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

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
>20 000 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.