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TABLE 5.3
Analytical Assessment
Quality Attribute Testing for Monoclonal Antibodies (TNF- α Blockers)
Category Product Quality Attributes Analytical Attributes
Physicochemical characterization
Primary structure Molecular weight
Amino acid sequence
Terminal sequence
Methionine oxidation
Deamidation
C- terminal and N- terminal variants
Disulde linkage mapping
Higher- order structure Protein secondary and tertiary
structures
Glycosylation N- linked glycosylation site
determination
N- glycan identication
N- glycan prole analysis
Aggregation Soluble aggregates SEC- UV, SEC- MALLS/ RI SV- AUC
Fragmentation Low molecular weight Non- reduced CE- SDS
Charge heterogeneity Acidic variants
Basic variants
Biological characterization
Fab- related biological
activity
TNF- α neutralization activity
TNF- α binding activity
Apoptosis activity
Transmembrane TNF- α binding assay
Fc- related biological
activity
FcRn binding
FcγRIIIa (V/ V type) binding
ADCC using healthy donor PBMCs
CDC
C1q binding
FcγRIa binding
FcγRIIa binding
FcγRIIb binding
FcγRIIIb binding
Notes: 2- AB, 2- aminobenzamide; AUC, analytical ultracentrifugation; CD, circular dichroism; DSC, differential scanning
calorimetry; ELISA, enzyme- linked immunosorbent assay; FACE, uorescence- activated cell sorting; PBMC, peripheral blood mononuclear cell; SPR, surface plasmon resonance; SV, sedimentation velocity; TNF, tumor necrosis
factor; UV, ultraviolet.
Intact mass under reducing/ non- reducing
conditions.
Peptide mapping by LC- ESI- MS/ MS using a
combination of digestion enzymes.
Peptide mapping under non- reducing conditions
Far- and near- UV CD spectroscopy, ITF
HDX- MS, antibody conformational array
DSC
LC- ESI- MS/ MS
Procainamide- labeling and LC- ESI- MS/ MS
2- AB labeling and HILIC- UPLC
Reduced CE- SDS
CEX- HPLC and icIEF
TNF- α neutralization assay by nuclear factor- κB
reporter gene assay
FRET
Cell- based assay
FACS
AlphaScreen
®
SPR
Cell- based assay
Cell- based assay
ELISA
FRET
SPR
SPR
SPR
• The degree of uncertainty surrounding a certain quality attribute: When there is limited
understanding regarding the correlation between changes in an attribute and its clinical
implications, that attribute should be ranked with higher risk due to the associated uncertainties.
• Classication of high- risk attributes: Any attribute deemed high risk concerning performance
categories (activity, PK/ PD, safety, efcacy, and immunogenicity) should be classied accordingly. The risk assessment tool should ideally present a prioritized list of attributes based on
patient risk. Risk scores for attributes should correspond to the level of patient risk. It is crucial
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Analytical Assessment
TABLE 5.4
99
Testing Methods for Oncology Monoclonal Antibodies
Category Attribute (Method or Methods to Query That Attribute)
Primary structure Primary sequence (e.g., UPLC peptide map, LC- MS/ MS, amino acid analysis,
Edman degradation, carboxypeptidase sequencing)
Disulde structure (e.g., LC- MS of nonreduced protein digest)
Intact mass (e.g., LC- MS)
Isoelectric point (e.g., IEF gels, cIEF, iCE)
Extinction coefcient (e.g., UV/ AAA or UV/ RI)
Secondary and tertiary structures Low- resolution secondary structure or indirect tertiary structure measurements (e.g.,
CD, DSC, FTIR, uorescence)
High- resolution measurements of higher- order structure (e.g., 2D- NMR, HDX- MS,
X- ray crystallography)
Glycosylation Glycosylation (e.g., HILIC, MS (MALDI, ESI), exoglycosidase sequencing, HPLC-
FLD, HPAEC- PAD, CE- LIF)
Glycosylation site mapping/ site occupancy (e.g., peptide mapping by LC- MS)
Dose Protein content (e.g., UV A280, RP- HPLC)
Deliverable volume (extractable volume)
Particulates Sub- visible particles (e.g., light obscuration, MFI, NTA)
Function Biological activity (e.g., for mAb: proliferative bioassay, cytotoxicity assay, ADCC,
and CDC; other assays may be appropriate for other proteins, e.g., enzyme kinetics
for the proposed biosimilar enzyme)
Receptor and ligand binding (e.g., SPR, ELISA)
Product variants (product- related
substances and impurities)
Notes: AUC, analytical ultracentrifugation; CD, circular dichroism; DSC, differential scanning calorimetry; HPLC, high-
performance liquid chromatography; LC, liquid chromatography; MS, mass spectroscopy; RP, reverse phase; SPR,
surface plasmon resonance; SDS- PAGE, sodium sulfate polyacrylamide gel electrophoresis.
High- molecular- weight species (e.g., SEC- MALS, AF4/ HF5, AUC, DLS)
Covalent dimers (e.g., SDS- PAGE, CE- SDS)
Purity and impurities (oxidation, deamidation, glycation, isomerization,
fragmentation, disulde reduction, e.g., RP- HPLC, CEX, SEC, IEX, IEF,
cIEF, LC- MS)
Amino acid misincorporations (e.g., LC- MS/ MS)
Micro- sequence heterogeneity (e.g., LC- MS)
C- and N- terminal modications (e.g., LC- MS, Edman degradation)
that the criteria used for scoring in the risk assessment are explicitly dened and justied.
Justication for the risk ranking of each attribute should be supported with relevant citations
from literature and data.
5.4.6 statistical consideRations
The evaluation of biosimilarity predominantly relies on statistical considerations. For clarity and to
illustrate how regulatory bodies interpret data, Table 5.5 presents statistical concepts as they pertain
to biosimilarity testing.
5.4.7 Quantitative and Qualitative data analyses
Thorough analyses of comparative analytical data are essential to demonstrate the similarity between
a proposed biosimilar and the reference product, accounting for minor differences in clinically
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TABLE 5.5
Analytical Assessment
Statistical Concepts Applied to Biosimilarity Determination
Subject Definition and Concepts Applicable to Biosimilarity Testing
A stepwise Analytical
Assessment
Signicance level (also
called “Size of a
Test”)
Condence level
Condence interval The condence interval expresses the degree of uncertainty associated with a sample statistic.
Statistical power
Evaluate quality attributes consistent with the risk assessment principles of the ICH Quality
Guidelines Q8, Q9, Q10, and Q11.
Consider criticality risk ranking of quality attributes concerning their potential impact on
activity, PK/ PD, safety, and immunogenicity.
Use a selective approach for assessment:
Equivalence interval testing for some critical attributes (K*σR+ ∆) and the default value of ∆is zero).
The equivalence margin can be determined by a proposed sample size/ power adjusted method.
Quality ranges (mean ± K*SD) for other less critical attributes; X= ≤3 unless otherwise justied
to be higher. The quality range can be determined by Mean ± K × SD, where K = 2- 3 based
on the targeted coverage; quality range tests will also assess all proposed biosimilar lots used
in equivalence tests; 90% of proposed biosimilar lots need to be within the quality range.
Raw/ graphical comparisons for other least critical attributes. Graphic data displays are a useful
tool to identify the potential issues with statistical methods listed above.
The size of a test, often called the signicance level, is the probability of committing a Type
I error. A Type I error occurs when a null hypothesis is rejected when it is true. This test size
is denoted by α (alpha). The 1– α is called the condence level, which is used in the form of
the (1– α)*100 percent condence interval of a parameter.
Conclusions about
Reality of attribute
attribute Description
Not similar Similar Type I error: patient (or regulatory) risk (α)
Similar Not similar Type II error: the developer’s risk (β)
Similar Similar Power (1- β)
The condence level refers to the percentage of all possible samples that can be expected to
include the true population parameter. For example, suppose all available samples were
selected from the same population, and a condence interval was computed for each sample.
A 90% condence level implies that 90% of the condence intervals would include the true
population parameter. The condence level can be lowered in some instances, and according
to the literature reports, the lowest condence level that it is recommended to evaluate
analytical similarity is approximately 80%.
A condence interval is an interval estimate combined with a probability statement. For example,
suppose an analysis allows the computation of an interval estimate; a condence level will
describe the uncertainty associated with the interval estimate. One may describe the interval
estimate as a “90% condence interval.” This means that if we used the same sampling method
to select different samples and computed an interval estimate for each sample, we would expect
the true population parameter to fall within the interval estimates 90% of the time.
Statistical power is a function of the sample size, the effect size, and the probability level chosen:
Kw
X
,)
RR
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TABLE 5.5 (Continued)
Statistical Concepts Applied to Biosimilarity Determination
Subject Definition and Concepts Applicable to Biosimilarity Testing
Equivalence interval
model
To meet the “equivalence interval” most critical quality attributes, a two one- sided t- test (TOST)
should be used for testing if the two- sided 90% condence interval of the mean differences
between a proposed biosimilar product and the reference product falls entirely within K*σR (σR
is the population standard deviation of the lots of the reference product that is estimated by the
sample standard deviation (
σ
) from the reference lots been tested.) Enough lots must be used
R
to reach at least 80% statistical power. TOST for testing the hypothesis is represented as follows:
𝐻0: 𝜇B − 𝜇R ≤−𝛿 or 𝜇B − 𝜇R ≥ 𝛿
𝐻a: −𝛿 < 𝜇B − 𝜇R < −𝛿
where μB and μR are the mean responses of a proposed biosimilar product and reference
product lots, respectively, and δ > 0 is the equivalence margin. If the null hypothesis is
accepted, the equivalence will be rejected.
101
Quality range model The reference product data dene the quality range for a specic quality attribute as
ˆˆˆˆ
µσµσ
(
*, )
RRRR
ˆ
here−
µ
r
Is the sample mean, a
R
ˆ
σ
Is
is the sample standard
R
deviation based on the reference product lots. The standard deviation multiplier (K) should
be scientically justied for that attribute. The statistical analysis will generally support
analytical similarity for the quality attribute if a sufcient percentage of test lot values
(e.g., 90%) falls within the quality range. For establishing the scientic justication for the
standard deviation multiplier (K), the two- sided tolerance intervals of the univariate normal
distribution are used to calculate the multiplier K. The following equation estimates K with
the given condence (1– α) and a targeted portion (P) of the univariate normal distribution:
Pr
Pr
{
XS,
− kS < X < X+ kS |X,s) > P} = 1 − α
(
X
For a sample size N= 10 per product, the two- sided tolerance interval of 90% coverage for
the distribution is 2.86 at the 95% condence level. If the signicant digit for an attribute
reported at one decimal, set the tier multiplier as 2.9 (generally 3.0) in this study to cover
90% of the reference product distribution at N= 10 per group. That is, the quality range is
ˆˆˆˆ
µσµσ
− 29
(
.*
if 90% lot values fall within the quality range.
RR
The errors in mean ±3SD method for quality ranges
(μ- 3σ, μ+ 3σ) = (- 3, 3) N = 6 N = 30 N = 100
Simulation 1 (−4.3, 4.4) too
(−3.4, 3.6) (−3.1, 3.3)
wide!
Simulation 2 (−1.4, 2.0) too
(−3.2, 3.1) (−3.0, 2.9)
tight!
(continued)
−≤
−>
µδ
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Analytical Assessment
TABLE 5.5 (Continued)
Statistical Concepts Applied to Biosimilarity Determination
Subject Definition and Concepts Applicable to Biosimilarity Testing
It is possible that either the equivalence margin or and equivalence range may fail or
vice versa. Products with similar standard deviation but different means may fail the
equivalence range test but nor equivalence margin. For products where the means are
the same, but the standard deviations are not, they may fail equivalence margin but not
equivalence testing.
Fixed margin approach The xed margin approach used for bioequivalence testing used the xed margin (δ1,
δ2) = (0.80, 1.25) or (0.90, 1.11) for the ratio of means regardless of the variability of the
tested CQAs. The testing requires a different number of lots for different CQA. However,
this approach may lead to equivalence with the out- of- specication product. The margins
are based on scientists’ prior knowledge, which may require excessively large EAC by
sponsors to assess the equivalence of a small number of the lot (e.g., a few lots) that may
be highly variable. Note that after decades of scientic discussions, the 80%– 125% limits
of bioequivalence testing were adopted, admitting that there is not any real scientic
rationale, except that it has been shown to work. Lately, there is a focus on the scaled average
bioequivalence, wherein the standard deviation of the reference product determines the
acceptance criteria.
Sample size and
variance approach
Minimum to maximum
approach
Raw and graphical
comparisons
Number of lots The number of lots tested side- by- side selected will depend on three factors:
The sample size and variance come from selecting an equivalence testing hypothesis:
H
:
µµδ
oT R
H
: −< −−<
δµ
aTR
or
µµδ
TR
Now we give lower power γ* (for target equivalence) to small sample sizes and solve
for (symmetric) Margin so that Power (Margin, Sample Sizes, True Mean Diff,
Variability) = γ*.
The rationale for this approach is that it captures the product and quality attribute variability,
allows an acceptable shift
δ
≥ 0, determined by scientists for each CQA, and rewards large
0
sample sizes by controlling small samples’ power.
The quality criterion regarding coverage cannot be dened using this approach as it is unstable
for small sample sizes and too wide for large sample sizes. For these reasons, Agencies reject
this approach.
This testing method involves an approach that uses raw data/ graphical comparisons for quality
attributes with the lowest risk ranking. The examination of similarity for QAs using this
method by no means is less stringent, which is acceptable because they have the least impact
on clinical outcomes in the sense that a notable dissimilarity will not affect clinical outcomes.
Depending on the nature of the testing output, if a numerical output is a result of graphical
output, this method of testing must always be made; a uorescence spectrum is tested on this
basis, and the maximum emission wavelength or its ratio with 310 nm can be subjected to an
equivalence range level testing.
There is no guarantee that a given QA, which passes the equivalence margin or equivalence
range test will pass this testing test and vice versa. Evaluation is based on raw data and
graphical presentation; it is somewhat subjective and biased.
The power of testing is accepted; generally, like the bioequivalence testing, and 80% (0.80)
power will be acceptance.
Condence level; generally, it is taken to be 5% giving CL of 0.90.
Standard deviation: Large standard deviations can be reduced either by increasing the number of
lots or by adjusting the analytical methods that are less variable.
nn
BB
.)
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TABLE 5.5 (Continued)
Statistical Concepts Applied to Biosimilarity Determination
Subject Definition and Concepts Applicable to Biosimilarity Testing
It is recommended that the developers include at least 6 to 10 lots of a proposed biosimilar
product in the comparative analytical assessment to ensure 1) adequate characterization
of a proposed biosimilar product and understanding of manufacturing variability, and
2) adequate comparison to the reference product should include lots manufactured with
the investigational– and commercial- scale process and may include validation lots, as well
as product lots manufactured at different scales, including engineering lots. These lots
should be representative of the intended commercial manufacturing process. If there is
a manufacturing process change pre- authorization, it may be possible, witan h adequate
scientic justication, to use data generated from lots manufactured with a different
process. However, data should be provided in the authorization application to support drug
substance and drug products manufactured with different processes and scales. The extent
of process development design (as described in guidelines ICH Q8 (R2) Pharmaceutical
Development and ICH Q11 Development and Manufacture of Drug Substances) and process
understanding should be used in support of the number of proposed biosimilar product lots
proposed for inclusion in the comparative analytical assessment in the application. The ICH
Q5 does not apply.
The number of lots used is more critical when statistical modeling is used to compare a
proposed biosimilar product with the RP; the statistical model may including using an
equivalence interval or an equivalence range where applicable; in such instances, the power
of the test is important as derived from the following equation:
Powern
() exp( ..
=− −− +1053948 0 14694 0 002052 Equation 1 [Niazi,
S. Biosimilarity— The FDA Perspective, CRC Press, Orlando, Florida 2018.]
Using four lots will give a power of 65%, ten lots to give 85% power, and 20 lots allow the
power of 92 percent. Generally, a power of 80% is recommended. Since not all testing is
conducted using statistical approaches, fewer lots may be sufcient for some testing.
Types of lots To the extent possible, proposed biosimilar lots included in the comparative analytical
assessment should be derived from different drug substance lots to adequately represent
the variability of attributes inherent to the drug substance manufacturing process. Drug
product lots derived from the same drug substance batch(es) are not considered sufciently
representative of such variability, except for use in testing certain drug product attributes
for which variability is mostly dependent on the drug product manufacturing process (e.g.,
protein concentration). Although it may be preferable to compare a proposed biosimilar
product lots to the reference product lots, it may be acceptable also to include independent
drug substance lots (if the drug substance was not used to make drug product), if needed, to
attain enough of lots for the comparative analytical assessment.
The comparative analytical assessment submitted with the marketing application to support
the demonstration of biosimilarity of a proposed biosimilar product to the reference product
should include lots of a proposed biosimilar product used in a principal clinical study, as
well as a proposed commercial product. After completing the initial comparative analytical
assessment or after completing clinical studies intended to support an application, the
developers considering manufacturing changes may need to conduct additional comparative
analytical studies of a proposed biosimilar product and the reference product. The nature and
extent of the changes may determine the extent of these additional analytical studies.
Replicates It is recommended that the same number of replicates be performed within each proposed
biosimilar lot as within each reference product lot and that the same lots be used for
equivalence testing, quality range testing, and visual assessment of graphical displays.
B
103
(continued)
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Analytical Assessment
TABLE 5.5 (Continued)
Statistical Concepts Applied to Biosimilarity Determination
Subject Definition and Concepts Applicable to Biosimilarity Testing
Equal number
side- by- side
Unbalanced samples Samples of reference and test products are blinded before the testing of analytical similarity;
Blinding Before conducting the side- by- side analytical similarity testing, the developers would create
Combining lots Biosimilar, the developer, conducts several biosimilarity studies at different stages and
Mixed graphic and
numerical data
Signicant gures Before subjecting the data to statistical analysis, it must be rounded off to match the analytical
There should be an equal number of lots when tested side- by- side. Suppose there are more
lots available for reference. In that case, an unbiased selection should be made to select the
equal number, and the rest can be used separately to develop the range acceptance criteria.
This recommendation is challengeable once we demonstrate sufcient power of the test.
If the number of samples of the reference product is higher, this will lead to a smaller
standard deviation and a larger difference range that may cause the test to fail because of β
error. The critical minimum number of lots tested side- by- side is approximately 8; however,
if there is an unbalanced selection between test and reference, a higher number may be
required.
if the reference samples to test out of specication, these are to be removed from the
statistical calculation, creating an unbalanced design since Agencies requires an equal
number of samples of test and reference product to be tested side by side. How can this be
resolved?
The developers should remove an equivalent number of test samples through a randomization
process; this process must be presented in the analytical testing SOP and properly
documented. It is important that if the reference product fails one attribute and is declared
out of specication, other values drawn from that samples can not be used, even if they are
not affected by the out of specication reading for one attribute. This exercise will create
a situation where the test’s power is reduced; to overcome this possibility, the developers
should try to start with a larger number of samples, such as 11 instead of 10, if the target is
ten samples. The developers may also adopt a procedure wherein the reference samples are
tested before being blinded to avoid this situation. However, the selection and blinding of the
reference product must still be made on a random basis.
a protocol that will include all acceptance criteria and blind the test and reference samples;
where a larger number of samples are available, this will be preceded by a random selection
of lots. However, when establishing EAC or other acceptance attributes using a separate set of
lots, there is no need for blinding the reference samples.
accordingly conducts analytical and functional similarity testing; can these lots be combined
to provide a composite description instead of one study?
Some testing provides a graphical output, but the peak height and area under the peak
curve can be quantitated. Whenever there is a graphical output, it should demonstrate no
extraordinary peak, no extraordinary heights of the peaks, and no extraordinary baseline—
this is an overall evaluation of the graphical output; however, where the peaks have
known signicance relating to potency, purity, and safety, and as a result, the quantiable
graphical attributes have clinical meaningfulness, these can be compared for equivalence
margin and equivalence range analysis; for visual testing, there is no further need to
perform any quantitative evaluation. However, if the numerical comparisons do not have
any clinical relevance, a graphical representation alone would be sufcient, regardless of
the attribute.
method’s sensitivity, instrument sensitivity, and other factors. For example, if a balance
is sensitive to 0.1 gram, the weight, all weight values will be rounded off to one decimal
point: 1.09 becomes 1.1 and 1.02 becomes 1.0; another approach is to express signicance
to a percentage of the value. For example, a method sensitive to giving ±1% will have the
following values: 1.00; 10.0; 100.
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inactive components. There exist multiple approaches to analyze the signicance of analytical data.
Regulatory bodies expect developers to possess comprehensive knowledge of statistical techniques
and to provide justication for the chosen statistical modeling. Any observed differences in clinical
response between the proposed biosimilar and the reference product are attributed to the variability
in critical quality attributes (CQAs). This premise forms the basis for identifying CQAs, considering
the wide dose- response relationship in biological products. Consequently, any modeling must factor
in these possibilities.
One viable approach to data analysis involves employing descriptive quality ranges for evaluating quantitative quality attributes of high and moderate risk. For attributes ranked with lower risk
or those that cannot be quantitatively measured (e.g., primary sequence), raw data and graphical
comparisons could be used.
Acceptance criteria for the quality ranges (QR) method in comparative analytical assessments
should be derived from the developer’s analysis of the reference product for a specic quality
attribute. The QR should be dened as the sample mean ( ), where is the sample standard deviation
based on the reference product lots. The multiplier (X) must be scientically justied and discussed
with regulatory bodies. Tolerance intervals are discouraged for establishing similarity acceptance
criteria due to the need for an extensive number of lots, as per our current experience. Developers
can propose alternative data analysis methods, including equivalence testing.
The main objective of comparative analytical assessment is to conrm that each attribute
observed in both the proposed biosimilar product and the reference product shares a similar population mean and standard deviation. Comparing a quality attribute typically supports the conclusion
that a proposed biosimilar product is akin to the reference product when a substantial percentage
of the proposed biosimilar lot values (e.g., 90%) fall within the dened QR for that attribute. It is
advisable to apply narrower acceptance criteria in the (e.g., a lower X value) QR method for higherrisk quality attributes.
Apart from risk ranking, other considerations should inuence the choice of quantitative data
analysis for an attribute or assay. Additional factors to contemplate in determining the appropriate
type of data evaluation and result analysis include
• Nature of the Attribute: Attributes identied as high risk should take precedence over attributes
with unknown but potentially high risk due to uncertainties.
• Distribution of the Attribute: Generally, it is recommended that the manufacturing process
aims to closely match the distribution centers of the reference product’s quality attributes.
Thus, the QR, assuming similar population means and standard deviation, serves as an appropriate approach to demonstrate similarity between the proposed biosimilar and the reference
product. Any concerns about distribution may necessitate further information or analyses to
support the QR method or alternative analysis approaches. For instance, if the distribution of
an attribute in a proposed biosimilar product is skewed compared to the reference product,
depending on the attribute’s nature and role in the product’s mechanism of action, it might
raise concerns. In such cases, appropriate scientic justication would be necessary to support
the comparative analytical assessment. When an attribute in the reference product has a nonnormal distribution, developers should engage with regulatory bodies for guidance.
• Attribute abundance: The inherent variability within protein products means that attributes
posing high risks in instances of high abundance (e.g., percent aggregation or oxidation) might
present signicantly lower or negligible risks when occurring in low abundance. It’s essential to conrm the attribute’s abundance in both the reference product (as determined by the
proposed biosimilar product, developer’s analysis of the RP) and the proposed biosimilar
product. While limit assays don’t always require QR evaluation, it is crucial to dene and justify the selected limits for the attribute amount, considering its changes over time.
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Analytical Assessment
• Assay Sensitivity in Attribute Assessment: Although using multiple assays is encouraged,
not all need the same evaluation approach. The most sensitive assay for detecting product
differences should undergo QR evaluation, while others assessing the same attribute may
use graphical comparisons. A rationale must be provided for each assay’s chosen evaluation
method.
• Attributes/ Assays Types: Certain attributes or assays (e.g., protein sequence, certain higherorder structure evaluation assays, or solely qualitative assays) may not be suitable for quantitative analysis. The comparative analytical assessment plan should clearly identify these assays
exempt from quantitative data analysis, along with the rationale for this exclusion.
• Utilizing Publicly Available Information: Publicly available information can inuence the data
analysis type and acceptance criteria in the comparative analytical assessment. Developers
should seek guidance from Agencies regarding the inclusion of such information in their
assessments.
Qualitative Analysis Recommendations: For lower- risk attributes, conducting qualitative analyses, presenting side- by- side data (e.g., spectra, thermograms, graphical data representations),
is recommended. This method facilitates visual comparison between the proposed biosimilar
product and the reference product. It is particularly useful for assays such as NMR, mass spectrometry, and biological activity, intended solely for analytical assessment and not product release
validation.
5.4.7.1 Risk Ranking
The nal comparative analytical assessment plan must include risk ranking for attributes, the
type of data evaluation for each attribute/ assay, and the nal data analysis plan. It should specify
the expected availability of proposed biosimilar and reference product lots for evaluating each
attribute/ assay and provide a rationale for why a specic number of lots is sufcient for evaluation. The comparative analytical assessment plan should be discussed with regulatory agencies
as early as possible in the proposed biosimilar product development program to agree on the
attributes/ assays for evaluation. This nal plan should be submitted to agencies before initiating
the nal analytical assessments, typically in a meeting with them. Developers must nalize and
approve an analytical evaluation plan before commencing testing, which will include acceptance
and rejection criteria.
5.4.7.2 Assay Variability
A high assay variability should not warrant a large σR. In such cases, optimizing the assay and
increasing the number of replicates can help reduce variability. Also, when equivalence margins or
quality ranges are excessively broad, it might be scientically justied to narrow them. In instances
where data do not adhere to a normal distribution, sponsors may opt for a non- parametric tolerance
interval, but this generally requires a large sample size.
Determining an acceptable high variability is challenging, but several guiding principles can help.
First, it involves assessing instrumentation variability— is there better equipment available? Second,
understanding the limit of critical quality attribute (CQA) variability is essential. For instance, many
pharmacodynamic responses inherently entail high variability in biological testing systems, similar
to bioassays or binding assays (often tested for equivalence range). For example, if literature, especially the originator data reports, conrm a variability of ±30%, that could be considered the limit to
achieve. Developers might consider using tests beyond those prescribed in the compendia or routine
testing methods by adopting higher sensitivity and repeatability testing. Regulatory agencies do not
make recommendations on the method to be used; it must be justied and shown to be appropriate
and suitable. Later, it can be validated for release purposes. Replicate analysis of samples can reduce
variability; however, agencies typically disallow multiple samples from the same lot.
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5.4.8 functional assessMents
107
Most biological products interact with the body in specic ways, such as binding to receptors,
ligands, or substrates. The resulting effects are usually detectable at a molecular or cellular level.
In vitro assays using human cells or receptors are commonly used to evaluate both the binding
to the target and the subsequent functional effects, aiding in the characterization of structure and
function. For monoclonal antibodies (mAbs), besides binding to the primary target’s complementarity determining region (CDR), the Fc portion also contains binding sites for different receptors,
potentially triggering various effector functions like complement activation, complement- dependent
cytotoxicity (CDC), and antibody- dependent cell- mediated cytotoxicity (ADCC). Evaluating these
Fc- related binding properties and effector functions can also be done in vitro.
5.4.9 oRthogonal studies
Data from many comparative in vitro studies, some of which may already be available from qualityrelated assays, should normally be provided to assess the possible potential differences in biological
activity between a proposed biosimilar product and the reference product.
These studies should cover relevant assays including:
• Binding to known target(s) (e.g., receptors, antigens, enzymes) involved in the pharmacological effects and pharmacokinetics of the reference product.
• Signal transduction, functional activity, or viability of cells relevant to the pharmacological
effects of the reference product.
• These studies need to be comparative and not solely focused on assessing the response itself.
To ensure clear results, appropriate methods suitable for their purpose should be used. These
test methods do not need to be validated.
They should be sensitive, specic, and sufciently discriminatory to demonstrate that observed
differences in quality attributes are not clinically relevant. Comparing the concentration– activity/
binding relationship of the proposed biosimilar product and the reference product at pharmacological targets is essential, particularly across a concentration range where potential differences are
most sensitively detected.
5.4.9.1 Lots Tested
It is crucial to conduct testing using an adequate number of lots of both the reference and proposed
biosimilar products, representing the intended material for clinical use. The number of lots tested
should account for assay and batch- to- lot variability. It should be enough to draw meaningful
conclusions regarding variability and similarity between the proposed biosimilar and the reference
product. While it’s generally recommended to use 6– 10 lots to achieve suitable test power, assays
providing non- quantitative data may be conducted on a smaller lot size. However, it is important to
acknowledge that using fewer lots increases the risk of lot failure.
These assays should collectively cover the entire range of pharmacological and toxicological
aspects relevant for both the reference product and the product class. In many cases, in vitro assays
can detect differences between a proposed biosimilar product and the reference product more effectively than animal studies. Hence, these assays are crucial for the non- clinical comparative evaluation
of proposed biosimilars.
Functional assays play a vital role in characterizing protein products, complementing physicochemical analyses, and quantifying the functional aspects of the protein.
If the reference product demonstrates multiple functional activities, developers must conduct
a suitable set of assays to assess the spectrum of pertinent activities associated with that product.
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