Data-driven analysis approach for biomarker discovery using molecular-profiling technologies.

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

Identification of the human cytochromes p450 responsible for in vitro formation of R- and S-norfluoxetine.

The formation of R- and S-norfluoxetine was analyzed in vitro in human liver microsomes. Low apparent K(m) values for R-norfluoxetine formation of < or =8 microM and S-norfluoxetine of <0.2 microM were determined. R-Norfluoxetine formation rates in a characterized microsomal bank correlated with the catalytic activities for cytochrome P450 (CYP) 2D6, CYP2C9, and CYP2C8. Expressed CYP2C9, CYP2C19, and CYP2D6 formed R-norfluoxetine following incubation with 1 microM R-fluoxetine and exhibited apparent K(m) values of 9.7, 8.5, and 1.8 microM, respectively. Multivariate correlation analysis identified CYP2C9 and CYP2D6 as significant regressors with R-norfluoxetine formation. Antibodies to the CYP2C subfamily and CYP2D6 each exhibited moderate inhibition of R-norfluoxetine formation. Therefore, CYP2D6 and CYP2C9 contribute to this biotransformation. At pharmacological concentrations of S-fluoxetine, S-norfluoxetine formation rates in the bank of microsomes were found to correlate only with CYP2D6 catalytic activity and only expressed CYP2D6 was found to be capable of forming S-norfluoxetine. Thus, it would appear that both CYP2D6 and CYP2C9 contribute to the formation of R-norfluoxetine, whereas only CYP2D6 is responsible for the conversion to S-norfluoxetine. Since the enantiomers of fluoxetine and norfluoxetine are inhibitors of CYP2D6, upon chronic dosing, the CYP2D6-mediated metabolism of the fluoxetine enantiomers would likely be inhibited, resulting in R-norfluoxetine formation being mediated by CYP2C9 and S-norfluoxetine formation being mediated by multiple high K(m) enzymes.

Cloning and sequencing of the Candida albicans C-4 sterol methyl oxidase gene (ERG25) and expression of an ERG25 conditional lethal mutation in Saccharomyces cerevisiae.

The ERG25 gene encoding the Candida albicans C-4 sterol methyl oxidase was cloned and sequenced by complementing a Saccharomyces cerevisiae erg25 mutant with a C. albicans genomic library. The Erg25p is comprised of 308 amino acids and shows 65 and 38% homology to the enzymes from S. cerevisiae and Homo sapiens, respectively. The protein contains three histidine clusters common to nonheme iron-binding enzymes and an endoplasmic reticulum retrieval signal as do the proteins from S. cerevisiae and humans. A temperature-sensitive (ts) conditional lethal mutation of the C. albicans ERG25 was isolated and expressed in S. cerevisiae. Sequence analysis of the ts mutant indicated an amino acid substitution within the region of the protein encompassed by the histidine clusters involved in iron binding. Results indicate that plasmid-borne conditional lethal mutants of target genes have potential use in the rescue of Candida mutations in genes that are essential for viability.

Determination of a novel alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate receptor antagonist (LY300164) and its N-acetyl metabolite in mouse, rat, dog, and monkey plasma using high-performance liquid chromatography with ultraviolet detection.

A method for the analysis of the AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate) receptor antagonist LY300164 (compound I) and its N-acetyl metabolite (compound II) in plasma was developed. The assay utilized solid-phase extraction on a C18 Bond Elut cartridge followed by reversed-phase HPLC with UV detection at 310 nm. The method exhibited a large linear range from 0.05 microgram/ml to 50 micrograms/ml with an intra-assay accuracy for compound I and compound II ranging from 89.0% to 114.5% and intra-assay precision ranging from 0.5 to 15.3% in mouse, rat, dog, and monkey plasma. The inter-assay accuracy of compound I and compound II was 93.3% to 101.8% and the inter-assay precision was 1.6% to 11.2% in dog plasma. The lower limit of quantitation was 0.05 microgram/ml for compound I in plasma from all species tested. The lower limit of quantitation for compound II was 0.05 microgram/ml in dog and monkey plasma and 0.1 microgram/ml in mouse and rat plasma. Extracts of compound I and II from dog plasma were shown to be stable for 24 h at room temperature, and both compounds were stable when spiked into rat and monkey plasma frozen at -70 degrees C for 27 days. The method has shown to be useful in the investigation of the pharmacokinetics of the parent compound (I) and metabolite (II) in preclinical studies.

Determination of dolasetron and its reduced metabolite in human plasma by GC-MS and LC.

Both a GC-MS and an LC method have been developed for the simultaneous quantitation of dolasetron and reduced dolasetron in human plasma. The GC-MS method has been utilized in preliminary human pharmacokinetic studies of dolasetron mesylate. Selected ion monitoring was used in these initial studies to obtain the sensitivity and specificity required for quantitation. The GC-MS method has been used in the range of 1-120 ng ml-1 for dolasetron and 1-240 ng ml-1 for reduced dolasetron in plasma. The limit of quantitation for both compounds by GC-MS was 1 ng ml-1. Recently, an LC method has been utilized for quantitation of both compounds on a routine basis. This method utilizes essentially the same sample preparation procedure as the GC-MS method. The LC method has been used in the range of 5-200 ng ml-1 in plasma for dolasetron and reduced dolasetron. In addition, the relationship between the LC and GC-MS methods has been assessed using data obtained from human male volunteers following intravenous administration of 3.0 mg kg-1 of dolasetron mesylate monohydrate.