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The authors compared the mortality and cardiac biomarker responses in three outbred stocks of Sprague Dawley rats (CD/IGS, Sasco, Harlan) treated with isoproterenol hydrochloride. Cardiac injury was confirmed by histologic evaluation, and increases in cardiac troponin I concentration in serum were measured by two methods. CD/IGS rats had a higher incidence and earlier mortality compared with Sasco or Harlan rats. Harlan rats had lower severity scores for cardiomyocyte degeneration/necrosis compared with the other stocks. Post-isoproterenol treatment cardiac troponin I concentrations were greater in CD/IGS and Sasco rats compared with Harlan rats. Concentrations of cardiac troponin T followed a similar pattern to that of cardiac troponin I in rats treated with isoproterenol. Myosin, light chain 3 concentrations increased in all rats treated with isoproterenol, but there was no difference between the three stocks in the magnitude or pattern of the dose response. Increases in fatty acid binding protein 3 concentrations were detected in only the highest dose group at the earliest timepoint postdose for all three stocks of rats. Results of these studies illustrate the need for investigators to recognize the potential differences in response between stocks of Sprague Dawley rats treated with cardiotoxicants or novel chemical entities.

Recent advances in the biological and analytical sciences have led to unprecedented interest in the discovery and quantitation of endogenous molecules that serve as indicators of drug safety, mechanism of action, efficacy, and disease state progression. By allowing for improved decision-making, these indicators, referred to as biomarkers, can dramatically improve the efficiency of drug discovery and development. Mass spectrometry has been a key part of biomarker discovery and evaluation owing to several important attributes, which include sensitive and selective detection, multi-analyte analysis, and the ability to provide structural information. Because of these capabilities, mass spectrometry has been widely deployed in search for new markers both through the analysis of large molecules (proteomics) and small molecules (metabonomics). In addition, mass spectrometry is increasingly being used to support quantitative measurement to assist in the evaluation and validation of biomarker leads. In this review, the dual role of mass spectrometry for biomarker discovery and measurement is explored for both large and small molecules by examining the key technologies and methods used along the continuum from drug discovery through clinical development.

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.