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Innovative Molecular Imaging for Clinical Research, Therapeutic Stratification, and Nosography in Neuroscience.

ABSTRACT: Over the past few decades, several radiotracers have been developed for neuroimaging applications, especially in PET. Because of their low steric hindrance, PET radionuclides can be used to label molecules that are small enough to cross the blood brain barrier, without modifying their biological properties. As the use of 11C is limited by its short physical half-life (20 min), there has been an increasing focus on developing tracers labeled with 18F for clinical use. The first such tracers allowed cerebral blood flow and glucose metabolism to be measured, and the development of molecular imaging has since enabled to focus more closely on specific targets such as receptors, neurotransmitter transporters, and other proteins. Hence, PET and SPECT biomarkers have become indispensable for innovative clinical research. Currently, the treatment options for a number of pathologies, notably neurodegenerative diseases, remain only supportive and symptomatic. Treatments that slow down or reverse disease progression are therefore the subject of numerous studies, in which molecular imaging is proving to be a powerful tool. PET and SPECT biomarkers already make it possible to diagnose several neurological diseases in vivo and at preclinical stages, yielding topographic, and quantitative data about the target. As a result, they can be used for assessing patients' eligibility for new treatments, or for treatment follow-up. The aim of the present review was to map major innovative radiotracers used in neuroscience, and explain their contribution to clinical research. We categorized them according to their target: dopaminergic, cholinergic or serotoninergic systems, ?-amyloid plaques, tau protein, neuroinflammation, glutamate or GABA receptors, or ?-synuclein. Most neurological disorders, and indeed mental disorders, involve the dysfunction of one or more of these targets. Combinations of molecular imaging biomarkers can afford us a better understanding of the mechanisms underlying disease development over time, and contribute to early detection/screening, diagnosis, therapy delivery/monitoring, and treatment follow-up in both research and clinical settings.

SUBMITTER: Beaurain M

PROVIDER: S-EPMC6890558 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Innovative Molecular Imaging for Clinical Research, Therapeutic Stratification, and Nosography in Neuroscience.

Beaurain Marie M Salabert Anne-Sophie AS Ribeiro Maria Joao MJ Arlicot Nicolas N Damier Philippe P Le Jeune Florence F Demonet Jean-François JF Payoux Pierre P

Frontiers in medicine 20191127

Over the past few decades, several radiotracers have been developed for neuroimaging applications, especially in PET. Because of their low steric hindrance, PET radionuclides can be used to label molecules that are small enough to cross the blood brain barrier, without modifying their biological properties. As the use of 11C is limited by its short physical half-life (20 min), there has been an increasing focus on developing tracers labeled with 18F for clinical use. The first such tracers allow ...[more]

PMID: 31828073

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Project description:BackgroundClinical research has frequently not been taught in a practical way, often resulting in a very didactic approach rendering it not very accessible for medical undergraduates. Simulation can provide an immersive, interactive, and reflective experience and may be applied to the clinical research curriculum.MethodsA 7-step model, modified from Kern's six-step approach and Khamis's stepwise model, was used to develop the curriculum. A questionnaire survey on undergraduates' attitude towards, knowledge and practice of clinical research and simulation education was conducted to generate a targeted needs assessment. The simulation framework was integrated into the development of educational strategies. Experts were consulted to assess the curriculum prior to implementation.ResultsTalent construction in China needs an innovative capability-enhanced clinical research curriculum. Sixty-six clinical undergraduates in our school completed the survey. 89.39% (59/66) of them hadn't participated in clinical research, while 93.94% (62/66) would like to conduct clinical trials if possible. 75.76% of respondents didn't have knowledge of or practical abilities in clinical trials. The mean score for practical ability (2.02 ± 0.92) was lower than that of knowledge (2.20 ± 0.93) (P < 0.01). The dimension of case report form got the lowest score among the five dimensions. Participating in clinical research (P = 0.04) and learning for themselves (P < 0.01) by a few students may have increased the total score. The curriculum was designed to simulate the whole process from protocol writing, registration, ethical approval, implementation, and data analysis to reporting based on one case study, and was divided into two parts to simulate different types of research: randomized controlled trials and observational studies. It was conducted in semesters 5 and 7 respectively, both including 16 sessions. After expert consultation, one session having a 29.01% coefficient of variation was adjusted and replaced. The final simulation class design scenario scripts are provided for reference.ConclusionsThe targeted needs assessment exposed medical undergraduates' poor knowledge of and abilities in clinical research. This is the first report of a simulation-based clinical research curriculum developed in China, and adds curriculum development and design details to the limited related published studies.

Project description:Background: Monoamine oxidases (MAOs) were discovered nearly a century ago. This article aims to analyze the research literature landscape associated with MAOs as privileged class of neuronal enzymes (neuroenzymes) with key functions in the processes of neurodegeneration, serving as important biological targets in neuroscience. With the accumulating publications on this topic, we aimed to evaluate the publication and citation performance of the contributors, reveal the popular research themes, and identify its historical roots. Methods: The electronic database of Web of Science (WoS) Core Collection was searched to identify publications related to MAOs, which were analyzed according to their publication year, authorship, institutions, countries/regions, journal title, WoS category, total citation count, and publication type. VOSviewer was utilized to visualize the citation patterns of the words appearing in the titles and abstracts, and author keywords. CRExplorer was utilized to identify seminal references cited by the MAO publications. Results: The literature analysis was based on 19,854 publications. Most of them were original articles (n = 15,148, 76.3%) and reviews (n = 2,039, 10.3%). The top five WoS categories of the analyzed MAO publications were Pharmacology/Pharmacy (n = 4,664, 23.5%), Neurosciences (n = 4,416, 22.2%), Psychiatry (n = 2,906, 14.6%), Biochemistry/Molecular Biology (n = 2,691, 13.6%), and Clinical Neurology (n = 1,754, 8.8%). The top 10 institutions are scattered in the United States, UK, France, Sweden, Canada, Israel, and Russia, while the top 10 countries/regions with the most intensive research on the field of MAOs are the United States, followed by European and Asian countries. More highly cited publications generally involved neurotransmitters, such as dopamine (DA), serotonin, and norepinephrine (NE), as well as the MAO-A inhibitors moclobemide and clorgyline, and the irreversible MAO-B inhibitors selegiline and rasagiline. Conclusion: Through decades of research, the literature has accumulated many publications investigating the therapeutic effects of MAO inhibitors (MAOIs) on various neurological conditions, such as Alzheimer's disease (AD), Parkinson's disease (PD), and depression. We envision that MAO literature will continue to grow steadily, with more new therapeutic candidates being tested for better management of neurological conditions, in particular, with the development of multi-target acting drugs against neurodegenerative diseases.

Project description:While Monte Carlo particle transport has proven useful in many areas (treatment head design, dose calculation, shielding design, and imaging studies) and has been particularly important for proton therapy (due to the conformal dose distributions and a finite beam range in the patient), the available general purpose Monte Carlo codes in proton therapy have been overly complex for most clinical medical physicists. The learning process has large costs not only in time but also in reliability. To address this issue, we developed an innovative proton Monte Carlo platform and tested the tool in a variety of proton therapy applications.Our approach was to take one of the already-established general purpose Monte Carlo codes and wrap and extend it to create a specialized user-friendly tool for proton therapy. The resulting tool, TOol for PArticle Simulation (TOPAS), should make Monte Carlo simulation more readily available for research and clinical physicists. TOPAS can model a passive scattering or scanning beam treatment head, model a patient geometry based on computed tomography (CT) images, score dose, fluence, etc., save and restart a phase space, provides advanced graphics, and is fully four-dimensional (4D) to handle variations in beam delivery and patient geometry during treatment. A custom-designed TOPAS parameter control system was placed at the heart of the code to meet requirements for ease of use, reliability, and repeatability without sacrificing flexibility.We built and tested the TOPAS code. We have shown that the TOPAS parameter system provides easy yet flexible control over all key simulation areas such as geometry setup, particle source setup, scoring setup, etc. Through design consistency, we have insured that user experience gained in configuring one component, scorer or filter applies equally well to configuring any other component, scorer or filter. We have incorporated key lessons from safety management, proactively removing possible sources of user error such as line-ordering mistakes. We have modeled proton therapy treatment examples including the UCSF eye treatment head, the MGH stereotactic alignment in radiosurgery treatment head and the MGH gantry treatment heads in passive scattering and scanning modes, and we have demonstrated dose calculation based on patient-specific CT data. Initial validation results show agreement with measured data and demonstrate the capabilities of TOPAS in simulating beam delivery in 3D and 4D.We have demonstrated TOPAS accuracy and usability in a variety of proton therapy setups. As we are preparing to make this tool freely available for researchers in medical physics, we anticipate widespread use of this tool in the growing proton therapy community.

Project description:Background:The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods:Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results:The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ? 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ? 0.01). Conclusions:Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

Dataset Information

Innovative Molecular Imaging for Clinical Research, Therapeutic Stratification, and Nosography in Neuroscience.

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Innovative Molecular Imaging for Clinical Research, Therapeutic Stratification, and Nosography in Neuroscience.

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