Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3)

Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation for identification of potential ERK inhibitors

As the downstream component of the mitogen-activated protein kinases (MAPK) pathway, the extracellular signal-regulated kinase (ERK) is responsible for phosphorylating a broad range of substrates in cell proliferation, differentiation, and survival. Direct targeting the ERK proteins by the piperidinopyrimidine urea-based inhibitors has been demonstrated to be an effective way to block the MAPK signaling pathway in inhibiting tumor growth. In order to discover better inhibitors, a computer-aided drug design (CADD) approach was employed to reveal the pharmacological characteristics and mechanisms of action. The pharmacophore model was generated on the basis of the compounds with eight features, i.e., four hydrogen bond acceptor atoms, one hydrogen bond donor atom, and three hydrophobic centers. A total of 14 hit compounds were obtained through virtual screening. Two potential inhibitors, namely VS01 and VS02, have been identified by molecular docking and molecular dynamics simulations. Both compounds are capable of attaching to the ERK pocket precisely. The binding free energies of VS01 and VS02 are about 15 kJ/mol and 4 kJ/mol stronger than that of the clinic Ulixertinib because of the characteristic hydrogen bonding, electrostatic, and hydrophilic interactions. The present theoretical investigations shed new light on the rational design of the potential ERK inhibitors to stimulate further experimental tests.Communicated by Ramaswamy H. Sarma.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r² ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.

Identification of novel Nicotinamide Phosphoribosyltransferase (NAMPT) inhibitors using computational approaches

Nicotinamide Phosphoribosyltransferase (NAMPT) is a rate-limiting enzyme in the biosynthesis of NAD. Cancer cells have elevated poly [ADP-Ribose] polymerase 1 (PARP) activity as well as the immense necessity of ATP: thereby consuming NAD at a higher rate than normal tissues. The perturbation of these intracellular processes is more sensitive and highly dependent on NAMPT to maintain the required NAD levels. Functional inhibition of NAMPT is, therefore, a promising drug target in therapeutic oncology. In this study, the importance of intermolecular contacts was realized based on contact occupancy and favorable energetic from molecular dynamic simulation to discern non-critical contacts of four different classes of potential NAMPT inhibitor bound complexes. Further, pharmacophore modeling, molecular docking, a quantum mechanical properties and MD simulation, as well as active site residual network communication were employed to identify potential leads. Present studies identified two leads, 2 and 3 which have better binding free energy compared to known inhibitors and showed stable hydrogen bonding and hydrophobic contacts with β barrel cavity lining residues in the active site of the dimer interface (A'B). Lead 2 containing fluorene as central core and lead 3 having phenyl-benzamide as a core showed stable moiety which was observed from electronic property analysis. Active site residual communication in identified leads bound complex also showed similarity to known inhibitor complexes. Compounds containing these moieties were not reported until now against NAMPT inhibition and can be considered as novel cores for future development of drugs to inhibit NAMPT function.