Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

Autor: José E. Rodríguez-Borges, Ivo E. Sampaio-Dias, Olga Caamaño, María Isabel Loza, Dolores Viña, José Brea, Xerardo García-Mera, Sonia Arrasate, Javier Llorente, Humberto González-Díaz, Víctor Yáñez-Pérez, Harbil Bediaga
Přispěvatelé: Fundação para a Ciência e a Tecnologia (Portugal), Collaborative Project of Genomic Data Integration, Ministerio de Economía y Competitividad (España), European Commission, Ikerbasque Basque Foundation for Science, Eusko Jaurlaritza, Xunta de Galicia, Sampaio-Dias, Ivo E., Arrasate, Sonia, González-Díaz, Humberto
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Popis: This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20â000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.
This research was funded by Fundação para a Ciência e Tecnologia (FCT, Portugal), through grants UIDB/50006/2020 (to LAQV-REQUIMTE Research Unit) and for project grants PTDC/BIA-MIB/29059/2017 and PEst-OE/QUI/UI0674/2013. This work was also supported by the Collaborative Project of Genomic Data Integration (CICLOGEN).
The support of Ikerbasque, Basque Foundation for Science and the research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016–74881-P), and Basque government (IT1045–16) are also acknowledged. The financial support (ED431G 2019/02) from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund—ERDF) is gratefully acknowledged. I.E.S.-D. thanks FCT for the doctoral grant SFRH/BD/93632/2013. X.G.-M. thanks Xunta de Galicia for financial support (GPC2014/003).
Databáze: OpenAIRE
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