Bioanalytical Method Validation: A

Personal Perspective on Small

Molecules

E. Dennis Bashaw, Pharm.D

Director, Division of Clinical Pharmacology-3

Office of Clinical Pharmacology

Office of Translational Sciences

US Food and Drug Administration

Disclaimer: The presentation today should not beconsidered, in whole or in part as beingstatements of policy or recommendation by theUS Food and Drug Administration.

The examples given in the presentation today arebased on actual situations seen by the presenter.All identifying information has been removed toprotect the confidentiality of the applicantsinvolved.

Trends in Drug Discovery

Scannell, JW, et al “Diagnosing the decline in pharmaceutical R&D efficiency” NatureReviews Drug Discovery, 11:191-200 (March 2012)

http://www.circare.org/info5.htm

http://modernmedicines.com/entry.php?id=45

Drug Development in the 21st Century

Risk/

Benefit

Do No Harm Science

Conservative Innovation

ClinicalPharmacology

INNOVATIVE ANALYSES•Improved Computing Resources •Quantitative drug-disease-trial models• Exposure-response models

INNOVATIVE TRIAL DESIGNS• Clinical trial simulations• Enrichment, adaptive, dose-response

KNOWLEDGE MANAGEMENT• Leverage prior data

New Clinical Pharmacology Tools

House of Cards

If you do not have confidence in the validation of the analytical method, how can you have confidence in the concentration values?

If you do not have confidence in the concentration values, how can you have confidence in the derived pharmacokinetic parameters or dose?

Safety & Efficacy

Clinical

Pharmacology

Bioanalytical

Validation

http://www.globalbioanalysisconsortium.org/

Relevant FDA Guidances

Bioanalysis. 2013 Mar;5(6):645-59. doi: 10.4155/bio.13.19.

J Pharm Sci. 2011 Mar;100(3):797-812

Anal Chem. 2012 Jan 3;84(1):106-12

The Importance of Methods Validation to Clinical Pharmacology

Bioanalytical Methods Validation Guidance, pg 2

MDS Canada

Cetero

Bioanalytical Review Problems

Mis-labeled samples Improper Shipping Flawed Extraction

Analytical Problems Calculation Issues Analysis/Reporting

Case Study #1

The Case of the “Come As You Are”Standard Curve

Case Study #1

IV drug, relative bioavailability study in patients with varying degrees of renal insufficiency

Analytical plan called for daily standard curves to be constructed at 2, 5, 10, 25, 100, 500, and 1000ng/ml using triplicate samples.

Analysis consisted of 5 runs over the course of 3 weeks by the same analyst

Case Study #1

Target (ng/mL) 2 5 10 25 100 500 1000

Run 1 1.76 4.2 10.2 22.3 120 515 1100

1.5 5.13 8.7 24.11 111 517.2 1070

2.13 4.8 11.4 27.2 102 505 1012

Mean 1.84 4.78 10.08 24.65 108.25 509.3 1045.5

Run 3 1.55 1005

2.42 1089

2.59 1070

Mean 2.19 1054.67

Run 4 1.76 4.76 10.35 21.45 114 535 1093

1.23 4.22 11.2 22.76 110 503 1125

1.16 3.98 10.22 31.22 102 487 1200

1.94 4.25 27.86 99 493 1103

1.87 5.15 22.4 1114

5.6 26.4

4.9

5.55

Mean 1.59 4.80 10.59 25.35 106.25 504.5 1127

NO DATA !

TRIPLICATE SAMPLES???

Case Study #1

Analyst chose the number of standards he would use to construct a standard curve differently between each assay run.

Worst case was two samples (low & high)!

Assay was essentially out of control

Over 5 runs over two weeks no two “standard” curves were constructed the same way

Report was “signed-off” by analyst, lab supervisor, Director of Analytical Services, and Vice-President for Pharmaceutics!

Case Study #2

The Case of History Repeating Itself

Case Study #2

Initial assay developed in the mid 1980s using, for the time, state of the art HPLC system

Original assay validation report showed adequate accuracy, precision, sensitivity, selectivity, etc.

Drug approved, but due to poor absorption the sponsor immediately began a series of formulation studies to improve bioavailability

Assay validation of the later studies was a copy of the earlier report, a table of standard curve results but no tracings

Case Study #2

Why didn’t the sponsor submit individual study validation reports or tracings?

A. They didn’t have them

B. The assay conditions had changed

C. The assay performance had changed

Due to changes in equipment/column the retention time

for the parent had gone from 3 minutes to 10 minutes.

While this is certainly manageable, the sponsor

decided to not provide this data to the FDA.

Case Study #3

The Case of the “U” Shaped Concentration Time Curve

Case Study #3

“U” Shaped Plasma Concentrations(mean of 12 subjects)

Not surprisingly, the calculation of half-life was “difficult” for Group 2!

Case Study #3

In two studies done in support of thisNDA more than a quarter of thesubjects in a treatment leg in both trials(assayed at the same time, by the sameanalyst) showed these results. Thesponsor chose to increase the half-lifeand denied there was an analyticalproblem!

What this is, is the “black boxphenomena” of data collection andanalysis.

A computer gets the output from thedetector, runs the statistical &pharmacokinetic analysis modules,produces standard tables that a reportis written from. But the report writer isnot necessarily exposed to the primarydata.

Study A

Study B

Case Study #4

The Case of The Missing Reagent

Case Study #4

A study was conducted in 24 subjects 12 normal and 12 with renal insufficiency.

Due to irreversible protein binding, the extracted plasma samples had to be acidified with 0.1N HCl within an hour of extraction.

Because of analytical problems at their prior lab, the company elected to send their samples to Europe for analysis.

A total of approximately 320 samples were shipped.

Case Study #4

Upon analysis, out of 300+ samplesall concentrations were BLOQ!

Subsequent investigation revealedthat the SOP did not specify anacidification target and that thestock bottle of HCl was sub-potent(age) and contaminated.

The cost of a bottle of acid and aproper SOP was approximately $1Million and six months ofdevelopment time.

Lessons

Case #1-Written SOPs are only effective when

followed. The fact that an analyst could change procedures on a daily basis and yet all levels of management signed off on the report should not be possible.

Case #2-Analytical methods need to be constantly

monitored for changes in performance and when found must be investigated. Hoping the FDA will not ask questions is not a risk management strategy.

Lessons

Case #3-Data analysis should include a program of

primary data examination. Over-reliance on the computer to catch errors is totally dependent upon an exhaustive programming of failure modes and is unlikely to ever be all inclusive.

Case #4-Common laboratory reagents play a key role

in analysis. A seemingly small detail caused a costly delay. Does your lab have an SOP on reagents and how well is it followed? Last inventory?

An Observation on Quality

Quality is neither necessarily expensive nor time consuming

Lack of Quality is always Costly

Financial, Cost to re-do work

Time, Delay to market

Reputational, Client loss of confidence in ability

Business, Loss of clients

Closing Thoughts

“It is not enough to do your best; you

must know what to do, and then do

your best.”W. Edwards Deming

Would you stake your reputation/ lab/

company right now on the quality of your

bioanalytical work?

If, not then WHY do you tolerate it?

WHY do you think anybody else will?