Tetrahydropyridine LIMK inhibitors: Structure activity studies and biological characterization.

LIM Kinases, LIMK1 and LIMK2, have become promising targets for the development of inhibitors with potential application for the treatment of several major diseases. LIMKs play crucial roles in cytoskeleton remodeling as downstream effectors of small G proteins of the Rho-GTPase family, and as major regulators of cofilin, an actin depolymerizing factor. In this article we describe the conception, synthesis, and biological evaluation of novel tetrahydropyridine pyrrolopyrimidine LIMK inhibitors. Homology models were first constructed to better understand the binding mode of our preliminary compounds and to explain differences in biological activity. A library of over 60 products was generated and in vitro enzymatic activities were measured in the mid to low nanomolar range. The most promising derivatives were then evaluated in cell on cofilin phosphorylation inhibition which led to the identification of 52 which showed excellent selectivity for LIMKs in a kinase selectivity panel. We also demonstrated that 52 affected the cell cytoskeleton by disturbing actin filaments. Cell migration studies with this derivative using three different cell lines displayed a significant effect on cell motility. Finally, the crystal structure of the kinase domain of LIMK2 complexed with 52 was solved, greatly improving our understanding of the interaction between 52 and LIMK2 active site. The reported data represent a basis for the development of more efficient LIMK inhibitors for future in vivo preclinical validation.

Randomized double-blind placebo-controlled cosmetic trial of a topical first-in-class Neutraligand targeting the chemokine TARC/CCL17 in mild-to-moderate atopic dermatitis.

BACKGROUND: Atopic dermatitis has a marked economic impact and affects the quality of life. A cosmetic compound with an innovative strategy is proposed here as a small chemical neutraligand, GPN279 (previously identified as a theophylline derivative), that binds and potently neutralizes the TARC/CCL17 chemokine, activating the Th2 cell-expressed CCR4 receptor. OBJECTIVE: Our objective was to evaluate the safety and activity of topically applied GPN279 in mild-to-moderate atopic dermatitis patients in a randomized, double-blind, placebo-controlled, parallel group trial. Such cosmetic active ingredient targeting dry skin with an atopic tendency would open a parallel strategy to the pharmaceutical approach, in particular for mild to moderate subjects, as an alternative to reduce the evolution towards severe forms of atopy. METHODS: This 4-week trial included adults with mild-to-moderate atopic dermatitis, according to the SCORAD index. Patients were randomized into two groups treated by topical applications of either an emulsion containing 0.44% GPN279 in placebo on skin lesions or the placebo (4.56% glycerin). Clinical activity was evaluated with the SCORAD as the primary objective. As secondary objectives, POEM, erythema, skin moisturization, its barrier function (TEWL) and safety were evaluated. RESULTS: Twenty-one patients in each group completed the study. SCORAD was significantly improved in the GPN279 group vs. placebo. GPN279 also significantly improved POEM, induced a rapid and significant decrease of erythema, and improved skin moisture. GPN279 and placebo were well tolerated throughout the study. CONCLUSION: A cosmetic cream comprising the CCL17 neutraligand GPN279 improved the skin barrier and physiology criteria in patients with mild-to-moderate atopic dermatitis.

GÉNÉRALITÉS: La dermatite atopique a un impact économique marqué et affecte la qualité de vie. Un composé cosmétique dote d'une stratégie innovante est proposé ici sous la forme d'un petit neutraligand chimique, le GPN279 (précédemment identifié comme un dérivé de la théophylline), qui se lie et neutralise puissamment la chimiokine TARC/CCL17, activant le récepteur CCR4 exprimé par les cellules Th2. OBJECTIF: Notre objectif était d'évaluer l'innocuité et l'activité du GPN279 appliqué localement chez des patients atteints de dermatite atopique légère à modérée dans un essai randomisé, en double aveugle contre placebo et en groupes parallèles. Un tel actif cosmétique ciblant les peaux sèches à tendance atopique ouvrirait une stratégie parallèle à l'approche pharmaceutique, notamment pour les sujets atteints de forme légère à modérée, comme alternative visant à réduire l'évolution vers des formes sévères d'atopie. MÉTHODES: Cet essai de 4 semaines incluait des adultes atteints de dermatite atopique légère à modérée, selon l'indice SCORAD. Les patients ont été randomisés en deux groupes traités par application topique sur les lésions cutanées soit d'une émulsion contenant 0,44% de GPN279 dans un placebo, soit du placebo seul (4,56% de glycérine). L'activité clinique a été évaluée selon l'indice SCORAD comme objectif principal. Les objectifs secondaires évaluaient le POEM, l'érythème, l'hydratation de la peau, sa fonction barrière (TEWL) et la sécurité. RÉSULTATS: Vingt et un patients de chaque groupe ont terminé l'étude. L'indice SCORAD a été significativement amélioré dans le groupe GPN279 par rapport au placebo. Le GPN279 a également amélioré de manière significative le POEM, a induit une diminution rapide et significative de l'érythème et amélioré l'hydratation de la peau. Le GPN279 et le placebo ont été bien tolérés tout au long de l'étude. CONCLUSION: Une crème cosmétique contenant le neutraligand CCL17 GPN279 améliore la barrière cutanée et les critères physiologiques chez les patients atteints de dermatite atopique légère à modérée.

The Identification of New c-FLIP Inhibitors for Restoring Apoptosis in TRAIL-Resistant Cancer Cells.

The catalytically inactive caspase-8-homologous protein, c-FLIP, is a potent antiapoptotic protein highly expressed in various types of cancers. c-FLIP competes with caspase-8 for binding to the adaptor protein FADD (Fas-Associated Death Domain) following death receptors' (DRs) activation via the ligands of the TNF-R family. As a consequence, the extrinsic apoptotic signaling pathway involving DRs is inhibited. The inhibition of c-FLIP activity in tumor cells might enhance DR-mediated apoptosis and overcome immune and anticancer drug resistance. Based on an in silico approach, the aim of this work was to identify new small inhibitory molecules able to bind selectively to c-FLIP and block its anti-apoptotic activity. Using a homology 3D model of c-FLIP, an in silico screening of 1880 compounds from the NCI database (National Cancer Institute) was performed. Nine molecules were selected for in vitro assays, based on their binding affinity to c-FLIP and their high selectivity compared to caspase-8. These molecules selectively bind to the Death Effector Domain 2 (DED2) of c-FLIP. We have tested in vitro the inhibitory effect of these nine molecules using the human lung cancer cell line H1703, overexpressing c-FLIP. Our results showed that six of these newly identified compounds efficiently prevent FADD/c-FLIP interactions in a molecular pull-down assay, as well as in a DISC immunoprecipitation assay. The overexpression of c-FLIP in H1703 prevents TRAIL-mediated apoptosis; however, a combination of TRAIL with these selected molecules significantly restored TRAIL-induced cell death by rescuing caspase cleavage and activation. Altogether, our findings indicate that new inhibitory chemical molecules efficiently prevent c-FLIP recruitment into the DISC complex, thus restoring the caspase-8-dependent apoptotic cascade. These results pave the way to design new c-FLIP inhibitory molecules that may serve as anticancer agents in tumors overexpressing c-FLIP.

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.

A perspective on the sharing of docking data.

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Design, synthesis and characterisation of a novel type II B-RAF paradox breaker inhibitor.

The mutation V600E in B-Raf leads to mitogen activated protein kinase (MAPK) pathway activation, uncontrolled cell proliferation, and tumorigenesis. ATP competitive type I B-Raf inhibitors, such as vemurafenib (1) and PLX4720 (4) efficiently block the MAPK pathways in B-Raf mutant cells, however these inhibitors induce conformational changes in the wild type B-Raf (wtB-Raf) kinase domain leading to heterodimerization with C-Raf, causing paradoxical hyperactivation of the MAPK pathway. This unwanted activation may be avoided by another class of inhibitors (type II) which bind the kinase in the DFG-out conformation, such as AZ628 (3) preventing heterodimerization. Here we present a new B-Raf kinase domain inhibitor, based on a phenyl(1H-pyrrolo [2,3-b]pyridin-3-yl)methanone template, that represents a hybrid between 4 and 3. This novel inhibitor borrows the hinge binding region from 4 and the back pocket binding moiety from 3. We determined its binding mode, performed activity/selectivity studies, and molecular dynamics simulations in order to study the conformational effects induced by this inhibitor on wt and V600E mutant B-Raf kinase. We discovered that the inhibitor was active and selective for B-Raf, binds in a DFG-out/αC-helix-in conformation, and did not induce the aforementioned paradoxical hyperactivation in the MAPK pathway. We propose that this merging approach can be used to design a novel class of B-Raf inhibitors for translational studies.

Estimation of Drug-Target Residence Time by Targeted Molecular Dynamics Simulations.

Drug-target residence time has emerged as a key selection factor in drug discovery since the binding duration of a drug molecule to its protein target can significantly impact its in vivo efficacy. The challenge in studying the residence time, in early drug discovery stages, lies in how to cost-effectively determine the residence time for the systematic assessment of compounds. Currently, there is still a lack of computational protocols to quickly estimate such a measure, particularly for large and flexible protein targets and drugs. Here, we report an efficient computational protocol, based on targeted molecular dynamics, to rank drug candidates by their residence time and to obtain insights into ligand-target dissociation mechanisms. The method was assessed on a dataset of 10 arylpyrazole inhibitors of CDK8, a large, flexible, and clinically important target, for which the experimental residence time of the inhibitors ranges from minutes to hours. The compounds were correctly ranked according to their estimated residence time scores compared to their experimental values. The analysis of protein-ligand interactions along the dissociation trajectories highlighted the favorable contribution of hydrophobic contacts to residence time and revealed key residues that strongly affect compound residence time.

Design and biological evaluation of substituted 5,7-dihydro-6H-indolo[2,3-c]quinolin-6-one as novel selective Haspin inhibitors.

A library of substituted indolo[2,3-c]quinolone-6-ones was developed as simplified Lamellarin isosters. Synthesis was achieved from indole after a four-step pathway sequence involving iodination, a Suzuki-Miyaura cross-coupling reaction, and a reduction/lactamization sequence. The inhibitory activity of the 22 novel derivatives was assessed on Haspin kinase. Two of them possessed an IC50 of 1 and 2 nM with selectivity towards a panel of 10 other kinases including the parent kinases DYRK1A and CLK1. The most selective compound exerted additionally a very interesting cell effect on the osteosarcoma U-2 OS cell line.

Design, Synthesis and SAR in 2,4,7-Trisubstituted Pyrido[3,2-d]Pyrimidine Series as Novel PI3K/mTOR Inhibitors.

This work describes the synthesis, enzymatic activities on PI3K and mTOR, in silico docking and cellular activities of various uncommon 2,4,7 trisubstituted pyrido[3,2-d]pyrimidines. The series synthesized offers a chemical diversity in C-7 whereas C-2 (3-hydroxyphenyl) and C-4 groups (morpholine) remain unchanged, in order to provide a better understanding of the molecular determinants of PI3K selectivity or dual activity on PI3K and mTOR. Some C-7 substituents were shown to improve the efficiency on kinases compared to the 2,4-di-substituted pyrimidopyrimidine derivatives used as references. Six novel derivatives possess IC50 values on PI3Kα between 3 and 10 nM. The compounds with the best efficiencies on PI3K and mTOR induced micromolar cytotoxicity on cancer cell lines possessing an overactivated PI3K pathway.

Equipping Coiled-Coil Peptide Dimers With Furan Warheads Reveals Novel Cross-Link Partners.

Using a coiled-coil peptide dimer as a model system to explore furan reactivity, we describe novel cross-link partners of furan warheads for site-specific cross-linking. We demonstrate that replacement of weak interhelical ionic contacts with a furan moiety and its potential cross-link partner affords covalently connected coiled-coil motifs upon furan activation. We describe for the first time the reaction of the activated furan warhead with cysteine and tyrosine, besides the previously reported lysine, thus enhancing the versatility of the furan cross-link methodology by the possibility to target different amino acids. The present in vitro validation of "furan-armed" α-helices provides further grounds for exploiting furan technology in the development of furan-modified ligands/proteins to target proteins in a covalent way through various amino acid side chains.

Design of new disubstituted imidazo[1,2-b]pyridazine derivatives as selective Haspin inhibitors. Synthesis, binding mode and anticancer biological evaluation.

Haspin is a mitotic protein kinase required for proper cell division by modulating Aurora B kinase localisation and activity as well as histone phosphorylation. Here a series of imidazopyridazines based on the CHR-6494 and Structure Activity Relationship was established. An assessment of the inhibitory activity of the lead structures on human Haspin and several other protein kinases is presented. The lead structure was rapidly optimised using a combination of crystal structures and effective docking models, with the best inhibitors exhibiting potent inhibitory activity on Haspin with IC50 between 6 and 100 nM in vitro. The developed inhibitors displayed anti-proliferative properties against various human cancer cell lines in 2D and spheroid cultures and significantly inhibited the migration ability of osteosarcoma U-2 OS cells. Notably, we show that our lead compounds are powerful Haspin inhibitors in human cells, and did not block G2/M cell cycle transition due to improved selectivity against CDK1/CyclinB.

Comparative Assessment of Protein Kinase Inhibitors in Public Databases and in PKIDB.

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of "flatland", we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.

Identification of a new series of flavopiridol-like structures as kinase inhibitors with high cytotoxic potency.

In this work, unique flavopiridol analogs bearing thiosugars, amino acids and heterocyclic moieties tethered to the flavopiridol via thioether and amine bonds mainly on its C ring have been prepared. The analogs bearing thioether-benzimidazoles as substituents have demonstrated high cytotoxic activity in vitro against up to seven cancer cell lines. Their cytotoxic effects are comparable to those of flavopiridol. The most active compound 13c resulting from a structure-activity relationship (SAR) study and in silico docking showed the best antiproliferative activity and was more efficient than the reference compound. In addition, compound 13c showed significant nanomolar inhibition against CDK9, CDK10, and GSK3ß protein kinases.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions.

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

Computational Analysis of Crystallization Additives for the Identification of New Allosteric Sites.

Allosteric effect can modulate the biological activity of a protein. Thus, the discovery of new allosteric sites is very attractive for designing new modulators or inhibitors. Here, we propose an innovative way to identify allosteric sites, based on crystallization additives (CA), used to stabilize proteins during the crystallization process. Density and clustering analyses of CA, applied on protein kinase and nuclear receptor families, revealed that CA are not randomly distributed around protein structures, but they tend to aggregate near common sites. All orthosteric and allosteric cavities described in the literature are retrieved from the analysis of CA distribution. In addition, new sites were identified, which could be associated to putative allosteric sites. We proposed an efficient and easy way to use the structural information of CA to identify allosteric sites. This method could assist medicinal chemists for the design of new allosteric compounds targeting cavities of new drug targets.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening.

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations.

In the early stage of a drug discovery process, the selection and optimization of a ligand is mainly based on equilibrium thermodynamic constants such as KD or IC50 values, which are representatives of the affinity of the compound for its target. However, these criteria are not able to correctly evaluate the efficacy of compounds in vivo and result in many failures of compound development during phase II of clinical trials. Residence time (RT) is an important parameter associated to an in vivo drug's safety and efficacy. The determination or modulation of kinetic rates correlated to RT may be performed to identify the best drug candidates in the early stages of a drug design project. For this purpose, a number of experimental methodologies were developed but remain costly in both time and money. Herein, we developed a novel protocol based on biased molecular dynamics simulations and transition-state theory in order to predict relative ligand kinetic rates and relative RTs of a series of compounds. First, we have repeatedly simulated the unbinding process of the ligand from its binding site to the outside of the target. Next, we sample the conformational space along the determined unbinding paths to allow relevant statistical distributions of the system. The free energy profiles associated to these distributions are then computed and used to predict the kinetics parameters. The studied set was composed of eight ligands with fast, intermediate, and slow dissociation rates and binding to the active and inactive states of p38α protein kinase. The proposed method provides an excellent correlation between the predicted values and the experimentally measured kinetic rates, in addition to a detailed characterization of the kinetic paths at the atomic level.

Cinchona-based zwitterionic stationary phases: Exploring retention and enantioseparation mechanisms in supercritical fluid chromatography with a fragmentation approach.

Chiralpak ZWIX(+) and ZWIX(-), are brush-type bonded-silica chiral stationary phases (CSPs), based on complex diastereomeric Cinchona alkaloids derivatives bearing both a positive and a negative charge. In the present study, we aimed to improve the understanding of retention and enantioseparation mechanisms of these CSPs employed in supercritical fluid chromatography (SFC). For this purpose, 9 other stationary phases were used as comparison systems: two of them are commercially available and bear only a positive charge (Chiralpak QN-AX and QD-AX) and the 7 others were designed purposely to be structurally similar to the parent ZWIX phases, but miss some portion of the complex ligand. First, cluster analysis was employed to identify similar and dissimilar behavior among the 11 stationary phases, where ionic interactions appeared to dominate the observed differences. Secondly, the stationary phases were characterized with linear solvation energy relationships (LSER) based on the SFC analysis of 161 achiral analytes and a modified version of the solvation parameter model to include ionic interactions. This served to compare the interaction capabilities for the 11 stationary phases and showed in particular the contribution of attractive and repulsive ionic interactions. Then the ZWIX phases were characterized for their enantioseparation capabilities with a set of 58 racemic probes. Discriminant analysis was applied to explore the molecular structural features that are useful to successful enantioseparation on the ZWIX phases. In particular, it appeared that the presence of positive charges in the analyte is causing increased retention but is not necessarily a favorable feature to enantiorecognition. On the opposite, the presence of negative charges in the analyte favors early elution and enantiorecognition. Finally, a smaller set of 30 pairs of enantiomers, selected by their structural diversity and different enantioseparation values on the ZWIX phases, were analyzed on all chiral phases to observe the contribution of each structural fragment of the chiral ligand on enantioselectivity. Molecular modelling of the ligands also helped in understanding the three-dimensional arrangement of each ligand, notably the intra-molecular hydrogen bonding or the possible contribution of ionic interactions. In the end, each structural element in the ZWIX phases appeared to be a significant contributor to successful enantioresolution, whether they contribute as direct interaction groups (ion-exchange functions) or as steric constraints to orientate the interacting groups towards the analytes.

Valuing Health Surveillance as an Information System: Interdisciplinary Insights.

The economic evaluation of health surveillance systems and of health information is a methodological challenge, as for information systems in general. Main present threads are considering cost-effectiveness solutions, minimizing costs for a given technically required output, or cost-benefit analysis, balancing costs with economic benefits of duly informed public interventions. The latter option, following a linear command-and-control perspective, implies considering a main causal link between information, decision, action, and health benefits. Yet, valuing information, taking into account its nature and multiple sources, the modalities of its processing cycle, from production to diffusion, decentralized use and gradual building of a shared information capital, constitutes a promising challenge. This work proposes an interdisciplinary insight on the value of health surveillance to get a renewed theoretical framework integrating information and informatics theory and information economics. The reflection is based on a typological approach of value, basically distinguishing between use and non-use values. Through this structured discussion, the main idea is to expand the boundaries of surveillance evaluation, to focus on changes and trends, on the dynamic and networked structure of information systems, on the contribution of diverse data, and on the added value of combining qualitative and quantitative information. Distancing itself from the command-and-control model, this reflection considers the behavioral fundaments of many health risks, as well as the decentralized, progressive and deliberative dimension of decision-making in risk management. The framework also draws on lessons learnt from recent applications within and outside of health sector, as in surveillance of antimicrobial resistance, inter-laboratory networks, the use of big data or web sources, the diffusion of technological products and large-scale financial risks. Finally, the paper poses the bases to think the challenge of a workable approach to economic evaluation of health surveillance through a better understanding of health information value. It aims to avoid over-simplifying the range of health information benefits across society while keeping evaluation within the boundaries of what may be ascribed to the assessed information system.

Development of Novel and Efficient Processes for the Synthesis of 5-Amino and 5-Iminoimidazo[1,2-a]imidazoles via Three-Component Reaction Catalyzed by Zirconium(IV) Chloride.

General and efficient approaches for the synthesis of new 5-amino and 5-iminoimidazo[1,2-a]imidazoles were developed through a three-component reaction of 1-unsubstituted 2-aminoimidazoles with various aldehydes and isocyanides mediated by zirconium(IV) chloride. The protocols were established considering the reactivity of the starting substrate, which varies depending on the presence of a substituent on the 2-aminoimidazole moiety. A library of new N-fused ring systems with wide structural diversification, novel synthetic, and potential pharmacological interest was obtained in moderate to good yields.