Nearly 50% of human malignancies exhibit unregulated RAS-ERK signaling; inhibiting it is a valid strategy for antineoplastic intervention. Upon activation, ERK dimerize, which is essential for ERK extranuclear, but not for nuclear, signaling. Here, we describe a small molecule inhibitor for ERK dimerization that, without affecting ERK phosphorylation, forestalls tumorigenesis driven by RAS-ERK pathway oncogenes. This compound is unaffected by resistance mechanisms that hamper classical RAS-ERK pathway inhibitors. Thus, ERK dimerization inhibitors provide the proof of principle for two understudied concepts in cancer therapy: (1) the blockade of sub-localization-specific sub-signals, rather than total signals, as a means of impeding oncogenic RAS-ERK signaling and (2) targeting regulatory protein-protein interactions, rather than catalytic activities, as an approach for producing effective antitumor agents.

Powerful methodologies for drug design and drug database screening and selection are presently available. Studies relating the structure of molecules to a property or a biological activity by means of statistical tools (QSPR and QSAR studies, respectively) are particularly relevant. An important point for this methodology is the use of good structural descriptors that are representative of the molecular features responsible for the relevant activity. Topological indices (TIs) are two-dimensional descriptors which take into account the internal atomic arrangement of compounds, and which encode in numerical form information about molecular size, shape, branching, presence of heteroatoms and multiple bonds. The usefulness of TIs in QSPR and QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity by their application to databases virtually generated by computer. In this article we will briefly review the history of TIs, their advantages and limitations with respect to other descriptors, and their possibilities in drug design and database selection. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse QSAR.

Molecular connectivity has been applied to the search of new compounds with activity against the protozoan Toxoplasma gondii, using a stepwise linear discriminant analysis (SLDA) which is able to classify a compound according its activity either as active or as inactive. Among the selected compounds, andrographolide and dibenzotiophene sulfone stand out, both with IC50 values lower than 1 microgram/ml, which are comparable to these of drugs such as sulfamethoxazole, pyrimethamine and trimethoprim, with IC50 values equal to 1.1, 0.04 and 2.31 micrograms/ml, respectively. These results confirm the usefulness of our topological approach for the selection and design of new-lead drugs active against Toxoplasma gondii.

The apicoplast, a plastid-like organelle of Toxoplasma gondii, is thought to be a unique drug target for quinolones. In this study, we assessed the in vitro activity of quinolones against T. gondii and developed new quantitative structure-activity relationship models able to predict this activity. The anti-Toxoplasma activities of 24 quinolones were examined by means of linear discriminant analysis (LDA) using topological indices as structural descriptors. In parallel, in vitro 50% inhibitory concentrations (IC 50 s) were determined in tissue culture. A multilinear regression (MLR) analysis was then performed to establish a model capable of classifying quinolones by in vitro activity. LDA and MLR analysis were applied to virtual structures to identify the influence of each atom or substituent of the quinolone ring on anti-Toxoplasma activity. LDA predicted that 20 of the 24 quinolones would be active against T. gondii. This was confirmed in vitro for most of the quinolones. Trovafloxacin, grepafloxacin, gatifloxacin, and moxifloxacin were the quinolones most potent against T. gondii, with IC 50 s of 0.4, 2.4, 4.1, and 5.1 mg/liter, respectively. Using MLR analysis, a good correlation was found between measured and predicted IC 50 s (r 2 ‫؍‬ 0.87, cross-validation r 2 ‫؍‬ 0.74). MLR analysis showed that the carboxylic group at position C-3 of the quinolone ring was not essential for anti-Toxoplasma activity. In contrast, activity was totally dependent on the presence of a fluorine at position C-6 and was enhanced by the presence of a methyl group at C-5 or an azabicyclohexane at C-7. A nucleophilic substituent at C-8 was essential for the activity of gatifloxacin and moxifloxacin.The discovery of a novel organelle in apicomplexan parasites and its characterization in Toxoplasma gondii offers new opportunities for pharmacological research on several protozoa of major medical importance (16). This organelle, the apicoplast, is a plastid-like structure which was probably acquired by secondary endosymbiosis from a green alga (15). The function of the apicoplast is still not clear, but the presence of this procaryotic structure within T. gondii presents a unique therapeutic target. Fichera and Roos showed that several antibiotics, such as azithromycin and ciprofloxacin, could inhibit DNA replication within the apicoplast and thus inhibited Toxoplasma growth (6). That study confirmed the previously wellknown effect of macrolides on T. gondii but also revealed fluoroquinolones as candidate anti-Toxoplasma drugs. However, other studies performed in vitro and in vivo failed to confirm the activity of ciprofloxacin against T. gondii and showed that, among the fluoroquinolones, only trovafloxacin and some of its derivatives inhibited Toxoplasma growth at micromolar concentrations (9, 10). Better knowledge of the structure-activity relationships of quinolones against T. gondii is thus needed.The aims of this work were (i) to assess quinolone activity against T. gondii by using a previously described model of virtual predicti...

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A QSAR model accounting for "average" G-protein-coupled receptor (GPCR) binding was built from a large set of experimental standardized binding data (1939 compounds systematically tested over 40 different GPCRs) and applied to the design of a library of "GPCR-predicted" compounds. Three hundred and sixty of these compounds were randomly selected and tested in 21 GPCR binding assays. Positives were defined by their ability to inhibit by more than 70% the binding of reference compounds at 10 microM. A 5.5-fold enrichment in positives was observed when comparing the "GPCR-predicted" compounds with 600 randomly selected compounds predicted as "non-GPCR" from a general collection. The model was efficient in predicting strongest binders, since enrichment was greater for higher cutoffs. Significant enrichment was also observed for peptidic GPCRs and receptors not included to develop the QSAR model, suggesting the usefulness of the model to design ligands binding with newly identified GPCRs, including orphan ones.

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In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.