Multi-Target Spectral Moment QSAR vs. ANN for antiparasitic drugs against different parasite species

The 14th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThere are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial Neural Networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysisF. acknowledges the financial support from program Angeles Albariño and González-Díaz, H. acknowledges financial support of the Programme Isidro Parga Pondal both funded by the Xunta de Galicia and European Social Fund (ESF

Ethyl 2-(Diisopropoxyphosphoryl)-2H-Azirine-3-Carboxylate: Reactions with Activated 1,3-Butadienes

The 12th International Electronic Conference on Synthetic Organic Chemistry session General Organic SynthesisEthyl 2-(diisopropoxyphosphoryl)-2H-azirine-3-carboxylate was generated “in situ” and reacted with a number of 1,3-butadienes. Cycloadducts, or in one case an ensuing rearranged product, were isolated with moderate to good yieldsThe authors thank Fundação para a Ciência e Tecnologia, CIQA-Centro de Investigação em Química do Algarve and Xunta de Galicia for financial support under project 07CSA008203P

Highly diastereoselective synthesis of 2-azabicyclo[2.2.1]hept-5-ene derivatives : bronsted acid catalized aza-diels-alder reaction between cyclopentadiene and imino-acetates with two chiral auxiliaries

The cycloaddition between protonated glyoxylate imines possessing two chiral auxiliaries, N-(S)- or N-(R)-1-phenylethyl and (-)-8-phenylmenthyl or (+)-8- phenylneomenthyl, and cyclopentadiene is described. The absolute configuration of all adducts formed was unequivocally assigned through NMR, specific optical rotation and X-ray data of appropriated derivatives. Experimental results confirm the highly exoselectivity for these aza-Diels–Alder reactions, single adducts being obtained from combinations of (8PM)-(R-PEA) and (8PNM)-(S-PEA).Thanks are due to Fundacao para a Ciencia e Tecnologia (FCT) for financial support given to Faculdade de Ciencias do Porto (project PTDC/QUI/67407/2006) and for financial support through the re-equipment program REDE/1517/RMN/2005

Aza-Diels-Alder versus 1,3-Dipolar Cycloadditions of Methyl Glyoxylate Oxime with Cyclopentadiene

The 12th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThe acid-catalyzed [3+2] and [4+2] cycloadditions between methyl glyoxylate oxime (1) and cyclopentadiene were investigated using various Lewis and/or Bronsted acids at different temperatures in dichloromethane as solvent. Besides the expected new adducts, (±)-methyl [(3-exo)-2-hydroxy-2-azabicyclo[2.2.1]hept-5-ene]-3-carboxylate (2) and (±)-methyl [(3-endo)-2-hydroxy-2-azabicyclo[2.2.1]hept-5-ene]-3-carboxylate (3) a third addict, (±) methyl (1R,4R,5R)-(2-ox-3-azabicyclo[3.3.0]oct-7-ene)-4-carboxylate (4), whose formation can be explained by a concerted 1,3-dipolar cycloaddition, was obtained. Yields and product ratios were found to be more dependent on the catalyst than on the temperature; these results and the stereochemistry of the adducts, confirmed by spectroscopic data (1H and 13C NMR) and by X-ray cycloadditionsThis work was supported by Centro de Investigação em Química of University of Porto. The authors thank the Fundação para a Ciência e Tecnologia (FCT) for financial support of this work under project POCTI/QUI/44471/2002 (Pluri-annual and Programmatic Funding) and for the grant to C. A. D. Sousa (SFRH/BD/31526/2006

A convenient synthesis of new 6-substituted purinylcarbanucleosides on cyclopenta[b]thiophene

The 12th International Electronic Conference on Synthetic Organic Chemistry session Bioorganic Chemistry and Natural ProductsThe first members of a new family of heterocarbobicyclic nucleoside analogues have been synthesized from the cis/trans mixture of (4-amino-5,6-dihydro-4H-cyclopenta[b]thiophen-6-yl)methanols cis/trans-7. The separation of cis and trans intermediates during preparation of the 6-chloropurine derivatives allowed separate preparation of the purine heterocarbanucleosides cis-10 and trans-11, from which cis-(12-14) and trans-(16-18) were obtained by replacement of the 6-chloro substituent with amino, hydroxy and cyclopropylamino groups. Additionally, the 6-phenyl-purinyl analogues cis-15 and trans-19 were prepared from cis-10 and trans-11 using Suzuki-Miyaura methodologyThe authors thank the Xunta de Galicia for financial support under project PGIDIT02BTF20305P

Synthesis and Antiviral Activities of Novel Purinyl- and Pyrimidinylcarbanucleosides Derived from Indan

The 12th International Electronic Conference on Synthetic Organic Chemistry session Bioorganic Chemistry and Natural ProductsStarting from (±)-trans- and (±)-cis-3-hydroxymethyl-1-indanol, novel 6-substituted purinylcarbanucleoside derivatives of indan (5, 6, 9, 10, 15 and 17) were synthesized through a key coupling reaction with 6-chloropurine under Mitsunobu conditions. Suzuki–Miyaura reactions of the protected 6-chloropurine derivative with different arylboronic acids afforded the corresponding 6-arylpurinylcarbanucleoside derivatives. Finally, three new 5-halouracilcarbanucleosides (19, 20 and 21) were prepared by reaction of uracilcarbanucleoside 18 with different N-halosuccinimides. All of the new analogues were evaluated for antiviral activity against a wide variety of virusesThe authors thank the Xunta de Galicia for financial support of this work under projects PGIDT05PXIB20301PR and 07CSA008203P

Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics

[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70–91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical–experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.Ministerio de Economía y Competitividad; CTQ2016-74881-PGobierno Vasco; IT1045-16Xunta de Galicia; GPC2014/003Xunta de Galicia; CN 2012/069Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; GRC2014/049Xunta de Galicia; ED431D 2017/2

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates

In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular OPEN ACCESS Int. J. Mol. Sci. 2014, 15 17036 targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentallyThe authors thank the Xunta de Galicia for financial support of this work under project 07CSA008203PRS

Pharmacological insights emerging from the characterization of a large collection of synthetic cannabinoid receptor agonists designer drugs

Synthetic cannabinoid receptor agonists (SCRAs) constitute the largest and most defiant group of abuse designer drugs. These new psychoactive substances (NPS), developed as unregulated alternatives to cannabis, have potent cannabimimetic effects and their use is usually associated with episodes of psychosis, seizures, dependence, organ toxicity and death. Due to their ever-changing structure, very limited or nil structural, pharmacological, and toxicological information is available to the scientific community and the law enforcement offices. Here we report the synthesis and pharmacological evaluation (binding and functional) of the largest and most diverse collection of enantiopure SCRAs published to date. Our results revealed novel SCRAs that could be (or may currently be) used as illegal psychoactive substances. We also report, for the first time, the cannabimimetic data of 32 novel SCRAs containing an (R) configuration at the stereogenic center. The systematic pharmacological profiling of the library enabled the identification of emerging Structure-Activity Relationship (SAR) and Structure-Selectivity Relationship (SSR) trends, the detection of ligands exhibiting incipient cannabinoid receptor type 2 (CB2R) subtype selectivity and highlights the significant neurotoxicity of representative SCRAs on mouse primary neuronal cells. Several of the new emerging SCRAs are currently expected to have a rather limited potential for harm, as the evaluation of their pharmacological profiles revealed lower potencies and/or efficacies. Conceived as a resource to foster collaborative investigation of the physiological effects of SCRAs, the library obtained can contribute to addressing the challenge posed by recreational designer drugsThis work was financially supported by the Consellería de Cultura, Educación e Ordenación Universitaria of the Galician Government: (grant: ED431B 2020/43), Centro Singular de Investigación de Galicia accreditation 2019–2022 (ED431G 2019/03), Ministerio de Ciencia e Innovación (PID2020-113430RB-I00) and the European Regional Development Fund (ERDF)S

Exploring Biginelli-based scaffolds as A2B adenosine receptor antagonists: Unveiling novel structure-activity relationship trends, lead compounds, and potent colorectal anticancer agents

Antagonists of the A(2B) adenosine receptor have recently emerged as targeted anticancer agents and immune checkpoint inhibitors within the realm of cancer immunotherapy. This study presents a comprehensive evaluation of novel Biginelli-assembled pyrimidine chemotypes, including mono-, bi-, and tricyclic derivatives, as A(2B)AR antagonists. We conducted a comprehensive examination of the adenosinergic profile (both binding and functional) of a large compound library consisting of 168 compounds. This approach unveiled original lead compounds and enabled the identification of novel structure-activity relationship (SAR) trends, which were supported by extensive computational studies, including quantum mechanical calculations and free energy perturbation (FEP) analysis. In total, 25 molecules showed attractive affinity (K-i < 100 nM) and outstanding selectivity for A(2B)AR. From these, five molecules corresponding to the new benzothiazole scaffold were below the K-i < 10 nM threshold, in addition to a novel dual A(2A)/A(2B) antagonist. The most potent compounds, and the dual antagonist, showed enantiospecific recognition in the A(2B)AR. Two A(2B)AR selective antagonists and the dual A(2A)AR/A(2B)AR antagonist reported in this study were assessed for their impact on colorectal cancer cell lines. The results revealed a significant and dose-dependent reduction in cell proliferation. Notably, the A(2B)AR antagonists exhibited remarkable specificity, as they did not impede the proliferation of non-tumoral cell lines. These findings support the efficacy and potential that A(2B)AR antagonists as valuable candidates for cancer therapy, but also that they can effectively complement strategies involving A(2A)AR antagonism in the context of immune checkpoint inhibition