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CASSS CMC STRATEGY FORUM EUROPE 2017, MAY 22-24JENNIFER LIU

APPLYING SCIENTIFIC CONSIDERATIONS AND STATISTICAL APPROACHES IN ANALYTICAL SIMILARITY ASSESSMENT

2 Amgen Proprietary—For Internal Use Only

BIOSIMILAR DEVELOPMENT BEGINS WITH THOROUGH KNOWLEDGE OF THE REFERENCE PRODUCT

Reference product

knowledge

Define critical quality

attributes

Establish Biosimilar’s

QTPP ranges

Refine QTPP as knowledge

increases

• Based on known mechanism of actions, safety and efficacy profiles

• Based on knowledge for the same class of molecules

• Characterize reference product for attribute profiles

• Establish QTPP based on reference product profiles

• Increase reference product knowledge over time with more lots

• Understand impact of attributes to bioactivity, pharmacokinetics, safety and immunogenicity

• Determine critical quality attributes to risk rank attributes

Clinical safety and efficacy

Human PK and PD

Nonclinical PK/PD and toxicology as appropriate

Match biological functions and pharmacological properties

Demonstrate analytical similarity


COMPREHENSIVE ANALYTICAL SIMILARITY ASSESSMENT IS THE FOUNDATION FOR APPROVABLE BIOSIMILARS

Impurities from host cellsand downstream process

Amino acid sequence and post-translational modifications, eg. glycans

Secondary, tertiary, and quaternary structure

Subvisible, submicron particles and aggregates

of various sizes

Quantitative levels of product variants and their identities

Degradation profiles denoting stability

Properties of the finished drug product including strength and

formulation attributes

Target binding and immunochemical

properties

Liu, BioDrugs (2016)

Stability

Primary structure

Biological function

Particles and

aggregates

General properties

and excipients

Process-related

impurities

Higher order structure

Product-related substances and

impurities


Objective assessment is based on criteria that can be measured against

Subjective assessment requires interpretation by a subject matter expert

HOW TO ASSESS COMPLEX DATASET WITH OBJECTIVITY

Liu, BioDrugs (2016)


• Quantitative results from reference product lots• Qualitative (profile) comparison with reference product side-by-side

Reference product data dependent

• Based on theoretical values of the reference product (chemical formula)• Consider reference product label information

Reference product attribute knowledge

• Stability trends of each product should be part of the assessment• Material ages at testing need to be considered

Stability-indicating product attributes

• Attributes and methods unique to individual manufacturing process• Attributes important for safety and routinely controlled by specifications

Manufacturing process controls in place

SCIENTIFIC CONSIDERATIONS FOR ASSESSMENT CRITERIA


Factors considered for assessment criteria

Reference product data dependent

Reference product attribute knowledge

Stability-indicating product attributes

Manufacturing process controls in place

Approaches to establish assessment criteria

Statistically derived

Scientifically justified (non-statistical)

Qualitatively compared(Visual)

STATISTICAL COMPARISON MAY INCREASE OBJECTIVITY IN SIMILARITY ASSESSMENT

Objectivity

Subjectivity


COMPARISON OF EMA AND FDA EXPECTATIONS FOR USE OF STATISTICS IN ANALYTICAL SIMILARITY ASSESSMENT

EURanges should be based primarily on the measured quality attribute ranges of the reference medicinal product and should not be wider than the range of variability of the representative reference medicinal product batches, unless otherwise justified.

A descriptive statistical approach to establish ranges for quality attributes could be used, if appropriately justified.

EMA/CHMP/BWP/247713/2012 Committee for Medicinal Products for Human Use (CHMP) Guideline on similar biological medicinal products containing biotechnology-derived proteins as active substance: quality issues (revision 1)

US Recommends a three-tier system with statistical assessments, where appropriate

1. Equivalence testing for some high risk attributes Based on 90% confidence interval for the difference in means of the biosimilar and RP being within 1.5 standard deviation based on the reference product

2. Quality ranges (mean ± X SD) for other high to low risk attributes, with >90% of the biosimilar lots falling with the quality range

3. Raw/graphical comparisons for other attributes

FDA presentation for ODAC meeting (Jan 2015)


Quality range (Tier 2)• The quality range of the RP for a specific

quality assay/attribute is defined as:• MeanRP ± K x SRP

– K = standard deviation multiplier – SRP = standard deviation of all the RP

lots.• Expect 90% lots are within quality range

STATISTICAL EVALUATION FOLLOWING FDA TIER APPROACH

Equivalence test (Tier 1)• The intent of equivalence test is to

demonstrate that: • MeanTP – MeanRP < EAC

– TP = Test product– RP = Reference product– EAC = Equivalence acceptance criterion

• If it can be demonstrated that difference in means between the TP and RP is less than the EAC, it is concluded that products are statistically equivalent.


Power of the equivalence test depends on number of lots

EAC = 1.5 times the standard deviation of the reference product lots tested

EQUIVALENCE TEST CONFIRMS VISUAL (MIN-MAX) TESTEU Min -Max US Min -Max

Number of lots Assigned Power

10 85%

15 90%

25 95%ABP vs EU EU vs US ABP vs US

EAC

- EAC

Circle represent the difference between the means. Vertical bar represents the 90% 2-sided confidence interval

Yi Tsong, Journal of Biopharmaceutical Statistics (2017)


Quality Range = mean ± 3 times standard deviation of the reference product lots

QR may underestimate RP variability• DP lots from same DS lot• Impossible to sample all

lots to cover RP clinical experience

Expect 90% lots fall within the quality range

ABP vs EU EU vs US ABP vs US

EU Min -Max US Min -Max

QUALITY RANGE CONFIRMS VISUAL (MIN-MAX) TEST


STATISTICS CAN OFFER AN OBJECTIVE VIEW OF DATASET

Seokkyun Kim, et al (2017) mAbs

• Distribution of the data points may provide insights to process and method variabilities• Statistically meaningful shift of reference product data over time may indicate a process

change• Changes in reference product profile can impact biosimilar development and analytical

similarity assessment


A symmetrical dataset should have close mean and median values

Median may be better suited for comparison for a skewed distribution.

SELECTION OF STATISTICAL METHOD MAY BASED ON THE REFERENCE DATA DISTRIBUTION

Comparison of mean, median and mode of two distributions with different skewness. (Wikipedia)

https://en.wikipedia.org/wiki/Mean

https://en.wikipedia.org/wiki/Mode_(statistics)

https://en.wikipedia.org/wiki/Log-normal_distribution

https://en.wikipedia.org/wiki/Skewness


Incorporating proper statistical tools in the data analysis can increase confidence in the overall assessment

Sufficiently large dataset and extensive investigation may be required to identify outliers

OUTLIERS CAN INFLATE PRODUCT ATTRIBUTE RANGE

Boxplot with whiskers from minimum to maximum

100% data point(Min-Max range)

Same Boxplot with whiskers with maximum 1.5 IQR

90% data point(60% of the Min-Max range)

third quartile

first quartile

maximum

minimum

median


Results not amenable to statistical analysis• Data close to or below limit of quantification (LOQ)• Side-by-side (visual) comparison of chromatograms and spectrum • Include objective criteria, e.g. similar profile with no new peaks above detection limitsMethod and instrument capability• Consider both precision and accuracy targets based on reference product knowledge• Intermediate precision expected based on method qualificationProcess and product uniquely controlled• Process-related impurities• Formulation-dependents attributes • Device-specific properties

NON-STATISTICAL ASSESSMENT CRITERIA SHOULD BE SCIENTIFICALLY JUSTIFIED FOR BIOSIMILAR COMPARISON


Qualitative criteria for profile comparison: No new peak or absence of major peaks compared to reference product

Scientific criteria for intact molecular weight: Conform to theoretical value within instrument precision

EXAMPLES OF NON-STATISTICAL CRITERIA

Peak Label: A B C D E

Glycosylation: A2G0F:A2G0F A2G0F:A2G0F+K* A2G0F:A2G1F A2G1F:A2G1FA2G0F:A2G2F A2G1F:A2G2F

Theoretical Mass (Da): 148080.6 148208.7 148242.7 148404.8 148567.0

Assessment Criterion (ppm): ± 50 ± 50 ± 50 ± 50 ± 50

Lot

EU 17 14 13 9 ND

ABP 19 15 18 15 11

US 20 23 14 16 ND

0.20.40.60.8

1

0.20.40.60.8

1

0.20.40.60.8

1

D l t d M (D )147800 148000 148200 148400 148600

A

B DC

E

x10 2

x10 2

x10 2

Co

unts

Deconvoluted Mass (Da)

AC

D EB

EU

ABP

USLiu, BioDrugs (2016)


It is impossible to obtain reference products at T=0

Observed differences maybe due to shelf life differences between tested biosimilar lots and reference product lots

CHALLENGES OF COMPARING STABILITY-INDICATING ATTRIBUTES FOR BIOSIMILARS

RP range from initial testing time point

Biosimilar T=0 data


APPROACH FOR COMPARING STABILITY-INDICATING PRODUCT ATTRIBUTES

Identify stability-indicating product attributes and develop appropriate analytical methods for quantitative measurement

Obtain reference product regularly and conduct testing at multiple time points

For biosimilar product, include results from stability studies performed for drug product lots manufactured from representative process and scale Stability trend of each product can be

generated from batches analyzed over time


STATISTICS HELP NORMALIZE DATA TO THE SAME TIMEPOINT

Comparing biosimilar T=0 to

RP first timepoints

Comparing biosimilar EoSto

RP EoS

Comparisons performed at end of shelf life represent worse case of both biosimilar and reference product

T= EoST=0

Overlapping age


SUMMARY

• Incorporating statistical tools in analytical similarity assessment can increase objectivity and confidence for the overall conclusion

• Selection of the appropriate statistical method for assessment should consider both product attribute and the data size and distribution

• Non-statistical criteria can be scientifically justified and should consider product knowledge and method capability

• Stability-indicating product attributes need to normalize to the same material age for meaningful comparisons

science statistics


ACKNOWLEDGEMENT

Jose Ramirez

Brad Jordan

Simon Hotchin

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