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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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75
μL (0.075 mL)
Semen specimen
Extraction
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300 μL
Pre-extraction
solution
200 μL
Sampled on
symphony
120 μL
Elution buffer
5 μL
qPCR sample
Possibly: additional
sample dilutions
x 1. 5
x 1
(assuming extraction is
100% efficient)
x 24 [x dilution factor]
copies/mL semen = [copies (in 5μL qPCR sample) x 24 x 1. 5]/0.075 mL semen
e.g. [75 copies(in 5 μL qPCR sample) x 24 x 1.5]/0.075 mL = 36000 copies/ mL semen.
Figure7.2 Exemplary formula to back-calculate copy numbers of vector genomes per mL semen.
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174
225 μL lysis buffer) are taken up during the process. Thus, a correction factor of
1.5 is introduced to determine the copy number of VGs in 75
μL (or 0.075 mL) of
the original semen specimen. Back‐calculated VG quantities are then normalized
to 0.075
mL to yield the concentration of VGs per mL semen. A back‐calculation
formula to report copies of VGs per milligram of a tissue specimen is derived analogously in Figure7.3.
Extraction efficiency is defined as the percentage yield of extracted vector DNA
normalized to input vector DNA in the original biological specimen. To determine
extraction efficiency, a treatment‐naïve biological specimen is spiked with vector
DNA, or intact vector capsids, at one or more concentrations, followed by extraction and PCR quantification. In practice, vector DNA is oftentimes represented by
a surrogate reference material, e.g. double‐stranded bacterial plasmid DNA or a
restriction enzyme‐derived plasmid DNA fragment that contains the GTx vector
sequence.
Whether vector DNA or intact vector capsids are used as spike material may
depend on study context. For nonclinical GTx biodistribution studies, the use of
vector DNA or surrogate vector DNA may better represent biological specimens,
since transduced cells in tissues collected several weeks after GTx administration
are expected to contain episomal double‐stranded vector DNA rather than single‐
stranded vector DNA packaged in intact AAV capsids that were present in dosing
solutions. For clinical shedding studies, vector DNA may still be packaged in capsids, and hence it is important to establish that encapsidated vector DNA can be
efficiently extracted from biological specimens. Encapsidated vector DNA may be
potentially transduction‐competent and its efficient detection is crucial to the key
objectives of shedding studies, which are to assess the potential risk of horizontal
transmission and the potential risk of release into the environment.
Using the back‐calculation formula, a nominal spike quantity is chosen based on
its anticipated target concentration in the extracted sample, assuming 100% recovery. An example of an extraction efficiency assessment is shown in Table7.2. The
target concentration in the extracted PCR test samples was chosen to be 500
reaction, which is 1log above the LLOQ of 50 copies/reaction. Consequently, each
biological specimen was spiked with a nominal quantity of 1.35E+04 copies of
VGs, based on the back‐calculation formula (nominally spiked copies=copies (in
5 μL PCR sample) × 24 × 1.125). It is important to determine extraction efficiencies
for specimen containing relatively low vector copy numbers, as extraction efficiency typically decreases with decreased vector DNA input. As a negative control,
biological specimens were also left unspiked.
All samples were extracted and tested in qPCR, and compared to the theoretically expected vector quantities representing complete spike recovery. In the
example shown in Table7.2, the spike working solution was also re‐quantified
copies/
20 mg
T
μ
Extraction
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issue specimen
235 μL
Pre-extraction
solution
200 μL
Sampled on
symphony
120 μL
Elution buffer
5 μL
qPCR sample
Possibly: additional
sample dilutions
x 1. 175
x 1
(assuming extraction is
100% efficient)
x 24 [x Dilution factor]
copies/mg tissue = [copies (in 5μL qPCR sample) x 24 x 1. 175] / 20 mg tissue
e.g. [75 copies(in 5
L qPCR sample) x 24 x 1. 175]/20 mg = 106 copies /mg tissue.
Figure7.3 Formula to back-calculate copy numbers of vector genomes per mg tissue.
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alongside spiked extraction efficiency validation samples to account for minor
variations that may occur during the preparation of the spike working solution.
Therefore, the detected copies of vector DNA in the extracted spiked specimen
were normalized to 426.72
based on re‐quantified concentration of the spike working solution at approximately 1.15E+04
Extraction efficiency may be evaluated on three different occasions (days) and
ideally by a different analyst on each occasion. The range and average of observed
extraction efficiencies may be reported to describe expected variability of the procedure. Each specimen type and each volume or mass used for extraction needs its
own assessment, since these parameters may impact vector DNA recovery.
Consequently, if specimen quantities are limiting during regulated sample analysis, this may require additional validation of the extraction method.
Extraction efficiency assessments for PCR‐based methods are informational
only since there is no regulatory method validation guidance from health authorities yet. The goal is to achieve relatively consistent recovery for each specimen,
since variable recovery within the same matrix would confound longitudinal monitoring and comparisons between individuals or dose levels. Variable recovery
would also confound assessments of biological specimen stability. As a general
rule, if extraction efficiency drops below 10%, then even minor variations in recovery of vector DNA may lead to high variability in the PCR data: For example, if
extraction efficiency varied between 3% and 9% of vector quantities present in a
biological specimen, then quantities recovered in extracted PCR test samples
would vary by a factor of up to 3 times (= 9% divided by 3%) for the same input
amount. This would significantly increase data variability, without even considering additional variability introduced by PCR. In contrast, if extraction efficiency
varied between 13% and 19%, or between 23% and 29%, then vector quantities
recovered in PCR test samples would vary by less than 1.5 times, thus facilitating a
more precise assessment. In addition, extraction efficiencies of less than 10% would
necessitate a correction factor when back‐calculating sample results, since the
assumption of complete vector DNA recovery would under‐estimate true vector
quantities in biological specimens by at least 10 times.
copies/5 μL.
copies instead of the nominal 500 copies/reaction,
7.5 Sensitivity Requirements
Regulatory guidance on sensitivity of PCR‐based methods exists only for nonclinical GTx biodistribution studies: “The assay should have a demonstrated limit of
quantification of ≤50 copies/μg gDNA, so that your assay can detect this limit with
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95% confidence”[4]. While the guidance refers to the lower limit of quantification
(LLOQ) in the first part of the sentence, the criterion referenced in the second part
specifies a reliable limit of detection (LOD). This duality is notable because limits
of quantification are usually based on acceptable precision and accuracy, and not
detectability. Hence, the guidance is usually interpreted as providing that PCR‐
based vector DNA detection methods must have a demonstrated LOD at which
95% of samples remain reliably detectable and that is equal to or lower than the
LLOQ, whereby the LLOQ can be as high as 50
copies per microgram gDNA.
Precision or accuracy need not be maintained at the LOD if it is lower than 50
microgram.
In the absence of guidance for the sensitivity of clinical shedding assays, one
may apply a similar criterion for method validation purposes, considering that
PCR procedures in nonclinical and clinical assays are oftentimes identical even if
DNA extraction procedures differ. One characteristic of clinical shedding specimens can be a lower gDNA content, for example, in plasma, urine, saliva, and
feces. Hence, it may suffice to assess LOD in buffer rather than matrix containing
1microgram of gDNA. LOD would then be reported as vector copies per reaction,
or vector copies per tested volume of extracted sample, instead of vector copies
per microgram gDNA.
To demonstrate reliable detection with 95% confidence for qPCR, a twofold dilution series of vector DNA or intact vector capsids is prepared in buffer or matrix
containing 1 μg gDNA, oftentimes starting at LLOQ of 50 copies per reaction as the
highest concentration. This dilution series is tested across 10 replicates for each
concentration level. The results are analyzed by logistic regression, whereby each
replicate is given a binary value of either “not detected” or “detected,” depending
on whether there was no increase in fluorescence after 40 PCR cycles or whether
there was an increase resulting in a measurable Ct or Cp value.
The percentage of detected replicates at each concentration forms the empirical basis for logistic regression that interpolates the concentration of vector DNA
at which 95% of the replicates would theoretically be detected. It may be helpful
to perform at least two sensitivity assessments over a period of 2days and either
pool the data or average the LOD values derived by separate logistic regression.
Areliable LOD value with 95% detectability derived by logistic regression is typically reported together with a 90% confidence interval, whereby the upper confidence limit should ideally remain at or below 50 copies per reaction, i.e. below
the desired LLOQ.
The experiments to establish a reliable LOD with 95% detectability for ddPCR
can be similar to those for qPCR but will differ in how replicate results are designated as “not detected” or “detected.” In qPCR, a cycle count of 40 is often applied
as the threshold by which a target template is considered detected or not, based on
collective experience with typical qPCR performance. Since ddPCR does not rely
copies/
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on cycle count for quantifying vector copies, a threshold for “not detected” vs.
“detected” may need to be established empirically prior to applying logistic regression. While there is no consensus on how to establish this threshold for ddPCR
assays, we oftentimes first characterize several replicates of a negative template
control (NTC) that contains the same amount of gDNA per reaction as would be
loaded with test samples. We then determine the maximum number of false‐
positive droplets that may be observed occasionally in the absence of any specific
DNA template, using an appropriate statistical distribution limit (e.g. 95th percentile). This approach focuses on the raw signal generated by the instrument (i.e.
positive droplets) to characterize background noise, rather than using low fractional copy numbers generated after application of Poisson statistics to positive
droplet counts (typically less than 5 per reaction). Once the false‐positive droplet
limit has been determined for NTC, it is applied as the threshold for designating
sample replicate results as “not detected” or “detected” in LOD experiments to
enable logistic regression.
Without clear regulatory guidance or bioanalytical consensus on LOD determinations, other approaches may be taken as long as they are based on scientific
rationale that supports context of use.
7.6 Specificity Requirements
Assessment of specificity determines the potential for detecting structurally similar
analytes of exogenous or endogenous origin that might lead to false‐positive results.
In addition, it includes a verification that positive samples are accurately quantified
despite the presence of similar analytes. Hence, PCR primers and probes need to be
specific for the target sequence and not generate off‐target amplicons. Specificity
can be evaluated in silico using NCBI BLAST to query oligo sequences against a
species‐specific nucleotide database. Primer and probes with the lowest percentage
of matched sequence coverage and identity are then further evaluated empirically
for nonspecific amplicons and target amplification efficiency in gDNA matrix.
This evaluation is performed in two ways, with and without spikes of a positive
control template into up to 1
not possible for low DNA‐content shedding matrices, then input may be guided by
conservative expectations for clinical samples. Firstly, absence of amplification in
unspiked gDNA demonstrates that primers and probe do not generate nonspecific
amplicons. Secondly, spikes of positive control DNA are tested at various concentrations in the presence or absence of gDNA, ideally extracted from a biological
specimen that has a high DNA content. A decrease in PCR reaction efficiency for
a spiked sample containing gDNA relative to an identically spiked sample without
gDNA suggests that primers or probe anneal to a nonspecific gDNA sequence.
μg of gDNA reaction input. If 1 μg reaction input is
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The sequestration of oligo reagents consequently decreases PCR amplification
efficiency of specific target template.
Assessing specificity early during PCR method development facilitates rapid
evaluation of other parameters. Confirming specificity by routinely testing gDNA‐
containing NTC samples can support previously established sensitivity thresholds
and monitor for potential contamination.
7.7 Standard Curve Performance, Colinearity,
Precision, and Accuracy
Given the fundamental differences in quantification between qPCR and ddPCR,
method development and validation diverge most strikingly in the parameters relevant for quantifying the target sequence. The most obvious difference is that
qPCR requires a standard/calibration curve against which unknown samples are
interpolated, whereas ddPCR does not require a standard curve but quantifies
absolutely.
Consequently, qPCR method development includes optimization of standard
curve performance, followed by validation. Standard curves for qPCR typically
meet acceptance criteria for reaction efficiency between 90% and 110% (corresponding to a slope of −3.60 and −3.10)
back‐calculated standard concentrations typically have Ct CV
and RE between −50% and +100% (accuracy). More narrow precision and wider
accuracy criteria compared to ligand binding assays can be justified by the doubling nature of PCR reactions. A change in Ct value of 1indicates a doubling in
the amount of target DNA, therefore the standard deviation must be small compared to the mean Ct for the amount of amplification product to not differ significantly between replicate reactions. Therefore, if CVs are evaluated for Ct values
rather than back‐calculated VG concentrations, they are oftentimes set as ≤3.00%.
In contrast, back‐calculated VG concentrations should be used to evaluate accuracy of quantification relative to nominal input. Due to the exponential nature of
PCR, sample quantifications can easily vary within 1 Ct cycle, translating into target DNA levels within one‐half of (−50%) or double (+100%) the nominal concentration. Therefore, a target RE range of −50% to +100% for VG quantities may be
adequate. A similar acceptance criterion is used to specify accuracy required for
antibody titer measurements, which is ±1log
implemented. This criterion corresponds to the last detectable sample dilution
shifting one dilution step up or down. Using the ±1 Ct cycle criterion for PCR
1
and a coefficient R2 ≥ 0.980. Furthermore,
≤ 3.00% (precision)
(titer) if a 1:2 dilution scheme was
2
1 PCR efficiency is calculated as: E=10
of standard curve plotted with Ct values on y‐axis and log(quantity) on x‐axis.
(−1/S)
−1, where E: theoretical PCR efficiency, S: slope
7.8 Selectivity Assessment and Matrix Interference 181
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methods ensures that raw Ct values greater than 10 (i.e. those within the core
measurement range) do not differ more than 10% from expectations. Moreover, the
decline of vector concentrations in clinical shedding studies is typically graphed
using a log
scale; hence, the use of a two‐fold RE range for measured quantities
10
appears to be reasonably accurate.
While ddPCR does not require a standard curve, linearity of assay response in
addition to precision and accuracy still needs to be verified using spiked quality controls. Acceptance criteria from bioanalytical validation of ligand binding assays can
be adopted in the absence of PCR‐specific regulatory guidance. Thus, quality controls at the limits of quantification may be allowed to have up to 25% CV and up to
±25% RE, while quality controls within these limits are allowed up to 20% CV and up
to ±20% RE, if following BMV guidance. Acceptance criteria determined through
method development and validation should be fit-for-purpose and appropriate for
the assay’s context of use. Linearity is evaluated in the same fashion as for qPCR, but
2
performed with a dilution series of spiked controls, and typically only the R
≥ 0.980
acceptance criteria is applied. Due to the greater independence of ddPCR quantification from PCR reaction efficiency, assessing efficiency is not a critical component for
validation. While ddPCR reaction efficiency is maximized during method development, as long as positive droplets can be adequately distinguished from negative
droplets with low “rain” (droplets with varying reaction efficiencies due to inhibition), quantification is not significantly impacted. As such, assessment of precision
and accuracy is the most critical parameter in ddPCR method validations.
Colinearity is another validation parameter for PCR assays on either platform.
Colinearity assesses if a surrogate positive control material (e.g. synthetic DNA or
linearized plasmid fragment) amplifies comparably to the GTx vector. In the simplest experiment, positive control template and GTx vector are identically diluted
2
serially and the slopes and coefficient R
≥ 0.980 are compared. If both dilution
series meet acceptance criteria and the curves are reasonably close and parallel,
the surrogate positive control material is suitable as a calibrator or quality control.
Using more easily procurable surrogate positive control material could help conserve GTx products when early manufacturing lots may be limited in quantity.
7.8 Selectivity Assessment and Matrix Interference
Selectivity of ligand‐binding assays is assessed by evaluating the degree and nature
of interference from components in sample matrix and its effect on analyte quantification. Taking plasma as an example, one would assess the impact of normal,
hemolyzed, and lipemic plasma, in addition to possible disease‐state or study‐driven
alterations in plasma composition. Since the analyte is measured directly in
matrixed sample, or in some minimally required dilution (MRD) thereof, all possible types of interfering matrix components could be present in study samples. By
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contrast, when testing DNA extracted from biological specimens, most potentially
interfering matrix components have been stripped away from the analyte.
Nonetheless, individual post‐extraction samples may contain a unique mix of co‐
extracted matrix contaminants that could still interfere with downstream PCR. There
may also be carry‐over of residual extraction reagents. Therefore, selectivity is still
an important component of method characterization for bioanalytical PCR assays.
Selectivity can be evaluated during method development to assess all matrices
but may be limited to target tissue or a key biofluid during validation. Off‐target
tissues and other biofluids may be included in validation if they are critical to
interpret study data. Since composition of biological specimens is variable, DNA
extractions may also vary in their final composition of potentially interfering factors. Selectivity can be assessed, for example, using 10individual donors from
which gDNA is extracted and tested either unspiked or spiked with vector DNA
near the LLOQ. At least 80% of the unspiked samples should yield a result below
the LOD, while at least 80% of the spiked samples should yield a positive result
within established precision and accuracy criteria.
7.9 Sample Stability Considerations
Depending on method procedures, stability may be evaluated for both vector DNA
in purified PCR test samples following extraction (if performed) and vector DNA
in non‐extracted biological specimens (tissues, organs, biofluids). The duration
and design of stability studies depend on study logistics, sample collection and
testing schedule, number of anticipated freeze/thaws, and storage conditions.
Collection of specimens in clinical trials may precede sample testing by only a few
weeks or months. In contrast, specimens in nonclinical GTx biodistribution studies may be held for years until analysis is triggered.
Biological specimens for PCR‐based testing of vector DNA are typically stored
frozen at −80
temperatures for varying amounts of time. For example, at‐home collection of clinical shedding specimen may require temporary storage at −20 or 4 °C. Extracted
DNA samples may also be stored differently than biological specimens during short‐
term storage at test sites. Retests of extracted DNA samples or retests requiring
re‐extraction of DNA from biological specimens could also trigger assessment of
freeze–thaw stability for all sample types involved.
To assess stability prior to a study, biological specimens from treatment‐naïve
individuals could be spiked with surrogate vector DNA or intact GTx capsids and
stored for repeated PCR testing over a defined time period. Spiked vector quantities could be selected analogously to vector quantities used in extraction efficiency
assessments, targeting at least one level within 1log of the LLOQ, for example,
°C, but study‐specific circumstances may necessitate storage at higher
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