ChemVassa: A New Method for Identifying Small Molecule Hits in Drug Discovery

Identification of C3b-Binding Small-Molecule Complement Inhibitors Using Cheminformatics

The complement system is an elegantly regulated biochemical cascade formed by the collective molecular recognition properties and proteolytic activities of more than two dozen membrane-bound or serum proteins. Complement plays diverse roles in human physiology, such as acting as a sentry against invading microorganisms, priming of the adaptive immune response, and removal of immune complexes. However, dysregulation of complement can serve as a trigger for a wide range of human diseases, which include autoimmune, inflammatory, and degenerative conditions. Despite several potential advantages of modulating complement with small-molecule inhibitors, small-molecule drugs are highly underrepresented in the current complement-directed therapeutics pipeline. In this study, we have employed a cheminformatics drug discovery approach based on the extensive structural and functional knowledge available for the central proteolytic fragment of the cascade, C3b. Using parallel in silico screening methodologies, we identified 45 small molecules that putatively bind C3b near ligand-guided functional hot spots. Surface plasmon resonance experiments resulted in the validation of seven dose-dependent C3b-binding compounds. Competition-based biochemical assays demonstrated the ability of several C3b-binding compounds to interfere with binding of the original C3b ligand that guided their discovery. In vitro assays of complement function identified a single complement inhibitory compound, termed cmp-5, and mechanistic studies of the cmp-5 inhibitory mode revealed it acts at the level of C5 activation. This study has led to the identification of a promising new class of C3b-binding small-molecule complement inhibitors and, to our knowledge, provides the first demonstration of cheminformatics-based, complement-directed drug discovery.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Inhibition of bacterial mevalonate diphosphate decarboxylase by eriochrome compounds

Mevalonate diphosphate decarboxylase (MDD; EC 4.1.1.33) catalyzes the irreversible decarboxylation of mevalonate diphosphate in the mevalonate pathway to form isopentenyl diphosphate, which is a precursor in the biosynthesis of many essential polyisoprenoid natural products, including sterols. In low G/C Gram-positive bacteria, which utilize the mevalonate pathway, MDD is required for cell viability and thus is a potential target for development of antibiotic drugs. To identify potential inhibitors of the enzyme, the National Cancer Institute's Mechanistic Diversity Set library of compounds was screened for inhibitors of Staphylococcus epidermidis MDD. From this screen, the compound Eriochrome Black A (EBA), an azo dye, was found to inhibit the enzyme with an IC50 value<5μM. Molecular docking of EBA into a crystal structure of S. epidermidis MDD suggested binding at the active site. EBA, along with the related Eriochrome B and T compounds, was evaluated for its ability to not only inhibit enzymatic activity but to inhibit bacterial growth as well. These compounds exhibited competitive inhibition towards the substrate mevalonate diphosphate, with Ki values ranging from 0.6 to 2.7μM. Non-competitive inhibition was observed versus ATP indicating binding of the inhibitor in the mevalonate diphosphate binding site, consistent with molecular docking predictions. Fluorescence quenching analyses also supported active site binding of EBA. These eriochrome compounds are effective at inhibiting S. epidermidis cell growth on both solid media and in liquid culture (MIC50 from 31 to 350μM) raising the possibility that they could be developed into antibiotic leads targeting pathogenic low-G/C Gram-positive cocci.

New design strategies for antidepressant drugs

INTRODUCTION

In spite of research efforts spanning six decades, the most prominent antidepressant drugs to date still carry several adverse effects, often serious enough to warrant discontinuation of the drug. Molecular mechanisms of depression are now better understood such that some of the specific receptors responsible can be targeted for activation or inhibition. This advance, coupled with the recent availability of crystal structures of relevant drug targets or their homologs, has opened the door for new antidepressant therapeutic compounds.

AREAS COVERED

The authors review the evolution of monoamine-based antidepressant drugs, up to the selective serotonin reuptake inhibitors (SSRIs). The authors discuss classic and contemporary antidepressant drug design strategies, with a focus on virtual screening and fragment-based drug design methods. Furthermore, they discuss the recent advancements in the understanding of the serotonin transporter (SERT) structure/function relationship in the context of recognition of SSRIs and outline a strategy for the use of computational approaches in producing new SSRI lead compounds.

EXPERT OPINION

The authors suggest that given the long-awaited availability of credible three-dimensional structures for the SERT and related monoamine transporter proteins, cutting-edge computational methods should be the linchpin of future drug discovery efforts regarding monoamine-based antidepressant lead compounds. Because these transporter inhibitors cause a ubiquitous increase in extraneuronal neurotransmitter levels leading to side and adverse therapeutic effects, the drug discovery should extend to appropriate manipulation of the 'downstream' receptors affected by the neurotransmitter boost. Efficient use of new computational strategies will accelerate the drug discovery process and reduce its economic burden.