The successful development of a catalytic imine asymmetric hydrogenation process for the reduction of the hydrochloride salt of 1-phenyl-3,4-dihydroisoquinoline to 1-(S)-phenyl-1,2,3,4-tetrahydroisoquinoline is described. This represents a novel approach to the key intermediate in preparing the urinary antispasmodic drug solifenacin, (1S)-(3R)-1-azabicyclo[2.2.2]oct-3-yl-3,4-dihydro-1-phenyl-2(1H)-isoquinoline carboxylate. Suitable reaction conditions were identified through an extensive screen of catalysts and combination of solvents and additives. The best reaction conditions: [Ir(COD)Cl]2-(S)-P-Phos, molar substrate to catalyst ratio (S/C) of >1000/1, THF, 1–2 equiv of H3PO4, 60 °C, 20 bar H2, were reproduced on a 200 g scale (95% isolated yield, 98% ee and >99% HPLC product purity).

No abstract

A four-step synthesis of 1-substituted 5-(2-aminophenyl)-1H-pyrazoles 5 as a novel type of histamine analogs and versatile building blocks for further transformations was developed. The synthesis starts from commercially available 2-nitroacetophenone (12), which is converted into the enamino ketone 13 as the key intermediate. Cyclization of the key intermediate 13 with monosubstituted hydrazines 14a -14l afforded the 5-(2-nitrophenyl)-1H-pyrazoles 17a -17l. Finally, catalytic hydrogenation of the nitro compounds 17a, 17c -17e, and 17g -17j furnished the title compounds 5a, 5c -5e, and 5g -5j, respectively, in good yields. As demonstrated by some further transformations, additional functionalization of compounds 17 and 5 is feasible, either by electrophilic substitution at C(4) of the pyrazole ring, or at the NH 2 group.Introduction. -In biological processes, histamine, tyramine, dopamine, tryptamine, serotonin, and melatonin play a crucial role as chemical messengers. Therefore, preparation of their novel synthetic analogs based on the 2-(heteroaryl)ethylamine scaffold is an important target in medicinal and synthetic organic chemistry [1].Pyrazoles are an important class of heterocyclic compounds. Despite their rare occurrence in nature, numerous pyrazole derivatives have found use in various applications, and a general interest in their chemistry is still continuing (for a review, see [2]). Among numerous synthetic options for the construction of the pyrazole ring, two classical approaches are most frequently employed: a) cyclocondensation of a 1,3-dicarbonyl compound with a hydrazine derivative and b) 1,3-dipolar cycloaddition of a CÀNÀN type 1,3-dipole (diazoalkane, nitrile imine, or azomethine imine) to a C,C multiple bond [2].Recently, a part of our research has been focused on the synthesis of functionalized pyrazoles utilizing the

No abstract

Two variations of enaminone-based parallel solution-phase synthesis of 1-substituted 4-(2-aminoethyl)-1 H-pyrazol-5-ols 8 and their NH-tautomers 8' were developed. The synthetic strategy comprises a two step preparation of the N-protected alpha-enamino lactams 3a and 3b from 2-pyrrolidinone (1), "ring switching" transformation of 3a, b with monosubstituted hydrazines 4a-u, and acidolytic removal of the N-protecting group. In order to ensure a clean and fast conversion, reactions of Cbz-enaminone 3a with hydrazines 4a-k were carried out under microwave irradiation to afford the "ring-switched" intermediates 7a-k. Deprotection of 7a-k with HBr-AcOH at 50 degrees C gave a library of 11 analytically pure 4-(2-aminoethyl)-1 H-pyrazol-5-ols (di)hydrobromides 8/ 8'a-k in 16-75% yields over two steps. The other reagent, Boc-enaminone 3b, was more reactive and ring switching transformations with hydrazines 4b, d, k proceeded smoothly and cleanly under conventional heating. Finally, a parallel one-pot transformation of the Boc-enaminone 3b with hydrazines 4a-u followed by subsequent deprotection of the intermediates 9a-u with HCl-EtOAc furnished a library of 21 analytically pure 4-(2-aminoethyl)-1 H-pyrazol-5-ols (di)hydrochlorides 8/ 8'a-u in 40-100% yields.

No abstract

No abstract

The intensive development of information and communication technologies in recent years has led to an increase in data size and complexity. Conventional approaches, with associated methods of analysis based on descriptive and inductive statistics, may no longer be suitable for extracting the valuable information that is hidden in the available data.Decision Support, Big Data niques, tools, methods and concepts, which conjointly form efficient systems for extracting the value from large and complex data.Heuristic methods of analysis turn out to be very efficient for fault-diagnosis problems in complex manufacturing processes, where the states are described by complex combinations of many parameters. Recently, in the manufacturing 30 domain, a lot of research was carried out regarding fault-diagnostics problems by incorporating machine-learning methods, data mining, and other evolving techniques, targeting the problems of sensing and describing multiple and dynamic faults, monitoring, real-time processing, searching for the best features, failure-condition identification, early prediction of failures, and assessing the 35 severity of failures [11].Many of so-far untouched challenges and opportunities remain for the development and introduction of useful heuristic techniques in manufacturing systems that would improve the process quality through an intelligent use of the available data [12]. 40The paper presents a holistic data-analysis method for the interpretative identification of faulty conditions in a cyclic manufacturing process (IIFC). The objective here is to demonstrate the usability of large volumes of data generated during a widespread type of the so called cyclic manufacturing processes, such as plastic injection moulding, for extracting valuable information and new 45 knowledge models for better understanding of the manufacturing process and its faulty operating conditions. The proposed approach uses well known datamining methods and addresses the arising issues of increasing data size and complexity.Cyclic-manufacturing processes are defined as manufacturing processes in 50 which the parts are produced repeating the same sequence of steps that forms the manufacturing cycle. The data that is generated in these processes describes the state of the process for each cycle. This is suitable for applying machinelearning and other advanced techniques of data analysis, because minimal effort is required to define the common features for each cycle due to the repetitive na-55 ture of the cycle's steps. Although machine-learning techniques are well known,