Ann. N.Y. Acad. Sci. ISSN 0077-8923

A N N A L S O F T H E N E W Y O R K A C A D E M Y O F SC I E N C E S Issue: Translational Neuroscience in Psychiatry

Translational psychiatry—light at the end of the tunnel Kenneth A. Jones,1 Frank S. Menniti,2 and Digavalli V. Sivarao3 1 Cyanaptic LLC, Glen Rock, New Jersey. 2 Mnemosyne Pharmaceuticals, Inc., Providence, Rhode Island. 3 Bristol–Myers Squibb Co., Wallingford, Connecticut

Address for correspondence: Kenneth A. Jones, Cyanaptic LLC, 25 Warren Place, Glen Rock, NJ 07452. [email protected]

Neuroscience has made tremendous progress delineating the cellular and molecular processes important for understanding neuronal development and behavior, but this knowledge has been slow to translate to new treatments for psychiatric illness. To accelerate this transfer of knowledge to the human condition requires the wide-scale adoption of biomarkers that can bridge preclinical and clinical discoveries, and serve as surrogate measures of efficacy before commencing expensive phase III studies. Several biomarker methodologies, including imaging, electroencephalography (EEG), and blood transcriptomics/proteomics, are now showing promise. From an industry perspective, we highlight the utility of quantitative EEG as one example of a translatable biomarker applicable to psychiatric drug development and discuss recent insights into glutamate system dysfunction in schizophrenia and depression gained through translational studies of the drug ketamine. Keywords: translational; psychiatry; biomarker; electroencephalography; glutamate; ketamine

Introduction: translational psychiatry Despite the tremendous growth in the field of neuroscience over the past 30 years, the number of truly novel treatments for psychiatric disorders has been disappointing. Many reasons come to mind to explain this apparent disconnect, including an early naivet´e about the complexity of the nervous system, lack of disease models, and the inadequacy of currently used end points for gauging the success of clinical trials. However, perhaps the most significant impediment has been the relative inaccessibility of the human brain to direct measurements of molecular and cellular function. There is no question that the complexity of the human brain is staggering, where numbers of neurons and permutations of synaptic connections are immense, and yet somehow from this complexity, sentience, dexterity, planning, creativity, and emotion are born. But what happens when something goes wrong?—when the developmental path deviates at some point or a cluster of genes fails to maintain the balance of synapses, and mood plummets, thoughts race, or hallucinations arise? Which are the circuits or neurotransmitters that manifest the

imbalance? While we may infer answers to these questions from animal studies, the limited ability to gather data directly from humans suffering from central nervous system (CNS) dysfunction has been a longstanding bottleneck to treatment development. The good news for brain research is that an increasingly sophisticated armament of tools is being applied by neuroscientists to the human brain to answer these questions, offering new insights into the nature of brain dysfunction that gives rise to psychiatric maladies. To discuss this rapidly advancing area was the impetus for a 1-day conferencea “Translational Psychiatry: Light at the End of the Tunnel,” the spirit of which can be captured in the following: nothing can be done to reduce the complexities of the brain so they should be embraced, and the heretofore inadequate accessibility of the brain, lack of disease models, and quantitative a

Held at the New York Academy of Sciences, New York, NY, on April 8, 2014. Papers prepared by speakers at the conference are published in Ann. N.Y. Acad. Sci. 1344: 1–119 (2015).

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clinical end points are essentially technical issues that ultimately will be addressed. Optimism for this comes, in part, from sophisticated imaging and electroencephalography (EEG) technologies that open a rich information window into human brain function and dysfunction. These insights, in turn, are being translated back to the preclinical realm to guide yet more sophisticated analyses in preclinical species, the development of better animal disease models, and the investigation of new drug targets. The circle is being completed by forward translation of this preclinical research into new biomarkers for target engagement and efficacy that may be used in the clinical evaluation of potential new drugs. This paper provides the background and impetus for the conference, an industry perspective on recent trends in clinical development in psychiatry, and a brief overview of some current approaches to find suitable biomarkers for psychiatric diseases. We focus our discussion on the emerging recognition of the role of glutamate neurotransmission dysfunction in psychiatric illness and the use of EEG methodology as a tool for novel discovery. An industry perspective and the growing need for biomarkers Despite the compelling need for new and improved medications to address the large unmet needs of psychiatry patients, currently there is a near standstill in industry discovery.1 This is particularly true at large pharmaceutical companies, traditionally the most prolific stakeholders in shepherding new drugs to clinical practice. Large pharmaceutical companies occupy a particularly important niche in the lengthy and costly process of the late stages of drug development: the planning and execution of phase II and phase III efficacy trials. However, the frequently ambiguous outcomes of phase II proof-of-concept studies, in the face of increased costs of running large clinical trials, have led to a recent exodus of pharma from their traditional niche. The perception, for better or for worse, is that the underlying science remains immature relative to the investment risks for success in clinical development. A major stumbling block often cited is the poor predictive value of animal models of psychiatric disease. A case in point is animal models of depression. Models such as the forced swim test were widely adopted because they showed responses to existing antidepressant drugs, not because they replicated a key neurophysiological 2

aspect of the human illness. As a result, new drugs discovered using these models looked very much like the ones used to validate the models in the first place; the current generation of antidepressant and antipsychotic drugs has provided limited improvements in efficacy relative to drugs described 30 years ago. Another major reason for the exit of a significant portion of the pharma industry from psychiatric drug R&D has been the lack of target engagement and efficacy biomarkers. A careful analysis2 of data from dozens of clinical drug development studies at Pfizer concluded that to maximize odds for success in phase III, it was imperative to ensure that the drug in question not only is present at the target site but also has a demonstrable pharmacodynamic activity commensurate with engagement.2 Not having a clear pharmacodynamic marker often leads to a highly unsatisfactory situation of being unable to determine whether the original hypothesis had been tested. While it may seem obvious that unequivocal evidence for functional engagement is an essential component of a drug’s clinical development, demonstrating such engagement has been a significant challenge for most psychiatric drug discovery to date.3–5 The current situation has drawn the attention of the National Institute of Mental Health (NIMH) and other thought leaders, who have reflected on past failures to suggest new approaches to accelerate scientific breakthroughs.1,6 Over the past several years, attention has shifted away from behavioral animal models based on predictive validity derived from currently used agents toward genetic approaches to understanding brain function and dysfunction at the molecular and circuit levels.7,8 The affordability of genomic sequencing has driven the discovery of multiple susceptibility genes for schizophrenia and autism,9–11 as but two examples. Although the polygenic nature of these and other psychiatric diseases presents a new challenge for those building genetically based disease models, collectively, the gene defects point to key foci of neural function, such as synaptic transmission and circuit development.7,9,10 These promising discoveries portend a more molecular and circuit-based understanding of the processes that are compromised in disease, and importantly, improved animal models and translatable endophenotypes that will have prognostic value in the clinic.8,12

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Genetics alone, however, will not solve the current drug discovery bottleneck. There is also new urgency for developing truly translatable measures of brain activity relevant to psychiatry that span both the laboratory and the clinic. In other CNS diseases such as Alzheimer’s disease, the first biomarkers will soon be available as surrogate end points for proof-of-concept clinical trials.13 The importance of these developments cannot be overstated especially for long-duration, and therefore expensive, human studies. Translatable biomarkers for psychiatric disorders may not be far behind. To spur the development of tools that will provide these measures, the NIMH recently released new guidelines for requiring the incorporation of biomarkers in every clinical trial sponsored by the institute.14 Although perhaps burdensome, this new requirement promises to provide critical data linking surrogate end points to primary measures of clinical efficacy. It is important to distinguish prognostic biomarkers that can be used to predict efficacy in humans from diagnostic biomarkers that can be used to correctly identify disease subtypes. It is primarily the former that is discussed here, particularly biomarkers with both prognostic and translational value, even though diagnostic biomarkers will be needed in psychiatry to correctly identify patient segments that may benefit from specific treatments. Examples of prognostic biomarker development include more sophisticated and widely available magnetic resonance imaging (MRI) and functional MRI technologies used in the study of depression that are zeroing in on areas of the brain, such as the amygdala and medial prefrontal cortex, that emit different signals in normal and depressed individuals.15,16 Likewise, in the field of schizophrenia, EEG methods are revealing electrical signatures of cognitive deficits whose early appearance in children predict the development of the illness later in life.17,18 When used in concert with event-related potentials, EEG exhibits the temporal resolution necessary to detect deficits in sensory processing or attention.19 Finally, blood-based biomarkers, particularly for depression, are revealing underappreciated biochemical links between the periphery and the brain. Interestingly, some of these biomarkers normalize with treatment while others do not, raising the possibility that certain blood biomarkers could be helpful for identifying patient subpopulations responsive to treatment.20–22 If sufficiently developed and validated, peripheral

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biomarkers may one day offer the practicality of use required for inclusion in large clinical trials. Use of quantitative EEG in drug discovery EEG-based techniques are used to illuminate circuit dysfunction underlying psychiatric illness and quantify circuit-relevant pharmacodynamic activity of novel drugs. Glutamatergic principal neurons form the core circuitry of the forebrain, and modulation of glutamatergic synapses is the brain’s informational currency. Activity in forebrain glutamatergic circuits, as modulated by the gammaaminobutyric acid (GABA)ergic interneuron network, gives rise to the translational imaging and electrophysiological signals currently under investigation by many investigators.b Emerging from these studies is insight into the circuit-level dysfunction that gives rise to schizophrenia and depression. Several EEG-based functional biomarkers, such as mismatch negativity and 40-Hz auditory steadystate response, lend themselves to interrogate specific circuit-level dysfunction.23 We highlight here the characteristics of quantitative EEG (qEEG) as a relatively inexpensive and accessible functional engagement measure that can be used to bridge preclinical and early clinical studies for glutamatergic and other targets. We then focus on the recent explosion of cross-species studies using glutamatergic drugs, notably ketamine, that are beginning to yield an understanding of the molecular mechanisms underlying circuit dysfunction in depression and schizophrenia. The convergence of these two themes may well represent the beginning of a renaissance in CNS drug discovery. On the basis of our experience from the application of qEEG to multiple CNS discovery programs as well as selected examples from the published literature, we will discuss several key factors that impact translatability. Many articles and reviews have been published on qEEG as a potential pharmacodynamic measure for early discovery.19,24–26 Some have focused on analytical methods for qEEG that need to be appreciated for effective application.27,28 We encourage the reader to consult these monographs for an in-depth review of qEEG as a technique, as well as the analytical approaches used. Here, we summarize key attributes that we

b

Including some of the speakers at the conference.

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believe are essential for using qEEG as a translational pharmacodynamic measure and illustrate how these have facilitated its use as a critical biomarker for N-methyl-D-aspartate (NMDA) channel blockers, especially in relation to treatment options for treatment-resistant depression. The EEG signal arises principally from summated postsynaptic activity of large populations of neurons within the cortical mantle. While neurons throughout the brain contribute to fluctuations in the field potential, the relatively large and numerous cortical pyramidal neurons with their extensive dendritic processes and laminar arrangement are the principal sources of fluctuating field potential registered by scalp electrodes. Quantitative EEG as a technique refers to dividing the oscillatory EEG signal into broad frequency bands and analyzing the signal power within these bands.29 While we do not completely understand the individual significance of these bands and how they may interact with each other in support of physiological function, we do know that in many regions of the cortex, higher-frequency oscillations are nested within a slower-frequency field potential change,30,31 and that inhibitory and excitatory neurons in the cortex and thalamic nuclei interact to produce these fluctuations.32,33 Rhythmic oscillations of a frequency band are often associated with particular physiological states. For example, delta frequency (0.5–4 Hz) oscillations occur in deep sleep as a result of thalamic burst firing. Sleep spindles in the alpha frequency range (9–13 Hz) are generated by the thalamic reticular nucleus whereas the occipital alpha rhythm, characteristic of relaxed wakefulness, is generated in deep layers of the visual cortex. Higher frequency beta (13–30 Hz) and gamma (30–100 Hz) oscillations, associated with mental alertness, appear to originate locally in the cortex,34,35 although these too can be markedly influenced by thalamic input.36,37 Since several classes of drugs affect specific oscillatory bands in an exposure- and time-dependent fashion, presumably owing to their effects on the underlying circuits, a tactical opportunity arises to use these changes as a pharmacodynamic biomarker as well as to model effects over time to make predictions regarding dose regimen and interval. It is important to recognize that the qEEG changes may or may not be related to efficacy of the drug, yet they can be used as an engagement marker to demonstrate 4

drug-related activity in the brain and to drive early clinical discovery.2,3,5 In the absence of any existing clinical data that support the use of qEEG as a pharmacodynamic biomarker for a given target, which often is the case for novel targets, a starting point would be to demonstrate a robust effect in experimental animals in a dose-ranging study. Using rodents as a starting point to investigate the potential for qEEG as a pharmacodynamic biomarker is a viable strategy, provided the cross-species target physiology is conserved. If qEEG effects in rodents are robust and are determined to be specific to the target, and in the absence of clinical data, moving on to a higher species, such as a nonhuman primate, may be considered as a bridge to clinical studies. Preclinically, EEG is frequently recorded from epidural electrodes that bypass the skull and sample directly from the brain where the signal registration is stronger and spectrally more complete. In order to have a greater probability for success across species, it is important to consider not only statistical significance but also how robust the drug-mediated effects are. Thus, an effect size analysis using an indicator such as Cohen’s d (mean difference between control and treatment in pooled standard deviation units) is warranted. As a rule of thumb, a Cohen’s d of 0.8 or greater could trigger further investigation for translatability. A recent study used two NMDA channel blockers, ketamine and lanicemine, to show robust and dose-dependent increases in gamma oscillations in rodents that were subsequently translated into healthy human volunteers.38 In addition to these two examples, there is a significant body of literature that suggests that this translational approach is indeed viable for certain classes of drugs.24,39,40 Despite showing consistent and strong effects, it is frequently not possible to explain the exact basis for qEEG changes because they ensue following drug exposure to very large and diverse populations of neurons in the brain. Moreover, multiple drug classes can in some cases produce the same qEEG response. For example, both benzodiazepine agonists and NMDA antagonists can increase cortical high-frequency oscillations, albeit through distinct molecular targets.41,42 However, it is possible to determine specificity by demonstrating a causal relationship between the target and the response. For example, benzodiazepine agonist effects on

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qEEG are blocked by selective antagonism with flumazenil, a benzodiazepine-selective antagonist, in rodents41 and in humans.43 On the other hand, gamma oscillations elicited by NMDA channel blockers are thought to result from the disinhibition of pyramidal cells owing to the NMDA block of parvalbumin+ inhibitory interneurons.44,45 In fact, MK-801–induced gamma oscillations have been shown to be attenuated in genetically engineered mice that express a deficient NMDA channel complex in parvalbumin+ interneurons.46 Additional means to ensure causality would be to demonstrate, where available, differentiation of response using enantiomers (e.g., R- versus S-baclofen47 ) or by contrasting two compounds with similar in vitro activity against the target but different CNS penetration. The characterization of specificity at the preclinical stage is important not only to ensure that the EEG response is linked to the target in question but also because, frequently, this is the only stage where it is practical to do so. Although any drug that works on the nervous system could in theory affect the qEEG response, in practice such changes are large and consistent enough only for certain classes of drugs. Since synchronization within cortical columns is largely responsible for the scalp-recorded EEG signal, it is important for any candidate drug to target, either directly or indirectly, cortical neurons. Immunohistochemical labeling with an antibody raised against the obligatory subunit NR1 of the NMDA channel demonstrates a robust presence of the receptor within the cortex, both on pyramidal cell layers and on inhibitory neurons across multiple species, including rodents and humans.48–50 Commensurate with the widespread expression of its molecular target, nonselective channel blockers, such as ketamine, produce a strong cortical disinhibition as evidenced by a selective reduction of fast-spiking putative GABAergic interneurons and a widespread increase in pyramidal cell firing in rodents.44 NMDA channel blockers produce a robust, dose-dependent, and long-lasting increase in high-frequency gamma oscillations (e.g., 30–55 Hz) in mice and rats, as well as in humans as discussed above.38,42,45,46 Perhaps for this reason alone, many of the clinical studies in psychiatry that employ EEG as a response measure are evaluating drugs that target the NMDA channel.

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An important attribute for a pharmacodynamic biomarker is for the signal to scale in a dose- or exposure-dependent manner. Apart from NMDA channel blockers, several other pharmacological classes of drugs evoke dose- or exposureproportional EEG responses, including opiates, barbiturates, benzodiazepines, and inhalational and injectable anesthetics, and lend their effects to pharmacokinetic–pharmacodynamic correlation.24,25 A corollary follows that the lack of a clear linearity of dose response in qEEG measures would suggest that this tool would be of limited utility for a phase-I biomarker. For example, nicotinic alpha7 receptor agonists frequently show an inverted U-shaped response across multiple paradigms,51,52 perhaps owing to rapid desensitization of the nicotinic channels at higher exposure. We have observed that EEG changes seen at lower doses frequently disappear at higher doses (unpublished data), making the use of qEEG for this particular target unsuitable as a biomarker. In summary, a significant challenge in psychiatric drug discovery is to have translational biomarkers that can inform the researchers about the unequivocal presence of the drug being tested in the brain and its time course, using affordable, noninvasive, and nondeleterious approaches. Translatability implies that the qualitative changes noted in preclinical species (e.g., rodents and/or primates) are essentially reproducible in humans. Quantitative EEG offers a simple and relatively inexpensive modality for translation across preclinical species and humans. While this measure cannot always be linked to the overall efficacy of a drug, it can offer a pharmacodynamic sign of the drug’s presence and activity in the brain. Such functional markers, as opposed to physical engagement markers (e.g., receptor occupancy or positron emission tomography imaging), may be especially relevant for high-efficacy agonists that produce pharmacological responses at very low occupancy, an observation explained by the concept of high receptor reserve.53,54 Quantitative EEG effects in early discovery can guide dose selection and dosing interval for subsequent phases of clinical discovery. However, qEEG is not suitable for all centrally acting targets. Its translational utility would be target-specific and determined by such factors as the cortical footprint of the target, effect size, and exposure–response linearity.

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New treatments targeting glutamate neurotransmission A second translational approachc is the use of the drug ketamine to probe the role of glutamate in psychiatric illness. Ketamine is an antagonist of the NMDA receptor, one of the four classes of receptors (AMPA, kainate, NMDA, and metabotropic) that mediate glutamate neurotransmission. NMDA receptors have a critical function in regulating the excitation/inhibition balance that governs information flow through glutamatergic/GABAergic networks.55,56 These receptors also play the principal role in regulating glutamate synaptic strength through the activation of pathways that insert or remove AMPA receptors from the synapse and that trigger protein translational and transcriptional pathways that support long-term modification of synapses.57–59 There is a rich pharmacology of NMDA receptor antagonists and these agents have been part of the experimental toolbox that has helped gain our current understanding of glutamate system physiology.56 Numerous NMDA receptor antagonists have been advanced into clinical trials for indications including anesthesia, neuroprotection in stroke, neuropathic pain, Parkinson disease, and Alzheimer’s disease. While these efforts failed to yield commercial successes (with the exception of memantine for Alzheimer’s disease), the clinical observations proved a treasure trove of insight into how glutamate system dysfunction may be involved in both schizophrenia and depression. Ketamine is an approved drug, albeit only for limited use as a pediatric anesthetic. However, because of its availability for clinical studies, ketamine has emerged as a powerful crossspecies translational tool in the systematic investigation of the previous clinical observations. This line of investigation has been, and continues to be, provocative of a renewed interest in the pursuit of drugs to treat psychiatric disease, as summarized below. In the late 1950s, Luby and coworkers found that in healthy volunteer studies, phencyclidine (PCP) produced a syndrome strikingly similar to acute c

Discussed by several speakers at the conference. See Ann. N.Y. Acad. Sci. 1344: 1–119 (2015), including the papers by Javitt, Featherstone et al., Sivarao, Abdallah et al., Leuchter et al., and Lener & Iosifescu.

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psychosis in schizophrenia.60 Subsequently, it was determined that these effects of PCP result from NMDA receptor inhibition. Ketamine, a PCP analog, was then adapted as the clinical translational tool to systematically probe these psychotomimetic effects of NMDA receptor blockade.61,62 In fact, clinical studies by Javitt et al. established that ketamine produces not only acute psychotic symptoms, but also broader cognitive and negative symptoms experienced by patients suffering from schizophrenia.62–64 Collectively, these clinical data have given rise to the hypothesis that NMDA receptor hypofunction is a key molecular defect underlying the expression of all of the complex symptoms of schizophrenia.64,65 Recent genetic evidence supports this hypothesis.66 Indeed, the NMDA hypofunction hypothesis is now driving the search for new treatments for schizophrenia.63,67 The more recent story of ketamine in depression arises from the drug’s use as a clinical translational probe, in this case of cognitive dysfunction in depression.68 Berman et al. gained approval in the late 1990s to use ketamine to investigate cognition in depression.68 In their study, a short intravenous infusion of ketamine resulted in the expected acute PCP-like psychotomimetic effects; these waned rapidly after the infusion was stopped, consistent with the drug’s short half-life and indicative that these were the direct result of on-target NMDA receptor inhibition. However, as patients waited to be discharged, a number reported lessening of their feelings of depression. Berman et al. documented this phenomenon to reveal a clinically significant antidepressant response to ketamine that developed in the hours after the drug had been cleared from the body.68 This finding, published in 2000, was replicated in 2006 by Zarate et al.69 Since then, interest in the rapid-onset antidepressant effects of ketamine gained momentum and well over a dozen replications have now been published.70–72 The robust clinical findings with ketamine, both as a model of schizophrenia and as an antidepressant, implicate glutamate system dysfunction at the core of two of the most debilitating and costly psychiatric illnesses. These clinical findings have spurred back-translational studies to understand the molecular underpinnings that account for these findings.73–78 What are the drug-on effects that induce schizophrenia-like symptoms, and what is the nature of the drug-off effect that

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alleviates depression? Are these effects interrelated and, if so, what might that imply regarding the relatedness of these psychiatric conditions? (See Refs. 79–84 for a discussion of these questions.) A prevalent hypothesis stems from the putative selectivity of ketamine for NMDA receptors on neurons that are most active. Essentially, NMDA receptor channel block by ketamine is activity dependent,85 and since NMDA receptors are most active on active neurons, such neurons are most sensitive to ketamine. Attention has focused on selective ketamine inhibition of cortical GABAergic fast-spiking, parvalbumin+ interneurons, which are believed to account for two functional effects of the drug.44,86 Inhibition of NMDA receptors on the parvalbumin interneurons disrupts their ability to regulate cortical synchrony,86 reflected in a shift of cortical gamma band frequency and power and decreased responsiveness to external stimuli.87 The disruption of gamma synchrony is hypothesized to underlie the acute, drugon cognitive-disrupting and psychotomimetic effects of the drug. Selective inhibition of NMDA receptors on parvalbumin interneurons also reduces inhibition by these neurons of glutamatergic principal neurons, resulting in a net cortical hyperglutamatergic state.44,86 This drug-on induction of gamma activity in the context of a hyperglutamatergic state is hypothesized to engage synaptic plasticity mechanisms that result in a longlasting synaptic potentiation, which may underlie the drug-off antidepressant response. Clinical support for this hypothesis is found in the observation of an increase in somatosensory-evoked potentials, measured in response to tactile stimulation as an increase in magnetoencephalographic gamma-band power that correlates with a drug-off reduction in depressive symptoms in patients that responded to ketamine, but not in those that failed to respond.88 Back-translational preclinical studies have provided biochemical73–77 evidence of such a long-lasting synaptic potentiation after short ketamine exposure. Thus, the hyperglutamatergic state hypothesis of ketamine action provides a mechanistic link between the drug-on and drug-off effects. Ketamine has proven to be a powerful translational tool to probe the glutamate system in psychiatric illness. The clinical behavioral data strongly implicate glutamate system dysfunction in schizophrenia and depression. These data set the

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stage for back-translational studies in both humans and preclinical species that have yielded an important mechanistic framework for understanding the nature of this dysfunction. Significantly, ketamine as a clinical probe continues to deliver new insights, with reports of beneficial therapeutic effects in patients suffering from bipolar disorder,89 obsessive compulsive disorder,90 and posttraumatic stress disorder.91 However, although the translational studies with ketamine shed light, they are not the end of the tunnel. For example, preclinical data suggest additional complexity to the mechanistic framework laid out above. There are multiple NMDA receptor subtypes, of which NR2A and NR2B subtypes predominate in the forebrain.58,92 Compounds highly selective for inhibition of the NR2B subtype have been developed, including Ro 25– 698193 and CP-10160694 that have been used extensively in preclinical studies to investigate the role of the NR2B subtype. Significantly, in humans, CP101606 causes dose-dependent psychotomimetic effects that, at high doses, appear to be similar to those produced by ketamine.95–98 Also similar to ketamine, a short exposure to CP-101606 produces a robust antidepressant response that occurs after the drug has been cleared from the body.98 These clinical data suggest that selective NR2B inhibition has therapeutic and adverse effects similar to ketamine in humans. Thus, back-translational studies of the mechanistic similarities and differences between ketamine and NR2B antagonists open a new path to further understanding of the role of glutamate system dysfunction in psychiatric illness. To date, preclinical data indicate that both drug classes induce similar drug-off synaptic potentiation.73,74,76,77 However, Sivarao et al. and others83,87,99 have found that the NR2B antagonists do not disrupt gamma synchrony in rodents. This lack of effect on gamma synchrony may be accounted for by the fact that there appears to be little NR2B expression by fast-spiking GABAergic interneurons, which predominately express NR2A.100 These data raise questions regarding the role of gamma disruption in relation to both psychotomimetic and antidepressant effects of NMDA receptor inhibition. Future preclinical studies should include investigations of whether NR2B inhibition induces a hyperglutamatergic state similar to that of ketamine. Follow-on human translational studies might include investigations of the effects of

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NR2B antagonists on EEG signatures, particularly gamma synchrony, and whether an antidepressant response to such a drug is accompanied by a drugoff synaptic potentiation. Fortunately, the translational approaches highlighted at the symposium are well suited to triangulating on mechanistic effects of these two classes of NMDA antagonists and thereby provide further insight into the role of glutamate system dysfunction in psychiatric illness, that is, bringing us steps closer to the end of the tunnel. Summary and perspective Advances in the understanding of psychiatric illnesses and the ability to improve current pharmacotherapy will benefit from new tools that bridge preclinical and clinical studies. The most powerful of these tools will provide mechanistically concordant insights in both patients and laboratory test species. We have provided some specific examples of some promising translatable approaches, with a focus on the use of EEG; other methods, such as functional and structural MRI and blood biomarkers, are being applied as well. EEG satisfies many criteria of a good translatable biomarker, namely large effect size, ability to inform about diseaserelevant brain circuits, homologous measurements in both patients and laboratory animals, and dosedependent effects of the drug. Historical and recent studies point to glutamatergic mechanisms as central to the understanding of mental disorders, and although the role of glutamatergic signaling in disease causation is poorly understood, glutamate and its receptors dominate synaptic signaling, especially in cortical areas affected in psychiatric disorders. Perturbation of cortical function by ketamine and other NMDA antagonists is readily detected by EEG methods that are now being used to define surrogate markers for both the adverse and therapeutic effects of these novel therapies.37 It may well be that a marriage of the renewed interest in glutamate pharmacology with robust translatable biomarkers will provide the impetus for an improved mechanistic understanding of psychiatric disorders. Acknowledgments We wish to acknowledge the Biochemical Pharmacology Discussion Group at the New York Academy of Sciences for their partnership in organizing the conference.

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Conflicts of interest F.S.M. is currently the Chief Scientific Officer of Mnemosyne Pharmaceuticals, Inc., which is developing glutamate receptor modulators to treat psychiatric disorders.

References 1. Hyman, S.E. 2012. Revolution stalled. Sci. Transl. Med. 4: 155cm11. 2. Morgan, P. et al. 2012. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov. Today 17: 419–424. 3. Berk, M. 2012. Pathways to new drug discovery in neuropsychiatry. BMC Med. 10: 151. 4. Brady, L.S. & T.R. Insel. 2012. Translating discoveries into medicine: psychiatric drug development in 2011. Neuropsychopharmacology 37: 281–283. 5. Singh, I. & N. Rose. 2009. Biomarkers in psychiatry. Nature 460: 202–207. 6. Insel, T.R. & N. Gogtay. 2014. National Institute of Mental Health clinical trials: new opportunities, new expectations. JAMA Psychiatry 71: 745–746. 7. Grayton, H.M. et al. 2012. Copy number variations in neurodevelopmental disorders. Prog. Neurobiol. 99: 81–91. 8. Markou, A. et al. 2009. Removing obstacles in neuroscience drug discovery: the future path for animal models. Neuropsychopharmacology 34: 74–89. 9. Corvin, A. & D.W. Morris. 2014. Genome-wide association studies: findings at the major histocompatibility complex locus in psychosis. Biol. Psychiatry 75: 276–283. 10. Gatt, J.M. et al. 2015. Specific and common genes implicated across major mental disorders: a review of metaanalysis studies. J. Psychiatr. Res. 60: 1–13. 11. Harrison, P.J. 2015. Recent genetic findings in schizophrenia and their therapeutic relevance. J. Psychopharmacol. 29: 85–96. 12. Donaldson, Z.R. & R. Hen. 2015. From psychiatric disorders to animal models: a bidirectional and dimensional approach. Biol. Psychiatry 77: 15–21. 13. Flood, D.G., G.J. Marek & M. Williams. 2011. Developing predictive CSF biomarkers—a challenge critical to success in Alzheimer’s disease and neuropsychiatric translational medicine. Biochem. Pharmacol. 81: 1422–1434. 14. Brady, L. 2014. Biomarker development and validation: establishing standards of evidence for their context of use in clinical trials. NAMHC Concept Clearance. National Institute of Mental Health. Accessed February 17, 2015. http://www.nimh.nih.gov/funding/grant-writing-and-app lication-process/concept-clearances/2014/biomarker-deve lopment-and-validation-establishing-standards-of-eviden ce-for-their-context-of-use-in-clinical-trials.shtml 15. Borsook, D. & L. Becerra. 2010. Using NMR approaches to drive the search for new CNS therapeutics. Curr. Opin. Investig. Drugs 11: 771–778.

C 2015 New York Academy of Sciences. Ann. N.Y. Acad. Sci. 1344 (2015) 1–11 

Jones et al.

16. Borsook, D., L. Becerra & R. Hargreaves. 2006. A role for fMRI in optimizing CNS drug development. Nat. Rev. Drug Discov. 5: 411–424. 17. Kaur, M. et al. 2013. Longitudinal associations between mismatch negativity and disability in early schizophreniaand affective-spectrum disorders. Prog. Neuropsychopharmacol. Biol. Psychiatry 46: 161–169. 18. Perez, V.B. et al. 2014. Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: forecasting psychosis risk with mismatch negativity. Biol. Psychiatry 75: 459–469. 19. Babiloni, C. et al. 2009. Fundamentals of electroencefalography, magnetoencefalography, and functional magnetic resonance imaging. Int. Rev. Neurobiol. 86: 67–80. 20. Pajer, K. et al. 2012. Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression. Transl. Psychiatry 2: e101. 21. Redei, E.E. et al. 2014. Blood transcriptomic biomarkers in adult primary care patients with major depressive disorder undergoing cognitive behavioral therapy. Transl. Psychiatry 4: e442. 22. Redei, E.E. & N.S. Mehta. 2015. Blood transcriptomic markers for major depression: from animal models to clinical settings. Ann. N.Y. Acad. Sci. 1344: 37–49. 23. Luck, S.J. et al. 2011. A roadmap for the development and validation of event-related potential biomarkers in schizophrenia research. Biol. Psychiatry 70: 28–34. 24. Bewernitz, M. & H. Derendorf. 2012. Electroencephalogram-based pharmacodynamic measures: a review. Int. J. Clin. Pharmacol. Ther. 50: 162–184. 25. Leiser, S.C. et al. 2011. Aligning strategies for using EEG as a surrogate biomarker: a review of preclinical and clinical research. Biochem. Pharmacol. 81: 1408–1421. 26. Mandema, J.W. & M. Danhof. 1992. Electroencephalogram effect measures and relationships between pharmacokinetics and pharmacodynamics of centrally acting drugs. Clin. Pharmacokinet. 23: 191–215. 27. Bronzino, J.D. 2000. “Principles of electroencephalography.” In The Biomedical Engineering Handbook. J.D. Bronzino, Ed.: 201–212. Boca Raton: CRC Publishers LLC. 28. Thakor, N.V. & S. Tong. 2004. Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng. 6: 453–495. 29. Buzsaki, G. & A. Draguhn. 2004. Neuronal oscillations in cortical networks. Science 304: 1926–1929. 30. Chrobak, J.J. & G. Buzsaki. 1998. Gamma oscillations in the entorhinal cortex of the freely behaving rat. J. Neurosci. 18: 388–398. 31. Lakatos, P. et al. 2005. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J. Neurophysiol. 94: 1904–1911. 32. Llinas, R.R. 1988. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242: 1654–1664. 33. Llinas, R.R. & M. Steriade. 2006. Bursting of thalamic neurons and states of vigilance. J. Neurophysiol. 95: 3297–3308. 34. Brett, B., G. Krishnan & D.S. Barth. 1996. The effects of subcortical lesions on evoked potentials and spontaneous

Translational psychiatry

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

C 2015 New York Academy of Sciences. Ann. N.Y. Acad. Sci. 1344 (2015) 1–11 

high frequency (gamma-band) oscillating potentials in rat auditory cortex. Brain Res. 721: 155–166. Gray, C.M. & D.A. McCormick. 1996. Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science 274: 109–113. Barth, D.S. & K.D. MacDonald. 1996. Thalamic modulation of high-frequency oscillating potentials in auditory cortex. Nature 383: 78–81. Steriade, M. et al. 1996. Synchronization of fast (30-40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. J. Neurosci. 16: 2788–2808. Sanacora, G. et al. 2014. Lanicemine: a low-trapping NMDA channel blocker produces sustained antidepressant efficacy with minimal psychotomimetic adverse effects. Mol. Psychiatry 19: 978–985. Buhrer, M. et al. 1990. Electroencephalographic effects of benzodiazepines. II. Pharmacodynamic modeling of the electroencephalographic effects of midazolam and diazepam. Clin. Pharmacol. Ther. 48: 555–567. Danhof, M. et al. 2008. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends Pharmacol. Sci. 29: 186–191. Mandema, J.W., E. Tukker & M. Danhof. 1992. In vivo characterization of the pharmacodynamic interaction of a benzodiazepine agonist and antagonist: midazolam and flumazenil. J. Pharmacol. Exp. Ther. 260: 36–44. Sebban, C. et al. 2002. Effects of phencyclidine (PCP) and MK 801 on the EEGq in the prefrontal cortex of conscious rats; antagonism by clozapine, and antagonists of AMPA-, alpha(1)- and 5-HT(2A)-receptors. Br. J. Pharmacol. 135: 65–78. Fiset, P. et al. 1995. Pharmacodynamic modeling of the electroencephalographic effects of flumazenil in healthy volunteers sedated with midazolam. Clin. Pharmacol. Ther. 58: 567–582. Homayoun, H. & B. Moghaddam. 2007. NMDA receptor hypofunction produces opposite effects on prefrontal cortex interneurons and pyramidal neurons. J. Neurosci. 27: 11496–11500. Pinault, D. 2008. N-methyl d-aspartate receptor antagonists ketamine and MK-801 induce wake-related aberrant gamma oscillations in the rat neocortex. Biol. Psychiatry 63: 730–735. Carlen, M. et al. 2012. A critical role for NMDA receptors in parvalbumin interneurons for gamma rhythm induction and behavior. Mol. Psychiatry 17: 537–548. Mandema, J.W. et al. 1992. Modeling of the effect site equilibration kinetics and pharmacodynamics of racemic baclofen and its enantiomers using quantitative EEG effect measures. J. Pharmacol. Exp. Ther. 261: 88–95. Cao, Z. et al. 2000. Postnatal development of NR1, NR2A and NR2B immunoreactivity in the visual cortex of the rat. Brain Res. 859: 26–37. Conti, F. et al. 1999. Neuronal and glial localization of NR1 and NR2A/B subunits of the NMDA receptor in the human cerebral cortex. Cereb. Cortex 9: 110–120. Mower, G.D. & L. Chen. 2003. Laminar distribution of NMDA receptor subunit (NR1, NR2A, NR2B) expression

9

Translational psychiatry

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62.

63. 64.

65.

66.

67.

10

Jones et al.

during the critical period in cat visual cortex. Brain Res. Mol. Brain Res. 119: 19–27. Huang, M. et al. 2014. The novel alpha7 nicotinic acetylcholine receptor agonist EVP-6124 enhances dopamine, acetylcholine, and glutamate efflux in rat cortex and nucleus accumbens. Psychopharmacology (Berl). 231: 4541– 4551. Rezvani, A.H. et al. 2009. Effect of R3487/MEM3454, a novel nicotinic alpha7 receptor partial agonist and 5-HT3 antagonist on sustained attention in rats. Prog. Neuropsychopharmacol. Biol. Psychiatry. 33: 269–275. Grimwood, S. & P.R. Hartig. 2009. Target site occupancy: emerging generalizations from clinical and preclinical studies. Pharmacol. Ther. 122: 281–301. Hume, S.P. et al. 2007. Low sensitivity of the positron emission tomography ligand [11C]diprenorphine to agonist opiates. J. Pharmacol. Exp. Ther. 322: 661–667. Niswender, C.M. & P.J. Conn. 2010. Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annu. Rev. Pharmacol. Toxicol. 50: 295–322. Traynelis, S.F. et al. 2010. Glutamate receptor ion channels: structure, regulation, and function. Pharmacol. Rev. 62: 405–496. Citri, A. & R.C. Malenka. 2008. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33: 18–41. Cull-Candy, S.G., S. Brickley & M. Farrant. 2001. NMDA receptor subunits: diversity, development and disease. Curr. Opin. Neurobiol. 11: 327–335. Malenka, R.C. & R.A. Nicoll. 1993. NMDA-receptordependent synaptic plasticity: multiple forms and mechanisms. Trends Neurosci. 16: 521–527. Domino, E.F. & E.D. Luby. 2012. Phencyclidine/ schizophrenia: one view toward the past, the other to the future. Schizophr. Bull. 38: 914–919. Javitt, D.C. et al. 2012. Has an angel shown the way? Etiological and therapeutic implications of the PCP/NMDA model of schizophrenia. Schizophr. Bull. 38: 958–966. Krystal, J.H. et al. 2003. NMDA receptor antagonist effects, cortical glutamatergic function, and schizophrenia: toward a paradigm shift in medication development. Psychopharmacology 169: 215–233. Javitt, D.C. et al. 2011. Translating glutamate: from pathophysiology to treatment. Sci. Transl. Med. 3: 102mr2. Olney, J.W., J.W. Newcomer & N.B. Farber. 1999. NMDA receptor hypofunction model of schizophrenia. J. Psychiatr. Res. 33: 523–533. Javitt, D.C. & S.R. Zukin. 1991. Recent advances in the phencyclidine model of schizophrenia. Am. J. Psychiatry. 148: 1301–1308. Hall, J. et al. 2015. Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity. Biol. Psychiatry 77: 52–58. Moghaddam, B. & J.H. Krystal. 2012. Capturing the angel in “angel dust”: twenty years of translational neuroscience studies of NMDA receptor antagonists in animals and humans. Schizophr. Bull. 38: 942–949.

68. Berman, R.M. et al. 2000. Antidepressant effects of ketamine in depressed patients. Biol. Psychiatry 47: 351–354. 69. Zarate, C.A. et al. 2006. A randomized trial of an N-methyl-D-aspartate antagonist in treatment-resistant major depression. Arch. Gen. Psychiatry 63: 856–864. 70. Aan Het Rot, M. et al. 2012. Ketamine for depression: where do we go from here? Biol. Psychiatry 72: 537–547. 71. Murrough, J.W. & D.S. Charney. 2010. Cracking the moody brain: lifting the mood with ketamine. Nat. Med. 16: 1384– 1385. 72. Niciu, M.J. et al. 2014. Glutamate receptor antagonists as fast-acting therapeutic alternatives for the treatment of depression: ketamine and other compounds. Annu. Rev. Pharmacol. Toxicol. 54: 119–139. 73. Duman, R.S. et al. 2012. Signaling pathways underlying the rapid antidepressant actions of ketamine. Neuropharmacology 62: 35–41. 74. Dwyer, J.M. & R.S. Duman. 2013. Activation of mammalian target of rapamycin and synaptogenesis: role in the actions of rapid-acting antidepressants. Biol. Psychiatry 73: 1189– 1198. 75. Kavalali, E.T. & L.M. Monteggia. 2012. Synaptic mechanisms underlying rapid antidepressant action of ketamine. Am. J. Psychiatry 169: 1150–1156. 76. Li, N. et al. 2010. mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists. Science 329: 959–964. 77. Li, N. et al. 2011. Glutamate N-methyl-D-aspartate receptor antagonists rapidly reverse behavioral and synaptic deficits caused by chronic stress exposure. Biol. Psychiatry 69: 754–761. 78. Maeng, S. et al. 2008. Cellular mechanisms underlying the antidepressant effects of ketamine: role of ␣-amino3-hydroxy-5-methylisoxazole-4-propionic acid receptors. Biol. Psychiatry 63: 349–352. 79. Lener, M.S. & D.V. Iosifescu. 2015. In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Ann. N.Y. Acad. Sci. 1344: 50–65. 80. Javitt, D.C. 2015. Neurophysiological models for new treatment development in schizophrenia: early sensory approaches. Ann. N.Y. Acad. Sci. 1344: 92–104. 81. Abdallah, C.G., L.A. Averill & J.H. Krystal. 2015. Ketamine as a promising prototype for a new generation of rapidacting antidepressants. Ann. N.Y. Acad. Sci. 1344: 66–77. 82. Featherstone, R.E. et al. 2015. EEG biomarkers of target engagement, therapeutic effect, and disease process. Ann. N.Y. Acad. Sci. 1344: 12–26. 83. Sivarao, D.V. 2015. The 40-Hz auditory steady-state response: a selective biomarker for cortical NMDA function. Ann. N.Y. Acad. Sci. 1344: 27–36. 84. Leuchter, A.F. et al. 2015. Rhythms and blues: modulation of oscillatory synchrony and the mechanism of action of antidepressant treatments. Ann. N.Y. Acad. Sci. 1344: 78– 91. 85. MacDonald, J.F., Z. Miljkovic & P. Pennefather. 1987. Usedependent block of excitatory amino acid currents in cultured neurons by ketamine. J. Neurophysiol. 58: 251–266.

C 2015 New York Academy of Sciences. Ann. N.Y. Acad. Sci. 1344 (2015) 1–11 

Jones et al.

86. Jackson, M.E., H. Homayoun & B. Moghaddam. 2004. NMDA receptor hypofunction produces concomitant firing rate potentiation and burst activity reduction in the prefrontal cortex. Proc. Natl. Acad. Sci. USA. 101: 8467– 8472. 87. Kocsis, B. 2012. Differential role of NR2A and NR2B subunits in N-methyl-D-aspartate receptor antagonistinduced aberrant cortical gamma oscillations. Biol. Psychiatry 71: 987–995. 88. Cornwell, B.R. et al. 2012. Synaptic potentiation is critical for rapid antidepressant response to ketamine in treatmentresistant major depression. Biol. Psychiatry 72: 555–561. 89. Zarate, C.A., Jr. et al. 2012. Replication of ketamine’s antidepressant efficacy in bipolar depression: a randomized controlled add-on trial. Biol. Psychiatry 71: 939–946. 90. Rodriguez, C.I. et al. 2013. Randomized controlled crossover trial of ketamine in obsessive-compulsive disorder: proof-of-concept. Neuropsychopharmacology 38: 2475–2483. 91. Feder, A. et al. 2014. Efficacy of intravenous ketamine for treatment of chronic posttraumatic stress disorder: a randomized clinical trial. JAMA Psychiatry 71: 681–688. 92. Paoletti, P., C. Bellone & Q. Zhou. 2013. NMDA receptor subunit diversity: impact on receptor properties, synaptic plasticity and disease. Nat. Rev. Neurosci. 14: 383–400. 93. Fischer, G. et al. 1997. Ro 25–6981, a highly potent and selective blocker of N-methyl-D-aspartate receptors

Translational psychiatry

94. 95.

96.

97.

98.

99.

100.

C 2015 New York Academy of Sciences. Ann. N.Y. Acad. Sci. 1344 (2015) 1–11 

containing the NR2B subunit. Characterization in vitro. J. Pharmacol. Exp. Ther. 283: 1285–1292. Menniti, F.S. et al. 1998. CP-101606: an NR2B-selective NMDA receptor antagonist. CNS Drug Rev. 4: 307–322. Bullock, M.R. et al. 1999. An open-label study of CP-101606 in subjects with a severe traumatic head injury or spontaneous intracerebral hemorrhage. Ann. N.Y. Acad. Sci. 890: 51–58. Merchant, R.E. et al. 1999. A double-blind, placebocontrolled study of the safety, tolerability and pharmacokinetics of CP-101606 in patients with a mild or moderate traumatic brain injury. Ann. N.Y. Acad. Sci. 890: 42–50. Nutt, J.G. et al. 2008. Effects of a NR2B selective NMDA glutamate antagonist, CP-101606 on dyskinesia and Parkinsonism. Mov. Disord. 23: 1860–1866. Preskorn, S.H. et al. 2008. An innovative design to establish proof of concept of the antidepressant effects of the NR2B subunit selective N-methyl-D-aspartate antagonist, CP-101606 in patients with treatment-refractory major depressive disorder. J. Clin. Psychopharmacol. 28: 631– 637. Sivarao, D.V. et al. 2014. NR2B antagonist CP-101606 abolishes pitch-mediated deviance detection in awake rats. Front. Psychiatry 5: 96. Matta, J.A. et al. 2013. Developmental origin dictates interneuron AMPA and NMDA receptor subunit composition and plasticity. Nat. Neurosci. 16: 1032–1041.

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