The emergence of pharmacogenomics within the pharmaceutical industry
The sequencing of the human genome represents one of the most significant scientific advances of the 20th century that will shape the foundation of medical research well into the 21st.This accomplishment was enabled by remarkable technological advances – high throughput sequencing, increased computing power, automated methods of analysis – that 25 years ago seemed unimaginable.Through this project, we have gained the understanding that human beings are an estimated 99.9% identical at the genetic level.Yet, it is the 0.1% of variation among individuals that serves as the foundation for the emerging discipline of pharmacogenomics. It is this variation that contributes to physical diversity in the human population as well as differences in disease susceptibility and response to pharmacological therapies.This contribution of the Human Genome Project offers the opportunity to shape the face of drug discovery and development in this century.
harmacogenetics was first described in the At first pharmacogenetics involved the charac- By Aidan Power, 1950s and can be defined, with broad agree- terisation of interindividual variation in drug Suzin Webb, Pment, as the study of DNA sequence varia- metabolising enzymes. It was determined by tradi- Dr Albert Seymour tion or genotype as it relates to differential drug tional phenotype measurements such as urinary and Dr Patrice Milos response. No general agreement on a definition of metabolite response to a probe drug. During the pharmacogenomics has yet been reached. The 1980s and 90s phenotypic variation began to be Pharmacogenetics Working Group (an informal understood at the level of gene mutations (defined collection of representatives from pharmaceutical as <1% frequency in the population) or polymor- companies that meet to discuss non-competitive phisms (≥1% in the general population) within issues related to pharmacogenetics) defines phar- major classes of drug metabolising enzymes. Today macogenomics as ‘the study of the genome and its the phenotype of interest is more diverse. It may be products (including RNA and protein) as they a biochemical measurement (eg fasting glucose relate to drug discovery and development’ (see level), a physical measurement (eg VO2), demo- Drug Information Association website at www.dia- graphic data (eg gender, smoking status, age) or home.org). This is the sense in which most compa- outcomes data (relapse, remission). nies use the term although recent authors have The simplest correlation involves DNA muta- attempted to define pharmacogenomics more nar- tions or polymorphisms within a gene that dis- rowly as the “study of differences in drug response rupt the normal activity, structure, or function of due to variation in the expression of the individual that gene’s product and result in an easily observ- genes in the cells of particular tissues”1. able phenotype. The earliest example of this was
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the discovery of a molecular basis for the ABO er and prescribe therapies that are tailored to an blood groups2. A more current example is the individual’s needs. The potential impact of per- surprisingly large interindividual variability sonalised medicine may be large: safer medicines, observed in the activity of critical drug metabolis- faster relief and, ultimately, cost savings for ing enzymes, particularly the cytochrome P450 healthcare. This is very much a vision for the enzyme family. There are numerous reports of future and we are just beginning to understand genetic contributions to this inherent variability. how to approach this goal. Pharmacogenetics In fact, for some genes (CYP2D6, CYP2C19, and could play a very important role in realising per- others) the genotype-phenotype correlation sonalised medicine as we begin to identify genetic approaches 100%3-6. markers associated with superior efficacy. In the Pharmacogenetics relies on the correlation of future, individuals with a particular disease may genetic polymorphisms with an observable pheno- be genetically screened to determine which thera- type. The emergence of new information on genet- peutic agent will have the highest probability of ic variation across the entire genome and the delivering efficacy. But this future is quite distant extensive cataloguing of this variation through the and the industry faces many hurdles. efforts of The SNP Consortium have provided us with a new foundation to further explore the sci- The requirements for ence of pharmacogenomics. Through scientific and pharmacogenomic studies technological advances in genetics and genomics Pharmacogenomics moves the pharmaceutical we see a future with a much more comprehensive industry into new avenues of research that require understanding of human disease, adding valuable technologies and methods of data analysis that are knowledge to putative therapeutic targets and not traditionally part of the core business. emerging insight into how individuals respond to Execution of pharmacogenomic studies is depend- drug therapies. ent on many diverse pieces of information that must be brought together to integrate genotype The challenge for the pharmaceutical and phenotype. These include access to: accurate industry clinical and demographic data; DNA samples There are a number of human diseases for which from well designed studies; single nucleotide poly- a specific causative genetic mutation in a particu- morphisms; genotyping technologies; informatics lar gene is known, such as cystic fibrosis, technologies to handle large quantities of data; Huntington disease, Tay-Sachs disease and sickle statistical methodologies for data analysis and cell anaemia2. The more common or ethnic specif- interpretation as well as general education within ic mutations causing these diseases can be detect- the pharmaceutical setting in the area of pharma- ed, and families with a history of these disorders cogenomics. Each of these is explored in more often use genetic information for family planning detail below. and counselling purposes. Fortunately, these dis- eases are relatively rare in most communities. Accurate clinical and demographic data However, the diseases that are most prevalent in The single most important requirement for phar- all communities have a complex etiology that macogenomics is well-defined information for the undoubtedly involves multiple genes and environ- disease or phenotype being studied. Without these mental effects. Overcoming this complexity is the data one can never expect to be successful in key challenge the pharmaceutical industry faces in exploring the relationship between subject differ- designing and developing therapeutic agents to ences and disease state or drug response. In the treat diseases such as obesity, diabetes, cardiovas- pharmaceutical industry we have the opportunity cular disease, atherosclerosis, osteoporosis, to conduct well-defined clinical studies in specific rheumatoid arthritis, osteoarthritis, infectious dis- disease populations. These studies often provide eases, chronic pain, neurodegenerative diseases, the opportunity to evaluate genetic contributions depression, schizophrenia, immunodeficiencies, to disease within the parameters of the entry crite- allergies, respiratory disorders and cancer. ria for that particular trial, for example subjects For most (if not all) of these diseases there is no with LDL levels within certain limits. However, it known cure, only therapeutic intervention. And must be realised that these are selected populations these therapeutic interventions are not effective in for the purpose of a clinical trial and not always all patients. Recently a common theme has representative of the wider population with dis- evolved among pharmaceutical companies – that ease. Many trials also offer the opportunity to of ‘personalised medicine’7. The goal is to discov- evaluate longer-term outcomes of morbidity and
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Anonymised New tube samples Remove PID 123 RELABEL
Sample Secure sample storage
New random ID (XYZ) DELETE KEY generated in secure file Anonymised Subject 123 at genetic study site analysis
Copy RELABEL Remove PID 123 and DOB Secure data in Clinical data pharmacogenetics database
Anonymised data
Figure 1 mortality – data that are not easily obtainable single nucleotide polymorphisms (SNPs), inser- Collection of DNA samples through other kinds of studies. tions and deletions of one or more bases and from consenting clinical trial variable numbers of tandem repeats (VNTRs). participants is anonymised DNA samples Some genetic polymorphisms have functional using the scheme detailed Currently our efforts in Pfizer, as in many other consequences, while others are benign on their here.This provides the opportunity to protect patient companies, focus on the collection of DNA from own but still useful from a genetic standpoint, as confidentiality while at the clinical trial subjects who voluntarily agree to they serve as ‘markers’ for particular regions of same time enabling donate a portion of their blood for research pur- a chromosome. It appears that SNPs are the pharmacogenomic poses. Informed consent is a critical part of this most common polymorphisms and, due to their investigations. Informatic process and a separate informed consent is provid- common frequency and binary nature which solutions are required at each step of the process ed to patients who agree to participate, informing enable high-throughput analysis, they have pre- them of the purpose of the research as well as the dominated in large scale pharmacogenomic process used to anonymise their sample for patient studies. SNP discovery used to be a long and protection (Figure 1). This allows the subsequent arduous task, but several new technologies, investigation of genes related to drug response and companies and collaborations have recently underlying disease state through the association of made SNPs much more accessible. genetic variations with observed phenotypes. In 1999 The SNP Consortium Ltd (TSC) was formed as a non-profit foundation with the mis- Polymorphic genetic markers sion of providing public genomic data in the form Several types of genetic polymorphisms can be of SNPs distributed evenly throughout the human found throughout the human genome, including genome and to make the information related to
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Figure 2 Genotyping technologies continue to mature and will ultimately enable cost-effective genotyping for whole genome association studies to address pharmacogenomic hyptheses
these SNPs available to the public without intellec- patient disease state and therapeutic response. tual property restrictions (http://snp.cshl.org). The Technological advances in genotyping for SNPs project started in April 1999 and was expected to have resulted in rapid, gel-free scoring systems that complete delivery of 300,000 SNPs by the end of rely on either fluorescence-based or mass-based 2001. During this period the TSC delivered 1.5 scoring of a particular SNP (Figure 2). These million SNPs into the public domain for academic advances have driven the cost of genotyping far and industry scientists throughout the world to below the $1 mark and costs continue to fall as the use. Complementing this genome-wide coverage of possibility of multiplexing becomes a reality. In SNPs, several biotechnology companies have also addition, many of these technologies enable pool- applied their technologies to discover novel SNPs ing of large populations of subjects (100-500), thus throughout the genome or to provide deep cover- further increasing data generation while decreasing age of SNP discovery within specific candidate overall cost. genes. Examples of these include Celera, Perlegen The design of a pharmacogenomics study is a and Genaissance, respectively. major factor in deciding which genotyping tech- It is estimated that there are between 3 and 10 nology is the most appropriate. The majority of million SNPs within the human genome, with an genotyping platforms are acceptable for analysis average spacing of one SNP every 500 to 1,000 of one or a few genes, interrogating data sets of base pairs. Lack of knowledge of these SNPs once moderate size. For example, investigation of a represented a major limitation to advances in phar- candidate gene potentially involved in obesity may macogenomics. Now, web-based access to compre- involve an association study design of 500 cases hensive SNP information is routine and there are and 500 controls and the interrogation of 10 SNPs few regions in the genome where SNP data are across the candidate gene locus. Thus, the lacking. Current limitations are the lack of data required 10,000 genotypes could realistically be relating to SNP frequency, population distribution delivered in most genetics laboratories within a and inheritance patterns and the challenge of assay reasonable period of time. However, to interrogate development for large numbers of SNPs. multiple SNPs in 50 candidate genes more sophis- ticated methods for sample handling, PCR prepa- Genotyping technologies ration and genotyping are required and capacity With the hurdle of SNP discovery disappearing, the quickly becomes an issue for many human genet- next challenge is the ability to perform sufficient ics laboratories. For whole genome analysis the genotyping to address specific questions related to scale increases dramatically. A 3,000 patient Phase
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III study without an a priori hypothesis related to The statistical analysis required for pharmacoge- References drug response could well require 300,000 or more nomic studies can be broken down into two cate- 1 Buchanan,A, Califano,A, genotypes across the genome for each patient! gories: population genetics and genotype/pheno- Kahn, J, McPherson, E, Robertson, J, Brody, B. Several companies, such as Perlegen and type analyses. The extent of each depends on the Pharmacogenetics: ethical Sequenom, are developing whole genome method- study design. issues and policy options. ologies and genotyping platforms capable of han- A study may test for a direct association Kennedy Institute of Ethics dling studies of this magnitude. Each uses propri- between a known functional SNP and a particular Journal 2002;12:1-15. etary technology although their ability to interro- phenotype. Alternatively, the study design may 2 Nussbaum, RL, McInnes, RR, Willard, HF. Genetic variation gate the whole genome for statistically relevant depend on linkage disequilibrium (LD) – a meas- in populations.Thompson and genetic associations with phenotypes has yet to be ure of relatedness between neighbouring SNPs Thompson Genetics in demonstrated. While polymorphism discovery and that can be used to detect association, indirectly, Medicine. 6th Edition. genotyping used to be rate-limiting, the amount of to an unknown functional variant. Philadelphia:W.B. Saunders data that can now be generated has pushed the Pharmacogenomic studies routinely rely on a Company. 2001; pp95-109. 3 Bertilsson, L. Geographical/ bottleneck further downstream to data manage- case/control design using unrelated subjects thus Interracial differences in ment, analysis and translation into disease rele- requiring statistical algorithms to quantitate LD polymorphic drug oxidation – vant information. and estimate haplotypes (SNPs along a common current state of knowledge of chromosome of a single parental origin) in the cytochromes P450 (CYP) 2D6 Informatic platforms absence of parental data. This information can and 2C19. Clin Pharmacokinet 1995;29:192-209. The ability to integrate disparate data sources into then be used to select SNPs for study in both a 4 Kroemer, HK and an organised data repository remains a significant candidate gene or a high-density genome scan Eichelbaum, M. Molecular basis challenge for pharmacogenomics research. study design. In addition, several groups are pro- and clinical consequences of Consider the volume of data collected in clinical viding very important clues to the extent of LD genetic cytochrome P450 2D6 trials; the anonymisation of the data to remove across the genome, the influences of genetic varia- polymorphism. Life Sciences 1995;56:2285-98. certain patient identifiers (Figure 1) and the tion and how this information can be applied to 5 Chen, S, Chou,W-H, Blouin, requirement of a separate database to retain this improve the design of studies for pharmacoge- RA, Mao, Z, Humphries, LL, information; the generation of massive numbers of nomics and human genetics in general8-9. Meek, QC et al.The genotypes associated with clinical data; the statis- The success of each pharmacogenomic study cytochrome P450 2D6 tical tools needed to establish relationships depends on how much of the variability in pheno- (CYP2D6) enzyme polymorphism: screening costs between genotype and phenotype; the mining of type is caused by variation within the candidate and influence on clinical the genotype-phenotype data in the hypothesis- gene(s) (referred to as the effect size), how well outcomes in psychiatry. Clin generating stage and the desire to replicate initial characterised the phenotype is (quantitative vs sub- Pharm Ther 1996;60:522-34. findings; and one quickly understands the chal- jective measurements) and the overall power of the 6 McElroy, SM, Sachse, C, lenges to delivery of informatic solutions. The study as determined by sample size. The complexi- Brockmoller, J, Richmond, JL, Lira, ME, Friedman, DL, Roots, pharmaceutical industry has developed many data ty of the diseases that the pharmaceutical industry I, Silber, BM, Milos, PM. CYP repository platforms to enable collection and studies means that specific statistical analysis 2D6 genotyping as an audit of clinical data, so we stand poised to methods and study design may be required to pro- alternative to phenotyping for address information platforms for such analysis. A vide the best chance of detecting a genetic signal in determination of metabolic number of biotech companies have sprung up with the presence of confounding effects such as envi- status in a clinical trial setting. AAPS PharmSci 2000;2(4) the goal of providing an integrated platform for ronmental factors, variation in phenotypic meas- article 33. such diverse data sources. urement and population specific effects such as 7 Mancinelli, L, Cronin, M, admixture. This is particularly the case when the Sadee,W. Pharmacogenomics: Statistical methodologies and data analysis pharmacogenomics study entails multiple biologi- the promise of personalized Technological advances in SNP discovery, genotyp- cally relevant candidate genes – with multiple SNPs medicine.AAPS PharmSci. 2000;2(1):E4. ing and informatics platforms to manage vast within each gene – being tested for association with amounts of information have provided the genetics a complex phenotype which may be the result of Continued on page 52 community with the tools to better understand several genes interacting with each other and with how genetic variation contributes to disease sus- environmental factors. Thus statistical approaches ceptibility and therapeutic efficacy and safety. to the analysis of these multivariate data sources, However, with these advances comes the need to such as regression and recursive portioning better understand the patterns of SNP distributions methodologies, are currently being employed. and environmental factors and the novel quantita- In addition, replication of the finding in a tive clinical measurements that are used to pheno- prospective study may be required to provide the type subjects in a clinical trial setting. Advances in confidence needed to assess the impact of phar- statistical analysis of these complex datasets are macogenomics in that particular disease or currently being tested and implemented. response variable.
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Continued from page 51 Education within the pharmaceutical industry Brown University. She has used molecular genetic Finally, there is a need to provide a comprehensive diversity to address questions in Australian prehis- 8 Patil, N, Berno,AJ, Hinds, DA, understanding of the science of human genetics tory forensics, conifer genomic evolution, marker- Barrett,WA, Doshi, JM, Hacker, CR, Kautzer, CR, Lee, DH, and the use of this information for the develop- assisted breeding and pharmacogenomics. Marjoribanks, C, McDonough, ment of pharmacogenomic approaches. Through DP,Nguyen, BT, Norris, MC, internal seminar series for clinicians, regulatory Dr Albert Seymour is a Senior Research Scientist in Sheehan, JB, Shen, N, Stern, D, staff, discovery scientists and management, togeth- Discovery Pharmacogenomics at Pfizer Global Stokowski, RP,Thomas, DJ, er with external education of physicians and Research and Development. He received a MS from Trulson, MO,Vyas, KR, Frazer, KA, Fodor, SP,Cox, DR. Blocks patients, we hope to develop a finer appreciation The Johns Hopkins University in Molecular Biology of limited haplotype diversity for the fruits emerging from the human genome and subsequent to that received his PhD from The revealed by high-resolution project. This education will continue to provide University of Pittsburgh in human genetics. scanning of human insight into potential research initiatives, as well as chromosome 21. Science,. clinical trial design and interpretation. Dr Patrice Milos currently oversees the Discovery 2001;294:1719-23. Reich, DE, Schaffner, SF, Daly, Pharmacogenomics, Clinical Biochemical MJ, McVean G, Mullikin, JC, Summary Measurements and DNA Sequencing Core for Higgins, JM, Richter, DJ, Lander The concept that genetic background can con- Pfizer Global Research and Development in ES,Altshuler, D. Human tribute to interindividual differences in disease sus- Groton, CT. Her previous position with Pfizer genomic sequence variation ceptibility and genetic variation and how it influ- involved molecular biology research in the and the influence of gene history, mutation and ences variation in response to medicines has led to Atherosclerosis disease area. She received her PhD recombination. Nature significant interest in pharmacogenomics within from Rensselaer Polytechnic Institute and complet- Genetics 2002;32:135-142 the pharmaceutical industry and biotechnology ed post-doctoral fellowships are Harvard and world. Combined with proteomics, expression Brown Universities. profiling, bioinformatics and animal models, it provides a unique opportunity to potentially influ- ence decision-making in the discovery and devel- opment of new medicines to better understand the relationship of targets and human disease, improve clinical trial design and select optimal doses for medicines. The field of pharmacogenomics has evolved dramatically over the last five years and will continue to do so. We have become adept at identifying polymorphic genetic markers and using genotyping assays to detect them in large, well- characterised patient populations. We have begun to develop bioinformatic tools, data management systems and statistical approaches to handle the wealth of data coming out of the genomics revolu- tion. We are only just beginning to understand what impact pharmacogenomics studies will have on the future of drug discovery and development. Success will need to be measured by scientific and clinical researchers, physicians, business leaders and the population at large. DDW
Aidan Power is Worldwide Head of Clinical Pharmacogenomics for Pfizer Global Research and Development based in New London, CT. He received his medical degree from University College Cork in Ireland, an MSc from University College London, UK and has post-graduate quali- fications in Psychiatry.
Suzin Webb received a BS in Genetics and Developmental Bio from Cornell University and her MS degree in Biology and Medicine from
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