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The control of Parkinson's disease (PD) is challenged by the motor and non-motor fluctuations as well as dyskinesias associated with levodopa long-term therapy. As such, pharmacological alternatives to reduce the reliance on this drug are needed. Melanostatin (MIF-1), a positive allosteric modulator (PAM) of D2 receptors (D2R), is being explored as a novel pharmacological approach focused on D2R potentiation. In this work, 3-furoic acid (3-Fu) was successfully employed as an l-proline (Pro) surrogate, affording two potent MIF-1 analogues, methyl 3-furoyl-l-leucylglycinate (4a) and 3-furoyl-l-leucylglycinamide (6a). In this series, the C-terminal carboxamide moiety was found crucial to enhancing the potency and toxicological profile, yet it is not considered a requisite for the PAM activity. Conformational analysis excludes 4a from adopting the claimed type II β-turn. The discovery and validation of 6a as a lead compound open a new avenue for the development of a novel class of anti-Parkinson therapeutics targeting the D2R.

The control of Parkinson's disease (PD) is challenged by the motor and non-motor fluctuations as well as dyskinesias associated with levodopa long-term therapy. As such, pharmacological alternatives to reduce the reliance on this drug are needed. Melanostatin (MIF-1), a positive allosteric modulator (PAM) of D

The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.