Pharmaceutical Research

, Volume 23, Issue 2, pp 312–328 | Cite as

Fit-for-Purpose Method Development and Validation for Successful Biomarker Measurement

  • Jean W. LeeEmail author
  • Viswanath Devanarayan
  • Yu Chen Barrett
  • Russell Weiner
  • John Allinson
  • Scott Fountain
  • Stephen Keller
  • Ira Weinryb
  • Marie Green
  • Larry Duan
  • James A. Rogers
  • Robert Millham
  • Peter J. O'Brien
  • Jeff Sailstad
  • Masood Khan
  • Chad Ray
  • John A. Wagner
Research Paper


Despite major advances in modern drug discovery and development, the number of new drug approvals has not kept pace with the increased cost of their development. Increasingly, innovative uses of biomarkers are employed in an attempt to speed new drugs to market. Still, widespread adoption of biomarkers is impeded by limited experience interpreting biomarker data and an unclear regulatory climate. Key differences preclude the direct application of existing validation paradigms for drug analysis to biomarker research. Following the AAPS 2003 Biomarker Workshop (J. W. Lee, R. S. Weiner, J. M. Sailstad, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development. A conference report. Pharm Res 22:499–511, 2005), these and other critical issues were addressed. A practical, iterative, “fit-for-purpose” approach to biomarker method development and validation is proposed, keeping in mind the intended use of the data and the attendant regulatory requirements associated with that use. Sample analysis within this context of fit-for-purpose method development and validation are well suited for successful biomarker implementation, allowing increased use of biomarkers in drug development.

Key Words

assay validation biomarkers drug development fit-for-purpose method development and validation 



American Association of Pharmaceutical Sciences


below quantifiable limit


Center for Drug Evaluation and Research


Centers for Medicare and Medicaid Services


Clinical Ligand Assay Society


Clinical Laboratory Improvement Amendments


Clinical and Laboratory Standards Institute


Good Laboratory Practices


Ligand Binding Assay Bioanalytical Focus Group


lower limit of detection


lower limit of quantification


minimum required dilution


National Committee for Clinical Laboratory Standards






Quality Controls


quantification limits


upper limit of quantification


vascular endothelial growth factor


validation sample



The authors are grateful to Ronald R. Bowsher, PhD (Linco Diagnostic Services), for providing encouragement and critical review on the manuscript. We also thank Wesley Tanaka, PhD, and Omar Laterza, PhD (Merck & Co.) for providing input and suggestions.


The following definitions are meant to be valid in the context of bioanalytical methods. Not all definitions will be consistent with terminology from other disciplines
1. Accuracy

Per the FDA Guidance on Bioanalytical Method Validation (May, 2001), Accuracy of an analytical method describes the closeness of mean test results obtained by the method to the true value (concentration) of the analyte. This is sometimes referred to as Trueness or Bias.

2. Advanced Validation

A method validation that requires more rigor and thorough investigation, both in validation tasks and documentation, to support pivotal studies or critical decisions; e.g., differentiating subtle graded drug effects, monitoring drug safety, or for submission to regulatory agencies for drug approval.

3. Biomarker

A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic response to a therapeutic intervention.

4. Clinical Endpoint

A characteristic or variable that reflects how a patient feels, functions, or survives.

5. Clinical Qualification

The evidentiary and statistical process linking biologic, pathologic and clinical endpoints to the drug effect, or linking a biomarker to biologic and/or clinical endpoints.

6. Definitive Quantitative Assay

An assay with well-characterized reference standards, which represents the endogenous biomarker, and uses a response–concentration standardization function to calculate the absolute quantitative values for unknown samples.

7. Dilution(al) Linearity

A test to demonstrate that the analyte of interest, when present in concentrations above the range of quantification, can be diluted to bring the analyte concentrations into the validated range for analysis by the method. Samples used for this test are, in general, the ones containing high concentrations of spiked analyte, not endogenous analyte.

8. Dynamic Range

The range of the assay that is demonstrated from the prestudy validation experiments to be reliable for quantifying the analyte levels with acceptable levels of bias, precision, and total error.

9. Exploratory Validation

Method validation that is less rigorous but adequate to meet study needs; e.g., looking for big effects in drug candidate screen, mechanism exploration, or internal decision with relatively minor impact to the final product, and not used for submission to regulatory agencies.

10. Interference

(1) Analytical interference: presence of entities in samples that causes a difference in the measured concentration from the true value. (2) Physicochemical interference (matrix interference): A change in measured physical chemical property of the specimen (e.g., excess bilirubin or hemoglobin, ionic strength, and pH) that causes a difference between the population mean and an accepted reference value.

11. Intermediate Precision

Closeness of agreement of results measured under changed operating conditions within a laboratory; e.g., different runs, analysts, equipments, or plates, etc. This is one of the three types of Precision.

12. Limit of Detection

A concentration resulting in a signal that is significantly different (higher or lower) from that of background. Limit of detection is commonly calculated from mean signal at background ± 2 or 3 standard deviations. This is often described as the analytical “sensitivity” of the assay in a diagnostic kit.

13. Limit of Quantification

Highest and lowest concentrations of analyte that have been demonstrated to be measurable with acceptable levels of bias, precision, and total error. The highest concentration is termed the Upper Limit of Quantification, and the lowest concentration is termed the Lower Limit of Quantification.

14. Minimum Required Dilution

The minimum dilution required to dilute out matrix interference in the sample for acceptable analyte recovery.

15. Parallelism

Relative accuracy from recovery tests on the biological matrix, incurred study samples, or diluted matrix against the calibrator calibrators in a substitute matrix. It is commonly assessed with multiple dilutions of actual study samples or samples that represent the same matrix and analyte combination of the study samples.

16. Pharmacodynamic

The relationship between drug concentrations and biochemical and physiological effects of drugs and mechanisms of drug action.

17. Precision

Precision is a quantitative measure (usually expressed as standard deviation and coefficient of variation) of the random variation between a series of measurements from multiple sampling of the same homogenous sample under the prescribed conditions. If it is not possible to obtain a homogenous sample, it may be investigated using artificially prepared samples or a sample solution. Precision may be considered at three levels: 1. Repeatability, 2. Intermediate Precision, and 3. Reproducibility.

18. Precision Profile

A plot of the coefficient of variation of the calibrated concentration vs. the concentration in log scale. It provides preliminary estimates of the quantification limits and feasibility assessments on the intended range.

19. Quality Controls

A set of stable pools of analyte, prepared in the intended biological matrix with concentrations that span the range claimed for the test method, used in each sample assay run to monitor assay performance for batch acceptance.

20. Qualitative Assay

The assay readout does not have a continuous proportionality relationship to the amount of analyte in a sample; the data is categorical in nature. Data may be nominal (positive or negative) such as presence or absence of a gene or gene product. Alternatively, data might be ordinal, with discrete scoring scales (1 to 5, −+, +++, etc.), such as immunohistochemistry assays.

21. Quasiquantitative Assay

(Quasi: “possesses certain attributes”) A method that has no calibrator, has a continuous response, and the analytical result is expressed in terms of a characteristic of the test sample. An example would be an antidrug antibody assay that is express as titer or % bound.

22. Relative Quantitative Assay

A method which uses calibrators with a response–concentration calibration function to calculate the values for unknown samples. The quantification is considered relative because the reference standard is either not well characterized, not available in a pure form, or is not fully representative of the endogenous biomarker.

23. Relative Accuracy

For relative quantitative methods, absolute accuracy is not possible to evaluate due to the unknown nature of the endogenous biomarker. Relative accuracy is the recovery (see below) of the reference standard spiked into the study matrix.

24. Recovery

The quantified closeness of an observed result to its theoretical true value, expressed as a percent of the nominal (theoretical) concentration. Recovery is often used as a measure of accuracy.

25. Repeatability

Closeness of agreement between results of successive measurements of the same samples carried out in the same laboratory under the same operating condition within short intervals of time. It is also termed intraassay or intrabatch precision. This is one of the three types of Precision.

26. Reproducibility

Closeness of agreement of results measured under significantly changed conditions; e.g., inter laboratory, alternate vendor of a critical reagent. This is also referred to as cross validation.

27. Robustness of the assay

A measure of the capacity of a method to remain unaffected by small, but deliberate changes in method parameters and provides an indication of its reliability during normal run conditions.

28. Selectivity

The ability of a method to determine the analyte unequivocally in the presence of components that may be expected to be present in the sample.

29. Sensitivity

The lowest concentration of analyte that an analytical method can reliably differentiate from background (limit of detection).

30. Specificity

The ability of assay reagents (e.g., antibody) to distinguish between the analyte, to which the reagents are intended to detect, and other components.

31. Surrogate Endpoint

A biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit or harm (or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence.

32. Target Range

Range of analyte concentrations where the study samples are expected to fall.

33. Total Error

The sum of all systematic bias and variance components that affect a result; i.e., the sum of the absolute value of the Bias and Intermediate Precision. This reflects the closeness of the test results obtained by the analytical method to the true value (concentration) of the analyte.

34. Validation

It is the confirmation via extensive laboratory investigations that the performance characteristics of an assay are suitable and reliable for its intended analytical use. It describes in mathematical and quantifiable terms the performance characteristics of an assay.

35. Validation samples

Test samples in biological matrix mimicking study samples, endogenous and/or spiked, used in prestudy validation to provide characterizations of assay performances; e.g., intra- and inter-run accuracy and precision, and analyte stability.


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Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  • Jean W. Lee
    • 1
    • 16
    Email author
  • Viswanath Devanarayan
    • 2
  • Yu Chen Barrett
    • 3
  • Russell Weiner
    • 3
  • John Allinson
    • 4
  • Scott Fountain
    • 5
  • Stephen Keller
    • 6
  • Ira Weinryb
    • 7
  • Marie Green
    • 8
  • Larry Duan
    • 9
  • James A. Rogers
    • 10
  • Robert Millham
    • 10
  • Peter J. O'Brien
    • 11
  • Jeff Sailstad
    • 12
  • Masood Khan
    • 13
  • Chad Ray
    • 14
  • John A. Wagner
    • 15
  1. 1.Formerly MDS Pharma ServicesLincolnUSA
  2. 2.Merck and Company, Inc.Blue BellUSA
  3. 3.Bristol-Myers SquibbPrincetonUSA
  4. 4.BAS Analytics Ltd.KenilworthUK
  5. 5.Pfizer Global Research and DevelopmentAnn ArborUSA
  6. 6.Protein Design Labs, Inc.FremontUSA
  7. 7.Wyeth ResearchCollegevilleUSA
  8. 8.Millenium PharmaceuticalsCambridgeUSA
  9. 9.Quest Pharmaceutical ServicesNewarkUSA
  10. 10.Pfizer Global Research and DevelopmentGroton–New LondonUSA
  11. 11.Therakos, Inc.ExtonUSA
  12. 12.Trimeris Inc.MorrisvilleUSA
  13. 13.Covance Laboratories, Inc.ChantillyUSA
  14. 14.Eli Lilly and CompanyIndianapolisUSA
  15. 15.Merck and Company, Inc.RahwayUSA
  16. 16.Thousand OaksUSA

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