Pharmacogenomics in Endocrinology

Richard D. Hockett, Sandra C. Kirkwood, Bruce H. Mitlak and Willard H. Dere

Eli Lilly & Company, Indianapolis, Indiana 46285

Address all correspondence and requests for reprints to: Richard D. Hockett, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285. E-mail: . hockett{at}lilly.com

The first draft of the human genome was published recently by two independent groups (1, 2). This important advance has led to a host of editorials describing how knowledge derived from the human genome will revolutionize medicine and drug development. The ability to predict the likely development of disease or appropriate response to therapy has been touted as a means to improve clinical outcome (3). The expression "the right drug into the right patient" has been used to describe the effects the genetic revolution will have on drug development. The term pharmacogenomics has often been employed to describe the application of genetics to certain aspects of drug development. Its classic meaning has been to understand the genetic influences on drug response. The most studied aspect has been the genetic variation in the drug metabolizing enzymes of the liver, which leads to altered enzyme kinetics for drugs metabolized by these enzymes (4, 5, 6). However, the sequencing of the genome and the development of newer technologies for exploring genes and proteins on a large scale have led to a redefinition of the term. In the broadest sense, pharmacogenomics now refers to the application of genetic technologies and information across the entire drug discovery and development platform. The overt goal is to understand the relationship between genotype and drug response, drug efficacy, or drug toxicity. This stated goal has relevance to the discovery, preclinical, and clinical development platforms.

A pharmacogenomics, or applied genomics, strategy leverages the human genetic sequence, a collection of sophisticated analytical tools, and genetic expertise to identify and develop better drugs faster, and to improve patient care. Although the term pharmacogenomics has application to the entire drug discovery chain, this paper will focus exclusively on applications of genetics in clinical use. In this context, the genetic applications are directed to the identification of biomarkers to diagnose disease, predict drug response, or predict toxicity. If we can tailor therapy to those individuals most likely to benefit and exclude those individuals who don’t respond or develop toxicity, the impact in medicine would be great. Such personalized therapy to improve the overall benefit-risk profile is as yet the unrecognized promise of genetics.

This long pursuit to improve patient care by customizing therapy will require genetic biomarkers to identify groups or subsets of patients with the same molecular basis of disease (rather than clinical or phenotypic categorization) who will respond with increased clinical efficacy to a specific therapy. As a consequence, subsets of patients who would be less responsive to a particular therapy should be provided alternatives. Moreover, because the goal of personalized therapy is to improve the overall benefit-risk profile, appropriate attention should be directed toward clinical safety. For example, patients who are predisposed toward particular toxicities (hepatic, cardiac, or bone marrow) should be identified and excluded.

As this overview will show, we are only just beginning to use the power of genetics in drug development. Unfortunately our ability to identify patients at risk for disease, stratify patients by clinical outcome and treatment response, or predict adverse event occurrences is, in reality, several years away.

Genetics in drug development

In drug discovery, the use of genetics has been positioned to fall into two broad categories: disease susceptibility genetics and drug activity genetics (Fig. 1Go). Disease susceptibility genetics refers to the identification of genetic polymorphisms that contribute to the risk for development of disease. This includes the identification of a single gene resulting in a well defined inherited disorder, such as sickle cell anemia, and the association of a number of genes with a complex disease, such as diabetes mellitus. On the other hand, drug activity genetics refers to the identification of genetic polymorphisms and the response to drugs, either efficacy, toxicity, or side effects. The important distinction lies in the specific use of a defined drug and ultimately the patient’s response to that drug.



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Figure 1. Schematic of drug activity genomics vs. disease susceptibility genomics. Each category is subdivided into discovery and clinical phases. For both drug activity and disease susceptibility genomics, genetic applications encompass the entire drug discovery and development value chain. Individual tasks associated with these functions are specific to parts of the pipeline, as with target identification in discovery or sample banking in the clinical setting, whereas other tasks appear in all three categories, such as biomarker development (shaded overlap region). MOA, Mechanism of action.

 
Drug activity genomics

Predicting how a patient will respond to a medication is the goal of drug activity genomics. Genetic biomarkers may facilitate classification of individuals, allowing for individualized prescriptions. Furthermore, predicting toxicity or adverse events to a specific drug would have enormous utility, decreasing a significant health care problem. Although the widespread clinical utility of such genetic biomarkers is yet to be proven, the recent literature has contained a few reports of the association of genetic variants with a clinical response.

Currently, as mentioned above, the most common genetic biomarkers used in drug development are the genetic polymorphisms present in the metabolic enzymes of the liver. A variety of mutations are found in the metabolizing enzymes, with single nucleotide polymorphisms the most common (7). Unlike disease susceptibility polymorphisms, the genetic variants in these enzymes do not predict disease. Nor do these enzyme variants predict response to therapy, but lead to the marked alteration in clearance of drugs, sometimes leading to marked drug toxicity (8, 9). As such, these enzymes are not targets for new therapeutics. The list of drugs metabolized by each enzyme system is large and is reviewed elsewhere (4, 5, 6).

As an example, the cytochrome P450 2D6 (CYP2D6) enzyme has approximately 37 known mutations, 6 of which have been shown to have no enzyme activity (CYP2D6*3, *4, *5, *6, *9, *21, see ref. 10) and another 2–3 that have been shown to have decreased activity (CYP2D6*10, *17). This enzyme is one of the major drug-metabolizing enzyme systems, with approximately 25% of the current cadre of commercially available drugs metabolized by CYP2D6 (11). A patient with two copies of a defective gene (e.g. homozygous CYP2D6*4 or CYP2D6*4, *6) has significantly reduced clearance of the parent drug, resulting in a prolonged half-life. The importance of reduced drug clearance is heightened when the therapeutic margin of safety is relatively low. Therefore, in individuals with decreased clearance of the drug due to certain polymorphisms in a metabolic enzyme, a reduced dose may be warranted. Even though these tests are not widely used outside of clinical trials, there is growing acceptance for their use in determining proper dosing regimens.

Two recent examples in the literature pertaining to the drug activity arena are in the field of asthma, and they associate drug response to genetic polymorphisms. One used the clinical trial results for ABT-761, a selective inhibitor of 5-lipoxygenase (ALOX5), with genetic variation in the ALOX5 promoter (12). Individuals homozygous for rare alleles demonstrated significantly decreased response as measured by FEV1 when compared with individuals heterozygous and homozygous for the wild-type allele. This study provides evidence for the importance of the regulatory region of genes, rather than in the coding regions of the genes themselves, in predicting response. Another example is the relationship between polymorphisms in the B2 adrenergic receptor gene and response to B agonists aimed at reversing acute bronchospasm in asthma (13). While predicting who will respond, both studies highlight problems inherent in this kind of analysis. Although the identified variants in the B2 adrenergic receptor gene have demonstrated functional consequences in vitro and in vivo, their relationship with bronchodilatory response to B agonists remains uncertain. The complexity in understanding response is further illustrated by the fact that only about 6% of asthma patients do not carry a wild-type allele at the ALOX5 promoter locus, but many more than 6% of asthmatics do not respond to ABT-761 (12). Thus, there may be other genetic defects in the pathway yet to be identified. Although these examples illustrate the potential applicability of genetic biomarkers for response, these markers clearly are in the early stages of development.

Disease susceptibility genomics

Genetic markers for disease susceptibility can have large impact in medicine, with many rare disorders explained by single gene mutations. For years, the identification of gene mutations predisposing to rare diseases, such as the expanded trinucleotide repeat (CAG) in the Huntington’s disease gene (14) and the mutations for cystic fibrosis, most frequently the {delta} F508 variant (15, 16), has been possible. The vast majority of these mutations are germline and inherited in a Mendelian fashion (dominant or recessive) with high penetrance, often resulting in a definitive disease phenotype. The identification of these rare variants has led to improved understanding of the pathophysiology of disease but has had relatively little impact on drug development. The two main reasons for this are the difficulty in relating the specific gene mutation to a "druggable" target and the fact that, because of their rarity, the mutations in the general population do not lend themselves to current drug development programs.

Conversely, the genetic markers with the greatest potential to impact medicine and drug development are those associated with complex diseases. Complex human disorders, caused by multiple genetic and environmental factors, are characterized by high population prevalence, lack of clear Mendelian patterns of transmission, etiological and phenotypic heterogeneity, and a continuum between disease and nondisease states (17, 18). Complex diseases cause significant morbidity and mortality, adding billions of dollars to the health care budget each year. In the field of endocrinology, diabetes mellitus, osteoporosis, and obesity are a few examples. Most of the genetic research in these fields has centered on the identification of disease susceptibility genes. However, as with single disease genetics above, to date no targeted therapeutics have been developed by first identifying a genetic association and then developing a drug to treat the associated disease. Although some may consider hormonal replacement therapy (such as insulin therapy for diabetes) an example of treating a complex disease, the replacement therapy doesn’t directly treat a specific genetic disorder, but rather provides the same treatment for all phenotypes. Although investigation of the susceptibility to complex diseases is an important area of research, the immediate impact on the development of new therapeutics is uncertain.

Review of the genetics of complex endocrine disorders

Both disease susceptibility and drug activity genetics will play key roles in shaping clinical practice in the future. Toward the goal of providing personalized medicine to patients with endocrine disorders, let’s review the promise and pitfalls of four areas – hypertension, diabetes mellitus, obesity, and osteoporosis. We will start with an in-depth review of osteoporosis, showing current status in both drug activity genomics and disease susceptibility genomics, and end with brief descriptions of the other disorders.

Osteoporosis is a global medical problem for older men and women, resulting in a huge economic burden. The National Osteoporosis Foundation currently estimates that the prevalence of osteoporosis and low bone mass in the United States is almost 44 million women and men age 50 or older (represents 55% of those over the age of 50 yr). In Europe, approximately one in eight citizens over the age of 50 yr will suffer a fracture of the spine, and one in three women over the age of 80 yr will have a hip fracture as a result of osteoporosis (International Osteoporosis Foundation website at http://www.osteofound.org/). It is hoped that understanding the pathophysiology and underlying genetics for this disease will lead to additional therapeutic options.

Evidence for the importance of genetic factors contributing to the development of osteoporosis has been obtained from heritability studies in twins and families. These studies have demonstrated that genetics plays an important role in achievement of peak bone mineral density (BMD) as well as other factors that also contribute to the risk of fracture such as bone size and geometry, bone turnover, and muscle strength (19). Further insights into the importance of genetic differences underlying variations in BMD have been gained by linkage studies in both humans and animals.

Focusing on disease susceptibility genetics, linkage studies have identified illuminating examples in which low or high BMD is inherited in simple Mendelian fashion. Examples of these include osteogenesis imperfecta (20), osteopetrosis (21), and osteoporosis due to inactivating mutations in the aromatase gene (22) or the estrogen receptor gene (23). An extended family has been described in which there is linkage between a genetic locus on chromosome 11 (11q12–13) and very high spinal BMD (24). A gene that appears to be responsible for these effects has recently been identified as LRP5 (25). As with most single gene mutations leading to disease, these are rare and account for only a small portion of individuals with osteoporosis.

As a complex disease, osteoporosis is a result of interactions between environmental and genetic factors (26), which may differ by skeletal site. One of the more promising genetic associations was reviewed recently by Mann et al. (27), where the authors evaluated the importance of allelic variation at a single nucleotide polymorphism in the COL1A1 gene at a site that interacts with the transcription activator Sp1. By meta-analysis of 16 prior studies, they showed an association between the COL1A1 "s" alleles and bone density, body mass index, and fractures. Furthermore, results from a cultured osteoblast system provide evidence for a molecular basis for the association of allelic variation at this site and skeletal fragility. It is not yet clear what proportion of osteoporosis can be attributed to the COL1A1 polymorphism.

To highlight further the phenotypic and genotypic complexities in osteoporosis, this disease can also be part of a syndrome. The osteoporosis-pseudoglioma syndrome (28) and autosomal recessive osteopetrosis (29) are two examples. Subsequent work by Koller et al. (30) provided evidence that the 11q12 chromosomal region is linked to variation in BMD in the normal population. It is interesting to note that osteoporosis-pseudoglioma syndrome has been mapped to the same gene on chromosome 11, 11q12–13 as noted above (31). It is unclear whether additional genes affecting BMD are also present in this region.

The aforementioned examples in osteoporosis are focused on disease susceptibility genetics. To date, none of the identified genes have resulted in novel treatments for this disease or changes in prescribing practices. Approaching drug activity genomics and getting closer to targeted therapeutics at defined genetic polymorphisms, one could hypothesize a plausible scenario using the vitamin D receptor. Studies from several groups have examined the relationship between allelic variations in the vitamin D receptor, BMD, bone turnover, and calcium metabolism. The initial report (32) suggested that a significant proportion of the gene effect on BMD could be explained by a polymorphism in the untranslated region of the vitamin D receptor gene. Although this finding continues to be controversial, with different groups providing both confirmatory and conflicting data, a meta-analysis (33) supports an association of vitamin D receptor polymorphisms and BMD. Although the clinical utility of determining polymorphisms in the vitamin D receptor remains uncertain, one could envision targeted therapy to increase the activity of this signaling system, if the specific genetic defect is present.

In the future, knowledge about relevant genetic polymorphisms may allow risk stratification as well as selection of specific dietary or pharmacologic therapies. Ultimately, a better understanding of the process by which specific genes and environmental factors affect skeletal health will serve as the basis for advances in clinical management of osteoporosis and other skeletal diseases. This will enable development of new biological markers for determining the risk of osteoporosis and fracture and may aid in optimal selection and use of a growing number of pharmacological treatments.

In hypertension, identifying simple genetic disorders, although uncommon, can lead to targeted therapy. Examples include glucocorticoid remediable aldosteronism and Liddle’s syndrome, in which treatment with glucocorticoids and amiloride, respectively, provides the best benefit-risk profile to affected individuals (8, 34). In the more complex area of essential hypertension, interesting patient subsets include those with the {alpha}-adducin, angiotensinogen T235, and angiotensin-converting enzyme polymorphisms (8, 34, 35, 36). Further work must be done to confirm that prospective identification of such subsets and providing specific therapy, e.g. diuretics for {alpha}-adducin polymorphisms, can actually enhance both individual blood pressure control and overall cardiovascular outcomes (36A ).

Maturity onset diabetes in the young (MODY) provides excellent examples in which specific genetic mutations are associated with clinical disorders that may not be phenotypically differentiated at the time of disease onset but have a very different course of disease progression. For example, MODY2, which is caused by a mutation in glucokinase, has a benign natural course characterized by mild hyperglycemia and a low risk of diabetic complications. By contrast, MODY1 and MODY3, caused by mutations in HNF-1{alpha} and 4{alpha}, have a progressive downhill course frequently requiring insulin and often characterized by microvascular complications. Genetic testing would likely alter clinical management; MODY2 may not require therapy, whereas insulin therapy and tight glucose control seem warranted for MODY1 and 3 (37, 38).

At this time, the value of genetic testing in type 2 diabetes mellitus is unproven and not recommended in routine clinical practice (38A ). Published studies identify mutations in insulin promoting factor 1 and sulfonylurea receptor 1 in subsets of type 2 diabetics (39, 40). These findings require confirmation in larger populations. In neither of the subsets is the differential benefit of either insulin or oral agents clear; however, in sulfonylurea receptor 1 polymorphisms, sulfonylureas may have a greater effect in lowering triglycerides.

Obesity is a syndrome with a rich variety of recently identified central and peripheral genes that influence fat mass. Like hypertension and MODY, single mutation diseases such as homozygosity for leptin deficiency can lead to the benefits of targeted therapy (leptin replacement) (41). An estimated 4% of morbidly obese youth have mutations in the MC4R (melanocortin 4 receptor) (41). Given the many potential targets and hopefully new medicines, one could imagine a future situation in which obese patients are segmented into genetic subsets and successfully treated with different medicines.

Final thoughts

Two concurrent development paths must be addressed before the impact of tailoring therapy based on pharmacogenomics can be realized. First, the underlying pathophysiology of complex disorders must be understood, so that more precise therapeutics can be developed. A critical tenet of personalized medicine is the ability to prescribe alternate treatment choices. If the same therapy will be instituted, regardless of the underlying cause, then one could question the necessity for stratifying any disease category (by genetics or other means). Understanding disease pathophysiology may arise from studying the genetic susceptibility of these disorders, or perhaps, from a combination of technologies and techniques. Although it has long been hoped that defining the genetics of complex disorders would lead directly to the development of rational therapeutics, this has not yet been the case.

The second area of development must be in the understanding of how polymorphisms in the population affects response to these new drugs. This understanding of drug-activity genomics would allow the customizing of medicine to target those individuals likely to respond to a given treatment. Conversely, those individuals who are unlikely to respond should not be exposed to a drug that will have little if any effect. The development of toxicity is also an important consideration, and those individuals likely to develop drug-induced toxicities would receive altered doses, or alternate therapy.

Pharmacogenomics holds great promise for the future of medicine. Tailoring therapy to individual patients by identification of genetic markers that predict drug response, drug efficacy, adverse events, or toxicity has enormous potential to improve therapeutic outcome. This personalizing of medicines has been the holy grail of pharmacogenomics since sequencing the human genome was conceptualized. Although the idea of tailored therapy is a good one, it is important to understand that we are at the beginning of a long process whose ultimate aim is improved patient care. Supporters in this field must be careful not to underestimate the time necessary to achieve this goal.

Acknowledgments

Footnotes

Abbreviations: ALOX5, 5-Lipoxygenase; BMD, bone mineral density; CYP2D6, cytochrome P450 2D6; MODY, maturity onset diabetes in the young.

Received March 26, 2002.

Accepted April 10, 2002.

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