Synthesis, evaluation, and docking study of adamantyl-1,3,4-oxadiazol hybrid compounds as CaMKIIδ kinase inhibitor

This study revealed a new inhibitor of Ca2+/calmodulin-dependent protein kinase II (CaMKII), a crucial factor in cardiovascular disease and hypertension. The study focuses on the bioactivity compounds that combine adamantane/1,3,4-oxadiazole, potentially inhibiting CaMKIIδ. Various adamantyl-1,3,4-oxadiazole derivatives were synthesized and tested for their efficiency against CaMKIIδ kinase, with 6f being the most potent with an IC50 value of 14.4 μM. Docking studies were carried out to determine the binding processes of these chemicals within the kinase’s active region. These discoveries are an important step toward the development of novel treatments for cardiovascular illnesses and hypertension, with the potential for more precise and efficient therapeutic interventions in the future.

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Preparation of novel shell-ionotropically crosslinked micelles based on hexadecylamine and tripolyphosphate for cancer drug delivery

AIMS

Micellar systems have the advantage of being easily prepared, cheap, and readily loadable with bioactive molecular cargo. However, their fundamental pitfall is poor stability, particularly under dilution conditions. We propose to use simple quaternary ammonium surfactants, namely, hexadecylamine (HDA) and hexadecylpyridinium (HDAP), together with tripolyphosphate (TPP) anion, to generate ionotropically stabilized micelles capable of drug delivery into cancer cells.

METHODS

optimized mixed HDA/HDAP micelles were prepared and stabilized with TPP. Curcumin was used as a loaded model drug. The prepared nanoparticles were characterized by dynamic light scattering, infrared spectroscopy, transmission electron microscopy, and differential scanning calorimetry. Moreover, their cellular uptake was assessed using flow cytometry and confocal fluorescence microscopy.

RESULTS

The prepared nanoparticles were found to be stable under dilution and at high temperatures and to have a size range from 139 nm to 580 nm, depending on pH (4.6-7.4), dilution (up to 100 times), and temperature (25 - 80 °C). They were effective at delivering their load into cancer cells. Additionally, flow cytometry indicated the resulting stabilized micellar nanoparticles to be non-cytotoxic.

CONCLUSIONS

The described novel stabilized micelles are simple to prepare and viable for cancer delivery.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Structure‐Based Approaches for the Prediction of Alzheimer's Disease Inhibitors: Comparative Interactions Analysis, Pharmacophore Modeling and Molecular Dynamics Simulations

Due to its significant role in neurodegeneration, Cyclin‐dependent kinase 5 (CDK5) has emerged as a potential target for addressing neuropathological disorders, including Alzheimer's disease (AD). The application of CDK5 inhibitors has demonstrated promise in the treatment of AD. This prompted us to model this interesting target using a computational workflow named Docking‐based Comparative Intermolecular Contacts Analysis (dbCICA). Approaches including 3D‐QSAR, genetic algorithm, and pharmacophore modeling were employed to discover new CDK inhibitors.

Synthesis and antitumor activity of model cyclopentene-[g]annelated isoindigos

A set of cyclopenten-[g]annelated isoindigos (5a-g) has been prepared and tested for their in vitro antiproliferative activities against MCF-7 and HL60 cells. Among, the N-1-methyl-5'-nitro derivative (5g) displayed the highest activity against HL60 cells (IC50 = 67 nM) and acted as the most potent Flt3 inhibitor. Compounds 5d-g exhibited moderate activity against MCF-7 (IC50 = 50-80 μM).

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Augmenting Bioactivity by Docking-Generated Multiple Ligand Poses To Enhance Machine Learning and Pharmacophore Modelling: Discovery of New TTK Inhibitors as Case Study

Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC ₅₀ of 1.0 µM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

Molecular docking, molecular dynamics simulations and in vitro screening reveal cefixime and ceftriaxone as GSK3β covalent inhibitors

GSK3β is a serine/threonine kinase that has been suggested as a putative drug target for several diseases. Recent studies have reported the beneficial effects of cephalosporin antibiotics in cancer and Alzheimer's disease, implying potential inhibition of GSK3β. To investigate this mechanism, four cephalosporins, namely, cefixime, ceftriaxone, cephalexin and cefadroxil were docked into the GSK3β binding pocket. The third-generation cephalosporins, cefixime and ceftriaxone, exhibited the best docking scores due to the exclusive hydrogen bonding between their aminothiazole group and hinge residues of GSK3β. The stability of top-ranked poses and the possibility of covalent bond formation between the carbonyl carbon of the β-lactam ring and the nucleophilic thiol of Cys-199 were evaluated by molecular dynamics simulations and covalent docking. Finally, the in vitro inhibitory activities of the four cephalosporins were measured against GSK3β with and without preincubation. In agreement with the results of molecular docking, cefixime and ceftriaxone exhibited the best inhibitory activities with IC₅₀ values of 2.55 μM and 7.35 μM, respectively. After 60 minutes preincubation with GSK3β, the IC₅₀ values decreased to 0.55 μM for cefixime and 0.78 μM for ceftriaxone, supporting a covalent bond formation as suggested by molecular dynamics simulations and covalent docking. In conclusion, the third-generation cephalosporins are reported herein as GSK3β covalent inhibitors, offering insight into the mechanism behind their benefits in cancer and Alzheimer's disease.

Asenapine as a Potential Lead Inhibitor against Central Ca2+/Calmodulin-Dependent Protein Kinase II: Investigation by Docking Simulation and Experimental Validation

Aim

The aim of this study is to investigated the potential inhibitory activity of asenapine against central nervous system CaMKII isozymes as a potential repurposing study using docking experiments and enzymatic assay.

Background

The Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a multifunctional protein kinase ubiquitously expressed throughout the brain. Emerging biological data have indicated that inhibiting central nervous system CaMKII isoforms, namely, CaMKIIα and CaMKIIβ, may be a promising therapeutic strategy for the potential treatment of many neurological diseases including schizophrenia, depression, epilepsy, and learning deficit.

Objective

1- Study the possible attractive interactions of asenapine within the binding sites of the central CaMKII isozymes. 2- Evaluate the inhibitory activities of asenapine against central CaMKII isozymes.

Method

Docking experiments of asenapine and other known CaMKII inhibitors were performed. Docking settings were validated using ROC analysis. After that, the inhibitory activities of asenapine against central CaMKII alpha and beta were evaluated by enzymatic assay.

Result

Docking and scoring experiments of asenapine showed several binding interactions anchoring asenapine within CaMKIIα and CaMKIIβ catalytic sites while enzymatic assay results revealed that asenapine can inhibit CaMKIIα and CaMKIIβ in the micromolar range.

Conclusion

Our study provides evidence that asenapine can serve as a promising lead for the development of new CaMKIIα and CaMKIIβ inhibitors. Moreover, this study reinforces how the investment in drug repurposing could boost the drug discovery process.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC₅₀ values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Biological Evaluation and Reverse Pharmacophore Mapping of Innovative Bis-Triazoles as Promising Anticancer Agents

Here, we describe further cytotoxic studies and reverse pharmacophore mapping (pharmacophore profiling) for bis-triazoles MS44-53, which were designed and synthesized previously to stabilize the G-quadruplex nucleic acids capable of being formed at the telomeric region and promoter sequences of genes involved in cellular proliferation and oncogenes. Pharmacophore-based activity profiling screen demonstrated some biological targets that MS44-53 may modulate their biological response, and thus can be considered as potential drugs to treat different kinds of diseases, such as carcinoma, diabetes type II, bacterial infection and cardiovascular diseases. Potent cell growth inhibitory properties were shown by ligands MS47 and MS49 against human melanoma MDA-MB-435, colon cancer HCT-116 and COLO 205, and pancreatic cancer MIA PaCa-2 cell lines, as evidenced by MTT assay. Both ligands were more potent against cancer cells than in skin normal CCD-1064Sk fibroblasts.

Aim:

The aim of this study is to identify the molecular target and mechanism of action of our promising anticancer bis-triazoles MS44-53, focusing specifically on the G-quadruplex stabilizers MS47 and MS49.

Background:

In molecular biology, G-quadruplexes (also known as G4-DNA), one of the higher-order structures of polynucleotides, are four stranded structures formed by nucleic acid sequences which are rich in guanine. They are formed mainly at the single-stranded G-overhang of telomeric DNA and within promoter sequences of genes involved in cellular proliferation and oncogenes such as c-myc, c-kit, and Hsp90. Stabilization of DNA G-quadruplexes is one of the anticancer strategies that has the potential to treat all cancers regardless of the type. A new series of bis-triazoles MS44-53 were developed to stabilize G-quadruplex structures selectively, as G4 ligands and experimental antitumour agents. FRET assay showed that MS47 and MS49 were only the best binders towards the Hsp90 promoter G-guadruplexes. While all bis-triazoles MS44-53 exhibited potent cell growth inhibitory activity against human carcinoma cell lines, suggesting that the ligands perturb molecular targets and mechanisms of action, other than stabilizing G-quadruplexes, contributing to antitumor activity. Therefore, the molecular targets and mechanisms of action of bis-triazoles MS44-53 in different types of human cancer cell lines should be determined by performing further computational studies to MS44-53 and in vitro evaluations for the G-quadruplex stabilizers MS47 and MS49.

Objectives:

1- Determining the exact IC50 for bis-triazoles MS47 & MS49 against four different types of human cancer cell lines; melanoma MDA-MB-435, pancreatic cancer MIA PaCa-2, and colon cancer HCT-116 and COLO 205 cell lines.

2- Predicting the biological targets that bis-triazoles MS44-53 may interact with to trigger or block their biological response.

Methods:

1- MTT assay was used for in vitro evaluation of the antiproliferative activities of MS47 and MS49, and determination of IC50 values.

2- Reverse pharmacophore mapping (pharmacophore profiling) was used for predicting the biological targets of bis-triazoles MS44-53, and determining the % binding probabilities.

Results:

MS49 exhibited more potent proliferation inhibitory activity than MS47 and higher IC50 value against skin normal fibroblasts. Pharmacophore profiling demonstrated FGFR1, PDGFR2, FLT3, mTOR, PPAR-gamma, MUR-F and CETP as biological targets for bis-triazoles MS44-53.

Conclusion:

Bis-triazoles MS47 and MS49 are promising selective innovative compounds with wide spectrum cytotoxic activities against distinct cancer types. Bis-triazoles MS44-53 can be considered as potential drugs to treat different types of carcinoma, in addition to diabetes type II, bacterial infection and cardiovascular diseases.

Other:

Further in vitro evaluations will be performed for bis-triazoles MS44-53 in order to identify their molecular targets and mechanisms of action in different types of human cancer cell lines.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand-receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure-activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand-receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM.