Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation

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.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

Deep neural network modeling based virtual screening and prediction of potential inhibitors for renin protein

Renin enzyme plays an essential role in the Renin-Angiotensin System (RAS), and it is involved in the pathogenesis of hypertension and several other cardiovascular diseases (CVDs). Inhibition of renin is an effective way to intervene with the pathogenesis of these diseases. Docking-based virtual screening, 3D-Quantitative Structure-Activity Relationship (3D-QSAR), and structure-based drug design are the most frequently used strategies towards discovering novel inhibitors targeting renin. In this study, we have developed a 2D fingerprint-based Deep Neural Network (DNN) classifier for virtual screening and a DNN-QSAR model for biological activity prediction. The resulting hits from the DNN-QSAR model were then subjected to the molecular docking to identify further top hits. Molecular Dynamics (MD) simulation was conducted to get a better insight into the binding mode of identified hits. We have discovered six compounds from the Maybridge chemical database with the predicted IC₅₀ values ranging from 24.2 nM to 83.6 nM. To the best of our knowledge, this is the first study that used a cascaded DNN model to identify potential lead compounds for the inhibition of renin target. Through the results presented in this study, we provide evidence of the DNN method being a useful approach to identify new chemical entities/novel lead compounds that may overcome the limitation of existing conventional strategies used in drug discovery research.Communicated by Ramaswamy H. Sarma.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

BACKGROUND

PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.

METHODS

Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).

RESULTS

The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.

CONCLUSION

Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand-Receptor Complexes

We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar IC₅₀s.

Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling

AIM

Ligand-based pharmacophore modeling requires long list of inhibitors, while pharmacophores based on single ligand-receptor crystallographic structure can be too restricted or promiscuous.

METHODOLOGY

This prompted us to combine simulated annealing molecular dynamics (SAMD) with ligand-receptor contacts analysis as means to construct pharmacophore model(s) from single ligand-receptor complex. Ligand-receptor contacts that survive numerous heating-cooling SAMD cycles are considered critical and are used to guide pharmacophore development.

RESULTS

This methodology was implemented to develop pharmacophores for acetylcholinesterase and protein kinase C-θ. The resulting models were validated by receiver-operating characteristic analysis and in vitro bioassay. Assay identified four new protein kinase C-θ inhibitors among captured hits, two of which exhibited nanomolar potencies.

CONCLUSION

The results illustrate the ability of the new method to extract valid pharmacophores from single ligand-protein complex.

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

The Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging virus that poses a major challenge to clinical management.

The 3C‐like protease (3CL^pro) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS‐CoV 3CL^pro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4‐CoV 3CL^pro. HKU4‐CoV 3CL^pro shares high sequence identity (81%) with the MERS‐CoV enzyme and thus represents a potential surrogate model for anti‐MERS drug discovery. We used 2 well‐established methods: Quantitative structure‐activity relationship (QSAR)‐guided modeling and docking‐based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding‐pocket regions involved in 3CL^pro‐ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CL^pro inhibitors. The identified hits were tested for HKU4‐CoV and MERS‐CoV 3CL^pro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS‐CoV 3CL^pro and represent a potential starting point for the development of novel anti‐MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS‐CoV 3CL^pro.

Highlights

MERS‐CoV is an emerging virus that is closely related to the bat HKU4‐CoV.

3CL^pro is a potential drug target for coronavirus infection.

HKU4‐CoV 3CL^pro is a useful surrogate model for the identification of MERS‐CoV 3CL^pro enzyme inhibitors.

dbCICA is a very robust modeling method for hit identification.

The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CL^pro inhibitors development.

Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors

Situin 2 (SIRT2) enzyme is a histone deacetylase that has important role in neuronal development. SIRT2 is clinically validated target for neurodegenerative diseases and some cancers. In this study, exhaustive unsupervised pharmacophore modeling was combined with quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent SIRT2 inhibitors using 146 known SIRT2 ligands. A computational workflow that combines genetic function algorithm with k-nearest neighbor or multiple linear regression was implemented to build self-consistent and predictive QSAR models based on combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve profiles. Optimal QSAR models and their associated pharmacophore hypotheses were experimentally validated by identification and in vitro evaluation of several new promising SIRT2 inhibitory leads retrieved from the National Cancer Institute structural database. The most potent hit illustrated IC₅₀ value of 5.4μM. The chemical structures of active hits were validated by proton nuclear magnetic resonance and mass spectroscopy.

Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors

Protein kinase B (PKB) is a key mediator of proliferation and survival pathways that are critical for cancer growth. Therefore, inhibitors of PKB are useful agents for the treatment of cancer. Herein, we describe pharmacophore-based virtual screening combined with docking study as a rational strategy for identification of novel hits or leads. Pharmacophore models of PKB β inhibitors were established using the DISCOtech and refined with GASP from compounds with IC50 values ranging from 2.2 to 246nM. The best pharmacophore model consists of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) site and two hydrophobic (HY) features. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods indicated that the model-3 was statistically valuable and reliable in identifying PKB β inhibitors. Pharmacophore model as a 3D search query was searched against NCI database. Several compounds with different structures (scaffolds) were retrieved as hits. Molecules with a Qfit value of more than 95 and three other known inhibitors were docked in the active site of PKB to further explore the binding mode of these compounds. Finally in silico pharmacokinetic and toxicities were predicted for active hit molecules. The hits reported here showed good potential to be PKB β inhibitors.

New diethylsilylacetylenic linker for parallel solid-phase synthesis of libraries of hydroxy acetylenic steroid derivatives with improved metabolic stability

Acetylenic tertiary alcohols are well-known to be compounds that are biologically more stable than their corresponding secondary alcohols. The linkage of an acetylenic compound to a polymer support and further introduction of molecular diversity was found to be an interesting way to generate libraries of hydroxy acetylenic derivatives and thus potentially improve their biological properties. For the first time, we describe the loading of an ethynyl steroid to a polystyrene-diethylsilane resin and its uses for the solid-phase synthesis of a model library of 21 steroid derivatives. Two levels of molecular diversity were introduced by successive addition of amino acids and carboxylic acids, and hydroxy acetylenic steroids were then released by an acidic treatment in high yield and purity without further purification step.