Discovery of New Antifungal Leads via Pharmacophore Modeling and QSAR Analysis of Fungal N-Myristoyl Transferase Inhibitors Followed by In Silico Screening

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.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

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.

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.

DPP-IV Inhibitory Phenanthridines: Ligand, Structure-Based Design and Synthesis

BACKGROUND

Lately, diabetes has become the main health concern for millions of people around the world. Dipeptidyl peptidase-IV (DPP-IV) inhibitors have emerged as a new class of oral antidiabetic agents. Formerly, acridines, N4-sulfonamido-succinamic, phthalamic, acrylic and benzoyl acetic acid derivatives, and sulfamoyl-phenyl acid esters were designed and developed as new DPP-IV inhibitors.

OBJECTIVE

This study aims to develop a pharmacophore model of DPP-IV inhibitors and to evaluate phenanthridines as a novel scaffold for inhibiting DPP-IV enzyme. In addition, to assess their binding interactions with the enzyme through docking in the binding site of 4A5S (PDB).

METHODS

Herein, Quantum-Polarized Ligand Docking (QPLD) and ligand-based pharmacophore modeling investigations were performed. Three novel 3,8-disubstituted-6-phenyl phenanthridine derivatives 3-5 have been designed, synthesized and characterized. In vitro biological testing against DPP-IV was carried out using fluorometric assay kit.

RESULTS

QPLD study demonstrates that compounds 3-5 forms H-bond with Lys554, Trp629, and Tyr631, besides charge transfer interaction between their aromatic rings and the aromatic rings of Tyr547 and Tyr666. Moreover, they fit the three pharmacophoric point features of DPP-IV inhibitors and were proven to have in vitro DPP-IV inhibitory activity where compound 5 displayed a % inhibition of 45.4 at 100 μM concentration.

CONCLUSION

Phenanthridines may serve as a potential lead compound for developing new DPP-IV inhibitors as a promising antidiabetic agent. Computational results suggest future structural simplification.

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.

Ligand-based computational modelling of platelet-derived growth factor beta receptor leading to new angiogenesis inhibitory leads

Platelet derived growth factor beta receptor (PDGFR- β) plays an important role in angiogenesis. PDGFR-β expression is correlated with increased vascularity and maturation of blood vessels in cancer. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for ligand-PDGFR-β recognition using 107 known PDGFR-β inhibitors. Genetic function algorithm (GFA) coupled to k nearest neighbor (kNN) and multiple linear regression (MLR) analysis were employed to generate predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new angiogenesis inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Two hits illustrated low micromolar IC₅₀ values in two distinct anti-angiogenesis bioassays.

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.

Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation

AIM

FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC₅₀ of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy.

CONCLUSION

Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.

Ligand-based modeling of diverse aryalkylamines yields new potent P-glycoprotein inhibitors

The P-glycoprotein (P-gp) efflux pump has an important role as a natural detoxification system in many types of normal and cancer cells. P-gp is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents, and therefore, it is clinically validated target for cancer therapy. Accordingly, in this study we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent P-gp inhibitors employing 130 known P-gp ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) or multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new promising P-gp inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Several potent hits were captured. The most potent hit decreased the IC50 of doxorubicin from 0.906 to 0.190 μM on doxorubicin resistant MCF7 cell-line.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.

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.

Synthesis of Some 4,5-Dihydrothieno[3,2-e][1,2,4]Triazolo[4,3-a] Pyrimi-dine-2-Carboxamides as Anti-Inflammatory and Analgesic Agents

A new series 4,5-dihydrothieno[3,2-e][1,2,4]triazolo[4,3-a]pyrimidine-2-carboxamide was synthesized. Twenty one newly synthesized compounds were investigated for their anti-inflammatory and analgesic activity using acute and subacute formalin-induced paw edema models and diclofenac Na as a reference. The acute toxicity (ALD50) and ulcerogenic effects of the active compounds were also determined. The thienotriazolopyrimidines 10a, 10c and 11c were found to exhibit remarkable anti-inflammatory activity at both models in addition to good analgesic activity with a delayed onset of action. Moreover, the active compounds showed high GI safety level and are well tolerated by experimental animals with high safety margin (ALD50 > 0.4 g/kg). Docking study using Molecular Operating Environment (MOE) version 2008.10 into COX-2 has been made for derivatives of highest anti-inflammatory activity.