The emerging field of personalized medicine and the prediction of side effects experienced due to pharmaceutical drugs is being studied intensively in the post genomic era. The molecular basis of inheritance and disease susceptibility is being unravelled, especially through the use of rapidly evolving new technologies. This in turn facilitates analyses of individual variations in the whole genome of both single subjects and large groups of subjects. Genetic variation is a common occurrence and although most genetic variations do not have any apparent effect on the gene product some do exhibit effects, such as an altered ability to detoxify xenobiotics. The human body has a highly effective detoxification system that detoxifies and excretes endogenous as well as exogenous toxins. Numerous studies have proved that specific genetic variations have an influence on the efficacy of the metabolism of pharmaceutical drugs and consequently the dosage administered. The primary aim of this project was the local implementation and assessment of two different genotyping approaches namely: the Applied Biosystems SNaPshot technique and Affymetrix DMET microarray. A secondary aim was to investigate if links could be found between the genetic data and the biochemical detoxification profile of participants. I investigated the approaches and gained insight into which method would be better for specific local applications, taking into consideration the robustness and ease of implementation as well as cost effectiveness in terms of data generated. The final study cohort comprised of 18 participants whose detoxification profiles were known. Genotyping was performed using the DMET microarray and SNaPshot techniques. The SNaPshot technique was used to genotype 11 SNPs relating to DNA repair and detoxification and was performed locally. Each DMET microarray delivers significantly more data in that it genotypes 1931 genetic markers relating to drug metabolism and transport. Due to the absence of a local service supplier, the DMET - microarrays were outsourced to DNALink in South Korea. DNALink generated raw data which was analysed locally. I experienced many problems with the implementation of the SNaPshot technique. Numerous avenues of troubleshooting were explored with varying degrees of success. I concluded that SNaPshot technology is not the best suited approach for genotyping. Data obtained from the DMET microarray was fed into the DMET console software to obtain genotypes and subsequently analysed with the help of the NWU statistical consultation services. Two approaches were followed: firstly, clustering the data and, secondly, a targeted gene approach. Neither of the two methods was able to establish a relationship between the DMET genotyping data and the detoxification profiling. For future studies to successfully correlate SNPs or SNP groups and a specific detoxification profile, two key issues should be addressed: i) The procedure for determining the detoxification profile following substrate loading should be further refined by more frequent sampling after substrate loading. ii) The number of participants should be increased to provide statistical power that will enable a true representation of the particular genetic markers in the specific population. The statistical analyses, such as latent class analyses to cluster the participants will also be of much more use for data analyses and interpretation if the study is not underpowered. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.

The emerging field of personalized medicine and the prediction of side effects experienced due to pharmaceutical drugs is being studied intensively in the post genomic era. The molecular basis of inheritance and disease susceptibility is being unravelled, especially through the use of rapidly evolving new technologies. This in turn facilitates analyses of individual variations in the whole genome of both single subjects and large groups of subjects. Genetic variation is a common occurrence and although most genetic variations do not have any apparent effect on the gene product some do exhibit effects, such as an altered ability to detoxify xenobiotics. The human body has a highly effective detoxification system that detoxifies and excretes endogenous as well as exogenous toxins. Numerous studies have proved that specific genetic variations have an influence on the efficacy of the metabolism of pharmaceutical drugs and consequently the dosage administered. The primary aim of this project was the local implementation and assessment of two different genotyping approaches namely: the Applied Biosystems SNaPshot technique and Affymetrix DMET microarray. A secondary aim was to investigate if links could be found between the genetic data and the biochemical detoxification profile of participants. I investigated the approaches and gained insight into which method would be better for specific local applications, taking into consideration the robustness and ease of implementation as well as cost effectiveness in terms of data generated. The final study cohort comprised of 18 participants whose detoxification profiles were known. Genotyping was performed using the DMET microarray and SNaPshot techniques. The SNaPshot technique was used to genotype 11 SNPs relating to DNA repair and detoxification and was performed locally. Each DMET microarray delivers significantly more data in that it genotypes 1931 genetic markers relating to drug metabolism and transport. Due to the absence of a local service supplier, the DMET - microarrays were outsourced to DNALink in South Korea. DNALink generated raw data which was analysed locally. I experienced many problems with the implementation of the SNaPshot technique. Numerous avenues of troubleshooting were explored with varying degrees of success. I concluded that SNaPshot technology is not the best suited approach for genotyping. Data obtained from the DMET microarray was fed into the DMET console software to obtain genotypes and subsequently analysed with the help of the NWU statistical consultation services. Two approaches were followed: firstly, clustering the data and, secondly, a targeted gene approach. Neither of the two methods was able to establish a relationship between the DMET genotyping data and the detoxification profiling. For future studies to successfully correlate SNPs or SNP groups and a specific detoxification profile, two key issues should be addressed: i) The procedure for determining the detoxification profile following substrate loading should be further refined by more frequent sampling after substrate loading. ii) The number of participants should be increased to provide statistical power that will enable a true representation of the particular genetic markers in the specific population. The statistical analyses, such as latent class analyses to cluster the participants will also be of much more use for data analyses and interpretation if the study is not underpowered. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.