Felix W. Frueh

Pharmacogenomic Biomarker Information in Drug Labels Approved by the United States Food and Drug Administration: Prevalence of Related Drug Use

Study Objectives. To review the labels of United States Food and Drug Administration (FDA)‐approved drugs to identify those that contain pharmacogenomic biomarker information, and to collect prevalence information on the use of those drugs for which pharmacogenomic information is included in the drug labeling.

Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential

BackgroundThe acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630–631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676–5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology.ResultsWe reanalyzed Tans dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tans study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall.ConclusionOur results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.

Biomarker qualification pilot process at the US Food and Drug Administration

New biomarkers of safety and efficacy are becoming powerful tools in drug development. Their application can be accelerated if a consensus can be reached about their qualification for regulatory applications. This consensus requires a review structure within the US Food and Drug Administration (FDA) that can evaluate qualification data for these biomarkers and determine whether these biomarkers can be qualified. A pilot process and corresponding Biomarker Qualification Review Team have been developed to test how the FDA can work on biomarker qualification.

From pharmacogenetics to personalized medicine: a vital need for educating health professionals and the community

The field of pharmacogenetics will soon celebrate its 50th anniversary. Although science has delivered an impressive amount of information in these 50 years, pharmacogenetics has suffered from lack of integration into clinical practice. There are several reasons for this, including the unmet need for education at medical schools and the lack of awareness about the impact of genetic medicine on healthcare in the community. Recently, the FDA announced that it considers pharmacogenomics one of three major opportunities on the critical path to new medical products. This notion by the FDA is filling the regulatory void that existed between drug developers and drug users. However, in order to bring pharmacogenetic testing to the prescription pad successfully, healthcare professionals and policy makers, as well as patients, need to have the necessary background knowledge for making educated treatment decisions. To effectively move pharmacogenetics into everyday medicine, it is therefore imperative for scientists and teachers in the field to take on the challenge of disseminating pharmacogenetic insights to a broader audience.

Microarray scanner calibration curves: characteristics and implications

BackgroundMicroarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear.ResultsBy scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias.ConclusionIt is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy.

Integration and use of biomarkers in drug development, regulation and clinical practice: a US regulatory perspective

The US FDA encourages the integration of biomarkers in drug development and their appropriate use in clinical practice. It is believed that this approach will help alleviate stagnation and foster innovation in the development of new medical products, and, ultimately, lead to more personalized medicine. To facilitate the use of biomarkers in drug development and clinical practice, the FDA organized workshops, issued guidances, established a voluntary submission process, developed online educational tools and, most importantly, strives to ensure the integration of this information into drug labels, for example, via the update of existing labels, or the inclusion of appropriate language in new drug labels. A pilot process has been set up to qualify novel biomarkers that are not associated with specific drug products, but are of more common use (e.g., biomarkers for drug safety). In addition, the FDA has initiated the creation of various consortia that are working towards the identification and characterization of exploratory biomarkers in order to qualify them for a specific use.

Strategic paths for biomarker qualification

Biomarkers may be qualified using different qualification processes. A passive approach for qualification has been to accept the end of discussions in the scientific literature as an indication that a biomarker has been accepted. An active approach to qualification requires development of a comprehensive process by which a consensus may be reached about the qualification of a biomarker. Active strategies for qualification include those associated with context-independent as well as context-dependent qualifications.

QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.

Process map proposal for the validation of genomic biomarkers

How can we encourage the application of novel genomic biomarkers in drug development? A major step in this direction would be a consensus on how to interpret results from measurements of these biomarkers in regulatory submissions. A transparent process for genomic biomarker validation would be of value both for the pharmaceutical industry as well as for regulatory agencies associated with it. A discussion on process map proposals for genomic biomarker validation can help with drafting of guidance documents for this process.

Impact of microarray data quality on genomic data submissions to the FDA

How can microarray data best be exploited and integrated into the regulatory decision-making process?