The estimation of the conceptual distance between patents is a critical issue for Computer-Aided patent portfolio analysis systems, an emerging class of computer tools for supporting R&D analyses and decisions, patent infringement risk evaluation, technology forecasting. The aim of the present work is the introduction of an original algorithm for patent comparison: since typical text analyses are biased by the writer's style, the inventions similarity is here estimated by comparing the components and their hierarchical and functional interactions automatically extracted by means of a custom software tool. The whole procedure is clarified with an exemplary application in the field of electric current circuit breakers.

Huntington’s disease (HD) is a neurodegenerative disease caused by polyglutamine expansion in the huntingtin protein. For drug candidates targeting HD, the ability to cross the blood–brain barrier (BBB) and reach the site of action in the central nervous system (CNS) is crucial for achieving pharmacological activity. To assess the permeability of selected compounds across the BBB, we utilized a two-dimensional model composed of primary porcine brain endothelial cells and rat astrocytes. Our objective was to use this in vitro model to rank and prioritize compounds for in vivo pharmacokinetic and brain penetration studies. The model was first characterized using a set of validation markers chosen based on their functional importance at the BBB. It was shown to fulfill the major BBB characteristics, including functional tight junctions, high transendothelial electrical resistance, expression, and activity of influx and efflux transporters. The in vitro permeability of 54 structurally diverse known compounds was determined and shown to have a good correlation with the in situ brain perfusion data in rodents. We used this model to investigate the BBB permeability of a series of new HD compounds from different chemical classes, and we found a good correlation with in vivo brain permeation, demonstrating the usefulness of the in vitro model for optimizing CNS drug properties and for guiding the selection of lead compounds in a drug discovery setting.

We investigated several strategies, based on the use of microwave-assisted solid-phase peptide synthesis (MW-SPPS) and scalable to kilogram-scale manufacturing, for the preparation of Eptifibatide, a disulfide-bridged cyclo-heptapeptide drug approved as an antithrombotic agent. Following the very fast microwave-assisted Fmoc/tBu synthesis of the linear precursor, we explored both the solution (off-resin) and the solid-phase (on-resin) disulfide formation. In order to optimize the oxidation in solution, we focused our attention on the mild disulfide formation procedure based on the use of air, observing some drawbacks, such as the formation of unwanted oxidation byproducts, such as dimers, or the use of large volumes of an environmentally unfriendly solvent (CH 3 CN). In order to overcome these difficulties, we studied four different on-resin strategies, with the final aim to develop a fully automated, single reactor procedure, exploring different strategies to protect the thiol side-chain functional group on the C-terminal Cys residue and to form the Eptifibatide ring. The main difference among these strategies is represented by the final cyclization mode that was obtained either by direct formation of an S−S disulfide bridge or by head to MPA on cysteine sidechain amide bond formation. In conclusion, the optimization of the latter strategy enabled us to devise an optimized scalable fully automated solid-phase microwave-assisted cGMP-ready process to prepare Eptifibatide.

Abstract. The application of standard Information Extraction techniques to Patent Analysis has several limitations partially due to the difference existing between patents and web pages, which are the object of the biggest majority of information search. Indeed, while in other fields customized processing techniques have been developed, the number of studies fully dedicated to patent text mining is very limited and the tools available on the market still require a relevant human workload. This paper presents an algorithm to identify the peculiarities of an invention through an automatic functional analysis of the patent text; as a result a ranked list of components and functions is provided as well as a selection of meaningful paragraphs disclosing the details of the invention. An example related to laser irradiation devices for medical treatment clarifies its basic steps.

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