Validation of Analytic Methods for Biomarkers Used in Drug Development

The role of biomarkers in drug discovery and development has gained precedence over the years. As biomarkers become integrated into drug development and clinical trials, quality assurance and, in particular, assay validation become essential with the need to establish standardized guidelines for analytic methods used in biomarker measurements. New biomarkers can revolutionize both the development and use of therapeutics but are contingent on the establishment of a concrete validation process that addresses technology integration and method validation as well as regulatory pathways for efficient biomarker development. This perspective focuses on the general principles of the biomarker validation process with an emphasis on assay validation and the collaborative efforts undertaken by various sectors to promote the standardization of this procedure for efficient biomarker development.

Biomarkers are playing an increasingly important role in drug discovery and development from target identification and validation to clinical application, thereby making the overall process a more rational approach. The potential use of biomarkers in each phase of the drug development process is summarized in Table 1 (1). The incorporation of biomarkers in drug development has clinical benefits that lie in the screening, diagnosing, or monitoring of the activity of diseases or in assessing therapeutic response. The development and validation of these mechanism-based biomarkers serve as novel surrogate end points in early-phase drug trials. This has created a much appreciated environment for protein biomarker discovery efforts and the development of a biomarker pipeline that resembles the various phases of drug development. The components of the biomarker development process include discovery, qualification, verification, research assay optimization, clinical validation, and commercialization (2).

The role of biomarkers in rational drug development has been a major focus of the Food and Drug Administration (FDA) critical path initiative and the NIH roadmap (3). Although the overwhelming majority of biomarkers are proteins used as surrogate end points for drug development, diagnostic biomarkers may also prove useful for understanding the biology of the disease. Successful biomarker development depends on a series of pathway approach that originates from the discovery phase and culminates in the clinical validation of an appropriately targeted biomarker. Much emphasis has been placed on the paradigm of biomarker translation specifically on the principles of biomarker validation in clinical trials, and the articles in this edition of CCR Focus will address various issues along the validation pathway, including the analysis of microarray data sets (4), the validation of predictive models (5), the design of clinical trials using genomics (6), and the overall statistical challenges that exist (7). New biomarkers can revolutionize both the development and use of therapeutics but are contingent on the establishment of a concrete validation process that addresses technology integration and method validation as well as regulatory pathways for efficient biomarker development. This perspective will feature highlights on the biomarker validation process and includes a discussion on analytic method validation.

Numerous publications have described the application of biomarkers in drug development using various nomenclatures to describe distinct aspects of this process. We begin with the standardization of terminology for ease of understanding the biomarker literature. A consensus definition of a biomarker is a factor that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention (8). A clinical end point is defined as a variable that measures how patients feel, function, or survive, whereas a surrogate end point is a biomarker that is intended to substitute for a clinical end point. In this case, a surrogate end point is expected to predict clinical benefit. Examples of surrogate end points and clinical end points are provided in Table 2.

Another critical distinction should be made when a biomarker undergoes analytic method validation versus clinical qualification. Analytic method validation is the process of assessing the assay, its performance characteristics, and the optimal conditions that will generate the reproducibility and accuracy of the assay. Clinical qualification is the evidentiary process of linking a biomarker with biological processes and clinical endpoints (9). Although validation, qualification, or evaluation has been used interchangeably in the literature, the distinction should be made to properly describe the particular phase the biomarker is transitioning through in the drug development process. As such, the term validation is reserved for analytic methods, and qualification for biomarker clinical evaluation to determine surrogate endpoint candidacy (8, 9). Both validation and qualification processes are intertwined, and hence, their integration guides biomarker development with the principle of linking the biomarker with its intended use (see Fit-for-Purpose Method Validation; ref. 10).

The FDA has issued guidance for industry on pharmacogenomic data submissions and in classifying the various types of genomic biomarkers and their degree of validity: exploratory biomarkers, probable valid biomarkers, and known valid biomarkers.³

Exploratory biomarkers lay the groundwork for probable or known valid biomarkers and can be used to fill in gaps of uncertainty about disease targets or variability in drug response and bridge the results of animal model studies to clinical expectation or used for the selection of new compounds (11). Examples of exploratory biomarkers include the use of gene panels used for preclinical safety evaluation or the evaluation of vascular endothelial growth factor as a target to assess the efficacy of angiogenesis inhibitors. For an exploratory biomarker to achieve the status of probable valid biomarker, it needs to be “measured in an analytic test system with well-established performance characteristics and for which there is an established scientific framework or body of evidence that elucidates the physiologic, toxicologic, pharmacologic, or clinical significance of the test results.” A probable valid biomarker seems to have predictive value for clinical outcomes but has not been independently replicated or widely accepted. The advancement from probable valid to known valid lies in the achievement of a broad consensus in cross-validation experiments, which include the independent validation of the biomarker by replicating the outcome at different sites. Thus, a known valid biomarker is defined as “a biomarker that is measured in an analytic test system with well-established performance characteristics and for which there is widespread agreement in the medical or scientific community about the physiologic, toxicologic, pharmacologic, or clinical significance of the test results.” That is, the scientific community at large accepts these known valid biomarkers to predict clinical or preclinical outcomes. Examples of such valid genomic biomarkers that are listed on the labels of FDA-approved drug products are presented in Table 3.

The qualification process is introduced to bridge the gap from an exploratory biomarker to a known valid biomarker status, keeping in mind that a validation process requires a consensus on an efficient and transparent process map for genomic biomarker validation. A qualification process map has been proposed by the FDA that evaluates exploratory genomic biomarkers of preclinical drug safety to assess the potential of genomic technologies in mock submission (12, 13) and identify key variables that can be used to determine the success of these biomarkers in voluntary genomic data submission (14). The proposal transitions an exploratory biomarker to a known valid genomic biomarker through a series of phases from discovery to method development to validation studies and cross-validation consortium (11). In the case of a process map that involves the validation of genomic biomarkers in clinical trials, the regulatory agency will review the biomarker validation package in terms of the usefulness of the biomarker in predicting clinical benefit (11). Although this particular qualification process addresses genomic biomarkers, its application can be further extended to other types of biomarkers (e.g., protein or diagnostic biomarkers) granted the qualification approach remains intact. Figure 1 shows the integration of the FDA biomarker qualification process along the phases of the drug development process.

The inclusion of biomarkers in drug development and regulatory review will improve the efficiency of the biomarker development process. Biomarker qualification is also observed in the codevelopment of biomarkers (in the form of diagnostic tests) and drugs with the use of these biomarkers limited to the application of the drug (11).⁴

Codevelopment imposes the necessity to generate specific guidelines describing analytic test validation (sensitivity and specificity of the assays), clinical test validation (ability of the assays to detect and predict diseases), and clinical utility.⁵ Furthermore, biomarkers play a significant role in adaptive trial designs in which patient population stratification and efficacy determination is based on biomarker readouts (15). Adaptive trials attempt to maximize the statistical power of the study with a small sample size. The integration of biomarkers into drug development is further discussed in the section below.

It is important to point out that biomarker method validation is distinct from pharmacokinetic validation and routine laboratory validation. The FDA has issued guidance for industry on bioanalytic method validation for assays that support pharmacokinetic studies that are specific for small-molecule drugs and that are not directly related to the validation of biomarker assays.⁶

Whereas routine laboratory validation refers to laboratories that do testing on human specimens for diagnosis, prevention, or treatment of any disease and falls under the jurisdiction of the Clinical Laboratory Improvement Amendments of 1988, there is little regulatory guidance on biomarker assay validation. In October 2003, the American Association of Pharmaceutical Scientists (AAPS) and the U.S. Clinical Ligand Society (CLAS) cosponsored a Biomarker Method Validation Workshop to address the validation challenges of biomarker assays in support of drug development (16). At this meeting, it was concluded that biomarker methods should not be validated by the same guiding principles developed for drug analysis used in bioanalytic method validation. Hence, a “fit-for-purpose” approach for biomarker method development and validation is derived with the idea that assay validation should be tailored to meet the intended purpose of the biomarker study.

Method validation should show the reliability of the assay for the intended application with the rigor of the validation process increasing from the initial validation proposed for exploratory purposes to the more advanced validation dependent on the evidentiary status of the biomarker (10). The fit-for-purpose method validation is an umbrella terminology that is used to describe distinct stages of the validation process, including pre-validation, exploratory and advanced method validation and in-study method validation (Fig. 1). Method validation is thus a continuous and iterative process of assay refinement with validation criteria that is driven by the application of the biomarkers with increasing rigor at each successive validation step and focusing on method robustness, cross-validation, and documentation control.

Before addressing the elements of biomarker assay development and method validation, it is important to recognize that the biomarker method validation process begins with choosing the right assay followed by developing this assay into a validated method. Indeed, the integration of various technologies proves pivotal to not only biomarker identification and characterization but also validation. In fact, the platform applied in biomarker discovery can also be further developed and used as an analytic platform. Biomarker measurement can be assessed at different biological levels with different technologies; thus, the appropriate choice of assay depends on the application of the biomarker and the limitations of the respective technology. Various types of assays can be used in the biomarker method validation process and range from the relatively low technology end, such as immunohistochemistry to immunoassays, to the high technology end, including platforms for genomics, proteomics, and multiplex ligand-binding assays.

A genomics approach consists of various methods that measure gene expression analysis, such as in microarrays, which has become the standard technology used for target identification and validation. Reverse transcription-PCR is a very sensitive, reproducible technology and often time used to validate microarray-generated data. Comparative genomic hybridization can be used to detect chromosomal alterations associated with certain cancers. Proteomics involves global protein profiling to provide information about protein abundance, location, modification, and protein-protein interactions. Whereas proteomics is a discovery technology, immunoassays are routinely used for protein biomarker assessments due to its straightforward clinical application and translation into a potential diagnostic assay. The multiplexing of protein assays can increase the throughput for simultaneous analysis of several proteins; however, it is limited by the need to standardize assay conditions, the lost of sensitivity over single assays, and the quality control (QC) of each analyte in the complete multiplex panel (17).

Metabonomics (or metabolomics) is the profiling of endogenous metabolites in biofluids or tissue for characterization of the metabolic phenotype. The analytic platforms used are based on nuclear magnetic resonance spectroscopy and the combination of liquid chromatography with mass spectroscopy. It is principally used in biomarker discovery, although by definition it is the ultimate end point measurement of biological events. Yet the technology is limited by the lack of comprehensive metabolite databases and throughput both of which affects data analysis and interpretation. The integration of these technologies lends to the field of bioinformatics where linking expression data derived from genomic/proteomic approaches to target biological pathways can provide a comprehensive understanding of the disease biology and further validating the application of the biomarker (18).

Furthermore, advances in novel imaging approaches also have profound implications for biomarker development. Molecular and functional imaging technologies are used to assess cell proliferation and apoptosis (e.g., ¹⁸F-fluoro-l-thymidine and ^99mTc-Annexin imaging), cellular metabolism (e.g., ¹⁸F-fluorodeoxyglucose positron emission tomography), and angiogenesis and vascular dynamics (e.g., dynamic contrast-enhanced computed tomography and magnetic resonance imaging). Therefore, making the right choice of assay is an important first step to successful biomarker method validation. Validating the developed method or assay to reliably measure the biomarker depends on a series of variables that is addressed below.

The key variable assay elements of biomarker method validation are more complicated than for the typical bioanalytic assay that follows good laboratory practice (GLP) guidelines. Table 4 compares these two validation paradigms and highlights some of the validation challenges encountered with biomarker assays. Biomarker assay development and method validation is a complex process that depends on several variables from the choice of the matrix to maintaining sample integrity to assay standardization and accuracy.

Specifications for biological matrices need to be determined taking into consideration the site of biomarker production and the physiology and distribution of the biomarkers. The first challenge is to identify and select a meaningful sample matrix that can be readily accessible, such as whole blood, plasma, serum, or urine. Sources of analytes can influence the validation process as evidenced by feasibility in the acquisition of biological material during the study, such as in the collection of noninvasive (sputum, urine, feces, and saliva) versus minimally invasive (blood or plasma) samples. If the method has sufficient sensitivity, then the preferred matrix choice is based on ease of sample collection and analysis. However, if sensitivity is a factor and measurement of the biomarker in the specified matrix poses as a challenge, then the preferred matrix is chosen based on sample concentration even if this presents as a greater challenge for sample collection and preparation.

In addition to the influence of sample sources, material collection and processing should be examined to maintain sample integrity. It is important for researchers to realize that biospecimen collection varies across populations; how they are handled and differences in sample processing variables can dramatically affect the results of a trial. Thus, appropriate conditions for collecting, handling, and storing study samples need to be standardized along with adequate training of the clinical trial management personnel to preserve the stability and integrity of the analyte. Sample integrity can be affected by repeated cycles of freeze-thawing specimens or by long-term storage, and hence, the stability of the sample becomes compromised. Depending on the type of samples (biological fluids or tissues), minimization in variability at each step of this procedure from collection to processing is critical to ensure consistent and valid analyte measurement at subsequent biomarker assays. For biological fluids, differences in the handling of urine versus whole blood samples can affect the analytic assay, and thus, optimization of a processing protocol is necessary and should be based on the specific biomarker in addition to the source of analyte. Thus, standardization of sample collection procedures and appreciation of the associated limitations allow this variability to be minimized. Moreover, the integrity of reagents is another variable that can also affect biomarker analysis. Reagents such as antibodies are subject to their own problems of supply, stability, and QC as they themselves are derived from biological sources.

QC measures should be undertaken to document analytic performance during clinical studies and to determine the acceptance or rejection of an analytic run during sample analysis (19). Similar to bioanalytic method validation, biomarker analysis requires a systematic review of the analyte stability in calibration standards, QCs, and study samples (20). In general, QCs are prepared to evaluate the lower, middle, and upper limits of standard curve ranges. Whereas QC samples are used during study sample analysis to judge the acceptability of assay runs, validation samples (VS) are used in assay validation experiments to estimate intra-run and inter-run accuracy/precision and stability. Whereas only three VS concentrations are required in GLP bioanalytic assays (21, 22), at least five different concentrations of VS should be analyzed in duplicate on at least six different runs during the prestudy validation because quantitative biomarker assays often exhibit nonlinear calibration curves; thus, more VS are required (10, 23).

Because biomarkers are endogenous substances, difficulty may arise in obtaining biomarker/analyte-free matrices either to do specificity studies on or to prepare for the calibration curve. Most of the time, the target biomarker molecule is not available to act as a certified calibration standard (19). Researchers then may rely on the use of a noncertified standard, a recombinant protein, or a surrogate matrix to construct the calibration curve (10). If assay standards are prepared in a nonauthentic matrix, QC samples should be prepared and tested in the same matrix as the study samples to show that the assay performance is similar between authentic and nonauthentic matrices (24). Parallelism studies should be conducted when surrogate standards and matrices are used for calibration purposes. Dilution linearity can also be problematic, as antibody and ligand-binding affinities can vary significantly in different media. Other important components of biomarker assay validation include the reference materials, precision and accuracy, dynamic range, sample recovery, sample volumes, and instrument validation. Additionally, the variability in method validation can also be affected by assay results at different locations and the correct calibration of the assay at different test sites.

The technical validation of biomarkers depends on all aspects of the analytic method, including assay sensitivity, specificity, reliability, and reproducibility (19, 25–27). Specificity refers to the ability of the assay to clearly distinguish the analyte of interest from structurally similar substances. Selectivity measures the degree to which unrelated matrix components cause analytic interference. Precision is determined by the repeatability and reproducibility of the assay, which are factors used to quantitatively express the closeness of agreement among results of measurements done under specific conditions (28). Repeatability describes the measurements that are done under the same conditions, whereas reproducibility addresses measurements done under different conditions. The reproducibility of the assay relies on its variability, levels of technical/instrumental and biological noise, as well as different validation phases of the method (pre-study and in-study validation of the method).

The acceptance criteria of the assay performance are established based on the study objectives and the known assay variability (29). The nature of the assay methodology and the data generated using that particular assay can influence the establishment of assay acceptance criteria. The categories of biomarker data that reflect the type of assay used have been defined at the AAPS/CLAS workshop (16). To aid in the formulation of a method validation plan, a biomarker assay can be placed into various functional categories, each requiring a distinct level of validation. A definitive quantitative assay makes uses of calibrators and a regression model to calculate absolute quantitative values for unknown samples. The reference standard must be well defined and fully representative of the biomarker. This type of assay can be validated to be accurate and precise. A relative quantitative assay uses a response-concentration calibration with reference standards that are not fully representative of the biomarker. Because the calibration curve may use either a noncertified standard or surrogate matrix or both, studies on parallelism and dilution linearity are necessary. Precision can be validated but accuracy can only be estimated. A quasi-quantitative assay (possesses certain attributes) does not use a calibration standard but has a continuous response that is expressed in terms of a characteristic of the test sample. Precision can be validated, but not accuracy (16).

A qualitative assay generates categorical data that lack proportionality to the amount of analyte in a sample. The data may be ordinal in that the assay relies on discrete scoring scales like those often used for immunohistochemistry or nominal such as the presence or absence of a gene product (10, 16). Qualitative assays are only required to show that they are sufficiently sensitive and specific to detect the target analyte. In addition to assay functionality, ensuring that the degree of validation done reflects the level of importance of the biomarker itself is equally important. Table 5 summarizes the validation variables for each category of biomarker assay as recommended by the AAPS/CLAS workshop.

What should be the acceptance criteria of the assay performance? Rather than setting the acceptance criteria for precision and accuracy at a fixed value, as in GLP assays, biomarker assays should but evaluated on a case per case basis, with ±25% acting as default value [±30% at the lower limit of quantitation (LLOQ); ref. 30]. In determining acceptance limits for QCs during sample analysis, either a 4-6-X rule or establishing confidence intervals should be considered (10, 30). Such is the case for bioanalytic assays of small molecules where the analytic run is accepted as valid when at least 67% (4/6) of the QCs fall within 15% of their nominal values (the 4:6:15 rule; refs. 21, 22, 31). Because the target molecule is often present in predose samples or in the QC matrix, limitations are often placed on LLOQ.

In summary, there are numerous factors that affect the biomarker method validation process, including the sources of variability in measurements, the intended application of a biomarker, patient selection, sample collection and processing, and analytic validation. As such proper method validation should be carried out in early clinical studies so that these analytic results can be used to assess whether the method affords the sensitivity, precision, and robustness of the assay. During early exploratory phases of drug development, it is not necessary to do full validation of biomarkers as long as the methods provide reliable data, information, and knowledge (24). As drug development progresses, validation should keep pace with the required precision and reliability needed to achieve the study objectives (24, 26). Pre-study validation should be completed before clinical studies are begun and should set the foundation for establishing method acceptance criteria. In-study sample analyses and validation must use QCs to document analytic performance during clinical studies (19). As we advance toward the later stages of drug development, the effect of the biomarker data on decisions around critical safety, efficacy, pharmacodynamic, or surrogate information increases. Thus, an increased rigor in advanced method validation is undertaken as described in the scaled, fit-for-purpose approach (Fig. 1).

Recognizing the importance and impact of biomarkers coupled together with the complexity that exists in the drug development process, researchers realized that there is much needed standardization for biomarker development and have therefore joined forces in an effort to integrate biomarkers into drug development. The most recent of these alliances is a consortium called the Cancer Biomarkers Collaborative (CBC) composed of the AACR, the FDA, and the National Cancer Institute with an initiative focused on facilitating the use of validated biomarkers in clinical trials (32). The goal of the CBC is to develop guidelines in the areas of biospecimens, assay validation, bioinformatics, and information sharing. The CBC will recommend standards and specifications on how to collect biospecimens and integrate them into drug trials such that the desired endpoint of the biomarker measurement is reached and these endpoints can then be compared among clinical trials. Effort in terms of validation is aimed at identifying and defining how to validate a biomarker assay and make it eligible for inclusion into clinical trials. Hence, the need for a well-defined process with consensus standards and guidelines for biomarker development, validation, qualification, and use is apparent and an important priority of the collaboration. The CBC intends to pave a regulatory pathway for biomarkers, as they transition from the development phase through the FDA approval process and then on to clinical utility.

In addition to the CBC, other alliances in existence include partnerships with government, industry, patient advocacy groups, and other nonprofit private sector organizations. The Biomarkers Consortium is a public-private biomedical research partnership formed by the Foundation for the NIH, the NIH, FDA, and the Pharmaceutical Research and Manufacturers of America. The Biomarkers Consortium aims to rapidly identify and qualify biomarkers, verify their individual value, and formalize their use in research and regulatory approval to guide clinical practice. Therefore, these collaborative efforts among various sectors have arisen to address the lack of standardized guidelines in biomarker validation, particularly for method validation, and in doing so hope to promote an efficient biomarker development process.

Up to this point, we have discussed the importance of biomarker method standardization and validation. However, another challenge that remains to be addressed is in understanding how to effectively and efficiently integrate biomarkers into the drug development process. Biomarkers can play a pivotal role in facilitating drug development (Table 1), particularly in oncology drug development where tumor markers seem to correlate with prognosis and potentially be a valuable measure of treatment outcome. The incorporation of biomarkers as surrogate endpoints of clinical efficacy and safety assessment is being intensely evaluated and pursued in rational drug development, especially in the case of biomarker and drug codevelopments such as HER2 (also called ErbB2 and Neu) and the development of trastuzumab.

HER2 is a proto-oncogene that became a potential biomarker when studies showed that its overexpression in breast cancer is associated with poor prognosis (33). The role of HER2 as a clinically relevant biomarker led directly to the development of trastuzumab, a specific targeted therapy of a recombinant monoclonal antibody directed against the extracellular domain of HER2. HER2 as a biomarker was used for the selection of the appropriate patient populations (that express or overexpress the oncogenic protein) in clinical trials and to evaluate potential efficacy after therapeutic intervention. Indeed, several clinical studies have confirmed that patients with high levels of HER2 receptor overexpression (via immunohistochemical staining) are likely to receive clinical benefit from therapy and that HER2 gene amplification (by fluorescence in situ hybridization) is most predictive (34). As such, trastuzumab was subsequently approved in 1998 by the FDA as second-/third-line monotherapy or first-line therapy in combination with paclitaxel for the treatment of HER2-overexpressing metastatic breast cancer (35–38). Thus, biomarker-based patient selection in the early stages of the clinical trial process proves to be critical to the evaluation of a targeted agent as shown by the successful development of trastuzumab.

Another example of the successful use of biomarkers in cancer drug development is the development of imatinib mesylate. This molecular-targeted drug is highly efficacious in chronic myeloid leukemia (CML; ref. 39) and gastrointestinal stromal tumor (GIST; refs. 40, 41). The BCR-ABL fusion protein translocation in CML provided a biomarker and a therapeutic target for this rationally designed small molecule. In clinical trials of imatinib, assessment of biomarker-based responses facilitated proof of its clinical benefit in CML. Clinical benefit was determined from evaluation of the molecular target in trials using conventional cytogenetics, fluorescence in situ hybridization assays of the BCR-ABL translocation, and reverse transcription-PCR detection of BCR-ABL transcripts, which are now used to guide treatment decisions (42). Treatment response and dose optimization can be based on measuring the level of BCR-ABL kinase inhibition achieved in vivo as determined by calculating the reduction in protein levels of phospho-CRKL in mononuclear blood cells taken from CML patients (43).

Imatinib also inhibited the receptor tyrosine kinase encoded by the oncogene c-KIT and expression of this oncogenic biomarker provided a rationale for its use in patients with GIST. In addition, the response to imatinib is closely correlated with the presence and type of c-KIT mutation, creating another role for these mutations to serve as biomarkers in treatment selection for individuals with GIST (44–46). GISTs with the most common c-KIT exon 11 mutations are associated with increased imatinib sensitivity, whereas the less common c-KIT exon 9 mutations are less sensitive to imatinib (47, 48). GISTs lacking mutations in c-KIT or the alternative receptor tyrosine kinase PDGFRA show much lower rates of response to imatinib (47, 48). Thus, the example of imatinib illustrates how biomarkers derived from BCR-ABL can both stimulate initial drug discovery efforts and serve as a useful end point assessment of treatment effect in biomarker-based patient and dose selection for imatinib therapy.

With these examples of biomarkers providing key rationale and end points in the development of molecular-targeted agents, biomarker-based drug development can be regarded as a proven, successful strategy for developing novel anticancer drugs. The scope of evaluation and validation of the biomarker will depend on its intended use as a marker of toxicity, safety, or efficacy. These biomarkers may serve as surrogate end points that predict clinical outcomes. As biomarkers are incorporated in drug development, regulatory approaches will be developed and will be more stringent than that needed to guide early drug development to ensure that the development, validation, and implementation of biomarkers into clinical trials is a safe, effective, and efficient process.

The need for a standardized pathway approach toward the biomarker validation process is becoming increasingly important given the recent surge in the biomarker development pipeline. As biomarker research progresses toward establishing the fundamentals of personalized medicine, the future of drug and biomarker codevelopment resides in identifying the right population that would benefit from that drug. The significant effort and resources that are invested in the development of these biomarkers will expand their roles as surrogate endpoints and diagnostic indicators for disease screening, monitoring disease progression and treatment efficacy, and in assessing patient outcome or identifying potential side effects such as in toxicity. The emphasis now would be placed on biomarker assay development and method validation to eliminate the failure of biomarkers that occur in the clinic as a result of poor assay choice and the lack of robust validation. Future advancements in biomarker research will be heavily focused on transitioning biomarkers from the development and validation phases to their clinical applications incorporated into drug trials.

No potential conflicts of interest were disclosed.