Combinatorial synthesis of nicotine analogs using an Ugi 4-CR/cyclization-reduction strategy

Stefano Marcaccini: a pioneer in isocyanide chemistry

Stefano Marcaccini was one of the pioneers in the use of isocyanide-based multicomponent reactions in organic synthesis. Throughout his career at the University of Florence he explored many different faces of isocyanide chemistry, especially those geared towards the synthesis of biologically relevant heterocycles. His work inspired many researchers who contributed to other important developments in the field of multicomponent reactions and created a school of synthetic chemists that continues today. In this manuscript we intend to review the articles on isocyanide multicomponent reactions published by Dr. Marcaccini and analyse their influence on the following works by other researchers. With this, we hope to highlight the immense contribution of Stefano Marcaccini to the development of isocyanide chemistry and modern organic synthesis as well as the influence of his research on future generations. We believe that this review will not only be a well-deserved tribute to the figure of Stefano Marcaccini, but will also serve as a useful inspiration for chemists working in this field.

A sequential Ugi–Smiles/transition-metal-free endo-dig Conia–ene cyclization: the selective synthesis of saccharin substituted 2,5-dihydropyrroles

A convenient, transition-metal-free access to a series of unprecedented saccharin substituted 2,5-dihydropyrroles is reported.

Post-Ugi Transformations for the Access to Pyrrolobenzodiazepine Scaffolds with Different Degrees of Unsaturation

The synthesis of three novel families of pyrrolo[2,1-c][1,4]benzodiazepine-5-ones is described. The compounds were prepared according to a three-step sequence, involving an Ugi reaction, building of the pyrrolo nucleus, and reduction-cyclization to the corresponding diazepine. Depending on the amine employed in the synthesis of the Ugi adducts, different unsaturation degrees could be obtained in the pyrrolo ring (saturated or with endo or exo unsaturations), a key feature determining their biological activity, as it affects the affinity of the pyrrolobenzodiazepines toward DNA and thus their cytotoxicity. This synthetic methodology represents a significant improvement with respect to those described in the literature so far, as it uses inexpensive and commercially available starting materials without needing derivatization or the use of protecting groups.

Machine Learning Neuroprotective Strategy Reveals a Unique Set of Parkinson Therapeutic Nicotine Analogs

AIMS

Present a novel machine learning computational strategy to predict the neuroprotection potential of nicotine analogs acting over the behavior of unpaired signaling pathways in Parkinson's disease.

BACKGROUND

Dopaminergic replacement has been used for Parkinson's Disease (PD) treatment with positive effects on motor symptomatology but low progression and prevention effects. Epidemiological studies have shown that nicotine consumption decreases PD prevalence through neuroprotective mechanisms activation associated with the overstimulation of signaling pathways (SP) such as PI3K/AKT through nicotinic acetylcholine receptors (e.g α7 nAChRs) and over-expression of anti-apoptotic genes such as Bcl-2. Nicotine analogs with similar neuroprotective activity but decreased secondary effects remain as a promissory field.

OBJECTIVE

The objective of this study is to develop an interdisciplinary computational strategy predicting the neuroprotective activity of a series of 8 novel nicotine analogs over Parkinson's disease.

METHODS

We present a computational strategy integrating structural bioinformatics, SP manual reconstruction, and deep learning to predict the potential neuroprotective activity of 8 novel nicotine analogs over the behavior of PI3K/AKT. We performed a protein-ligand analysis between nicotine analogs and α7 nAChRs receptor using geometrical conformers, physicochemical characterization of the analogs and developed manually curated neuroprotective datasets to analyze their potential activity. Additionally, we developed a predictive machine-learning model for neuroprotection in PD through the integration of Markov Chain Monte-Carlo transition matrix for the 2 SP with synthetic training datasets of the physicochemical properties and structural dataset.

RESULTS

Our model was able to predict the potential neuroprotective activity of seven new nicotine analogs based on the binomial Bcl-2 response regulated by the activation of PI3K/AKT.

CONCLUSION

Hereby, we present a robust novel strategy to assess the neuroprotective potential of biomolecules based on SP architecture. Our theoretical strategy can be further applied to the study of new treatments related to SP deregulation and may ultimately offer new opportunities for therapeutic interventions in neurodegenerative diseases.

An efficient microwave-assisted synthesis of cotinine and iso-cotinine analogs from an Ugi-4CR approach

A convenient synthesis of cotinine and iso-cotinine analogs featuring an Ugi-4CR/cyclization approach.