Scientific and Regulatory Considerations for the ... · White Papers – DeSilva (2003),...

U.S. FOOD & DRUG ADMINISTRATION Duke MARGOLIS CENTER

for Health Policy I CRITICAL PATH \~ INSTITUTE

Scientific and Regulatory Considerations for the Analytical Validation of Assays

Used in the Qualification of Biomarkers in Biological Matrices

June 14-15, 2017 [email protected]

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mailto:[email protected]



Opening Remarks

John-Michael Sauer, Critical Path Institute

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PTC White Paper Comments

[email protected]

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In Scope

• Defining the scientific expectations for the validation of assays used in the regulatory qualification of fluid based biomarkers

Out of Scope

• Approaches used in the development and approval of In Vitro Diagnostics

• Validation of assay used in the application of biomarker under an IND

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Goals of the meeting

• Align around the core expectations for the validation of a fluid biomarker assay used in qualification.

• Agree upon the concept that the benefit/risk, the context of use, and the intrinsic biological behavior associated with the biomarker defines additional assay performance requirements.

• Codify these concept in the points to consider white paper in an agreed upon manner across all biomarker qualification stakeholders.

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FDA Efforts to Support Biomarker Development and Qualification

Overview of the Analytical Validation White Paper Objectives and Key Terminology

Session Ia: Review of the Draft Analytical Validation Framework Assay Design, Development, and Validation & Pre-analytical Considerations

Session Ib: Review of the Draft Analytical Validation Framework Assay Performance & Assay Validation Acceptance Criteria

Session II: Applying the Framework in a Real-World Context: Biomarkers of Drug-induced Nephrotoxicity

Session III: Facilitated Discussion of the Framework

6



What did we hear yesterday

• There are core expectations for the validation of a fluid biomarker assay used in qualification, regardless of the context of use.

• However, the benefit/risk, the context of use, and the intrinsic biological behavior associated with the biomarker defines additional assay performance requirements beyond the core expectations.

• Although the approach to the validation of biomarkers to be used under an IND is similar to those described for the qualification of biomarkers, the regulatory expectation around the assay validation are much lower for biomarkers used under an IND.

7



What did we hear yesterday (continued)

• Biomarker assay validation in he biomarker qualification space requires can not be done in isolation. I requires in put from other scientific disciplines – clinicians, biologists, and statisticians.

• It is impractical to derive a checklist for the validation of biomarker assay, but a framework can be derived to guide the development of assays for biomarker qualification.

• Although the Clinical & Laboratory Standards Institute (CLSI) documents provide approaches to solving many of the issues associated with assay development and characterization for biomarker assay, they need to properly fitted for use in the biomarker qualification space.

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What did we hear yesterday (continued)

• There is an expectation that the biomarker is “regulatory ready” before beginning the qualification process. From an assay standpoint, the full validation of the bioanalytical assay need to be completed prior to finalizing the Qualification Plan.

• There are many sections of the points to consider white paper that can be improved with additional information (qualification process road map) and more examples.

9



ALIDATED

As good scientists we want the best assays to produce high quality data in order to answer our scientific questions.

However, we must ensure that our expectations are not too high?

Appropriate assay characterization practices must be applied:

Relative Accuracy

Reproducibility

Analytical Measurement Range (LLOQ, ULOQ, LOD)

Parallelism (MRD and Prozone)

Specificity

Selectivity

Stability

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Session IV: Applying the Framework in a Real-World Context: Glutamate Dehydrogenase (GLDH) as a Biomarker of Hepatotoxicity

Session V: Reflecting on the Framework: An Industry Perspective

Session VI: Facilitating Dissemination and Implementation of the Draft Analytical Validation Framework

Session VII: Summary of Key Themes, Major Takeaways, and Next Steps

11






12




Session IV: Applying the Framework in a Real-World Context: Glutamate Dehydrogenase (GLDH) as a Biomarker of Hepatotoxicity

Jiri Aubrecht, Pfizer

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Panelists

• Shelli Schomaker, Pfizer

• Juliane Lessard, U.S. Food and Drug Administration

• John-Michael Sauer, Critical Path Institute

14

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Unmet medical need

ALT cannot detect liver injury in subjects with underlying muscle disease

B) Patients with acquired muscle impairments

A) Patients with hereditary muscle diseases ALT correlates with muscle injury

ALT (u/L) AST (u/L) CK (u/L)

Healthy adults (364) 20 ± 6 22 ± 4 104 ± 57

Healthy boys (3) 21 ± 10 29 ± 2 131 ± 42

DMD boys (41) 378 ± 214 235 ± 145 11,162 ± 7,977

APAP tox adults (8) 3,788 ± 1,730 3614 ± 2824 884 ± 1,456

15

Biomarker qualification goal • Address the lack of liver specificity of ALT with Glutamate dehydrogenase

(GLDH) as a liver specific biomarker of DILI

GLDH • Converts glutamate to ketoglutarate in mitochondria

• Tissue distribution:

– Liver>>kidney, pancreas and intestinal mucosa

– Only trace amount present in muscle, lymphocyte and others

• Utility of GLDH as liver specific biomarker of liver injury shown in

preclinical species

• Impact • Drug development - Facilitate development of therapies for subjects with muscle

disease by enabling to monitor liver safety in drug development

• Patient care - Improve medical care for patients with muscle disease • Improve diagnosis of liver inherited muscle dystrophies

• Enable diagnosis of the onset of liver damage in subjects with muscle impairments

16

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Time (Days ]

GLDH - liver specific biomarker of hepatocellular injury

ALT (u/L) AST (u/L) CK (u/L) GLDH (u/L)

Healthy adults (364) 20 ± 6 22 ± 4 104 ± 57 3 ± 2

Healthy boys (3) 21 ± 10 29 ± 2 131 ± 42 3 ± 0

DMD boys (41) 378 ± 214 235 ± 145 11,162 ± 7,977

APAP tox adults (8) 3,788 ± 1,730 3614 ± 2824 884 ± 1,456 963 ± 1,000

Muscle disease prevents diagnosis of liver injury by ALT

5 ± 2

GLDH has an application for diagnosis of liver injury in drug development and clinical practice

17

your patient population or drug

mechanism of action confound ALT results for monitoring

hepatic safety?

No

Do you th ink you have Al T changes from extra-hepatic sources?

No

Sponsors should implement clini ca l )--... Ye'"'~------~ measurement of GLDH with standard safety

panel for monitoring hepatic safety.

-:-------Ye~---

Sponsors should util ize the standard sa fety panel for monitoring hepatic safety.

Context of Use • Elevated serum GLDH is a measure of

hepatocellular liver injury that can be used in target populations including healthy subjects and patients with increases in serum ALT from suspected extrahepatic sources to evaluate liver specificity of observed increases in ALT

• GLDH will be used in conjunction with standard measures of hepatotoxicity including total bilirubin

Risk and benefits

• No additional risks associated with application of GLDH as it will be used in conjunction with the current diagnostic paradigm for DILI and will not eliminate or supersede current safeguards.

• Application of GLDH provides benefits by enabling the diagnosis of the onset of liver injury in subjects with suspected extrahepatic increases of serum ALT.

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GLDH assay

• GLDH kit manufactured by Randox in the United Kingdom (UK) with ISO13485 certification as evidence of GMP.

α-oxoglutarate +NADH+NH4

GLDH

glutamate+NADH+ +H2O

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- - - -

- ---- ---- -

Analytical Validation Continuum

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Exploratory studies • Characterization of

biomarker performance • Publication (Schomaker et

al, Tox Sci 2013)

Assay validation • Single laboratory • Limited fit for purpose

validation • “Good Science” - publication

standards

Exploratory context of use studies

• Refining of biomarker characterization according to CoU

• Publication (in progress)

Assay Validation • Single laboratory • Fit for purpose considering

CoU • “Good science” addressing

conditions of use

Accepted use under IND • Use accepted by FDA for a specific

drug development program under IND

• Use limited to specific clinical trials for decision making (liver safety in subjects with muscle diseases

Assay Validation • Comply with CLIA standards as

laboratory developed test (LDT) • Single laboratory • Comprehensive validation,

including interference • Proficiency testing • Conduct in CLIA certified high

complexity laboratory

Qualification as DDT • Use allowed across all drug

development program according to its CoU

• Multiple institutions conduct studies

Assay Validation • Comply with minimum evidentiary

standards • Comprehensive validation,

including interference • Support use according to CoU

In vitro diagnostics • Applied in drug development

and clinical practice • Diagnosis of disease

Assay validation • Implemented in routine

laboratories • Highest requirements for

assay performance/validation • 510k filing

To maximize the impact of biomarkers in drug development we need to develop a seamless transition for assays from qualification as DDT to implementation in clinical trials (CLIA, LDT) by harmonizing validation standards

Progressive Validation of Biomarkers

Biomarker Laboratory Developed IVD Assay

Parameter Exploratory Validation for Test Validation (CLIA, Validation

Biomarker Validation Regulatory LDT) Qualification

QC Samples + + + + LOB and LOD + + + + Precision

Intra-assay + + + + Inter-assay + + + +

Accuracy (Relative) Spike Recovery + + + +

Reportable Range LLOQ + + + + ULOQ + + + +

Parrallelism / Dilutional Linearity + + + + Stability

Short Term Room Temperature - + + + Short Term 4° degrees C + + + + Long Term -80° degrees C - + + + Freeze/Thaw + + + +

Reference Range Interval + + + + Analytical Specificity/Interference - + + + Medically Relevant Precision Recovery and QC Samples

- + + +

Method comparison - -/+ + + 21 Proficiency Testing - - + +

Comparison of Regulatory Expectations for Precision Validation Studies

Crystal City CDER CDRH CDRH

White Papers Bioanalytical Full Method 510(k) PMA

Partial Method Validation For Clinical Use (Class II For Clinical Use (Class III

Validation For Use in Biomarker Medical Devices) d Medial Devices) d

Exploratory/Feasibility Qualification

Phase of Testing a b, c

Controls 3 6 2 3

Duplicates, analytical 2 2 2 2

Samples 5 5 - -

Sites 1 1 2 3

Operators 1 e 1 e 2 3

Reagent Lots 1 1 2 3

Calibration Cycles NA NA 5 5

Runs 6 6 2 f 2 f

Days 3 g 3 g 20 20

Runs/Day 1 1 2 2

Minimum Data Points / Sample 60 120 640 2160 a White Papers – DeSilva (2003), Viswanathan (2007a,b), Lee (2007), Lee (2009); b FDA Bioanalytical Method Validation Final 2001; c FDA Bioanalytical Method Validation Draft 2013; d

Harmonized w/ CLSI Approved Guideline Method Evaluation Protocol EP05-A3; e DeSilva (2003), Viswanathan (2007a,b), Lee (2006), Lee (2009) recommend two (2); f Two runs per day (AM & PM) for 20 days yielding a total of 40 runs; g Not per day, but over three days, ergo a total of 6 runs

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Progressive Validation of Biomarkers

Biomarker Laboratory Developed IVD Assay

Parameter Exploratory Validation for Test Validation (CLIA, Validation

Biomarker Validation Regulatory LDT) Qualification

QC Samples + + + + LOB and LOD + + + + Precision

Intra-assay + + + + Inter-assay + + + +

Accuracy (Relative) Spike Recovery + + + +

Reportable Range LLOQ + + + + ULOQ + + + +

Parrallelism / Dilutional Linearity + + + + Stability

Short Term Room Temperature - + + + Short Term 4° degrees C + + + + Long Term -80° degrees C - + + + Freeze/Thaw + + + +

Reference Range Interval + + + + Analytical Specificity/Interference - + + + Medically Relevant Precision Recovery and QC Samples

- + + +

Method comparison - -/+ + + 23 Proficiency Testing - - + +

ptable and/or

ormation in the

pa ge insert.

<15% are acceptable

etection level of the a

% to

the linearity using the

ent as guides.

Analytical validation of GLDH assay Requirement Testing strategy Acceptance Criteria

Limit of Blank (LOB) – the

manufacturer’s claim of LOB must be

verified.

Limit of Detection (LOD) – the lowest

analyte concentration that can be

reliably distinguished from the LOB

must be determined.

At least 20 replicates of a blank and 10 replicates of the low concentration

sample will be analyzed in a single run. The mean and standard deviation

(SD) of the of the blank will be used to calculate the LOB and the LOB

and the SD of the of the low concentration sample will be used to

calculate the LOD utilizing CLSI guidance EP17A.

The LOB will be calculated according to the following

formula:

LOB = mean blank + 1.645(SD blank )

The LOD will be calculated according to the following

formula:

)LOD = LOB + 1.645(SDlow concentration sample

Precision – the assay must be

acceptably precise within run,

between runs and day to day over a

time course.

Minimum of 3 concentrations (Low, High and Near Detection Level (NDL))

in addition to both levels of Quality Control in duplicate 1- 2 times per day

over 20 days. Calculate the SD and/or CV within run, between run, day to

day and total variation

Values < 10% will be considered acce

should be consistent with inf

manufacturers’ cka

Values for the samples near the

d ssay.

Accuracy (trueness)/ Recovery –

spiked sample recovery studies must

be evaluated using a species specific

matrix.

Investigations are run primarily as an

indication of sample matrix effects

and to test the ability of the assay to

measure the “true” known

concentration of the analyte in the

sample .

Recovery evaluations will be made by utilizing serum samples (working

dilution) with low, but still measurable analyte concentrations. The working

diluted sample is ‘spiked’ with different concentrations of kit calibrator.

Recovery should be evaluated at several concentrations over the assays

dynamic range.

Acceptable performance will be based on percent

recovery at each spiked concentration dilution (80

120%) and/or visual assessment of

slope and correlation coeffici

Reportable range/ Linearity – the

instrument’s analytic measurement

range must be established for each

analyte tested

7-9 concentrations in duplicate or triplicate across anticipated measuring

range (or 20-30% beyond the anticipated measurment range to ascertain

widest possible range) or a series of dilutions of a highly elevated patient

sample with concentrations across the anticipated measuring range.

Linear regression analysis of the data will be performed using Microsoft

Excel.

Acceptable performance will be based on percent

recovery at each dilution (80% to 120%) and visual

assessment of the linearity using the slope and

correlation coefficient as guides.

24

o e matrix lots tested

ria:

on (%CV) of ≤30%

in Lot accuracy (%RE

respective no

ativ

25

Analytical validation of GLDH assay

Sample Stability – long-term room Sample Storage Stability will be evaluated using Acceptable long-term storage stability: Measured

temperature, refrigerated and frozen freshly collected samples/ sample pools that are aliquoted as soon as concentration would be within the inter-assay precision

sample stability must be defined. possible after collection and stored at room temperature (20-25°C),

refrigerated temperature (1-8°C) and frozen at (-70 to -80°C). Ideally,

three sample sets with low, mid, or high analyte concentrations are

assayed at 4, 24, 48, 72 and 96 hours for both room and refrigerated

temperatures plus 1, 2, 4, 8, 10, 14, 21, and 28 day refrigerated. Three

sample sets with low, mid, or high analyte concentrations are assayed

within the first week of collection and after storage of approximately: 1

week, 2 weeks, 1, 3, 6, 12, and 18 months frozen. The percent recovery

for the each storage timepoint will be calculated relative to the baseline

value.

for the assay and/ or 80%- 120% recovery of the baseline

(initial thaw) concentration.

Reference Interval Establishment At least 60 specimens representative of the population. Historical data was generated using 552 human

samples. See Reference 10.

Analytical Specificity/Interference Matrix components can potentially interfere with assay performance.

Therefore, the potential for variable matrix-related interferences will be

evaluated in at least 1 validation run with independent sources of icterus

in patient serum (n≥5 sources), hemolysis (n≥5 sources), and lipemia (n≥5

sources), spiked at different concentrations (n≥1).

Results are acceptable if ≥80% f th

meet the following crite

With-in lot precisi

With- ) within ±30% of the

minal concentrations.

Method to Method Comparison At least 40 human serum samples will be analyzed in duplicate over 5

operating days by both methodologies. A linear regression analysis will be

performed and a correlation coefficient (R), slope and %bias will be

calculated. Method to Method comparison data will be used to

characterize the %bias between the Advia 1800 and a similar platform

utilizing the same Randox GLDH reagent system at a second site. EP

Evaluator version 7.0 will be used for the regression analysis.

Neg e and positive biases are expected due to

differences in methodology and will be used to assess

impact on correlation. An R value of ≥ 0.90 is expected in

most analytes in which the dynamic range is adequately

tested.

Proficiency Testing GLDG proficiency testing samples were established by using Randox

Serum Level 2 (Calibrator 2) catalog number CAL 2350.

Acceptable performance will be based on obtaining

values within the defined acceptance range for the

proficiency testing samples

Questions •What are your thoughts on a “fit for purpose” validation that supports context of use vs.

rigid “fits all” assay validation requirements

•How we can integrate assay validation requirements for qualification as DDT and

CLIA/LDT assay validation requirements for biomarker implementation in clinical trials?

•Why does FDA recommend CLSI guidelines for analytical validation of assays during

biomarker qualifications?

•What are FDA’s expectations for the performance of assays in biomarker qualification

•applications?

•How could manufacturers leverage data from biomarker qualifications for subsequent IVDs

•Can we design the assay validation requirements that would facilitate the development of

IVD if needed?

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27




Session V: Reflecting on the Framework: An Industry Perspective

Moderator: Mark E. Arnold, Covance

Panelists: Lakshmi Amaravadi, Sanofi

Stephen Lowes, Q Solutions

Chad Ray, Zoetis

Lauren Stevenson, Biogen

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Central focus

• “Assays that measure biomarkers seeking qualification are used to produce the evidence required to establish and confirm decision points”

• “Demonstration that the analytical procedure will measure what it is supposed to measure”

• Paper highlights critical topics for the biology of the biomarker, collection and processing, and required analytical characteristics • Relationship of analytical validation and biomarker qualification

• The key is how do we appropriately achieve this for each biomarker in its Context Of Use

29

Central focus • “Assays that measure biomarkers seeking qualification are used

to produce the evidence required to establish and confirm decision points”

• “Demonstration that the analytical procedure will measure what it is supposed to measure”

• Paper highlights critical topics for the biology of the biomarker, collection and processing, and required analytical characteristics • Relationship of analytical validation and biomarker qualification

• The key is how do we appropriately achieve this for each biomarker in its Context Of Use

30

Scope - Agreement • Focus is on those biomarkers and assays that confirm disease

modification • Assays used to support the Qualification of a Biomarker for a disease

• Proposed framework is not required for biomarker assays used in internal decision making • But, provide topics for consideration in developing assays for internal

decision making

• LBA and LC-MS/MS assays are within scope but technology platform alone does not differentiate assay validation strategy

31

Context of Use (COU), Specificity

• Context of use (COU) considerations • Important to understand before launching into assay development/validation

• The iterative process of Analytical vs Clinical validation

• Appreciation of this linkage is important in defining assay performance criteria and interpreting the results

• Full validation vs partial validation: terms may lead to misinterpretation (Table1; Exploratory)

• Specificity • Essential to know what you are measuring

• Irrespective of small or large molecule

• Consider COU, a specific molecule, specific isoform, catabolite or family of molecules

• Specific reagents/antibodies selected during assay development for LBA or immuno-extraction for LC-MS

32

Reference Material • PK Assays ≠ Biomarker Assays

• The notion that PK BMV as the default state • Risk of misinterpretation

• Reference Material/Calibrator • Notes the limitations of protein biomarkers

• Well known and typically addressed

• Provides guidance to address source, structural and lot differences

• The term and practices of ‘commutability’ not addressed/linked • Clarify that when calibrator ≠ endogenous analyte

• Assay validation parameters should be assessed using samples with endogenous analyte

• This includes Stability assessment

• Stability performed with calibrator may lead to misleading/erroneous results

33

Statistics • Statistics Related to Biomarker and Assay

• Agree that understanding the variance components (within and between subject variance along with analytical variance) is essential to defining the required analytical performance characteristics

• Defining assay bias requires higher order reference material, pre-existing method, or spiked recovery

• Total Allowable Error (TAE = BA + 1.65*CVA) • Inclusion seen as positive • Implementation will require a different approach

• Split perspective - relevant and needed vs not needed to define the assay performance characteristics • Not needed – concern that it would be too prescriptive • Needed – need to take into account the biological and analytical variability, not

always a consideration of BMV bioanalytical scientists

34

Relative Accuracy (Bias)

•Various Perspectives Remain •What constitutes a “true” value for endogenous & how is that established? •Measuring accuracy of spiked standard/calibrator has limited utility without

correlation to the endogenous analyte •Current ‘accepted’ assay* is the level to which subsequent assays must meet or

exceed •Relative accuracy (bias) being inherently linked to COU precludes prescriptive

criteria •Commutability is predicated on having a handle on accuracy •Defining Accuracy/Bias of a bioanalytical assay is a consensus need but the

means of doing so has induced confusion

•Protein vs Small Molecule Biomarkers •Where a well-characterized biomarker reference material is available (i.e.

synthesized small molecule) then achieving analytical accuracy as with established small molecule bioanalytical practice is a reasonable objective

*’current accepted assay’ is that assay used to generate the data for biomarker qualification for a particular disease and COU. The assay itself is not qualified, nor is the biomarker qualification a statement that the assay has been formally cleared or approved by the FDA.

35

Parallelism and Dilutional Linearity • Dilutional Linearity

• Control samples (spiked with reference material) • Measured Concentration vs Expected Concentration of diluted samples should yield linear

response with slope = 1 • Requires that an expected concentration is known • Relevance to biomarker assays is debatable

• Parallelism • Incurred samples (for biomarker assays = samples with endogenous analyte) • Sample Dilution-Response Curve should be parallel to Standard Concentration-Response

Curve • Relevance for biomarker assays is fundamental

• Complexity in practice • When samples with adequately high endogenous levels are not available during validation • Utility of spiking recombinant material on top of endogenous analyte needs alignment • Later, when appropriate samples are available, parallelism should be revisited • Need to address surrogate matrix/analyte parallelism with LC-MS assays

36

Parallelism vs Spiked Recovery •Application of parallelism data

•Demonstration that sample dilution-response curve is parallel to standard concentration-response curve •Endogenous analyte ≠ calibrator material •Validates the use of surrogate matrix for calibrator preparation

•Identification of minimum required dilution (MRD) to achieve acceptable %bias and precision

•Estimation of LLOQ for endogenous analyte •Assessment of parallelism in multiple individuals = selectivity

•Spike recovery experiments •Do not replace parallelism experiments •Do not inform selectivity with respect to endogenous analyte •Should be interpreted conservatively – accurate recovery of spiked recombinant

material does not necessarily apply to endogenous analyte – beware false sense of security

37

Validation vs Use • Framework is familiar to BMV

• More clarity needed on • Prevalidation testing (e.g., parallelism)

• Validation assessments using samples with endogenous analyte

• Use

• Expectation that assay may need to undergo iterative improvements as more is learned about biomarker • Improve characteristics as variability and population differences refined • Leads to optimized assay to support Biomarker Qualification

• Context • Differentiating drug development biomarkers/surrogate endpoints from

FDA recognized Qualified Biomarkers

38

Areas for Discussion • Context of Use (COU)

• Specificity

• Reference Material • quality and similarity to endogenous

• Statistics • Biomarker vs Assay Variability in COU

• TAE of assay

• Relative Accuracy (Bias)

• Parallelism, Dilutional Linearity & Spiked Recovery • Validation vs Use

• Other

39






40




Session VI: Facilitating Dissemination and Implementation of the Draft Framework

Moderator: Gregory Daniel, Duke-Margolis Center for Health Policy

Panelists: Chris Leptak, U.S. Food and Drug Administration

Lisa McShane, National Cancer Institute

Joseph Menetski, Foundation for the National Institutes of Health

Martha Brumfield, Critical Path Institute

41

!,! __

:;;; ••

www.fda.gov

Session III: Analytical Validation Framework in light of

21st Century Cures legislation and PDUFA VI

• 21st Century Cures and PDUFA VI increasingly places FDA as an active participant in drug development, broadening our traditional regulatory role

• Requires expanded efforts to enhance drug development

• Patient-focused drug development: collect / analyze patient experience, to use in designing drug development programs (endpoints), and in regulatory decision making (endpoints and risk/benefit considerations)

• Novel, innovative trial designs: use of complex adaptive and other novel trial designs – and how such clinical trials can be used to satisfy the substantial evidence standard

• Real world evidence: using data regarding use or potential benefits and risks of a drug derived from sources other than randomized clinical trials – in support of new indications and

42

post-approval study requirements

• Drug development tools: biomarkers and COAs

" ~--~ ••

www.fda.gov

CURRENT OR “LEGACY” BQ PROGRAM

• Current process: Consultation & Advice to Review

• Letter of Intent: provides overview and rationale, statement of specific intended context of use (COU), and initial supporting information

• Key components: biomarker need/value, clearly defined COU, reasonable evidence of feasibility (e.g., preliminary validation of assay)

• Qualification packages: briefing documents and full submission

• Key components: assay validation, demonstration that in appropriate COU, biomarker acts as expected

• 43

" ~--~ ••

www.fda.gov

21ST CC DDT PROCESS: WHAT’S DIFFERENT?

• New important features, but also much continuity with existing BQ program

• Formalizes a three-step process

• LOI

• Qualification Plan

• Full Qualification Package

• Encourages use of external consultants – input into qualification plan and review of qualification package

• A transparent process – so all stakeholders are aware of tools in development, stage, and FDA determinations and rationale

•Requires setting and implementing “reasonable timeframes” for the FDA review of LOIs, qualification plans, or full qualification packages

•Requires development of a “taxonomy” of biomarkers (BEST) and new guidances to support clarity on qualification pathway for requestors (Process, Framework, Evidence)

44

" ~--~ ••

www.fda.gov

WHERE DOES ANALYTICAL VALIDATION FIT IN?

•LOI Submission: Feasibility assessment of proposal will include information to support that measurement of the novel biomarker is, in fact, possible.

•QP Submission: Project development plan from concept to information to be developed/provided to support the biomarker’s COU. To determine clinical utility and clinical validation, important to know that the analytical validation has been completed and information submitted to QP.

•FQP Submission: Review of data to support the clinical validation of the biomarker for the COU

45

Timeline of communication and response Oct. 20, 2016 Dec. 6, 2016 Dec. 6 – 24, 2016April 2016 Cvent email to FNIH eblast FDA listserv& andWorkshop

attendees website update

358 sent Email 168 opened (47%)

Website

39,196 sent by FNIH 7,215 opened (18%)

Program Webpage 324 page views 6:59 average time

Program Webpage 1,233 page views 6:28 average time on page 30 came from FDA site

Additional Website coverage * • Regulatory Affairs Professionals Society • Myotonic Dystrophy Foundation

& FDA listserv ~98,000 addresses * Story picked up on their own

46

https://fnih.org/what-we-do/biomarkers-consortium/programs/framework-for-safety-biomarkers

https://fnih.org/what-we-do/biomarkers-consortium/programs/framework-for-safety-biomarkers



Roles and challenges for the academic community in assurance of analytically valid

biomarker assays fit for drug development

Lisa M McShane, PhD Biometric Research Program

Division of Cancer Treatment and Diagnosis National Cancer Institute

47

Background • A substantial number of drug discoveries occur in academia or in

collaboration with academia

• Biomarkers increasingly needed to identify patients with most potential to benefit from new therapies and to avoid safety risks

• Integration of laboratory and clinical sciences is suboptimal in some academic institutions

• Resources and expertise for analytical validation may differ between “research” and clinical laboratories

• Drug development in academia may occur through “one-off” grants rather than through a coordinated, well-resourced and streamlined development program •Laboratory scientists sometimes inadequately recognized or compensated in clinical

programs

48

Rigor and Reproducibility in NIH Applications: Resource Chart

NI H Grants Policy Website: http://grants .nih.gov/reproducibility/index.htm

NIH Website: https: /{www.nih .gov/research-training/rigor-reproducibility

WHAT DOES IT MEAN?

The scientific premise for an app li cation is the research that is used to form the basis for the proposed research question(s).

Scient ific Premise Describe the general strengths and weaknesses of the prior research be ing cited as crucial to support the application. Consider discussing the rigor of previous experimental designs, as well as the incorporation of re levant biological variables and authent icat ion of key resources.

Scientific Rigor (Designl

Biological Variables

Authentication

'See related £Ml,,~ Scientific rigor is the strict applicat ion of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretat ion and reporting of resu lts.

Emphas ize how the experimental design and methods proposed will achieve robust and unbiased resu lts.

*See related ffiili, blog post examples from pilots

Biological variables, such as sex, age, weight, and underlying health conditions, are often critica l factors affecting health or disease. In particular, sex is a biological variable that is frequently ignored in animal study designs and analyses, leading to an incomplete understanding of potential sex-based

differences in basic biological function, disease processes and treatment response.

Explain how relevant biological variables, such as the ones noted above, are factored into research designs, analyses, and report ing in vertebrate animal and human studies. Strong just ification from the scientific literature, preliminary data or other relevant considerations must be provided for applications proposing to study only one sex.

' See related £Ml,, blog posts article @ Key biological and/or chemical resources include, but are not limited to, ce ll lines, specia lty chemica ls, antibodies and other biologics.

Briefly describe methods to ensure the identity and validity of key biological and/or chemica l resources used in the proposed studies. These resources may or may not be generated with NIH funds and:

may differ from laboratory to laboratory or over time;

may have qualities and/ or qualifications that cou ld influence the research data;

are integral to the proposed research. The authentication plan should state in one page or less how you will authenticate key resources, including the frequency, as needed for your research. Note: Do not include authentication data in your plan.

*See related .Eruh, blog post

WHERE SHOULD IT BE INCLUDED IN THE

APPLICATION?

Research Strategy ', Significance

Research Strategy :,. Approach

Research Strategy , Approach

Other Research Plan Section

:,. Include as an

attachment ;. Do not include in

the Research Strategy.

**This chart is based on general instructions for research grant and mentored career development applications. It should only be

used as a guide. For all applications, please read the applicable Funding Opportunity Announcement IFOA) & Application Guide

for specific instructions.

Rigor and reproducibility in NIH grant applications Area of focus Relevance to analytical validation

Scientific premise of the proposed research

• Evidence demonstrating biological effects as assessed by reliable measurements

Rigorous experimental design for robust and unbiased results

•

•

Design of sufficiently informative assay analytical validation studies Design of clinical studies to account for factors affecting assay performance (e.g., control pre-analytics, adequate replication)

Consideration of relevant • Biological factors affecting analytical biological variables performance identified and controlled to

extent feasible • Measure key biological characteristics

affecting associations between treatment and clinical outcome

Authentication of key biological • Specification and stability of assay reagents and/or chemical resources • Document that assay measures what it is

supposed to, with acceptable performance

49https://grants.nih.gov/reproducibility/index.htm 49

Goals

• Raise awareness of importance and impact of assay analytical performance

• Educate all stakeholders about how to properly evaluate analytical performance

• Provide adequate incentives and resources to promote best practices and achieve fit-for-purpose analytical performance

• Promote sharing of good quality biomarker data and assay protocols to support and expedite biomarker qualification efforts

50

( CRITICAL PATH INSTITUTE a decade of excellence

A Perspective

Advancing Education on Biomarker Qualification

Martha A. Brumfield, PhD President & CEO Critical Path Institute

CRITICAL PATH INSTITUTE C-Path’s Experience

• C-Path has worked/is working on eleven different biomarker programs including fluid-based (7) and imaging (4) - Some programs have multiple biomarkers

• Variation across programs encompasses

- Quality of data

- Quantity of data

- Extent of validation of assay(s)

- Ability to bridge from one assay to another

- Ability to determine cut points by assay

- Legacy assays that may not meet current expectations

52

( CRITICAL PATH INSTITUTE

Transparency Requirements of Cures

21st Century Cures Act will require greater transparency in the entire drug development tool qualification pathway

Letter of Intent (LOI)

Qualification Plan (QP)

Final Qualification Submission (FQP)

FDA posted update on DDT webpage indicating how, when, what will be made publicly accessible https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationP rogram/ucm561587.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery

Benefits from transparency

Provides opportunity to learn

Prevents duplication of effort and offers chance to combine efforts across groups with similar objectives

53

https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/ucm561587.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery

( CRITICAL PATH INSTITUTE Next Steps for White Paper Review

Comments on white paper should be sent to: [email protected]

• Circulate via professional societies and other networks

• Comments will be accepted through end of August

• Writing group will review comments

• Subsequent revision to be circulated to workshop attendees and others

54




Session VII: Summary of Key Themes, Major Takeaways, and Next Steps

Moderators: Mark McClellan, Duke-Margolis Center for Health Policy

ShaAvhrée Buckman-Garner, U.S. Food and Drug Administration

Panelists: Shashi Amur, U.S. Food and Drug Administration

Sue-Jane Wang, U.S. Food and Drug Administration

John-Michael Sauer, Critical Path Institute

Steve Piccoli, Neoteric Consulting

Russell Grant, LabCorp

John Kadavil, U.S. Food and Drug Administration

55



Key Themes and Takeaways

• There are core expectations for the validation of a fluid biomarker assay used in qualification, regardless of the context of use.

• However, the benefit/risk, the context of use, and the intrinsic biological behavior associated with the biomarker defines additional assay performance requirements beyond the core expectations.

• Exploratory biomarkers are outside of the scope of the white paper; however, principles of the white paper may be applied at the discretion of the investigator in exploratory situations.

56




• Biomarker assay validation in the biomarker qualification space cannot be done in isolation and requires input from other scientific disciplines – clinicians, biologists, and statisticians.

• It is impractical to create a universal checklist for the validation of biomarker assay, but a framework can be derived to guide the development of assays for biomarker qualification.

• Although the Clinical & Laboratory Standards Institute (CLSI) documents provide approaches to addressing many of the issues associated with assay development and characterization for biomarker assay, they need to appropriately adapted for use in the biomarker qualification space.

57




• There is an expectation that the biomarker is “regulatory ready”before beginning the qualification process. From an assay standpoint, the full validation of the bioanalytical assay needs to be completed prior to finalizing the Qualification Plan.

• There are many sections of the white paper that can be improved with additional information and more examples.

58



Next Steps

• Finalize whitepaper based on workshop and future comments

• Work towards the publication of topics outlined in whitepaper in peer reviewed journal

• Under 21st Century Cures Act and the PDUFA VI reauthorization goals, evidentiary considerations for biomarker qualification are clearly needed…analytical validation is a critical component

59

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