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

Abstract

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 (22 ± 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 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of peptidomimetic 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 data herein reported was used, along with >20000 outcomes of preclinical assays, to seek a general model to assess the potential of a series of compounds as allosteric modulators for a myriad of receptors targets, organisms, cell lines, and biological activity parameters based on Perturbation Theory (PT) ideas and Machine Learning techniques (ML), abbreviated as ALLOPTML. By doing so, ALLOPTML shows specificity Sp = 89.2/89.4% and sensitivity Sn = 71.3/72.2% 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 multi-output and multi-condition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery.