Synthesis, biological evaluation, and molecular modeling of nitrile-containing compounds : exploring multiple activities as anti-Alzheimer agents

Funding: EC COST Actions D34 and CM1103 for Short-term Scientific Mission funding (EM, DS, MM); the School of Biology at the University of St. Andrews (EJS, RRR); the Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa (AN, ACJ, TR, MCC); FCT, the Portuguese Foundation for Science and Technology (Project PTDC/SAU-NEU/64151/2006 (MCC), and project grant (DS) Vega 2/0127/18 and the contract No. APVV-15-0455 of Slovak Research and Development Agency (MM).Based on the monoamine oxidase (MAO) inhibition properties of aminoheterocycles with a carbonitrile group we have carried out a systematic exploration to discover new classes of carbonitriles endowed with dual MAO and AChE inhibitory activities, and Aβ anti‐aggregating properties. Eighty‐three nitrile‐containing compounds, 13 of which are new, were synthesized and evaluated. in vitro screening revealed that 31 , a new compound, presented the best lead for trifunctional inhibition against MAO A (0.34 μM), MAO B (0.26 μM), and AChE (52 μM), while 32 exhibited a lead for selective MAO A (0.12 μM) inhibition coupled to AChE (48 μM) inhibition. Computational analysis revealed that the malononitrile group can find an advantageous position with the aromatic cleft and FAD of MAO A or MAO B. However, the total binding energy can be handicapped by an internal penalty caused by twisting of the ligand molecule and subsequent disruption of the conjugation ( 32 in MAO B compared to the conjugated 31 ). Conjugation is also important for AChE as well as the hydrophilic character of malononitrile that allows this group to be in close contact with the aqueous environment as seen for 83 . Although the effect of 31 and 32 against Aβ1–42, was very weak, the effect of 63 and 65 , and of the new compound 75 , indicated that these compounds were able to disaggregate Aβ1–42 fibrils. The most effective was 63 , a (phenylhydrazinylidene)propanedinitrile derivative that also inhibited MAO A (1.65 μM), making it a potential lead for Alzheimer's disease application.PostprintPeer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Electron Ionization Mass Spectrometric Study of Some Substituted 1,3-oxazoles

Comunicação oral sob a forma painelHeterocyclic compounds are widely used as pharmaceuticals. Tacrine (THA) was the first drug approved for the treatment of Alzheimer’s disease (AD) and a great effort has been made to design and synthesize new THA-analogues in order to evaluate their beneficial effects as therapeutic agents in this disease.1-6 Contrasting with the pharmaceutical interest, mass spectrometric studies of these compounds and of some of its precursors, namely the oxazole and oxazole derivatives, are scarce. In order to obtain its structural information, a mass spectrometric study was performed with some 5-amino-2-aryl-4-cyano-1,3-oxazoles (Compounds 1 - 5) using a quadrupole mass spectrometer, equipped with an electron ionization (EI) ion source. Detailed fragmentation pathways have been established for all significant ions, including some fragment ions characteristic of the oxazole family. 1. Marco JL, Ríos C, Carreiras MC, Baños JE, Badía A, Vivas NM. Bioorg. Med. Chem. 2001; 9: 727. 2. Ros E, Aleu J, Gomez de Aranda I, Canti C, Pang YP, Marsal J, Solsona C. J Neurophysiol. 2001; 86: 183. 3. Marco JL, Ríos C, Carreiras MC, Baños JE, Badía A, Vivas NM. Arch. Pharm. Pharm. Med. Chem. 2002; 7: 347. 4. Barreiro EJ, Câmara CA, Verli H, Brazilmás L, Castro NG, Cintra WM, Aracava Y, Rodrigues CR, Fraga CAM. Journal of Medicinal Chemistry 2003; 46: 1144. 5. Marco JL, Carreiras MC. Mini-Rev. Med. Chem. 2003; 3: 518. 6. Marco JL, Ríos C, García AG, Villarroya M, Carreiras MC, Martins C, Eleutério A, Morreale A, Orozco M, Luque FJ. Bioorg. Med. Chem. 2004; 12: 2199.Centro de Química e Bioquímica, Faculdade de Ciências, Universidade de Lisbo. Centro de Estudos de Ciências Farmacêuticas, Faculdade de Farmácia, Universidade de Lisboa

Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging

Acetylcholinesterase inhibition was modeled for a set of 136 tacrine analogues using Bayesian-regularized Genetic Neural Networks (BRGNNs). In the BRGNN approach the Bayesian-regularization avoids overtraining/overfitting and the genetic algorithm (GA) allows exploring an ample pool of 3D-descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of diverse-training set neural network ensembles (NNEs). The ensemble averaging provides reliable statistics. When 40 members are assembled, the NNE provides a reliable measure of training and test set R values of 0.921 and 0.851 respectively. In other respects, the ability of the nonlinear selected GA space for differentiating the data was evidenced when the total data set was well distributed in a Kohonen Self-Organizing Map (SOM). The location of the inhibitors in the map facilitates the analysis of the connection between compounds and serves as a useful tool for qualitative predictions

The multifactorial nature of Alzheimer's disease for developing potential therapeutics

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder with several target proteins contributing to its aetiology. Pathological, genetic, biochemical, and modeling studies all point to a critical role of Aβ aggregation in AD. Though there are still many enigmatic aspects of the Aβ cascade, none of the gaps invalidate the hypothesis. The amyloid hypothesis determines that the production, aggregation and accumulation of Aβ in the brain gives rise to a cascade of neurotoxic events that proceed to neuronal degeneration. Different targets of the disease include APP pathogenic cleavage, cytoskeletal destabilization, neurotransmitter and ion dyshomeostasis, metal ion accumulation, protein misfolding, oxidative stress, neuronal death and gene mutations. Thus, disease-modifying treatments for AD must interfere with the pathogenic steps responsible for the clinical symptoms: the deposition of extracellular Aβ plaques, the intracellular neurofibrillary tangles, inflammation, oxidative stress, iron deregulation, among others. The observations supporting the development of multifunctional compounds in association with the perception that several dual binding site AChEIs were able to reach different targets guided the development of a new drug design strategy, the multi-target-directed-ligand (MTDL) approach. This may be regarded as the buildup of hybrid molecules composed of distinct pharmacophores of different drugs. Thus, each pharmacophore of the new hybrid drug would preserve the capacity of interacting with their specific sites on the targets and, therefore, generate multiple specific pharmacological responses which would enable the treatment of multi-factorial diseases. This review summarizes a few current therapeutic trends on MTDL strategy intended to halt or revert the progression of the disease. © 2013 Bentham Science Publishers.Peer Reviewe

A reinvestigation of the acid-promoted heterocyclization of 2-(2-oxo-2-arylethyl)malononitriles in the presence of amines

The reinvestigation of the acid-promoted cyclization of 2-(2-oxo-2-arylethyl)malononitriles, in the presence of benzylamine or aniline, in ethanol or acetonitrile, has confirmed that this is a long-time reaction process for a low-yielding synthesis of 2-amino-5-arylfuran-3-carbonitriles (2), or 2-amino-5-aryl-1-phenyl-1H-pyrrole-3-carbonitriles (4), depending on the base used. However, the microwave-assisted synthesis of 2-amino-5-(4'-methoxyphenyl)furan-3(4)-(di)carbonitriles (2c and 3c) proceeds in shorter reaction times and higher yields than does the classical thermal heating protocol. In these reactions we have observed for the first time, and characterized by their spectroscopic data and X-ray analysis, the unexpected formation of 2-amino-5-aryl-3 (4)-(di)carbonitriles (3), whose formation has been rationalized by density functional theory (DFT) analysis of the proposed reaction mechanism.. - Portuguese Foundation for Science and Technology (FCT) [SFRH/BD/17577/2004]; MEC [SAF2006-08764-C02-01]; Comunidad deMadrid [S/SAL-0275-2006]; Instituto de Salud Carlos III [RD06/0026/1002]; CSIC-GRICES project [2007PT13]. - This work was supported by the Portuguese Foundation for Science and Technology (FCT) funds as well as by C. M. Ph. D. fellowship SFRH/BD/17577/2004. M. C. C. thanks Carolina Duarte and Paula Santos for technical assistance. The present work has been supported by MEC Grants SAF2006-08764-C02-01, Comunidad deMadrid (S/SAL-0275-2006), Instituto de Salud Carlos III [Retic RENEVAS (RD06/0026/1002)], and CSIC-GRICES project (2007PT13)