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Biomedical subjects

S Wold

Publications and source records attributed to S Wold.

At least 37 records · Page 2Linked to original sources
PubMed1991

A strategy for ranking environmentally occurring chemicals. Part VI. QSARs for the mutagenic effects of halogenated aliphatics.

A strategy for the systematic analysis and priority ranking of environmental chemicals has been applied to a class of 58 halogenated aliphatic hydrocarbons. A training set of ten compounds representing this class, was selected by statistical design. The training set compounds were then subjected to biological testing in the Salmonella typhimurium reverse mutation assay (Ames test). The measured biological data, recorded as dose-response curves, were analyzed to determine the mutagenic potency (slope of the initial portion) and the mutagen dose (MD 50) required to increase the number of revertants above the background by 50%. For each compound, four mutagenic potency estimates and four MD 50 values were determined, all originating from the tester strains TA 100 and TA 1535 with and without metabolic activation. The obtained responses were analyzed with multivariate techniques to give QSAR models relating the mutagenic potency data to the physico-chemical properties of the compounds. Finally, the derived QSARs were used to predict the mutagenic potencies and the MD 50S for the non-tested compounds in the class.

Chemical Phenomena↗
PubMed1990

Peptide QSAR on substance P analogues, enkephalins and bradykinins containing L- and D-amino acids.

Peptide QSARs are constructed for substance P analogues, enkephalins (two examples) and bradykinins containing both L- and D-amino acids. As descriptors in the QSARs, the previously developed descriptors z1 (hydrophobicity), z2 (bulk) and z3 (electronic effect) are used together with a qualitative variable coding for variation in chirality. Two parametrizations of the peptide sequences are tested. In the first no chiral description is used at all, and in the second chirality is described by the qualitative variable. It is concluded that for the current series of peptides, the biological response to variation in amino acid sequence and chirality can be modelled.

Amino Acid Sequence↗
PubMed1989

A multivariate approach to saccharide quantitative structure-activity relationships exemplified by two series of 9-hydroxyellipticine glycosides.

Multivariate saccharide quantitative structure-activity relationships (QSARs) have been developed for two series of 9-hydroxyellipticine glycosides. In order to describe the structural variation of the glycoside moieties, thirteen chromatographic variables were used. Eleven D-glycosides and seven L-glycosides were used in separate QSARs to model 9-hydroxyellipticine glycoside antitumour activity against L 1210 leukemia. The multivariate partial least squares (PLS) method was used to establish the QSARs.

Animals↗
PubMed1987

Signal peptide amino acid sequences in Escherichia coli contain information related to final protein localization. A multivariate data analysis.

With few exceptions, the signal peptides from proteins inserted into, or translocated through, the membranes of gram-negative bacteria or the endoplasmic reticulum of eukaryotes have no sequence homologies. Therefore these signal peptides have not been considered to contain information related to the different final localizations of the proteins. In this study, 43 signal peptide amino acid sequences from proteins with different final localizations in Escherichia coli have been subjected to a multivariate data analysis. Each amino acid residue was characterized by 20 physico-chemical properties, yielding a multivariate property profile for each peptide. The similarities/dissimilarities in the property profiles for the signal peptides from different classes were compared with each other by generating few-dimensional partial least squares (PLS) discriminant plots. With this approach, signal peptides from proteins localized to the periplasmic space (PS), the outer membrane (OM), and the extracellular surroundings (excreted proteins), were separated into distinct groups. Signal peptides from pili proteins were not separated from the OM signal peptides and only partly from the PS signal peptides, but were clearly different from the signal peptides of the excreted proteins. Signal peptides from inner membrane proteins were similar to those of the PS peptides. The size and the hydrophobicity of different peptide segments were responsible for the separation of the signal peptide classes. For example, the hydrophobicity of the N-terminal segment of the signal peptides increased with an increased distance from the cytoplasm of the final localization for the corresponding proteins. Thus, many signal peptides from proteins with different final localizations in E. coli have different discernible physico-chemical profiles.

Amino Acid Sequence↗
PubMed1987

Peptide quantitative structure-activity relationships, a multivariate approach.

The variation in amino acid sequence within sets of peptides is described by three principal properties, z1, z2, and z3, per varied amino acid position. These principal properties are derived from a principal components analysis of a matrix of 29 physicochemical variables for the 20 coded (in mRNA) amino acids. The scales z1, z2, and z3 are used to construct informative sets of analogues for exploring and developing quantitative structure-activity relationships (QSAR) of peptides. For the QSARs, the multivariate partial least squares (PLS) method is used. Multivariate QSARs are developed for four families of peptides, and it is shown how these QSARs can predict the activity of new peptide analogues.

Bradykinin↗
PubMed1986

The formation of ES of cytochrome-c peroxidase: a comparison with lactoperoxidase and horseradish peroxidase.

The activation energy for the formation of the first red compound, ES, for cytochrome-c peroxidase (ferrocytochrome-c: hydrogen-peroxide oxidoreductase, EC 1.11.1.5) by i-propyl hydroperoxide and the rate constants for the formation of ES with various hydroperoxides have been determined. Multivariate data analysis by the partial least-squares model in latent variables has been used to compare the rate constants with the corresponding rate constants for the formation of compound I from lactoperoxidase and two isoenzymes of horseradish peroxidase. The results show that the rate of formation of ES from cytochrome-c peroxidase is highly correlated with the pKa of the hydroperoxides. The activation energy for the formation of ES with i-propyl hydroperoxide is close to the corresponding value for hydrogen peroxide.

Cytochrome-c Peroxidase↗
PubMed1986

On the use of some multivariate statistical methods in pharmacological research.

Using an interaction experiment with apomorphine and scopolamine effects on exploratory behavior as an illustrative example, four multivariate statistical methods are described and compared with univariate statistical methods, with respect to their utility in pharmacological research. The utility of multivariate analysis of variance and Hotelling's T2 test is compared with univariate analysis of variance and Student's t-test. A novel use of principal component analysis is reported. This latter method transfers knowledge about the dose-response pattern of an agonist to subsequent interaction experiments involving the agonist and putative antagonists. The procedure considerably increases the sensitivity of the statistical analysis and reduces the risk for spurious results (statistical type I errors). Finally, some possibilities of a recently developed method for modeling with latent variables, the partial least squares method, are explored. It is demonstrated by the example how the interaction between apomorphine and scopolamine can be decomposed into one apomorphine-related pattern unaffected by scopolamine, one scopolamine-related pattern sensitive to apomorphine, and one interaction pattern. The similarities and differences between principal components and partial least squares analyses are also discussed.

Animals↗
PubMed1986

Relationships between induction of anesthesia and mitotic spindle disturbances studied by means of principal component analysis.

A dataset comprising the activity of 30 compounds in 4 biological tests--anesthesia of tadpoles, anesthesia of frog heart, abnormal growth and spindle disturbances in Allium root tips--was re-evaluated by means of principal component analysis. A two-component model is required to explain the variation in biological activity of the compounds. It is found that abnormal growth is different from the other biological responses. When this test is excluded, as much as 90% of the variation is explained by a one-component model, the determining factor most probably being the lipophilic character of the compounds. Mammalian mitotic cells respond in a similar way to mitotic cells of Allium root tips. It is suggested that possible regularities in the dose-response relationships for anesthesia, teratogenic effects and generation of abnormal chromosome numbers require further exploration.

Anesthetics↗
PubMed1986

The prediction of bradykinin potentiating potency of pentapeptides. An example of a peptide quantitative structure-activity relationship.

The variation in amino acid sequence, in a set of bradykinin potentiating pentapeptides, is described by three variables per amino acid position. The variables were derived from a principal components analysis of a property matrix for the 20 coded amino acids. The resulting structure descriptor matrix describes the observed activity of the peptides to 97% by means of a multivariate partial least squares (PLS) model. It is demonstrated that this quantitative structure-activity relationship (QSAR) can be used to predict the activity of new peptide analogs.

Bradykinin↗
PubMed1985

A multivariate pattern recognition study of risk-factors indicating postoperative thromboembolism despite low-dose heparin in major abdominal surgery.

The object of the present investigation was to identify those who, among high-risk patients, would "break through" low-dose heparin prophylaxis and develop thromboembolism after major abdominal surgery. Twenty-nine variables (clinical characteristics, pre- and postoperative coagulation and fibrinolytic factors) from 19 patients with and 26 patients without thromboembolism were analyzed by means of a multivariate supervised pattern recognition technique (SIMCA). We found no statistically significant difference between patients with and without thromboembolism. Thus, in the studied group of high-risk patients it was not possible to identify a predictive index for selection of individual patients liable to develop postoperative thromboembolism despite low-dose heparin prophylaxis in major abdominal surgery.

Abdomen↗
PubMed1985

A multivariate study of the relationship between the genetic code and the physical-chemical properties of amino acids.

The 20 naturally occurring amino acids are characterized by 20 variables: pKNH2, pKCOOH, pI, molecular weight, substituent van der Waals volume, seven 1H and 13C nuclear magnetic resonance shift variables, and eight hydrophobicity-hydrophilicity scales. The 20-dimensional data set is reduced to a few new dimensions by principal components analysis. The three first principal components reveal relationships between the properties of the amino acids and the genetic code. Thus the amino acids coded for by adenosine (A), uracil (U), or cytosine (C) in their second codon position (corresponding to U, A, or G in the second anticodon position) are grouped in these components. No grouping was detected for the amino acids coded for by guanine (G) in the second codon position (corresponding to C in the second anticodon position). The results show that a relationship exists between the physical-chemical properties of the amino acids and which of the A (U), U (A), or C (G) nucleotide is used in the second codon (anticodon) position. The amino acids coded for by G (C) in the second codon (anticodon) position do not participate in this relationship.

Amino Acids↗
PubMed1985

Toxicity modeling and prediction with pattern recognition.

Empirical models can be constructed relating the change in toxicity to the change in chemical structure for series of similar compounds or mixtures. The first step is to translate the variation in structure to quantitative numbers. This gives a data table, a data matrix denoted by X, which then is analyzed. The same type of the models can be used to relate the variation of in vivo data to the variation of a battery of in vitro tests. A single data analytical model cannot be applied to a set of compounds of diverse chemical structure. For such data sets, separate models must be developed for each subgroup of compounds. The data analytical problem then partly is one of classification, pattern recognition (PARC). The assumption of structural and biological similarity within each subset of modeled compounds is then essential for empirical models to apply. PARC is often used to classify compounds as active (toxic) or inactive. The data structure is then often asymmetric which puts special demands on the data analysis, making the traditional PARC methods inapplicable. Depending on the desired information from the data analysis and on the type of available data, four levels of PARC can be distinguished: (I) the data X are used to develop rules for classifying future compounds into one of the classes represented in X; (II) same as I, but the possibility of future compounds belonging to "unknown" classes not represented in X is taken into account; (III) same as II, plus the quantitative prediction of one activity variable (here toxicity) in some classes; (IV) same as III, but several quantitative activity (toxicity) variables are predicted.

Mathematics↗