Rational screening of oligonucleotide combinatorial libraries for drug discovery.

Combinatorial strategies offer the potential to generate and screen extremely large numbers of compounds and to identify individual molecules with a desired binding specificity or pharmacological activity. We describe a combinatorial strategy for oligonucleotides in which the library is generated and screened without using enzymes. Freedom from enzymes enables the use of oligonucleotide analogues. This dramatically extends the scope of both the compounds and the targets that may be screened. We demonstrate the utility of the method by screening 2'-O-Methyl and phosphorothioate oligonucleotide analogue libraries. Compounds have been identified that bind to the activated H-ras mRNA and that have potent antiviral activity against the human herpes simplex virus.     

BEAR, a novel virtual screening methodology for drug discovery

BEAR (binding estimation after refinement) is a new virtual screening technology based on the conformational refinement of docking poses through molecular dynamics and prediction of binding free energies using accurate scoring functions. Here, the authors report the results of an extensive benchmark of the BEAR performance in identifying a smaller subset of known inhibitors seeded in a large (1.5 million) database of compounds. BEAR performance proved strikingly better if compared with standard docking screening methods. The validations performed so far showed that BEAR is a reliable tool for drug discovery. It is fast, modular, and automated, and it can be applied to virtual screenings against any biological target with known structure and any database of compounds.     

Structure-based virtual screening approach to the discovery of novel PTPMT1 phosphatase inhibitors

Dual-specificity protein tyrosine phosphatase localized to mitochondrion 1 (PTPMT1) has recently proved to be a promising therapeutic target for the treatment of type II diabetes. Herein we report the first example for a successful application of the structure-based virtual screening to identify the novel inhibitors of human PTPMT1. These inhibitors were computationally screened for having desirable physicochemical properties as a drug candidate and reveal a high potency with IC(50) values ranging from 0.7 to 17.3μM. Therefore, they deserve consideration for further development by structure-activity relationship studies to optimize the antidiabetic activities. Structural features relevant to the stabilization of the newly identified inhibitors in the active site of PTPMT1 are addressed in detail.     

Structure-based virtual screening approach to the discovery of phosphoinositide 3-kinase alpha inhibitors

Phosphoinositide 3-kinase alpha (PI3Kα) has proved to be an attractive target for the development of therapeutics for the treatment of cancer. Herein we report a successful application of the structure-based virtual screening to identify the novel inhibitors of PI3Kα. These inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with IC₅₀ values ranging from 20 to 40 μM. Therefore, they deserve a consideration for further development by structure-activity relationship (SAR) studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the newly identified inhibitors in the ATP-binding site of PI3Kα are addressed in detail.     

Structure-based virtual screening approach to the discovery of p38 MAP kinase inhibitors

p38 Mitogen-activated protein kinase (MAPK) has been considered to be a promising target for the development of therapeutics for various immunologic diseases. Herein we report an example for a successful application of the virtual screening with protein-ligand docking to identify the novel inhibitors of p38α MAPK. These inhibitors were screened for having desirable physicochemical properties as a drug candidate and compound 1-3 revealed a moderate inhibitory activity with IC(50) values ranging from 0.7 to 20 μM. Therefore, they deserve a consideration for further development by structure-activity relationship (SAR) studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the newly identified inhibitors in the ATP-binding site of p38 MAPK are addressed in detail.     

Structure-based virtual screening of Src kinase inhibitors

Src is an important target in multiple processes associated with tumor growth and development, including proliferation, neovascularization, and metastasis. In this study, hit identification was performed by virtual screening of commercial and in-house compound libraries. Docking studies for the hits were performed, and scoring functions were used to evaluate the docking results and to rank ligand-binding affinities. Subsequently, hit optimization for potent and selective candidate Src inhibitors was performed through focused library design and docking analyses. Consequently, we report that a novel compound ‘43’ with an IC₅₀ value of 89 nM, representing (S)-N-(4-(5-chlorobenzo[d][1,3]dioxol-4-ylamino)-7-(2-methoxyethoxy)quinazolin-6-yl)pyrrolidine-2-carboxamide, is highly selective for Src in comparison to EGFR (IC₅₀ ratio > 80-fold) and VEGFR-2 (IC₅₀ ratio > 110-fold). Compound 43 exerted anti-proliferative effects on Src-expressing PC3 human prostate cancer and A431 human epidermoid carcinoma cells, with calculated IC₅₀ values of 1.52 and 0.78 μM, respectively. Moreover, compound 43 (0.1 μM) suppressed the phosphorylation of extracellular signal-regulated kinases and p90 ribosomal S6 kinase, downstream molecules of Src, in a time-dependent manner, in both PC3 and A431 cell lines. The docking structure of compound 43 with Src disclosed that the chlorobenzodioxole moiety and pyrrolidine ring of C-6 quinazoline appeared to fit tightly into the hydrophobic pocket of Src. Additionally, the pyrrolidine NH forms a hydrogen bond with the carboxyl group of Asp348. These results confirm the successful application of virtual screening studies in the lead discovery process, and suggest that our novel compound 43 can be an effective Src inhibitor candidate for further lead optimization.     

Structure-based drug discovery of carbonic anhydrase inhibitors

Inhibition of the metalloenzyme carbonic anhydrase (CA; EC 4.2.1.1) has pharmacologic applications in the field of anti-glaucoma, anti-convulsant and anti-cancer agents. But recently, it has also emerged that these enzymes have the potential for designing anti-infective drugs (anti-fungal and anti-bacterial agents) with a novel mechanism of action. Sulphonamides and their isosteres (sulphamates/sulphamides) constitute the main class of CA inhibitors (CAIs), which bind to the metal ion from the enzyme active site. Recently, the dithiocarbamates (DTCs), possessing a similar mechanism of action, were reported as a new class of inhibitors. These types of CAIs will be discussed in detail in this review. Novel drug design strategies have been reported ultimately based on the tail approach for obtaining sulphonamides/DTCs, which exploit more external binding regions within the enzyme active site (in addition to coordination to the metal ion), leading thus to isoform-selective compounds. Most of the promising data have been obtained by combining x-ray crystallography of enzyme-inhibitor adducts with novel synthetic approaches for generating chemical diversity. Whereas sulphonamide – NO donating hybrid drugs were reported as effective anti-glaucoma agents, most of the interesting new inhibitors were designed for inhibiting specifically the tumour-associated isoforms CA IX and XII, validated targets for imaging and treatment of hypoxic tumours. Promising compounds that inhibit CAs from bacterial and fungal pathogens, of the DTC and carboxylate types, will be also reviewed.     

High-throughput microsomal stability assay for screening new chemical entities in drug discovery

In this work, the authors present a novel, robotic, automated protocol for assessing a metabolic stability protocol assembled on a Hamilton platform and a new strategy for pooling samples (cassette analysis). To increase the high throughput of the liquid chromatography (LC) step, fast chromatography and automated liquid chromatography tandem mass spectrometry (LC/MS/MS) analytical methods were also developed, and a rapid data analysis system was generated that converts peak areas obtained by LC/MS/MS in intrinsic clearance values. All of the steps of the microsomal stability assay were carefully studied and optimized. Standard errors and confidence intervals of the measured clearances were also automatically generated in the process to allow an immediate evaluation of the significance of observed values. Methods based on pooling analysis of 2 and 4 different analytes were compared with a standard method without pooling. A simple statistical treatment was used to show their equivalence. The different protocols developed were analyzed in terms of the best compromise between accuracy and high-throughput capabilities.     

Miniaturized screening technologies for drug discovery

Adaptive Profiling (APL) and other biochip companies aim to harness the power of microsystems technology together with advances in chemistry and molecular biology, to become service and technology providers to organizations involved in pharmaceutical research and development. By supplying a unique range of decision-making tools that aid an earlier identification of qualified drug candidates for clinical development, the company should gain a significant share of the 10 billion US dollar biological screening, bioavailability and toxicity assessment market.     

Integrating virtual screening in lead discovery

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.     

DNA-encoded chemical libraries for the discovery of MMP-3 inhibitors

Encoded self-assembling chemical (ESAC) libraries are characterized by the covalent display of chemical moieties at the extremity of self-assembling oligonucleotides carrying a unique DNA sequence for the identification of the corresponding chemical moiety. We have used ESAC library technology in a two-step selection procedure for the identification of novel inhibitors of stromelysin-1 (MMP-3), a matrix metalloproteinase involved in both physiological and pathological tissue remodeling processes, yielding novel inhibitors with micromolar potency.     

Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA

The ecdysone receptor (EcR) is an insect nuclear receptor that is activated by the molting hormone, 20-hydroxyecdysone. Because synthetic EcR ligands disrupt the normal growth of insects, they are attractive candidates for new insecticides. In this study, the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method was used to predict the binding activity of EcR ligands. Validity analyses using 40 known EcR ligands showed that the binding activity was satisfactorily predicted when the ligand conformational free energy term was introduced. Subsequently, this MM/PBSA method was applied to structure-based hierarchical virtual screening, and 12 candidate compounds were selected from a database of 3.8 million compounds. Five of these compounds were active in a cell-based competitive binding assay. The most potent compound is a simple proline derivative with low micromolar binding activity, representing a valuable lead compound for further structural optimization.     

Development and virtual screening of target libraries.

The concomitant development of in silico screening technologies and of three-dimensional information on therapeutically relevant macromolecular targets makes it possible to navigate in the structural proteome and to identify targets fulfilling user-defined queries. This review illustrates some in-house recent advances in the development of target libraries and how they can be browsed to unravel chemogenomic information.     

NMR screening in drug discovery

NMR methods in drug discovery have traditionally been used to obtain structural information for drug targets or target-ligand complexes. Recently, it has been shown that NMR may be used as an alternative approach for identification of ligands that bind to protein drug targets, shifting the emphasis of many NMR laboratories towards screening and design of potential drug molecules, rather than structural characterization.     

Structure-based virtual screening and identification of a novel androgen receptor antagonist

Hormonal therapies, mainly combinations of anti-androgens and androgen deprivation, have been the mainstay treatment for advanced prostate cancer because the androgen-androgen receptor (AR) system plays a pivotal role in the development and progression of prostate cancers. However, the emergence of androgen resistance, largely due to inefficient anti-hormone action, limits the therapeutic usefulness of these therapies. Here, we report that 6-(3,4-dihydro-1H-isoquinolin-2-yl)-N-(6-methylpyridin-2-yl)nicotinamide (DIMN) acts as a novel anti-androgenic compound that may be effective in the treatment of both androgen-dependent and androgen-independent prostate cancers. Through AR structure-based virtual screening using the FlexX docking model, fifty-four compounds were selected and further screened for AR antagonism via cell-based tests. One compound, DIMN, showed an antagonistic effect specific to AR with comparable potency to that of the classical AR antagonists, hydroxyflutamide and bicalutamide. Consistent with their anti-androgenic activity, DIMN inhibited the growth of androgen-dependent LNCaP prostate cancer cells. Interestingly, the compound also suppressed the growth of androgen-independent C4-2 and CWR22rv prostate cancer cells, which express a functional AR, but did not suppress the growth of the AR-negative prostate cancer cells PPC-1, DU145, and R3327-AT3.1. Taken together, the results suggest that the synthetic compound DIMN is a novel anti-androgen and strong candidate for useful therapeutic agent against early stage to advanced prostate cancer.     

Hypothesis testing in high-throughput screening for drug discovery

Following the success of small-molecule high-throughput screening (HTS) in drug discovery, other large-scale screening techniques are currently revolutionizing the biological sciences. Powerful new statistical tools have been developed to analyze the vast amounts of data in DNA chip studies, but have not yet found their way into compound screening. In HTS, characterization of single-point hit lists is often done only in retrospect after the results of confirmation experiments are available. However, for prioritization, for optimal use of resources, for quality control, and for comparison of screens it would be extremely valuable to predict the rates of false positives and false negatives directly from the primary screening results. Making full use of the available information about compounds and controls contained in HTS results and replicated pilot runs, the Z score and from it the p value can be estimated for each measurement. Based on this consideration, we have applied the concept of p-value distribution analysis (PVDA), which was originally developed for gene expression studies, to HTS data. PVDA allowed prediction of all relevant error rates as well as the rate of true inactives, and excellent agreement with confirmation experiments was found.     

Large-scale integrated super-computing platform for next generation virtual drug discovery

Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand-receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed.     

Structure-based prediction of Mycobacterium tuberculosis shikimate kinase inhibitors by high-throughput virtual screening

A structure-based virtual screening protocol was used to predict Mycobacterium tuberculosis shikimate kinase (MtSk) inhibitors. Docking simulations were performed using eHiTS software and 644 drug-like compounds were identified as potential inhibitors. Forty-two percent of such inhibitors had a structural relationship to a triazole or a tetrazole heteroaromatic system which may provide a candidate lead for the discovery of MtSk inhibitors.