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  PREDICTING AND REDUCING IMMUNOGENICITY As discussed the mechanisms leading to antibody induction by therapeutic proteins are still not completely understood. As a consequence it is impossible based on our current knowledge to fully predict the immunogenicity of a new product in patients. For nonhuman proteins which induce the classical immune response, the level of nonself is a relative predictor of an immune response. However, it is not an absolute predictor. Sometimes a single amino acid change is sufficient to make a self protein highly immunogenic. With other proteins substantial diver-gence from the natural sequence has no effect. For foreign proteins a number of in vitro stimulation and binding tests and computational models are adver-tised as predictors of immunogenicity. However, all these tests have their limitations. T-cell proliferation assays, for example, have the drawback that many antibodies are capable of inducing some level of T-cell activation or inhibit cell prolif

  Genomics, Other “Omics” Technologies,Personalized Medicine, and Additional  Biotechnology-Related Techniques INTRODUCTION Pharmaceutical biotechnology and the products result-ing for biotechnologies continue to grow at an exponential rate. Note that 2005 was a record setting year with the U.S. Food and Drug Administration (FDA) approval of 21 biopharmaceutical products. There were 15 FDA approvals in 2004, 20 in 2003, and 13 in 2002 since the last edition of this textbook (Rader 2006; Staff, 2006). Early 2006 saw approvals of several unique new biopharmaceutical entities including a recombinant vaccine widely hailed as the first vaccine for the prevention of an oncogenic virus-associated cancer. However, until recently, the techniques made available by advances in molecular biology and biotechnology that have provided currently approved therapeutic agents generally fell into two broad areas: recombinant DNA (rDNA) technology and hybridoma techniques (to produce monoclonal antibodies)

  AN INTRODUCTION TO “OMICS” TECHNOLOGIES Since the discovery of DNA’s overall structure in 1953, the world’s scientific community has continued to gain a better understanding of the genetic information encoded by DNA and the genetic information carried by a cell or organism. In the 1980s and 1990s,biotechnology techniques produced novel therapeutics and a wealth of information about the mechanisms of various diseases such as cancer. Yet the etiology of many other diseases, including obesity and heart disease, remained unknown at the genetic and the molecular level, presenting no obvious target to attack with a small molecule drug or biotechnology-produced therapeutic agent. The answers were hidden in what was unknown about the human genome. Despite the increasing knowledge of DNA structure and function in the 1990s, the genome, the entire collection of genes and all other functional and non-functional DNA sequences in the nucleus of an organism, had yet to be sequenced. DNA may well b

  Genomics   Sequencing the human genome and the genomes of other organisms has lead to an enhanced under-standing of human biology and disease. Many industry analysts predicted a tripling of pharmaceutical R&D productivity due to the sequencing of the human genome (Williams, 2007). Success to date has been limited and there is significant debate as to the impact of genomics on successful drug discovery and development (Nirmala, 2006; Caldwell et al., 2007). Validation of viable drug targets identified by genomics has been challenging (Bagowski, 2005). An interesting concept is that of the “druggable” genome (Hopkins and Groom, 2002). This is an estimate at 600 to 1,500 valid molecular targets for drug discovery, as assessed as the intersection of the number of human genes identified linked to disease with the subset of the human genome products that could be modulated by small-molecule targets. However, the genomics revolution has been the foundation for an explosion in “omics” te

  “Omics” Enabling Technology: Bioinformatics Structural genomics, functional genomics, proteomics, and other “omic” technologies have generated an enormous volume of genetic and biochemical data to store. The entire encoded human DNA sequence alone requires computer storage of approximately 10 9  bits of information: the equivalent of a thousand 500-page books! GenBank (managed by the National Center for Biotechnology Information, NCBI, of the National Institutes of Health), the European Molecular Biology Laboratory (EMBL), and the DNA Data Bank of Japan (DDBJ) are three of the many centers worldwide that collaborate on collecting DNA sequences. These databanks, (both public and private) store tens of millions of sequences (Martin et al., 2007). Metabolic databases and other collections of biochemical and bioactivity data add to the complexity and wealth of information (Olah and Oprea, 2007). Once stored, analyzing the volumes of data, (i.e., comparing and relating information from va

  Transcriptomics This technology examines the complexity of messen-ger RNA transcripts of an organism (i.e., transcrip-tome), under a variety of internal and external conditions reflecting the genes that are being actively expressed at any given time (with the exception of mRNA degradation phenomena such as transcrip-tional attenuation) (Subramanian et al., 2005). Therefore, the transcriptome can vary with external environmental conditions while the genome is roughly fixed for a given cell line (excluding  mutations). The transcriptomes of stem cells and cancer cells are of particular interest to better under-stand the processes of cellular differentiation and carcinogenesis. High-throughput techniques based on microarray technology are used to examine the expression level of mRNAs in a given cell population.

  Proteomics, Structural Proteomics, and Functional Proteomics Functional genomics research will provide an unprece-dented information resource for the study of biochemical pathways at the molecular level. Certainly a large number of the  ~ 25,000 genes identified in sequencing the human genome will be shown to be functionally important in various disease states (see druggable genome discussion above). This will result in the identification of a vast array of proteins implicated as playing pivotal roles in disease processes (Evans, 2003). These key identified proteins will serve as potential new sites for therapeutic intervention (Fig. 1). The research area called proteomics seeks to define the function and correlate that with expression profiles of all proteins encoded within an organism’s genome or “proteome” (Edwards et al., 2000; Kreider, 2001; Voshol et al., 2007). The  ~ 25,000 human genes can produce 100,000 proteins. The number, type and concentration may vary depending on cell

  “Omics” Enabling Technology: Microarrays The biochips known as DNA microarrays and oligonucleotide microarrays are a surface collection of hundreds to thousands of immobilized DNA sequences or oligo-nucleotides in a grid created with specialized equipment that can be simultaneously examined to conduct expression analysis (Khan et al., 1999; Southern, 2001; Amaratunga et al., 2007). Biochips may contain representatives of a particular set of gene sequences (i.e., sequences coding for all human cytochrome P450 isozymes) or may contain sequences representing all genes of an organism. They can produce massive amounts of genetic information (Butte, 2002; Vaince et al., 2006). While the in vitro diagnostics market has been difficult to enter, Roche Diagnostics AmpliChip CYP 450 is a FDA-approved diagnostic tool able to determine a patient’s genotype with respect to two genes that govern drug metabo-lism. This information obtained may be useful by a physician to select the appropriate drug

  “Omics” Enabled Technology: BriefIntroduction to Biomarkers Biomarkers are clinically relevant substances used as indicators of a biologic state (Shaffer, 2006; DePrimo, 2007). Detection or concentration change of abiomarker may indicate a particular disease state (e.g., the presence of an antibody may indicate an infection), physiology, or toxicity. A change in expression or state of a protein biomarker may correlate with the risk or progression of a disease, with the susceptibility of the disease to a given treatment, or the drug’s safety profile. Implemented in the form of a medical device, a measured biomarker becomes an in vitro diagnostic tool (Williams et al., 2006). While it is well beyond this chapter to provide a detailed discussion of biomar-kers, it is important to note that omics technologies including omics-enabled technologies such as micro-arrays are being developed as clinical measuring devices for biomarkers. Biomarkers enable character-ization of patient population

  Metabonomics and Metabolomics   The vast information revealed with the sequencing of the human genome has yet to produce the advances in personalized medicine expected. However, the tech-niques and processes of identifying clinically signifi-cant biomarkers of human disease and drug safety have fostered the systematic study of the unique chemical fingerprints that specific cellular processes leave behind, specifically, their small-molecule meta-bolite profiles (Nicholson and Wilson, 2003; Fernie et al., 2004; Delnomdedieu and Schneider, 2005; Lindon et al., 2005; Weckwerth and Morgenthal, 2005) The human “metabolome” represents the collection of all metabolites in a biological organism, which are the end products of its gene expression and  gene product function. Thus, while genomics and proteomics do not tell the whole story of what might be happening within a cell, metabolic profiling can give an instantaneous snapshot of the physiology of that cell.   High performance liquid chrom

  Pharmacogenetics and Pharmacogenomics It has been noted for decades that patient response to the administration of a drug was highly variable  within a diverse patient population. Efficacy as  determined in clinical trials is based upon a standard dose range derived from the large population studies. Better understanding of the molecular interactions occurring within the pharmacokinetics phase of a drug’s action, coupled with new genetics knowledge and then genomics knowledge of the human have advanced us closer to a rational means to optimize drug therapy. Optimization with respect to the patients’ genotype, to ensure maximum efficacy with minimal adverse effects is the goal. Environment, diet, age, lifestyle, and state of health all can influence a person’s response to medicines, but understanding an individual’s genetic makeup is thought to be the key to creating personalized drugs with greater efficacy and safety. Approaches such as the related pharmacogenetics and pharmacogenomi

  Toxicogenomics   Toxicogenomics is related to pharmacogenomics, combining toxicology, genetics, molecular biology, and environmental health to elucidate the response of living organisms to stressful environments or toxic agents. Likewise, new drug candidates can be screened through a combination of gene expression profiling and toxicol-ogy to understand gene response and possibly predict safety (Furness, 2002). Genomic techniques utilized include gene expression level profiling and single-nucleotide polymorphism analysis of the genetic variation of individuals generally employing micro-array technology. Toxicogenomic studies are corre-lated to adverse toxicological effects in clinical trials so that suitable biomarkers for these adverse effects can be developed. Using such methods, it would then theoretically possible to test an individual patient forhis or her susceptibility to these adverse effects before administering a drug. Patients that would show the marker for an adverse effe

  Glycomics and Glycobiology The novel scientific field of glycomics, or glycobiol-ogy, may be defined most simply as the study of the structure, synthesis and biological function of all glycans (may be referred to as oligosaccharides or polysaccharides, depending on size) and glycoconju-gates in simple and complex systems (Varkin et al., 1999; Fukuda and Hindsgauld, 2000; Alper, 2001). The application of glycomics or glycobiology is sometimes called glycotechnology to distinguish it from biotech-nology (referring to glycans rather than proteins and nucleic acids). However, many in the biotech arena consider glycobiology one of the research fields encompassed by the term biotechnology. In the postgenomic era, the intricacies of protein glycosyla-tion, the mechanisms of genetic control, and the internal and external factors influencing the extent and patterns of glycosylation are important to under-standing protein function and proteomics. Like proteins and nucleic acids, glycans are bi

  “Omics” Integrating Technology: Systems Biology The massive scientific effort embodied in the HGP and the development of bioinformatics technologies have catalyzed fundamental changes in the practice of modern biology (Aderem and Hood, 2001). Biology has become an information science defining all the elements in a complex biological system and placing them in a database for comparative interpretation. As seen in Figure 2, the hierarchy of information collec-tion goes well beyond the biodata contained in the genetic code that is transcribed and translated. It involves a complex interactive system. Systems biol-ogy is often described as a non-competitive technology by the pharmaceutical industry (a foundational technology that must be developed to better succeed at the competitive technology of drug discovery and development). It is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of that syst