Discovery of new selective cytotoxic agents against Bcl-2 expressing cancer cells using ligand-based modeling

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.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

BACKGROUND

Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics.

OBJECTIVE

In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed.

RESULTS

Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design.

CONCLUSION

This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC₅₀ of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors

Background:

Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately.

Objective:

In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2).

Method:

We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching.

Results:

We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds.

Conclusions:

Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on- andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease-modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the β-amyloid precursor protein (βAPP) and β-amyloid (Aβ) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.