| Literature DB >> 34033232 |
Aristea S Galanopoulou1, Wolfgang Löscher2, Laura Lubbers3, Terence J O'Brien4, Kevin Staley5, Annamaria Vezzani6, Raimondo D'Ambrosio7, H Steve White8, Harald Sontheimer9, John A Wolf10,11, Roy Twyman12, Vicky Whittemore13, Karen S Wilcox14, Brian Klein13.
Abstract
Epilepsy is one of the most common chronic brain diseases and is often associated with cognitive, behavioral, or other medical conditions. The need for therapies that would prevent, ameliorate, or cure epilepsy and the attendant comorbidities is a priority for both epilepsy research and public health. In 2018, the National Institute of Neurological Disease and Stroke (NINDS) convened a workshop titled "Accelerating the Development of Therapies for Antiepileptogenesis and Disease Modification" that brought together preclinical and clinical investigators and industry and regulatory bodies' representatives to discuss and propose a roadmap to accelerate the development of antiepileptogenic (AEG) and disease-modifying (DM) new therapies. This report provides a summary of the discussions and proposals of the Preclinical Science working group. Highlights of the progress of collaborative preclinical research projects on AEG/DM of ongoing research initiatives aiming to improve infrastructure and translation to clinical trials are presented. Opportunities and challenges of preclinical epilepsy research, vis-à-vis clinical research, were extensively discussed, as they pertain to modeling of specific epilepsy types across etiologies and ages, the utilization of preclinical models in AG/DM studies, and the strategies and study designs, as well as on matters pertaining to transparency, data sharing, and reporting research findings. A set of suggestions on research initiatives, infrastructure, workshops, advocacy, and opportunities for expanding the borders of epilepsy research were discussed and proposed as useful initiatives that could help create a roadmap to accelerate and optimize preclinical translational AEG/DM epilepsy research.Entities:
Keywords: acquired epilepsy; animal model; comorbidity; epilepsy; genetic epilepsy; translation; treatment
Mesh:
Year: 2021 PMID: 34033232 PMCID: PMC8166793 DOI: 10.1002/epi4.12490
Source DB: PubMed Journal: Epilepsia Open ISSN: 2470-9239
Initiatives on funding or advancing AEG/DM preclinical studies
| Initiative | Sponsor | Purpose |
|---|---|---|
| ETSP | NINDS (Division of Translational Research) | Identification of new therapeutic agents for the unmet medical needs in the epilepsies, including AEG, DM, and prevention |
| EpiBioS4Rx | NINDS | Center without walls (CWoW) for biomarker discovery and screening for therapies to prevent PTE |
| TAPTE | CURE Epilepsy and the US Department of Defense Psychological Health and Traumatic Brain Injury Research Program, award W81XWH‐15‐2‐0069 | Team Approach to the Prevention and Treatment of PTE |
| EpiXchange/Epicluster | European Community, EBRA | Present major findings from European Community‐funded collaborative projects on epilepsy and discuss challenges and strategies to bring epilepsy research closer to clinical application. Establish collaborative framework for coordinated actions of epilepsy research in Europe |
| ILAE/AES Joint Translational Task Force |
ILAE, AES CURE Epilepsy, Epilepsy Therapy project, Autism Speaks | Undertake initiatives to optimize epilepsy therapy development and translation to the clinics. Initiatives that have been undertaken include harmonization of vEEG studies, interpretation and seizure classification in animal models, systematic reviews, preclinical CDEs for epilepsy, infrastructure development |
| Translational Research Programs | NINDS (Division of Translational Research) | Translational programs at NINDS were established with the goal of helping to ensure project preparedness in the early stages and resources to develop small molecules, biologics and neural devices are available for promising AEG/DM interventions and biomarkers from academic investigators and small businesses. Program details depending on project stage and modality are further described: |
Abbreviations: AEG, antiepileptogenesis; AES, American Epilepsy Society; CDE, common data elements; Citizens United for Research in Epilepsy doing business as CURE Epilepsy; CWoW, Center Without Walls; DM, disease modification; EBRA, European Brain Research Area; EpiBioS4Rx, Epilepsy Bioinformatics Study for antiepileptogenic therapy; ETSP, Epilepsy therapy Screening Program; ILAE, International League Against Epilepsy; NINDS, National Institutes of Neurological Disorders and Stroke; PTE, posttraumatic epilepsy; TAPTE, Team Approach to the Prevention and Treatment of PTE; vEEG, video encephalography.
FIGURE 1Antiepileptogenesis, disease modification, and drug resistance in epilepsy. A, Disease modification (DM) may lead to prevention, cure, or amelioration of epilepsy [ie, antiepileptogenesis (AEG)] or associated comorbidities (ie, anticomorbidity DM therapy) through actions that cannot be simply attributed to antiseizure effects of the treatment. AEG and DM treatments can alter the development, progression, type, severity, pathology and system dysfunction, or treatment response of epilepsies and associated comorbidities. B, Drug resistance in epilepsy has been proposed to be a manifestation of the severity of the intrinsic epilepsy network. Alternative hypotheses on mechanisms of drug resistance have been published. An AEG/DM therapy may mitigate drug resistance, cure, or prevent epilepsy
Opportunities and challenges for preclinical AEG/DM therapy development
| Opportunities in preclinical models | Challenges/gaps | |
|---|---|---|
| Epilepsy types | ||
| Known vs unknown etiology |
Etiology‐relevant models enable rational, precision medicine treatments Phenotypic screening or targeting common pathologies may identify treatments for epilepsies in which pathogenic mechanisms are heterogeneous, complex, or unknown |
Induction methods may introduce bias toward specific mechanisms Invasive methods of inducing or monitoring for seizures in animals may introduce confounders: Need better technologies Most naturally occurring epilepsies, including unknown etiologies, cannot be modeled. Exceptions: veterinarian clients and inbred models of few epilepsy types Need more systematic studies on natural history and incidence of epilepsies in experimental animals |
| Generalized vs focal onset |
Many models exist Many molecular‐, cellular‐, and network‐related mechanisms or pathologies have parallels in both preclinical models and humans Staging of epileptogenesis and treatment windows is more feasible than in humans Across‐model comparisons help dissociate the role of induction methods from epilepsy‐specific pathologies |
Gaps in knowledge on mechanisms/targets for initiation, maintenance, progression, or remission of epileptogenesis Infrastructure and strategies to identify promising therapy targets in human epilepsy populations for reverse translation Challenges in validating and translating preclinical findings to the clinics Data/specimen repositories for across species validation Optimize study design and outcomes of preclinical/clinical studies to allow comparisons Biomarkers for translation and validation |
| Rare epilepsy syndromes and pediatric epilepsies |
Remarkable growth in genetic and nongenetic models of rare and pediatric epilepsies Efficacy/tolerability treatment trials can be done using controlled and powered studies Innovative experimental techniques Maturational factors and other biological factors can better be studied in models |
Numerous rare epilepsy etiologies (genetic or nongenetic) have not yet been modeled Validation of preclinical data to humans remains challenging Data/specimen repositories to allow across species comparisons Consortia and data sharing to enrich knowledge on rare epilepsies Biomarkers to translate and validate data Optimize preclinical and clinical study designs to permit comparisons and address regulatory and ethical concerns |
| Drug‐resistant epilepsies |
Models have revealed possible mechanisms, diagnostics, and treatments for drug resistance |
Validation of preclinical data to humans is challenging Optimize preclinical study design to model drug resistance |
| Strategies/study design | ||
| Target populations |
Preclinical trials are more amenable to: Exploring targets, biomarkers, risk factors, and confounders Detect a positive drug effect by minimizing confounders Designs that can differentiate true from false successes and failures |
Optimization of study designs is needed to: Improve translation, validation of findings Implement clinically relevant strategies Enable the identification of likely to benefit populations |
|
Studies specifically controlling for specific breeding, housing, environmental factors, genetic substrates, and/or premorbid conditions may be conducted to clarify their impact on phenotypes and drug effects Across‐model confirmation may reveal common pathways and evolutionarily preserved aspects of epileptogenesis Infrastructure to build and disseminate expertise in creating specialized models and increase availability of animal models could be useful |
Species‐specific limitations: Breeding/housing habitat and handling are simpler and not the natural ones cannot recapitulate complex factors inherent to human everyday life conditions and stressors Source/genetic substrate affects phenotype; animal studies conducted in same strain may not extend to different strains or heterogeneous genetic substrate of human subjects enrolled in clinical trials Premorbid health condition of animal research models is not fully characterized. Premorbid factors that could influence results cannot be ascertained as in clinical studies Incidence and type of natural epilepsy and underlying pathologies unknown in models. Studies on epilepsies of unknown etiology are therefore challenging in models. Animal models of acquired epilepsies are typically induced by methods that may not fully simulate natural causes and may contribute partially to resultant phenotypes and/or pathologies | |
|
Access to appropriate models may not be optimal | ||
| AEG/DM strategies |
More feasible in models due to more controlled experimental conditions, flexibility to probe any stage of epilepsy development and progression, or utilize techniques not feasible yet in clinical trials |
Differences in study design, assessments and outcomes between preclinical and clinical trials may hinder translation and validation Need to develop more clinically relevant in vivo methods to monitor and validate treatments and targets across species Certain aspects cannot be easily modeled in animals: enrollment/compliance issues AEG effects from |
| Treatments |
More flexibility to test Easier to test interventions to delineate the mechanism of AEG treatment effects for future improvements More flexibility to monitor treatment effects on targeted mechanisms Preclinical multicenter studies may address issues related to adequate powering of AEG/DM trials and reduce potential bias stemming from a single‐center trials |
Optimization of clinically relevant methods for treatment delivery and monitoring in vivo Tolerability and safety in humans cannot be always predicted from animal studies Improve access to and transparency of data from treatment trials to minimize unnecessary duplications and/or facilitate repurposing of drugs AEG/DM trials in rodents are laborious, time‐consuming, and effort‐consuming, making it challenging to perform dose‐effect studies in powered studies from single centers, particularly in models with low epilepsy rates |
| Outcomes and measurements |
Seizure monitoring can be more systematic and objective in models, through the use of vEEG, rather than self‐reporting Many methods to assess for comorbidities in models with greater flexibility in incorporating in the study design |
Definition, scoring, and classification of seizures and epilepsies needs optimization Agreement on clinically relevant outcomes and measurements, comorbidity assessment is needed |
| Transparency, data sharing and reporting | ||
|
First efforts to address gaps in access to data information have been under way: PANAChE database for ETSP screened compounds ( Registries of preclinical studies (eg, Registries of funded preclinical AEG studies (eg, ICARE) Publishing/reporting of data of preclinical studies (negative, replications, preliminary, etc) in journals, including open access publications Improved capacity for big data storage and reporting and big data analyses Development of preclinical epilepsy common data elements (CDEs) |
Collaborations are needed to share expertise and address all aspects of AEG/DM studies Supporting AEG/DM research: May require more effort/resources/cost/expertise than usual drug trials Special considerations may be needed for pediatric and early‐onset epilepsies | |
Abbreviations: AEG, antiepileptogenesis; AES, American Epilepsy Society; CDE, common data elements; DM, disease modification; ETSP, Epilepsy therapy Screening Program; ICARE, Interagency Collaborative to Advance Research in Epilepsy; ILAE, International League Against Epilepsy; PANAChE, Public Access to Neuroactive & Anticonvulsant Chemical Evaluations; PTE, posttraumatic epilepsy; vEEG, video encephalography.
Preclinical working group suggestions to optimize AEG/DM therapy development
| Recommendation | Description |
|---|---|
| Research initiatives | |
| Epilepsy brain initiative |
Development and validation of a battery of tests/tools to identify targets and circuits involved in ictogenesis, epileptogenesis or comorbidities |
| Novel technologies for in vivo control of therapy targets |
Development and validation of novel technologies applicable to humans that may affect targeted regulation of genes, pathways, cells, circuits in vivo |
| Validation and optimization of preclinical findings for clinical use |
Creation of platforms to validate and optimize preclinical discoveries for use in clinical trials for AEG, disease prevention of modification. This may include retro‐ or prospective studies, shared big databases and repositories for investigation and development of omics, imaging, electrophysiological or network probing tools, bioinformatics |
| Infrastructure | |
| Data sharing and big data analyses tools |
Big databases for data sharing, including published, unpublished or preliminary Servers to host big data and tissue or sample repositories for sharing Policies for data sharing and usage, considering intellectual properties |
| Workshops | |
| Roadmap to accelerate the advancement to clinical trials for disease prevention, modification or AEG. |
Define minimal/best preclinical dataset needed to advance to a clinical trial for disease prevention, modification or AEG for both adult and pediatric epilepsies. Define best endpoints and predicting biomarkers for outcomes (eg, epilepsy, seizures, remission, comorbidities, consequences, drug resistance, disease progression or improvement) |
| Data sharing and big data analyses |
Planning workshop to:
Plan for infrastructure for big databases for data sharing and optimize conditions that would enable and encourage researchers to utilize them Identify server to house data or tissue repositories for sharing in research, including storage cataloging databases, policies for collaboration and data exchange, intellectual properties, big data analyses |
| Advocacy/expanding borders of epilepsy research | |
| Broaden epilepsy community expertise by engaging experts outside the field |
Encourage collaborations with people with expertise outside epilepsy Encourage creation of interoperable big databases to facilitate data exchange and processing with big databases with no epilepsy focus |
| Systematic utilization of medical record data |
Systematic utilization of medical record data (HIPAA compliant) to obtain insight into candidate drugs for repurposing, epilepsy comorbidities, factors influencing progression/remission, etc |
| Systematic probing of mechanisms through which treatments work in humans (and in animals) with epilepsies |
Systematic probing of mechanisms through which treatments work in humans (and in animals) with epilepsies |