3D-QSAR study of corticotropin-releasing factor 1 antagonists and pharmacophore-based drug design

Synthesis, in vitro biological investigation, and molecular dynamics simulations of thiazolopyrimidine based compounds as corticotrophin releasing factor receptor-1 antagonists

Corticotrophin releasing factor receptor-1 (CRFR1) is a potential target for treatment of depression and anxiety through modifying stress response. A series of new thiazolo[4,5-d]pyrimidine derivatives were designed, prepared and biologically evaluated as potential CRFR1 antagonists. Four compounds produced more than fifty percent inhibition in the [125I]-Tyr0-sauvagine specific binding assay. Assessment of binding affinities revealed that compound (3-(2,4-dimethoxyphenyl)-7-(dipropylamino)-5-methylthiazolo[4,5-d]pyrimidin-2(3H)-one) 8c was the best candidate with highest binding affinity (Ki = 32.1 nM). Further evaluation showed the ability of compound 8c to inhibit CRF induced cAMP accumulation in a dose response manner. Docking and molecular dynamics simulations were used to investigate potential binding modes of synthesized compounds as well as the stability of 8c-CRFR1 complex. These studies suggest similar allosteric binding of 8c compared to that of the co-crystalized ligand CP-376395 in 4K5Y pdb file.

The Discovery of Novel BCR-ABL Tyrosine Kinase Inhibitors Using a Pharmacophore Modeling and Virtual Screening Approach

Chronic myelogenous leukemia (CML) typically results from a reciprocal translocation between chromosomes 9 and 22 to produce the bcr-abl oncogene that when translated, yields the p210 BCR-ABL protein in more than 90% of all CML patients. This protein has constitutive tyrosine kinase activity that activates numerous downstream pathways that ultimately produces uncontrolled myeloid proliferation. Although the use of the BCR-ABL tyrosine kinase inhibitors (TKIs), such as imatinib, nilotinib, dasatinib, bosutinib, and ponatinib have increased the overall survival of CML patients, their use is limited by drug resistance and severe adverse effects. Therefore, there is the need to develop novel compounds that can overcome these problems that limit the use of these drugs. Therefore, in this study, we sought to find novel compounds using Hypogen and Hiphip pharmacophore models based on the structures of clinically approved BCR-ABL TKIs. We also used optimal pharmacophore models such as three-dimensional queries to screen the ZINC database to search for potential BCR-ABL inhibitors. The hit compounds were further screened using Lipinski’s rule of five, ADMET and molecular docking, and the efficacy of the hit compounds was evaluated. Our in vitro results indicated that compound ZINC21710815 significantly inhibited the proliferation of K562, BaF3/WT, and BaF3/T315I leukemia cells by inducing cell cycle arrest. The compound ZINC21710815 decreased the expression of p-BCR-ABL, STAT5, and Crkl and produced apoptosis and autophagy. Our results suggest that ZINC21710815 may be a potential BCR-ABL inhibitor that should undergo in vivo evaluation.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.