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European Biopharmaceutical Review

Gene Expression Analysis

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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Jari Rautio is Co-Founder of Plexpress and the leading expert on the company’s novel TRAC technology. Prior to this, Rautio was head of the Plexpress R&D team that successfully commercialised the technology, opening up a wide range of highthroughput applications in drug research. This wealth of scientific experience is illustrated via a number of publications, including his doctoral thesis discussing the development and applications of the technology. As well as his scientifi c role, Rautio is deeply involved with business strategy and development, including a pivotal role in fund raising.
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