Addizione di Michael organocatalitica asimmetrica di composti 1,3-dicarbonilici ad orto-chinoni metidi: un nuovo e semplice approccio alla preparazione di 4H-cromeni e croman-2-oni otticamente attivi

The work described in this thesis deals with the development of the asymmetric organocatalytic conjugate addition reaction of 1,3-dicarbonyl compounds to ortho-quinone methides. Due to their instability, these synthetically appealing intermediates have not been fully exploited in catalytic asymmetric settings. In this work, the instability of ortho-quinone methides is overcome by their generation in situ under mild basic conditions, starting from the corresponding sulfonyl derivatives. The bifunctional catalysts used are able to activate both substrates for the reaction, by means of a synergic action of the two catalytic sites, inducing at the same time high enantioselection in the addition step. The reaction leads to the generation of a 2-alkylphenolic framework, featuring a chiral centre at the benzylic position. In particular, the employment of acetylacetone and Meldrum acid as nucleophiles has allowed the obtainment of 4H-chromenes and chroman-2-ones in good yields and generally excellent enantioselectivities. These compounds are synthetic precursors of several natural products, some of which showing interesting biological activity, and of some active pharmaceutical ingredients used in commercial drugs.

Synthesis and functionalization of a lactam-pyrazole molecular scaffold as a promising anticancer compound

The importance of pyrazole and lactam-based molecules in medical and pharmaceutical fields is underlined by the multitude of active ingredients on trade, such as Sildenafil or Apixaban, by Pfizer. In this work, a synthesis of an organic molecule with promising anticancer activity has been developed. This molecular scaffold is characterized by a δ-lactam-fused pyrazolic core, with a well-known biological activity and amenable of further functionalization. The synthetic strategy adopted for the obtainment of the core was based on a 1,3-dipolar cycloaddition of a nitrilimine with an α,β-unsaturated δ-lactam. Secondly, in order to give the final compound an elevated pharmacological activity, a functionalization with a double “side chain”, namely molecular fragment able to improve the interaction with particular biological receptors, was achieved. The target compound was thus obtained, with a highly convergent synthesis, and will be tested for antiproliferative activities towards different cellular lines.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.