Expert Review of Clinical Pharmacology
ISSN: 1751-2433 (Print) 1751-2441 (Online) Journal homepage: http://www.tandfonline.com/loi/ierj20
Optimizing early Go/No Go decisions in CNS drug development William Z Potter To cite this article: William Z Potter (2015) Optimizing early Go/No Go decisions in CNS drug development, Expert Review of Clinical Pharmacology, 8:2, 155-157 To link to this article: http://dx.doi.org/10.1586/17512433.2015.991715
Published online: 24 Dec 2014.
Submit your article to this journal
Article views: 123
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ierj20 Download by: [University of Wisconsin Oshkosh]
Date: 14 November 2015, At: 10:14
Editorial
Optimizing early Go/No Go decisions in CNS drug development Expert Rev. Clin. Pharmacol. 8(2), 155–157 (2015)
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
William Z Potter National Institute of Mental Health, Bethesda, MD [email protected]
Go/No Go decisions concerning development of any single compound determine investment in increasingly costly studies from Phases I–III. Such decisions are problematic for CNS drug development where the variety of molecular targets in the brain have stimulated decades of studies without major therapeutic advances. Many costly studies do not even yield interpretable results as to whether the mechanism being pursued has therapeutic potential. Therefore, both industry and the public sector have implemented a decision making strategy based on whether a compound can test a molecular hypothesis of drug action. One requires, at a minimum, compelling evidence in humans that a compound both interacts with its presumed molecular targets in brain and ideally documents a CNS functional consequence of the interaction prior to efficacy studies. This strategy will much more quickly rule out ineffective mechanisms although it does not address the problem of poorly predictive models of novel CNS drug efficacy.
Early ‘Go/No Go’ decisions in drug development increasingly control the degree of subsequent investment in either a compound or a molecular mechanism for one or more indications. Ideally, one knows the specific mechanism(s) engaged by a compound but this is often not the case. Early ‘No Go’ decisions are prevalent within industry and are now being applied in National Institutes of Health (NIH)-funded studies. The underlying assumptions and risks remain a matter of debate, especially when the pathophysiology of a clinical syndrome is unknown and animal models are poorly predictive, as is the case for many CNS diseases. How to optimize decision making remains an open question and is largely dependent on which unproven assumptions are embraced. For industry, decisions in the aggregate must support profitability although there are examples of ‘Go’ decisions to meet a specific medical need and market a medication that never generates enough income to cover its development costs. For the public and advocacy sectors, the goal is to most effectively translate basic and preclinical science into better
treatments for disorders with great medical need or societal impact. Several CNS disorders, including syndromal conditions such as depression, fall into the category of high burden medical diseases and will be used as examples [1]. But how to prioritize the many molecular targets that may modulate brain processes involved in a complex syndrome like depression? ‘Go/No Go’ decisions are critical to determine the balance between the depth and breadth of investment in any single compound or mechanism. Making ‘No Go’ decisions early in the process may increase Type 2 errors but allows for the exploration of a greater diversity of possibilities. With this strategy, only robust early effects justify undertaking follow-on Phase II studies. Studies with many compounds such as D-serine yield equivocal efficacy results and are administered in the absence of a direct measure of interaction with the molecular target or resultant functional changes in the brain [2]. One cannot specify an optimized decision-making process as to next steps when confronted with this type of data in the absence of prior specification of what constitutes an adequate
KEYWORDS: brain target engagement . CNS drug development . Go/No Go decisions . mechanistic hypotheses
informahealthcare.com
.
RDoC
10.1586/17512433.2015.991715
2014 Informa UK Ltd
ISSN 1751-2433
155
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
Editorial
Potter
test of the compound. Conversely, by focusing on tests of mechanistic hypotheses according to specifications which follow, a basis for decision-making emerges. Hypothesis testing requires knowledge of what a compound does in the human brain. In the absence of a method for ruling in or out a prespecified measure of function in human brain, as in the D-serine example, one would make an early decision not to pursue clinical efficacy studies. The risk of requiring this level of evidence is prematurely abandoning a potentially beneficial treatment. However, no major advances in CNS therapeutics have emerged from the efficacy-based clinical trial approach over the last four decades and newer US FDA approved drugs are mechanistically of the same types as those identified through serendipity [3]. Thus, there seems to be a low probability of missing an effective treatment by requiring the compelling chain of evidence needed to test a hypothesis prior to investing in efficacy studies. Moreover, given that lack of efficacy in later phase trials now constitutes a larger proportion for failures in drug development [4], non-reproducible positive findings from early studies (Type 1 errors) are a high probability risk. Instead of trying to address Type 2 error risk, one focuses on the potential benefits of being able to accept or reject hypotheses based on a state-of-the-art understanding of the relationship between: dose; concentration/molecular interaction at primary site of action; downstream functional brain effects; and clinical effects. A study funded by National Institute of Mental Health (NIMH) provides an example of this approach, testing the hypothesis that selective kappa opioid receptor antagonism will improve clinical anhedonia (lack of ability to experience pleasure). In this example, the required elements are: .
. .
.
Preclinical safety data must allow doses that produce blood concentrations that exceed what is projected to be necessary to achieve full antagonism (given an orthosteric mechanism) in human brain. PET ligands to safely explore the full range of receptor occupancy by the kappa antagonist. The ability of a specified degree of antagonism (as defined by degree of occupancy) to alter an functional magnetic resonance imaging (fMRI) signal associated with a specific reward task to assess whether, and how much, receptor occupancy by the drug subsequently affects the circuits hypothesized to be related to clinical anhedonia. Having demonstrated both interaction with kappa receptors and a specific functional brain effect, a conclusive assessment of the relationship to traditional clinical measures of depression and anhedonia is possible.
A ‘Go’ decision to efficacy studies is well supported if all conditions are met. A ‘No Go’ decision to future clinical studies can be made if criteria are not met at any step depending on how closely one wishes to adhere to a hypothesis testing model. Alternatively, one could decide to go ahead to a clinical efficacy study using traditional subjective measures based solely on findings of high selective kappa antagonism in the absence of 156
affecting the fMRI signal. Such an approach would no longer be optimal from a hypothesis testing perspective and, in the given example, was explicitly rejected by the company originating the compound in the absence of any tests of functional effects. Depending on one’s confidence in the fMRI signal as reflecting a necessary mediator process of hedonic state, if an exploration of the full receptor occupancy range were accompanied by a null response, one either rejects the hypothesis of a role for kappa antagonism in hedonic responses or speculates that kappa antagonism may beneficially affect a circuit function not detected by the fMRI paradigm. When confronted with negative findings from pharmacodynamics studies, advocates for a mechanism routinely evoke the possibility that the wrong function was measured. Scientifically, one argues that we do not know enough to test a specific hypothesis about a molecular target’s link to a function as a mediator of clinical effect. This argument puts the burden on establishing which measurable brain functions are linked to which clinical domains – the goal of the recently NIMH initiated Research Domain Criteria project [5] which will require years to decades to fully implement. Each specific hypothesis will generate a different series of potential ‘Go/No Go’-based milestones with the degree of confidence in any decision subject to revision as more knowledge about the relationship between interaction with the target and brain function emerges. The step of requiring reasonable evidence of a molecular interaction at the target organ (human brain) before launching full scale efficacy studies seems essential to any ‘optimal’ decision-making plan. But meeting this requirement has not been feasible in important instances. There are many large negative clinical trials, most recently in Alzheimer’s disease, which are misinterpreted as refuting a mechanism when in fact there was inadequate evidence of the degree to which the agent interacted with targeted sites in brain. From the perspective of interpreting results, trials of COX-2 inhibitors, g-secretase inhibitors or monoclonal antibodies should have waited for more robust evidence of intended effects on the brain targets [6–8]. The counterargument is that the methods to rule in or out robust effects do not exist for many mechanisms and it is worth testing these based on theoretical and preclinical considerations. There is, however, no compelling data to support this counterargument. Once evidence of the targeted molecular interaction is established, designing the next optimal decision-making study entails choosing between two differing strategies which are influenced by many factors: .
Take evidence of molecular interaction (even if functionally ‘silent’) as sufficient to justify an efficacy trial. A negative efficacy finding will discourage further investment in that mechanism for a specific indication and save the field from future futile studies. Patient welfare is advanced even if one does not have good evidence as to how this was achieved from a mechanistic hypothesis perspective. Moreover, since generating evidence of molecular interactions in brain has
Expert Rev. Clin. Pharmacol. 8(2), (2015)
Optimizing early ‘Go/No Go’ decisions in CNS drug development
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
.
become feasible for a wider class of compounds, many more mechanisms can be explored in efficacy trials than if evidence of alteration in brain function is required. Require evidence of functional brain effect as well as molecular interaction prior to efficacy trials. For example, if one can cover the receptor occupancy range with an orthosteric antagonist but detects no functional brain effect, then one either stops clinical development of that class of antagonists or invests in other measures of brain function as a basis for moving forward. If a molecular interaction is the only ‘Go’ criterion, as in ‘A’ above, the system may be flooded with expensive efficacy studies that are likely to be negative in the absence of a functional brain effect.
The tension between the belief systems arguing for either of these strategies manifests itself in both the public and private sectors whether within companies, academia, NIH and philanthropic funding authorities, or public advocacy groups. The model being pursued by the NIMH captured in its recent funding announcements for early stage and exploratory clinical trials of novel interventions (PAR-14-107 and RFAMH-15-300/310 [9]) takes the latter strategy ‘B’, whereby investigators are requested to establish dose response relationships of physiological effects in brain as well as, where possible, measures of molecular interaction such as receptor occupancy. This is based on a belief that, to move beyond serendipitously identified treatments emerging from clinical observation, we should harness the tools of modern neuroscience to create testable hypotheses with the chain of evidence linking an intervention to identifiable mechanisms in the brain. Whether this approach proves optimal in terms of advancing novel treatments remains to be seen and other avenues remain open. In certain instances, References 1.
2.
Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013;382(9904): 1575-86 Nunes EA, MacKenzie EM, Rossolatos D, et al. D-serine and schizophrenia: an update. Expert Rev Neurother 2012;12: 801-12
3.
Kinch MS, Patridge E. An analysis of FDA-approved drugs for psychiatric disorders. Drug Discov Today 2014; Available from: http://dx.doi.org/10.1016/
4.
Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3(8):711-15
informahealthcare.com
5.
6.
Editorial
even in the absence of measures of molecular interactions in the brain, if there is strong clinical evidence of a brain effect (e.g., ketamine), one may pursue an efficacy study. The attractiveness of efficacy studies under these circumstances is increased if sufficiently powerful trial designs can be brought to bear that consistently distinguish drug from placebo such as the double-blind randomized withdrawal studies in apparent drug responders [10]. But, from a scientific perspective, it is optimal to only make ‘Go’ decisions when one is clear that results of a study will prove interpretable about the potential of an intervention in the absence of a positive finding. At a minimum, by adhering to the framework for decisions being adopted by NIMH as well as much of industry, we should avoid uninterpretable data as to whether an agent tested a mechanistic hypothesis. By utilizing clinical trial resources for compounds that have a brain effect, which has been understood, more rapid progress can be achieved in terms of ruling in or out the therapeutic utility of specific mechanisms. Acknowledgements
The author would like to acknowledge J Heemskerk, National Institute of Mental Health, M Hillefors, NIMH and S Zalcman, for their suggestions on content for this article. Financial & competing interests disclosure
The author has acted as a part-time senior advisor in the last 2 years to the following companies: Amgen, Lilly, Theravance, Taisho, Ironwood, Takeda, AgeneBio and Neurotrope. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med 2013;11:126. Available from: www.biomedcentral.com/1741-7015/ 11/126 Counts SE, Lahiri DK. Editorial: overview of immunotherapy in Alzeimer’s Disease (AD) and mechanisms of IVIG neuroprotection in preclinical models of AD. Curr Alzheimer Res 2014;11:623-5
7.
Alzheimer’s Disease Anti-inflammatory Prevention Trial Research Group. Results of a follow-up study to the randomized Alzheimer’s Disease anti-inflammatory prevention trial (ADAPT). Alzheimers Dement 2013;9:714-23
8.
Imbimbo BP, Giardina GA. g-secretase inhibitors and modulators for the treatment
of Alzheimer’s Disease: disappointments and hopes. Curr Top Med Chem 2011;11: 1555-70 9.
Clinical trials funding opportunity announcements. Available from: www.nimh. nih.gov/funding/opportunitiesannouncements/clinical-trials-foas/index. shtml
10.
Borges S, Chen YF, Laughren TP, et al. Review of maintenance trials for major depression disorder: a 25-year perspective from the US Food and Drug Administration. J Clin Psychiatry 2014;75: 205-14
157
ISSN: 1751-2433 (Print) 1751-2441 (Online) Journal homepage: http://www.tandfonline.com/loi/ierj20
Optimizing early Go/No Go decisions in CNS drug development William Z Potter To cite this article: William Z Potter (2015) Optimizing early Go/No Go decisions in CNS drug development, Expert Review of Clinical Pharmacology, 8:2, 155-157 To link to this article: http://dx.doi.org/10.1586/17512433.2015.991715
Published online: 24 Dec 2014.
Submit your article to this journal
Article views: 123
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ierj20 Download by: [University of Wisconsin Oshkosh]
Date: 14 November 2015, At: 10:14
Editorial
Optimizing early Go/No Go decisions in CNS drug development Expert Rev. Clin. Pharmacol. 8(2), 155–157 (2015)
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
William Z Potter National Institute of Mental Health, Bethesda, MD [email protected]
Go/No Go decisions concerning development of any single compound determine investment in increasingly costly studies from Phases I–III. Such decisions are problematic for CNS drug development where the variety of molecular targets in the brain have stimulated decades of studies without major therapeutic advances. Many costly studies do not even yield interpretable results as to whether the mechanism being pursued has therapeutic potential. Therefore, both industry and the public sector have implemented a decision making strategy based on whether a compound can test a molecular hypothesis of drug action. One requires, at a minimum, compelling evidence in humans that a compound both interacts with its presumed molecular targets in brain and ideally documents a CNS functional consequence of the interaction prior to efficacy studies. This strategy will much more quickly rule out ineffective mechanisms although it does not address the problem of poorly predictive models of novel CNS drug efficacy.
Early ‘Go/No Go’ decisions in drug development increasingly control the degree of subsequent investment in either a compound or a molecular mechanism for one or more indications. Ideally, one knows the specific mechanism(s) engaged by a compound but this is often not the case. Early ‘No Go’ decisions are prevalent within industry and are now being applied in National Institutes of Health (NIH)-funded studies. The underlying assumptions and risks remain a matter of debate, especially when the pathophysiology of a clinical syndrome is unknown and animal models are poorly predictive, as is the case for many CNS diseases. How to optimize decision making remains an open question and is largely dependent on which unproven assumptions are embraced. For industry, decisions in the aggregate must support profitability although there are examples of ‘Go’ decisions to meet a specific medical need and market a medication that never generates enough income to cover its development costs. For the public and advocacy sectors, the goal is to most effectively translate basic and preclinical science into better
treatments for disorders with great medical need or societal impact. Several CNS disorders, including syndromal conditions such as depression, fall into the category of high burden medical diseases and will be used as examples [1]. But how to prioritize the many molecular targets that may modulate brain processes involved in a complex syndrome like depression? ‘Go/No Go’ decisions are critical to determine the balance between the depth and breadth of investment in any single compound or mechanism. Making ‘No Go’ decisions early in the process may increase Type 2 errors but allows for the exploration of a greater diversity of possibilities. With this strategy, only robust early effects justify undertaking follow-on Phase II studies. Studies with many compounds such as D-serine yield equivocal efficacy results and are administered in the absence of a direct measure of interaction with the molecular target or resultant functional changes in the brain [2]. One cannot specify an optimized decision-making process as to next steps when confronted with this type of data in the absence of prior specification of what constitutes an adequate
KEYWORDS: brain target engagement . CNS drug development . Go/No Go decisions . mechanistic hypotheses
informahealthcare.com
.
RDoC
10.1586/17512433.2015.991715
2014 Informa UK Ltd
ISSN 1751-2433
155
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
Editorial
Potter
test of the compound. Conversely, by focusing on tests of mechanistic hypotheses according to specifications which follow, a basis for decision-making emerges. Hypothesis testing requires knowledge of what a compound does in the human brain. In the absence of a method for ruling in or out a prespecified measure of function in human brain, as in the D-serine example, one would make an early decision not to pursue clinical efficacy studies. The risk of requiring this level of evidence is prematurely abandoning a potentially beneficial treatment. However, no major advances in CNS therapeutics have emerged from the efficacy-based clinical trial approach over the last four decades and newer US FDA approved drugs are mechanistically of the same types as those identified through serendipity [3]. Thus, there seems to be a low probability of missing an effective treatment by requiring the compelling chain of evidence needed to test a hypothesis prior to investing in efficacy studies. Moreover, given that lack of efficacy in later phase trials now constitutes a larger proportion for failures in drug development [4], non-reproducible positive findings from early studies (Type 1 errors) are a high probability risk. Instead of trying to address Type 2 error risk, one focuses on the potential benefits of being able to accept or reject hypotheses based on a state-of-the-art understanding of the relationship between: dose; concentration/molecular interaction at primary site of action; downstream functional brain effects; and clinical effects. A study funded by National Institute of Mental Health (NIMH) provides an example of this approach, testing the hypothesis that selective kappa opioid receptor antagonism will improve clinical anhedonia (lack of ability to experience pleasure). In this example, the required elements are: .
. .
.
Preclinical safety data must allow doses that produce blood concentrations that exceed what is projected to be necessary to achieve full antagonism (given an orthosteric mechanism) in human brain. PET ligands to safely explore the full range of receptor occupancy by the kappa antagonist. The ability of a specified degree of antagonism (as defined by degree of occupancy) to alter an functional magnetic resonance imaging (fMRI) signal associated with a specific reward task to assess whether, and how much, receptor occupancy by the drug subsequently affects the circuits hypothesized to be related to clinical anhedonia. Having demonstrated both interaction with kappa receptors and a specific functional brain effect, a conclusive assessment of the relationship to traditional clinical measures of depression and anhedonia is possible.
A ‘Go’ decision to efficacy studies is well supported if all conditions are met. A ‘No Go’ decision to future clinical studies can be made if criteria are not met at any step depending on how closely one wishes to adhere to a hypothesis testing model. Alternatively, one could decide to go ahead to a clinical efficacy study using traditional subjective measures based solely on findings of high selective kappa antagonism in the absence of 156
affecting the fMRI signal. Such an approach would no longer be optimal from a hypothesis testing perspective and, in the given example, was explicitly rejected by the company originating the compound in the absence of any tests of functional effects. Depending on one’s confidence in the fMRI signal as reflecting a necessary mediator process of hedonic state, if an exploration of the full receptor occupancy range were accompanied by a null response, one either rejects the hypothesis of a role for kappa antagonism in hedonic responses or speculates that kappa antagonism may beneficially affect a circuit function not detected by the fMRI paradigm. When confronted with negative findings from pharmacodynamics studies, advocates for a mechanism routinely evoke the possibility that the wrong function was measured. Scientifically, one argues that we do not know enough to test a specific hypothesis about a molecular target’s link to a function as a mediator of clinical effect. This argument puts the burden on establishing which measurable brain functions are linked to which clinical domains – the goal of the recently NIMH initiated Research Domain Criteria project [5] which will require years to decades to fully implement. Each specific hypothesis will generate a different series of potential ‘Go/No Go’-based milestones with the degree of confidence in any decision subject to revision as more knowledge about the relationship between interaction with the target and brain function emerges. The step of requiring reasonable evidence of a molecular interaction at the target organ (human brain) before launching full scale efficacy studies seems essential to any ‘optimal’ decision-making plan. But meeting this requirement has not been feasible in important instances. There are many large negative clinical trials, most recently in Alzheimer’s disease, which are misinterpreted as refuting a mechanism when in fact there was inadequate evidence of the degree to which the agent interacted with targeted sites in brain. From the perspective of interpreting results, trials of COX-2 inhibitors, g-secretase inhibitors or monoclonal antibodies should have waited for more robust evidence of intended effects on the brain targets [6–8]. The counterargument is that the methods to rule in or out robust effects do not exist for many mechanisms and it is worth testing these based on theoretical and preclinical considerations. There is, however, no compelling data to support this counterargument. Once evidence of the targeted molecular interaction is established, designing the next optimal decision-making study entails choosing between two differing strategies which are influenced by many factors: .
Take evidence of molecular interaction (even if functionally ‘silent’) as sufficient to justify an efficacy trial. A negative efficacy finding will discourage further investment in that mechanism for a specific indication and save the field from future futile studies. Patient welfare is advanced even if one does not have good evidence as to how this was achieved from a mechanistic hypothesis perspective. Moreover, since generating evidence of molecular interactions in brain has
Expert Rev. Clin. Pharmacol. 8(2), (2015)
Optimizing early ‘Go/No Go’ decisions in CNS drug development
Downloaded by [University of Wisconsin Oshkosh] at 10:14 14 November 2015
.
become feasible for a wider class of compounds, many more mechanisms can be explored in efficacy trials than if evidence of alteration in brain function is required. Require evidence of functional brain effect as well as molecular interaction prior to efficacy trials. For example, if one can cover the receptor occupancy range with an orthosteric antagonist but detects no functional brain effect, then one either stops clinical development of that class of antagonists or invests in other measures of brain function as a basis for moving forward. If a molecular interaction is the only ‘Go’ criterion, as in ‘A’ above, the system may be flooded with expensive efficacy studies that are likely to be negative in the absence of a functional brain effect.
The tension between the belief systems arguing for either of these strategies manifests itself in both the public and private sectors whether within companies, academia, NIH and philanthropic funding authorities, or public advocacy groups. The model being pursued by the NIMH captured in its recent funding announcements for early stage and exploratory clinical trials of novel interventions (PAR-14-107 and RFAMH-15-300/310 [9]) takes the latter strategy ‘B’, whereby investigators are requested to establish dose response relationships of physiological effects in brain as well as, where possible, measures of molecular interaction such as receptor occupancy. This is based on a belief that, to move beyond serendipitously identified treatments emerging from clinical observation, we should harness the tools of modern neuroscience to create testable hypotheses with the chain of evidence linking an intervention to identifiable mechanisms in the brain. Whether this approach proves optimal in terms of advancing novel treatments remains to be seen and other avenues remain open. In certain instances, References 1.
2.
Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013;382(9904): 1575-86 Nunes EA, MacKenzie EM, Rossolatos D, et al. D-serine and schizophrenia: an update. Expert Rev Neurother 2012;12: 801-12
3.
Kinch MS, Patridge E. An analysis of FDA-approved drugs for psychiatric disorders. Drug Discov Today 2014; Available from: http://dx.doi.org/10.1016/
4.
Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3(8):711-15
informahealthcare.com
5.
6.
Editorial
even in the absence of measures of molecular interactions in the brain, if there is strong clinical evidence of a brain effect (e.g., ketamine), one may pursue an efficacy study. The attractiveness of efficacy studies under these circumstances is increased if sufficiently powerful trial designs can be brought to bear that consistently distinguish drug from placebo such as the double-blind randomized withdrawal studies in apparent drug responders [10]. But, from a scientific perspective, it is optimal to only make ‘Go’ decisions when one is clear that results of a study will prove interpretable about the potential of an intervention in the absence of a positive finding. At a minimum, by adhering to the framework for decisions being adopted by NIMH as well as much of industry, we should avoid uninterpretable data as to whether an agent tested a mechanistic hypothesis. By utilizing clinical trial resources for compounds that have a brain effect, which has been understood, more rapid progress can be achieved in terms of ruling in or out the therapeutic utility of specific mechanisms. Acknowledgements
The author would like to acknowledge J Heemskerk, National Institute of Mental Health, M Hillefors, NIMH and S Zalcman, for their suggestions on content for this article. Financial & competing interests disclosure
The author has acted as a part-time senior advisor in the last 2 years to the following companies: Amgen, Lilly, Theravance, Taisho, Ironwood, Takeda, AgeneBio and Neurotrope. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med 2013;11:126. Available from: www.biomedcentral.com/1741-7015/ 11/126 Counts SE, Lahiri DK. Editorial: overview of immunotherapy in Alzeimer’s Disease (AD) and mechanisms of IVIG neuroprotection in preclinical models of AD. Curr Alzheimer Res 2014;11:623-5
7.
Alzheimer’s Disease Anti-inflammatory Prevention Trial Research Group. Results of a follow-up study to the randomized Alzheimer’s Disease anti-inflammatory prevention trial (ADAPT). Alzheimers Dement 2013;9:714-23
8.
Imbimbo BP, Giardina GA. g-secretase inhibitors and modulators for the treatment
of Alzheimer’s Disease: disappointments and hopes. Curr Top Med Chem 2011;11: 1555-70 9.
Clinical trials funding opportunity announcements. Available from: www.nimh. nih.gov/funding/opportunitiesannouncements/clinical-trials-foas/index. shtml
10.
Borges S, Chen YF, Laughren TP, et al. Review of maintenance trials for major depression disorder: a 25-year perspective from the US Food and Drug Administration. J Clin Psychiatry 2014;75: 205-14
157
