Screening protocol for the identification of modulators by immunofluorescent cell-based assay.

High-throughput assays are a common strategy for the identification of compounds able to modulate a certain cellular activity. Here, we show an automatized analysis platform for the quantification of nuclear foci as inhibitory effect of compounds on a target protein labeled by fluorescent antibodies. Our experience led us to a fast analysis platform that combines cell-based assays, high-content screening, and confocal microscopy, with an automatic and user-friendly statistical analysis of plate-based assays including positive and negative controls, able to identify inhibitory effect of compounds tested together with the Z-prime and Window of individual plate-based assays to assess the reliability of the results. The platform integrates a web-based tool implemented in Pipeline Pilot and R, and allows computing the inhibition values of different parameters obtained from the high-content screening and confocal microscopy analysis. This facilitates the exploration of the results using the different parameters, providing information at different levels as the number of foci observed, the sum of intensity of foci, area of foci, etc, the detection and filtering of outliers over the assay plate, and finally providing a set of statistics of the parameters studied together with a set of plots that we believe significantly helps to the interpretation of the assay results.

Non-genotoxic activation of p53 through the RPL11-dependent ribosomal stress pathway.

Nucleolar disruption has recently emerged as a relevant means to activate p53 through inhibition of HDM2 by ribosome-free RPL11. Most drugs that induce nucleolar disruption also possess important genotoxic activity, which can have lasting mutagenic effects. Therefore, it is of interest to identify compounds that selectively produce nucleolar disruption in the absence of DNA damage. Here, we have performed a high-throughput screening to search for nucleolar disruptors. We have identified an acridine derivative (PubChem CID-765471) previously known for its capacity to activate p53 independently of DNA damage, although the molecular mechanism underlying p53 activation had remained uncharacterized. We report that CID-765471 produces nucleolar disruption by inhibiting ribosomal DNA transcription in a process that includes the selective degradation of the RPA194 subunit of RNA polymerase I. Following nucleolar disruption, CID-765471 activates p53 through the RPL11/HDM2 pathway in the absence of detectable DNA damage. In a secondary screening of compounds approved for medical use, we identify two additional acridine derivatives, aminacrine and ethacridine, that operate in a similar manner as CID-765471. These findings provide the basis for non-genotoxic chemotherapeutic approaches that selectively target the nucleolus.

Predicting the enantioselectivity of the copper-catalysed cyclopropanation of alkenes by using quantitative quadrant-diagram representations of the catalysts.

We present a new methodology to predict the enantioselectivity of asymmetric catalysis based on quantitative quadrant-diagram representations of the catalysts and quantitative structure-selectivity relationship (QSSR) modelling. To account for quadrant occupation, we used two types of molecular steric descriptors: the Taft-Charton steric parameter (ν(Charton)) and the distance-weighted volume (V(W) ). By assigning the value of the steric descriptors to each of the positions of the quadrant diagram, we generated the independent variables to build the multidimensional QSSR models. The methodology was applied to predict the enantioselectivity in the cyclopropanation of styrene catalysed by copper complexes. The dataset comprised 30 chiral ligands belonging to four different oxazoline-based ligand families: bis- (Box), azabis- (AzaBox), quinolinyl- (Quinox) and pyridyl-oxazoline (Pyox). In the first-order approximation, we generated QSSR models with good predictive ability (r(2) =0.89 and q(2) =0.88). The derived stereochemical model indicated that placing very large groups at two diagonal quadrants and leaving free the other two might be enough to obtain an enantioselective catalyst. Fitting the data to a higher-order polynomial, which included crossterms between the descriptors of the quadrants, resulted in an improvement of the predicting ability of the QSSR model (r(2) =0.96 and q(2) =0.93). This suggests that the relationship between the steric hindrance and the enantioselectivity is non-linear, and that bulky substituents in diagonal quadrants operate synergistically. We believe that the quantitative quadrant-diagram-based QSSR modelling is a further conceptual tool that can be used to predict the selectivity of chiral catalysts and other aspects of catalytic performance.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Centralizing discovery information: from logistics to knowledge at a public organization.

Due to the huge amount of data generated in drug discovery programs, their success strongly depends on both the workflows and platforms to manage and, more importantly, to integrate different chemical and biological data sources. At Experimental Therapeutics Program in the Spanish National Cancer Research Center (CNIO), we have addressed our efforts in the design and optimal implementation of those key processes that enable dynamic workflows and interfaces between the different information blocks. Our approach focuses on the development of a common chemical and biological repository (CCBR) that gathers all data that pass quality control criteria. An integral web application (WACBIP) was designed to query against CCBR while providing decision making tools. Currently, our CCBR contains more than 43,000 unique structures as well as experimental data from more than 350 different biological assays. As input sources of the CCBR, we federated a series of Laboratory Information Management Systems (LIMS) which cover sections as follows: chemical synthesis, analytical department, compound logistics, biochemical and cellular data (including high-throughput and high-content screenings; HTS and HCS), computational chemistry (in-silico chemogenomics and physico-chemical profiling) and in-vivo pharmacology. With regard to the last section, an integral In-Vivo Management e-Biobook (IVMB) that handles the entire workflow of in-vivo labs was designed and implemented. Herein we describe the processes and tools that we have developed and implemented, balancing purchase and development, for centralizing discovery information as well as providing decision-making and project management tools - a clear unmet need in public organizations and networks.

Discovery of mitogen-activated protein kinase-interacting kinase 1 inhibitors by a comprehensive fragment-oriented virtual screening approach.

Mitogen-activated protein kinase-interacting kinases 1 and 2 (MNK1 and MNK2) phosphorylate the oncogene eIF4E on serine 209. This phosphorylation has been reported to be required for its oncogenic activity. To investigate if pharmacological inhibition of MNK1 could be useful for the treatment of cancers, we pursued a comprehensive virtual screening approach to rapidly identify pharmacological tools for target validation and to find optimal starting points for a plausible medicinal chemistry project. A collection of 1236 compounds, selected from a library of 42 168 compounds and a database of 18.8 million structures, were assayed. Of the identified hits, 26 were found to have IC(50) values less than 10 µM (2.10% hit rate). The most potent compound had an IC(50) value of 117 nM, and 73.1% of these hits were fragments. The hits were characterized by a high ligand efficiency (0.32-0.52 kcal/mol per heavy atom). Ten different chemical scaffolds were represented, giving a chemotype/hit ratio of 0.38.

Data fusion of similarity and dissimilarity measurements using Wiener-based indices for the prediction of the NPY Y5 receptor antagonist capacity of benzoxazinones.

In this paper, we present the advantages of using data fusion of similarity and dissimilarity measurements for the development of quantitative structure-activity relationship models. Nonisomorphic fragments extracted in the matching process were considered to obtain dissimilarity values employed for correcting similarity measurements, thus, leading to finer chemical information. The purpose was to correlate similarity and dissimilarity matrices with pharmacological activities of drugs (the inhibitory capacity presented by 30 benzoxazinone derivatives for the NPY Y5 receptor). Wiener and hyper-Wiener descriptors computed over distance and weighted distance matrices were used for the calculation of dissimilarity values. A comparison with classical and fingerprints-based similarity was also carried out. The best approaches were achieved by means of dissimilarity and of fusion data spaces that take into account isomorphic and nonisomorphic information (Q2 = 0.88, SECV = 0.18, slope = 1.06, and intercept = 0.09). The study of anomalous behavior presented by some compounds was also undertaken.

New quantum mechanics-based three-dimensional molecular descriptors for use in QSSR approaches: application to asymmetric catalysis.

This paper presents a new protocol based on 3D molecular descriptors using QM calculations for use in CoMFA-like 3D-QSSR. The new method was developed and then applied to predict catalytic selectivity in the asymmetric alkylation of aldehydes catalyzed by Zn-aminoalcohols. The molecular descriptors are obtained straightforwardly from the electronic charge density function, rho(r), and the molecular electrostatic potential (MEP) distributions. The chemically meaningful Molecular Shape Field (MSF) descriptor that accounts for the shape properties of the catalyst is defined from rho(r). Alignment independence was achieved by computing the product of the MSF and MEP values of pairs of points over a given distance range on a molecular isosurface and then selecting the product with the highest value. The new QSSR method demonstrated good predictive ability (q2 = 0.79) when full cross-validation procedures were carried out. Accurate predictions were made for a larger data set, although some deviations occurred in the predictions for catalytic systems with low enantiodiscrimination. Analysis of this QSSR model allows for the following: (1) evaluation of the contribution of each functional group to enantioselectivity and (2) the molecular descriptors to be related to previously proposed stereochemical models for the reaction under study.

Refinement and use of the approximate similarity in QSAR models for benzodiazepine receptor ligands.

Several considerations for refining the approximate similarity measurements have been introduced in this paper: the use of topological invariants for the calculation of similarity indexes and the development of new similarity correction processes. The quality of the new similarity measurements obtained with the proposed methods has permitted the development of fast, cheap, and simple quantitative structure-activity relationship models for the prediction of biological activities of nonbenzodiazepine gamma-aminobutyric acid(A)/benzodiazepine receptor ligands (58 compounds). Internal and external validations were carried out for the approximate similarity matrices computed using different approaches. Satisfactory results which compare reasonably well with a 3D approach were obtained: Q2= 0.65 and standard error in cross validation SECV= 0.83 for the training stage; r = 0.79 and error in external prediction = 0.82 for the test step. In addition, the method proposed was compared with other topological approaches based on constitutional similarity and on fingerprints. Satisfactory results were obtained.

A steroids QSAR approach based on approximate similarity measurements.

A new QSAR method based on approximate similarity measurements is described in this paper. Approximate similarity is calculated using both the classical similarity based on the graph isomorphism and a distance computation between nonisomorphic subgraphs. The latter is carried out through a parametric function where different topological invariants can be considered. After optimizing the contribution of nonisomorphic distance to the new graph similarity, predictive models built with approximate similarity matrixes show higher predictive ability than those using traditional similarity matrixes. The new method has been applied to the prediction of steroids binding to the corticosteroid globulin receptor. The proposed model allows us to obtain valuable external predictions (r=0.82 and SEP=0.30) after training the model by cross-validation (Q2=0.84 and SECV=0.47). Slope and bias parameters are also given.