Discovery and optimization of novel, non-steroidal glucocorticoid receptor modulators

Structure-based virtual screening, molecular docking, molecular dynamics simulation and MM/PBSA calculations towards identification of steroidal and non-steroidal selective glucocorticoid receptor modulators

Glucocorticoids have been used in the treatment of many diseases including inflammatory and autoimmune diseases. Despite the wide therapeutic effects of synthetic glucocorticoids, the use of these compounds has been limited due to side effects such as osteoporosis, immunodeficiency, and hyperglycaemia. To this end, extensive studies have been performed to discover new glucocorticoid modulators with the aim of increasing affinity for the receptor and thus less side effects. In the present work, structure-based virtual screening was used for the identification of novel potent compounds with glucocorticoid effects. The molecules derived from ZINC database were screened on account of structural similarity with some glucocorticoid agonists as the template. Subsequently, molecular docking was performed on 200 selected compounds to obtain the best steroidal and non-steroidal conformations. Three compounds, namely ZINC_000002083318, ZINC_000253697499 and ZINC_000003845653, were selected with the binding energies of -11.5, -10.5, and -9.5 kcal/mol, respectively. Molecular dynamic simulations on superior structures were accomplished with the glucocorticoid receptor. Additionally, root mean square deviations, root mean square fluctuation, radius of gyration, hydrogen bonds, and binding-free energy analysis showed the binding stability of the proposed compounds compared to budesonide as an approved drug. The results demonstrated that all the compounds had suitable binding stability compared to budesonide, while ZINC_000002083318 showed a tighter binding energy compared to the other compounds.Communicated by Ramaswamy H. Sarma.

PPL catalyzed four-component PASE synthesis of 5-monosubstituted barbiturates: Structure and pharmacological properties

Enzymatic four-component reactions are very rare although three-component enzymatic promiscuous reactions are widely reported. Herein, we report an efficient PASE protocol for the synthesis of potentially lipophilic zwitterionic 5-monosubstituted barbiturates by four component reaction of mixture of ethyl acetoacetate, hydrazine hydrate, aldehyde and barbituric acid in ethanol at room temperature. Seven different lipases were screened for their promiscuous activity towards the synthesis of 5-monosubstituted barbiturates and the lipase from porcine pancreas (PPL) found to give optimum efficiency. The zwitterionic 5-monosubstituted barbiturates with pyrazolyl ring showed promising pharmacological activity upon screening for antibacterial and apoptotic properties.

Antiprogestins in gynecological diseases

Antiprogestins constitute a group of compounds, developed since the early 1980s, that bind progesterone receptors with different affinities. The first clinical uses for antiprogestins were in reproductive medicine, e.g., menstrual regulation, emergency contraception, and termination of early pregnancies. These initial applications, however, belied the capacity for these compounds to interfere with cell growth. Within the context of gynecological diseases, antiprogestins can block the growth of and kill gynecological-related cancer cells, such as those originating in the breast, ovary, endometrium, and cervix. They can also interrupt the excessive growth of cells giving rise to benign gynecological diseases such as endometriosis and leiomyomata (uterine fibroids). In this article, we present a review of the literature providing support for the antigrowth activity that antiprogestins impose on cells in various gynecological diseases. We also provide a summary of the cellular and molecular mechanisms reported for these compounds that lead to cell growth inhibition and death. The preclinical knowledge gained during the past few years provides robust evidence to encourage the use of antiprogestins in order to alleviate the burden of gynecological diseases, either as monotherapies or as adjuvants of other therapies with the perspective of allowing for long-term treatments with tolerable side effects. The key to the clinical success of antiprogestins in this field probably lies in selecting those patients who will benefit from this therapy. This can be achieved by defining the genetic makeup required - within each particular gynecological disease - for attaining an objective response to antiprogestin-driven growth inhibition therapy.Free Spanish abstractA Spanish translation of this abstract is freely available at http://www.reproduction-online.org/content/149/1/15/suppl/DC1.

A green four-component synthesis of zwitterionic alkyl/benzyl pyrazolyl barbiturates and their photophysical studies

A novel series of unsymmetrically substituted alkyl/benzyl pyrazolyl barbiturates incorporating highly biologically active pyrazolone and barbiturate moieties was synthesized by four-component reactions of a mixture of ethyl acetoacetate, hydrazine hydrate, aldehydes and barbituric acid/thiobarbituric acid in ethanol without using a catalyst. The photophysical properties of the newly designed alkyl/benzyl pyrazolyl barbiturates were studied, and good quantum yield of some products indicated a definitive scope in the field of biochemical applications. Single-crystal X-ray crystallographic studies revealed that the newly synthesized compounds exist in zwitterionic form. The zwitterionic nature of the new chimera makes them interesting candidates for drug delivery as zwitterionic drugs are known to have highly water soluble properties, specific protein absorption, slow recognition by immune system, slow blood clearance from body and can constantly diffuse and deposit throughout the physiological pH.

Discovery of a novel non-steroidal GR antagonist with in vivo efficacy in the olanzapine-induced weight gain model in the rat

We report the optimization of a series of non-steroidal GR antagonists that led to the identification of compound 7. This compound is efficacious when dosed orally in an olanzapine-induced weight gain model in rats.

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.

Rational ligand-based virtual screening and structure-activity relationship studies in the ligand-binding domain of the glucocorticoid receptor-α

The interest in developing synthetic glucocorticoids (GCs) arises from the utility of endogenous steroids as potent anti-inflammatory and immunosuppressant agents. The first GCs to be discovered, such as cortisol or dexamethasone, still represent the main treatment for conditions of the inflammatory process, despite the fact that they carry a significant risk of side effects. Hence, there is a continuing need to find drugs that preserve the immune effects of GCs without the side effects, such as those on metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. In this review, we focus on the recent use of ligand-based computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of ligand-based (similarity searches, pharmacophore screens and quantitative structure-activity relationships) approaches that have been implemented in recent years. A recent virtual high-throughput screening similarity search was successful in developing a novel series of nonsteroidal GR antagonists. Additionally, there has been considerable success in ligand-based structure-analysis relationship generation and lead optimization studies for the GR. Future trends toward integrated GR ligand design incorporating ligand- and structure-based methodologies are inevitable.

Sequential one-pot combination of multi-component and multi-catalysis cascade reactions: an emerging technology in organic synthesis

Creating sequential one-pot combinations of multi-component reactions (MCRs) and multi-catalysis cascade (MCC) reactions is a challenging task that has already emerged as a new technology in synthetic organic chemistry. Through one-pot sequential combination of MCRs/MCC reactions, the chemical products (fine chemicals, agrochemicals and pharmaceuticals) that add value to our lives can be produced with less waste and greater economic benefits. Within this Emerging Area, we describe our recent developments and designs for sequential one-pot MCRs/MCC reactions to facilitate their realization as biomimetics in organic chemistry.

A general approach to chiral building blocks via direct amino acid-catalyzed cascade three-component reductive alkylations: formal total synthesis of HIV-1 protease inhibitors, antibiotic agglomerins, brefeldin A, and (R)-gamma-hexanolide

Multicatalysis cascade (MCC) process for the synthesis of highly substituted chiral building blocks (2-alkyl-CH-acids, 2-alkylcyclohexane-1,3-diones, 2-alkylcyclopentane-1,3-diones, and H-P ketone analogues) is presented based on the cascade three-component reductive alkylation's (TCRA) platform. Herein, we developed the high-yielding alkylation of a variety of CH-acids with (R)-glyceraldehyde acetonide/(S)-Garner aldehyde and Hantzsch ester through amino acid-catalyzed TCRA reaction without racemization at the alpha-position to carbonyl. Direct sequential combination of the L-proline-catalyzed TCRA reaction with other reactions like cascade alkylation/ketenization/esterification (A/K/E), alkylation/ketenization/esterification/alkylation (A/K/E/A), Brønsted acid-catalyzed cascade hydrolysis/lactonization/esterification (H/L/E), hydrolysis/esterification (H/E), hydrolysis/oxy-Michael/dehydration (H/OM/DH), and Robinson annulation (RA) of CH-acids, chiral aldehydes, Hantzsch ester, diazomethane, methyl vinyl ketone, various active olefins, and acetylenes furnished the highly functionalized chiral building blocks in good to high yields with excellent diastereoselectivities. In this context, many of the pharmaceutically applicable chiral building blocks were prepared via MCC reactions.

Rational structure-based drug design and optimization in the ligand-binding domain of the glucocorticoid receptor-α

Background: Endogenous glucocorticoids (GCs) are involved in a range of endocrine functions including the metabolism of lipids, carbohydrates and proteins, stress response, fluid and electrolyte balance, as well as the maintenance of immunological, renal and skeletal homeostasis. There is a need to find agents that preserve the immune effects of GCs without side effects such as those affecting metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. Discussion: In this review, we focus on the use of recent computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of structure-based (e.g., homology modeling and docking) studies that have been implemented and evaluate their success. Conclusion: By the end of 2008, there had been limited achievements utilizing docking studies and no published successes in the area of virtual high-throughput screening. However, the availability of novel crystal structures and the use of induced-fit docking protocols are improving docking success rates and promising to aid the future delivery of nonsteroidal ligands.

Understanding nuclear receptors using computational methods

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.