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.

MPK12 in stomatal CO2 signaling: function beyond its kinase activity.

Protein phosphorylation is a major molecular switch involved in the regulation of stomatal opening and closure. Previous research defined interaction between MAP kinase 12 and Raf-like kinase HT1 as a required step for stomatal movements caused by changes in CO2 concentration. However, whether MPK12 kinase activity is required for regulation of CO2 -induced stomatal responses warrants in-depth investigation. We apply genetic, biochemical, and structural modeling approaches to examining the noncatalytic role of MPK12 in guard cell CO2 signaling that relies on allosteric inhibition of HT1. We show that CO2 /HCO3 - -enhanced MPK12 interaction with HT1 is independent of its kinase activity. By analyzing gas exchange of plant lines expressing various kinase-dead and constitutively active versions of MPK12 in a plant line where MPK12 is deleted, we confirmed that CO2 -dependent stomatal responses rely on MPK12's ability to bind to HT1, but not its kinase activity. We also demonstrate that purified MPK12 and HT1 proteins form a heterodimer in the presence of CO2 /HCO3 - and present structural modeling that explains the MPK12:HT1 interaction interface. These data add to the model that MPK12 kinase-activity-independent interaction with HT1 functions as a molecular switch by which guard cells sense changes in atmospheric CO2 concentration.

Synthesis of 6'-galactosyllactose, a deviant human milk oligosaccharide, with the aid of Candida antarctica lipase-B.

Research on human milk oligosaccharides (HMOs) has increased over the past decade showing great interest in their beneficial effects. Here we describe a method for the selective deacetylation using immobilised Candida antarctica lipase-B, Novozyme N435 (N435), of pyranose saccharides in organic media with the aim of simplifying and improving the pathways for the synthesis of HMOs. By first studying in depth the deacetylation reaction of peracetylated D-glucose two reaction conditions were found, which were used on different HMO building blocks, peracetylated saccharides and thioglycosides. D-Glucose based saccharides showed selectivity towards the fourth and the sixth position deacetylation. While α-anomer of peracetylated D-galactose remained unreactive and ß-anomer favoured the first position deacetylation. Peracetylated L-fucose, on the other hand, had no selectivity as the main product was fully unprotected L-fucose. Taking the peracetylated D-glucose deacetylation reaction product and selectively protecting the primary hydroxyl group in the sixth position left only the fourth position open for the glycosylation. Meanwhile, the deacetylation product of D-galactose thioglycoside, with the sixth position deacetylated, had both acceptor and donor capabilities. Using the two aforementioned products derived from the N435 deacetylation reactions a deviant HMO, 6'-galactosyllactose (6'-GL) was synthesised.

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.

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.

Discovery and Validation of Lmj_04_BRCT Domain, a Novel Therapeutic Target: Identification of Candidate Drugs for Leishmaniasis.

Since many of the currently available antileishmanial treatments exhibit toxicity, low effectiveness, and resistance, search and validation of new therapeutic targets allowing the development of innovative drugs have become a worldwide priority. This work presents a structure-based drug discovery strategy to validate the Lmj_04_BRCT domain as a novel therapeutic target in Leishmania spp. The structure of this domain was explored using homology modeling, virtual screening, and molecular dynamics studies. Candidate compounds were validated in vitro using promastigotes of Leishmania major, L. amazonensis, and L. infantum, as well as primary mouse macrophages infected with L. major. The novel inhibitor CPE2 emerged as the most active of a group of compounds against Leishmania, being able to significantly reduce the viability of promastigotes. CPE2 was also active against the intracellular forms of the parasites and significantly reduced parasite burden in murine macrophages without exhibiting toxicity in host cells. Furthermore, L. major promastigotes treated with CPE2 showed significant lower expression levels of several genes (α-tubulin, Cyclin CYCA, and Yip1) related to proliferation and treatment resistance. Our in silico and in vitro studies suggest that the Lmj_04_BRCT domain and its here disclosed inhibitors are new potential therapeutic options against leishmaniasis.

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.

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.

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.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-7.

Breakthroughs in Medicinal Chemistry [...].

Natural Variation in Arabidopsis Cvi-0 Accession Reveals an Important Role of MPK12 in Guard Cell CO2 Signaling.

Plant gas exchange is regulated by guard cells that form stomatal pores. Stomatal adjustments are crucial for plant survival; they regulate uptake of CO2 for photosynthesis, loss of water, and entrance of air pollutants such as ozone. We mapped ozone hypersensitivity, more open stomata, and stomatal CO2-insensitivity phenotypes of the Arabidopsis thaliana accession Cvi-0 to a single amino acid substitution in MITOGEN-ACTIVATED PROTEIN (MAP) KINASE 12 (MPK12). In parallel, we showed that stomatal CO2-insensitivity phenotypes of a mutant cis (CO2-insensitive) were caused by a deletion of MPK12. Lack of MPK12 impaired bicarbonate-induced activation of S-type anion channels. We demonstrated that MPK12 interacted with the protein kinase HIGH LEAF TEMPERATURE 1 (HT1)-a central node in guard cell CO2 signaling-and that MPK12 functions as an inhibitor of HT1. These data provide a new function for plant MPKs as protein kinase inhibitors and suggest a mechanism through which guard cell CO2 signaling controls plant water management.

Synthesis, In Silico, and In Vitro Evaluation of Anti-Leishmanial Activity of Oxadiazoles and Indolizine Containing Compounds Flagged against Anti-Targets.

Due to the lack of approved vaccines against human leishmaniasis and the limitations of the current chemotherapy inducing side effects and drug resistance, development of new, effective chemotherapeutic agents is essential. This study describes the synthesis of a series of novel oxadiazoles and indolizine-containing compounds. The compounds were screened in silico using an EIIP/AQVN filter followed by ligand-based virtual screening and molecular docking to parasite arginase. Top hits were further screened versus human arginase and finally against an anti-target battery to tag their possible interactions with proteins essential for the metabolism and clearance of many substances. Eight candidate compounds were selected for further experimental testing. The results show measurable in vitro anti-leishmanial activity for three compounds. One compound with an IC50 value of 2.18 µM on Leishmania donovani intramacrophage amastigotes is clearly better positioned than the others as an interesting molecular template for further development of new anti-leishmanial agents.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes-6.

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of Editorials that is published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...].

Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking.

We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall's τ of 0.26 ± 0.06 and a Spearman's ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R 2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7-8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1-0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.

Cobalt Complex with Thiazole-Based Ligand as New Pseudomonas aeruginosa Quorum Quencher, Biofilm Inhibitor and Virulence Attenuator.

Pseudomonas aeruginosa is one of the most dreaded human pathogens, because of its intrinsic resistance to a number of commonly used antibiotics and ability to form sessile communities (biofilms). Innovative treatment strategies are required and that can rely on the attenuation of the pathogenicity and virulence traits. The interruption of the mechanisms of intercellular communication in bacteria (quorum sensing) is one of such promising strategies. A cobalt coordination compound (Co(HL)2) synthesized from (E)-2-(2-(pyridin-2-ylmethylene)hydrazinyl)-4-(p-tolyl)thiazole (HL) is reported herein for the first time to inhibit P. aeruginosa 3-oxo-C12-HSL-dependent QS system (LasI/LasR system) and underling phenotypes (biofilm formation and virulence factors). Its interactions with a possible target, the transcriptional activator protein complex LasR-3-oxo-C12-HSL, was studied by molecular modeling with the coordination compound ligand having stronger predicted interactions than those of co-crystallized ligand 3-oxo-C12-HSL, as well as known-binder furvina. Transition metal group 9 coordination compounds may be explored in antipathogenic/antibacterial drug design.

Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors.

A set of top-ranked compounds from a multi-objective in silico screen was experimentally tested for toxicity and the ability to inhibit the activity of HIV-1 reverse transcriptase (RT) in cell-free assay and in cell-based assay using HIV-1 based virus-like particles. Detailed analysis of a commercial sample that indicated specific inhibition of HIV-1 reverse transcription revealed that a minor component that was structurally similar to that of the main compound was responsible for the strongest inhibition. As a result, novel s-triazine derivatives were proposed, modelled, discovered, and synthesised, and their antiviral activity and cellular toxicity were tested. Compounds 18a and 18b were found to be efficient HIV-1 RT inhibitors, with an IC50 of 5.6±1.1µM and 0.16±0.05µM in a cell-based assay using infectious HIV-1, respectively. Compound 18b also had no detectable toxicity for different human cell lines. Their binding mode and interactions with the RT suggest that there was strong and adaptable binding in a tight (NNRTI) hydrophobic pocket. In summary, this iterative study produced structural clues and led to a group of non-toxic, novel compounds to inhibit HIV-RT with up to nanomolar potency.

Arginase Flavonoid Anti-Leishmanial in Silico Inhibitors Flagged against Anti-Targets.

Arginase, a drug target for the treatment of leishmaniasis, is involved in the biosynthesis of polyamines. Flavonoids are interesting natural compounds found in many foods and some of them may inhibit this enzyme. The MetIDB database containing 5667 compounds was screened using an EIIP/AQVN filter and 3D QSAR to find the most promising candidate compounds. In addition, these top hits were screened in silico versus human arginase and an anti-target battery consisting of cytochromes P450 2a6, 2c9, 3a4, sulfotransferase, and the pregnane-X-receptor in order to flag their possible interactions with these proteins involved in the metabolism of substances. The resulting compounds may have promise to be further developed for the treatment of leishmaniasis.

Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets.

A delicate balance exists between a drug molecule's toxicity and its activity. Indeed, efficacy, toxicity, and side effect problems are a common cause for the termination of drug candidate compounds and development projects. To address this, an antitarget interaction profile is built and combined with virtual screening and cross docking for new inhibitors of HIV-1 integrase, in order to consider possible off-target interactions as early as possible in a drug or hit discovery program. New ranking techniques using triangular numbers improve ranking information on the compounds and recovery of known inhibitors into the top compounds using different docking programs. This improved ranking arises from using consensus of ranks between docking programs and ligand efficiencies to derive a new rank, instead of using absolute score values, or average of ranks. The triangular number rerank also allowed the objective combination of results from several protein targets or screen conditions and several programs. Triangular number reranking conserves more information than other reranking methods such as average of scores or averages of ranks. In addition, the use of triangular numbers for reranking makes possible the use of thresholds with a justified leeway based on the number of available known inhibitors, so that the majority of the compounds above the threshold in ranks compare to the compounds that have known experimentally determined biological activity. The battery of anti- or off-targets can be tailored to specific molecular or drug design challenges. In silico filters can thus be deployed in successive stages, for prefiltering, activity profiling, and for further analysis and triaging of libraries of compounds.

Benford's Law and distributions for better drug design.

INTRODUCTION: Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively. AREAS COVERED: The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD. EXPERT OPINION: As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.

Hydration properties of ligands and drugs in protein binding sites: tightly-bound, bridging water molecules and their effects and consequences on molecular design strategies.

Some water molecules in binding sites are important for intermolecular interactions and stability. The way binding site explicit water molecules are dealt with affects the diversity and nature of designed ligand chemical structures and properties. The strategies commonly employed frequently assume that a gain in binding affinity will be achieved by their targeting or neglect. However, in the present work, 2332 high-resolution X-ray crystal structures of hydrated and nonhydrated, drug and nondrug compounds in biomolecular complexes with reported Ki or Kd show that compounds that use tightly bound, bridging water molecules are as potent as those that do not. The distribution of their energies, physicochemical properties, and ligand efficiency indices were compared for statistical significance, and the results were confirmed using 2000 permutation runs. Ligand cases were also split into agonists and antagonists, and crystal structure pairs with differing tightly bound water molecules were also compared. In addition, agonists and antagonists that use tightly bound water bridges are smaller, less lipophilic, and less planar; have deeper ligand efficiency indices; and in general, possess better physicochemical properties for further development. Therefore, tightly bound, bridging water molecules may in some cases be replaced and targeted as a strategy, though sometimes keeping them as bridges may be better from a pharmacodynamic perspective. The results suggest general indications on tightly hydrated and nontightly hydrated compounds in binding sites and practical considerations to adopt a strategy in drug and molecular design when faced with this special type of water molecules. There are also benefits of lower log P and better developability for tightly hydrated compounds, while stronger potency is not always required or beneficial. The hydrated binding site may be one of the many structure conformations available to the receptor, and different ligands will have a different ability to select either hydrated or nonhydrated receptor binding site conformations. Compounds may thus be designed, and if a tightly bound, bridging water molecule is observed in the binding site, attempts to replace it should only be made if the subsequent ligand modification would improve also its ligand efficiency, enthalpy, specificity, and pharmacokinetic properties. If the modification does succeed in replacing the tightly bound, bridging water molecule, it will have at least achieved benefits for ligand optimization and development independently of either positive or negative change in binding affinity outcome.