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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5943_Библиотеки_им_академика_М_И_Перельмана
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Bioanalytical Methods to Study Biodistribution and
Christian Vettermann and Russell Soon
Translational Sciences, Assay Strategy & Development, BioMarin Pharmaceutical, Inc., Novato, CA, USA
7.1 Introduction
Pharmacokinetic studies monitor the presence and fate of a therapeutic following
administration to animals or clinical trial participants. For adeno‐associated virus
(AAV)‐based gene therapies (GTx), pharmacokinetic studies typically address tissue biodistribution, intracellular vector processing, and vector shedding. Given
their invasive nature, biodistribution and vector processing studies are typically
conducted non‐clinically, and only limited data exist in humans[1, 2]. In contrast,
while vector shedding studies may be conducted non‐clinically, they are more
routinely implemented in clinical GTx trials. Regulatory expectations for nonclinical biodistribution and clinical shedding studies are covered in Chapter4. In this
chapter, we focus on bioanalytical methods to detect GTx vectors in tissues and
biological fluids.
Polymerase Chain Reaction (PCR)‐based methods to detect GTx vectors are
generally more sensitive than cell‐based or ligand‐binding assays[3]. In addition
to PCR being highly specific, it can broadly detect both encapsidated and non‐
encapsidated (i.e. processed) vector DNA. Regulatory guidance for qualification
or validation of PCR‐based methods is limited[4, 5]. Thus, bioanalytical practices
are often rooted in regulatory guidances for validation of non‐PCR‐based methods. These guidances focus on methodologies that have dominated the field so far,
namely ligand‐binding and chromatographic assays[6], and therefore do not fully
address the specific needs of PCR‐based methods. Consequently, while these
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Drug Development for Gene Therapy: Translational Biomarkers, Bioanalysis, and Companion
Diagnostics, First Edition. Edited by Yanmei Lu and Boris Gorovits.
© 2024 John Wiley & Sons, Inc. Published 2024 by John Wiley & Sons, Inc.
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guidances may serve as reference points, specific recommendations require consideration of context‐of‐use and fit‐for‐purpose aspects for PCR methods, as the
field acquires the critical mass necessary for establishing consensus and specific
regulatory guidance.
Some industry white papers and regulatory reflection papers on PCR have
emerged within the past few years[7–9]. These publications started to delve into
the details underpinning critical areas of set‐up, validation, and sample testing for
PCR‐based methods in gene and cell therapy development. A valuable summary
of regulatory and technical aspects for PCR‐based biodistribution and shedding
methods was recently provided[10]. In this chapter, we will discuss the application of real‐time (q) PCR and digital PCR to bioanalytical assessments that support GTx development. Our focus is on areas where practical use of these methods
still requires clarification, or where strategic and tactical considerations come into
play in choosing one PCR platform over the other. While another book chapter
will focus in‐depth on reverse transcription PCR methods for the detection and
measurement of GTx vector RNA transcripts, this chapter will focus on detection
of GTx vector DNA.
7.2 Choice ofPlatform: qPCR vs. Digital PCR
Two technology platforms exist for PCR‐based methods that differ in instrumentation, measurement principle, and applicability to translational or clinical questions (Table7.1). On the one hand, quantitative polymerase chain reaction (qPCR)
has been a standard molecular biology technique since its invention in the 1980s.
On the other hand, digital PCR is a relative newcomer to the world of quantitative
assays, and even newer to the bioanalytical suite of assays used in drug development. Understanding the principles of each technology should help inform platform selection within the intended contexts of use.
In qPCR, a sample containing target DNA sequence is mixed with DNA polymerase and primers plus probe, and subjected to several reaction cycles at set
temperatures for specific durations to amplify the DNA and generate a fluorescent
signal in real time. The fluorescent signal increases as the target DNA sequence
amplifies with each thermal cycle until a given detection threshold is reached.
The cycle number at which this occurs is designated Cq or Ct value and is proportional to the starting amount of target DNA in the sample. The Cq or Ct value
from test samples with an unknown concentration of target DNA is then interpolated against a calibration curve of reference GTx drug material, vector DNA, or
surrogate vector DNA, generating a result reported as vector genome (VG) copy
number. Sample volumes in qPCR reactions range from 20 to 200 μL, and real‐
time measurements are carried out on qPCR instruments, such as Roche
Table7.1 Characteristics ofqPCR and ddPCR assays.
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qPCR ddPCR
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Standard real‐time PCR instruments, such
as Roche LightCycler systems. Validated
methods require system to be CFR Part 11
compliant.
Microfluidic chamber (e.g. BioMark digital
PCR, Fluidigm), micro‐well chip (e.g.
QuantStudio3D digital PCR, Life
Technologies), droplet‐digital (e.g. QX200
and QXONE, Bio‐Rad), or Crystal™
digital‐based (Naica, Stilla) instruments.
Validated methods require system to be
CFR Part 11 compliant.
Real‐time fluorescence measurement. End‐point fluorescence measurement.
Calibration curve prepared from GTx
reference material, vector DNA, or
surrogate material.
No calibration curve required, absolute
quantification through application of
Poisson statistics to number of positive/
negative reaction partitions.
Unit of measurement: single‐stranded or
double‐stranded copies of target DNA per
Unit of measurement: molecules of target
DNA per volume.
volume.
Dynamic range of about 8–9logs. Dynamic range of about 4–5logs.
Limited precision for lower template
amounts.
Tolerance for PCR inhibitors can vary
with reagents, sensitivity may be
negatively impacted in interfering matrix
components.
Sequences with low PCR efficiency
difficult to quantify accurately.
Higher precision for lower template
amounts.
High tolerance for PCR inhibitors due to
reaction partitioning and end‐point
measurement, greater sensitivity in the
presence of interfering matrices.
Quantification less dependent on PCR
efficiency, advantageous for sequences
with secondary structures, such as ITRs.
Multiple‐amplicon reactions possible, but
no read‐out of connectivity between
amplicons.
Multiple‐amplicon reactions possible
(‘drop‐phase’), read‐out of linked
amplicons possible with systems allowing
for multiple detectors per reaction
partition.
Generally lower cost ($25,000–$50,000 for
instrument, $2 reagent cost per sample)
Average run‐time is around 2 h per plate,
option to run 384‐well plate offers
increased throughput over 96‐well plate.
Data analysis includes calibration curve
and quality control performance, followed
by sample interpolation.
a
Basu, A.S. (2017). Digital assays part I: partitioning statistics and digital PCR. SLAS Technol.
Transl. Life Sci. Innov. 22 (4): 369–386. doi:10.1177/2472630317705680.
Generally higher cost ($80,000‐$100,000 for
a
instrument, $5 reagent cost per sample)
Average run‐time is around 5 h per plate,
current instruments limited to 96‐well
plate.
Data analysis simplified since calibration
curve is not required, and quality controls
and samples absolutely quantified.
a
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LightCycler. Conventional qPCR is well‐established, and a variety of commercial
master‐mixes and instruments are available to provide optimal PCR conditions,
even for difficult samples or matrices that may otherwise impact PCR efficiency,
accuracy, or precision of quantification.
The concept of digital PCR was introduced in the 1990s [11, 12] and found
immediate application in the detection of rare genetic targets and mutations. The
breadth and depth of the platform’s application have grown steadily in both academia and industry, as the technology is being continually refined and greater
technical understanding facilitates increasingly varied applications. At its core,
digital PCR enables absolute quantification of a nucleic acid target through
endpoint‐measured PCR amplification; the platform does not require interpolation
against a standard curve to generate a quantitative measurement. In contrast to
qPCR, where an analog signal is generated in real time in a microliter scaled (typically 20
μL) PCR reaction, a digital PCR reaction employs the same thermal‐cycling
and detection chemistry, but the amplification reaction is partitioned into tens of
thousands of smaller reactions on the nano or femtoliter scale. At sufficiently low
target template concentrations, partitioning a sample into a high number of reactions enables each partition to generate a binary (digital) endpoint signal depending on the presence or absence of a single copy of target DNA in the partition,
following a Poisson distribution. This partitioning of a sample allows for advantages over qPCR, namely absolute quantification of target DNA molecules, greater
tolerance to PCR inhibitors, improved precision, and potentially greater sensitivity.
In essence, an absolute count of target amplicon copies is derived from applying
Poisson statistics to the number of negative and positive partitions in a sample.
While various commercialized digital PCR platforms exist on the market, the
differences between them boil down to how the reaction is partitioned. Microfluidic
chamber (e.g. BioMark dPCR, Fluidigm), micro‐well chip (e.g. QuantStudio3D
dPCR, Life Technologies), droplet‐digital (e.g. QX200 and QXONE, Bio‐Rad), and
Crystal™ digital‐based (Naica, Stilla) systems generate digital PCR data using the
same underlying principle: PCR reaction partitioning to achieve a digital readout.
Bio‐Rad’s droplet digital PCR (ddPCR) platform is presently widely used and
therefore the focus of this chapter.
The difference in measurement principles between qPCR and ddPCR if used
in single‐amplicon mode can be illustrated by the following theoretical examples: A monomeric circular episome of an AAV GTx vector would be detected as
one copy of vector DNA using qPCR and as one molecule of vector DNA using
ddPCR. However, a concatemeric circular episome consisting of two vector copies fused head‐to‐tail at the ITRs would be detected as two copies of vector DNA
using qPCR but as one molecule of vector DNA using ddPCR. The reason behind
this theoretical discrepancy is that the two covalently joined vector copies in a
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circular episome would be co‐partitioned in ddPCR into the same droplet and
counted as one positive event after amplification.
Similarly, if using an appropriate calibration standard, qPCR can resolve the
number of single‐stranded VGs (the DNA packaging form in AAV capsids), while
ddPCR indistinguishably counts both single‐stranded and double‐stranded copies
of vector DNA, due to co‐partitioning of both individual strands of a double‐
stranded DNA molecule. In summary, there may be a tendency to under‐estimate
VG copies using single‐amplicon ddPCR compared to using single‐amplicon
qPCR, if higher‐order molecular aggregates exist. This line of thought may warrant further consideration when ddPCR is used to compare the presence of vector
DNA in longitudinal GTx biodistribution studies since the fraction of double‐
stranded concatemeric vector DNA is expected to increase in transduced tissues
over the first few weeks following GTx administration.
ddPCR has clear advantages over qPCR if used for structural characterization of
vector DNA. The ability of ddPCR to accurately quantify DNA sequences with
substantial secondary structure, such as ITR fusions, makes it the method of
choice for estimating the number of fully circularized AAV vector episomes in
transduced tissue. In contrast, qPCR measurements of ITR fusions are oftentimes
unreliable due to lower PCR amplification efficiency, a circumstance that does not
affect ddPCR as long as positive droplets remain clearly separated from negative
droplets.
In addition, ddPCR offers the unique feature to establish the contiguity
(linkage)
between two or more target sequences, when ddPCR is used in multi‐amplicon
mode (“drop‐phase”)[2]. This feature can be utilized to confirm the length of
detected vector DNA fragments and ensure that adequate DNA repair processes
have occurred when 5′ and 3′ truncated single strands of an over‐sized VG are
packaged in AAV capsids and need to be assembled in transduced target cells. The
same linkage analysis can be applied to characterize transgene‐derived mRNA in
reverse transcription ddPCR (RT‐ddPCR). As an example, multiplex RT‐ddPCR
could quantify two distinct amplicons at the 5′ and 3′ ends of a vector transcript.
Measuring the number of “linked” (double‐positive) vector transcripts provides
an indication of productive vector expression and proper splicing.
Program‐specific considerations also influence which PCR platform is most
appropriate to detect vector DNA. While there may not always be an immanent
reason to choose one over the other in nonclinical biodistribution or clinical
vector‐shedding studies, it may be helpful to use the same platform in both types
of studies for a particular program (Part 3 of the 2020White Paper on Recent
Issues in Bioanalysis). This ensures better comparability of results since data
interpretations remain unaffected by potential differences in sample concentrations that may be attributable to platform differences.
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Study‐specific details, such as anticipated sample testing load, also deserve consideration: Technologies for improving ddPCR throughput are still nascent, and
hence the reagent cost of ddPCR on a per sample basis currently remains about
twice as high as that of qPCR. Another consideration could be the anticipated
peak vector concentration in test samples: Given qPCR’s greater dynamic range,
the method may prove more efficient than ddPCR from a labor and cost perspective when testing samples, since some shedding matrices or target tissues are
likely to have high vector concentrations requiring dilution. Samples with higher
concentrations would require fewer dilutions to quantitate in qPCR, translating
into less time and lower consumable costs over the duration of a study. This aspect
might also contribute to overall qPCR data accuracy since fewer sample dilutions
translate into fewer opportunities for analyst errors or instrument malfunctions.
Other context‐of‐use considerations include availability of a well‐characterized
reference calibration DNA standard for qPCR: if unavailable, ddPCR may be preferable since this platform relies on absolute quantification and hence does not
require interpolation from a calibration standard. Finally, very large template
DNA fragments can negatively affect droplet generation in ddPCR, thus requiring
enzymatic pre‐digestion of samples, which increases sample preparation time and
cost. All these considerations should be taken into account when deciding on the
most suitable PCR platform for GTx studies.
7.3 Aspects ofMethod Development
Once a PCR platform has been selected, the development of the bioanalytical assay
can begin. Many aspects of method development were covered in‐depth recently
[10]. Consequently, the aim of this chapter is to highlight only a few notable points
and frame them within the context of planned applications and logistical considerations. While this section will discuss method development, subsequent sections
will delve deeper into assay parameters that are critical for method characterization and validation.
With knowledge of pharmacokinetic study design, model species, and test
sample matrices, the first step in PCR‐based method development is sourcing of
appropriate reference material that serves as a calibration standard (qPCR) and
as a positive control (qPCR and ddPCR). Ideally, the structure/form of the reference material should resemble that of the GTx vector DNA in future test samples. If surrogate reference materials are used, comparable PCR efficiency and
colinearity may be evaluated between the chosen material and the anticipated
form of the GTx vector. Early in assay development, synthetic, commercially
manufactured, single‐stranded, or double‐stranded DNA fragments or bacterial
plasmids are often used as reference material. These synthetic materials should
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be characterized thoroughly since commercial products show varying degrees of
quality. For example, a commercially purchased synthetic DNA fragment product that contains only 80% DNA molecules with complete fidelity to the ordered
sequence would result in an immediate negative 20% relative error (RE) from the
nominal concentration measured by UV absorbance or nucleic acid‐intercalating
dyes. This potential impact on accuracy precedes any manipulation of the material, and hence the initial negative bias would be additive to any subsequent error
introduced during preparation of reference material stocks or during in‐assay
dilutions. One potential solution could be to use the known purity of DNA reference material to compensate and adjust reported nominal concentrations.
ddPCR is also increasingly being used to characterize reference materials because
absolute quantification allows for selective measurement of functionally intact
sequences that can be amplified in a PCR reaction [13–17]. This experimental
approach to assigning nominal reference material concentrations is largely unaffected by the presence nontarget or low‐fidelity sequences that may interfere in
spectrophotometric or fluorometric methods. The advent of ddPCR has also led to
a better understanding of potential impact from passive adsorption to plasticware,
which may result in loss of nucleic acids during preparation of reference material
stocks or when performing bioanalytical assays[18]. While passive‐adsorption loss
of an encapsidated genome or naked nucleic acid template may not have a quantitatively significant impact on highly concentrated stock solutions, under‐recovery
of template DNA may become more evident at low‐
assay controls. Addition of surfactants like Pluronic® F‐68 can increase accurate
quantification of encapsidated VGs[19], while addition of carrier nucleic acids can
mitigate passive adsorptive loss of non‐
Moreover, manufacturers of ddPCR instruments recommend addition of carrier
nucleic acids, such as tRNA or polyA, to mitigate loss of DNA template due to nonspecific binding to plasticware[21].
Another focus of early method development is the selection of sample matrix
into which the reference material will be spiked. In pharmacokinetic studies, GTx
vector DNA can be measured in target as well as off‐target tissues and various
biofluids. Therefore, tissues and biofluids from naïve subjects of the test species
should be acquired and gDNA extracted for use as matrix into which reference
material is spiked. The extraction method employed for a specific tissue or biofluid during development should be selected with consideration of future testing
needs, such as suitability for study samples and throughput. Other considerations
for nucleic acid extraction are discussed below.
Un‐spiked and spiked calibrator and/or controls are then used to demonstrate
specific, precise, and accurate detection of target vector DNA within endogenous
gDNA. Some biofluids (e.g. urine, saliva, CSF) may not yield a high concentration
of gDNA since they are acellular in nature. Thus, it may be helpful to characterize
encapsidated vector DNA templates[20].
concentration calibrators or
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typical nucleic acid yield early in development and decide how to best report assay
sensitivity and other method parameters that are typically expressed as a quantity
of VG per microgram of gDNA.
Given that GTx biodistribution and shedding studies often measure VGs in different tissues and biofluids, assay development may also need to demonstrate
equivalency of gDNA extracted from each source. Validation of bioanalytical
assays can be costly and time‐intensive, so while it may be ideal to validate every
parameter using gDNA from each matrix, scientific rigor needs to be balanced
against logistical feasibility, return of investment, and assay development timelines. Early development experiments can establish if a given parameter requires
evaluation in gDNA extracted from each matrix, or whether DNA from a representative matrix or pooled matrix can be leveraged to limit the number of validation experiments. These decisions should be guided by the assay’s context of use
and scientific rationale.
Another key aspect for assay development is the design of PCR amplification
primer and probes. Since the mechanics of PCR amplification do not change
between qPCR and ddPCR, we will not delve into specific recommendations for
in silico primer and probe design that have been covered elsewhere [10]. In
addition, the steady increase in PCR applications has resulted in development and
constant improvement of software tools that simplify PCR reagent design and
allow one to predict the performance of those reagents under various reaction
conditions (e.g. PrimerQuest by Integrated DNA Technology, Geneious by
Dotmatics, and the public software Primer3).
Even though in silico tools may be used for designing PCR assays, a myriad of
experimental and biological variables necessitates empirical screening of PCR
amplicon, primer, and probe candidates, usually followed by optimizing PCR conditions. Performing a thermal‐gradient experiment to optimize ddPCR annealing
temperature can have a large impact on amplification efficiency and resolution of
droplet clusters (Figure7.1). Empirical verification of in silico predicted performance becomes especially important in multiplexed PCR assays where the target
amplicon may be amplified together with invariant genes for normalization of
results. Design considerations may also extend beyond amplification of a specific
sequence. For example, if using amplicon linkage capability offered by RT‐ddPCR,
the method employed for RT priming becomes critical– one needs to ensure that
RT is primed so that both targeted amplicons are reverse transcribed into one
contiguous first strand of cDNA. This would be possible by using Oligo(dT) to
prime from the poly(A) tail of a transcript, or by using a sequence‐specific primer
that is downstream of the most 3′ amplicon, but it would not be possible by using
random hexamers for RT priming.
Once a PCR‐based method format has been established, its performance should
be further characterized and/or validated. The following sections will highlight
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64.0 °C
63.6 °C
62.9 °C
61.8 °C
60.4 °C
59.3 °C
58.5 °C
58.0 °C
Figure7.1 Optimizing the annealing temperature for thermal cycling in a duplexed
ddPCR assay. Changes in annealing temperature improve spatial resolution of droplet
clusters and reduce ddPCR “rain” or the appearance of multiple double-positive droplet
populations due to different reaction efficiencies. This is visible as better fluorescent
signal separation between double-positive (orange), single-positive (blue and green), and
double-negative (gray) droplet populations. Two double-positive droplet populations
arise at lower annealing temperatures, likely due to varying reaction efficiencies arising
from partial inhibition. Increasing the annealing temperature to 62.9 °C from the default
of 60.0 °C resulted in one defined double-positive population, which permitted more
accurate thresholding and quantification of target.
key bioanalytical parameters to assess, such as extraction efficiency, sensitivity,
specificity, standard curve performance, range of quantification, linearity, precision, accuracy, selectivity/matrix interference, and stability of extracted DNA
samples and original biological specimens. Strategic and tactical considerations
related to these assessments or how they are affected by critical method development choices will also be described.
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7.4 Back-Calculation Formulas and Extraction
Efficiency Assessments
PCR‐based methods may be performed directly with biological specimens, for
example, when testing saliva, cell lysate, or cerebrospinal fluid (CSF) at appropriate dilutions[22, 23]. Nonetheless, to ensure reliable and consistent method performance in pharmacokinetic studies, it is oftentimes advisable to implement
DNA extraction procedures that break down cells and/or tissues and remove
impurities that might otherwise interfere with PCR amplification. Hence, efficiency of the extraction procedure needs to be evaluated during method validation, in addition to PCR performance characteristics.
Prior to assessing extraction efficiency, one should select a suitable extraction
kit/method for each matrix. While many organ and tissue samples may be
extracted using the same commercial kit, specialized kits are available for more
difficult clinical specimens, such as human feces where rapid DNA degradation is
a concern. Commercial manual kits or automated extraction instruments employ
a variety of techniques, such as DNA binding to solid‐phase supports (silica‐
coated spin columns or magnetic beads). Forms of vector DNA anticipated in biological specimen, type of biological fluid or tissue, and sample throughput should
also factor into the selection of extraction methods.
One peculiarity of qPCR compared to other pharmacokinetic methods, such as
LC/MS, is that the calibration curve is not typically co‐extracted with test samples
but prepared directly in gDNA matrix or buffer. Consequently, PCR‐based quantification of GTx VGs is primarily obtained for extracted DNA samples, while the
concentration of VGs in the original (neat) biological specimens remains unknown
and may need to be extrapolated. Similarly, absolute quantification by ddPCR is
obtained only for extracted DNA samples. Hence, primary PCR data may require
back‐calculation to determine quantities of VGs per milliliter or milligram of biological specimens. Establishing back‐calculation formulas is also essential when
assessing extraction efficiency during method validation.
To derive back‐calculation formulas, volume changes and sample losses during
the extraction process are considered while working backwards from primary
quantities measured in an extracted PCR test sample, as shown in Figure7.2. In
the example shown, the copy number of VGs detected in 5
test sample derived from a semen specimen is first multiplied by 24 to obtain the
number of VGs in total elution volume of 120 μL. An identical number of VGs
would then be expected in 200 μL pre‐extraction solution, assuming all vector
DNA was adsorbed by and eluted from the DNA purification column (100%
extraction efficiency), resulting in a multiplication factor of 1. Due to sample dead
volumes on the automated extraction instrument, only 200 μL of the initially pre-
pared 300 μL pre‐extraction solution (consisting of 75 μL semen samples plus
μL of an extracted PCR
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