Preclinical bridging studies: understanding dried blood spot and plasma exposure profiles.

BACKGROUND: Understanding the distribution of the analyte between the cellular and noncellular (plasma) components of the blood is important, especially in situations where dried blood spot (DBS) data need to be compared with plasma data, or vice versa. RESULTS: Pearson's coefficient, Lin's coefficient and the Bland-Altman analysis are appropriate to evaluate the concordance between DBS and plasma data from bridging studies. Percent recovery plots generated using the ex vivo blood:plasma ratio and the regression equations demonstrate the best approach for predicting plasma concentrations from DBS. CONCLUSION: Statistical analysis of bridging study data is needed to characterize the relationship or concordance between blood (DBS) and plasma. The outcomes also provide guidance on selecting the most appropriate approach to transform DBS data to plasma, or vice versa. However, the biological and statistical evidence must be weighed together when deciding if DBS is suitable for preclinical and/or clinical development.

Dried blood spot sampling: coupling bioanalytical feasibility, blood-plasma partitioning and transferability to in vivo preclinical studies.

BACKGROUND: The adoption of dried blood spot (DBS) sampling and analysis to support drug discovery and development requires the understanding of its bioanalytical feasibility as well as the distribution of the analyte in blood. RESULTS: Demonstrated the feasibility of adopting DBS for four test analytes representing diverse physico-chemical as well as pharmacokinetic parameters. The key findings include the use of a single extraction procedure across all four analytes, assay range of 1 to 5000 ng/ml, stability in whole blood as well as on-card, and the non-impact of blood volume. In vivo data were used to calculate the blood-to-plasma ratio (using both AUC and average of individual time points), which was then used to predict plasma concentration from DBS data. The predicted data showed an excellent correlation with actual plasma data. CONCLUSION: Transition from plasma to DBS can be supported for preclinical studies by conducting a few well-defined bioanalytical experiments followed by an in vivo bridging study. Blood:plasma ratio derived from the bridging study can be used to predict plasma concentrations from DBS data.

Proteomic analysis of rat liver phosphoproteins after treatment with protein kinase inhibitor H89 (N-(2-[p-bromocinnamylamino-]ethyl)-5-isoquinolinesulfonamide).

Therapeutic strategies focused on kinase inhibition rely heavily on surrogate measures of kinase inhibition obtained from in vitro assay systems. There is a need to develop methodology that will facilitate measurement of kinase inhibitor activity or specificity in tissue samples from whole animals treated with these compounds. Many of the current methods are limited by the use of antibodies, many of which do not cross-react between several species. The proteomics approach described herein has the potential to reveal novel tissue substrates, potential new pathway interconnections, and inhibitor specificity by monitoring differences in protein phosphorylation. We used the protein kinase inhibitor H89 (N-(2-[p-bromocinnamylamino]-ethyl)-5-isoquinolinesulfonamide) as a tool to determine whether differential profiling of tissue phosphoproteins can be used to detect treatment-related effects of a protein kinase A (PKA) inhibitor in vivo. With a combination of phosphoprotein column enrichment, high-throughput two-dimensional gel electrophoresis, differential gel staining with Pro-Q Diamond/SYPRO Ruby, statistical analysis, and matrix-assisted laser desorption ionization/time of flight mass spectrometry analysis, we were able to show clear differences between the phosphoprotein profiles of rat liver protein extract from control and treated animals. Moreover, several proteins that show a potential change in phosphorylation were previously identified as PKA substrates or have putative PKA phosphorylation sites. The data presented support the use of differential proteomic methods to measure effects of kinase inhibitor treatment on protein phosphorylation in vivo.