets for these compounds, we used information from previous screenings and identified as putative targets the histone acetyltransferase P-300 (EP300) and the acyl-protein thioesterase 2 (LYPLA2), both known to be involved in epigenetic regulation. Advantageous pharmacological properties were predicted for these compounds such as good total surface area of binding to aromatic and hydrophobic units in the enzyme active site. In addition, we found down-regulation of LYPLA2 and EP300 in both the MCF7 and MDA-MB-231 breast cancer cells treated with our inhibitors, but no significant effect was detected in normal breast cells MCF10A. We also observed upregulation of EP300 mRNA expression in the MCF10A cell line for some of these compounds and the same effect for LYPLA2 mRNA in MCF7 for one of our compounds. These results suggest an effect at the transcriptional regulation level and associated with oncological contexts.

A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors.

Sdox is a hydrogen sulfide (H2S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma.

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.

Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances.

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.

Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features.

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine-learning classifiers and regressors and to evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evaluation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and prediction, improving assessment and design of compounds. Source code and data are available on github.

Repurposing old drugs to fight multidrug resistant cancers.

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

Activity to Breast Cancer Cell Lines of Different Malignancy and Predicted Interaction with Protein Kinase C Isoforms of Royleanones.

Plants have been used for centuries to treat several illnesses. The Plectranthus genus has a vast variety of species that has allowed the isolation of cytotoxic compounds with notable activities. The abietane diterpenes 6,7-dehydroroyleanone (DeRoy, 1), 7α-acetoxy-6ß-hydroxyroyleanone (Roy, 2), and Parvifloron D (ParvD, 3) were obtained from Plectranthus spp. and showed promising biological activities, such as cytotoxicity. The inhibitory effects of the different natural abietanes (1-3) were compared in MFC7, SkBr3, and SUM159 cell lines, as well as SUM159 grown in cancer stem cell-inducing conditions. Based on the royleanones' bioactivity, the derivatives RoyBz (4), RoyBzCl (5), RoyPr2 (6), and DihydroxyRoy (7), previously obtained from 2, were selected for further studies. Protein kinases C (PKCs) are involved in several carcinogenic processes. Thus, PKCs are potential targets for cancer therapy. To date, the portfolio of available PKC modulators remains very limited due to the difficulty of designing isozyme-selective PKC modulators. As such, molecular docking was used to evaluate royleanones 1-6 as predicted isozyme-selective PKC binders. Subtle changes in the binding site of each PKC isoform change the predicted interaction profiles of the ligands. Subtle changes in royleanone substitution patterns, such as a double substitution only with non-substituted phenyls, or hydroxybenzoate at position four that flips the binding mode of ParvD (3), can increase the predicted interactions in certain PKC subtypes.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Structural insights and binding analysis for determining the molecular bases for programmed cell death protein ligand-1 inhibition.

Programmed cell death protein 1 (PD-1) and PD-ligand 1 (PD-L1) interaction plays an important role in cancer immunotherapy. Several PD-1/PD-L1 inhibitors have been approved with remarkable impact on overall patient survival rates. Inhibitors in clinical practice are presently limited to monoclonal antibodies. However, their severe shortcomings expose the need for a new generation of PD-L1 inhibitors. Understanding the tumor microenvironment, identifying specific biomarkers and X-ray crystalline structures of PD-1/PD-L1 complexes, including molecular and genomic signature studies are essential to determine the success for the development of PD-1/PD-L1 inhibitors into safer and efficient cancer immunotherapeutics. Currently, the development of immune-modulatory small molecules is being explored due to their benefits over recombinant protein approaches. Nevertheless, their development is hampered in part due to lack of structural information. The current study builds on PD-L1 small-molecule inhibitor structural information and provides insights into the design of new inhibitors. To this end, a comprehensive analysis of crystallographic structures and benchmarking studies were performed, showing the specific structure model and software best suited to study PD-L1. The use of in silico methodologies can give a deeper insight to guide the design of novel PD-L1 small-molecule inhibitors.

Selenazolyl-hydrazones as Novel Selective MAO Inhibitors With Antiproliferative and Antioxidant Activities: Experimental and In-silico Studies.

The novel approach in the treatment of complex multifactorial diseases, such as neurodegenerative disorders and cancer, requires a development of efficient multi-targeting oriented drugs. Since oxidative stress significantly contributes to the pathogenesis of cancer and neurodegenerative disorders, potential drug candidates should possess good antioxidant properties. Due to promising biological activities shown for structurally related (1,3-thiazol-2-yl)hydrazones, a focused library of 12 structurally related benzylidene-based (1,3-selenazol-2-yl)hydrazones was designed as potential multi-targeting compounds. Monoamine oxidases (MAO) A/B inhibition properties of this class of compounds have been investigated. Surprisingly, the p-nitrophenyl-substituted (1,3-selenazol-2-yl)hydrazone 4 showed MAO B inhibition in a nanomolar concentration range (IC50 = 73 nM). Excellent antioxidant properties were confirmed in a number of different in vitro assays. Antiproliferative activity screening on a panel of six human solid tumor cell lines showed that potencies of some of the investigated compounds was comparable or even better than that of the positive control 5-fluorouracil. In-silico calculations of ADME properties pointed to promising good pharmacokinetic profiles of investigated compounds. Docking studies suggest that some compounds, compared to positive controls, have the ability to strongly interact with targets relevant to cancer such as 5'-nucleotidase, and to neurodegenerative diseases such as the small conductance calcium-activated potassium channel protein 1, in addition to confirmation of inhibitory binding at MAO B.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Optimization of in vivo DNA delivery with NickFect peptide vectors.

As the field of gene therapy progresses, an increasingly urgent need has arisen for efficient and non-toxic vectors for the in vivo delivery of nucleic acids. Cell-penetrating peptides (CPP) are very efficient transfection reagents in vitro, however, their application in vivo needs improvement. To enhance in vivo transfection we designed various CPPs based on previous knowledge of internalization studies and physiochemical properties of NickFect (NF) nanoparticles. We show that increment of the helicity of these Transportan10 analogues improves the transfection efficiency. We rationally design by modifying the net charge and the helicity of the CPP a novel amphipathic α-helical peptide NF55 for in vivo application. NF55 condenses DNA into stable nanoparticles that are resistant to protease degradation, promotes endosomal escape, and transfects the majority of cells in a large cell population. We demonstrate that NF55 mediates DNA delivery in vivo with gene induction efficiency that is comparable to commercial transfection reagents. In addition to gene induction in healthy mice, NF55/DNA nanoparticles showed promising tumor transfection in various mouse tumor models, including an intracranial glioblastoma model. The efficiency of NF55 to convey DNA specifically into tumor tissue increased even further after coupling a PEG2000 to the peptide via a disulphide-bond. Furthermore, a solid formulation of NF55/DNA displayed an excellent stability profile without additives or special storage conditions. Together, its high transfection efficacy and stability profile make NF55 an excellent vector for the delivery of DNA in vivo.

Peptide-ligand binding modeling of siRNA with cell-penetrating peptides.

The binding affinity of a series of cell-penetrating peptides (CPP) was modeled through docking and making use of the number of intermolecular hydrogen bonds, lipophilic contacts, and the number of sp3 molecular orbital hybridization carbons. The new ranking of the peptides is consistent with the experimentally determined efficiency in the downregulation of luciferase activity, which includes the peptides' ability to bind and deliver the siRNA into the cell. The predicted structures of the complexes of peptides to siRNA were stable throughout 10 ns long, explicit water molecular dynamics simulations. The stability and binding affinity of peptide-siRNA complexes was related to the sidechains and modifications of the CPPs, with the stearyl and quinoline groups improving affinity and stability. The reranking of the peptides docked to siRNA, together with explicit water molecular dynamics simulations, appears to be well suited to describe and predict the interaction of CPPs with siRNA.