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10 Substrate and Distal Pharmacodynamic Biomarker Measurements forGene Therapy
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when used for quantitation. For the quantitation of target gene (mRNA) expression, the RNA is first reverse transcribed to its complementary DNA using reverse
transcriptase and then quantitated by qPCR (RT‐qPCR or qRT‐PCR)[31]. DNA
microarray technology was popular a decade ago due to the surge of omics‐level
studies. This found applications in studying expression levels throughout the
genome for transcriptome profiling and biomarker discovery[32, 33]. After the
human genome project, the importance and the business of sequencing has been
realized, resulting in the development of Next Generation Sequencing (NGS)
technologies of which RNAseq is a part. Advances in NGS technologies and
RNAseq, shifted the focus preferring sequencing‐based methods over microarrays[34, 35]. RNA sequencing using NGS can detect both known and novel transcripts. The NanoString nCounter gene expression system captures and counts
individual mRNA transcripts by direct measurement without amplification[36]
10.2.4.1 RT-qPCR for Relative Gene Expression Analysis
RT‐qPCR was originally developed from real‐time PCR to amplify distinct nucleic
acid sequences for the detection and relative quantification of mRNA levels in
human monocyte‐derived macrophages [37]. In some publications, the term
RT‐PCR was used for real‐time PCR. In this chapter, RT is used for reverse transcription and qPCR for quantitative (Real‐Time) PCR, i.e. RT‐qPCR. If the technique is used only for detection, the term reverse transcription Real‐Time PCR
(rRT‐PCR) was also used by some groups, such as CDC[38]. There are tremendous
advancements in the design of primers and probes (Minor Grove Binding), master
mixes with mutated enzymes, such as hot‐start DNA polymerase, reverse transcriptase, and efficient real‐time PCR instruments, that can detect multiple fluorophores (multiplex RT‐qPCR). This made the qPCR or RT‐qPCR a routine method
of choice to quantitate PD biomarker analysis in several gene and cell therapy studies. The availability of kits for single‐step RT‐qPCR reduced the time and improved
efficiency to a greater extent[39]. In addition, there are several tools available
by the vendors, such as IDT‐DNA or ThermoFisher Scientific, for the design of
primers and probes for real‐time PCR (https://www.thermofisher.com/order/
catalog/product/4316034?SID=srch- hj- 4316034 and https://www.idtdna
.com/pages/products/qpcr- and- pcr/gene- expression/primetime- qpcr- probes).
Quantitation of mRNA in a sample by RT‐qPCR can be performed either as absolute quantification or relative quantification. As in the case of any analyte quantitation, for absolute quantification of mRNA also, serially diluted standards that
produce a linear relationship between Ct values and initial amounts of total RNA
or cDNA is used allowing the determination of the concentration of unknowns
based on their Ct values assuming that all standards and samples have approximately equal amplification efficiencies. In the case of single‐step RT‐qPCR reactions, the standards, and any QC must be RNA only[40]. In addition, the range of
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the standard curve should cover all sample concentrations and in the range of accurately quantifiable and detectable levels specific for both the instrument and assay.
However, the digital PCR technology allows us to perform absolute quantitation
without the need of a standard curve as it provides a direct number rather than a Ct
value. The main disadvantage of these systems is extended sample preparation time
5
and the upper limit of quantitation (ULOQ) is only 10
thermofisher.com/order/catalog/product/4316034?SID=srch- hj-
www.
copies/reaction (https://
4316034)[41].
Relative Quantitation: Relative quantitation of gene expression analysis, which
will be discussed more in this chapter also uses regular real‐time PCR instrumentation and the same general reagents for amplification. In relative quantitation,
variations in target gene expression are measured based on the levels of a reference gene used as either internal or external sample[42]. The results are expressed
as a target/reference ratio, for accurate expression. In several biodistribution studies, the data are expressed as the mRNA copy number per unit of total RNA in the
sample [29, 43]. Several mathematical models, for example, geNorm, geNorm
Kits, REST‐2009‐standalone application software, Bestkeeper applet, NormFinder,
and “R,” Genevestigator are available to calculate the mean normalized gene
expression from relative quantitation assays. As the levels are expressed in comparison to a selected reference gene, standard curve preparation and calculation
of copy number are not required. Relative quantitation is also used in microarray
technology to study the variations in transcriptome levels[33]. The main factor
and issue in the relative quantitation of target gene is the selection of the reference gene(s) and normalization of the data. In clinical trials, relative quantification by RT‐qPCR has been successfully used when there is availability of untreated
or normal groups [44, 45]. An ideal reference gene is one whose expression is,
(1)constitutive, (2) unaffected by the drug target and experimental conditions,
(3)unaltered by the developmental stage, and (4) equal in different tissue or cell
types[43]. Amplification efficiency is another important factor that affects relative quantitation. A reference gene is completely different from the target gene.
So, unless by chance, it is not possible to have the same amplification efficiency as
target gene mRNA. This mandates the requirement of a correction factor. A simple equation to calculate the amplification efficiency from the data derived using
–1/slope
a standard curve, e=
10
[46], where: e= theoretical efficiency, Slope=the
slope of the standard curve, plotted with the y‐axis as Ct and the x‐axis as
log(quantity). Alternatively, several data analysis models have been developed
that enable the calculation of PCR amplification efficiencies from individual
amplification plots, such as data analysis for real‐time PCR (DART‐PCR), LinReg,
and the real‐time PCR Miner algorithms. Some of the reference genes found to be
useful in different studies are listed in Table10.4.
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10.2 Technologies to Quantify Substrate and Distal PD Biomarker 259
Gene Expression Ratio (EGOI)/(EHKG)
Ct GOI Ct HKG
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Methods popularly used in different studies for relative quantification of tran-
script mRNA are,
a) Standard curve method –copy numbers quantitated using a standard curve
and relative quantitation calculated in comparison to a selected calibrator
sample[42].
b) The comparative Ct method–a mathematical model that calculates changes to
the relative fold difference between an experimental and calibrator sample,
with no standard curve required[43].
c) Pfaffl model– calculates relative gene expression data while accounting for
differences in primer efficiencies using the equation:
where E= Efficiency, GOI=Gene of Interest, HKG=House Keeping Gene or
Reference gene. The term housekeeping gene has been discontinued[44].
The d) Q‐gene method[45], e) Liu and Saint method[46], and f) Amplification
plot method[47] are other methods used to calculate amplification efficiencies
and normalize the data. However, it was found from several studies that the reference genes cannot be considered universal even in broadly similar conditions.
It is necessary to confirm that the selected reference gene is expressed to the
same level in the selected tissues under experimental conditions[47]. However,
the new tools available from different vendors or institutes to design the primer/
probe sets allow to have them good efficiency. For example, a) IDT DNA Inc.
(https://www.idtdna.com/pages/tools/), b) ThermoFisher Scientific Inc (https://
www.thermofisher.com/us/en/home/life-
science/oligonucleotides- primersprobes- genes/custom- dna- oligos/oligo- design- tools.html), c) National Center
for Bioinformatics, NIH (https://www.ncbi.nlm.nih.gov/tools/primer-
blast/).
Relative quantitation is easier as it does not require standard curve preparation,
but it is applicable only when variations in levels of the target are under study for
a PD biomarker. To reduce the time and effort for method development, testing
the efficiency of three or more sets of primer/probes has been suggested for biodistribution studies[48]. Specific regulatory guidance for qPCR is warranted for
uniformity in the qPCR‐based clinical studies.
10.2.4.2 RNA-seq
Rapid advances in sequencing technologies and reagents simplified the sequencing methods. No wonder, over 13million SARS‐CoV2whole genome sequences
are uploaded to GISAID by September 2022 (https://gisaid.org). There are several
PD studies where in RNA‐seq has been used successfully to monitor the levels of
known PD biomarkers and also to identify any novel genes affected. In a US FDA
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coordinated study RNA‐seq showed agreement with RT‐qPCR at relative gene
expression measurement levels[41]. The analysis of RNA‐seq data are complex.
Unless there is a good pipeline developed for data analysis, quality check, and
quantitation, the results could be erroneous. Designing an appropriate pipeline
with reference sequences is essential for quality sequence data. With an appropriate pipeline, the RNA‐seq was used successfully in several AAV‐based gene therapy studies to find out the changes in tropism[49]. In addition, the minimum
number of copies of the target RNA required to get high‐quality sequence also
causes issues in RNAseq. A general flowchart for RNA‐seq analysis[50] was provided for successful processing of the samples. However, RNA‐seq possesses the
potential to check multiple targets in a single run and is very useful in discovery
or genomic studies.
10.2.4.3 Nanostring Technology
This technique has been developed by integrating the ligand‐protein interaction,
primer binding, and fluorescence. It consists of two oligonucleotide probes of
35–50 bases in length. The two probes are complementary to the target mRNA
separated by a gap of some bases. One probe is biotinylated and is called, “Capture
Probe”. The other one is called, “Reporter Probe” which is linked to a unique fluorescent barcode. In brief, both probes are hybridized to the target mRNA. The
probe‐target complex is purified using a streptavidin‐coated surface by the binding of the biotinylated capture probe, excess probe is removed by washing. The
complexes are aligned, and the reporter probes are scanned and counted using a
barcode imaging device, Figure 10.1. High level of multiplexing is possible by
using different reporter barcodes. The number of counts is equal to the number of
target RNA molecules. Normalization of the data with internal controls and reference genes is performed to produce highly precise relative counts. Nanostring
technology is considered best suitable for relative quantification rather than absolute quantification[31].
In a single reaction, relative quantitation of hundreds of target genes can be
performed. Several comparative studies of RNA‐seq and Nanostring technology in
relative quantitation of gene expression showed that both of them are equally
effective and complementary[51]. Nanostring‐based detection and quantitation
are also found to be very sensitive in the rAAV biodistribution studies with a titer
10
of[52] 1–2 × 10
genomes, ~10–40 times less virus than what is typically used for
intravenous or peripheral administration for targeting the spinal cord.
If the task is only the detection of biomarkers, all three techniques–RT‐qPCR,
RNA‐Seq, and Nanostring work well with limitations specific to each. For absolute quantification, RT‐qPCR is still the method of choice either by regular real‐
time PCR platform or a digital PCR platform. Parallel studies performed with
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Total RNA
Single m-RNA molecule
Biotinylated capture probe
Avidin-Biotin affinity purication
of probe bound RNAs
Counting of immobilized transcripts with different reporter
probes
Color imaging of immobilized reporter probes (Ref. 8)
Reporter probe, barcode
Probe set
hybridization to
specic
complementary RNA
Figure10.1 Principle of NanoString nCounter for RNA relative quantification.
RT‐qPCR, RNA‐seq, and Nanostring showed that they all qualify for relative
quantitation[50, 53, 54]. A comparison of the three techniques for detection and
relative quantitation has been made in Table10.5.
10.2.4.4 Regulatory Considerations for RNA Quantitation in GLP Studies
As mentioned earlier, due to the low cost of the reagents, sensitivity, precision
qRT‐PCR is still the method of choice for measuring transcript levels. Efforts to
have a consensus on experimental design and data analysis of real‐time PCR‐based
studies were made for regular publications and other research work for both absolute and relative quantitation[42, 43, 46, 47, 54–56]. For cell and gene therapy
studies, FDA, USA (FDA) and EMA have released bioanalytical guidance documents for clinical and nonclinical studies[57, 58]. FDA guidance recommends the
10 Substrate and Distal Pharmacodynamic Biomarker Measurements forGene Therapy
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262
Table10.5 Comparison between RT-qPCR– RNA-seq and Nanostring.
Parameters RT-qPCR RNA-Seq Nanostring
Instrument
operation/Sample
preparation
Sample quantity Minimal (Less
Prior Knowledge of
Target Sequence
Sample Throughput Very High Medium High
Number of Targets
per run
(Multiplexing)
Novel transcript/
Sequence Discovery
Power
Pharmacokinetics
Application‐ Target
gene Transcript
Pharmacodynamic
Studies
Absolute/Relative
Quantification
QC Review and Data
Analysis
Simple and
established in
many labs,
familiar
platform.
than
femtograms, <5
copies of target)
Essential Not essential Essential
One to five One to hundreds One to several
Nil High Nil
Very Useful Not Likely, as the target
Good for one to
a limited
number of
marker gene
expression
studies.
Works for both Works for both,
Simple Needs pipeline
Needs more skills than
real‐time PCR and
Nanostring, increasingly
used where
transcriptome/
multitarget analysis is
required.
Minimum 25 ng Total
RNA
number is 1 or a few
Very useful for larger
number to
transcriptome level
studies in variation in
gene expression.
sensitivity lesser than
RT‐qPCR.
development and
bioinformatics
knowledge.
Simple, platform is
still not as popular
to replace
RT‐qPCR.
Minimum 25 ng
Total RNA
Useful
Good for one to a
large number of
gene expression
studies possible.
Good for relative
quantification.
Simple
LLOQ of a qPCR assay to be at least 50 copies per μg of host gDNA with consideration given to the sample size used for analysis relative to the size of the tissue in its
entirety [59, 60]. However, many of them are easily applicable to techniques such
as ligand‐binding assays (LBA) but are difficult to apply for qPCR‐based assays due
to their distinctive characteristics[55]. The EMA has released guidelines for PCR
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assays but FDA has not yet required validation of qPCR/qRT‐PCR assays[61].
However, pharma industries and contract research organizations are trying to harmonize divergent global practices for method development, qualification/validation, and sample analysis through publications and conferences [62]. Recently,
different serotypes of AAV have emerged as preferred viral vectors for gene therapy
applications due to several advantages such as broad tropism, nonpathogenic
nature, low immunogenicity, and prolonged period of transgene expression[63].
Quantitative biodistribution of the viral vector and expression of the target gene
(PK) is essential to draw conclusions on the effect and relationship between the
dosage vs. PD marker levels[49, 64]. Extensive discussion and recommendations
for “best practice” for qPCR/qRT‐PCR assay design during method development,
validation for gene therapy sample analysis with a special focus on AAV‐based
clinical and nonclinical studies were made recently[48, 65]. These practices are
followed for biodistribution analysis in AAV‐based gene therapy studies for vector
transgene or its mRNA quantitation by qPCR or RT‐qPCR in both nonclinical tissue distribution and shedding in clinical samples, respectively. Several white
papers are published by various institutes and scientific forums on the regulatory
guidelines. One of them discussed the upcoming digital PCR in comparison to
regular real‐time PCR. The steps/workflow of the guidelines in practice and recommendations are provided in Figure10.2.
Recently, the use of digital PCR with (ddPCR) or without droplet is also being
used as it does not require a standard curve for absolute quantification. In view of
the changing instrumentation and reagents, there is a need to regularly update
the specific steps per the requirement of the method. Also, quantitative RNA‐
seq‐based methods are being put into use in AAV‐based transcriptome
Due to the complexity of the sequencing methodology, quality control, and analysis pipelines, it is very difficult to form regulatory guidelines for RNA‐seq‐based
quantitation of the target gene, its product or PD biomarkers [66]. Signal
amplification‐based branched‐DNA assay technology is also considered as a viable alternative to RT‐qPCR but recent advances in the probe design and technology made real‐time PCR a more sensitive technique[67]. In addition, unless there
is a need for transcriptome analysis or large‐scale biomarker level changes, RT‐
qPCR by either regular or digital platforms serves the needs for current gene therapy projects, including the preferred AAV‐based platforms. Though the suggestions
and guidelines from forums and labs are available, there remains a need for FDA/
health authorities to refine the best practices and provide unambiguous guidelines for qPCR in regulated bioanalysis.
levels[49].
10.2.5 Single-cell Analysis
Another application of histology to support gene therapy programs is target
engagement assessment in nonclinical studies to understand the mechanism of
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