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Preclinical research has facilitated the discovery of valuable drugs for the symptomatic treatment of epilepsy. Yet, despite these therapies, seizures are not adequately controlled in a third of all affected individuals, and comorbidities still impose a major burden on quality of life. The introduction of multiple new therapies into clinical use over the past two decades has done little to change this. There is an urgent demand to address the unmet clinical needs for: (a) new symptomatic anti-seizure treatments for drug-resistant seizures with improved efficacy/tolerability profiles, (b) disease modifying treatments that prevent or ameliorate the epileptogenic state, and (c) treatments for the common comorbidities that contribute to disability in people with epilepsy. New therapies also need to address the special needs of certain subpopulations, i.e. age- or gender-specific treatments. Preclinical development in these treatment areas is complex due to heterogeneity in presentation and etiology, and may need to be formulated with a specific seizure, epilepsy syndrome or comorbidity in mind. The aim of this report is to provide a framework that will help define future guidelines that improve and standardize the design, reporting, and validation of data across preclinical anti-epilepsy therapy development studies targeting drug-resistant seizures, epileptogenesis and comorbidities.
Epilepsy affects 50 million people worldwide (World Health Organization 2006), with an estimated 2–3 million living in the United States (Epilepsy Foundation of America, Hirtz et al. 2007), 6 million in Europe (World Health Organization 2010), and at least 40 million in the developing world (World Health Organization et al. 2005). Epilepsy poses a significant burden on the quality of life of affected individuals and their families. Since the introduction of bromide as an anti-seizure drug in 1857, there has been an impressive expansion of therapies that are clinically effective in decreasing the frequency and severity of seizures in people with epilepsy. This class of symptomatic treatments is widely referred to as “antiepileptic drugs” (AEDs). In this article, we will avoid this term and use instead “anti-seizure drug”, to prevent confusion with disease-modifying therapies that have a sustained modulatory effect on the underlying epileptic state (i.e. the predisposition to generate spontaneous recurrent seizures) and/or with treatments that ameliorate associated comorbidities. The newer anti-seizure drugs have been identified through systematic screening in batteries of an increasing number of in vivo and in vitro seizure and epilepsy models (Loscher and Schmidt 2011). Undoubtedly, the clinical availability of a broad range of anti-seizure treatments has significantly improved the management of the disorder. At present, two thirds of all individuals with epilepsy will achieve seizure freedom with available medications. This translates into better quality of life and reduces the risk of seizure-associated injuries and death. However, a third of people with epilepsy will not have adequate seizure control with the current medications. For these patients the situation has improved very little in the last few decades. In addition, current screening methods have failed to elucidate which drugs are more or less likely to produce clinically significant adverse effects that may impair the quality of life or limit dosing to levels insufficient to completely control seizures. Other important concerns are the risks related to drug-drug interactions and the potential for teratogenicity, which may limit the use of effective anti-seizure medications in women of child bearing potential.
There is an urgent need for more effective and better tolerated treatments to control drug-resistant seizures, as well as for innovative therapies to prevent, stop or reverse the development of epilepsy and epilepsy-related comorbidities (White 2003, Smith et al. 2007, Jacobs et al. 2009, Loscher and Schmidt 2011). Such treatments may include not only individual pharmacological compounds or combination therapies but also devices and other novel therapeutic interventions. Here we will use the general term anti-epilepsy treatment (AET) to include all these types of treatments. Where appropriate, more specific terms will be used to indicate the treatment indication, adopting the following definitions modified from those recommended by Pitkanen (Pitkanen 2010).
These treatments alter the development or progression of epilepsy, comorbidities, and also the associated pathology. They include:
The validation of new AETs that address currently unmet clinical needs is a multistep process, requiring rigorous testing through all stages of development, using experimental models of seizures or epilepsy (Table 1). While these models have provided new candidate molecular targets for drug-resistant seizures, epileptogenesis and certain comorbidities (Loscher and Potschka 2005, Loscher 2007, Pitkanen 2010, Pitkanen and Lukasiuk 2011), there is concern that these findings may not translate into clinically meaningful interventions. This situation poses a significant burden and barrier to investment, as the costs associated with development of a new anti-seizure treatment currently approximates $1 billion.
The purpose herein is to provide a framework for a discussion that will lead to recommendations for improved design, analysis, and reporting of preclinical AET studies. The Working Group on Recommendations for Preclinical Epilepsy Drug Discovery of the Neurobiology Commission of the International League Against Epilepsy (ILAE), the Basic Science Committee of the American Epilepsy Society (AES), and the US National Institute for Neurological Diseases and Stroke (NINDS), and the have spurred initiatives to optimize the bench-to-bedside translation of preclinical research to develop new AETs. In this document, we highlight pitfalls in translating preclinical data to successful clinical treatments for epilepsy, comparing them with the experience from other disciplines. Proof of principle experimental studies and projects aimed at identifying new mechanisms and targets for new therapies are not included in the present commentary. We will focus on preclinical assessments of treatments that are intended to be ultimately translated into human trials.
A major concern highlighted in other neurological and non-neurological disease areas is the poor reproducibility of preclinical data for compounds progressing from academic laboratories to industrial development programs and, ultimately, to clinical trials (Stroke Therapy Academic Industry Roundtable (STAIR) 1999, Ioannidis 2005, Benatar 2007, Fisher et al. 2009, Mullard 2011, Prinz et al. 2011). Given the high cost of clinical drug development, factors such as low reproducibility and translatability, or heterogeneity in study design that hinders the comparison of preclinical data are major disinsentives for investment in developing novel treatments (Mullard 2011). The reasons for these obstacles are multiple and varied, but methodological issues related to the design, execution and reporting of preclinical studies are important components. For example, methodological pitfalls identified by a meta-analysis of preclinical studies using the superoxide dismutase 1 (SOD1) transgenic mouse model of amyotrophic lateral sclerosis (Benatar 2007) included lack or insufficient study blinding, small sample sizes, initiation of treatments at a presymptomatic stage which may not be clinically relevant, publication bias favoring positive studies, measures of statistical significance with questionable clinical relevance, and failure to address issues related to translation of findings to the clinical setting.
In response to similar concerns within the spinal cord research field, the NINDS contracted a replication program where 10 replication studies were conducted in an attempt to validate the preclinical data. The majority of these data could not be replicated (Pinzon et al. 2008, Steward et al. 2008). In the stroke research field, guidelines and criteria have been proposed to identify data quality problems and offer directions for future studies (Stroke Therapy Academic Industry Roundtable (STAIR) 1999). The STAIR report outlines recommendations for preclinical study design, emphasizing the importance of sample size calculations, preset inclusion and exclusion criteria, group allocation and blinding, appropriate reporting of excluded animals as well as of conflicts of interest (Fisher et al. 2009). The report provided a set of minimal criteria to be met before selecting a candidate treatment for clinical testing. The example of the putative neuroprotectant NXY-059 which failed in a randomized multicenter clinical trial, despite successful preclinical testing in accordance with the STAIR guidelines, demonstrates that guidelines may not necessarily guarantee success in drug development (Ginsberg 2007). However, they may help in teasing out the factors that contribute to discordant results between preclinical and clinical studies, if such studies follow consistent designs and methodology.
In an attempt to address some of these issues with preclinical studies of drugs for neurological diseases, the “Rigor in Science Working Group” of the NINDS has recently released an essential list of “Points to consider for improving the quality of NINDS-Supported Preclinical and Clinical Research through Rigorous Study Design and Transparent Reporting” (http://www.ninds.nih.gov/funding/transparency_in_reporting_guidance.pdf) (Table 2).
Epilepsy differs from many other neurological conditions due to its extreme heterogeneity in etiologies and phenotypes. Even in clinical trials, the reproducibility of efficacy results can vary across studies targeting the same seizure type, possibly due to population differences in placebo response, and genetic, societal or biological factors, including etiologic and diagnostic heterogeneity (Sperling et al. 2010). To optimize translation of preclinical results to the clinical setting, it would be helpful if the precisely defined terminology and criteria were used in both phases of development (Tables 3 and and44).
This article briefly discusses some of the challenges that this goal will present. It is important to note that, unlike other neurological disease areas mentioned above, the failure of AETs introduced over the last two decades to significantly impact unmet clinical needs in the epilepsy population does not necessarily represent a failure in translating preclinical results to the clinical setting. Recent drugs have shown substantially similar anti-seizure profiles to those of older generation drugs in preclinical models, and similar efficacy profiles in clinical trials. Nonetheless, valuable lessons learned from other neurological diseases have important implications for the preclinical translation of potential new AETs.
Crucial aspects in preclinical AET development include: (a) study design and execution, (b) unbiased reporting of both positive and negative outcomes, (c) replication studies, and (d) translation of data into clinical trials. There is no ideal design and protocol that is suitable for all purposes. Protocols may need to be adapted to account for the mechanism of action of the AET (where known), etiology and features of the seizure/epilepsy syndrome studied, specific targeted population, and the utilized endpoints. However, standardizing designs, testing paradigms and outcome assessments across preclinical AET studies for the same indication, and anticipating their clinical applicability, will be needed to assess the replicability of data. Certain aspects of these steps are common for all treatment goals, whereas others relate to the specific objectives. Below, and in Tables 3 and and4,4, we outline problems related to these issues. Our objective is not to provide definitive guidelines, but rather a framework for discussion leading to a future set of recommendations that will be widely shared by the scientific community.
Accurate prediction of a drug’s efficacy and toxicity from preclinical data can be limited by inter-species/strain differences in seizure threshold, pharmacodynamics and pharmacokinetics (Frankel et al. 2001, Baillie and Rettie 2011). Consideration should be given to whether preclinical studies should examine effects in both sexes and different age groups, as deemed appropriate for the specific epileptic syndrome. Genetic background may influence not only the susceptibility to seizures and epilepsy, but may also modulate the consequences of an initial precipitating insult. When considered clinically relevant, the effects of different genetic substrates could be investigated in preclinical AET development.
Ideally, animal models used for preclinical drug development should: (a) allow identification of the components of the seizure type and epilepsy syndrome that are relevant to the human condition, (b) address the age, sex or etiology-specific features of the human syndrome, (c) manifest comorbidities or pathologies that are characteristic of the human condition and relevant to goals of the study, and (d) allow for monitoring of outcomes using reliably quantifiable and clinically-relevant endpoints or disease biomarkers. There are no animal models that fulfill all of these characteristics. However, studies carried out on existing models can still yield useful proof of principle data, provided that the model displays phenotypic features that the proposed treatment is expected to address. Discussion of how the advantages and limitations of any proposed model provide information that will be useful in the design of future clinical studies would be helpful.
To minimize bias, randomization and blinding are desirable. In some circumstances, such as presence of specific abnormal behavioral phenotypes or tissue pathology, complete blinding is not possible and this should be discussed when reporting the data. Inclusion and exclusion criteria may influence applicability of the results, and thus may also be a source of bias if they are not predefined and applied by an investigator blinded to the group assignment. Attention to a rigorous, predefined, statistical analysis plan, including sample size calculations, can also help minimize bias.
Outcomes that are appropriate for the study goals and can be assessed by quantitative, unbiased methods have obvious advantages. Video-EEG or equivalent biomarkers are currently considered the standard methods for the assessing seizure outcomes in models of epileptogenesis and spontaneous epilepsy, but not necessarily for anti-seizure studies utilizing electrical or chemically induced seizures, where the threshold required to trigger a seizure is a primary outcome measurement, nor in neonatal rodents when the skull size may not permit the placement of EEG electrodes. The optimal duration and timeline of monitoring to assess a treatment’s effect is also variable, and may depend on the specific objectives. Consideration or discussion of possible confounding variables such as age, sex, or other experimental parameters would be important, as well as reporting adverse outcomes (mortality, toxicity etc).
Assessment of dose-response curves, preferably using routes of administration that could be easily applied in humans, and identification of minimally effective and maximally tolerated doses is helpful for the design of future clinical studies. Investigating pharmacokinetic/pharmacodynamic (PK/PD) relationships, including the rate and extent of brain penetration, can also facilitate the design of proof-of concept studies in the clinic. For non-pharmacological AETs, dose-response experiments may need to be adapted to the specific modality.
In principle, replication studies using the same model or different models of the same epilepsy syndrome or seizure type are essential for determining whether to proceed to human investigation. For example, the demonstration that a new AET shows efficacy in more than one model relevant to the same seizure or epilepsy type or comorbidity would strengthen the evidence supporting a decision to progress to clinical development. Preclinical replication studies could be facilitated by organizing a system whereby independent preclinical AET testing centers are supported to undertake replication of promising preclinical data from academic laboratories. This system would need to address issues such as funding sources, protocol standardization, intellectual property rights, financial and non-financial conflicts of interest, objectivity in testing and analysis, as well as scientific merit (Table 4). Publication bias is arguably even more of a problem for preclinical than clinical studies, as it is very difficult to publish studies in animal models that produce negative results. A system of registration for preclinical studies, as currently exists for clinical trials, would be helpful in minimizing this bias.
Familiarization with the preclinical regulatory requirements, as established by the regulatory agencies of the country where preclinical research is conducted, can strengthen preclinical AED development research and anticipate issues to be addressed in an Investigational New Drug (IND) application. Relevant guidelines relate to good laboratory practices (GLP), good manufacturing practices (GMP), methods and procedures for animal PK/PD and toxicology studies, as well as assays for quantitation of drugs and metabolites. The investigators are referred to publications of the Food and Drug Administration (FDA) in the U.S.A., the European Medicines Agency (EMA) in Europe, the Japanese Regulatory Agency, or specific regulatory bodies of the individual country where research is conducted.
Potential intellectual property and financial conflicts of interests for investigators involved in preclinical studies should be stated in publications.
There is a pressing need for new anti-seizure treatments that can control drug-resistant seizures and/or offer a better tolerability/efficacy profile for specific target patient populations. To date, preclinical anti-seizure drug development has primarily been carried out using a few well-characterized acute or chronic animal models of seizures or epilepsy (Table 1). Yet, the reiteration of the same algorithms and models might create a bias towards identifying anti-seizure therapies with indications and efficacy profiles similar to those of currently clinically available drugs (Coatsworth 1971, Perucca et al. 2007, Perucca 2010, Loscher and Schmidt 2011). Validating an anti-seizure therapy in an adult animal may not necessarily guarantee its efficacy in neonates or infants, where brain physiology and connectivity are still immature and the underlying pathophysiology may affect anti-seizure efficacy. Expanding the range of seizure models used in preclinical testing to include models of drug-resistant seizures or specific populations (i.e. age, gender, epilepsy syndrome etc) could be useful.
Compared with preclinical anti-seizure drug screening, studies on anti-epileptogenesis effects are expensive and time-consuming because they require prolonged video-EEG monitoring and large sample sizes to account for significant inter-animal variability in seizure frequency, latency to onset, and other phenotypic endpoints. Consequently, it is imperative to ensure that the study design and utilization of data are appropriate, not only to minimize costs but also to facilitate comparisons across studies. Optimizing the design may also reduce the number of inconclusive studies and ultimately reduce the number of laboratory animals required.
Most of the currently used chronic epilepsy models are models of adult genetic generalized or acquired limbic epilepsy (Table 1). Epileptogenesis however is unlikely to be a single process across epilepsy syndromes. More models representing other important epilepsy syndromes and developmental ages should be developed and rigorously characterized across multiple laboratories to facilitate the identification of syndrome-appropriate anti-epileptogenic therapies.
There are major unmet needs in early life epilepsies. Most preclinical development has focused on adult animal models, partly to avoid confounding effects of developmental processes. Yet human epilepsies often begin at very young ages and, by the time these children mature into adults, the effects of seizures, therapies, and comorbid conditions have altered brain biology and functioning, and consequently the way epileptogenesis has progressed. How applicable for young patients are therapies discovered using adult-onset epilepsy models? Validation and utilization of the emerging early life models of epileptogenesis and epilepsies will be necessary for the identification of more effective age-specific anti-epileptogenic therapies.
Some, but not all, models are created using an inciting insult. Factors expected to modify the epileptogenic response to the initial insult or induction method may be investigated as to their potential impact on antiepileptogenic efficacy. These include the severity or duration of the initial epileptogenic insults, as well as the impact of different pathologies and genetic background. Pre-existing genetic mutations may alter the response not only to the initial insult but also to anti-epileptogenic therapies (Glasscock et al. 2007, Chiu et al. 2008). Studies that account for etiology and severity of the initial insult may help to characterize etiology-specific, anti-epileptogenic therapies, thereby guiding eligibility criteria for future clinical trials. It is important to differentiate anti-seizure effects from anti-epileptogenic effects if the initial insult is an induced seizure, as the former may confound the assessment of the latter (e.g. kindling or post-status epilepticus models).
AETs may have different effects if administered before or after the onset of seizures, during different stages of the disease, and if given for short or long periods (Goodkin and Kapur 2003, Buckmaster et al. 2009, Jozwiak et al. 2011). Since in many clinical situations treatment can only be initiated after the initiating epileptogenic insult (e.g., after a stroke or head injury) or after the occurrence of spontaneous recurrent seizures, it is important to demonstrate preclinical efficacy under similar conditions. Application of therapies prior to an epileptogenic insult may provide limited relevant information. If treatments are effective only when given at the presymptomatic stage, biomarkers or surrogate endpoints may need to be identified to guide the timing and duration of anti-epileptogenic therapy prior to onset of clinical symptoms. The identification of clinically relevant biomarkers for treatment administration, progression or epileptogenesis and treatment-related side effects and confirmation of prevention or cure could greatly increase the efficacy/safety profile of the antiepileptogenic therapy and will reduce the costs and efforts invested in these studies (Roch et al. 2002, Glauser 2007, Engel 2008, Shinnar et al. 2008, Bragin et al. 2010, Dube et al. 2010). The STAIR experience with tissue-type plasminogen activator (tPA) is an encouraging example, because the therapeutic window has been found to be about the same in animals and humans (Papadopoulos et al. 1987, Ding et al. 2004, Hatcher and Starr 2011).
For antiepileptogenesis studies, a full reporting of the methods, duration, and frequency of seizure detection and analysis is important. Blinded analysis revealing the sensitivity achieved for seizure detection, information regarding the number of data gaps (i.e. periods without EEG monitoring), and calculations of the seizure frequencies in a manner that accounts for the duration and timing of monitoring as well as seizure clustering can facilitate interpretation of preclinical data and subsequent clinical development (Williams et al. 2009). Determination of the optimum observation period to assess the outcomes (e.g. spontaneous seizures) should consider the expected frequency and time of appearance in the model being used. If the study aims at evaluating the persistence of an anti-epileptogenic effect following withdrawal of treatment, the time taken to wash out the effects of the treatment needs to be considered.
Preclinical development of symptomatic and disease modifying treatments affecting comorbidities, such as neurocognitive and developmental impairments, neuropsychiatric conditions and cardiovascular events, shares many of the challenges described for anti-seizure and anti-epileptogenic studies. The importance of early intervention to minimize comorbidities has been advocated in several early life human epilepsy syndromes, such as infantile spasms and tuberous sclerosis (Lux et al. 2005, Jozwiak et al. 2011). Identifying the therapeutic time window and appropriate clinically relevant biomarkers may facilitate the design of future clinical trials. The major challenge in the designing these studies is the delineation of appropriate and easily quantifiable outcomes, including cognitive, behavioral, cardiorespiratory, and neurological outcomes, that are relevant to the respective comorbidities in people with epilepsy. The likelihood of some of those outcomes, i.e cognitive/behavioral, to change as a result of experience, handling, biological, genetic or other epigenetic factors requires special care and standardization in the monitoring of the experimental animals. In particular, developmental studies face the challenge in interpreting these outcomes as a function of age, sex, maturation rate, in addition to treatment and the previously mentioned modifiers.
A biomarker can be defined as an objectively measured characteristic of a normal or pathological biological process, or a biological response to a therapeutic intervention (Biomarkers Definitions Working Group 2001, Engel 2011). A surrogate endpoint is a biomarker that can substitute for a clinical endpoint (Biomarkers Definitions Working Group 2001) and can therefore provide an indirect measure of disease presence or progression (Engel 2011). Diagnosis and treatment of epilepsy suffer from the lack of reliable biomarkers and/or surrogate endpoints for the presence and severity of an epilepsy condition, epileptogenesis or comorbidities. Biomarkers or surrogate endpoints appropriate for the epileptic syndrome under study would be of great benefit in selecting the patient populations likely to benefit, in guiding treatment timing and selection and in monitoring clinical outcomes (Engel 2011). According to the goals, different modalities may be used to identify clinically relevant biomarkers, i.e. electrophysiological measures, biochemical markers, magnetic resonance imaging, positron emission tomography, genetic and epigenetic markers, and cardio-respiratory monitoring (Roch et al. 2002, Glauser 2007, Engel 2008, Shinnar et al. 2008, Bragin et al. 2010, Dube et al. 2010). There is specifically a need for clinically applicable biomarkers that: (a) differentiate epileptogenic changes from reactive changes or epiphenomena, especially at the early stage following the initial epileptogenic insult, to guide selection and early implementation of symptomatic and disease modifying treatments; (b) permit early and sensitive monitoring of disease progression, prior to appearance of clinical symptoms; (c) identify accurately the boundaries of the epileptogenic focus to allow implementation of more selective therapies (i.e. resection surgery); (d) reliably predict the time of seizure recurrence, the response or resistance to AETs, or the occurrence of AET-related adverse effects, (e) identify the risk for developing comorbidities of epilepsy and (f) document prevention or cure. Assessment of the limitations in translating preclinical markers to the clinical arena should be encouraged, including whether and how selected markers could be applied in the design of a clinical trial.
The discovery of better treatments for patients with epilepsy requires improved ways of evaluating preclinical models, more robust protocols, and a more consistent assessment of results. The present commentary should not be considered as a definitive list of recommendations but, rather, a framework for the development of specific guidelines. Such guidelines could also inform the grant review process, to ensure support of those proposals which are most likely to lead to clinically relevant results. Future work could involve definition of a hierarchical list of preclinical evidence recommended to progress to formal clinical testing, plus a separate list of optional, complementary information. This would allow the comparison of different AETs in terms of translational value, attributing different degrees of likelihood of clinical success to each, based on evidence deriving from preclinical studies.
Regular systematic reviews of published preclinical AET data would be helpful. Such reviews should address appropriateness and best utilization of animal models, tools for behavioral or outcome assessment, methodological design, and critical, comparative evaluation of the preclinical efficacy data for specific seizure types or syndromes. This would allow adaptation of guidelines to the evolving needs of the field. A Cochrane-like collaboration may be useful to pursue this aim (www.cochrane.org). To objectively evaluate the promise of new AETs, it would be useful to provide a forum to publish not only positive but also negative studies.
Thank you to members of the Basic Science Committee of the American Epilepsy Society and of the International League Against Epilepsy Neurobiology Commission Working Group on Recommendations for Preclinical Epilepsy Drug Discovery for reviewing and making constructive suggestions on the manuscript.
*Members of the AES Basic Science Committee: Paul Buckmaster (chair), Kevin Staley (Ex-Officio), Douglas A. Coulter (Ex-Officio), Matthew Anderson, Amy Brooks-Kayal, Aristea S. Galanopoulou, L. John Greenfield Jr, Daryl W. Hochman, Frances E. Jensen, Manisha N. Patel, Nicholas P. Poolos, David L. Sherman, Bret N. Smith, Libor Velisek, Karen S. Wilcox.
**Members of the ILAE Working Group of the Neurobiology Commission: Michele Simonato (co-chair), Terence O’Brien (co-chair), Alexis Arzimanoglou, Edward H. Bertram III, Paul Buckmaster, Stephen Collins, Jerome Engel Jr., Jacqueline French, Aristea S. Galanopoulou, Gregory L. Holmes, Henrik Klitgaard, Merab Kokaia, Wolfgang Löscher, Holger Lerche, John Messenheimer, Solomon L. Moshé, Astrid Nehlig, Jeffrey L. Noebels, Emilio Perucca, Asla Pitkänen, Dieter Schmidt, James Stables, Kevin Staley, Eugene Trinka, Matthew Walker, H. Steve White, Samuel Wiebe.
We envision that the contents of this commentary will form the basis for accurate and detailed guidelines to be developed in the future. The ultimate goal is to improve the study design and promote the publication of high-quality research, permit meaningful comparisons of results across studies, and improve their translation into successful clinical studies. It is also not the intention of this commentary to provide recommendations for proof of principle or target identification studies or for appropriateness of preclinical studies for publication in scientific journals.
This editorial has been reviewed, edited and approved by the Working Group on Recommendations for Preclinical Epilepsy Drug Discovery of the Neurobiology Commission of the International League Against Epilepsy and by the Basic Science Committee of the American Epilepsy Society. Opinions expressed by the authors, however, do not necessarily represent official policy or position of the International League Against Epilepsy and the American Epilepsy Society.
ASG has received research support from NIH NINDS/NICHD grant NS62947 (PI) and recent research grant by Johnson & Johnson. A.S.G. has also received consultancy fees from Novartis and royalties from Morgan and Claypool Life Sciences. PSB has no conflicts to disclose. KJS has no conflicts to disclose. SLM has received research support from NIH: R01 NS20253 (PI), R01-NS43209 (Investigator), 2UO1-NS45911 (Investigator), and the Heffer Family Foundation. SLM is serving on the Editorial Board of Neurobiology of Disease, Epileptic Disorders, Brain and Development and Physiological Research and has received a consultancy fee from Eisai and speaker’s fee and travel from GSK. EP received speaker’s or consultancy fees and/or research grants from Bial, Eisai, GSK, Johnson and Johnson, Novartis, Pfizer, Pfizer, UCB Pharma, Upsher-Smith and Vertex. EP receives research support from the Italian Ministry of Health, the Italian Ministry for University and Research, the Italian Medicines Agency and the European Commission of the EU. EP also serves in the editorial boards of Acta Neurologica Scandinavica, CNS Drugs, Epileptic Disorders, Epilepsy Research, Seizure, Lancet Neurology, Expert Reviews in Neurotherapeutics, Clinical Pharmacokinetics, Therapeutic Advances in Drug Safety, Frontiers in Clinical Trials and Pharmacotherapy and Clinical Drug Investigation. JEJr has received research funding from NIH grants P01 NS02808, R01 NS33310, U01 NS42372, honoraria from Medtronic, Eisai, Johnson & Johnson, Lippincott, and royalties from Medlink, Wolters Kluwer, Blackwell, and Elsevier. W.L. has no conflicts to disclose. JLN has no conflicts to disclose. AP has no conflicts to disclose. JS has no conflicts to disclose. HSW has served as a paid consultant to Johnson & Johnson Pharmaceutical Research and Development, GlaxoSmithKline, Valeant Pharmaceuticals, Eli Lilly & Co., and Upsher-Smith Laboratories, Inc., is a member of the UCB Pharma Speakers Bureau, the NeuroTherapeutics Scientific Advisory Board, has received research funding from NeuroAdjuvants, Inc., and is one of two scientific co-founders of NeuroAdjuvants, Inc., Salt Lake City, UT. TJO’B has received unrestricted research grants from UCB Pharma Janssen-Cilag, Sanofi-Synthelabo, and Novartis. TJO’B has received speaking honorarium from UCB Pharma Janssen-Cilag, Sanofi-Synthelabo, and SciGen. MS has received research funding from GlaxoSmithKline, Chiesi Pharmaceuticals (Italy), Sanofi-Synthelabo, and Schering-Plough.
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