Molecular PET Imaging in Alzheimer’s Disease

Screening of [18F]Florbetazine for Aβ Plaques and a Head-to-Head Comparison Study with [11C]Pittsburgh Compound-B ([11C]PiB) in Human Subjects

Positron emission tomography (PET) imaging of amyloid-β (Aβ) has emerged as a crucial strategy for early diagnosis and monitoring of therapeutic advancements targeting Aβ. In our previous first-in-human study, we identified that [18F]Florbetazine ([18F]92), featuring a diaryl-azine scaffold, exhibits higher cortical uptake in Alzheimer's disease (AD) patients compared to healthy controls (HC). Building upon these promising findings, this study aimed to characterize the diagnostic potential of [18F]92 and its dimethylamino-modified tracer [18F]91 and further compare them with the benchmark [11C]PiB in the same cohort of AD patients and age-matched HC subjects. The cortical accumulation of these tracers was evident, with no significant radioactivity retention observed in the cortex of HC subjects, consistent with [11C]PiB images (correlation coefficient of 0.9125 and 0.7883 between [18F]Florbetazine/[18F]91 and [11C]PiB, respectively). Additionally, quantified data revealed higher standardized uptake value ratios (SUVR) (with the cerebellum as the reference region) of [18F]Florbetazine/[18F]91 in AD patients compared to the HC group ([18F]Florbetazine: 1.49 vs 1.16; [18F]91: 1.33 vs 1.20). Notably, [18F]Florbetazine exhibited less nonspecific bindings in myelin-rich regions, compared to the dimethylamino-substituted [18F]91, akin to [11C]PiB. Overall, this study suggests that [18F]Florbetazine displays superior characteristics to [18F]91 in identifying Aβ pathology in AD. Furthermore, the close agreement between the uptakes in nontarget regions for [18F]Florbetazine and [11C]PiB in this head-to-head comparison study underscores its suitability for both clinical and research applications.

Novel Flurbiprofen Derivatives as Antioxidant and Anti-Inflammatory Agents: Synthesis, In Silico, and In Vitro Biological Evaluation

In this study, we present the synthesis of five novel compounds by combining flurbiprofen with various substituted 2-phenethylamines. The synthesized derivatives underwent comprehensive characterization using techniques such as 1H- and 13C-NMR spectroscopy, UV-Vis spectroscopy, and high-resolution mass spectrometry (HRMS). Detailed HRMS analysis was performed for each of these newly created molecules. The biological activities of these compounds were assessed through in vitro experiments to evaluate their potential as anti-inflammatory and antioxidant agents. Furthermore, the lipophilicity of these derivatives was determined, both theoretically using the cLogP method and experimentally through partition coefficient (RM) measurements. To gain insights into their binding affinity, we conducted an in silico analysis of the compounds' interactions with human serum albumin (HSA) using molecular docking studies. Our findings reveal that all of the newly synthesized compounds exhibit significant anti-inflammatory and antioxidant activities, with results statistically comparable to the reference compounds. Molecular docking studies further explain the observed in vitro results, shedding light on the molecular mechanisms behind their biological activities. Using in silico method, toxicity was calculated, resulting in LD50 values. Depending on the administration route, the novel flurbiprofen derivatives show lower toxicity compared to the standard flurbiprofen.

Recent Developments in PET and SPECT Radiotracers as Radiopharmaceuticals for Hypoxia Tumors

Hypoxia, a deficiency in the levels of oxygen, is a common feature of most solid tumors and induces many characteristics of cancer. Hypoxia is associated with metastases and strong resistance to radio- and chemotherapy, and can decrease the accuracy of cancer prognosis. Non-invasive imaging methods such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) using hypoxia-targeting radiopharmaceuticals have been used for the detection and therapy of tumor hypoxia. Nitroimidazoles are bioreducible moieties that can be selectively reduced under hypoxic conditions covalently bind to intracellular macromolecules, and are trapped within hypoxic cells and tissues. Recently, there has been a strong motivation to develop PET and SPECT radiotracers as radiopharmaceuticals containing nitroimidazole moieties for the visualization and treatment of hypoxic tumors. In this review, we summarize the development of some novel PET and SPECT radiotracers as radiopharmaceuticals containing nitroimidazoles, as well as their physicochemical properties, in vitro cellular uptake values, in vivo biodistribution, and PET/SPECT imaging results.

Position estimation using neural networks in semi-monolithic PET detectors

Objective. The goal of this work is to experimentally compare the 3D spatial and energy resolution of a semi-monolithic detector suitable for total-body positron emission tomography (TB-PET) scanners using different surface crystal treatments and silicon photomultiplier (SiPM) models.Approach. An array of 1 × 8 lutetium yttrium oxyorthosilicate (LYSO) slabs of 25.8 × 3.1 × 20 mm³separated with Enhanced Specular Reflector (ESR) was coupled to an array of 8 × 8 SiPMs. Three different treatments for the crystal were evaluated: ESR + RR + B,with lateral faces black (B) painted and a retroreflector (RR) layer added to the top face; ESR +RR, with lateral faces covered with ESR and a RR layer on the top face and; All ESR, with lateral and top sides with ESR. Additionally, two SiPM array models from Hamamatsu Photonics belonging to the series S13361-3050AE-08 (S13) and S14161-3050AS-08 (S14) have been compared. Coincidence data was experimentally acquired using a²²Na point source, a pinhole collimator, a reference detector and moving the detector under study in 1 mm steps in thex- andDOI- directions. The spatial performance was evaluated by implementing a neural network (NN) technique for the impact position estimation in thex- (monolithic) andDOIdirections.Results. Energy resolution values of 16 ± 1%, 11 ± 1%, 16 ± 1%, 15 ± 1%, and 13 ± 1% were obtained for theS13-ESR + B + RR,S13-AllESR,S14-ESR + B + RR,S14-ESR + RR,andS14-AllESR, respectively. Regarding positioning accuracy, mean average error of 1.1 ± 0.5, 1.3 ± 0.5 and 1.3 ± 0.5 were estimated for thex- direction and 1.7 ± 0.8, 2.0 ± 0.9 and 2.2 ± 1.0 for theDOI- direction, for the ESR + B + RR, ESR + RR and All ESR cases, respectively, regardless of the SiPM model.Significance. Overall, the obtained results show that the proposed semi-monolithic detectors are good candidates for building TB-PET scanners.

Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning

18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes.