the impact of genomics on the practice of medicine and ......genomics, medicine and pharmaconomics...

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© 2005 Prentice Hall Inc. / A Pearson Education Company / Upper Saddle River, New Jersey 07458

Genomics, Medicine and Pharmaconomics

The impact of genomics on the practice of medicine and pharmacy

This chapter describes the impact of genomics on the practice of medicine.

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Contents

1. Medical promise of genomics2. High-throughput methods for genotyping3. Cancer genomics4. The problem of variable drug response5. Polymorphisms affecting drug efficacy

a. In drug metabolismb. In drug transportersc. In drug targets

6. Pharmacogenomics in drug development

The topics covered in this chapter include the medical promise of genomics, high-throughput methods for genotyping, the study of cancer genomics, and the ways in which microbial genomics has facilitated drug and vaccine development. The chapter ends with a discussion of gene therapy.

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What we hope to gain from genomics1. Drug, diagnostics, and prognostics development2. Genotyping to predict patient susceptibility to

disease3. Personalized healthcare based on an individual’s

genomic features

decision support systemsgenotype

molecular profilepatient history

knowledge base

drugs diagnostics prognostics

genome health

pharma R&D patient care

The plan for the human genome project was presented to the United States Congress in 1990 by the National Institutes for Health (NIH) and the Department of Energy (DOE). The proposal requested $200 million per year for fifteen years. At roughly the same time (1993) Congress decided to eliminate funding for another multibillion dollar science project called the Superconducting Super Collider, designed to deepen our understanding of particle physics. The positive political reaction to the human genome project can be attributed to the medical promise of a fully sequenced human genome. Even before the genome project was underway, it was easy for biologists to convince politicians that this undertaking would improve healthcare dramatically. New drugs, diagnostics, and prognostics would be developed. The genetic basis for disease would be better understood and more easily detectable. A personalized approach to healthcare would be possible based on an individual’s genotype. The anticipated impact of the human genome project is schematized in the slide. Knowledge of the genome sequence would inform decisions in patient care and pharmaceutical research and development. These decisions would be based on an individual’s genotype, molecular profile, medical history, and our biological understanding of disease. Examples of anticipated genomics applications that will revolutionize healthcare are shown in the next slide.

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Long-term returns

1. Personalized genotype databasesa. Used to assess health risks throughout lifeb. Prescribe adjustments to lifestyle and medical

treatment2. Simulated cells

a. Reduce the need for time-consuming experimentsb. Allow experiments that would otherwise be

impossible3. New frameworks for clinical trials

a. Pharmacogenomics

Although predictions of the future are notoriously unreliable, the progression of genomics research suggests several outcomes that will revolutionize the way healthcare is practiced. For example, personalized genotype databases generated at birth could be used to assess a patient’s health risks throughout life. Medical practitioners would use this information to adjust medical treatments and suggest changes in lifestyle that would maximize a patient’s longevity and quality of life. Of course, building such a database would require a much greater understanding of genetic disease and methods to quickly, accurately, and cheaply assess genotypes. To fully attain these goals, billions of dollars of public and private funds and years of research will be needed. Additionally, such personalized databases raise a host of ethical, legal, and political issues that may take decades to completely resolve. The complete sequencing of the human genome should therefore be considered only the first step in this process. As our understanding of cellular components increases, we may reach the point where human cells can be fully simulated on a computer allowing biologists to perform previously time-consuming or impossible experiments with ease. Such “e-cells” could also be used to understand the role that individual cellular components play in disease. A discussion of an “e-cell” project is presented in the “Computational Foundations of Genomics” chapter of this book. Genomics is also expected to change the framework for drug development. Drug design guided by the information gleaned from genomics and the genotyping of individuals showing adverse drug reactions should greatly increase the efficiency of drug development, clinical trials, and the administration of drug therapies. It will likely take many years for all of the goals mentioned in this slide to be realized. For the remainder of this chapter, we focus on the impact genomics will have on the more immediate future of medicine.

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Short-term returns

1. Faster characterization of disease genes2. Better disease diagnosis and prognosis with

microarrays3. Better methods for genotyping4. More efficient drug and vaccine development

The impact of genomics has already been felt in the characterization of disease genes. Examples of this are given in the chapters on “Monogenic Disease Traits” and “Complex Disease Traits.” An acceleration of disease gene discovery is expected in the near and long term. Microarrays have been proven to be clinically useful in the prognosis and diagnosis of a number of different types of cancer and better methods for genotyping promise to make the assessment of genetic disease faster and cheaper. Lastly, microbial genomics has already revealed a number of promising drug targets and possible vaccine candidates that were previously unknown. These genomics applications are likely to become routine in the clinical environment in the near future.

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Advances in disease genetics

1. Detection of disease genes is most direct medical use of genomics information

2. Over 1,000 disease genes were characterized by 2000

3. How to exploit this information?

250

0

Year of discovery

Dis

ease

gen

es

1981 2000

The most direct application of data from the human genome sequence is the characterization of disease genes. Even before the first draft of the sequence had been published, more than one thousand disease genes had been discovered with a steady increase in discoveries every year. But given this information, how can it be applied clinically. A person possessing a disease gene may know the probability of developing a disease or the probability of passing it on to an offspring, but can medical science help a person beyond allowing them to make an informed decision? The ethical issues associated with knowledge of disease susceptibility genes are discussed in the chapter on Ethics and Genomics.

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Preimplantation diagnosis

1. Couples with at least one child suffering from cystic fibrosis underwent preimplantation diagnosis

2. Biopsied cells from in vitro three-day-old embryos were genotyped

3. Implanted embryos (NN and ΝΔ) in one couple resulted in a healthy baby girl

ΔΔ ΝΝ ΔΔΝΝBAD ΝΔ diagnosis

homoduplexheteroduplex

biopsied cellΔΝ ΔΝΔΝ ΔΝ ΔΝΔΝ DNA added

1 2 3 4 5 6

In 1992, a group of British and American scientists and medical doctors demonstrated the feasibility of selecting human embryos that will not develop a genetic disease, even when the parents are carriers of a mutant gene. All of the couples chosen for this study were carriers for cystic fibrosis and already had at least one child that suffered from the disease. Eggs and sperm were taken from each couple and used for in vitro fertilization. One or two cells were removed for genotyping from embryos that were at the four to eight cell stage of development. Previous experiments had shown that removing a single cell from an embryo at this stage did not cause problems later in life. Using PCR with primers for the mutation that most commonly causes cystic fibrosis, the team was able to determine which embryos were free of the disease gene, carried the disease gene, or were homozygous for the disease gene and thus would definitely develop the disease. The results of the genotyping for one couple are shown in the slide. Segments of the wild-type gene were also amplified using PCR. Samples from each genotyped cell were mixed in one case with known amplified mutant sequences, and in the other case with known wild-type sequences. In the slide, wild-type and mutant sequences are denoted by N and Δ respectively. The mixtures were then run on a gel. If a sample from a biopsied cell showed two bands in a lane that contained embryo DNA and mutant DNA this indicated two hybridizations, one between copies of the mutant DNA (a homoduplex with higher gel mobility) and one between copies of mutant and wild-type embryo DNA (a heteroduplex with lower gel mobility). This would indicate at least one wild-type allele in the embryo. If a second lane containing embryo DNA and wild-type DNA showed a similar pattern it would indicate the presence of the mutant DNA allele in the embryo. An example of this scenario is shown for biopsied cell 4 in the slide. An embryo that was h f th t t ll l ld h h t d l d h d l

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Single-nucleotide polymorphisms (SNPs)

• Benefits of characterizing SNPs• High-density SNP map will greatly facilitate the

identification of disease genes• Detection of SNPs can serve as a diagnostic for genetic

diseases• Millions of SNPs presently in public and private

databases• Fast, cheap, and accurate genotyping of SNPs still a

challenge• Smallest linkage-disequilibrium studies still out of

reach• Genotyping 30,000 SNPs in 1,000 individuals would

have required tenfold increase in technological capacity at end of 2001

Single nucleotide polymorphisms (SNPs), as their name implies, are characterized by a single nucleotide difference in DNA sequence. They are quite common, occurring once every one thousand bases in the human genome. When they appear inside the coding region of a gene, they can give rise to a monogenic disease. Some biologists also speculate that SNPs in the regulatory regions of genes may be involved in common diseases. Given that greater than 95% of the human genome contains non-coding DNA, it should not be surprising that SNPs are most common in those regions, where they have no impact on phenotype. Nonetheless, SNPs can be quite useful regardless of where they occur. Obviously, genetic illnesses caused by mutations associated with SNPs can be diagnosed accurately by genotyping the polymorphism, but even SNPs in non-coding regions can be useful as genetic markers in searches for disease genes. At present, there are millions of SNPs stored in public and private databases, but their exploitation is limited by our inability to quickly, accurately, and cheaply genotype SNPs in the population at large. For example, even the smallest genomewide linkage disequilibrium studies that are essential in understanding polygenic diseases are still out of reach. At the end of 2001, the genotyping of 30,000 SNPs in 1,000 individuals (a minimal linkage disequilibrium study) would have required a 10-fold increase in technological capacity. In the following slides, we discuss some of the different strategies currently used to genotype SNPs and what will be needed to make high-throughput genotyping possible.

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Cancer genome projects

1. Cancer Genome Anatomy Project (CGAP)a. Established 1997 by National Cancer Institute (USA)b. Specializes in EST sequencing

2. Human Cancer Genome Project (HCGP)a. Established 1999 by Brazilian research groupsb. Specializes in SAGE analysis

3. Cancer Genome Project (CGP)a. Established 2000 by Wellcome Trust and Sanger

Institute (United Kingdom)b. Specializes in genomic mutations leading to cancer

One in three people will suffer from cancer in his or her lifetime. For this reason, cancer research is central in medicine and politics. At the time that this chapter was written, there were three major sequence-based cancer genome projects underway in three countries. The Cancer Genome Anatomy Project (CGAP) was established by the National Cancer Institute of the United States in 1997. It specializes in expressed sequence tag (EST) sequencing and aims to understand gene expression in cancerous cells. The Human Cancer Genome Project (HCGP) was launched in 1999 by a collaboration of several Brazilian research groups. Like the CGAP, it aims to understand gene expression in cancerous cells, but uses a different technology called serial analysis of gene expression (SAGE). The Cancer Genome Project was established in the United Kingdom in 2000 by the Wellcome Trust and the Sanger Institute. Unlike the other two projects, it focuses on understanding the genomic mutations that give rise to cancer. Funding for the cancer genome projects ranges from $15 million to $60 million.

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Searching for cancer-causing mutations in genomic DNA

1. Use human genome sequence to make PCR primers for target genes

2. Compare PCR products from normal tissue and tumors, using automated heteroduplex analysis

3. Sequence genes when heteroduplex analysis suggests differences between tumor and normal cell

4. Find genomic mutants

PCR

heteroduplex analysis

sequence mutants

Make PCR primers oftarget genes from normal

and tumor tissue

1

2

3

The cancer genome project takes a more targeted approach. Using the human genome sequence, it makes PCR primers for genes that are likely to be implicated in cancer. These primers are applied to normal and cancerous tissue and the amplified sequences are used in automated heteroduplexanalysis, which determines whether or not the cancerous and normal sequences are identical. If they differ, this suggests that the amplified gene may contain a cancer causing mutation. The mutant gene is then sequenced and cataloged in a database. This process is outlined in the figure in the slide.

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Microarrays and cancer

1. Histology not always effective tool for prognosis and diagnosis

2. Microarrays distinguish cancerous tissues on the basis of a gene expression profile

a. Use in diagnosisi. Example: characterizing acute lymphoblastic

leukemiab. Use in prognosis

i. Example: assessing the likelihood of metastasis in medulloblastoma

Histology is often used to diagnose cancer on the basis of various morphological features; however, this is not always reliable. Many researchers have begun to use microarrays to analyze cancerous tissue on the basis of gene expression. Clinicians hope that such experiments will lead to the development of more accurate tools in the diagnosis and prognosis of cancer. Already, there are numerous promising results. In 1999, microarrays were first applied to cancerous tissue to accurately distinguish acute myelogenous leukemia from acute lymphoblastic leukemia. In the chapter on pharmacogenomics, we discuss the use of microarrays in providing doctors with prognoses of breast cancer patients that were more accurate than existing tools. In the next slide we show how microarrays can be used to predict metastasis in medulloblastoma, the leading cause of malignant brain tumors in children.

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Microarrays in the prognosis of metastasis

1. Identified 85 genes with different levels of expression in metastatic and nonmetastatic tumors

2. 72% accuracy in predicting metastasis

3. Identified genes induced in metastasisa. Could serve as

potential drug targets

M+M–

green = downregulated

red = upregulated

The metastasis of medulloblastomas indicates a poor prognosis for patients. This cancer is especially common among children, half of whom succumb to the disease. Detecting metastasis is considered vital to determining the intensity with which the cancer is treated. A group of researchers used microarrays to determine the gene expression profile indicative of metastasis in medulloblastoma. They examined medulloblastomas that had been previously characterized as metastatic and nonmetastatic. The results of this experiment are shown in the slide. They found that 59 genes exhibited an increase and 26 showed a decrease in gene expression in the metastatictissue. In the slide, metastatic tissue is denoted by ‘M+ ’ and nonmetastatictissue by ‘ M-.’ Rows of pixels in the microrray readout represent the levels of gene expression associated with individual genes from different tissue samples. The gene expression signature derived from this study could accurately predict metastatic medulloblastomas 72% of the time. Among the genes with enhanced expression was one coding for platelet derived growth factor receptor alpha (PDGFRα). When antibodies inhibiting the function of PDGFRα were added to medulloblastoma tissue in vitro, the researchers were able to prevent migration. A similar result was found when applying a chemical that inhibited members of a downstream biochemical pathway. Thus, the application of microarrays to medulloblastoma not only provided a method for prognosis, but also several candidate drug targets for halting metastasis.

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The social cost of drug variability

1. Adverse drug reactions affect roughly 2 million hospital patients every year

2. Approximately 100,000 fatalities per year can be attributed to adverse drug effects

3. On the other hand, drugs administered to treat serious illnesses will sometimes have no effect at all

The problem of variable drug response is enormous. Every year in the United States, adverse drug effects are estimated to affect roughly 2 million hospital patients. Approximately 100,000 of these patients will die as a direct result of the drugs they are taking. A 1997 study of two large hospitals in the United States found that 4.5 per 100 admissions suffered from nonpreventable adverse drug reactions. The authors of the study estimated that such cases added an average of $2.5 million to the annual budgets of large American hospitals. This calculation excluded expenses relating to litigation, the cost of injuries to the patients, and admissions precipitated by adverse drug events. An additional negative factor associated with drug variability is that sometimes a therapeutic agent, while not causing injury, will have no effect at all. For example, 20%–70% of all cancer patients will not respond to drug therapy.

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Causes of variability in drug response

1. Environmenta. Foodsb. Other drugsc. Patient’s condition

2. Hereditya. Inability of the body to metabolize drugb. Drug receptor polymorphisms

3. Combination of environment and hereditya. Example: drug allergies

Both environmental and hereditary factors are responsible for drug side effects. For example, patients being treated with a class of antidepressants called monoamine oxidase inhibitors (MAOIs) must be extremely careful with their diet. Wine, aged cheese, cured meat, and other foods containing the chemical tyramine can induce potentially fatal strokes and heart attacks in these individuals. In other cases, a patient taking multiple drugs may find that one drug has an adverse effect on another. For example, the antiseizuremedication phenytoin reduces the efficacy of some oral contraceptives. Frequently, the cause of drug response variability is genetic, which is the focus of this chapter. For example, the body’s ability to respond to a drug can be determined entirely by genetics, due to drug receptor polymorphisms. Some adverse drug reactions such as drug allergies are due to a combination of environment and heredity.

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Background

1. 1950sa. Inherited differences in drug response

observed2. 1980s

a. CYP2D6 gene cloned3. 1990s–present

a. Human genome sequencedb. Microarray technology

Although the hereditary basis of variable drug response was first discovered in the 1950s (see the next slide), it was not until the late 1980s that researchers cloned a gene, CYP2D6, involved in metabolizing drugs. Today, the sequencing of the human genome promises to uncover many morepolymorphisms affecting drug response. Moreover, new technologies like microarrays offer the promise of practical clinical applications.

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Enzymatic deficiency in primaquine-sensitive blood cells

1. The antimalarial drug primaquine causes adverse side effects in some patients

2. A 1956 study determined that this condition was caused by a deficiency in a blood enzyme

3. Today, military personnel can be tested for this deficiency, and affected individuals are exempt from primaquine treatment

Normalsubject

Primaquine-sensitivesubject

Hemolysate (ml)R

educ

ed g

luta

thio

ne, G

SH

(mg)

1 5 100.00

0.25

0.50

0.75

1.00

During the 1950s, scientists were able to show for the first time that variability in drug response could be affected by a patient’s genetics. In a classic 1956 study, researchers determined the biochemical basis for the toxicity caused by primaquine, an antimalarial agent, in some individuals. This drug was known to adversely affect roughly 10% of African-Americans, but a much smaller percentage of European-Americans. This issue was important for the American armed forces, because there was no way of knowing prior to treatment if soldiers would be adversely affected. The toxic effects of primaquine include shortness of breath, fatigue, and abdominal and back pain. A high proportion of red blood cells in affected individuals is destroyed by clumps of denatured hemoglobin called Heinz bodies. The authors of the 1956 study took blood samples from normal individuals of African and European ancestry, as well as from African-Americans that were known to be primaquine sensitive. A previous study had shown that primaquine sensitivity is associated with abnormal glutathione (a blood protein) levels. The researchers assayed enzymes known to be involved in the metabolism of glutothione. The panel in the slide shows the single enzyme, G-6-P dehydrogenase, that exhibited abnormal behavior. Primaquine-sensitive individuals appeared to be deficient in their ability to chemically reduce glutathione by use of the G-6-P dehydrogenase enzyme. This study and others gave rise to the new field of pharmacogenetics. Today, military personnel are screened for G-P-6 dehydrogenase deficiency, and affected individuals are no longer given primaquine.

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The CYP2D6 gene

1. Human CYP2D6 gene cloned in 1988a. Shown to be involved in metabolism of a hypertension

drug2. Developments since 1988

a. More than 40 SNPs discoveredb. CYP2D6 found to be involved in the metabolism of

20% of all prescription drugs

promoter exon intron

CYP2D6 gene

known SNPs

In 1988, Frank Gonzalez and colleagues cloned the human CYP2D6 gene, a member of the cytochrome P450 family, and showed that it was involved in the metabolism of a hypertension drug. Since then, more than 40 single-nucleotide polymorphisms have been identified in this gene, which has been found to be involved in the metabolism of roughly 20% of all prescription drugs. These drugs range from heart medications to antidepressants and antipsychotics. A schematic of the gene is shown in the slide, with exons in red (at the top of the figure) and the locations of SNPs color coded according to their location in the gene. The cloning of the CYP2D6 gene marked a breakthrough for pharmacogenetics.

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Pharmacogenetics

1. Variations used since the 1950s2. A classic genetics approach

a. Look for unusual drug response or metabolism phenotype

b. Conduct family studies to understand the pattern of inheritance

c. Clone the responsible genei. 1980s and 1990s

The field of pharmacogenetics currently follows an approach used through the 1980s and much of the 1990s. Drug-response phenotypes are observed and then analyzed through family studies that reveal their genetics. Researchers subsequently attempt to clone the responsible genes.

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Pharmacogenomics

1. Since the late 1990s2. Uses high-throughput sequencing methods3. Takes advantage of human genome databases

to identify candidate mutations a. Especially single-nucleotide polymorphisms

(SNPs)4. Finally, looks for correlations between

polymorphisms and drug-response phenotypes

Pharmacogenomics takes advantage of the recently completed humangenome sequence and efficient genotyping techniques to look for candidate genotypes that might be indicative of drug-response phenotypes.

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Possible physiological barriers to drug efficacy

1. Drug metabolism2. Drug disposition3. Drug transport4. Drug targets5. Environmental and genetic factors can play a

role in all of these aspects

The physiological processes that interfere with the intended effects of drugs fall into four broad categories: drug metabolism, drug disposition, drug transport, and drug targets. Environmental as well as genetic factors can play a role in each of these processes. For example, drug disposition (i.e., drug absorption) can be affected if a patient is dehydrated. Similarly, the response of a drug target may be affected by other drugs in a patient’s body. The GABAA receptor in the brain is responsible for inhibiting neural activity. It is also a drug target for a class of tranquilizers called benzodiazapines. The response of the GABAA receptor to benzodiazapines is greatly amplified in the presence of alcohol, and a patient taking benzodiazapines under the influence of alcohol risks fatal consequences. For the remainder of the chapter, we focus on how genetic polymorphisms play a role in these physiological processes.

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Phenotype distribution depends on mutation type

1. Mutations accounting for typical variabilitya. Minor a.a. substitutionsb. Promoter SNPsc. Regulatory SNPs

2. Mutations accounting for atypical variabilitya. Early stop codonsb. Exon skippingc. Deletionsd. Major amino acid

substitutions

Normal drug response

Abnormal drug

response

Enzyme activity or drug clearance

frequ

ency

1 10 100

Genetic polymorphisms are responsible for both normal and abnormal drug response; however, different kinds of polymorphisms play a role in each case. Normal levels of variability are expected to be the result of minor mutations that affect, but preserve, the function of an enzyme involved in drug response. For example, an amino acid substitution that replaces one hydrophobic amino acid with another hydrophobic one will likely have only a minor effect on the functioning of an enzyme. Similarly, SNPs in promoter and regulatory regions of a gene will have an effect on the amount of enzyme expressed, but will usually not eliminate enzymatic activity completely. In contrast, the mutations underlying abnormal drug response typically show that a protein’s function has been completely lost. Mutations found to result in loss of function include deletions, early stop codons, major amino acid substitutions, and exon skipping. Such mutations are expected to eliminate enzyme function through shortened transcripts and the creation of unstable proteins. We now turn to the relationship between genotype and the physiological processes underlying drug response.

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The physiology of drug metabolism

1. Aspects of drug metabolism that can affect drug response in patients

a. Lack of enzymatic activity to break down toxic drug by-products

b. No enzymatic activity to alter a drug so that it can reach its intended target

c. Persistence or rapid purging of drug because of mutant enzyme

In some cases, metabolic enzymes are required to break down a drug or drug metabolite that is toxic to the body. An example of this situation is given in later slides. In other cases, a drug must be properly modified by an endogenous protein so that it can reach the drug target. Enzyme activity can also have more continuous effects that lead to the persistence or overly rapid purging of a pharmaceutical agent.

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The physiology of drug transport

1. Drugs must pass through various barriers to reach their target

a. Tissue barriersi. Example: the blood–brain barrier

b. Barriers at the cellular leveli. Example: transporters that ferry molecules

across the cell membrane2. Very few polymorphisms affecting drug

transport have been found

Drugs often have to be ferried to a target before they can have a beneficial effect. A variety of barriers can prevent a drug from reaching its target. For example, the blood–brain barrier isolates neurons from compounds that are present in blood. At the cellular level, numerous transporters and channels regulate the entry of molecules across the plasma membrane. Only a handful of polymorphisms affecting drug transport have been found.

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The physiology of drug targets

1. It is a drug’s interaction with its target that creates benefit for the patient

2. Polymorphisms in drug targets can affect efficacy

3. Some examplesa. Drug may bind weakly to target, resulting in

decreased efficacyb. Drug may bind too strongly to target,

resulting in dangerously high level of response

The drug target is the intended site of action for a drug. It is the drug’s interaction with the target that makes the drug beneficial to a patient. Polymorphisms in drug-target genes can affect the binding of the drug or the type of response the drug elicits.

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The economics of drug development

• Annual worldwide expenditures on pharmaceuticals in 2001 were over $300 billion.

• Cost of drug development from discovery to approval estimated at $350–$500 million

• Drug development costs are increasing faster than inflation

Worldwide pharmaceutical sales in 2001 were estimated to be over $300 billion. At present, pharmaceutical companies have the highest profit margins of any industry group in the United States. There has been a great deal of debate about how critical these profits are to developing new drugs. Because the U.S. government does not require companies to report precise research and development expenditures, reports of drug development costs vary widely. Modest estimates range from $300–$500 million per drug, but a recent study by Tufts University put the cost at $802 million. Regardless of the precise numbers, the cost of drug development is enormous and increasing at a rate faster than inflation despite quicker clinical trials and better staffing at the Food and Drug Administration, which approves all new pharmaceuticals in the United States. One aim of pharmacogenomics is to decrease drug development costs through better technology.

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Overview of drug trial process

1. ~12 years for experimental drugs to reach the marketplace

2. Five out of 5,000 compounds that are preclinicallytested make it to human trials

a. One of those five makes it to market3. Clinical trials take 6 years4. FDA review process

a. ~100,000 pages of trial data to analyzeb. Law requires process to be completed in 6 monthsc. More typically, it takes 2.5 years

The trial phase of the drug development process is expensive and time consuming. The slide lists some discouraging statistics concerning drug development. By applying pharmacogenomics to clinical and preclinical trials, it may be possible to reduce the expense and time for bringing new drugs to the marketplace. At present, it takes approximately 12 years of research, development, and testing for a new drug to reach the consumer. Despite all this work, the drug may have a range of side effects that will require additional investigations.

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Using pharmacogenomics to improve preclinical trials

• Interspecies polymorphisms can make it difficult to interpret preclinical results

• Pharmacogenomics pinpoints potentially troublesome polymorphisms

• Inserting human genes into test animals might improve quality of preclinical trials

WT 1 2 3 WT 1 2 3

Mouse CCK2 Human CCK2

PD

1351

58 a

ctiv

ity(%

pro

duct

ion)

HumanMouse

… VR MLLVIVVLFFLC WLPVYSANTWR …L V

1 23

Receptor

Preclinical trials must be conducted on laboratory animals before any drug is tested on humans. Unfortunately, the animal receptors and enzymes that interact with a drug can differ significantly from those found in humans. This fact makes drawing conclusions from animal studies difficult and can result in financial loss for a pharmaceutical company which discovers that its new compound does not work in humans after spending millions of dollars on preclinical trials. The cholecystokinin (CCK2) receptor illustrates the problem of animal–human variability in drug response. Like the β2 adrenoreceptor, the CCK2 receptor belongs to the medically important class of G-protein-coupled receptors. This receptor is of great interest to drug companies because it regulates several gastrointestinal functions and modulates pain perception and anxiety in the central nervous system. The graph in the slide illustrates how polymorphisms distinguishing the human CCK2 receptor from the mouse receptor can affect drug response. Researchers constructed an in vitro system to determine how the activity of the compound PD135158 depends on receptor amino acids. The receptor gene was expressed in cells where it was coupled to inositol phosphate production, which could be measured to assess the ability of PD135158 to activate the CCK2 receptor. The yellow bars in the graph show the response of the mouse receptor, and the green bars show the response of the human receptor. The wild-type mouse receptor was much more sensitive to PD135158 than the wild-type human receptor. Researchers performed site-directed mutagenesis to alter individual amino acids in order to determine which ones accounted for differences in the CCK2 receptor’s response. They were able to make the mouse receptor behave more like the human receptor by changing one leucine to a valine and one valine to a leucine, thereby Pharmacogenomics 587 replicating the amino acids at those positions in the human receptor. When the converse was done, the human receptor showed a response level similar to that of the murine receptor. This example illustrates the promise of pharmacogenomics in improving the cost and value of preclinical trials. By id tif i th i t i l hi ibl f diff i

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Clinical Trials

1,000–3,000 patient volunteers

3 yearsVerify drug efficacy, study effects of long-term use

III

100–300 patient volunteers

2 yearsSearch for side effects, determine drug efficacyII

20–80 healthy volunteers1 yearAssess safety and

dosage in humansI

Test Population

Average DurationPurposePhase

The table in the slide illustrates the purpose and process of the three phases of clinical trials required by the U.S. government’s Food and Drug Administration. Successful completion of all three phases of clinical testing requires six years and thousands of human volunteers.

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Using pharmacogenomics to improve clinical trials

1. Phase Ia. Genotype subjects to ensure sufficient diversityb. Identify genotypes of poor metabolizersc. Archive genotypes to assess patient risk in later trials

2. Phase IIa. Determine genotypes in patients experiencing toxicityb. Exclude patients with toxicity risk from further trials,

using pharmacogenomic markers 3. Phase III

a. Excluding patients with at-risk genotypes allows pharmaceutical companies to improve drug efficacy numbers and reduce the number of volunteers needed to show benefits of a new drug

Pharmacogenomics is expected to improve every phase of clinical trials. Phase I trials may be improved by genotyping subjects to ensure sufficient genetic diversity and by archiving genotypes indicative of poor drug metabolizers that can be informative for later trials. In phase II trials, the genotypes of patients experiencing drug toxicity can be used to exclude other patients with similar risks from further trials. By phase III, a drug company may have a sufficient catalog of at-risk genotypes, allowing it more effectively to prove the efficacy of its drug on patients who show no side effects.

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Improving the effectiveness of treatments with pharmacogenomics

All patients with same diagnosis

1 Removetoxic and

nonresponders

TreatResponders and patientsnot predisposed to toxic

side effects

2

In the future, pharmacogenomics will boost the effectiveness of drugs by prescreening individuals who would otherwise not respond or would have an adverse response to treatment. These individuals could then be treated by some alternative method. Such a process will reduce the amount of time to develop an effective treatment and will eliminate unnecessarily harmful side effects, making treatment more humane as well as cost-effective. Returning to the example of schizophrenia, it is possible that pharmacogenomics will eventually allow psychiatrists to suggest a treatment plan based on a genomic or expression profile, rather than on trial and error.

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Microarray applications

1. Different patterns of gene expression can account for disease that is symptomatically similar in two patients

a. Example: breast cancerb. Pattern of gene expression can affect treatment

efficacy2. Microarrays allow clinicians to simultaneously

measure levels of gene expression for thousands of genes

3. Gene expression signature can be used in prognoses and decisions about treatment

Often, a single disease that has been defined by its symptomaticmanifestation is actually the result of different patterns of gene expression in different patients. These patterns of gene expression are expected to correlate to differences in prognosis and effective treatment. Microarrays are well suited to differentiating patterns of gene expression because of their ability to measure gene expression across thousands of genes simultaneously. Recently, microarrays were used successfully to determine prognoses for breast cancer patients.

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Breast cancer

1. Statisticsa. 13.3% of American women will suffer from breast

cancer in their lifetimeb. Breast cancer accounts for 15% of all cancer deaths

among women, second only to lung cancerc. Estimate of new cases in 2001: 192,000

2. Difficulties in treating breast cancera. Predictors of metastasis that work for other cancers

fail for breast cancerb. Chemotherapy and hormonal therapy reduce risk of

metastasis c. But 70–80% of breast cancer patients will do well

without chemotherapy and associated discomforts

Breast cancer is a common disease that affects 1 in 8 American women and was estimated to have killed approximately 40,000 people in 2001. Like so many other diseases, breast cancer is the result of both hereditary and environmental factors. While breast cancer is often completely treatable, devising a therapy strategy can be tricky. For example, lymph-node status, which accurately predicts metastasis in other cancers, does not accurately predict the behavior of breast cancer tumors. Also, treatments that reduce the possibility of metastasis, like chemotherapy, have no effect on 70–80% of breast cancer patients, who would have survived anyway. Because chemotherapy attacks all growing cells (not just cancerous ones), there are a number of unpleasant side effects, including hair loss, nausea, and pain. Determining which cancers do not require chemotherapy could significantly increase patient morale and decrease health-care expenditures.

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Clinical implications

1. Microarray gene expression profile outperforms current clinical techniques used to determine breast cancer prognosis

2. Gene expression signature studies allow for patient-tailored therapies

3. Gene expression pattern found in poor-prognosis patients suggests targets for future anticancer drugs

This microarray study was among the first to show that cancer treatment strategies can be developed based on gene expression signatures from patients. When compared with traditional prognosis methods, the microarray classifier performed just as well in finding patients with poor prognoses and even better in finding those with good prognoses. In addition, by having found genes that are significantly up regulated in breast cancer tumors, this microarray study suggests a number of possible targets for future anticancer drugs.

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Pharmacogenomics databases

On-lineGenotypeDatabase

The full promise of pharmacogenomics will be fulfilled when individual patients can be genotyped for polymorphisms affecting drug response and disposition. One approach would genotype patients based on their illness. For example, a breast cancer patient would be genotyped for polymorphisms known to affect treatment regimens for that disease. The patient would then authorize the release of this information to physicians, pharmacists, and others. Although the information technology is presently available, an ethical and legal framework for such a system is still under development. Soon, everyone may have an individualized database that contains all of his or her drug-related polymorphisms.

the impact of genomics on the practice of medicine and ......genomics, medicine and pharmaconomics...

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