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Tuesday
Dec112007

Evidence-Based Medicine

Psychiatry in the Postgenomic Era

Kathy L. Kopnisky, PhD
Steven E. Hyman, MD

Dr. Kopnisky is assistant to the director, and Dr. Hyman is director, both at the National Institute of Mental Health in Bethesda, MD.

 Abstract
A rough draft of the human genome sequence is now available in the public domain and the “finished” sequence of the 3 billion base pair genome will be available. Genes play a critical role in the risk of mental illness, but deciphering their role has proven to be extremely difficult. It is only with information from the genome project that we can succeed in finding risk conferring genes, but other challenges, including the boundaries of the illnesses themselves, still await us. Even while this research is proceeding, we should question how genomics and genetics will change our understanding of mental illness and, ultimately, the practice of psychiatry.

Introduction
As we enter the 21st century, we are still uncertain about the mechanisms underlying mental disorders. Powerful imaging technologies have been developed that permit the examination of the working brain. Technologies for postmortem examination of the brain have steadily improved. Genetic information will markedly enhance such approaches, at a minimum by decreasing the heterogeneity of populations studied while providing tools for molecular and cellular studies of pathogenesis. Based on experience in other areas of medicine, it appears absolutely necessary to use genetics to identify the genes that produce risk of mental disorders if we are to ultimately understand the disease processes. Beyond insights into the pathogenesis of mental disorders, gene discovery will undoubtedly provide important insights into normal brain function as well. Moreover, discovery of risk genes should hasten the development of treatments that are not simply aimed at symptoms, but that alter the disease process.

Why is it that gene discovery is so important and why has the task been so difficult? The importance of genes as tools of investigation lies in the complexity of what it is that we are trying to understand. The brain is the most complex object in the history of human scientific inquiry and mental disorders represent problems with the highest integrated functioning of the human brain—thinking, emotion, and behavioral control. To succeed in understanding, we will need both “top down” approaches (moving from epidemiology to neurocircuitry systems on to genes) and “bottom up” approaches (moving from genes and proteins to cell biology to neurocircuitry to pathophysiology) and we will need to find ways of ultimately combining these approaches.

While the precise definition of a “gene” can be arguable, in the simplest terms a gene is considered to be a stretch of DNA that encodes one (or a family of related) RNAs and ultimately their protein
products. Genes provide not only the sequences of possible proteins that can be synthesized but also information about where, when, and under what circumstances these proteins will be made; proteins, in turn, are the building blocks of cells. Such regulatory information is far less well understood than information that codes for RNAs. We are still learning to identify stretches of DNA involved in the regulation of gene expression. Important clues are coming from the sequencing of mammalian species other than humans (eg, the recently completed mouse sequence). DNA regions that are conserved by evolution across species are likely to be functionally important, and these will include regulatory regions.
Disease risk genes can be defined as genes that contain a variation in DNA sequence that leads a change in location, timing, levels of gene expression, or the nature of the RNA or protein encoded by the gene, such that it contributes to risk of mental illness. Genetic variations may take the form of “mutations,” which are clearly deleterious errors (eg, an error that would truncate a protein or block its expression altogether) or polymorphisms, which might produce slight alterations in expression pattern or function, but which cannot be said to be clearly abnormal. The most common type of variation in the human genome is the substitution of a single nucleotide base for another; such variations are referred to as single nucleotide polymorphisms (SNPs).

Discovery of disease risk genes means that we will be able to ask fundamental pathophysiologic questions. We hope to be in a position to ask how one version of a gene (allele) confers vulnerability toward or protection against disorders such as manic depressive illness or schizophrenia, while a slightly different version of the same gene does not. Additionally, by determining at what point during brain development a relevant gene is activated, we will be able to detect the earliest moments during which normal development gets diverted to result in such illnesses as the autism spectrum disorder or perhaps schizophrenia. Most importantly, protein products of gene expression function in complex biochemical networks that subserve all cellular functions. Identification of genes that confer risk of mental illness will point us toward biochemical pathways that, in turn, may suggest entirely novel treatments or even preventive interventions for mental illness.

We are painfully aware that the majority of drug-based therapeutics currently used to treat mental disorders was discovered serendipitously while being used to treat other medical conditions. For too long we have relied on the clever exploitation of these findings which led to the use of initial reference compounds, such as chlorpromazine or imipramine, from which our current treatments evolved. Today, we sorely need pathophysiologically-based pharmaceuticals that are safer and more efficacious for the treatment of illnesses such as schizophrenia, manic depressive illness, major depression and a host of anxiety disorders. Our field needs pathophysiologically relevant protein targets for drug development. As discoveries are made regarding the identity of the genes and protein products involved in specific disorders, the development of truly novel pharmacologic compounds is increasingly likely.

Challenges to Identification of Mental Illness Genes
Why has the discovery of disease risk genes for mental illness been so difficult? Unlike the situation for diseases such cystic fibrosis or Huntington’s disease, both of which are caused by a single gene in either a Mendelian dominant or recessive pattern, family and genetic linkage studies of mental illnesses have demonstrated that the pattern of inheritance of risk for these disorders is highly complex, comprised of both genetic and environmental components. Based upon Lander and Kruglyak’s1 seminal paper establishing guidelines for determining the statistical significance of linkages associated with disease, evidence indicates that most psychiatric disorders are polygenic. One case in point, Botstein and colleagues2 excluded the possibility that bipolar illness is the result of a monogenic or even bigenic disorder based upon results from a full-genome scan of linkages associated with bipolar disorder.

Additional challenges in tackling the genetics of complex psychiatric disorders lies in the realization that no specific gene or group of genes is sufficient or necessary for producing a mental illness, clearly unlike in the cases of cystic fibrosis or Huntington’s disease. In other words, it may be that there are multiple genetic pathways leading to similar disease
phenotypes. Each disease risk gene may contribute a small increment of risk and some may have no role at all in subsets of families. Thus the “signal” provided by risk genes may be very small and technically difficult to discern. Or, it is possible that there is a single dominant gene (or group of genes) which confer most of the risk to a given mental illness, but because there may be several such dominant-like genes in the population, it is statistically difficult to identify them.

In addition to polygenic and multiple small-effect genes, the complexity of deciphering contributors to mental illness is further complicated by “epigenetics”—the heritable regulation of gene expression or activity that does not involve changes in DNA sequence. For instance, it is well recognized that methylation (which typically silences but occasionally activates gene expression) of specific DNA sequences is frequently based upon whether the sequence is of maternal or paternal allelic origin.3 It is not yet known what confers these heritable modifications, but they ultimately result in functional differences during development. For example, the regulation of X chromosome inactivation in mammalian females (XX) is a classic example of epigenetics.4

As prefaced earlier, multiple gene-gene and gene-environment interactions ultimately coalesce to produce genetically complex phenotypes.5,6 A clear role for the environment in eliciting one particular neuropsychiatric brain disease was brought to the foreground when individuals taking an illicit drug containing 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) acquired phenotypic symptoms and brain pathology indistinguishable from Parkinson’s disease.7 In general, it appears that in certain psychiatric illnesses, such as bipolar disorder and schizophrenia, genetics plays a greater role in pathophysiology, whereas in others, such as depression and anxiety disorders, the environment is thought to have a greater modulatory role. Regardless, the role of the environment is indisputable. For example, despite sharing 100% of their DNA, monozygotic twins do not exhibit 100% concordance for schizophrenia,8 manic-depressive illness, or any other mental disorder. Furthermore, findings from adoption studies show that the biological relatives of adopted children who are affected with mental illness are more likely to suffer from the mental illness than are the foster parents or foster family members.9,10

Gene hunting in psychiatry has been made yet more difficult as a result of a few additional factors. First, there are no laboratory tests for which a mental disorder phenotype is unambiguously determined. The diagnoses of mental disorders relies on behavioral and phenotypic diagnostic criteria as listed in the Diagnostic and Statistical Manuals of Mental Disorders, Fourth Edition11 (DSM-IV), or the International Classification of Diseases12 (ICD). Second, several illnesses can have overlapping features. For instance, a subset of patients diagnosed with manic-depressive illness or schizophrenia both present with psychotic symptoms, and the distinction between schizophrenia and schizophreniform disorders, for example, is often subtle and uncertain.

The definitive discrimination of many mental disorders may not be possible until their genetic basis is known as was the case with the spinocerebellar ataxia (SCA) disorders, many of which clinically present with similar behavioral and phenotypic features. The identification of a new mechanism of pathogenesis, via genes containing expanded triple repeats, led to the discovery of the independent etiologies responsible for each ataxic disorder and a genetic basis for differentiating between them. Finally, mental disorders are extremely common. The prevalence of major depression, for example, may exceed 10% in some populations. But, associating depression with specific alleles—even those that are retained at high frequency in the population—is not straightforward, particularly because they are likely to represent simple single base changes (polymorphisms) and not deleterious mutations such as easily recognizable deletions of large segments of DNA. Additionally, the polymorphisms, or stretches thereof, may represent silent variants on their own, but other certain infelicitous combinations may produce vulnerability to illness.

Despite these difficulties, as we are lacking a deep understanding of the pathophysiology of mental disorders, we must rely on reverse genetics or positional cloning approaches to find genes. These terms refer to approaches to gene identification which assume no prior knowledge of that disease mechanism but rather are based on relating the transmission of an illness to the presence of identified chromosomal segments. For Mendelian or single-gene disorders, such as cystic fibrosis or Huntington’s disease, the identification and cloning of disease risk genes is now relatively straightforward. For complex traits such as mental disorders, the approach is still extraordinarily difficult and there is no complex phenotype that has been fully dissected genetically.

Past Efforts to Identify Genes
Many reverse genetic studies have been performed using multiple strategies in populations with mental disorders. The most numerous of these have been for schizophrenia, manic-depressive illness and, more recently, autism. The major strategy employed is called “linkage analysis,” whereby DNA from families having a high number of affected individuals is examined in order to identify genetic markers (indicative of specific chromosomal regions) that segregate in affected members. In other words, since genes or DNA sequences at a specific location tend to be inherited together due to their proximity, the linkage of a genetic marker with a disease is indicative of a causative gene in close proximity to chromosomal marker. This linkage approach has worked extremely well for Mendelian disorders in which a given locus is fully responsible for an illness, but such linkage approaches have not had the resolving power to find loci that confer risk of complex phenotypes such as mental illness.

Attempts have been made to avoid the effort required for reverse genetic approaches by employing a candidate gene approach whereby genes that are likely to be involved in a disorder are manipulated and studied in various experimental systems. For example, a candidate gene can be knocked out or overexpressed in a transgenic mouse, its protein can be
pharmacologically manipulated up or down, and surgical “treatments” can ablate specific neuroanatomical structures and neurocircuitry connections thought to be pertinent in the disorder. Unfortunately, it is still too early in our understanding of the pathophysiology of mental illness to be in possession of many compelling candidates. Thus, almost all of the candidates that have been investigated are derived from the putative mechanism of action of therapeutic agents rather than from any real knowledge of pathophysiology.

Most efforts to date have been directed toward genes responsible for the biosynthesis, release, or reuptake of serotonin, dopamine, or other neurotransmitters. In addition, many of these association studies have not achieved adequate statistical power to be decisive. As a result, we remain in limbo regarding claims about any certain associations.

The Genomic Revolution and Major Developments
The genomic revolution should markedly aid in determining the genetic liability of risk for developing mental disorders and other complex phenotypes, ergo, the rest of this essay will focus more specifically on psychiatry in the postgenomic era. The near completion of the full sequencing of the human genome was published for the record and comparison by a large public and private effort.13,14 The sequencing of the 3 billion base pair human genome not only provides a referenced sequence, but in sequencing chromosomes from many individuals, we have also derived maps of human diversity. Here we will focus on two major developments.

First, it has been recognized that there is a most common type of variation in the genome, which, as described above is the single nucleotide polymorphism or SNP. At the time of this writing, 3 million SNPs, or one in approximately every 1,000 bases, have been identified and many of them have had their precise location within the genome determined.15,16 Thus, in theory, the collection of SNPs provides literally millions of markers throughout the genome. Traditionally, human
disease has been attributed to mutations in specific genes, but now, with the recognition that humans have sequence variations at specific chromosomal loci, we are provided with new insight into how specific sequence variations in a gene or genes may make us more vulnerable to, or protected from, disease and mental illness.Furthermore, other discoveries have made SNPs even more useful. As we have learned, in any human population SNPs may be inherited together representing the descent of ancestral chromosomes leading to a linkage disequilibrium (LD) at certain chromosomal locations.17 Because humanity is a young species having radiated out of east Africa perhaps only 5,000 generations ago, human genetic diversity is not so great as to have permitted time for an enormous number of new mutations and recombinations. Every extended human family has new mutations that have occurred within the last 5,000 generations (indeed, such new mutations are almost certainly responsible for most of the Mendelian disorders such as Huntington’s disease). However, the most common variations in the human genome seen in about 85% of cases to be shared across all ethnic groups and are probably a part of the human genetic heritage since the time that humans radiated out of Africa. In addition, because of population bottlenecks, some groups have relatively no levels of genetic diversity. Thus, for example, northern Europeans (perhaps as a result of the ice age), seem to have large blocks of ancestral chromosomes resulting in LD with other population samples.

The direct outcome of these and other SNP-related discoveries is the development of new research methods of finding disease-related genes. A mixture of SNPs traveling together may be described as an LD block or a haplotype block having “stretches” of polymorphisms that seem to occur in a limited number of heritable combinations. This sets the stage for haplotype mapping which is based on the discovery that some long stretches of DNA, comprising some 50,000–100,000 contiguous bases, have very few variations in SNP patterns, indicating that human genetic diversity is not only a function of individual SNPs, but also of the combination of alleles (haplotypes) at a given region.16 Therefore, rather than attending to each SNP individually to address whether or not it has a role in a disease, much larger chunks of DNA can be analyzed for variations in SNP patterns and linkage to disease. Rather than analyzing the role of each of 50 SNPs in 50,000 bases of DNA (approximately 1 per 1,000 bases), researchers would only have to analyze, perhaps, five SNP patterns that occur in those 50,000 bases and the relationship of the SNP pattern to disease.

If there should turn out to be a significant number of haplotype blocks in the human genome, each of significant length (or 50,000–100,00 contiguous bases), this approach would substantially reduce the amount of DNA needed to be “covered” in order to find disease-related genes. Maps of linkage disequilibrium among different human populations will provide us with extraordinarily powerful tools with which we can trace ancestral chromosomal segments as they are transmitted from parents to children. Such tools can be used to supplement current linkage analysis studies, or, alternatively, to perform independent whole genome association studies in which the question is asked whether a particular gene associated with a certain linkage disequilibrium block or chromosomal segment is marked by certain SNPs. Ultimately, the fruits of the genome project will give us tools of unprecedented power to identify disease genes.

New Tools for Pathophysiology
In addition to permitting gene finding, genomic tools will hasten our understanding of both pathophysiology and drug action. It is now a well-established fact that the genome does not simply set in motion processes of development and then become inert. Rather, in every cell the genome is a repository of information for appropriate responses to the environment. In the brain, these responses include not only adaptations to stresses, drugs, illness, and injury, but also they permit learning and memory.18

Specifically, current models of learning and memory suggest that memories are stored by the remodeling of synapses and related neural circuitry. This remodeling depends on the neuronal transmission of chemical and electrical signals that
ultimately activate certain genes and suppress others so that proteins are made which will appropriately remodel the nervous system. New powerful techniques based upon the completion of the human genome sequence will more readily
allow for the rapid identification of genes critical to brain remodeling in response to its environment.

More specifically, whole new fields have arisen in functional genomics and proteomics which have the power of looking in aggregate at the genes that are activated or suppressed and the proteins that are expressed or modified in response to mental stimuli, disease, or other physiologic probes. As we collect the entire complement of expressed genes—DNA segments that get transcribed into RNA in cells—we are increasingly able to create microarrays that contain these segments of DNA spotted or etched onto glass slides or other positive supports which can then be used as tools of inquiry. Rather than studying one or a few genes at a time, we can look at expression levels of thousands of genes in a given condition. For example, it will be possible to obtain a specific “DNA chip” representing a desired brain region or even individual cell type. Investigators will be able to test what genes are activated or suppressed in normal as compared with diseased tissue, in control as compared with drug-treated tissue, and in “young” as compared with “old” tissue.
While it is true that there is a long  way to go before perfection of these technologies (especially in proteomics), we already have early and exciting examples relevant to psychiatric research, particularly in the area of schizophrenia. For example, Mirnics and coworkers19,20 compared the gene expression profiles from the prefrontal cortex brain region of
postmortem schizophrenic and “normal” subjects and (based on more than 7,000 partial DNA sequences) found that schizophrenia may be a disease of the synapse because transcripts that encode proteins involved in synaptic function are decreased in patients as compared with normal individuals. Follow-up experiments from studies such as this one
highlight the “bottom-up” approach to understanding mental illness whereby data from cDNA microarray analysis
leads to identification of specific disease-regulated genes, the related protein functions and its role in the nervous system, and, finally, in disease. Our previous inability to probe all the regulated genes and biochemical pathways
will no longer be a major issue. We will literally have the universe of possibilities or candidates for these physiologic responses before us to facilitate subsequent biological investigations.

Discussion
Thus far, we have reviewed technological developments that are the most potentially useful in the study of mental disorders. We have also highlighted the challenges that still lie ahead due to the great difficulty of specifically identifying genetic and environmental contributors to multifactorial disorders such as mental illnesses. Thus, despite the excitement, we must recognize that progress will remain hard won and will take time. On the other hand, we are finally increasingly in possession of the tool kit that will permit true progress after a period of relative stagnation in understanding causes of mental disorders. However, this does not imply that with enough time and hard work all will be well. There will be critical conceptual difficulties and none are more important than readdressing the phenotypes of mental disorders. The ability of genomic tools to find the appropriate disease-related gene(s) is limited by the “quality” or homogeneity of the phenotypic sample. For instance, the definition of someone affected by schizophrenia or manic depressive illness, is used in the collection of DNA. The current (DSM-IV) and ICD classifications, not to be belittled, reflect a substantial improvement in communication about patients, their prognosis, and their treatment. On the other hand, there has been a worrisome and highly problematic reification of these diagnostic categories that, if anything, has served to stymie scientific progress, not only in genetics but also in critical neuroscience and behavioral studies of mental disorders. Thus, the complex ideologies of these disease phenotypes remind us that these illnesses will remain fuzzy. There will be a somewhat circular process of understanding phenotype as we gain a better understanding of genotype; this, in turn, will affect our understanding of phenotype. All of this circularity may seem unsettling and unsatisfying to philosophical purists and it is difficult to see any way out of a process of constant adjustment. However, in the meantime, it is critical that we collect broad and thoughtful phenotypic information and not be handcuffed by diagnostic criterion sets that have reliability as their strong suit but were never meant to represent valid diagnostic entities.

The road to fully understanding how genes contribute to psychiatric disorders will be long and difficult. In addition, psychiatry cannot succeed unless it recruits a generation of new investigators deeply steeped in the most advanced genomic technologies. If, however, we fully avail ourselves of the tools that rest on the foundation of the genome project and proceed with open minds, we will ultimately gain information of extraordinary value for mentally ill patients. 

 

  1    Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241-247.
  2.     Friddle C, Koskela R, Ranade K, et al. Full-genome scan for linkage in 50 families segregating the bipolar affective disease phenotype. Am J Hum Genet. 2000;66:205-215.
  3.    Ferguson-Smith AC, Surani MA. Imprinting and the epigenetic asymmetry between parental genomes. Science. 2001;293:1086-1089.
  4.     Park Y, Kuroda MI. Epigenetic aspects of X-chromosome dosage compensation. Science. 2001;293:1083-1085.
  5.    Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265:2037-2048.
  6.    Frankel WN, Schork NJ. Who’s afraid of epistasis? Nat Genet. 1996;14:371-373.
  7.     Langston JW, Ballard P, Tetrud JW, Irwin I. Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science. 1983;219:979-980.
  8.     Gottesman II. Schizophrenia Genesis: The Origins of Madness. New York, NY: Freeman; 1991.
  9.    Kallman FJ. The Genetics of Schizophrenia. Locust Valley, NY: J.J. Augustin; 1938.
10.     Heston LL. The genetics of schizophrenic and schizoid disease. Science. 1970;167:249-256.
11.     Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.
12.     The ICD-10 Classificatin of Mental and Behavioral Disorders. Geneva, Switzerland: World Health Organization; 1992.
13.     Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860-921.
14.     Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291:1304-1351.
15.    Gura T. Genetics. Can SNPs deliver on susceptibility genes? Science. 2001;293:593-595.
16.     Sachidanandam R, Weissman D, Schmidt SC, et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature. 2001;409:928-933.
17.    Reich DE, Cargill M, Bolk S, et al. Linkage disequilibrium in the human genome. Nature. 2001;411:199-204.
18.     Berke JD, Hyman SE. Addiction, dopamine, and the molecular mechanisms of memory. Neuron. 2000;25:515-532.
19.     Mirnics K, Middleton FA, Lewis DA, Levitt P. Analysis of complex brain disorders with gene expression microarrays: schizophrenia as a disease of the synapse. Trends Neurosci. 2001;24:479-486.
20.    Mirnics K, Middleton FA, Marquez A, Lewis DA, Levitt P. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron. 2000;28:53-67.

Tuesday
Dec112007

Evidence-Based Medicine

Pharmacogenomics of Antidepressants:
Drug Discovery, Treatment, and Ethical Considerations


Julio Licinio, MD
Jonas Hannestad, MD, PhD
Ma-Li Wong, MD


Dr. Licinio is professor of psychiatry and biobehavioral sciences and medicine/endocrinology at the University of California School of
Medicine in Los Angeles. He is also director of the University’s Interdepartmental Clinical Pharmacology Center. Dr. Hannestad is postdoctoral fellow, and Dr. Wong is professor of psychiatry and biobehavioral sciences and director, both at the Laboratory of Pharmacogenomics at Neuropsychiatric Institute of the University of California School of Medicine, Los Angeles.  

 

Abstract
Pharmacogenomics is a new area of medicine that uses the data emerging from the sequencing of the human genome to predict drug responses and to identify new targets for treatment. Pharmacogenomics is of great relevance to depression, a common and complex disorder of unknown cause for which prediction of treatment response and identification of new targets for therapeutics are of crucial importance. The clinical reality is that weeks of continued antidepressant treatment are required before therapeutic effects occur. Moreover, it is not possible to know in advance if a patient is likely to respond to a specific drug. While therapeutic effects take long to emerge, adverse reactions manifest themselves rather
soon. This impacts negatively on compliance, leading to incomplete and failed treatments, and potentially disastrous outcomes, such as suicide, which is now the eighth leading cause of death in the United States. The identification of the genomic substrates underlying antidepressant treatment would facilitate not
only the formulation of individualized treatment approaches, but also the development of new classes of drugs that would affect those genomic targets more directly than existing compounds, leading to more rapidly effective clinical responses. As the research needed to achieve these goals is being conducted, myriad ethical questions emerge: Which ethnic groups will participate in—and consequently benefit from—such research? How will genomic information related to drug response be handled in ethical, legal, economic, and social terms? Progress in the pharmacogenomics of depression needs to be paralleled by thoughtful consideration of the implications of such work at the individual and societal levels.

“Doctors are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of whom they know nothing.”
—Voltaire (1694–1778)

But now that the human genome is an open book, personalized medicines—drugs tailored to our genetic idiosyncrasies—will soon be possible. In a decade or so, pills geared to particular “genotypes” are expected to begin arriving in pharmacies along with tests to show who should get them. Eventually we’ll look back in wonder at how we used to play guinea pig in the primitive therapeutic experiments our doctors carried out each time they wrote new prescriptions for us, just as we now shake our heads about the poor chumps who got blood transfusions before it was possible to match donors’ and recipients’ blood types (The adverse reactions included kidney failure and fatal clots).1


Introduction
Pharmacogenomics is a new area of medicine in which information databases that are emerging from the sequencing
of the human genome and related high throughput technology are used to improve therapeutics. The term “psychogenomics”2 has been used to describe the process of applying the powerful tools of genomics and proteomics to achieve a better understanding of the biological substrates of normal behavior and of diseases of the brain that manifest themselves as behavioral abnormalities. This article will cover two topics that are of direct relevance to genomics in neuroscience. The first is the application of genomics to the treatment of a specific psychiatric disorder. We will discuss depression, which is a common and complex disorder of unknown case with high prevalence that costs the US economy over $50 billion per year, and which is the fourth cause of disability worldwide (second in developed countries).3-6 The second topic we address in this article is the issue of conducting research in ethnically identified groups.Pharmacogenomics can offer identification of novel therapeutic targets and individualization of treatment
for depression.

Pharmacogenomics of Depression: Drug Discovery
The psychosocial substrates of depression are fully acknowledged and recognized. For example, it is undeniable that stress and loss can precipitate episodes of depression. For example, the work of Lerer’s group in Jerusalem has shown that loss (divorce being worse than death) of a parent (mother more than father) before age 9 years was highly associated with depression in adulthood.7 Additionally, structured psychosocial treatments, such
as cognitive or interpersonal psychotherapies, can be highly effective in the treatment of depression.8,9 Thus, one cannot deny the impact of psychosocial approaches to depression treatment. Nevertheless, clinical experience also supports a biological basis for treatment response. Many patients in clinical trials who receive only medication achieve full remission in a few weeks. It is widely accepted that there is a biological substrate to depression and that interventions at
the level of that substrate can lead to remission. A key question in the field of depression research is: What are the final therapeutic targets of antidepressants?

As clinicians and patients know, not everybody responds well to each of the more than 20 antidepressants approved
by the Food and Drug Administration. Approximately 60% to 70% of patients respond to any specific antidepressant. Therefore, there is a need to develop new treatments for the substantial minority of patients who are currently labeled as “treatment-resistant” or “refractory.” The greatest challenge to the development of new drugs is the identification of new therapeutic targets. For example, the reason fluoxetine was such a blockbuster was because it was the first selective
serotonin reuptake inhibitor (SSRI) on the market. Potential new targets for depression treatment include neuropeptides. They have been thought to have a role in the biology of depression, and consequently, there has been an effort to develop drugs that modify neuropeptidergic function. Drugs acting at the level of the receptor for the neuropeptides
corticotropin-releasing hormone (CRH) and substance P have been used experimentally in the treatment of depression with success, but have not made their way to the market yet.10,11 What other targets are there for depression? The simple answer is that we do not know.

It is obvious that antidepressants act on the brain to affect the substrate of depressive symptoms. Their initial and acute targets are central monoaminergic systems. Drugs that at least initially affect one or more of three monoamines (serotonin, norepinephrine, dopamine)12,13 can be fully effective in treating depression. The effects of antidepressants on one or more of those amines takes place within hours, yet the clinical response to antidepressants takes weeks to occur. Therefore, some targets that are still unknown,

and common to various classes of drugs, are being activated and causing the antidepressant effect. Many decades of psychopharmacology research have failed to elucidate the identity of those targets that, once activated, can lead to remission of depressive symptoms. Genomics opens up new doors to such efforts. The use of advanced molecular biology techniques coupled with our enhanced understanding of genomic sequences will facilitate the search for genomic targets of antidepressant drugs. As an example, our laboratory has used techniques such as differential messenger mRNA display and DNA microarrays (“DNA chips”) to identify new transcripts that are expressed in the brain in response to chronic treatment with both fluoxetine and impramine (Figure 1). Such transcripts may be involved in the pathways that are modulated by antidepressants as they exert their therapeutic effects. We are currently validating those findings by a variety of independent methods and characterizing those genes. Once these genes are fully characterized they will
be natural new targets for antidepressant drug development.


Pharmacogenomics of Depression:
Individualized Treatment

In any area of medicine, including psychiatry, the outcome of treatment is never certain. All of us who practice
medicine work with patients who respond in the most diverse ways to the same
treatment. After receiving the same dose of the same medication, some patients have no response whatsoever, others have only severe side effects, while others can experience complete remission of their symptoms. In some cases, it is possible to predict drug response based on the patient’s personal or family history of treatment response. Physicians also try to balance the patient’s health status and possible drug interactions with the side-effect profile of various antidepressants.

Current work conducted by our group and others on the clinical pharmacogenomics of depression is aimed at using genetic markers to identify predictors of treatment response. For this to occur, it is necessary that rigorous clinical research be developed to thoroughly examine the relation between genotype and the phenotype of drug response. This way, in well-conducted clinical trials, favorable clinical responses and adverse events are related to specific genetic polymorphisms. The goal of such work is to identify markers associated with treatment responses. This line of investigation offers enormous promise for the individualization of treatment. We would all benefit from knowing the likelihood of favorable responses or adverse reactions before taking a drug. In spite of enormous promise, a variety of problems emerge in the conduction of such work. These include clinical factors such the confounding variable presented by the placebo response, the issues of sample size, patients’ genetic background, ethnic stratification, use of continual versus categorical outcome measures, choice of drugs and treatment strategy, treatment compliance, and environmental contributions to treatment outcome.6

The challenges presented by the genetic approaches to such studies are also considerable and have been discussed elsewhere.6 A key dilemma is the choice of which genetic polymorphisms to choose for associations with drug responses, and how to statistically ascertain small effects in
the context of multiple comparisons of variables that have a varying (but not fully characterized) degree of partial relationship to one another.

Serotonin Transporter Gene
A natural candidate gene for such studies is the one encoding the serotonin transporter. After serotonin is released in the synaptic cleft, the serotonin transporter (5-HTT) brings serotonin back into the presynaptic neuron, thereby decreasing the amount of bioavailable serotonin in the synapsis. Blocking the 5-HTT by drugs such the SSRIs leads to increased synaptic concentrations of serotonin.14 The 5-HTT displays a polymorphism in its regulatory region, the presence or absence of a 44 base-pair insertion. The short variant of the polymorphism reduces the transcriptional efficiency of the 5-HTT gene promoter, resulting in decreased 5-HTT expression and 5-HTT uptake
in lymphoblasts.15

The group at the San Raffaele Hospital in Milano has showed that patients with the “long” form of the 5-HTT regulatory region show a higher response to fluvoxamine and paroxetine.16,17 Furthermore, a not uncommon problem with the
treatment of bipolar disorder with antidepressants is the precipitation of a manic phase. This risk is higher if the patient has the “short” form of the 5-HTT regulatory region.18 Additionally, the group from Milano also showed that antidepressant response to a nonpharmacologic intervention, namely sleep deprivation, was more likely to occur in patients with the long genotype.19 The short and long forms of the 5-HTT regulatory region can also influence the occurrence of extrapyramidal side effects and akathisia, which can be induced by SSRIs. These side effects are known to affect compliance.20 In Korea, Kim and colleagues21 studied the effects of the long and short forms of the 5-HTT regulatory region and they also examined the association of treatment response to the presence of an insert in the second intron of that gene.21
It is noteworthy that while the group from Milano found that the long (l/l or l/s) form of the 5-HTT gene was associated with better treatment response to an SSRI, the group from Korea found that the short (s/s) form was associated with better treatment response. These results suggest that factors other than a specific genotype may have important roles in determining the effect of gene-drug interactions. The differences between the two studies, including culture, diet, type of medical care, psychosocial support, and genetic background, are so vast that it is not possible to attribute a specific cause for such disparities in the data. These discrepancies raise the important
point that it in order to document replicability, it is essential that genomic and pharmacogenomic studies be conducted in the same manner in at least two different populations.

In conclusion, genetic variations in one target of antidepressant action, the 5-HTT, are associated with treatment response. However, these variations do not fully predict the response to treatment. It is highly likely that no single genetic marker will fully explain the genetic components of antidepressant treatment responses. To achieve full understanding of this topic, a variety of markers in multiple genes that contribute to treatment responses will have to be identified. Current research efforts are aimed at identifying genetic targets of antidepressant action. Polymorphisms in those genes will be natural candidates for future pharmacogenomics studies of
antidepressant responses.22

Cytochrome P450 system
A body of research has been conducted on the relation between the cytochrome P450 (CYP 450) superfamily23 and antidepressant response. The CYP 450 is a group of related enzymes located in endoplasmic reticulum. Those enzymes are expressed mainly in liver, and also in the gut and the brain. They use oxygen to transform endogenous (eg, steroids) or exogenous (eg, drugs) substances into more polar products that can be eliminated in
the urine. The electrons are supplied by reduced nicotinamide adenine
dinucleotide phosphate (NADPH) CYP 450 reductase, a flavoprotein that transfers electrons from NADPH to CYP 450.
The CYP superfamily is divided into 14 families and 17 subfamilies of enzymes defined on the basis of similarities in
their amino-acid sequences. The enzymes transforming drugs in humans belong to CYP families 1–4. The antidepressants are extensively metabolized by these enzymes. Consequently, genetic variations that affect enzyme activity will impact on the metabolism of antidepressant drugs and will affect clinical responses to treatment. Among the CYP 450 superfamily, CYP 2D6 has an important role in the metabolism of various antidepressants, as well as other commonly used drugs (Table 1).

The activity of CYP 2D6 is bimodal, some people (6% of Caucasians) have no copy of the gene, while others have gene duplication. One third of Ethiopians have such gene duplication. Overall, the CYP 2D6 cluster has 48 mutations and
50 alleles.24-28 A dramatic case report illustrates the clinical relevance of this gene cluster. A 9-year-old diagnosed with attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, and Tourette’s disorder was treated with a combination of methylphenidate, clonidine, and fluoxetine. After treatment was initiated, the patient had generalized seizures that evolved to status epilepticus followed by cardiac arrest and death. The medical examiner’s report indicated death caused by fluoxetine toxicity. At autopsy, blood, brain, and other tissue concentrations of fluoxetine and norfluoxetine were several-fold higher than expected based on literature reports for overdose situations. This led authorities to charge the parents with murder and prompted juvenile authorities to take away their other two children pending the outcome of a homicide investigation. Subsequent testing of autopsy tissue revealed the presence of a gene defect at the CYP 450 CYP 2D locus, which is known to result in poor metabolism of fluoxetine.29 Criminal charges to the parents were then dismissed. The fact that the population frequency of such clinically relevant mutant alleles and duplicated genes is dependent on ethnicity raises critical ethical considerations.

Ethical Considerations
Ethnically-Identified
Minority Groups
The frequency of polymorphisms in genes that are relevant to antidepressant treatment response may vary among ethnic groups. It is therefore important to consider ethnicity in clinical research in this area. Our work on the clinical pharmacogenomics of antidepressants in the Los Angeles Mexican-American population has brought to our attention a number of important issues and considerations that emerge when studying an ethnically-
identified group. First, we are often asked about the rationale for studying a specific group. Our rationale has been that because we will examine polymorphisms in genes associated with drug response, including CYP 450 genes, we want to avoid in our study factors other than drug response that could be responsible for variations in the frequency of polymorphisms. One such factor would be ethnic stratification.

Ethnic stratification is particularly important in Los Angeles, which is said to be the world’s most ethnically diverse metropolitan area. For example, it is possible that if we study a general population we may identify a genetic polymorphism X that could be 15% more prevalent in the antidepressant responder group than in nonresponders.
Let us imagine that the responder group is predominantly of ethnicity A and the nonresponder group is mostly of ethnicity B. If there is ethnically-related variation in the frequency of polymorphism X, it would be very hard to
determine if the variation in the rates of polymorphism X between the two groups is due to their differences in drug response profiles or to their different ethnic composition. To avoid this type of confounding scenario, we opted to study one population group, so that all patients (responders and nonresponders) would belong to the same group, and therefore would not stratify divergently during the course of the study. Such measures do not fully eliminate genetic stratification as we are not studying an ethnic homogeneous population. Different individuals of Mexican origin have varying rates of European, Native-American, and, to a smaller extent, African backgrounds. Nevertheless, even though Mexican-Americans are not a homogenous group, they are certainly less heterogeneous than the population of Los Angeles at large.

During the course of this study, several topics emerged. There are sensitive issues when one recruits patients not only because they have a disease, but also because of who they are ethnically.30 The potential for stigma is ever present. According to the Census Bureau, Mexican-Americans are the most rapidly-growing minority group in the US. Today, 1 in every 14 Americans is Mexican-American. This group is among the poorest and least educated in the nation. What will happen if we find that this population is far less responsive to a common treatment than the general population? Could our data become an obstacle to obtaining health insurance for a group that already has considerable difficulty gaining access to adequate medical care? We have developed strategies to consult the community in order to inform them about the research and obtain their impressions. Such a complex and logistically-challenging process involves the following considerations:
(A)    Whom does one consult?
(B)    In a large county that has 3 million Mexican-Americans, where does one go to develop such a process of community consultation?
(C)    How does one deal with dissent? For example, what should the investigator do if some members
of the community are highly supportive of the research effort while others are against it?
(D)    What if the research results can potentially worsen existing stigma?
(E)    How does one motivate community members and community leaders to participate in a process that will be of no immediate benefit to them?
(F)    What is the cost of such consultation and who pays for it?
In general, we have found that multiple meetings in different locations are better than one large and highly engineered meeting. The creation of a community advisory group can facilitate an ongoing dialogue with the community and help in dealing with conflicting views. Research results should be reported in a sensitive and careful manner to avoid ethnic stereotypes and stigma. As the study of ethnically identified groups evolves, strategies to consult with communities will be refined and become more widespread.

Additional Ethical Considerations
There are multiple other ethical considerations in pharmacogenomics. A detailed review of this topic by Robertson discusses issues of confidentiality (which is always a problem in any type of genetics test) and labeling patients as “nonresponders.”31 Such a label could affect the patient’s perception of self, future medical care, and ability to obtain insurance or employment. An important possible complication of drugs that are tested and approved for people with specific genetic markers is the issue of what to do with those who do not have the markers that are associated with favorable outcome. For example, if a new antidepressant is approved for patients with specific genetic markers, it might be difficult to use that drug in individuals who do not have that genotype. A chronically depressed patient who is refractory to existing treatment may not have those genetic markers. Her physician may still think that the new drug is the best option for that patient, despite the risk of adverse reactions or low efficacy. Will such
“off-label” use be covered by the patient’s insurance? In that scenario, what would the physician’s liability be if the patient experiences severe adverse reactions?


Conclusions
Pharmacogenomics, a new area of investigation that integrates genomics and therapeutics, has much to offer the field of psychiatry. While the promise of individualized therapeutics is considerable, the obstacles cannot be overlooked. Those include clinical, technical, and ethical issues that are only now being fully addressed. For all groups to benefit from progress in pharmacogenomics, it is crucial to include members of various ethnically identified groups in such studies. However, as we do that, additional ethical issues emerge. Future research will determine whether the concepts of race or ethnicity are relevant to pharmacogenomics. Even if they are not, it is necessary to conduct studies to achieve that conclusion. Those studies are themselves fraught with ethical issues. Careful consideration of the
interplay of genomics, psychiatry, and ethics should guide a conscientious effort to advance the science of pharmacogenomics in a manner that maximizes the translation of scientific advances into better health care for all, while avoiding stigma and stereotypes. 

Acknowledgment: Dr. Licinio received grants GM61394, DK58851, and HL04526 from the National Institute of Health (NIH), and a monetary award from the Dana Foundation. Dr. Hannestad is supported by a UCLA Norman Cousins Center PNI fellowship. Dr. Wong received NIH grants MH/NS62777, GM61394, HL04526, and AT00151 and a monetary award from the National Alliance for Research on Schizophrenia and Depression. �

 

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Tuesday
Aug212007

Scientific articles

1515708-807312-thumbnail.jpg
Wednesday
May162007

Drug Disposition & Pharmacokinetics

Efficacy of Olanzapine and Risperidone in Schizophrenia:
A Randomized Double-Blind Crossover Design

Open article by Clicking Title Above.
When Open, Click Thumbnails to Load Images.


By Jose M. Canive, MD, Gregory A Miller, PhD, Jessica G. Irwin, BS, Sandra N. Moses, PhD, Robert J. Thoma, PhD, J. Christopher Edgar, PhD, Andrea Sherwood, PhD, Fernando Torres, MD, Marianna LaNoue, PhD, Stephen Lewis, MD, Faith M. Hanlon, PhD, Michael P.Weisend, PhD, Valerie Mead, MD, and Vicente B.Tuason, MD

 Psychopharmacology Bulletin. 2006;39(1):105-116.


Friday
May042007

Original Research—Suicide

Brief Report: Paroxetine in Younger and Adult Individuals at High Risk for Suicide

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By Mark S. Bauer,MD, Stephen R.Wisniewski, PhD, Jane N. Kogan, PhD, Lauren B. Marangell, MD, Michael E. Thase, MD,
Gary Sachs, MD, for the STEP-BD Investigators

 Psychopharmacology Bulletin. 2006;39(1):31-37.