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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5423_Библиотеки_им_академика_М_И_Перельмана
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10 Substrate and Distal Pharmacodynamic Biomarker Measurements forGene Therapy
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the signature peptide after tryptic digestion might be the same, or slightly different in selected amino acids. A signature peptide within
± 2 Da of molecular weight
from the signature peptide of the analytical targeted protein might be chosen as
the surrogate analyte, and since a typical Sciex mass spectrometer does not have
the capability to differentiate such small mass‐to‐charge ratio, the same multiple
reaction monitoring (MRM) transition can be used. However, if the molecular
weight is equal to or greater than 3Daltons on a Sciex mass spectrometer, different
MRM transitions have to be monitored which creates additional variables in the
methodology but is still feasible.
Glycosaminoglycans, including heparan sulfate (HS), dermatan sulfate (DS),
keratan sulfate (KS), and chondroitin sulfate (CS), are unbranched linear sulfated
anionic polysaccharides, composed of a series of structurally different disaccharides. When a large molecule analytical target such as glycosaminoglycans is to be
quantitated, neither top‐down intact analysis on a large‐molecule level nor
up analysis is feasible, due to their highly heterogeneous structures, without a consistent sequence or molecular weight. In this case, a different analytical strategy
must be adopted. The quantitation of HS can be achieved by quantitating individual disaccharides after chemical or enzymatic digestion. A similar approach can be
taken for the quantitation of DS. The disaccharides to be used in quantitation are
carefully investigated in healthy and diseased matrices and the most abundant
and/or clinically meaningful disaccharides are selected for quantitation. Individual
or total disaccharide concentrations can be reported using this approach. An alternative approach for data processing can be utilized by adding up the individual
peak area of different disaccharides of the same sample, which represents the total
HS or DS. This approach is useful in reporting the total HS and DS but lacks information on individual disaccharide concentrations. Based on the purpose of the
biomarker quantitation, different strategies can be considered and adopted.
Even though the LC‐MS platform provides advanced tools for biomarker quantitation, compared with immunoassays, the sensitivity of the LC‐MS technique
limits its application in the large molecule biomarker fields. Immunoassays usually allow the detection of protein biomarkers at pg/mL levels. Using high‐
sensitive platforms such as Quanterix SIMOA or MSD (S‐plex assays), the
sensitivity can reach low fg/mL levels. However, typical LC‐MS methods are only
capable of detecting biomarkers at ng/mL to μg/mL levels. Recently, a few highly
sensitive protein biomarker quantitation methods using LC‐MS were also reported
using nano‐flow HPLC coupled with a Q‐Evacuative mass spectrometer. These
methods also feature high selectivity and provide an alternative option for protein
biomarker quantitation. Secondly, after validation is complete, the immunoassay
workflows are usually more concise and have a higher throughput than LC‐MS
assays. After a sample batch is prepared, the LC‐MS method requires samples to
be quantitated by sequential injections and is more time‐consuming in data
bottom‐
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acquisition than immunoassays, most of which only take a few seconds to minutes for data acquisition. On the other hand, the LC‐MS platform is superior to
immunoassays with respect to assay selectivity, assay linearity, and reduced matrix
effect. According to the physiological or pathological processes, the observed biomarker response change may be an increase or decrease and the percent change
may be great or very slight. The normal range can vary both within a patient and
between patients.
Choosing the appropriate platform is important and each platform has its
unique advantages and disadvantages. Investigators should carefully evaluate the
platforms, reagents, and context of use for the biomarkers to develop the most
fit‐for‐purpose methodologies.
10.2.1.2 Method Validation by LC-MS/MS
To determine the extent of biomarker method validation needed, it is necessary to
establish how the data will be used in the context of the entire project being supported. The associated method validation parameters and their stringency will be
defined based on:
● Intended use of the biomarker data,
● The importance of the data with respect to the conclusion of the study,
● The type of study in which it is placed, and
● How the data are used in a regulated submission.
Some questions that may help define the current validity of the biomarker,
which will drive the degree of method validation in return, for example, include:
● Is this biomarker part of a panel of biomarkers whose significance in this indi-
cation is not yet fully understood?
● Is there data in the literature describing this biomarker for the current indica-
tion or drug class?
● Are references available?
● Is this a biologically qualified biomarker for the context of use (i.e. animal
model, efficacy, safety endpoint)?
● Is this biomarker understood in terms of:
○ Anticipated individual physiologic/biologic variability
○ Subtle or robust changes in values depending on the disease state or intended
indication
● Is there a baseline level in the matrix? Does it vary with disease state?
● What is known about the target (e.g. identity, possibility of cross‐reactivities,
and stability in matrix, etc.)?
An example of the tiered approach to defining the intended use of the bio-
marker data is summarized in Table10.1.
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Table10.1 Biomarker purpose and suggested fit-for-purpose validation extent.
Stage of drug
program
Discovery Exploratory Exploratory To screen for possible makers; to help
Proof of
Concept
Established
markers
Surrogate
endpoint
Biomarker
qualification Method Examples where is will be useful
rank drug candidates.
Selection Qualified Make decisions on the biomarker’s
Further
corroboration
Surrogacy Validated Can be used in place of traditional
Validated Safety Studies; submission studies
utility; useful in discovery studies but
not for safety or submission studies.
endpoints, including primary,
secondary, and exploratory endpoints.
Biomarker validation, including LC‐MS biomarker validation, should follow US
2018 Guidance: Bioanalytical method validation guidance for industry, European
Medicines Agency (EMA) guidance: Guideline on bioanalytical method validation, and ICH M10 guidance. However, the criteria for accuracy and precision for
protein biomarker analysis is generally following that of immunoassays, where
accuracy should be within 100
and 100
± 25% for lowest calibration standard and LLOQ samples; and percent CV
± 20% for non‐LLOQ calibration standards or QCs,
should be within 20% for non‐LLOQ intra‐ and inter‐ QC precision and 25% for
LLOQ intra‐ and inter‐precision. In terms of validation parameters, since the
selectivity cannot be evaluated due to the endogenous nature of the biomarkers in
biological matrices, a matrix sample screening run, including healthy or diseased
matrices, from at least 25 different donors should be performed. For recovery and
matrix effect evaluation, if the endogenous concentration is too high to perform at
one or more concentration levels (e.g. QC‐Low and/or QC‐Mid concentration levels), the stable isotope‐labeled reference material can be used instead as an analyte to perform the matrix effect testing. For protein or other large molecule
bioanalysis involving chemical or enzymatic digestion, the digestion efficiency
should be evaluated whenever possible.
10.2.2 Histology
Histologic assessment of tissue biomarkers can range from histochemistry such as
hematoxylin and eosin (H&E) to in situ hybridization (ISH) for nucleic acid and
immunohistochemistry (IHC) for protein detection. It has traditionally been used
in drug development for invivo diagnosis, evaluating morphological changes, eval-
uating response to therapy, nonclinical safety assessment, and basic research[18].
However, there are cases where histology evaluation was used as a surrogate
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endpoint to reasonably predict clinical benefit to support drug approval.
Histological reduction of the accumulated GL‐3inclusion burden in biopsied kidney interstitial capillaries (KIC) was used as a surrogate endpoint[2] to support
the approval of Fabrazyme® and Galafold® in the United States, Fabrazyme for
patients with confirmed Fabry disease and Galafold to treat Fabry patients with
amenable GLA gene variants. For both approved drugs, the reduction of GL‐3inclusions in kidney KIC was evaluated on renal tissues that were fixed, embedded, and
stained using similar procedures [19, 20]; however, each approved drug used a
different method for GL‐3 inclusion evaluation and scoring. The Fabrazyme
Scoring System (FSS) based on a semiquantitative scoring scale of 0 to 3 evaluated
on fresh glass slides under light microscopy by three independent renal pathologists in a blinded manner (0= none/trace, 1 =mild, 2=moderate, 3=severe
accumulation) was used in Fabrazyme trials to support the traditional approval of
the therapy[2, 19]. Whereas Galafold, also evaluated by three independent renal
pathologists in a blinded manner, used the quantitative Barisoni Lipid Inclusion
Scoring System (BLISS) method based on KIC GL‐3inclusions per renal peritubular capillaries using whole‐slide digital (WSI) images to support the accelerated
approval of the chaperon therapy[2, 20].
Traditionally, the interpretation of the histology results is performed by observation using light or immunofluorescence microscopy. However, the digital WSI
workflow has become a trend in modern pathology practice and is increasingly
adopted in clinical diagnostic and clinical trials[20–23]. Additionally, the incorporation of artificial intelligence and machine‐learning techniques for computation image analysis also takes the histology field to the next level. The incorporation
of these new capabilities will enable information extraction and quantitative analysis that is not possible using the conventional microscopy method[22]. There are
advantages and disadvantages to either approach. For conventional microscopy,
pathologists regularly evaluate multiple focus planes; however, individual practice could potentially introduce variations, bias, and errors. Although Digital WSI
offers many advantages over the conventional microscopy approach, it does have
some limitations. These include the lack of Z‐dimensional focus (depth on an
image) unless using the Z‐stacking approach to produce a composite multiplane
image, the imperfect control of the scanner and scanning software that can contribute to imaging artifacts, the need to use microscopy scanner with high objective lens (e.g. ×40, ×100) results in the large file size, which will require a
substantial network bandwidth to handle these data sets, and complicated workflow to securely store, share images and data[21, 22]. Additionally, the individual
pathologist bias may still exist with WSI if images are evaluated by an individual
pathologist only.
From clinical trials support perspective, whether it is through traditional
microscopy‐based with manual evaluation or scoring by an individual pathologist
through a digital platform, there are many method developments, validation,
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logistical, and operation challenges when it comes to using histology in clinical trials to support endpoints evaluation. Testing logistics of histology evaluations may
involve many laboratories from sample collection, fixation, staining, and image
scanning to data outputs and on‐boarding experienced pathologists. Each clinical
site may have different procedures for sample collection, processing, and fixation. It
is crucial to understand variations in procedures used by different clinical sites and
implement a strategy to best unify these procedures. During method development,
the preanalytical variables, such as tissue collection, fixation, processing, and
embedding, should be evaluated carefully as differences in the procedure applied
will likely impact the results[24]. Improper tissue handling and mechanical manipulation during tissue processing may introduce artifacts or impact RNA integrity for
ISH assessments. Inappropriate fixation or prolonged formalin‐fixation may impact
staining results or cause excessive protein cross‐linking and reduce the biomarker
availability for antibody binding for IHC evaluations. During the analytical histology phase, the sectioning and staining procedures should be optimized and evaluated for consistency. Utilizing the same laboratory to perform the analytical phase
will help to reduce variability. If a digital platform is used, the selection of the scanner, image and monitor resolutions, data analysis and interpretation of results, and
reporting of data process will need to be vetted and planned out early[21].
The draft Food and Drug Administration (FDA) guidance for the development
of Fabry disease indicated a standardized and validated method conducted by
experienced pathologists in a blinded and systematic manner should be used if
the surrogate endpoint is based on histological assessment[25]. However, guidance on how to systematically validate this type of histology evaluation is still
lacking. FDA guidance of histopathology to support biomarker qualification for
nonclinical studies [18] and the white paper from the College of American
Pathologists and Laboratory Quality Center for diagnostic purposes[23] can be
used as a general guide for method setup and validation. During validation, the
validation study design should closely mimic the clinical environment and the
specific technology for the intended use. Demonstration of accuracy, intra‐ and
inter‐observer agreement and the concordance between digital and glass slides
are some recommended parameters if a digital platform is the selected method[23].
Additionally, the data analysis, data reporting, and the software used for the analysis will also need to be evaluated and incorporated as part of the validation if
applicable.
10.2.3 Functional Activity and Immunoassays
Functional protein activity and immunoassays have also been used in drug development for PD biomarkers measurement. Some examples of PD biomarkers
using protein activity are chitotriosidase for Gaucher disease and serum ceruloplasmin activity for Wilson disease[26–28]. Method development and validation
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challenges and solutions for functional activity were discussed extensively in
Chapter9 and the presented information can be applied to this application.
Immunoassays are still considered the gold standard for biomarker testing,
especially in the field of large molecule protein biomarker testing. Over the past
two decades, immunoassays and technologies have developed in various aspects,
and recent innovations have advanced in many directions: sensitivity, multiplexing, robustness, reproducibility, time, and cost‐effectiveness. It covers a wide spectrum of immunoassay quantitation platforms, including traditional ELISAs and
novel platforms, such as Quanterix SIMOA, MSD, Ella™ Automated Immunoassay
System (ELLA), Luminex, Quansys Q‐view imaging system, SMC, and O‐Link.
Table10.2 lists current platforms that are commonly applied for quantitative
immunoassays of biomarkers. In this table, a comprehensive cross‐platform and
cross‐assay evaluation was performed, using different technology platforms, and
immunoassays of different analytes, by comparing a set of common assay parameters: precision, sensitivity, parallelism, frequency of endogenous analyte detection (FEAD), and data correlation between platforms.
Immunoassays are trending in two main directions: high sensitivity and multiplexing. The driving force for these two directions is the ultra‐low amounts of a lot
of newly discovered and important biomarkers (picomolar to femtomolar range)
in the body, their dynamic secretion processes, and short half‐lives. Moreover, to
understand the mechanism of drug functionality and to lend support to the proof
of concept of the biomarker, more than one biomarker needs to be quantitated at
the same time and these biomarkers can be selected in return to facilitate the disease diagnosis and treatment, patient selection, companion diagnosis, etc. All
these highlight the value of biological specimens from disease and healthy populations and the urgent need for sensitive methods that are capable of multiplexing. Currently, the Quanterix SIMOA demonstrates superior sensitivity due to
their unique technology. Competitors like MSD S‐plex assays and the SMC Pro
assays also showcase the ability for high‐sensitivity testing and quantitation.
There are various platforms capable of supporting multiplexing, such as Luminex
assays, ELLA assays, MSD V‐plex and U‐plex assays, and O‐link assays. Most of
these multiplexing assays demonstrate stable, reliable performance. However,
when looking to combine the sensitivity and the multiplexing capabilities, there is
still a gap in the technology. It is challenging to find a multiplexing assay that
demonstrates ultra‐high sensitivity in many fields, such as Alzheimer’s biomarkers. Various companies continue their efforts in building high‐performing instruments capable of multiplexing without compromising sensitivity and throughput.
10.2.3.1 Method Validation ofImmunoassay
Biomarker validation using immunoassay techniques follows FDA, EMA, and
ICH M10 guidelines for immunoassays. Biomarker validation is deemed as fit‐for‐
purpose, and the extent of the validation should be appropriate for the intent of
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the study that the biomarker supports. Fit‐for‐purpose method validation ranges
from a simple method qualification, a including one precision and accuracy
(P&A) run to test accuracy and precision, to a full validation covering all required
parameters defined in the guidance.
Elements of method qualification and method validation are usually assessed
on a case‐by‐case basis and depend on many factors, including the importance of
the study in terms of decision‐making and whether the biomarker data will be
used to support submission. Documentation is always expected, even if the earlier
stages of method development are less complete in terms of validation.
Once the biomarker is positioned in its importance to the study protocol, the
next step should be to collect the available information about the proposed method:
● Are there any proprietary or commercial kits available?
● How is this study to be used and how is the sample collected? Will the sample
be used fresh or stored?
● Platform
● What are the species (and dynamic range and sensitivity required?) to be
researched on: rat/dog/monkey/human?
● What is the matrix type: urine/plasma/serum/CSF/other?
● What are the sample volume requirements: microliter/nanoliter?
● Are critical reagents available, supplied consistently, and meet the standard for
the context of use?
● Is there a WHO or other recognized or commercially available reference standard?
● What is the sensitivity required: ng/mL or pg/mL or fg/mL?
● What is the nature of throughput required (approximate samples per study/
submission)?
Once the method is established and the quantitation range is identified, a proposal for method qualification/validation can be developed.
● Partial Validations (bridging studies)
The stage of validation and the extent of the modifications made to the current
method determines the degree of testing required. For some modifications, e.g.
change in the reagent lot, reagent “bridging” is necessary to verify the new lot
acceptability. For other modifications, e.g. change in platform, critical reagent,
matrix, etc., at least three assays must be performed to verify the validity of the
method and should include at least recovery, P&A, and linearity.
● Cross‐Site Verification
Optimally cross‐site verification should be conducted using both spiked QCs and
incurred (study) samples. The method transfer should follow an established SOP.
● Commercial Kits
Never assume a kit meets the criteria described in the package insert. The kit
should follow the stringencies described above, depending on its intended use.
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Table10.3 Comparison ofmethod validation parameters between fully validated
regulated bioanalytical assays support clinical studies against full validation under
CLIA regulations.
Regulated
Method Validation Requirement
Dynamic Range, Sensitivity and Calibration Standards ✓ ✓
Dilution Linearity and Parallelism ✓ ✓
Intra‐ and Inter‐run Accuracy and Precision, Spike Recovery ✓ ✓
Matrix Selectivity ✓ ✓
Endogenous Screening ✓ ✓
Stability (Short and Long Term) ✓ ✓
Inter‐lot Bridging ✓ ✓
Drug Interference Test, Hemolysis, Lipemic Effect ✓ ✓
QA Audit ✓ ✓
Normal Population Range Determination ✗ ✓
Personnel Competency Test ✗ ✓
Lab Accreditation and Certification per Regulations ✗ ✓
CLIA Director Review and Signature on Validation Protocols
and Reports
External Proficiency Test ✗ ✓
Bioanalysis CLIA
✗ ✓
Moreover, when biomarker data is used for physicians to make the diagnosis
decision, a CLIA validation should be performed in a CLIA‐certified laboratory.
The details of the comparison for parameters between full validation of regulated
bioanalytical methods against full validation under CLIA regulations are listed in
Table10.3.
10.2.4 mRNA Detection of Downstream Target Expression as a
PD Biomarker
Transcription is a highly regulated process in both prokaryotes and eukaryotes.
Hence, specific transcript or transcriptome analysis, either qualitative or quantitative is preferentially performed to assess health conditions or a therapeutic intervention by monitoring variations in PD biomarker levels. Though Northern
blotting and DNA microarrays also are in use, PCR‐based molecular methods are
still preferred for the detection and quantitation of target expression. Real‐time
PCR is considered by many as the gold standard in nucleic acid quantification
because of its accuracy and sensitivity[29, 30]. Real‐time PCR is termed qPCR
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