Current Molecular Medicine - Volume 5, Issue 1, 2005

What will drug development look like in 10, 20 or 40 years? Opinions abound on this interesting question, ranging from totally personalized development of medicines, (visions of Scotty in Star Trek using his scanner to diagnose and treat Captain Kirk on the spot!) to multifunctional drug targets that can be used across a wide range of disease indications. As the landscape in drug development is rapidly evolving, there are heightened expectations that we should reap the benefits of the genomic revolution, increased pressure for better drug safety, and price pressure from generic medicines and consumer demand. Within this rapidly evolving environment, new technologies are being developed that will likely change the face of drug discovery and development. Prominent among these is the fledgling enterprise of transcriptome analysis. Transcriptome analysis provides the opportunity to evaluate global changes in gene transcript expression in a cell, tissue, organ or whole organism. Within the past decade there has been an exponential increase in publications on transcriptomics (see the Figure), with several significant advances already seen. This issue is dedicated to exploring how transcriptome analysis is impacting drug discovery and development, and to allow the reader to become familiar with the application of this technology within this context. The issue is divided thematically into two groups of articles. The first five articles discuss the technology, how it is used, how to approach data analysis, and how this technology fits into the larger picture of available and developing technologies. Fan and Hedge explore the use of blood as a tissue for transcriptome analysis, guiding the reader into the intricacies and potential pitfalls that can be encountered. Yue and Residorf discuss the various methods of data analysis that can be applied to transcriptomic data, and which analysis tools are currently available. Manasco discusses the ethical and legal implications for the use of transcriptome and genomic data, with potential solutions to various issues of confidentiality and consent proposed. Hu, Kaplow and He describe the lessons we have learned from use of traditional biomarkers and how this can be applied to transcriptomics. To put the use of this technology into a broader context, Bilello discusses how “OMIC” technologies can complement each other, and how the parallel fields of proteomics, metabonomics etc. are developing. These papers should serve to orient the reader to understanding in a general sense how transcriptomics can be applied in studying drugs. The papers that follow deal more specifically with the use of transcriptome analysis in the various stages of drug development. Searfoss, Ryan and Jolly describe use in pre-clinical toxicology studies. In the clinical realm, Hsu, Cass and Williams discuss the use of transcriptomics in clinical pharmacology, which in general comprises the earliest stage of drug development. This is followed by three papers that discuss what has been to date the most widespread application of transcriptomics in drug development, namely in oncology. Burczynski and his collaborators discuss early phase oncology trials, while the papers by Dracopoli and Wadlow & Ramaswamy discuss later stage and additional uses in the oncology setting. Overall, the reader should come away with a good understanding of how transcriptomics is being used and its potential applications. Clearly we are on the verge of a new era in drug discovery and development. The papers within this issue set the stage for the revolutionary impact transcriptomics will have on every aspect of pharmaceutical research and development. I would like to thank all the authors for their diligent work on these manuscripts. It has been a pleasure and a privilege to edit this special issue.

Peripheral blood may be the most feasible tissue source in clinical assessment of differences in gene expression between diseases and drug treatments due to accessibility. Yet, gene expression profiling from blood remains a challenge. Blood is a complicated biological system consisting of a variety of cell types at different stages of development. In addition, blood is also one of the most variable tissue types for gene expression analysis. The success of a blood microarray study depends on the choice of cell isolation method and preparation technique. In this review, we give a brief overview of the current status of using blood as a source for expression profiling and discuss potential applications of this method in the practices of clinical research.

The increasing use of gene expression profiling offers great promise in clinical research into disease biology and its treatment. Along with the ability to measure changing expression levels in thousands of genes at once, comes the challenge of analyzing and interpreting the vast sets of data generated. Analysis tools are evolving rapidly to meet such challenges. The next step is to interpret observed changes in terms of the biological properties or relationships underlying them. One powerful approach is to make associations between the genes that are under investigation and well-known biochemical or signaling pathways, and further to assess the significance of such associations. Similarly, genes can be mapped to standardized biological categories via an ontology resource. We discuss these approaches and several web-based resources and tools designed to facilitate such analyses. This information can be used to facilitate understanding and to help design more focused experiments for validating the relevance and importance of these biological pathways and processes in human disease and therapeutics.

Many ethical and legal issues surround genomic technologies, some of which are present for other kinds of medical data, but some of which are specific to genomic data. Specifically the global nature of genomic data and the life-long implications of genetic defects on the health of the individual subject produce challenges in the ethical and legal handling of this data. In general, data derived from transcriptome analysis, which studies gene expression, as well as proteomics and metabolomics, carry less ethically-charged information than measures of the germ line genome. However, theoretical issues that have been raised related to withholding therapy based on a specific genotype which could also apply to a specific expression profile. Potential solutions for these challenges are discussed, such as maintaining a connection with research participants through a trusted third party, using electronic means to manage that contact and reconsent subjects. A flexible, secure information technology infrastructure is proposed to manage and search consent forms, provide the ability to collect additional data and consent while maintaining participant confidentiality.

Traditional biomarkers have played an important role in drug development as well as patient care. A single traditional biomarker or surrogate endpoint is unlikely to either characterize the complete pathophysiology of a complex disease or capture all the therapeutic benefits or potential adverse effects that a drug will have in a diverse patient population. Transciptome analysis, on the other hand, can provide a large-scale survey of gene expression associated with the etiology of a human disease or pharmacological responses to a therapeutic intervention. The quantitative and qualitative readouts can provide increased power to identify novel drug targets or biomarkers indicative of drug safety or efficacy. Transcriptomics has positively impacted drug development and will continue to improve the medicines of the future. Here, we describe the increasingly important roles that traditional biomarkers and transcriptome analysis have played in various phases of drug discovery and development as well as the opportunities and challenges that they present to the pharmaceutical industry.

Over the last decade we have witnessed a fundamental change in how biomedical research is carried out and we can now assess the impact of the Human Genome Project on drug discovery and development. Advances in “omics” technologies (genomics, transcriptomics, proteomics and metabonomics) were touted as having the potential to revolutionise our approach to disease diagnosis, prognostication and development of novel therapeutics. However, the promise of rapid advances in medicine “from the lab bench to the bedside” has not manifested as of yet. Indeed it appears that the translational applications of genomic-based research have preceded the development of both (i) a conceptual framework for disease understanding and (ii) effective tools that can exploit the vast amounts of data derived from these efforts. In reality great progress has been made, however understanding processes such as disease progression (or drug response) requires systematic insight into dynamic (and temporal) differences in gene regulation, interaction and function. This review will discuss “omic” technologies with the emphasis upon advances in our understanding of the human genome derived transcriptome (RNA), and its proteome (proteins), while focusing upon the translation of this information into the drug development paradigm.

A major benefit of the genomics revolution in biomedical research has been the establishment of transcriptome analysis as an enabling technology in the drug development process. Nowhere in the realm of drug development has the expectation of the impact of transcriptome analysis been greater than in the area of pre-clinical toxicology. Transcriptome analysis, along with other new high-content data generating technologies, has the potential to radically improve the drug safety assessment process by allowing drug development teams to identify potential toxicity liabilities earlier, and thus proceed only with those molecules that have both efficacy at the target and a low potential for toxicity in the human population. In this review we will briefly describe the major ways in which transcriptome analysis is being applied in the pre-clinical safety assessment process, focusing primarily on four areas where transcriptome analysis has already begun to have impact. These include using transcriptome analysis to: 1) understand mechanisms of toxicity: 2) predict toxicity: 3), develop in vivo and in vitro surrogate models and screens; and, 4) develop toxicity biomarkers. We will close by briefly addressing future trends and needs in the application of transcriptome analysis to drug safety assessment.

Clinical pharmacology is the investigation of drug effects in humans. This review discusses the basic tenets of clinical pharmacology research, including pharmacokinetic and pharmacodynamic analysis, therapeutic window, and clinical trial design, and the issues that may arise in the application of transcriptome analysis to clinical pharmacology studies. Examples of how transcriptome analysis can be applied to clinical pharmacology research are described, including a model system of endotoxin challenge (in vitro and in vivo), and an example of a cross-over drug study in normal volunteers. Various data display and analysis methods are also illustrated, including principal component analysis, hierarchical cluster analysis, and pathways analysis.

Microarray-based expression profiling studies in the field of oncology have demonstrated encouraging correlations between tumor transcriptional profiles and eventual patient outcomes. These findings have fueled great interest in the application of transcriptional profiling to samples available from real-time clinical trials, and clinical pharmacogenomic objectives utilizing transcriptional profiling strategies are becoming increasingly incorporated into clinical trial study designs. Over the last few years several retrospective studies based on the profiling of archival tumor tissues suggest that transcriptional analysis of oncology samples may provide general prognosis measures, and in some cases may even predict response to specific therapies. Recently the FDA released a voluntary genomic data guidance meant to assist both regulatory agencies and pharmaceutical companies alike in evaluating the potential benefit of implementing expression profiling studies during the preclinical and clinical phases of drug development. Despite the great promise afforded by this technology, the ultimate benefit of applying transcriptional profiling in prospective clinical trials has yet to be realized because a number of practical impediments to this process exist. The multi-fold purpose of the current review is to highlight the increasing evidence from studies that have identified transcriptional signatures in archived tumors prognostic of patient outcome, to describe some of the drivers for the implementation of transcriptional profiling strategies in real-time drug development, to discuss the use of transcriptional profiling in the context of increasingly complex translational medicine strategies, and to highlight the practical issues and potential approaches involved in the successful application of clinical pharmacogenomic objectives during real-time clinical trials. Strategic implementation of transcriptional profiling in early oncology clinical trials can provide an opportunity to identify predictive markers of clinical response and eventually provide a substantial step forward towards the era of personalized medicine.

Transcriptional profiling of a tumor's entire genomic complement has become a key tool in the analysis of human cancers and identification of novel markers to predict disease state, outcome, and response to therapy. At present, this technology provides the most comprehensive approach for the analysis of somatic changes altering critical pathways during transformation of stable diploid cells into unstable tumor cells. Such analyses are impacting the development of novel anti-cancer drugs through the early detection of cancer, development of targeted therapies, identification of optimal dose and regimens for new drugs, and segmentation of patients to enrich response rates and reduce risk of adverse events.

The recent sequencing of the human genome, coupled with advances in biotechnology, is enabling the comprehensive molecular “profiling” of human tissues. In particular, DNA microarrays are powerful tools for obtaining global views of human tumor gene expression. Complex information from tumor “expression profiling” studies can, in turn, be used to create novel molecular cancer diagnostics. We discuss the utility of DNA microarray-based tumor profiling in clinical cancer research, highlight some important recent studies, and identify future avenues of research in this evolving field.