Bulimia nervosa (BN) has been characterized as similar to an addiction, though the empirical support for this characterization is limited. This study utilized PET imaging to determine whether abnormalities in brain dopamine (DA) similar to those described in substance use disorders occur in BN.PET imaging with [(11) C]raclopride, pre/post methylphenidate administration, to assess dopamine type 2 (D(2)) receptor binding (BP(ND)) and striatal DA release (ΔBP(ND)).There was a trend toward lower D(2) receptor BP(ND) in two striatal subregions in the patient group when compared with the control group. DA release in the putamen in the patient group was significantly reduced and, overall, there was a trend toward a difference in striatal DA release. Striatal DA release was significantly associated with the frequency of binge eating.These data suggest that BN is characterized by abnormalities in brain DA that resemble, in some ways, those described in addictive disorders.
Positron Emission Tomography (PET) can be used to quantify proteins of interest in the brain, assess the function of these proteins, and quantify cerebral glucose metabolism and blood flow. Its value in neuropsychiatric pharmaceutical drug development is extensive, from the identification of relevant pathophysiology in disease states, to measurement of blood-brain barrier penetration and regional cerebral occupancy of a pharmaceutical agent, to predictions of treatment outcome from a specific pharmacologic intervention in a specific patient. In this paper, we briefly review some basics of brain imaging using PET, and describe its applications to the field of neuropsychiatric pharmaceutical development, including relevant examples from the existing literature. We conclude with a discussion of future developments that will make PET increasingly available and useful for such purposes. Keywords: Radioligands, investigational new drug, major depressive disorder, Alzheimer Disease, serotonin 1A receptor
Objectives. To determine the frequency of depression in patients with idiopathic parkinsonism presenting to a tertiary care Hospital in Karachi. Material and methods. This case study was conducted at the neurology department Jinnah Postgraduate Medical Centre, Karachi (JPMC). The duration of the study was six months from 22nd January 2019 to 2nd June 2019. A total of 114 patients of parkinsonism (idiopathic Parkinson’s disease) were included in this study. Patients were assured of confidentiality. They were given questionnaire with Beck depression inventory while waiting in the neurology outpatient clinic. Questionnaire was taken back after 25 minutes. Patient score more than 9 was diagnosed as depression. The identified depressed patient was offered treatment. Results. Frequency of depression in patients with idiopathic Parkinson’s disease was observed in 48.25% (55/114) cases. Conclusions. It is concluded that our study indicates the burden of depression in Parkinson disease (PD) patients. However, even with stable or mild deficit in motor function, the wide prevalence of depression indicates that it should be suspected and treated. Over the past several years, systematic studies of depression and its treatment have contributed significantly to this most challenging psychiatric problem in PD. Hence, there is a need of policy for screening and prompt treatment of such patients so they could lead to enhance quality of life.
Alzheimer's disease (AD) is a neurodegenerative condition that is hallmarked by senile dementia, worsens over time, and has no proven treatment. It causes a decline in cognitive abilities. Effective automated procedures are required for early prediction and diagnosis since it is imperative to stop the progression of the disease. The creation of precise computer diagnostic systems was made possible by the nature of several aspects of neural data, which were primarily retrieved from neuroimaging with computer-aided algorithms. In the field of computer vision, deep learning, a high-tech machine learning strategy, has demonstrated exceptional ability in identifying detailed structures in complicated, high-dimensional data. Presently, a rising body of research suggests that deep learning techniques can act as a crucial pillar for the diagnosis, categorization, and prediction of AD. In order to develop targeted therapies, it is crucial to comprehend the genetic aetiology of AD. Several researchers had tried to find possible biomarkers for future therapy by using machine learning techniques to analyze the expressed genes in AD patients. Technology advancements in genomic research, like genome-wide association studies (GWAS), which enable the identification of polymorphisms and have been extensively used in investigations of AD, have identified certain genes as significant clinical risk factors for AD. In addition, a number of deep learning models are currently being used in research investigations to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in light of the most recent developments in neuroimaging and genetics. The chapter enlightens many studies that apply deep learning algorithms to predict AD using genomes or neuroimaging data along with the supportive tactics. On the basis of combining both neuroimaging and genome data, pertinent integrative neuroimaging genomics studies that make use of deep learning techniques to forecast AD have been discussed. The limitations of the most recent deep learning combined with neuroimaging and genomics AD investigations have also been described. Lastly, a summary of the research findings, challenges, and future directions for integrating deep learning methods into therapeutic settings is deliberated.
Aim: A breakthrough in modern medicine, in terms of treatment of Alzheimer's disease, is yet to be seen, as the scene is currently plagued with numerous clinical trial failures. Here, we are exploring multifunctional hybrid sulfonamides for their anti-Alzheimer activity due to the complex nature of the disease. Results & methodology: Compound 41 showed significant inhibition of MMP-2 (IC50: 18.24 ± 1.62 nM), AChE (IC50: 4.28 ± 0.15 μM) and BuChE (IC50: 1.32 ± 0.02 μM). It also exhibited a metal-chelating property, as validated by an in vitro metal-induced Aβ aggregation assay using confocal fluorescence imaging. Whereas, MTT and DPPH assays revealed it to be nontoxic and neuroprotective with substantial antioxidant property. Conclusion: The present study puts forth potent yet nontoxic lead molecules, which foray into the field of multitargeted agents for the treatment of Alzheimer's disease.
181 Objectives: Amyotrophic lateral sclerosis (ALS) is a fatal, progressive, mostly adult onset neurodegenerative disorder belonging to the class of motor neuron disease (MND), causing muscle weakness, spasticity, paralysis and death. There is currently no cure or validated diagnostic tools available for ALS. Among the biomarkers of ALS, significant loss of microtubule, a cytoskeleton protein is reported in postmortem ALS brain. Pharmacological stabilization of the microtubule network offers an attractive therapeutic strategy in ALS. Therefore, a non-invasive measurement of neuro microtubules, using PET imaging would robustly help clinicians to understand the complex ALS biochemical changes, thus aiding in personalizing treatment strategies. In this study, we evaluated the in vivo efficacy of [11C]MPC6827, the first BBB penetrating microtubule PET radiotracer, to image microtubule in transgenic ALS mice model using microPET imaging.
Methods: The radiochemical synthesis of [11C]MPC-6827 was automated on a GE-FX2MeI/FX2M radiochemistry module by alkylating the corresponding desmethyl-MPC-6827 with [11C]MeI in DMF using NaOH. MicroPET/CT imaging with [11C]MPC-6827 was performed in 3 month old ALS mice model of superoxide dismutase mutation (SOD1[asterisk]G93A). Dynamic 0-60 min microPET/CT scanning studies were performed in anesthetized SOD1[asterisk]G93A transgenic and wild type mice (n=3) using Trifoil PET/CT scanner. Time Activity Curves (TACs) were derived from the whole brain, sub brain regions, and cervical spinal cord in both the transgenic and wild type mice.
Results: [11C]MPC-6827 was synthesized with high radiochemical purity (>98%) and specific activity (1.8+0.5 Ci/µmol) in 40+5% radiochemical yield, decay corrected to EOS (n =15). MicroPET imaging in SOD1[asterisk]G93A transgenic and wild type mice demonstrated BBB penetration and retention in brain. Based on regions of interest (ROI) analysis, transgenic SOD1[asterisk]G93A mice showed approximately 2-fold lower brain and spinal cord uptake when compared to wildtype littermates. Additionally, whole brain and cervical spinal TACs were also low in SOD1[asterisk]G93A transgenic mice over the wild type ones
Conclusions: Initialin vivo studies in SOD1[asterisk]G93A mice indicated lower binding of [11C]MPC-6827 in transgenic mice compared to wild types. Our initial evaluations suggest that [11C]MPC-6827 can be a potential radiotracer for imaging ALS and other neurodegenerative disorders in which alteration of microtubule is hypothesized.
Abstract The overall objectives of this research are to (i) develop azulene-based PET probes and (ii) image COX2 as a potential biomarker of breast cancer. Several lines of research have demonstrated that COX2 is overexpressed in breast cancer and that its presence correlates with poor prognoses. While other studies have reported that COX2 inhibition can be modulated and used beneficially as a chemopreventive strategy in cancer, no viable mechanism for achieving that approach has yet been developed. This shortfall could be circumvented through in vivo imaging of COX2 activity, particularly using sensitive imaging techniques such as PET. Toward that goal, our laboratory focuses on the development of novel 18F-labled COX2 probes. We began the synthesis of the probes by transforming tropolone into a lactone, which was subjected to an [8+2] cycloaddition reaction to yield 2-methylazulene as the core ring of the probe. After exploring numerous synthetic routes, the final target molecule and precursor PET compounds were prepared successfully using convergent synthesis. Conventional 18F labeling methods caused precursor decomposition, which prompted us to hypothesize that the acidic protons of the methylene moiety between the azulene and thiazole rings were readily abstracted by a strong base such as potassium carbonate. Ultimately, this caused the precursors to disintegrate. This observation was supported after successfully using an 18F labeling strategy that employed a much milder phosphate buffer. The 18F-labeled COX2 probe was tested in a breast cancer preclinical mouse model. The data obtained via successive whole-body PET/CT scans indicated probe accumulation and retention in the tumor. Overall, the probe was stable in vivo and no defluorination was observed. A biodistribution study, using the PET probe and Western blot analysis corroborate with the imaging data. Taken together, these data demonstrate the potential for the use of this novel PET probe for imaging COX2 as a biomarker for the early detection and prediction of the progression of colorectal cancer. Currently, several probe optimization processes are presently being carried out in our laboratory, including the toxicity study and in vivo metabolite analysis. Detailed results of the biometric validation of the probe will be reported in a timely manner. In conclusion, this novel COX2 PET probe was shown to be a promising agent for cancer imaging and deserves further investigation. Citation Format: Wellington Pham, Donald D. Nolting, Dileep J.S. Kumar, John J. Mann, Mohammed N. Tantawy, Todd E. Peterson, Larry Marnett, John C. Gore. Development and validation of the specificity of a novel azulene-based COX2 probe for cancer imaging. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5585. doi:10.1158/1538-7445.AM2013-5585
Arachidonic acid (AA) is involved in signal transduction, neuroinflammation, and production of eicosanoid metabolites. The AA brain incorporation coefficient (K*) is quantifiable in vivo using [11 C]AA positron emission tomography, although repeatability remains undetermined. We evaluated K* estimates obtained with population-based metabolite correction (PBMC) and image-derived input function (IDIF) in comparison to arterial blood-based estimates, and compared repeatability. Eleven healthy volunteers underwent a [11 C]AA scan; five repeated the scan 6 weeks later, simulating a pre- and post-treatment study design. For all scans, arterial blood was sampled to measure [11 C]AA plasma radioactivity. Plasma [11 C]AA parent fraction was measured in 5 scans. K* was quantified using both blood data and IDIF, corrected for [11 C]AA parent fraction using both PBMC (from published values) and individually measured values (when available). K* repeatability was calculated in the test-retest subset. K* estimates based on blood and individual metabolites were highly correlated with estimates using PBMC with arterial input function (r = 0.943) or IDIF (r = 0.918) in the subset with measured metabolites. In the total dataset, using PBMC, IDIF-based estimates were moderately correlated with arterial input function-based estimates (r = 0.712). PBMC and IDIF-based K* estimates were ∼6.4% to ∼11.9% higher, on average, than blood-based estimates. Average K* test-retest absolute percent difference values obtained using blood data or IDIF, assuming PBMC for both, were between 6.7% and 13.9%, comparable to other radiotracers. Our results support the possibility of simplified [11 C]AA data acquisition through eliminating arterial blood sampling and metabolite analysis, while retaining comparable repeatability and validity.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)