Copyright © 2010 Quintiles. 16.15.15-102010

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Executive Summary

The sequencing of bacterial, viral, fungal, parasite, and human genomes that began during the closing years of the 20th century has ushered in an era of profound advancement in understanding with regard to microbial virulence; host response to and control of infection; and disease pathogenesis. In parallel, this knowledge has been exploited in the creation of vaccines, drugs, and biologics tailored to optimize target presentation and effectiveness in preventing and treating infectious diseases. Equally exciting has been the development of biological indicators, or biomarkers, based upon associations increasingly recognized between genes, their downstream products (i.e., transcripts, proteins, metabolites) and pathological processes that can be used to diagnose, monitor, and manage infections. There is evidence that these technological advances can be leveraged to improve the quality of healthcare delivery in certain disciplines (e.g., oncology, hematology, cardiology, neurology), and it is anticipated that the totality of these recent advances, coupled with integrative/systems biology approaches, will deliver effi ciencies in drug and vaccine development, will improve product effectiveness and minimize “off-target” side effects, and will be a major force in containing the unsustainable growth in the cost of healthcare delivery around the globe as we continue wrestling with the microbial world.

Pharmacobiomics and Infectious Diseases: Progress and OpportunitiesKelly T. McKee Jr., M.D., M.P.H.Vice PresidentPublic Health and Government Services, Quintiles

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Table of Contents

Executive Summary 01

Introduction 03

Drug and Vaccine Discovery in the Biomics Era 05

Pharmacogenomics: The Leading Edge of Personalized Medicine 06

Pharmacogenomics in Infectious Disease 07

Biomarker Discovery 10

Near-Term Challenges 12

A Peek at the Future 13

Conclusions 15

References 16

About the Author 21

Acknowledgements 21

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Introduction

Development costs for new molecular (NME) and biological (NBE) entities have reached staggering heights; published estimates range upwards of $1.5–2B (capitalized) for the average investment required to bring an NME to market.1,2 Sadly, these costs are continuing to rise. The PhRMA trade group recently reported that the current industry spend for R&D has risen to nearly $50B per year.3 Preclinical drug discovery accounts for at least a third of drug development costs, while clinical testing accounts for more than 60%.2 Attrition rates for NMEs are enormous: Fewer than 1 in 10 NMEs survive from candidate selection to launch, and NBEs fare only marginally better.2 In this environment, pressured constantly by both healthcare consumers and payers to whom these expenses are logically passed, developers are anxiously seeking ways to improve effi ciency and productivity to reverse the trend in escalating R&D expenditures.

While the proportion of overall pipeline development cost attributable to products designed to prevent and treat infectious diseases has not been specifi cally identifi ed, it is surely signifi cant. Success rates from fi rst-in-man trials to registration for NMEs targeting infectious diseases appear to be slightly higher (16–17%) than the overall success rate across all therapeutic areas (11%)4, but it is clear that much still needs to be done to improve effi ciency. Fortunately, recent technological advances in molecular genetics, biopharmaceutical engineering and bioinformatics have enhanced our understanding of the structure and functioning of microbes and the scientifi c basis of the host-pathogen interface. These insights have, in turn, provided a way forward in the quest for more rapid and effi cient design of antimicrobial drugs and vaccines, and improved understanding and quantifi cation of the biological basis for the variance observed in response to administered compounds.

Since publication of the sequence for the fi rst full bacterial genome (Haemophilus infl uenzae) in 19955, the genetic makeups of more than 1,000 bacteria and 3,000 viruses have been elucidated. At least one genomic sequence is now known for essentially every major human pathogen.6 Sequencing of the human genome during the early years of the current decade7,8,9 has provided insights into the genetic basis for variability in the immune response following interaction with pathogens, as well as protection following vaccination. In addition, gene polymorphisms have helped to explain the epidemiology of selected adverse drug reactions. From the cracking of these codes and study of genes, proteins, and the processes that underlie their structure and function has emerged a universe of new “omics” disciplines, the study or application of which can be collectively termed “biomics” (see Table 1). Biomics focuses on the building blocks of the complex systems (e.g., genes, proteins, metabolites) that underlie cells, tissues, organs, and ultimately, the organism. These approaches have led to advances in understanding disease pathogenesis (via functional genomics, epigenomics, metabolomics), to identifi cation of causative disease agents (via metagenomics), and to identifi cation of vaccine and therapeutic candidates using a multiplicity of screening techniques (via structural and functional genomics, transcriptomics, proteomics, and immunomics). These multi-scale approaches to the study of pathogens and their interaction with their human hosts have revolutionized microbiology and are having a profound effect on drug design and vaccine development.

Biomics focuses on the building blocks of the complex systems (e.g., genes, proteins, metabolites) that underlie cells, tissues, organs, and ultimately, the organism.

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Table 1. Selected Biomics Tools Applied to Drug, Biologic and Vaccine Discovery

Tool Definition Typical Application

Genomics Approach to sequencing and bioinformatic processing of genetic data to identify candidate genes meeting selected predictive criteria

Identification of vaccine targets

Functional genomics Approach to assessing patterns of gene expression under various conditions

Studies of disease pathogenesis

Epigenomics Study of enzyme and protein structures that impact high order DNA structure and gene expression

Mechanisms of disease/ pathogenesis

Metagenomics Study of genomic material in environmental/biological samples

Identification of unknown/emerging pathogens in pathological samples

Proteomics Study of set of proteins coded by genome of interest

Diagnostic biomarkers

Transcriptomics Study of RNA molecules expressed under defined conditions

Genome expression profiling

Metabolomics Study of small molecule metabolic intermediates produced by organisms under various conditions

Toxicology, phenotypic profiling in conjunction with functional genomics

Immunomics Study of the effectors of the mammalian immune system

Identification of B and T cell epitopes in vaccine design

Vaccinomics Study of the response of the mammalian immune system to vaccines, biologics, or drugs

Identification of gene polymorphisms underlying differential responses to vaccines

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About the Author

Kelly T. McKee Jr., M.D., M.P.H.Vice President, Public Health and Government Services, Quintiles

Dr. McKee has dedicated his career to researching and preventing infectious diseases. Before joining Quintiles in 2006, he served as Senior Director of Clinical Research and Chief Medical Officer for a vaccine development company. After finishing a 20-year career in the U.S. Army Medical Department, he served as the Head of General Communicable Disease Control and State Epidemiologist for North Carolina, but returned to government work at the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID). He has written more than 100 peer-reviewed publications and textbook chapters and reviews manuscripts for several professional journals.

Acknowledgements

The author wishes to thank Drs. Oren Cohen, Claude Hughes and Sandra Silberman for their reviews and thoughtful comments on this manuscript.

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63. Meisner SJ, Mucklow S, et al. Association of NRAMP1 polymorphism with leprosy type but not susceptibility to leprosy per se in west Africans. Am J Trop Med Hyg. 2001;65:733-735.

64. Malhotra D, Darvishi K, et al. IL-10 promoter single nucleotide polymorphisms are significantly associated with resistance to leprosy. Hum Genet. 2005;118:295-300.

65. Roy S, Frodsham A, et al. Association of vitamin D receptor genotype with leprosy type. J Infect Dis. 1999;179:187-91.

66. Wong SH, Hill AVS, et al. Genomewide association study of leprosy [letter]. N Engl J Med. 2010;362:1446-1447.

67. Querec TD, Akondy RS, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116-125.

68. Butcher EC, Berg EL, et al. Systems biology in drug discovery. Nat Biotech. 2004;22:1253-1259.

69. Beloqui A, Guazzaroni ME, et al. Reactome array: forging a link between metabolome and genome. Science. 2009;326:252-257.

70. Alberts B. Editorial expression of concern. Science. 2010;327:144.

71. Lupski JR, Reid JG, et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N Engl J Med. 2010;362:1181-1191.

72. Roach JC, Glusman G, et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. In press.

73. Lifton RP. Individual genomes on the horizon. N Engl J Med. 2010;362:1235-1236.

74. Ng SB, Turner EH, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272-276.

75. Reif DM, Motsinger-Reif AA, et al. Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun. 2009;10:112-119.

76. Schadt EE, Friend SH, et al. A network view of disease and compound screening. Nat Rev Drug Discov. 2009;8:286-295.

77. Pujol A, Mosca R, et al. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci. 2010;31:115-123.

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Drug and Vaccine Discovery in the Biomics Era Traditional methods of drug and vaccine discovery are time- and labor-intensive, costly, and plagued by inaccuracies. With the introduction of biomics, greater precision has been introduced, and high-throughput tools have vastly enhanced the speed with which compounds are used to probe for suitable targets. It is estimated that over one to two years, as many as 10–100 times more candidate antimicrobial drugs and vaccines can be identifi ed using various biomics-based approaches than might be found using traditional approaches over a comparable interval.10

Principles that increase the probability of success include fi nding targets that should, at a minimum, be 1) expressed or accessible to the host immune system (or to a therapeutic agent) during human disease, 2) important for pathogenesis or pathogen survival, 3) genetically conserved, and 4) free of measurable homology or similarity to host proteins or other factors.10 Adherence to these tenets has resulted in numerous creative approaches to drug and vaccine design, and there has been an accompanying explosion of corporate entities through which these approaches have been scaled and commercialized.

The application of genomics to pathogen target identifi cation has seen success in the “intelligent design” of promising new vaccines in silico via what has been termed “reverse vaccinology”; candidate products for N. meningiditis serogroup B, group B streptococci, and several viruses, among others, have been produced and are under evaluation (reviewed in 6, 10). Transcriptomics has been applied to garner an understanding of the genetic basis for hyperinfection seen with V. cholera. Functional genomics has shed light on the genes required by H. pylori to colonize its host. Proteomics has been applied to the identifi cation and characterization of specifi c group A streptococcal surface proteins. Exploiting the pathogen-host interface has found application through development of host cell drug targets based upon functional genomic, transcriptomic, and proteomic analyses of host gene up-regulation following infection. Some approaches not directly pertinent to target screening (e.g., use of metagenomics to probe biological samples for the genetic material of unidentifi ed or “emerging” infectious agents, and application of epigenomics or metabolomics to studies of disease pathogenesis) have also proven relevant to developers’ requirements and needs. The data generated from these technologies, coupled with advances in computational biology and information technology, have been fundamental to the current revolution in drug and vaccine design. The litany of discoveries attributable to biomics-based technologies over the past 5–10 years far exceeds the scope of this overview, but there is no doubt that we have seen the future of drug and vaccine development for infectious diseases, and it is here.

Biomics are ushering in the future of drug and vaccine development for infectious diseases.

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Pharmacogenomics: The Leading Edge of Personalized Medicine

Pharmacologic compounds cannot be expected to behave the same way in every person dosed, as there are multiple determinants of effectiveness, tolerability, and safety. In the process of clinical evaluation of drugs, biologics, and vaccines, there is an attempt to control as many extrinsic (e.g., diet, co-administered drugs, smoking) and intrinsic (e.g., age, gender, underlying disease/organ dysfunction) influences as possible to provide a clean profile ahead of regulatory approval. The fact is, however, that only 20–30% of investigational new drugs (INDs) clear the safety and efficacy hurdles necessary to secure new drug/biological license approval (NDA/BLA), with a significant proportion of failures occurring in large and expensive late phase trials.1,2,4 This failure to eliminate ineffective investigational products early in development, or to identify “rescue” opportunities for products of value in population subsets, is an enormous drag on R&D spending, a significant factor in corporate valuation, and a compromise of the industry’s commitment to stewardship of study volunteer and patient health. Moreover, because clinical trials represent, by design, statistical samples of target populations, newly licensed compounds are fraught with further costly and potentially destructive adverse event or efficacy profiles as experience with usage grows over time and prescription number.

The haploid human genome (23 chromosomes) contains approximately 30,000 genes, made up of more than 3 billion nucleotide base pairs. Since every person typically carries one copy of each gene (i.e., an allele) inherited from each parent, individuals generally carry two gene copies. An individual’s genotype reflects the specific alleles for each of the ≈30,000 gene sites, or loci, on his/her chromosomes. While >99.9% of the DNA sequences that comprise genes are identical among individuals, about 1 in 1,200 may differ between any two persons. Sites on the genome where DNA sequences vary by a single nucleotide base are called single nucleotide polymorphisms (SNPs). If these SNPs occur in coding regions that cause amino acid changes, they have the potential to change the structure of the translated proteins. Such coding differences may be clinically important, particularly if they affect gene function or the level of gene expression.

The International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/index.html.en) estimates that about 10 million common SNPs exist in human populations, where the minor (rarer) allele occurs at a frequency of >1%. Although the Project has catalogued more than 3.1 million SNPs in some 270 individuals, it should be noted that this particular dataset is limited by its target population (based on Utah Caucasians, Han Chinese, Japanese, and Nigerian Yoruba).11 It is evident that, given the vast number of genes that make up the human genome (about half of which have, as yet, undefined functions), most gene polymorphisms likely have little to do with the response of an individual to a given drug, biologic, or vaccine. However, it has become increasingly clear that much of the individual variability observed in responses to pharmacologic agents is, in fact, attributable to genotype differences in certain chromosome regions; notably, those that affect metabolism, immune regulation, and gene expression. A regularly updated compendium of literature pertaining to the influence of human genetic polymorphisms on drug response is maintained through the Pharmacogenomics Knowledge Library (PharmGKB) at http://www.pharmgkb.org/index.jsp. The reader is encouraged to consult this database for additional details on illustrative examples included in the current discussion, as well as for additional associations not noted.

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49. Zilliox MJ, Moss WJ, et al. Gene expression changes in peripheral blood mononuclear cells during measles virus infection. Clin Vaccine Immunol. 2007;14:918-923.

50. Gaucher D, Therrien R, et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J Exp Med. 2008;205:3119-3131.

51. Zhu W, Higgs BW, et al. A whole genome transcriptional analysis of the early immune response induced by live attenuated and inactivated influenza vaccines in young children. Vaccine. 2010;28:2865-2876.

52. Vahey MT, Wang Z, et al. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J Infect Dis. 2010;201:580-589.

53. Jacobsen M, Mattow J, et al. Novel strategies to identify biomarkers in tuberculosis. Biol Chem. 2008;389:487-495.

54. Coen M, O’Sullivan M, et al. Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin Infect Dis. 2005;41:1582-1590.

55. Beger RD, Sun J, et al. Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol Appl Pharmacol. 243:154-166.

56. Colombo S, Rauch A, et al. The HCP5 single-nucleotide polymorphism: a simple screening tool for prediction of hypersensitivity reaction to abacavir. J Infect Dis. 2008;198:864-867.

57. Kim SH, Kim SH, et al. Genetic polymorphisms of drug-metabolizing enzymes and anti-TB drug-induced hepatitis. Pharmacogenomics. 2009;10:1767-1779.

58. Sun F, Chen Y, et al. Drug-metabolising enzyme polymorphisms and predisposition to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2008;12:994-1002.

59. Yamada S, Tang M, et al. Genetic variations of NAT2 and CYP2E1 and isoniazid hepatotoxicity in a diverse population. Pharmacogenomics. 2009;10:1433-1445.

60. Yue J, Peng R. Does CYP2E1 play a major role in the aggravation of isoniazid toxicity by rifampicin in human hepatocytes? Br J Pharmacol. 2009;157:331-333.

61. Relman DA. Learning to appreciate our differences. J Infect Dis. 2008;198:4-5.

62. Engineer F, Tharmaratnam A. Realizing the promise of Asia Pacific: the region’s strategic shift from outsourcing to innovation. Quintiles white paper. 2010.

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32. Zhang FR, Huang W, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361:2609-2618.

33. Mahasirimongkol S, Yanai H, et al. Genome-wide SNP-based linkage analysis of tuberculosis in Thais. Genes Immun. 2008;10:77-83.

34. Sam-Agudu NA, Greene JA, et al. TLR9 polymorphisms are associated with altered IFN-γ levels in children with cerebral malaria. Am J Trop Med Hyg. 2010;82:548-555.

35. Khor CC, Vannberg FO, et al. CISH and susceptibility to infectious diseases. N Engl J Med. 2010;362:2092-2101.

36. Wang C, Tang J, et al. HLA and cytokine gene polymorphisms are independently associated with responses to hepatitis B vaccination. Hepatology. 2004;39:978-988.

37. Poland GA, Ovsyannikova IG, et al. Immunogenetics of seasonal influenza vaccine response. Vaccine. 2008;26(4)(suppl):D35-40.

38. Poland GA, Ovsyannikova IG, et al. Personalized vaccines: the emerging field of vaccinomics. Expert Opin Biol Ther. 2008;8:1659-1667.

39. Poland GA, Ovsyannikova IG, et al. Application of pharmacogenomics to vaccines. Pharmacogenomics. 2009;10:837-852.

40. Stanley SL Jr, Frey SE, et al. The immunogenetics of smallpox vaccination. J Infect Dis. 2007;196:212-219.

41. Reif DM, McKinney BA, et al. Genetic basis for adverse events after smallpox vaccination. J Infect Dis. 2008;198:16-22.

42. Tozzi V. Pharmacogenetics of antiretrovirals. Antiviral Res. 2010;85:190-200.

43. Motsinger AA, Haas DW, et al. Human genomic association studies: a primer for the infectious diseases specialist. J Infect Dis. 2007;195:1737-1744.

44. Ge D, Fellay J, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature. 2007;461:399-401.

45. Thomas DL, Thio CL, et al. Genetic variation in IL28B and spontaneous clearance of hepatitis C virus. Nature. 2009;461:798-801.

46. Fellay J, Thompson AJ, et al. ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C. Nature. 2010;464:405-408.

47. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89-95.

48. Parida SK, Kaufmann SH. The quest for biomarkers in tuberculosis. Drug Discov Today. 15:148-157.

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The U.S. Food and Drug Administration (FDA) defi nes pharmacogenomics as “the study of variations in DNA and RNA characteristics as related to drug response.”12 Therefore, the goal of pharmacogenomics is to identify genetic variants (i.e., polymorphisms) and use them to predict responses to drugs, biologics, and vaccines. These responses include those to therapy and prophylaxis (e.g., immune responses), to susceptibility to diseases, and to adverse events based upon differences in genetic makeup.

The correlations of human gene polymorphisms with drug response have led to advances in clinical therapeutics that have enabled a more individualized approach to disease management, or “personalized medicine.” The impact has perhaps been most notable in oncology (e.g., KRAS gene screening to identify colorectal cancer patients likely to benefi t from cetuximab), but discoveries in transplantation immunology, rheumatology, and other areas have been made as well. Certain genetic polymorphisms have been found to correlate with the probability of response to existing drugs (e.g., warfarin and CYP2C9 and VKORC1 alleles, clopidrogel and the CYP2C19 allele), and so-called “genetic biomarkers” have also been used to identify patients at increased risk for drug toxicity (e.g., adverse dermal reactions with carbamazipine and the HLA-B*1502 allele). While the cost-effectiveness of genetic testing in conjunction with pharmacotherapy has yet to be systematically proven, a recent study sponsored by Medco Health Solutions suggests that signifi cant impacts are possible: genotyping for the CYP2C9 and VKORC1 alleles applied in conjunction with warfarin prescribing was found to cut hospitalization rates by approximately 30%.13 The tailoring of pharmacotherapy to an individual’s genetic profi le as a focal point of both drug discovery and healthcare debate for the foreseeable future is made a virtual certainty by these observations.

Pharmacogenomics in Infectious Disease

Classical immunogenetics and genomics have yielded signifi cant insights into understanding the genetic basis underlying normal immunological responses and susceptibility to infections, with clues to the molecular underpinnings of both immunological responsiveness and protection following vaccination. The foundations of genetic determinism were laid with the predisposition to multiple and varied infectious diseases observed among children with primary immunodefi ciency disorders (e.g., severe congenital neutropenia, X-linked agammaglobulinemia, severe combined immunodefi ciency) (reviewed in 14). Subsequently, increasing numbers of immunologic disorders were found to confer susceptibility to single pathogens (e.g., invasive Neisseria infections associated with X-linked properdin defi ciency or defects in the terminal components of the complement cascade, inherited susceptibility to mycobacterial disease associated with defects in IL-12/IL-23-dependent IFN-λ-mediated immunity, and recently unc93 homolog B [UNC93B] and TLR3 defi ciencies associated with susceptibility to sporadic HSV encephalitis) (reviewed in 14). Genetically determined resistance to infectious agents has been elucidated as well.

Perhaps the three most widely recognized genomic associations with infectious diseases are the natural resistance to severe P. falciparum malaria among hemoglobin S (HbS) heterozygotes due to the HBB allele, P. vivax malaria resistance conferred by the absence of the Duffy blood group (the chemokine DARC, and the erythrocyte chemokine co-receptor for P. vivax)15, and the 32 base pair deletion in the gene encoding the primary HIV-1 co-receptor, CC chemokine receptor 5 (i.e., the CCR5 Δ32 variant), which confers high-level resistance among individuals of European descent to infection with this deadly virus.16 These polymorphisms are clearly evolutionarily complex: There

The goal of pharmacogenomics is to identify genetic variants and use them to predict responses to drugs, biologics and vaccines.

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is a selective advantage of HBB heterozygosity in protection against severe malaria over both types of homozygotes; P. vivax is transmitted in some populations lacking DARC on their red cells17; additional relationships have been identified between the CCR5 Δ32 variant and development of symptomatic and/or severe disease following infection with two neurotropic flaviviruses (West Nile and tickborne encephalitis)18,19,20; and there is an inverse correlation between CCR5 Δ32 prevalence and the incidence of Kawasaki disease, a condition suspected to have an infectious trigger.21 Untangling the mysteries underlying these and other gene polymorphisms thus promises to be enlightening on multiple fronts.22

With the profusion of genomic analyses, numerous other relationships between genetics and response to infection have come to light. For example:

> Genes coding for proteins involved in antigen processing for HLA class I presentation (e.g., TAP1, TAP2, LMP2, LPM7, Tapasin), HLA-DR223,24, and polymorphisms in IL-10 genes25, have all been linked to HPV-associated cervical cancer susceptibility.

> Several HLA molecules have been associated with immune control of HIV (primarily via cytotoxic T-cell response), the most important of which appears to be HLA B5726,27. A polymorphism located in a gene (HCP5) near the HLA-B locus has been found to account for almost 10% of the variation in HIV-1 set point among individuals of European ancestry28; interestingly, this gene variant is in high linkage disequilibrium (i.e., it is not randomly associated) with the HLA allele B*5701, which not only has a very strong protective effect on HIV-1 disease progression, but is associated as well with abacavir hypersensitivity (see below). In the same study, a different polymorphism located in the nearby HLA-C gene explained another 6.5% of the set-point effect (together, the 2 polymorphisms thus accounting for almost 15% of the variation), while an additional set of polymorphisms encoding an RNA polymerase 1 subunit, had nearly a 6% effect on viral progression.28 Follow-up studies among African Americans have identified the same HLA-C-associated SNP linked to viral load set point.29 However, a different polymorphism in the HLA-B gene than that found for European Americans, one associated with the HLA-B*5703 allotype, appears to be the most important common variant influencing viral load among African Americans.30

> Polymorphisms in several genes have been associated with susceptibility to leprosy, as well as its clinical spectrum, in different populations. Variants in as-yet unidentified genes have been mapped to chromosome 10p13 (India), to the regulatory region shared by a gene coding for the E3-ubiquitin ligase Parkin (PARK2) and the Parkin coregulated gene (PACRG) (Viet Nam), and to the lymphotoxin-α gene (LTA) ( for early-onset leprosy in Viet Nam) (reviewed in 14). More recently, polymorphisms in the nucleotide-binding oligomerization domain containing 2 (NOD2) signaling pathway, which regulate innate immune response, have been associated with susceptibility to M. leprae infection in China and Nepal.31,32 Intriguingly, SNPs in the NOD2 region have previously been associated with Crohn’s disease susceptibility in diverse populations, raising the question of common etiology or immunopathogenesis.

> Regions on chromosomes 5q, 17p, and 20p have been statistically associated with tuberculosis via linkage analysis (i.e., using genotype and phenotype data from multiple biologically related family members to assess heritability) in Thai families, suggesting a genetic basis for failure to contain infection in at least one ethnic population.33

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17. Ryan JR, Stoute JA, et al. Evidence for transmission of Plasmodium vivax among a Duffy antigen negative population in Western Kenya. Am J Trop Med Hyg. 2006;75:575-581.

18. Kindberg E, Mickiene A, et al. A deletion in the chemokine receptor 5 (CCR5) gene is associated with tickborne encephalitis. J Infect Dis. 197: 266-269.

19. Lim JK, Louie CY, et al. Genetic deficiency of chemokine receptor CCR5 is a strong risk factor for symptomatic West Nile virus infection: a meta-analysis of 4 cohorts in the US epidemic. J Infect Dis. 2008;197:262-265.

20. Lim JK, McDermott DH, et al. CCR5 deficiency is a risk factor for early clinical manifestations of West Nile virus infection but not for viral transmission. J Infect Dis. 2010;201:178-185.

21. Burns JC, Shimizu C, et al. Genetic variations in the receptor-ligand pair CCR5 and CCL3L1 are important determinants of susceptibility to Kawasaki disease. J Infect Dis. 2005;192:344-349.

22. Ahuja SK, He W. Double-edged genetic swords and immunity: lesson from CCR5 and beyond. J Infect Dis. 2010;201:171-174.

23. Deshpande A, Wheeler CM, et al. Variation in HLA class I antigen-processing genes and susceptibility to human papillomavirus type 16-associated cervical cancer. J Infect Dis. 2008;197:371-381.

24. Gostout BS, Poland GA, et al. TAP1, TAP2, and HLA-DR2 alleles are predictors of cervical cancer risk. Gynecol Oncol. 2003;88:326-332.

25. Shrestha S, Wang C, et al. Interleukin-10 gene (IL10) polymorphisms and human papillomavirus clearance among immunosuppressed adolescents. Cancer Epidemiol Biomarkers Prev. 2007;16:1626-1632.

26. Altfeld M, Addo MM, et al. Influence of HLA-B57 on clinical presentation and viral control during acute HIV-1 infection. AIDS. 2003;17:2581-2591.

27. Haynes BF, Pantaleo G, et al. Toward an understanding of the correlates of protective immunity to HIV infection. Science. 1996;271:324-328.

28. Fellay J, Shianna KV, et al. A whole-genome association study of major determinants for host control of HIV-1. Science. 2007;317:944-947.

29. Shrestha S, Aissani B, et al. Host genetics and HIV-1 viral load set-point in African-Americans. AIDS. 2009;23:673-677.

30. Pelak K, Goldstein DB, et al. Host determinants of HIV-1 control in African Americans. J Infect Dis. 201;201:1141-1149.

31. Berrington WR, Macdonald M, et al. Common polymorphisms in the NOD2 gene region are associated with leprosy and its reactive states. J Infect Dis. 2010;201:1422-1435.

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16. Samson M, Libert F, et al. Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature. 1996;382:722-725.

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A number of studies have highlighted the importance of gene polymorphism in vaccine responsiveness. HLA-DRB1*07, SNPs in IL-2 and IL-4 gene loci, and variants at the IL-12B locus have all been found to be independently associated with hepatitis B vaccine non-response.36 Investigators at the Mayo Clinic Vaccine Research Program have observed signifi cant associations between antibody responses and polymorphisms of HLA and cytokine/cytokine response genes for infl uenza and smallpox (vaccinia) vaccines.37,38,39 These investigators have also identifi ed associations between Class I and II HLA, cytokine, cytokine receptor, SLAM and CD46 (measles virus receptor molecules), and other immune response gene polymorphisms with varying humoral and cellular immune responses to MMR (measles, mumps, rubella) vaccine. There are also strong indicators that host genetics play a signifi cant role in host response after smallpox vaccination. In one study, an association between development of fever and IL-1 complex and IL-18 gene polymorphisms (and reduced risk of fever associated with an IL-4 gene SNP) was observed among vaccinia-naïve individuals.40 In another, more comprehensive analysis, polymorphisms in genes associated with adverse reactions to several pharmacologic agents (methylenetetrahydrofolate reductase [MTHFR]) and with interferon regulatory factor-1 (IRF1) were signifi cantly associated with the occurrence of adverse events following vaccination.41

There is a growing body of knowledge linking the occurrence of drug effectiveness and adverse drug reactions to human genomic variation. In the realm of infectious diseases, perhaps the most widely studied, and best understood, is with antiretroviral compounds.42 However, examples with other anti-infective compounds have emerged as well.

Although a large number of associations have been reported between antiretrovirals and host genetic polymorphisms, broad generalizations of these fi ndings have been diffi cult, due to small study size, inadequate statistical power, and selection biases. In some cases, however, associations have been replicated in multiple studies, lending credence to their potential utility for tailoring or “personalizing” pharmacotherapy. Some examples of associations observed through multiple independent assessments (reviewed by 42,43) include:

> Abacavir: hypersensitivity reaction (4-8% of Caucasian patients) associated with HLA-B*5701 allele

> Efavirenz: CNS side effects associated with CYP2B6 G T polymorphism at position 516

> Nelfi navir: metabolism (hence pharmacokinetics) infl uenced by CYP2C19 G A polymorphism at position 681

> Indinavir/atazanavir: hyperbilirubinemia associated with UGT 1A1 polymorphism

> Nucleoside reverse transcriptase inhibitors: lipoatrophy associated with TNF-α promoter gene polymorphisms

There is a growing body of knowledge linking the occurrence of drug effectiveness and adverse drug reactions to human genomic variation.

> A relatively small study showed that two polymorphisms in the TLR9 gene were associated with higher levels of IFN-γ in Ugandan children with cerebral malaria, but not in children with uncomplicated malaria, suggesting a possible genetic predisposition to altered pro-infl ammatory cytokine signaling.34

> Recently, fi ve polymorphisms in the gene coding for one of the cytokine signaling (SOCS) family of proteins, namely the cytokine-inducible SRC-homology domain protein (CISH) that plays a critical role in controlling IL-2 signaling, were associated with increased susceptibility to multiple categories of infectious pathogens.35 In a study of blood samples from >8,000 persons from seven case-control series in Southeast Asia and Africa, the risk of contracting malaria, tuberculosis, or bacteremia was increased by 18% (for those carrying one “risk” allele) to as much as 81% (for those carrying 4 or more “risk” alleles).

9www.quintiles.com

Recent genomics discoveries relating to therapeutic response in patients infected with hepatitis C virus (HCV) have raised hopes for major advances in managing this diffi cult disease. Treatment of patients infected with genotype 1 HCV has long been problematic. Recently, investigators from Duke and Johns Hopkins, using genome-wide association (GWAS) to explore host gene variation as predictors of outcome in patients with HCV, identifi ed extremely strong associations between sustained virological response (SVR) after pegylated interferon-α and ribavirin therapy for U.S. patients of European, African, and Hispanic ancestry with chronic genotype 1 HCV infection and an SNP on chromosome 19. This SNP, ≈3kb upstream of the region of the IL28B gene, codes for a type III interferon (IFN-λ3).44 The magnitude of the association between the relevant allele (CC genotype) was greater than all other clinical factors currently used to predict SVR (baseline viral load, baseline fi brosis, ethnicity), accounting for approximately half the difference in treatment response rates. These investigators subsequently associated this same SNP with spontaneous HCV clearance across multiple study cohorts.45

Studies elsewhere, incorporating disparate ethnic backgrounds, have replicated these fi ndings, adding the contribution of minor alleles in the IL28a, IL28b, and IL29 regions of chromosome 19 that infl uence virological response. The Duke and Johns Hopkins investigators also have recently identifi ed a group of SNPs on chromosome 20 that were associated with ribavirin-induced hemolytic anemia in HCV-treated patients.46 The strongest association was in the region of the ITPA gene, which encodes a protein that hydrolyses inosine triphosphate. The study suggested that two functional ITPA gene variants were responsible for conferring protection against anemia. The clinical ramifi cations of these collective fi ndings suggest that biomarkers for predicting SVR and possibly ribavirin-induced hemolytic anemia will emerge to guide therapeutic decision-making, and that a new appreciation for the signifi cance of the IL28 gene region is likely to ignite interest in IFN-λ as a potential therapeutic agent for patients with chronic HCV infection.

Biomarker Discovery

A biological marker, or biomarker, is a characteristic that is objectively measured and evaluated as an indicator of a physiological or pathological process, or pharmacologic response to a therapeutic intervention.47 In the context of drug and vaccine development, the value of biomarkers lies in their potential for replacing clinical endpoints; in other words, functioning as surrogate endpoints to reduce attrition, accelerating pre-clinical and clinical testing, and reducing costs.48

In addition to the use of human gene polymorphisms as “genetic biomarkers,” gene transcripts, proteins, metabolites, and other molecular signatures are potential biomarkers that can improve clinical management of disease and speed the drug discovery process. Transcriptomics, the analysis of global changes to gene expression profi les via the detection and analysis of differential up- and down-regulation of genes and gene clusters, provides a link between the genome and the set of proteins it expresses (the proteome). Transcriptomics has been at the heart of major advances

Recent genomics discoveries have raised hopes for major advances in managing hepatitis C virus (HCV).

www.quintiles.com10

While the details of these approaches far exceed the scope of the present review, the methodologies employed offer novel ways of examining biomic, tissue pathology, and clinical data to further understand disease pathogenesis, accelerate drug discovery, and assess pharmaceutical toxicities. By developing models that place gene or protein targets in their physiological context, a “global” perspective of compound impact can be assessed. Furthermore, the high-level interconnectivity described for both physiologic and pathologic (i.e., disease) networks suggests new strategies for targeting pathways, as opposed to single entities, through drug/biologic or vaccine cocktails, or even via assessment of more “promiscuous” compounds that affect multiple targets.

Conclusions

The application of biomics to infectious disease drug and vaccine development offers great promise in the quest to control burgeoning development costs and to impact the overall cost of healthcare delivery via both direct and indirect effects. As genetic predispositions for disease and drug response are increasingly identified and validated, and as biomarker development matures, leveraging these technologies becomes essential. This will enable a more informed approach to compound development and a shift in clinical development strategy to more aggressive, but ultimately less costly, proof-of-concept evaluation of NMEs and NBEs. When applied during earlier phase studies, this paradigm will begin to yield even greater efficiencies and lower costs across the industry.

15www.quintiles.com

GWAS and other association analyses currently constitute the technological underpinnings of pharmacogenomics. While the SNP approach in GWAS analysis is useful for single gene polymorphisms, the extent to which susceptibility to positive or negative pharmacologic responsiveness is due to multi-gene uncommon mutations, or mutations outside so-called “working genes,” is presently unknown. Technological advances in whole genome sequencing have begun to yield results in identifying rare, single-gene mutations underlying inherited diseases.71,72 Currently, costs for whole genome sequencing limit widespread application (prices currently run around $4,000–$10,000/sequence). However, “next generation” sequencers, utilizing technologies such as Ion Torrent Systems’ semiconductor-like chips etched with nanoscopic sample wells atop ion-sensitive layers that detect voltage changes, or Life Technologies’ “quantum dot” nanocrystals linked to DNA polymerase that transfer energy to fl uorescent-tagged bases upon laser excitation, promise signifi cantly improved effi ciency and give hope to dropping costs to the $1,000/sequence range. Alternatively, whole-exon sequencing, which focuses on the protein-encoding exons that constitute the approximately 1% of the genome that harbor about 90% of mutations with large effects, offers considerable cost effi ciency by shrinking the sequencing target.73,74 The relevance of whole genome and/or whole exon sequencing to pharmacogenetics in infectious diseases has yet to be defi ned, but the technology bears watching as it becomes increasingly more cost effi cient.

Finally, severe but uncommon outcomes from a number of infectious diseases are well recognized (e.g., Japanese encephalitis, poliomyelitis) but remain mechanistic puzzles; assessment of the bases for underlying susceptibility to these outcomes represent prime opportunities to apply biomics to characterize the molecular changes underlying these pathologies. Likewise, severe vaccine-associated side effects (e.g., cardiac and encephalitic complications of vaccinia vaccine, neurotropic reactions to 17-D yellow fever vaccine, Guillain-Barré following infl uenza and other vaccines) are readily identifi able candidates for studies to investigate genetic predictors or predispositions, regulatory abnormalities, or other underlying variances from normal response. While GWAS or linkage studies may prove useful in illuminating the basis for these clinical outcomes in some instances, the dynamic nature of the genome’s regulation, its genetic and epigenetic structural modifi cations, changing levels of expression, and other aspects of control make it highly likely that additional analytic approaches will be necessary to more thoroughly unravel the mysteries. Moreover, it has become fi rmly established that responses to infections or vaccines, adverse drug reactions, and other phenomena have their genesis in the complex interplay among the myriad components of the human biological system. Genetic factors conspire at varying levels with proteomic, metabolomic, environmental, and other effects to generate observed clinical outcomes.

Unraveling the contributions of these infl uences is one of the signifi cant future challenges to understanding mechanisms underlying clinical effects of drugs and vaccines, and tailoring more effective pharmacologic interventions. The complex computational methodologies required to integrate and analyze such multi-level biological data are only now being developed and exploited. A recently published analysis of genomic and proteomic associations with adverse events following vaccinia vaccination, for example, highlighted the use of machine-learning and decision analysis tools to model a pathogenetic mechanism involving prolonged stimulation of monocyte-based infl ammatory pathways and imbalance of tissue repair pathways.75 Indeed, efforts are under way to understand key pathways and organism-level responses to a number of disease states and host-pathogen interactions through application of complex network and systems biology approaches.67,76,77

The dynamic nature of the genome’s regulation and other aspects of control make it highly likely that more analytic approaches will be necessary to unravel the mysteries.

www.quintiles.com14

in oncology (e.g., HER2 over-expression in breast cancer and development/use of trastuzumab [Herceptin]), and has begun to enlighten understanding of the genetic basis for host response to infections, vaccine responsiveness, and vaccine effi cacy. For example, studies of gene expression in circulating mononuclear cells during acute and convalescent phases of measles infection in children have revealed that there is an up-regulation of cytokine and other immune response regulator genes (e.g., IL-1β, TNFα, IL-8, CXCL-2, ICAM-1, CIAS-1, and others), and a down-regulation of selected transcription, signal transduction, and other immune response genes.49 A transcriptomic analysis of host gene activity following vaccination with the 17D yellow fever vaccine documented the coordinated interplay between up-regulation of genes for specifi c transcription factors (e.g., Stat1, IRF7, ETS2) and those downstream in the effector arms of the immune response.50 Studies of children receiving trivalent inactivated (TIV) live-attenuated (LAIV) infl uenza vaccines demonstrated up-regulation of genes encoding proteins in the type 1 interferon pathway and cell cycle regulation, with LAIV recipients showing signifi cantly higher and earlier over-expression of type 1 interferon-stimulated genes compared with recipients of TIV vaccines.51 Differential expression of genes in the immunoproteasome pathway (integral to MHC Class I-linked antigen processing) was observed in recipients protected from malaria parasite challenge following vaccination with adjuvanted RTS,S vaccine.52

Proteomics allows for the evaluation of global changes in the full complement of proteins produced by cells or tissues at a defi ned point in time. Between alternative gene splicing and post-translational modifi cations, the number and nature of proteins is not a simple refl ection of the information contained on the genome. Changes in protein structure, function, or abundance detected in advance of clinically evident manifestations can serve as diagnostic or prognostic biomarkers. Recent technological advances have considerably enhanced both accuracy and sensitivity of detection of polypeptides, enzymes, and other proteins. For example, sophisticated technology (e.g., surface-enhanced laser desorption-ionization time-of-fl ight mass spectrometry [SELDI-TOF-MS]) coupled with pattern recognition analysis has been applied to fl uids and tissues to identify peptide and protein signals that can serve as biomarkers for identifying active tuberculosis infection with a high degree of accuracy.53

Metabolomics enables evaluation of global changes to the profi les of small-molecule metabolites, representing the chemical fi ngerprints of cellular processes.48 During an active infection, this consists of metabolites produced by the host, by the pathogen, and by the environment (including normal fl ora). Early successes with identifying clusters of metabolic biomarkers, or biosignatures, that distinguished active from latent tuberculosis have incited enthusiasm for this technology in managing this diffi cult problem.53 Metabolic profi ling of spinal fl uid for distinguishing viral, bacterial, and fungal meningitis and ventriculitis has also been demonstrated.54 This approach also holds considerable promise in biomarker discovery to improve accuracy of hepatic and renal toxicity screening of compounds.55

Changes in protein structure, function or abundance detected in advance of clinically evident manifestations can serve as diagnostic or prognostic biomarkers.

Transcriptomics has been at the heart of major advances in oncology and is shedding light on the genetic basis for host response to infections, vaccine responsiveness and vaccine effi cacy.

11www.quintiles.com

Near-Term Challenges

The application of genetic, transcriptomic, proteomic, and other “omic” data analyses to identify biomarkers that can be used to predict vaccine adverse events, to predict immunological responsiveness, and to predict effi cacy is emerging from its infancy. Patterns of susceptibility to infectious pathogens and/or vaccine response based upon broad categories have been recognized for some time. For example, hemoglobinopathies [β-thalassemia, HbS, HbC, HbE] and G6P-D defi ciency have been associated with protection from malaria; females typically generate stronger humoral immune responses than do males to vaccine antigens; individuals carrying certain Ig heavy- and k light chain [Km/Gm] allotypes respond poorly to polysaccharide antigens. The time is now upon us, when biomics and other new tools and technologies are beginning to clarify the genetic and molecular bases for these and other relationships. While pharmacogenomic screening has become established to a limited degree in infectious disease practice (e.g., HCP5/HLA-B*5701 screening prior to initiating therapy with abacavir in HIV positive patients56), much needs to be done to bring these powerful tools into the mainstream of clinical medicine.

With the expanding and increasing complexity of genomic, proteomic, and host metabolic and immunologic profi les come additional challenges related to validating biomarkers and other diagnostic or predictive profi les, and understanding their underlying mechanisms. For example, despite the prevalence and public health signifi cance of isoniazid- and other anti-tuberculosis (e.g., rifampin) drug-induced hepatoxicity, reliable biomarkers for these have yet to be defi ned. While investigations of a genetic basis for predisposition toward (or susceptibility to) hepatotoxicity among persons receiving these compounds have hinted at associations between polymorphisms in genes coding for drug metabolizing enzymes (e.g., the cytochrome P450 2EI [CYP2E1], N-acetyltransferase [NAT2], glutathione S transferase [GSTM1], and several carboxylesterases [CES1, CES2, CES4]), fi ndings have been inconsistent and sometimes confl icting.57,58,59,60 The answer to these and similar puzzles will require thoughtfully designed clinical trials involving large, prospective, replicated cohorts; focused experimental observation; and application of standardized specimen collection accompanied by cross-referenced clinical analysis (i.e., metadata). Equally important, criteria for causal relationships must be considered (or reconsidered) without a priori assumptions.61

A growing appreciation for the impact, both clinically and economically, of non-US/EU interests on matters pertaining to healthcare delivery offers both challenge and opportunity for biomics. For example, Asia’s segment of the healthcare market is climbing dramatically: estimates of Compound Annual Growth Rates range from 3% in SE Asia to 12% in India to 25% in China between 2009-2014, with Asia projected to capture as much as 16% of global pharmaceutical market share over this same interval.62 It is clear, therefore, that the relevance of genomic associations and biomarkers, primarily established in “western” populations, must be studied and validated in a much broader base of race/ethnicity backgrounds. Even within non-Western populations, heterogeneity will become increasingly relevant. Several assessments of SNPs associated with leprosy among Chinese32, African63, and Indian64,65 (summarized in 66) patients highlight commonalities and discrepancies perhaps based on ancestral allele frequencies. In a similar vein, the business case for pharmacogenomic and biomarker discovery pertaining to infections of primary importance in these “developing” markets (e.g., dengue, malaria, helminth infestation) grows ever stronger.

Biomics and other new tools and technologies are beginning to clarify the genetic and molecular bases for immunological responsiveness.

The relevance of genomic associations and biomarkers, primarily established in “western” populations, must be studied in a broader base of race/ethnicities.

www.quintiles.com12

As informative and productive as genetic/genomic, transcriptomic, proteomic, metabolomic, and other types of profi ling of biological samples have been in understanding disease pathogenesis, drug discovery, and identifying biomarkers, each of these single approaches is logically restricted to the level of biological organization that it addresses. To truly understand physiology, host response, disease, and drug toxicity at the level of molecular pathways, regulatory networks, cells, tissues, and ultimately the entire organism, integration of the diverse types of data revealed by these biomic analyses is required.

A great deal of effort is being expended in developing the computational and informatics tools necessary to “mine” the large amounts of data being generated. Learning algorithms have been developed that can be applied to identify predictive interactions across different “omics” levels, biases and noise specifi c to certain “omics” levels can be balanced or weighted by information at another, and general themes of how molecules interact with each other and infl uence measurable outcomes can be identifi ed (adapted from 53). Applications of this type of approach have already begun to bear fruit: A systems biology approach to the immune response of individuals to 17-D yellow fever vaccine enabled identifi cation of distinct gene “signatures” for predicting vaccine-specifi c CD8+ T-cell and neutralizing antibody responses with exceptional (up to 100%) accuracy.67 Translation of the knowledge derived from integrating biomics data into systems biology models of ever-increasing complexity that simulate biological systems in silico and allow for experimental perturbation to predict responses that can then be tested in vivo, offer tremendous promise for streamlining the drug discovery process.68 Incorporating systems biology into product development planning thus offers the potential for predicting vaccine and drug effectiveness far in advance of expensive and time-consuming clinical testing.

A Peek at the Future

Associations observed between HLA, cytokine, cytokine receptor, and virus receptor polymorphisms and measles vaccine responsiveness are already being exploited to develop next-generation vaccine candidates based upon peptides selected to circumvent HLA polymorphic restrictions (reviewed in 38). Validation of some of the exciting preliminary fi ndings highlighted in the paragraphs above, such as the association of MTHFR, IL-4, and IRF1 gene polymorphisms with the occurrence of adverse events after vaccinia vaccination, the association of over-expressed Type I interferon response genes with LAIV response, and the association of over-expressed immunoproteosome genes with protection after RTS,S malaria vaccination, could eventually lead to the development of predictive biomarkers as well as identifi cation of next-generation vaccines tailored to specifi c immune system targets.

Recently, another entity was added to the biomics armamentarium in the form of the “reactome array.”69 Unfortunately, there are outstanding questions regarding the methods for synthesizing the dye-labeled metabolites that are central to the physical array described in the seminal publication.70

However, assuming vindication of the questioned science, this technology will enable genome-independent profi ling of metabolic activity for an organism, population or tissue. By providing a “metabolic barcode” of study samples, a “reactome” can enable comparison across population members, and it may be possible to reconstruct much of the metabolic network of the material under study without pre-existing genetic information. In the presence of genetic data, a reactome array can also provide a direct link between the metabolome and the genome. The reactome array thus has wide potential application in diagnostics, enzyme discovery and will help us to further understand physiological and pathological processes.

Validation of some preliminary fi ndings could lead to the development of predictive biomarkers as well as identifi cation of next-generation vaccines.

13www.quintiles.com

Near-Term Challenges

The application of genetic, transcriptomic, proteomic, and other “omic” data analyses to identify biomarkers that can be used to predict vaccine adverse events, to predict immunological responsiveness, and to predict effi cacy is emerging from its infancy. Patterns of susceptibility to infectious pathogens and/or vaccine response based upon broad categories have been recognized for some time. For example, hemoglobinopathies [β-thalassemia, HbS, HbC, HbE] and G6P-D defi ciency have been associated with protection from malaria; females typically generate stronger humoral immune responses than do males to vaccine antigens; individuals carrying certain Ig heavy- and k light chain [Km/Gm] allotypes respond poorly to polysaccharide antigens. The time is now upon us, when biomics and other new tools and technologies are beginning to clarify the genetic and molecular bases for these and other relationships. While pharmacogenomic screening has become established to a limited degree in infectious disease practice (e.g., HCP5/HLA-B*5701 screening prior to initiating therapy with abacavir in HIV positive patients56), much needs to be done to bring these powerful tools into the mainstream of clinical medicine.

With the expanding and increasing complexity of genomic, proteomic, and host metabolic and immunologic profi les come additional challenges related to validating biomarkers and other diagnostic or predictive profi les, and understanding their underlying mechanisms. For example, despite the prevalence and public health signifi cance of isoniazid- and other anti-tuberculosis (e.g., rifampin) drug-induced hepatoxicity, reliable biomarkers for these have yet to be defi ned. While investigations of a genetic basis for predisposition toward (or susceptibility to) hepatotoxicity among persons receiving these compounds have hinted at associations between polymorphisms in genes coding for drug metabolizing enzymes (e.g., the cytochrome P450 2EI [CYP2E1], N-acetyltransferase [NAT2], glutathione S transferase [GSTM1], and several carboxylesterases [CES1, CES2, CES4]), fi ndings have been inconsistent and sometimes confl icting.57,58,59,60 The answer to these and similar puzzles will require thoughtfully designed clinical trials involving large, prospective, replicated cohorts; focused experimental observation; and application of standardized specimen collection accompanied by cross-referenced clinical analysis (i.e., metadata). Equally important, criteria for causal relationships must be considered (or reconsidered) without a priori assumptions.61

A growing appreciation for the impact, both clinically and economically, of non-US/EU interests on matters pertaining to healthcare delivery offers both challenge and opportunity for biomics. For example, Asia’s segment of the healthcare market is climbing dramatically: estimates of Compound Annual Growth Rates range from 3% in SE Asia to 12% in India to 25% in China between 2009-2014, with Asia projected to capture as much as 16% of global pharmaceutical market share over this same interval.62 It is clear, therefore, that the relevance of genomic associations and biomarkers, primarily established in “western” populations, must be studied and validated in a much broader base of race/ethnicity backgrounds. Even within non-Western populations, heterogeneity will become increasingly relevant. Several assessments of SNPs associated with leprosy among Chinese32, African63, and Indian64,65 (summarized in 66) patients highlight commonalities and discrepancies perhaps based on ancestral allele frequencies. In a similar vein, the business case for pharmacogenomic and biomarker discovery pertaining to infections of primary importance in these “developing” markets (e.g., dengue, malaria, helminth infestation) grows ever stronger.

Biomics and other new tools and technologies are beginning to clarify the genetic and molecular bases for immunological responsiveness.

The relevance of genomic associations and biomarkers, primarily established in “western” populations, must be studied in a broader base of race/ethnicities.

www.quintiles.com12

As informative and productive as genetic/genomic, transcriptomic, proteomic, metabolomic, and other types of profi ling of biological samples have been in understanding disease pathogenesis, drug discovery, and identifying biomarkers, each of these single approaches is logically restricted to the level of biological organization that it addresses. To truly understand physiology, host response, disease, and drug toxicity at the level of molecular pathways, regulatory networks, cells, tissues, and ultimately the entire organism, integration of the diverse types of data revealed by these biomic analyses is required.

A great deal of effort is being expended in developing the computational and informatics tools necessary to “mine” the large amounts of data being generated. Learning algorithms have been developed that can be applied to identify predictive interactions across different “omics” levels, biases and noise specifi c to certain “omics” levels can be balanced or weighted by information at another, and general themes of how molecules interact with each other and infl uence measurable outcomes can be identifi ed (adapted from 53). Applications of this type of approach have already begun to bear fruit: A systems biology approach to the immune response of individuals to 17-D yellow fever vaccine enabled identifi cation of distinct gene “signatures” for predicting vaccine-specifi c CD8+ T-cell and neutralizing antibody responses with exceptional (up to 100%) accuracy.67 Translation of the knowledge derived from integrating biomics data into systems biology models of ever-increasing complexity that simulate biological systems in silico and allow for experimental perturbation to predict responses that can then be tested in vivo, offer tremendous promise for streamlining the drug discovery process.68 Incorporating systems biology into product development planning thus offers the potential for predicting vaccine and drug effectiveness far in advance of expensive and time-consuming clinical testing.

A Peek at the Future

Associations observed between HLA, cytokine, cytokine receptor, and virus receptor polymorphisms and measles vaccine responsiveness are already being exploited to develop next-generation vaccine candidates based upon peptides selected to circumvent HLA polymorphic restrictions (reviewed in 38). Validation of some of the exciting preliminary fi ndings highlighted in the paragraphs above, such as the association of MTHFR, IL-4, and IRF1 gene polymorphisms with the occurrence of adverse events after vaccinia vaccination, the association of over-expressed Type I interferon response genes with LAIV response, and the association of over-expressed immunoproteosome genes with protection after RTS,S malaria vaccination, could eventually lead to the development of predictive biomarkers as well as identifi cation of next-generation vaccines tailored to specifi c immune system targets.

Recently, another entity was added to the biomics armamentarium in the form of the “reactome array.”69 Unfortunately, there are outstanding questions regarding the methods for synthesizing the dye-labeled metabolites that are central to the physical array described in the seminal publication.70

However, assuming vindication of the questioned science, this technology will enable genome-independent profi ling of metabolic activity for an organism, population or tissue. By providing a “metabolic barcode” of study samples, a “reactome” can enable comparison across population members, and it may be possible to reconstruct much of the metabolic network of the material under study without pre-existing genetic information. In the presence of genetic data, a reactome array can also provide a direct link between the metabolome and the genome. The reactome array thus has wide potential application in diagnostics, enzyme discovery and will help us to further understand physiological and pathological processes.

Validation of some preliminary fi ndings could lead to the development of predictive biomarkers as well as identifi cation of next-generation vaccines.

13www.quintiles.com

GWAS and other association analyses currently constitute the technological underpinnings of pharmacogenomics. While the SNP approach in GWAS analysis is useful for single gene polymorphisms, the extent to which susceptibility to positive or negative pharmacologic responsiveness is due to multi-gene uncommon mutations, or mutations outside so-called “working genes,” is presently unknown. Technological advances in whole genome sequencing have begun to yield results in identifying rare, single-gene mutations underlying inherited diseases.71,72 Currently, costs for whole genome sequencing limit widespread application (prices currently run around $4,000–$10,000/sequence). However, “next generation” sequencers, utilizing technologies such as Ion Torrent Systems’ semiconductor-like chips etched with nanoscopic sample wells atop ion-sensitive layers that detect voltage changes, or Life Technologies’ “quantum dot” nanocrystals linked to DNA polymerase that transfer energy to fl uorescent-tagged bases upon laser excitation, promise signifi cantly improved effi ciency and give hope to dropping costs to the $1,000/sequence range. Alternatively, whole-exon sequencing, which focuses on the protein-encoding exons that constitute the approximately 1% of the genome that harbor about 90% of mutations with large effects, offers considerable cost effi ciency by shrinking the sequencing target.73,74 The relevance of whole genome and/or whole exon sequencing to pharmacogenetics in infectious diseases has yet to be defi ned, but the technology bears watching as it becomes increasingly more cost effi cient.

Finally, severe but uncommon outcomes from a number of infectious diseases are well recognized (e.g., Japanese encephalitis, poliomyelitis) but remain mechanistic puzzles; assessment of the bases for underlying susceptibility to these outcomes represent prime opportunities to apply biomics to characterize the molecular changes underlying these pathologies. Likewise, severe vaccine-associated side effects (e.g., cardiac and encephalitic complications of vaccinia vaccine, neurotropic reactions to 17-D yellow fever vaccine, Guillain-Barré following infl uenza and other vaccines) are readily identifi able candidates for studies to investigate genetic predictors or predispositions, regulatory abnormalities, or other underlying variances from normal response. While GWAS or linkage studies may prove useful in illuminating the basis for these clinical outcomes in some instances, the dynamic nature of the genome’s regulation, its genetic and epigenetic structural modifi cations, changing levels of expression, and other aspects of control make it highly likely that additional analytic approaches will be necessary to more thoroughly unravel the mysteries. Moreover, it has become fi rmly established that responses to infections or vaccines, adverse drug reactions, and other phenomena have their genesis in the complex interplay among the myriad components of the human biological system. Genetic factors conspire at varying levels with proteomic, metabolomic, environmental, and other effects to generate observed clinical outcomes.

Unraveling the contributions of these infl uences is one of the signifi cant future challenges to understanding mechanisms underlying clinical effects of drugs and vaccines, and tailoring more effective pharmacologic interventions. The complex computational methodologies required to integrate and analyze such multi-level biological data are only now being developed and exploited. A recently published analysis of genomic and proteomic associations with adverse events following vaccinia vaccination, for example, highlighted the use of machine-learning and decision analysis tools to model a pathogenetic mechanism involving prolonged stimulation of monocyte-based infl ammatory pathways and imbalance of tissue repair pathways.75 Indeed, efforts are under way to understand key pathways and organism-level responses to a number of disease states and host-pathogen interactions through application of complex network and systems biology approaches.67,76,77

The dynamic nature of the genome’s regulation and other aspects of control make it highly likely that more analytic approaches will be necessary to unravel the mysteries.

www.quintiles.com14

in oncology (e.g., HER2 over-expression in breast cancer and development/use of trastuzumab [Herceptin]), and has begun to enlighten understanding of the genetic basis for host response to infections, vaccine responsiveness, and vaccine effi cacy. For example, studies of gene expression in circulating mononuclear cells during acute and convalescent phases of measles infection in children have revealed that there is an up-regulation of cytokine and other immune response regulator genes (e.g., IL-1β, TNFα, IL-8, CXCL-2, ICAM-1, CIAS-1, and others), and a down-regulation of selected transcription, signal transduction, and other immune response genes.49 A transcriptomic analysis of host gene activity following vaccination with the 17D yellow fever vaccine documented the coordinated interplay between up-regulation of genes for specifi c transcription factors (e.g., Stat1, IRF7, ETS2) and those downstream in the effector arms of the immune response.50 Studies of children receiving trivalent inactivated (TIV) live-attenuated (LAIV) infl uenza vaccines demonstrated up-regulation of genes encoding proteins in the type 1 interferon pathway and cell cycle regulation, with LAIV recipients showing signifi cantly higher and earlier over-expression of type 1 interferon-stimulated genes compared with recipients of TIV vaccines.51 Differential expression of genes in the immunoproteasome pathway (integral to MHC Class I-linked antigen processing) was observed in recipients protected from malaria parasite challenge following vaccination with adjuvanted RTS,S vaccine.52

Proteomics allows for the evaluation of global changes in the full complement of proteins produced by cells or tissues at a defi ned point in time. Between alternative gene splicing and post-translational modifi cations, the number and nature of proteins is not a simple refl ection of the information contained on the genome. Changes in protein structure, function, or abundance detected in advance of clinically evident manifestations can serve as diagnostic or prognostic biomarkers. Recent technological advances have considerably enhanced both accuracy and sensitivity of detection of polypeptides, enzymes, and other proteins. For example, sophisticated technology (e.g., surface-enhanced laser desorption-ionization time-of-fl ight mass spectrometry [SELDI-TOF-MS]) coupled with pattern recognition analysis has been applied to fl uids and tissues to identify peptide and protein signals that can serve as biomarkers for identifying active tuberculosis infection with a high degree of accuracy.53

Metabolomics enables evaluation of global changes to the profi les of small-molecule metabolites, representing the chemical fi ngerprints of cellular processes.48 During an active infection, this consists of metabolites produced by the host, by the pathogen, and by the environment (including normal fl ora). Early successes with identifying clusters of metabolic biomarkers, or biosignatures, that distinguished active from latent tuberculosis have incited enthusiasm for this technology in managing this diffi cult problem.53 Metabolic profi ling of spinal fl uid for distinguishing viral, bacterial, and fungal meningitis and ventriculitis has also been demonstrated.54 This approach also holds considerable promise in biomarker discovery to improve accuracy of hepatic and renal toxicity screening of compounds.55

Changes in protein structure, function or abundance detected in advance of clinically evident manifestations can serve as diagnostic or prognostic biomarkers.

Transcriptomics has been at the heart of major advances in oncology and is shedding light on the genetic basis for host response to infections, vaccine responsiveness and vaccine effi cacy.

11www.quintiles.com

Recent genomics discoveries relating to therapeutic response in patients infected with hepatitis C virus (HCV) have raised hopes for major advances in managing this diffi cult disease. Treatment of patients infected with genotype 1 HCV has long been problematic. Recently, investigators from Duke and Johns Hopkins, using genome-wide association (GWAS) to explore host gene variation as predictors of outcome in patients with HCV, identifi ed extremely strong associations between sustained virological response (SVR) after pegylated interferon-α and ribavirin therapy for U.S. patients of European, African, and Hispanic ancestry with chronic genotype 1 HCV infection and an SNP on chromosome 19. This SNP, ≈3kb upstream of the region of the IL28B gene, codes for a type III interferon (IFN-λ3).44 The magnitude of the association between the relevant allele (CC genotype) was greater than all other clinical factors currently used to predict SVR (baseline viral load, baseline fi brosis, ethnicity), accounting for approximately half the difference in treatment response rates. These investigators subsequently associated this same SNP with spontaneous HCV clearance across multiple study cohorts.45

Studies elsewhere, incorporating disparate ethnic backgrounds, have replicated these fi ndings, adding the contribution of minor alleles in the IL28a, IL28b, and IL29 regions of chromosome 19 that infl uence virological response. The Duke and Johns Hopkins investigators also have recently identifi ed a group of SNPs on chromosome 20 that were associated with ribavirin-induced hemolytic anemia in HCV-treated patients.46 The strongest association was in the region of the ITPA gene, which encodes a protein that hydrolyses inosine triphosphate. The study suggested that two functional ITPA gene variants were responsible for conferring protection against anemia. The clinical ramifi cations of these collective fi ndings suggest that biomarkers for predicting SVR and possibly ribavirin-induced hemolytic anemia will emerge to guide therapeutic decision-making, and that a new appreciation for the signifi cance of the IL28 gene region is likely to ignite interest in IFN-λ as a potential therapeutic agent for patients with chronic HCV infection.

Biomarker Discovery

A biological marker, or biomarker, is a characteristic that is objectively measured and evaluated as an indicator of a physiological or pathological process, or pharmacologic response to a therapeutic intervention.47 In the context of drug and vaccine development, the value of biomarkers lies in their potential for replacing clinical endpoints; in other words, functioning as surrogate endpoints to reduce attrition, accelerating pre-clinical and clinical testing, and reducing costs.48

In addition to the use of human gene polymorphisms as “genetic biomarkers,” gene transcripts, proteins, metabolites, and other molecular signatures are potential biomarkers that can improve clinical management of disease and speed the drug discovery process. Transcriptomics, the analysis of global changes to gene expression profi les via the detection and analysis of differential up- and down-regulation of genes and gene clusters, provides a link between the genome and the set of proteins it expresses (the proteome). Transcriptomics has been at the heart of major advances

Recent genomics discoveries have raised hopes for major advances in managing hepatitis C virus (HCV).

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While the details of these approaches far exceed the scope of the present review, the methodologies employed offer novel ways of examining biomic, tissue pathology, and clinical data to further understand disease pathogenesis, accelerate drug discovery, and assess pharmaceutical toxicities. By developing models that place gene or protein targets in their physiological context, a “global” perspective of compound impact can be assessed. Furthermore, the high-level interconnectivity described for both physiologic and pathologic (i.e., disease) networks suggests new strategies for targeting pathways, as opposed to single entities, through drug/biologic or vaccine cocktails, or even via assessment of more “promiscuous” compounds that affect multiple targets.

Conclusions

The application of biomics to infectious disease drug and vaccine development offers great promise in the quest to control burgeoning development costs and to impact the overall cost of healthcare delivery via both direct and indirect effects. As genetic predispositions for disease and drug response are increasingly identified and validated, and as biomarker development matures, leveraging these technologies becomes essential. This will enable a more informed approach to compound development and a shift in clinical development strategy to more aggressive, but ultimately less costly, proof-of-concept evaluation of NMEs and NBEs. When applied during earlier phase studies, this paradigm will begin to yield even greater efficiencies and lower costs across the industry.

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References

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2. Paul SM, Mytelka DS, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010;9:203-214.

3. PhRMA. Pharmaceutical Industry Profile 2010.

4. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3:711-718.

5. Fleischmann RD, Adams MD, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science. 1995;269:496-512.

6. Rinoudo CD, Telford JL, et al. Vaccinology in the genome era. J Clin Invest. 2009;119:2515-2525.

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8. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931-945.

9. Venter JC, Adams MD, et al. The sequence of the human genome. Science. 2001;291:1304-1351.

10. Seib KL, Dougan G, et al. The key role of genomics in modern vaccine and drug design for emerging infectious diseases. PLoS Genet. 2009;5(10):1-8.

11. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851-861.

12. Guidance for Industry: E15 Definitions for Genomic Biomarkers, Pharmacogenomics, Pharmacogenetics, Genomic Data and Sample Coding Categories. U.S. Department of Health and Human Services, Food and Drug Administration. 2008.

13. Epstein R. Effect of genotyping warfarin patients on outcomes: results from the national community-based Medco-Mayo Warfarin Effectiveness Study (MM-WES). Presented at: American College of Cardiology Annual Scientific Session/i2 Summit; March 2010; Atlanta, GA.

14. Alcais A, Abel L, et al. Human genetics of infectious diseases: between proof of principle and paradigm. J Clin Investig. 2009;9:2506-2514.

15. Miller LH, Mason SJ, et al. The resistance factor to Plasmodium vivax in blacks: the Duffy-blood-group genotype. N Engl J Med. 1976;169:1795-1802.

16. Samson M, Libert F, et al. Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature. 1996;382:722-725.

www.quintiles.com16

A number of studies have highlighted the importance of gene polymorphism in vaccine responsiveness. HLA-DRB1*07, SNPs in IL-2 and IL-4 gene loci, and variants at the IL-12B locus have all been found to be independently associated with hepatitis B vaccine non-response.36 Investigators at the Mayo Clinic Vaccine Research Program have observed signifi cant associations between antibody responses and polymorphisms of HLA and cytokine/cytokine response genes for infl uenza and smallpox (vaccinia) vaccines.37,38,39 These investigators have also identifi ed associations between Class I and II HLA, cytokine, cytokine receptor, SLAM and CD46 (measles virus receptor molecules), and other immune response gene polymorphisms with varying humoral and cellular immune responses to MMR (measles, mumps, rubella) vaccine. There are also strong indicators that host genetics play a signifi cant role in host response after smallpox vaccination. In one study, an association between development of fever and IL-1 complex and IL-18 gene polymorphisms (and reduced risk of fever associated with an IL-4 gene SNP) was observed among vaccinia-naïve individuals.40 In another, more comprehensive analysis, polymorphisms in genes associated with adverse reactions to several pharmacologic agents (methylenetetrahydrofolate reductase [MTHFR]) and with interferon regulatory factor-1 (IRF1) were signifi cantly associated with the occurrence of adverse events following vaccination.41

There is a growing body of knowledge linking the occurrence of drug effectiveness and adverse drug reactions to human genomic variation. In the realm of infectious diseases, perhaps the most widely studied, and best understood, is with antiretroviral compounds.42 However, examples with other anti-infective compounds have emerged as well.

Although a large number of associations have been reported between antiretrovirals and host genetic polymorphisms, broad generalizations of these fi ndings have been diffi cult, due to small study size, inadequate statistical power, and selection biases. In some cases, however, associations have been replicated in multiple studies, lending credence to their potential utility for tailoring or “personalizing” pharmacotherapy. Some examples of associations observed through multiple independent assessments (reviewed by 42,43) include:

> Abacavir: hypersensitivity reaction (4-8% of Caucasian patients) associated with HLA-B*5701 allele

> Efavirenz: CNS side effects associated with CYP2B6 G T polymorphism at position 516

> Nelfi navir: metabolism (hence pharmacokinetics) infl uenced by CYP2C19 G A polymorphism at position 681

> Indinavir/atazanavir: hyperbilirubinemia associated with UGT 1A1 polymorphism

> Nucleoside reverse transcriptase inhibitors: lipoatrophy associated with TNF-α promoter gene polymorphisms

There is a growing body of knowledge linking the occurrence of drug effectiveness and adverse drug reactions to human genomic variation.

> A relatively small study showed that two polymorphisms in the TLR9 gene were associated with higher levels of IFN-γ in Ugandan children with cerebral malaria, but not in children with uncomplicated malaria, suggesting a possible genetic predisposition to altered pro-infl ammatory cytokine signaling.34

> Recently, fi ve polymorphisms in the gene coding for one of the cytokine signaling (SOCS) family of proteins, namely the cytokine-inducible SRC-homology domain protein (CISH) that plays a critical role in controlling IL-2 signaling, were associated with increased susceptibility to multiple categories of infectious pathogens.35 In a study of blood samples from >8,000 persons from seven case-control series in Southeast Asia and Africa, the risk of contracting malaria, tuberculosis, or bacteremia was increased by 18% (for those carrying one “risk” allele) to as much as 81% (for those carrying 4 or more “risk” alleles).

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is a selective advantage of HBB heterozygosity in protection against severe malaria over both types of homozygotes; P. vivax is transmitted in some populations lacking DARC on their red cells17; additional relationships have been identified between the CCR5 Δ32 variant and development of symptomatic and/or severe disease following infection with two neurotropic flaviviruses (West Nile and tickborne encephalitis)18,19,20; and there is an inverse correlation between CCR5 Δ32 prevalence and the incidence of Kawasaki disease, a condition suspected to have an infectious trigger.21 Untangling the mysteries underlying these and other gene polymorphisms thus promises to be enlightening on multiple fronts.22

With the profusion of genomic analyses, numerous other relationships between genetics and response to infection have come to light. For example:

> Genes coding for proteins involved in antigen processing for HLA class I presentation (e.g., TAP1, TAP2, LMP2, LPM7, Tapasin), HLA-DR223,24, and polymorphisms in IL-10 genes25, have all been linked to HPV-associated cervical cancer susceptibility.

> Several HLA molecules have been associated with immune control of HIV (primarily via cytotoxic T-cell response), the most important of which appears to be HLA B5726,27. A polymorphism located in a gene (HCP5) near the HLA-B locus has been found to account for almost 10% of the variation in HIV-1 set point among individuals of European ancestry28; interestingly, this gene variant is in high linkage disequilibrium (i.e., it is not randomly associated) with the HLA allele B*5701, which not only has a very strong protective effect on HIV-1 disease progression, but is associated as well with abacavir hypersensitivity (see below). In the same study, a different polymorphism located in the nearby HLA-C gene explained another 6.5% of the set-point effect (together, the 2 polymorphisms thus accounting for almost 15% of the variation), while an additional set of polymorphisms encoding an RNA polymerase 1 subunit, had nearly a 6% effect on viral progression.28 Follow-up studies among African Americans have identified the same HLA-C-associated SNP linked to viral load set point.29 However, a different polymorphism in the HLA-B gene than that found for European Americans, one associated with the HLA-B*5703 allotype, appears to be the most important common variant influencing viral load among African Americans.30

> Polymorphisms in several genes have been associated with susceptibility to leprosy, as well as its clinical spectrum, in different populations. Variants in as-yet unidentified genes have been mapped to chromosome 10p13 (India), to the regulatory region shared by a gene coding for the E3-ubiquitin ligase Parkin (PARK2) and the Parkin coregulated gene (PACRG) (Viet Nam), and to the lymphotoxin-α gene (LTA) ( for early-onset leprosy in Viet Nam) (reviewed in 14). More recently, polymorphisms in the nucleotide-binding oligomerization domain containing 2 (NOD2) signaling pathway, which regulate innate immune response, have been associated with susceptibility to M. leprae infection in China and Nepal.31,32 Intriguingly, SNPs in the NOD2 region have previously been associated with Crohn’s disease susceptibility in diverse populations, raising the question of common etiology or immunopathogenesis.

> Regions on chromosomes 5q, 17p, and 20p have been statistically associated with tuberculosis via linkage analysis (i.e., using genotype and phenotype data from multiple biologically related family members to assess heritability) in Thai families, suggesting a genetic basis for failure to contain infection in at least one ethnic population.33

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17. Ryan JR, Stoute JA, et al. Evidence for transmission of Plasmodium vivax among a Duffy antigen negative population in Western Kenya. Am J Trop Med Hyg. 2006;75:575-581.

18. Kindberg E, Mickiene A, et al. A deletion in the chemokine receptor 5 (CCR5) gene is associated with tickborne encephalitis. J Infect Dis. 197: 266-269.

19. Lim JK, Louie CY, et al. Genetic deficiency of chemokine receptor CCR5 is a strong risk factor for symptomatic West Nile virus infection: a meta-analysis of 4 cohorts in the US epidemic. J Infect Dis. 2008;197:262-265.

20. Lim JK, McDermott DH, et al. CCR5 deficiency is a risk factor for early clinical manifestations of West Nile virus infection but not for viral transmission. J Infect Dis. 2010;201:178-185.

21. Burns JC, Shimizu C, et al. Genetic variations in the receptor-ligand pair CCR5 and CCL3L1 are important determinants of susceptibility to Kawasaki disease. J Infect Dis. 2005;192:344-349.

22. Ahuja SK, He W. Double-edged genetic swords and immunity: lesson from CCR5 and beyond. J Infect Dis. 2010;201:171-174.

23. Deshpande A, Wheeler CM, et al. Variation in HLA class I antigen-processing genes and susceptibility to human papillomavirus type 16-associated cervical cancer. J Infect Dis. 2008;197:371-381.

24. Gostout BS, Poland GA, et al. TAP1, TAP2, and HLA-DR2 alleles are predictors of cervical cancer risk. Gynecol Oncol. 2003;88:326-332.

25. Shrestha S, Wang C, et al. Interleukin-10 gene (IL10) polymorphisms and human papillomavirus clearance among immunosuppressed adolescents. Cancer Epidemiol Biomarkers Prev. 2007;16:1626-1632.

26. Altfeld M, Addo MM, et al. Influence of HLA-B57 on clinical presentation and viral control during acute HIV-1 infection. AIDS. 2003;17:2581-2591.

27. Haynes BF, Pantaleo G, et al. Toward an understanding of the correlates of protective immunity to HIV infection. Science. 1996;271:324-328.

28. Fellay J, Shianna KV, et al. A whole-genome association study of major determinants for host control of HIV-1. Science. 2007;317:944-947.

29. Shrestha S, Aissani B, et al. Host genetics and HIV-1 viral load set-point in African-Americans. AIDS. 2009;23:673-677.

30. Pelak K, Goldstein DB, et al. Host determinants of HIV-1 control in African Americans. J Infect Dis. 201;201:1141-1149.

31. Berrington WR, Macdonald M, et al. Common polymorphisms in the NOD2 gene region are associated with leprosy and its reactive states. J Infect Dis. 2010;201:1422-1435.

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32. Zhang FR, Huang W, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361:2609-2618.

33. Mahasirimongkol S, Yanai H, et al. Genome-wide SNP-based linkage analysis of tuberculosis in Thais. Genes Immun. 2008;10:77-83.

34. Sam-Agudu NA, Greene JA, et al. TLR9 polymorphisms are associated with altered IFN-γ levels in children with cerebral malaria. Am J Trop Med Hyg. 2010;82:548-555.

35. Khor CC, Vannberg FO, et al. CISH and susceptibility to infectious diseases. N Engl J Med. 2010;362:2092-2101.

36. Wang C, Tang J, et al. HLA and cytokine gene polymorphisms are independently associated with responses to hepatitis B vaccination. Hepatology. 2004;39:978-988.

37. Poland GA, Ovsyannikova IG, et al. Immunogenetics of seasonal influenza vaccine response. Vaccine. 2008;26(4)(suppl):D35-40.

38. Poland GA, Ovsyannikova IG, et al. Personalized vaccines: the emerging field of vaccinomics. Expert Opin Biol Ther. 2008;8:1659-1667.

39. Poland GA, Ovsyannikova IG, et al. Application of pharmacogenomics to vaccines. Pharmacogenomics. 2009;10:837-852.

40. Stanley SL Jr, Frey SE, et al. The immunogenetics of smallpox vaccination. J Infect Dis. 2007;196:212-219.

41. Reif DM, McKinney BA, et al. Genetic basis for adverse events after smallpox vaccination. J Infect Dis. 2008;198:16-22.

42. Tozzi V. Pharmacogenetics of antiretrovirals. Antiviral Res. 2010;85:190-200.

43. Motsinger AA, Haas DW, et al. Human genomic association studies: a primer for the infectious diseases specialist. J Infect Dis. 2007;195:1737-1744.

44. Ge D, Fellay J, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature. 2007;461:399-401.

45. Thomas DL, Thio CL, et al. Genetic variation in IL28B and spontaneous clearance of hepatitis C virus. Nature. 2009;461:798-801.

46. Fellay J, Thompson AJ, et al. ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C. Nature. 2010;464:405-408.

47. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89-95.

48. Parida SK, Kaufmann SH. The quest for biomarkers in tuberculosis. Drug Discov Today. 15:148-157.

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The U.S. Food and Drug Administration (FDA) defi nes pharmacogenomics as “the study of variations in DNA and RNA characteristics as related to drug response.”12 Therefore, the goal of pharmacogenomics is to identify genetic variants (i.e., polymorphisms) and use them to predict responses to drugs, biologics, and vaccines. These responses include those to therapy and prophylaxis (e.g., immune responses), to susceptibility to diseases, and to adverse events based upon differences in genetic makeup.

The correlations of human gene polymorphisms with drug response have led to advances in clinical therapeutics that have enabled a more individualized approach to disease management, or “personalized medicine.” The impact has perhaps been most notable in oncology (e.g., KRAS gene screening to identify colorectal cancer patients likely to benefi t from cetuximab), but discoveries in transplantation immunology, rheumatology, and other areas have been made as well. Certain genetic polymorphisms have been found to correlate with the probability of response to existing drugs (e.g., warfarin and CYP2C9 and VKORC1 alleles, clopidrogel and the CYP2C19 allele), and so-called “genetic biomarkers” have also been used to identify patients at increased risk for drug toxicity (e.g., adverse dermal reactions with carbamazipine and the HLA-B*1502 allele). While the cost-effectiveness of genetic testing in conjunction with pharmacotherapy has yet to be systematically proven, a recent study sponsored by Medco Health Solutions suggests that signifi cant impacts are possible: genotyping for the CYP2C9 and VKORC1 alleles applied in conjunction with warfarin prescribing was found to cut hospitalization rates by approximately 30%.13 The tailoring of pharmacotherapy to an individual’s genetic profi le as a focal point of both drug discovery and healthcare debate for the foreseeable future is made a virtual certainty by these observations.

Pharmacogenomics in Infectious Disease

Classical immunogenetics and genomics have yielded signifi cant insights into understanding the genetic basis underlying normal immunological responses and susceptibility to infections, with clues to the molecular underpinnings of both immunological responsiveness and protection following vaccination. The foundations of genetic determinism were laid with the predisposition to multiple and varied infectious diseases observed among children with primary immunodefi ciency disorders (e.g., severe congenital neutropenia, X-linked agammaglobulinemia, severe combined immunodefi ciency) (reviewed in 14). Subsequently, increasing numbers of immunologic disorders were found to confer susceptibility to single pathogens (e.g., invasive Neisseria infections associated with X-linked properdin defi ciency or defects in the terminal components of the complement cascade, inherited susceptibility to mycobacterial disease associated with defects in IL-12/IL-23-dependent IFN-λ-mediated immunity, and recently unc93 homolog B [UNC93B] and TLR3 defi ciencies associated with susceptibility to sporadic HSV encephalitis) (reviewed in 14). Genetically determined resistance to infectious agents has been elucidated as well.

Perhaps the three most widely recognized genomic associations with infectious diseases are the natural resistance to severe P. falciparum malaria among hemoglobin S (HbS) heterozygotes due to the HBB allele, P. vivax malaria resistance conferred by the absence of the Duffy blood group (the chemokine DARC, and the erythrocyte chemokine co-receptor for P. vivax)15, and the 32 base pair deletion in the gene encoding the primary HIV-1 co-receptor, CC chemokine receptor 5 (i.e., the CCR5 Δ32 variant), which confers high-level resistance among individuals of European descent to infection with this deadly virus.16 These polymorphisms are clearly evolutionarily complex: There

The goal of pharmacogenomics is to identify genetic variants and use them to predict responses to drugs, biologics and vaccines.

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Pharmacogenomics: The Leading Edge of Personalized Medicine

Pharmacologic compounds cannot be expected to behave the same way in every person dosed, as there are multiple determinants of effectiveness, tolerability, and safety. In the process of clinical evaluation of drugs, biologics, and vaccines, there is an attempt to control as many extrinsic (e.g., diet, co-administered drugs, smoking) and intrinsic (e.g., age, gender, underlying disease/organ dysfunction) influences as possible to provide a clean profile ahead of regulatory approval. The fact is, however, that only 20–30% of investigational new drugs (INDs) clear the safety and efficacy hurdles necessary to secure new drug/biological license approval (NDA/BLA), with a significant proportion of failures occurring in large and expensive late phase trials.1,2,4 This failure to eliminate ineffective investigational products early in development, or to identify “rescue” opportunities for products of value in population subsets, is an enormous drag on R&D spending, a significant factor in corporate valuation, and a compromise of the industry’s commitment to stewardship of study volunteer and patient health. Moreover, because clinical trials represent, by design, statistical samples of target populations, newly licensed compounds are fraught with further costly and potentially destructive adverse event or efficacy profiles as experience with usage grows over time and prescription number.

The haploid human genome (23 chromosomes) contains approximately 30,000 genes, made up of more than 3 billion nucleotide base pairs. Since every person typically carries one copy of each gene (i.e., an allele) inherited from each parent, individuals generally carry two gene copies. An individual’s genotype reflects the specific alleles for each of the ≈30,000 gene sites, or loci, on his/her chromosomes. While >99.9% of the DNA sequences that comprise genes are identical among individuals, about 1 in 1,200 may differ between any two persons. Sites on the genome where DNA sequences vary by a single nucleotide base are called single nucleotide polymorphisms (SNPs). If these SNPs occur in coding regions that cause amino acid changes, they have the potential to change the structure of the translated proteins. Such coding differences may be clinically important, particularly if they affect gene function or the level of gene expression.

The International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/index.html.en) estimates that about 10 million common SNPs exist in human populations, where the minor (rarer) allele occurs at a frequency of >1%. Although the Project has catalogued more than 3.1 million SNPs in some 270 individuals, it should be noted that this particular dataset is limited by its target population (based on Utah Caucasians, Han Chinese, Japanese, and Nigerian Yoruba).11 It is evident that, given the vast number of genes that make up the human genome (about half of which have, as yet, undefined functions), most gene polymorphisms likely have little to do with the response of an individual to a given drug, biologic, or vaccine. However, it has become increasingly clear that much of the individual variability observed in responses to pharmacologic agents is, in fact, attributable to genotype differences in certain chromosome regions; notably, those that affect metabolism, immune regulation, and gene expression. A regularly updated compendium of literature pertaining to the influence of human genetic polymorphisms on drug response is maintained through the Pharmacogenomics Knowledge Library (PharmGKB) at http://www.pharmgkb.org/index.jsp. The reader is encouraged to consult this database for additional details on illustrative examples included in the current discussion, as well as for additional associations not noted.

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49. Zilliox MJ, Moss WJ, et al. Gene expression changes in peripheral blood mononuclear cells during measles virus infection. Clin Vaccine Immunol. 2007;14:918-923.

50. Gaucher D, Therrien R, et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J Exp Med. 2008;205:3119-3131.

51. Zhu W, Higgs BW, et al. A whole genome transcriptional analysis of the early immune response induced by live attenuated and inactivated influenza vaccines in young children. Vaccine. 2010;28:2865-2876.

52. Vahey MT, Wang Z, et al. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J Infect Dis. 2010;201:580-589.

53. Jacobsen M, Mattow J, et al. Novel strategies to identify biomarkers in tuberculosis. Biol Chem. 2008;389:487-495.

54. Coen M, O’Sullivan M, et al. Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin Infect Dis. 2005;41:1582-1590.

55. Beger RD, Sun J, et al. Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol Appl Pharmacol. 243:154-166.

56. Colombo S, Rauch A, et al. The HCP5 single-nucleotide polymorphism: a simple screening tool for prediction of hypersensitivity reaction to abacavir. J Infect Dis. 2008;198:864-867.

57. Kim SH, Kim SH, et al. Genetic polymorphisms of drug-metabolizing enzymes and anti-TB drug-induced hepatitis. Pharmacogenomics. 2009;10:1767-1779.

58. Sun F, Chen Y, et al. Drug-metabolising enzyme polymorphisms and predisposition to anti-tuberculosis drug-induced liver injury: a meta-analysis. Int J Tuberc Lung Dis. 2008;12:994-1002.

59. Yamada S, Tang M, et al. Genetic variations of NAT2 and CYP2E1 and isoniazid hepatotoxicity in a diverse population. Pharmacogenomics. 2009;10:1433-1445.

60. Yue J, Peng R. Does CYP2E1 play a major role in the aggravation of isoniazid toxicity by rifampicin in human hepatocytes? Br J Pharmacol. 2009;157:331-333.

61. Relman DA. Learning to appreciate our differences. J Infect Dis. 2008;198:4-5.

62. Engineer F, Tharmaratnam A. Realizing the promise of Asia Pacific: the region’s strategic shift from outsourcing to innovation. Quintiles white paper. 2010.

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63. Meisner SJ, Mucklow S, et al. Association of NRAMP1 polymorphism with leprosy type but not susceptibility to leprosy per se in west Africans. Am J Trop Med Hyg. 2001;65:733-735.

64. Malhotra D, Darvishi K, et al. IL-10 promoter single nucleotide polymorphisms are significantly associated with resistance to leprosy. Hum Genet. 2005;118:295-300.

65. Roy S, Frodsham A, et al. Association of vitamin D receptor genotype with leprosy type. J Infect Dis. 1999;179:187-91.

66. Wong SH, Hill AVS, et al. Genomewide association study of leprosy [letter]. N Engl J Med. 2010;362:1446-1447.

67. Querec TD, Akondy RS, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116-125.

68. Butcher EC, Berg EL, et al. Systems biology in drug discovery. Nat Biotech. 2004;22:1253-1259.

69. Beloqui A, Guazzaroni ME, et al. Reactome array: forging a link between metabolome and genome. Science. 2009;326:252-257.

70. Alberts B. Editorial expression of concern. Science. 2010;327:144.

71. Lupski JR, Reid JG, et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N Engl J Med. 2010;362:1181-1191.

72. Roach JC, Glusman G, et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. In press.

73. Lifton RP. Individual genomes on the horizon. N Engl J Med. 2010;362:1235-1236.

74. Ng SB, Turner EH, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272-276.

75. Reif DM, Motsinger-Reif AA, et al. Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun. 2009;10:112-119.

76. Schadt EE, Friend SH, et al. A network view of disease and compound screening. Nat Rev Drug Discov. 2009;8:286-295.

77. Pujol A, Mosca R, et al. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci. 2010;31:115-123.

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Drug and Vaccine Discovery in the Biomics Era Traditional methods of drug and vaccine discovery are time- and labor-intensive, costly, and plagued by inaccuracies. With the introduction of biomics, greater precision has been introduced, and high-throughput tools have vastly enhanced the speed with which compounds are used to probe for suitable targets. It is estimated that over one to two years, as many as 10–100 times more candidate antimicrobial drugs and vaccines can be identifi ed using various biomics-based approaches than might be found using traditional approaches over a comparable interval.10

Principles that increase the probability of success include fi nding targets that should, at a minimum, be 1) expressed or accessible to the host immune system (or to a therapeutic agent) during human disease, 2) important for pathogenesis or pathogen survival, 3) genetically conserved, and 4) free of measurable homology or similarity to host proteins or other factors.10 Adherence to these tenets has resulted in numerous creative approaches to drug and vaccine design, and there has been an accompanying explosion of corporate entities through which these approaches have been scaled and commercialized.

The application of genomics to pathogen target identifi cation has seen success in the “intelligent design” of promising new vaccines in silico via what has been termed “reverse vaccinology”; candidate products for N. meningiditis serogroup B, group B streptococci, and several viruses, among others, have been produced and are under evaluation (reviewed in 6, 10). Transcriptomics has been applied to garner an understanding of the genetic basis for hyperinfection seen with V. cholera. Functional genomics has shed light on the genes required by H. pylori to colonize its host. Proteomics has been applied to the identifi cation and characterization of specifi c group A streptococcal surface proteins. Exploiting the pathogen-host interface has found application through development of host cell drug targets based upon functional genomic, transcriptomic, and proteomic analyses of host gene up-regulation following infection. Some approaches not directly pertinent to target screening (e.g., use of metagenomics to probe biological samples for the genetic material of unidentifi ed or “emerging” infectious agents, and application of epigenomics or metabolomics to studies of disease pathogenesis) have also proven relevant to developers’ requirements and needs. The data generated from these technologies, coupled with advances in computational biology and information technology, have been fundamental to the current revolution in drug and vaccine design. The litany of discoveries attributable to biomics-based technologies over the past 5–10 years far exceeds the scope of this overview, but there is no doubt that we have seen the future of drug and vaccine development for infectious diseases, and it is here.

Biomics are ushering in the future of drug and vaccine development for infectious diseases.

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Table 1. Selected Biomics Tools Applied to Drug, Biologic and Vaccine Discovery

Tool Definition Typical Application

Genomics Approach to sequencing and bioinformatic processing of genetic data to identify candidate genes meeting selected predictive criteria

Identification of vaccine targets

Functional genomics Approach to assessing patterns of gene expression under various conditions

Studies of disease pathogenesis

Epigenomics Study of enzyme and protein structures that impact high order DNA structure and gene expression

Mechanisms of disease/ pathogenesis

Metagenomics Study of genomic material in environmental/biological samples

Identification of unknown/emerging pathogens in pathological samples

Proteomics Study of set of proteins coded by genome of interest

Diagnostic biomarkers

Transcriptomics Study of RNA molecules expressed under defined conditions

Genome expression profiling

Metabolomics Study of small molecule metabolic intermediates produced by organisms under various conditions

Toxicology, phenotypic profiling in conjunction with functional genomics

Immunomics Study of the effectors of the mammalian immune system

Identification of B and T cell epitopes in vaccine design

Vaccinomics Study of the response of the mammalian immune system to vaccines, biologics, or drugs

Identification of gene polymorphisms underlying differential responses to vaccines

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About the Author

Kelly T. McKee Jr., M.D., M.P.H.Vice President, Public Health and Government Services, Quintiles

Dr. McKee has dedicated his career to researching and preventing infectious diseases. Before joining Quintiles in 2006, he served as Senior Director of Clinical Research and Chief Medical Officer for a vaccine development company. After finishing a 20-year career in the U.S. Army Medical Department, he served as the Head of General Communicable Disease Control and State Epidemiologist for North Carolina, but returned to government work at the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID). He has written more than 100 peer-reviewed publications and textbook chapters and reviews manuscripts for several professional journals.

Acknowledgements

The author wishes to thank Drs. Oren Cohen, Claude Hughes and Sandra Silberman for their reviews and thoughtful comments on this manuscript.

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Copyright © 2010 Quintiles. 16.15.15-102010

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Executive Summary

The sequencing of bacterial, viral, fungal, parasite, and human genomes that began during the closing years of the 20th century has ushered in an era of profound advancement in understanding with regard to microbial virulence; host response to and control of infection; and disease pathogenesis. In parallel, this knowledge has been exploited in the creation of vaccines, drugs, and biologics tailored to optimize target presentation and effectiveness in preventing and treating infectious diseases. Equally exciting has been the development of biological indicators, or biomarkers, based upon associations increasingly recognized between genes, their downstream products (i.e., transcripts, proteins, metabolites) and pathological processes that can be used to diagnose, monitor, and manage infections. There is evidence that these technological advances can be leveraged to improve the quality of healthcare delivery in certain disciplines (e.g., oncology, hematology, cardiology, neurology), and it is anticipated that the totality of these recent advances, coupled with integrative/systems biology approaches, will deliver effi ciencies in drug and vaccine development, will improve product effectiveness and minimize “off-target” side effects, and will be a major force in containing the unsustainable growth in the cost of healthcare delivery around the globe as we continue wrestling with the microbial world.

Pharmacobiomics and Infectious Diseases: Progress and OpportunitiesKelly T. McKee Jr., M.D., M.P.H.Vice PresidentPublic Health and Government Services, Quintiles

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