Introduction to the EQIPD quality system
- PAASP, Germany
- Charité Universitätsmedizin, Germany
- Janssen Pharmaceutica NV, Belgium
- PAASP GmbH, Germany
- AAALAC International, Spain
- Porsolt, France
- Sanofi, France
- UCB, United Kingdom
- Novartis pharma, Switzerland
- Pfizer, United States
- University of Groningen, Netherlands
- Tel Aviv University, Israel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Germany
- Charles River Laboratories, France
- Cohen Veterans Bioscience, United States
- German Mouse Clinic, Germany
- Helmholtz Zentrum München, Germany
- Broad Institute of MIT & Harvard, United States
- PAASP, France
- Polish Academy of Sciences, Poland
- Institute of Pharmacology, Toxicology, and Pharmacy, Germany
- National Council of Science and Technology, Mexico
- The University of Lausanne, Switzerland
- University of Aberdeen, United Kingdom
- Radboud University Medical Center, Netherlands
- University of Belgrade, Serbia
- Institute of Science and Technology, Austria
- University of Helsinki, Finland
- Imperial College London, United Kingdom
- Avertim, Belgium
- University of Edinburgh, United Kingdom
Abstract
While high risk of failure is an inherent part of developing innovative therapies, it can be reduced by adherence to evidence-based rigorous research practices. Numerous analyses conducted to date have clearly identified measures that need to be taken to improve research rigor. Supported through the European Union's Innovative Medicines Initiative, the EQIPD consortium has developed a novel preclinical research quality system that can be applied in both public and private sectors and is free for anyone to use. The EQIPD Quality System was designed to be suited to boost innovation by ensuring the generation of robust and reliable preclinical data while being lean, effective and not becoming a burden that could negatively impact the freedom to explore scientific questions. EQIPD defines research quality as the extent to which research data are fit for their intended use. Fitness, in this context, is defined by the stakeholders, who are the scientists directly involved in the research, but also their funders, sponsors, publishers, research tool manufacturers and collaboration partners such as peers in a multi-site research project. The essence of the EQIPD Quality System is the set of 18 core requirements that can be addressed flexibly, according to user-specific needs and following a user-defined trajectory. The EQIPD Quality System proposes guidance on expectations for quality-related measures, defines criteria for adequate processes (i.e., performance standards) and provides examples of how such measures can be developed and implemented. However, it does not prescribe any pre-determined solutions. EQIPD has also developed tools (for optional use) to support users in implementing the system and assessment services for those research units that successfully implement the quality system and seek formal accreditation. Building upon the feedback from users and continuous improvement, a sustainable EQIPD Quality System will ultimately serve the entire community of scientists conducting non-regulated preclinical research, by helping them generate reliable data that are fit for their intended use.
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Article and author information
Author details
Sandrine Bongiovanni
Esmeralda Castaños-Vélez
Alexander Dityatev
Ernesto Prado Montes de Oca
Funding
Innovative Medicines Initiative (777364)
- Malcolm R MacLeod
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States
Publication history
- Received: September 21, 2020
- Accepted: May 20, 2021
- Accepted Manuscript published: May 24, 2021 (version 1)
- Version of Record published: June 7, 2021 (version 2)
Copyright
© 2021, Bespalov et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.
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- Medicine
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Background: Deep Brain Stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MER) or local field potential recordings (LFP) can be used to extend neuroanatomical information (defined by magnetic resonance imaging) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced.
Methods: Here we present a tool that integrates resources from stereotactic planning, neuroimaging, MER and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (𝑁 = 52) offline and present single use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool.
Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages.
Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luftund Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), Foundation for OCD Research (FFOR).
