Biomedical subjects

J C Kiburis

Publications and source records attributed to J C Kiburis.

3 recordsLinked to original sources

PubMed1996

Quantitative structure-retention relationships in doping control.

Regression equation modelling was used for the correlation of gas chromatographic relative retention times tRR of anabolic steroids, stimulants and narcotics with their molecular characteristics in order to create a model for the prediction of tRR values of unanalyzed molecules. Predicting chromatographic retention parameters is one of the main goals of the quantitative structure-retention relationships (QSRR) methodology. To be performed, QSRR studies require two tools; a methodology for the extraction of the structural characteristics and a statistical program for the correlation of these characteristics with the chromatographic data.

Anabolic Agents↗

PubMed1991

Prediction of gas chromatographic relative retention times of stimulants and narcotics.

The ADAPT software system was used to create models for the prediction of gas chromatographic relative retention times (RRTs) of stimulants and narcotics that are analyzed in doping control of athletes. The two main methods that were followed for building the models were the quantitative structure-retention relationship (QSRR) and multiple linear regression analysis. The main proposed model for the entire data set had a multiple correlation coefficient R = 0.991 and standard error s = 0.046 or approximately 4.5%. Because of the relatively high standard error of the main model, a second model was built on a subset of compounds with R = 0.982 and s = 0.027 or approximately 2.5%.

Central Nervous System Stimulants↗

PubMed1991

Prediction of gas chromatographic relative retention times of anabolic steroids.

The prediction of gas chromatographic relative retention times (RRTs) of anabolic steroids, used in the doping control of athletes, was performed by a quantitative structure-retention relationship (QSRR) and multiple linear regression analysis study. A nine-variable model was generated with a multiple correlation coefficient R = 0.991 and relative standard error of less than 3%. Preliminary results indicated that the application of the model, especially in the prediction of RRTs of metabolites of the anabolic steroids, will be helpful.

Anabolic Agents↗