How molecular profiling could revolutionize drug discovery

Information from genomic, proteomic and metabolomic measurements has already benefited target discovery and validation, assessment of efficacy and toxicity of compounds, identification of disease subgroups and the prediction of responses of individual patients. Greater benefits can be expected from the application of these technologies on a significantly larger scale; by simultaneously collecting diverse measurements from the same subjects or cell cultures; by exploiting the steadily improving quantitative accuracy of the technologies; and by interpreting the emerging data in the context of underlying biological models of increasing sophistication. The benefits of applying molecular profiling to drug discovery and development will include much lower failure rates at all stages of the drug development pipeline, faster progression from discovery through to clinical trials and more successful therapies for patient subgroups. Upheavals in existing organizational structures in the current 'conveyor belt' models of drug discovery might be required to take full advantage of these methods.

[1]  Emanuel F. Petricoin,et al.  Medical applications of microarray technologies: a regulatory science perspective , 2002, Nature Genetics.

[2]  Amalia M. Issa,et al.  Ethical perspectives on pharmacogenomic profiling in the drug development process , 2002, Nature Reviews Drug Discovery.

[3]  Robert L. Phillips,et al.  Identification of biomarkers for tumor endothelial cell proliferation through gene expression profiling , 2005, Molecular Cancer Therapeutics.

[4]  Andrew J. Wilson,et al.  Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin. , 2003, Cancer research.

[5]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[6]  Jorge R. Oksenberg,et al.  Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis , 2002, Nature Medicine.

[7]  E. Neumann,et al.  Knowledge assembly for the life sciences. , 2002, Drug discovery today.

[8]  Stephen M. Hewitt,et al.  Post-analysis follow-up and validation of microarray experiments , 2002, Nature Genetics.

[9]  Silvio Albertini,et al.  Discriminating Different Classes of Toxicants by Transcript Profiling , 2004, Environmental health perspectives.

[10]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[11]  U. Certa,et al.  Temporal and spatial gene expression patterns after experimental stroke in a rat model and characterization of PC4, a potential regulator of transcription , 2003, Molecular and Cellular Neuroscience.

[12]  J. Waring,et al.  Identifying toxic mechanisms using DNA microarrays: evidence that an experimental inhibitor of cell adhesion molecule expression signals through the aryl hydrocarbon nuclear receptor. , 2002, Toxicology.

[13]  R. Stoughton.  Applications of DNA microarrays in biology. , 2005, Annual review of biochemistry.

[14]  R G Ulrich,et al.  Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. , 2001, Toxicology and applied pharmacology.

[15]  P. Brown,et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays , 1998, Nature Medicine.

[16]  J. Waring.  Development of a DNA Microarray for Toxicology Based on Hepatotoxin-Regulated Sequences , 2002, EHP toxicogenomics : journal of the National Institute of Environmental Health Sciences.

[17]  Silvio Albertini,et al.  Gene expression in two hepatic cell lines, cultured primary hepatocytes, and liver slices compared to the in vivo liver gene expression in rats: possible implications for toxicogenomics use of in vitro systems. , 2003, Toxicological sciences : an official journal of the Society of Toxicology.

[18]  Yusuke Nakamura,et al.  Prediction of response to neoadjuvant chemotherapy for osteosarcoma by gene-expression profiles. , 2004, International journal of oncology.

[19]  Matej Oresic,et al.  Integrative biological analysis of the APOE*3-leiden transgenic mouse. , 2004, Omics : a journal of integrative biology.

[20]  A. Zlotnik,et al.  Targeting tumor cells. , 2003, Current opinion in cell biology.

[21]  R. Weinshilboum,et al.  Pharmacogenomics: bench to bedside , 2004, Nature Reviews Drug Discovery.

[22]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[23]  Alan G. E. Wilson,et al.  Role of predictive metabolism and toxicity modeling in drug discovery--a summary of some recent advancements. , 2003, Current opinion in drug discovery & development.

[24]  E E Schadt,et al.  A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets. , 2003, Biochemical Society transactions.

[25]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[26]  I. Wilson,et al.  Understanding 'Global' Systems Biology: Metabonomics and the Continuum of Metabolism , 2003, Nature Reviews Drug Discovery.

[27]  Amanda Clare,et al.  How well do we understand the clusters found in microarray data? , 2002, Silico Biol..

[28]  Harm van Bakel,et al.  TEAM: a tool for the integration of expression, and linkage and association maps , 2004, European Journal of Human Genetics.

[29]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[30]  M. Lindsay.  Target discovery , 2003, Nature Reviews Drug Discovery.

[31]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[32]  R. Christopher,et al.  Data‐Driven Computer Simulation of Human Cancer Cell , 2004, Annals of the New York Academy of Sciences.

[33]  Yudong D. He,et al.  A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. , 2005, Cancer research.

[34]  Elaine Holmes,et al.  Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis , 2004, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[35]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[36]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[37]  S. Ekins.  Predicting undesirable drug interactions with promiscuous proteins in silico. , 2004, Drug discovery today.

[38]  D. Stone,et al.  Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Reuven Agami,et al.  A large-scale RNAi screen in human cells identifies new components of the p53 pathway , 2004, Nature.

[40]  L. Hartwell,et al.  Integrating genetic approaches into the discovery of anticancer drugs. , 1997, Science.

[41]  Yudong D. He,et al.  Expression profiling predicts outcome in breast cancer , 2002, Breast Cancer Research.

[42]  Alan Engelberg.  Iconix Pharmaceuticals, Inc.--removing barriers to efficient drug discovery through chemogenomics. , 2004, Pharmacogenomics (London).

[43]  Clayton M. Christensen.  The Innovator's Dilemma , 1997 .