Screening of Suitable Solvents in Organic Synthesis. Strategies for Solvent Selection.

Carlson, Rolf; Lundstedt, Torbjörn; ALBANO, C.

doi:10.3891/acta.chem.scand.39b-0079

Cited by 102 publications

(71 citation statements)

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“…Multivariate design (MVD) 21,22 is a combination of multivariate characterization (MVC), [23][24][25] principal component analysis (PCA), and Statistical Experimental Design (SED) to select a diverse set of objects that represents all objects, that is, spans the variation. There is a number of different experimental designs that can be applied to span the variation in a…”

Section: Multivariate Designmentioning

confidence: 99%

“…To be able to do this, we have to characterize the objects with both measured and observed descriptors. This often includes setting up specific inclusion and exclusion criteria for the study, such as age span (e.g., 18-45 years), body mass index (e.g., [20][21][22][23][24][25][26][27][28][29][30], medicinal chemistry profiles (e.g., lipids, glucose), gender, tobacco habits, and use of drugs. In addition to those criteria, additional information regarding each object is collected by questionnaires that include life style factors, food and drinking habits, social situation, and so on.…”

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confidence: 99%

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Chemometrics in Metabonomics

2006

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We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.

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Section: Multivariate Designmentioning

confidence: 99%

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confidence: 99%

Chemometrics in Metabonomics

2006

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“…The values of ET(30) for many different solvents are available from literature [35,361; those relevant for the present study are given in Table 2.…”

Section: Thermodynamic Model O J E N Z Y M E (Protein) Denaturation Bmentioning

confidence: 99%

Denaturation capacity: a new quantitative criterion for selection of organic solvents as reaction media in biocatalysis

Khmelnitsky

Mozhaev

Belova

et al. 1991

European Journal of Biochemistry

230

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The process of reversible denaturation of several proteins (a-chymotrypsin, trypsin, laccase, chymotrypsinogen, cytochrome c and myoglobin) by a broad series of organic solvents of different nature was investigated using both our own and literature data, based on the results of kinetic and spectroscopic measurements. In all systems studied, the denaturation proceeded in a threshold manner, i. e. an abrupt change in catalytic and/or spectroscopic properties of dissolved proteins was observed after a certain threshold concentration of the organic solvent had been reached. To account for the observed features of the denaturation process, a thermodynamic model of the reversible protein denaturation by organic solvents was developed, based on the widely accepted notion that an undisturbed water shell around the protein globule is a prerequisite for the retention of the native state of the protein. The quantitative treatment led to the equation relating the threshold concentration of the organic solvent with its physicochemical characteristics, such as hydrophobicity, solvating ability and molecular geometry. This equation described well the experimental data for all proteins tested. Based on the thermodynamic model of protein denaturation, a novel quantitative parameter characterizing the denaturing strength of organic solvents, called the denaturation capacity (DC), was suggested. Different organic solvents, arranged according to their DC values, form the DC scale of organic solvents which permits theoretical prediction of the threshold concentration of any organic solvent for a given protein. The validity of the DC scale for this kind of prediction was verified for all proteins tested and a large number of organic solvents. The experimental data for a few organic solvents, such as formamide and N-methylformamide, did not comply with equations describing the denaturation model. Such solvents form the group of so-called 'bad' solvents; reasons for the occurrence of 'bad' solvents are not yet clear. The DC scale was further extended to include also highly nonpolar solvents, in order to explain the wellknown ability of enzymes to retain catalytic activity and stability in biphasic systems of the type water/waterimmiscible organic solvent. It was quantitatively demonstrated that this ability is accounted for by the simple fact that nonpolar solvents are not sufficiently soluble in water to reach the inactivation threshold concentration.

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“…The diversity of solvents in the solvent PCA map will ensure the 8 solvents are very different from each other. PCA solvent maps have been developed and used for chemical reactions and polymorph screening [7][8][9][10]. PCA has also been used successfully for ligand mapping by us in collaboration with the University of Bristol [11][12] as well as for amines, aldehydes, ketones and Lewis acids [13][14][15].…”

Section: Introductionmentioning

confidence: 99%