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Working with biological matrices and understanding the intended use are crucial.
Approaches to drug development are changing, as pharmaceutical companies use advanced technologies to achieve targeted goals. Today, rational drug development strategies based on modeling, simulation, and biomarkers complement such traditional drug discovery methods as structure-activity relationship (SAR) studies. Biomarkers are also being used to evaluate drug performance during preclinical testing and clinical trials. Assay development and validation, however, can be more challenging for biomarkers than it is for APIs. This article will examine some of these issues.
Many of the challenges of working with biomarkers stem from the fact that, unlike APIs in neat solutions, they are endogenous compounds in biological fluids. For example, isobaric analytes such as bile acids in matrices can lead to higher background noise and interference in biomarker assays. In addition, biomarkers are typically present in trace amounts, unlike APIs, according to Stephanie Pasas-Farmer, senior director of bioanalytical labs with Frontage Laboratories.
Furthermore, analytical method development for APIs tends to be more straightforward because reference materials with known concentrations are available and can be used to spike into the matrix. With biomarker assays, internal standards are not often available, and, generally, an endogenous level of the biomarker is already in the matrix, which makes quantitation a challenge, according to April Brys, director of biomarker services with Battelle.
Regulatory requirements for analytical methods also differ for APIs and biomarkers. API analytical method development and validation must meet US Pharmacopeial Convention requirements or guidelines set by the International Conference on Harmonization (ICH).
Biomarker assay development and validation, meanwhile, depends on the assay’s intended use. If it is being used as a diagnostic tool, it must meet requirements of the Clinical Laboratory Improvement Amendments (CLIA). If it is being used to establish a pharmacokinetic/pharmacodynamics (PK/PD) endpoint, it must meet good laboratory practice (GLP) standards.
Many of the same analytical techniques, however, are used for API methods and biomarker assays. “Small-molecule biomarker assays are typically developed using a liquid chromatography/mass spectrometry (LC/MS) platform, which provides a great balance of sensitivity, selectivity, robustness, precision, accuracy, and ease of operation,” states Patrick Bennett, executive director of biomarker operations at PPD. The LC/MS platform is also beneficial for biomarker analysis because it can be used to perform multi-component analyses simply, including metabolomics-based analyses of hundreds of molecules per sample.
Dealing with matrix issues
For biomarker assays, regulatory agencies prefer that calibration standards and quality control samples be prepared using the same biological matrix (whether blood, urine, plasma, or other biological fluid) found in the samples to be analyzed. However, they should be free of biomarker, which can influence the accuracy of readings. Unfortunately, it is difficult to obtain analyte-free matrices.
One option, according to Pasas-Farmer, is to use a surrogate matrix that is biomarker free. A second option is to remove the endogenous analyte/biomarker from the matrix through chemical treatment, charcoal stripping, selective extraction, or some other method. In both of these cases, however, the chosen matrix will be different from the study sample matrix. “With surrogate, stripped, and depleted matrices, there may be issues with solubility, stability, non-specific binding, or ionization matrix effects due to the differences in the modified matrix and the authentic matrix,” says Bennett. Consequently, it will be necessary to design validation experiments to confirm that the ability to quantify analytes in a modified matrix is similar to that for analytes in the authentic matrix. If an authentic matrix is combined with standard addition, Bennett adds, experiments and processes will be necessary to determine the baseline and final concentrations and generate bridging data from newer lots.
These issues may be avoided by using a standard addition approach with the true biological matrix once the basal levels of the endogenous biomarker have been calculated for the specific lot of control matrix. Stable, labeled reference material can be used as a surrogate standard, and labeled internal standards can be effective. These approaches, however, can also be challenging because, often, reference standards are not available, or are difficult to synthesize, for biomarker assays. Even if they are available, whether they are labeled or unlabeled, they can be expensive.
In some cases, depending on the detection method and the type of matrix, samples may have to be purified before a biomarker assay can be completed. This, in turn, can create various problems, including recovery and matrix interference issues, notes Brys.
Intended use and validation
Before method validation, it is crucial to identify the intended use of the biomarker assay. “The investigator must understand the ultimate use of the data generated,” says Pasas-Farmer. If biomarker data are to be used for early screening and evaluation of the mechanism of action (MOA) of a drug candidate, then a qualified assay that is semi-quantitative or relatively quantitative can be used, she says, but if the biomarker data are used to support claims for the safety/efficacy of dosing in a pivotal preclinical or clinical study, and if they will be used in a regulatory filing in support of a new drug application (NDA), then a fully validated assay should be implemented.
Finally, if the assay will be used as a diagnostic tool to determine the disease state of an individual, then CLIA validation requirements must be met, she explains.
Discussions are still ongoing, however, about whether biomarker assays should be fully validated, or if qualification of an assay can still be used if the new chemical entity is in the early stages of development. Bennett notes, for example, that, even if no compliance is deemed necessary, extensive method performance qualification may still be warranted to ensure that the biomarker assay can provide adequate confidence in the data required.
Validation of an API analytical method is typically focused on the validation of specific assay performance parameters such as the precision, robustness, linearity, and lower limit of quantification (LLOQ)/lower limit of detection (LLOD). In contrast, validation of biomarker assays involves determination of “fitness for purpose” and must demonstrate that the assay meets the predetermined purpose, according to Brys, who defines the stages of biomarker validation as follows:
If stable labeled material is used as a surrogate reference standard, Bennett says, additional experiments must be performed immediately before each analytical run, to demonstrate similar responses for equimolar amounts of labeled and unlabeled analyte. Because biomarkers are endogenous and the goal is to measure the change in the biomarker concentration in response to some other change (e.g., use in diseased vs. healthy patients or in comparing different therapies), he says, experiments must also be run to determine the ability to detect a statistically meaningful change.
While it might seem obvious, Bennett stresses that, even before assay development starts, it is crucial to ensure that the correct biomarker(s) is being evaluated for the right endpoint(s). Brys adds that selected biomarkers should have a strong biological rationale and correlation with efficacy in preclinical studies and, thus, focusing on a few biomarkers is more effective. “A basic starting point for the development of any biomarker assay is an understanding of relevant precedents for the assay,” agrees Donna Eash, director of client services with Frontage.
As important is ensuring that the extent of qualification or validation is aligned with the objective of the biomarker data that is obtained, according to Bennett. “If the data are used for dose selection, formulation comparisons, or research, then compliance may not be as important as ensuring that the method meets an appropriate acceptance criteria. However, if the data are to be used for patient enrollment, safety, or efficacy, meeting required regulatory compliance standards also should be included in the strategy,” he observes.
Developers must also outline an effective development plan, considering that endogenous biomarkers are often identified in discovery stages. “A good strategy must be in place for initial discovery into preclinical, followed by preclinical into clinical, as each stage will have its own level of rigor, and follow the fit-for-purpose assay qualification and validation steps,” Eash notes.
Given the low levels of biomarkers present in most samples, Brys adds, it is important to identify an analytical method with sufficient sensitivity. Access to actual study or patient samples can be important during the pre-qualification/validation stage to ensure that the right concentration range(s) are being evaluated and to determine differences in the concentrations of the biomarker(s) in treated and untreated samples, according to Bennett. He also reiterates that validation experiments should be performed to quantify any matrix effects and non-specific binding that may occur when using modified or surrogate matrices.
The following are also important to the successful development and validation of biomarker assays, Brys says:
Multiplexing and other advances
New advances in analytical technology, such as multiplexing systems that allow for 10–100 biomarkers to be analyzed in a single sample, offer a number of benefits, including lower costs and fewer invasive procedures, says Pasas-Farmer.
High resolution/accurate mass (HR/AM) instruments are now commercially available with triple quadrupole-like sensitivity and robustness. Bennett says that they offer significantly higher resolving power with a much greater signal-to-noise ratio, even when using single-ion monitoring (SIM). SIM is advantageous because it does not require the fragmentation and subsequent loss of signal that occurs in LC-MS/MS experiments. This means that, for many analytes, particularly those with poor or excessive fragmentation or high background noise levels, even when MS/MS is used, it allows for greater selectivity and sensitivity, he says. Examples would include bile, which has poor fragmentation, and steroids, with excessive fragmentation.
Easier and more robust nanospray technologies have also allowed researchers to achieve significantly better LLOQs than was possible with high-flow liquid chromatography and traditional electrospray ionization techniques, Bennett says. Pairing this technology with either HR/AM or triple quadrupole instruments can provide sensitivity benefits that are further magnified when multidimensional chromatography and an immunoaffinity cleanup step are used, he adds.
Evolving regulatory environment
Regulatory expectations for biomarker methods are changing, and Bennett expects them to continue to evolve over the next three to five years. FDA’s recent guidance suggests that the agency will take a more rigorous and less semi-quantitative approach to overseeing assay qualification and validation. In addition, Incurred Sample Reanalysis, which is used to measure the quality of data and robustness of an assay and has been required for small- and large-molecule drug substances, is now being requested for biomarker development, Pasas Farmer points out.
About the Author
Cynthia A. Challener, PhD, is a contributing editor at Pharmaceutical Technology.
Article DetailsPharmaceutical Technology
Vol. 39, No. 2
Citation: When referring to this article, please cite it as C. Challener, “Developing and Validating Assays for Small-Molecule Biomarkers,” Pharmaceutical Technology39 (2) 2015.