It is well known that chemical assays of high quality (i.e., adequate sensitivity, selectivity, and reproducibility) are essential to obtaining credible data in clinical pharmacology studies (e.g., PK) and biopharmaceutics studies (e.g., BE). However, in the future, assay development that includes more sophisticated technologies and more attention to detail will be needed.
For example, there are many pharmacological or physiological biomarkers of drug activity which are used in analyzing exposure-response relationships for the purpose of making decisions in drug development or regulatory review, where evidence of validation of the measurement of the response component is incomplete or missing. In addition, with the evolution of PGt and PGx, principles of validation of new technologies such as mass spectrometry (proteomics), high-throughput DNA sequencing, and expression profiling (microarrays) will need to be established to ensure credible interpretation and use of these data. Each of these newer technologies, in contrast to traditional technologies, will provide a tremendous amount of information about changes in gene expression and potentially useful biomarker panels. The bioinformatics software used to mine these data sets is not standardized at the moment, and as a result various association algorithms, cluster analyses, and SNP and haplotype identification methods are used from company to company. The potential for interlaboratory differences in interpretation is enormous and consensus on how to use these tools reliably will be important in clinical pharmacology and biopharmaceutics studies of the future.
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