Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome researchThe authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galici

QSAR Study for Macromolecular RNA Folded Secondary Structures of Mycobacterial Promoters with Low Sequence Homology

The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThe general belief is that quantitative structure-activity relationships (QSAR) techniques work only for small molecules and, proteins sequences or, more recently, DNA sequences. However, with non-branched graph for proteins and DNA sequences the QSAR often have to be based on powerful non-linear techniques such as support vector machines. In our opinion linear QSAR models based in RNA could be useful to assign biological activity when alignment techniques fail due to low sequence homology. The idea bases in the high level of branching for the RNA graph. This work introduces the so called Markov electrostatic potentials k?M as a new class of RNA 2D-structure descriptors. Subsequently, we validate these molecular descriptors solving a QSAR classification problem for mycobacterial promoter sequences (mps), which constitute a very low sequence homology problem. The model developed (mps = –4.664·0cM + 0.991·1cM – 2.432) was intended to predict whether a naturally occurring sequence is an mps or not on the basis of the calculated kcM value for the corresponding RNA secondary structure. The RNAQSAR approach recognises 115/135 mps (85.2%) and 100% of control sequences. Average predictability and robustness were greater than 95%. A previous non-linear model predicts mps with slightly higher accuracy (97%) but uses a very large parameter space for DNA sequences. Conversely, the kcM-based RNA-QSAR encodes more structural information and needs only two variablesGonzález-Díaz, H. thanks the Xunta de Galicia (BTF20301PR) for partial financial suppor

Coumarin-Chalcone Hybrids as new scaffolds in drug discovery

The 13th International Electronic Conference on Synthetic Organic Chemistry session General Organic SynthesisThe first hydroxilated series of coumarin-chalcone derivatives has been synthesize starting from the corresponding salicyl aldehyde and β-ketoester precursors by a Knoevenagel reaction in order to obtain the methoxy derivatives which have been further hydrolyzed with a Lewis aci

Exploring the Multi-Target Performance of Mitochondriotropic Antioxidants against the Pivotal Alzheimer’s Disease Pathophysiological Hallmarks

Alzheimer disease (AD) is the most common neurodegenerative disease featuring progressive and degenerative neurological impairments resulting in memory loss and cognitive decline. The specific mechanisms underlying AD are still poorly understood, but it is suggested that a deficiency in the brain neurotransmitter acetylcholine, the deposition of insoluble aggregates of fibrillar β-amyloid 1–42 (Aβ42), and iron and glutamate accumulation play an important role in the disease progress. Despite the existence of approved cholinergic drugs, none of them demonstrated effectiveness in modifying disease progression. Accordingly, the development of new chemical entities acting on more than one target is attracting progressively more attention as they can tackle intricate network targets and modulate their effects. Within this endeavor, a series of mitochondriotropic antioxidants inspired on hydroxycinnamic (HCA’s) scaffold were synthesized, screened toward cholinesterases and evaluated as neuroprotectors in a differentiated human SH-SY5Y cell line. From the series, compounds 7 and 11 with a 10-carbon chain can be viewed as multi-target leads for the treatment of AD, as they act as dual and bifunctional cholinesterase inhibitors and prevent the neuronal damage caused by diverse aggressors related to protein misfolding and aggregation, iron accumulation and excitotoxicityThis work was funded by FEDER funds through the Operational Programme Competitiveness Factors-COMPETE and national funds by FCT-Foundation for Science and Technology under research grants (UID/QUI/00081, NORTE-01-0145-FEDER-000028, PTDC/DTP-FTO/2433/2014, PTDC/BIA-MOL/28607/2017, POCI-01-0145-FEDER-028607). S. Benfeito and C. Fernandes grants are supported by FCT, POPH and QREN. The authors also thank the COST action CA15135 for supportS

QSAR & Network-based multi-species activity models for antifungals

The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThere are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model, which outputs were the inputs of the above-mentioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extent model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power-law network with few mechanisms (network hubs)Gonzalez-Díaz H. acknowledges contract/grant sponsorship from the Program Isidro Parga Pondal of the “Dirección Xeral de Investigación y Desenvolvemento” of “Xunta de Galicia”. This author also acknowledges two contracts as guest professor in the Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Spain in 2006. The authors thank the Xunta de Galicia (projects PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (project PI061457) for partial financial suppor

Unify QSAR approach to antibacterial activity of organic drugs against different species

The 10th International Electronic Conference on Synthetic Organic Chemistry session Bioorganic Chemistry and Natural ProductsThere are many different kinds of pathogen bacteria species with very different susceptibility profile to different antibacterial drugs. One limitation of QSAR models are the biological activity of drugs against only one bacteria species. In previous paper we develop one unified Markov model to describe the biological activity of different drugs tested in the literature against some of the antimicrobial species. Consequently predicting the probability with which a drug is active against different bacteria species with a single unify model is a goal of the major importance. This work develops one unified Markov model to describe the biological activity of more than 70 drugs tested in the references against to 96 bacteria species. Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested bacteria species processed the data. The model correctly classifies 199 out of 237 active compounds (83.9%) and 168 out of 200 non-active compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carring out by means of external predicting series, classifying the model 202 out 243, 83.13% of compounds. In order to show how the model function in practice a virtual screening was carring out recognizing the model as active 84.5%, 480 out of 568 antibacterial compounds not used in training or predicting series. The present is an attempt to calculate withing a unify framework probabilities of antibacterial action of drugs against many different speciesAuthors thank projects PXIB20304PR and BTF20302PR from Xunta de Galiza for partial financial suppor

Detection of drug-drug interactions by modeling interaction profile fingerprints

Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4–0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.This work was supported by grants R01 LM010016 (CF), R01 LM010016-0S1 (CF), R01 LM010016-0S2 (CF), R01 LM008635 (CF), “Plan Galego de Investigación, Innovación e Crece-mento 2011–2015 (I2C)”, European Social Fund (ESF) and Angeles Alvariño program from Xunta de Galicia (Spain)S

Alternative Methodologies for the Synthesis of Substituted 3-arylcoumarins: Perkin Reactions and Palladium-Catalyzed Synthesis

With the aim to find out the best methodology to prepare different series of substituted coumarins, thinking on their pharmacological evaluation, in the present communication we report the synthesis of 3-phenylcoumarin with different number and position of substituent groups in both 3-phenyl and coumarin rings. The substituents in this new scaffold were introduced in the 6 and 8 positions of the coumarin moiety and in 3'and 4' positions of the 3-phenyl ring. The synthesized compounds 1-7, 8 and 9-11 were prepared and characterized by different methodologies. Perkin modified reaction (method A and B) and Palladium-catalyzed synthesis (method C) were the methodologies described in this communicationWe are grateful to the Spanish Ministerio de Sanidad y Consumo (PS09/00501) and to Xunta da Galicia (CSA030203PR). M.J.M. also thanks Fundação de Ciência e Tecnologia for a PhD gran

Multi-Target Spectral Moment: QSAR for antiviral drugs vs. different viral species

The 13th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryPrado-Prado, F. acknowledges the financial support from the Xunta de Galicia for a one-year post-doctoral position (Research Project IN89A 2008/75-0). González-Díaz, H. acknowledges the financial support from the Isidro Parga Pondal Programme and one-year post-doctoral position (Research Project IN89A 2008/117-0), both financed by the Xunta de Galicia and the European Research funds from the European Social Fund (ESF