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European Biopharmaceutical Review
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In the drug discovery process, eradicating unsuitable compounds at an
early stage translates to considerable cost savings. Determining the
ADME-Tox (absorption, distribution, metabolism,excretion and toxicity)
profi le of each compound is crucial in the identification of candidates
to invest in and pursue through clinical trials
Drug development is an expensive business, especially when
considering the attrition rates of new molecular entities (NMEs), as
over the last decade it is estimated that a NME entering Phase 1
clinical trials only has an eight per cent chance of reaching the market
(1). Furthermore, the cost of a failed compound rises exponentially as
it progresses through the pipeline. Therefore, it is easy to imagine how
early stage elimination of unsuitable compounds incurs substantial cost
savings.
Part of the pre-clinical study phase looks at the characteristics of
absorption, distribution, metabolism, excretion and toxicity, forming
the ADME-Tox profile specific to each individual compound. This ADME-Tox
profi le is vital in determining which compounds to invest and pursue
throughout clinical trials, and ADME-Tox effects in fact account for the
majority of compound failures.
The accurate evaluation of ADME-Tox profi les in cellular and animal
models during pre-clinical drug discovery remains a challenge.
Approximately 40 per cent of drug-induced liver injury cases are not
detected in pre-clinical studies using conventional biochemical markers
such as aminotransferase and total bilirubin. Alternative approaches are
required to provide more accurate and representative ADME-Tox data in
order to reduce late-stage attrition, and this is where gene expression
analysis comes into play. Signifi cantly, the Food and Drug
Administration (FDA) has recently drafted updated guidelines to back the
use of gene expression analysis in ADME-Tox studies, by suggesting that
CYP mRNA analysis could be more informative than measuring CYP protein
activity (2).
The Importance of Gene Expression Analysis
Gene expression is the most fundamental level at which the genotype
of an organism gives rise to the phenotype. A good way to consider gene
expression is as a mediator that interprets the information stored in a
cell’s DNA to create a phenotypic output via gene transcription and
messenger Ribonucleic Acid (mRNA) processing. The final influence on
phenotype is predominantly exerted through the synthesis of proteins,
some of which are structural and control the shape and characteristics
of the organism, while others may be the enzymes responsible for
catalysing particular metabolic pathways.
However, recent results from the ENCODE project – a 10-year effort
by hundreds of scientists to characterise the human genome in depth –
have indicated that a much larger proportion of our DNA is likely to be
expressed and functional than previously estimated (3). This has put the
focus back on Ribonucleic Acid (RNA) as a key component of organism
growth and development, meaning that the measurement of gene expression
continues to be a critical tool employed across many disciplines,
including drug development programmes. Indeed, we now have the
technological ability to quantify the level at which a particular gene
is expressed within a cell, tissue or organism, providing access to a
wealth of information. Of course, this shift has been a primary driving
force behind the updated FDA guidelines, and gene expression analysis is
becoming an integral part of ADME-Tox studies, within both
translational research and routine compound screening.
Which Approach for Gene Expression Analysis?
Gene expression analysis can be roughly split into two distinct but
overlapping approaches, depending on the needs of the study in question.
When breaking new ground using models or disease subsets where little
is known, gene expression analysis for ADME-Tox begins with the initial
biomarker discovery phase. This relies upon genome-wide approaches to
identify these key genes of interest. However, the complexity of
analysing the complete genome does not lend this type of analysis to
in-depth validation and high sample throughput. Therefore, once a set of
potential biomarkers has been identifi ed, targeted approaches for gene
expression analysis are employed for in-depth validation across
multiple samples.
The Global Landscape of Gene Expression
Microarray technology has yielded much important information about
the transcriptome and as such has been invaluable in providing the link
between information encoded in the genome and ADME-Tox responses. The
great benefit of this approach is that it allows a researcher to
investigate the expression of every gene in the genome in a single
experiment. Unfortunately, it can be time-consuming and potentially
expensive to explore more than a handful of samples per study.
Some of the technical variation seen in the early days of
microarrays has been largely eliminated, and data quality much enhanced.
This improvement has been aided by the efforts of the Microarray
Quality Control (MAQC) consortium, which has set quality control
standards to ensure the effi cacy of microarray experiments (4). Now any
systematic variation between research groups and laboratories can be
dealt with through experimental and computational methods, making
comparison much easier and more insightful (5,6). As a well-established
technology, microarrays provide an excellent method for the study of
global gene expression.
A relatively new and rapidly developing technology, RNA sequencing
(also termed Whole Transcriptome Shotgun Sequencing or RNA-Seq) uses
high throughput deep sequencing technologies in order to determine the
expression level and exact nucleotide sequence of each transcript
expressed in a sample (7). This is achieved by accurately quantifying
the amount of starting material, and then comparing the frequency of
each sequence read against the number of total reads produced by the
sequencing run.
Other normalisation steps are also required, for example to account
for differences in gene lengths or sequence read lengths, before the
expression level of each gene can be estimated. For this reason, the
quantitative analysis of RNA-Seq data is undergoing continual
improvement and in its current form may not be as robust or reliable as
other methods, especially those that measure transcript numbers more
directly (8). There are also issues related to processing time and cost
to consider as well as the analysis challenges associated with the
accurate assembly and interpretation of next generation sequence data.
However, a great benefi t of RNA-Seq datasets is that they can be used
to identify the existence of unexpected nucleotide variations, such as
those introduced by mutations in the DNA template, alternative
transcript splicing or RNA editing. No other technology currently offers
this level of nucleotide resolution or the ability to detect de novo
RNA variations.
Targeted Gene Expression Analysis with Advanced PCR-based Technologies
At the other end of the spectrum, targeted approaches are employed
for validating genome-wide studies, providing more accurate quantitative
data than current microarray and RNASeq technologies (9). RT-qPCR
remains the method of choice for this stage of routine ADME-Tox
screening, and is relatively easy to design and set up. However, as the
sample and/or gene target number increases, RT-qPCR tends to become more
expensive and equally time-intensive.
Novel Technologies in Gene Expression Analysis
The last decade has witnessed the emergence of several novel
technologies that aim to combine increased sample throughput with
efficient gene multiplexing, while reducing assay time and cost. One
such method, Transcript Analysis with the aid of Affinity Capture
(TRAC), developed by Plexpress (Helsinki, Finland), utilises labelled
oligonucleotide probes complementary to the RNA of known target genes
(10).
TRAC enables the rapid and costeffective detection of target gene
transcripts from a large number of samples in a single assay. Unlike
multiplex RT-qPCR, TRAC provides reliable and accurate readouts of up to
30 transcripts per sample, including in-well normalisation for data
reliability. With labelled probes directly hybridising to target
transcripts, there is no need for RNA extraction, cDNA synthesis or
amplification. This minimises any technical bias caused due to variable
reaction efficiencies, as is the case for RT-qPCR sample preparation and
analysis (TRAC intra- and inter-assay CVs are typically less than 10
per cent). As far as instrument requirements are concerned, TRAC is easy
to set up in any laboratory, without the need for specialist equipment
other than a magnetic bead processor and capillary electrophoresis
device. Both are common in molecular biology laboratories and can often
be automated, thus enabling high-throughput and walk-away sample
processing. This makes TRAC simple to set up, while the technique is
also more cost-effective than RT-qPCR thanks to lower reagent usage and
up to 10 times faster due to short hands-on time with few manual steps
(total time for 96 samples is three to four hours using TRAC, with only
one to two hours hands-on time). Using TRAC, hundreds or thousands of
samples can be processed in a day using standard 96 well plates, with
one sample per well. This allows researchers to generate a dynamic
perspective of gene expression by studying many genes across numerous
samples, for unparalleled insight into ADME-Tox responses.
TRAC from Gene to Genome
TRAC is a versatile tool for both genomewide studies and the
targeted validation phases of ADME-Tox studies. The former application
is achieved by the integration of TRAC with the microarray, enabling
full genome-wide screens of mRNA and microRNA profiles. With this
approach, ADME-Tox biomarker sets have been identified and validated
across a range of samples, leading to the creation of a custom panel
suiting the specific experimental requirements. Alternatively,
pre-validated panels of genes for ADMETox profiles in both rat and human
can be incorporated with the TRAC platform for routine ADME-Tox
screening studies. As TRAC can multiplex up to 30 genes per well, these
probes can be combined into highly informative panels for measuring
genes involved in hepatotoxicity, drug transport and drug metabolism.
Conclusion
The quantitative analysis of gene expression is becoming an
increasingly integral part of modern biological investigations
surrounding drug development. This is especially true as the FDA
guidance has been updated to advocate the use of gene expression
analysis for ADME-Tox studies, while final European Medicines Agency
guidelines state that mRNA analysis should be used in drug interaction
studies in order to increase assay sensitivity. Advances in molecular
biology and bioinstrumentation have been required in order to meet the
increasing demands of the drug development industry, and these have led
to the development of new techniques offering a range of sensitivities,
throughputs and quantitative capabilities.
TRAC technology facilitates multiplex assays of up to 30 genes per
sample. With samples processed using standard 96 well plates, the
technology allows researchers to examine panels of target genes in many
samples quickly, easily and cost-effectively. Such studies provide a
more dynamic perspective of gene expression across many biological
states. Providing high sample throughput without comprising on target
breadth, TRAC is perfect for studying gene expression in a wide range of
systems. Furthermore, when TRAC is combined with microarrays as part of
an integrated workflow, it provides a full gene expression solution,
from ADME-Tox biomarker discovery all the way through to in-depth
analysis.
References
1. Food and Drug Administration, Innovation or Stagnation: Challenge
and opportunity on the critical path to new medical products, 2004
2. Guidance for industry: drug interaction studies – study design,
data analysis, implications for dosing, and labelling recommendations.
Visit: www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatory
InformationGuidances/ucm292362.pdf
3. The ENCODE Project Consortium, An integrated encyclopedia of DNA elements in the human genome, Nature 489: pp57-74, 2012
4. Shi L et al, The MicroArray Quality Control (MAQC) project shows
interand intraplatform reproducibility of gene expression measurements,
Nat Biotechnol 24: pp1,151-1,161, 2006
5. Fan X et al, Consistency of predictive signature genes and
classifi ers generated using different microarray platforms,
Pharmacogenomics J 10: pp247-257, 2010
6. Luo J et al, A comparison of batch effect removal methods for
enhancement of prediction performance using MAQC-II microarray gene
expression data, Pharmacogenomics J 10: pp278-291, 2010
7. Morin RD et al, Profi ling the HeLa S3 transcriptome using
randomly primed cDNA and massively parallel short-read sequencing,
BioTechniques 45(1): pp81-94, 2008
8. Lee S et al, Accurate quantifi cation of transcriptome from
RNA-Seq data by effective length normalization, Nucleic Acids Res 39(2):
e9, 2011
9. Canales RD et al, Evaluation of DNA microarray results with
quantitative gene expression platforms, Nat Biotechnol 24:
pp1,115-1,122, 2006
10. Plexpress, Gene expression analysis: a review, Visit: http://bit.ly/ V3SSx1, 2012
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