Design,Synthesis and Preclinical Assessment of 99mTc-iFAP for In Vivo Fibroblast Activation Protein (FAP) Imaging

Fibroblast activation protein (FAP) is expressed in the microenvironment of most human epithelial tumors. ⁶⁸Ga-labeled FAP inhibitors based on the cyanopyrrolidine structure (FAPI) are currently used for the detection of the tumor microenvironment by PET imaging. This research aimed to design, synthesize and preclinically evaluate a new FAP inhibitor radiopharmaceutical based on the ^99mTc-((R)-1-((6-hydrazinylnicotinoyl)-D-alanyl) pyrrolidin-2-yl) boronic acid (^99mTc-iFAP) structure for SPECT imaging. Molecular docking for affinity calculations was performed using the AutoDock software. The chemical synthesis was based on a series of coupling reactions of 6-hidrazinylnicotinic acid (HYNIC) and D-alanine to a boronic acid derivative. The iFAP was prepared as a lyophilized formulation based on EDDA/SnCl₂ for labeling with ^99mTc. The radiochemical purity (R.P.) was verified via ITLC-SG and reversed-phase radio-HPLC. The stability in human serum was evaluated by size-exclusion HPLC. In vitro cell uptake was assessed using N30 stromal endometrial cells (FAP positive) and human fibroblasts (FAP negative). Biodistribution and tumor uptake were determined in Hep-G2 tumor-bearing nude mice, from which images were acquired using a micro-SPECT/CT. The iFAP ligand (Ki = 0.536 nm, AutoDock affinity), characterized by UV-Vis, FT-IR, ¹H–NMR and UPLC-mass spectroscopies, was synthesized with a chemical purity of 92%. The ^99mTc-iFAP was obtained with a R.P. >98%. In vitro and in vivo studies indicated high radiotracer stability in human serum (>95% at 24 h), specific recognition for FAP, high tumor uptake (7.05 ± 1.13% ID/g at 30 min) and fast kidney elimination. The results found in this research justify additional dosimetric and clinical studies to establish the sensitivity and specificity of the ^99mTc-iFAP.     

A New Series of Aryloxyacetic Acids Endowed with Multi-Target Activity towards Peroxisome Proliferator-Activated Receptors (PPARs), Fatty Acid Amide Hydrolase (FAAH), and Acetylcholinesterase (AChE)

A new series of aryloxyacetic acids was prepared and tested as peroxisome proliferator-activated receptors (PPARs) agonists and fatty acid amide hydrolase (FAAH) inhibitors. Some compounds exhibited an interesting dual activity that has been recently proposed as a new potential therapeutic strategy for the treatment of Alzheimer’s disease (AD). AD is a multifactorial pathology, hence multi-target agents are currently one of the main lines of research for the therapy and prevention of this disease. Given that cholinesterases represent one of the most common targets of recent research, we decided to also evaluate the effects of our compounds on the inhibition of these specific enzymes. Interestingly, two of these compounds, (S)-5 and 6, showed moderate activity against acetylcholinesterase (AChE) and even some activity, although at high concentration, against Aβ peptide aggregation, thus demonstrating, in agreement with the preliminary dockings carried out on the different targets, the feasibility of a simultaneous multi-target activity towards PPARs, FAAH, and AChE. As far as we know, these are the first examples of molecules endowed with this pharmacological profile that might represent a promising line of research for the identification of novel candidates for the treatment of AD.     

Recent Analytical Approaches for the Study of Bioavailability and Metabolism of Bioactive Phenolic Compounds

The study of the bioavailability of bioactive compounds is a fundamental step for the development of applications based on them, such as nutraceuticals, functional foods or cosmeceuticals. It is well-known that these compounds can undergo metabolic reactions before reaching therapeutic targets, which may also affect their bioactivity and possible applications. All recent studies that have focused on bioavailability and metabolism of phenolic and terpenoid compounds have been developed because of the advances in analytical chemistry and metabolomics approaches. The purpose of this review is to show the role of analytical chemistry and metabolomics in this field of knowledge. In this context, the different steps of the analytical chemistry workflow (design study, sample treatment, analytical techniques and data processing) applied in bioavailability and metabolism in vivo studies are detailed, as well as the most relevant results obtained from them.     

Monthly Increase in Vitamin D Levels upon Supplementation with 2000 IU/Day in Healthy Volunteers: Result from “Integriamoci”, a Pilot Pharmacokinetic Study

Vitamin D (VD) is a calcium- and phosphate-controlling hormone used to treat bone disorders; yet, several other effects are progressively emerging. VD deficiency is highly prevalent worldwide, with suboptimal exposure to sunlight listed among the leading causes: oral supplementation with either cholecalciferol or calcitriol is used. However, there is a scarcity of clinical studies investigating how quickly VD concentrations can increase after supplementation. In this pilot study, the commercial supplement ImmuD3 (by Erboristeria Magentina^®) was chosen as the source of VD and 2000 IU/day was administered for one month to 21 healthy volunteers that had not taken any other VD supplements in the previous 30 days. Plasma VD levels were measured through liquid chromatography coupled to tandem mass spectrometry after 7, 14, and 28 days of supplementation. We found that 95% of the participants had insufficient VD levels at baseline (<30 ng/mL; median 23.72 ng/mL; IQR 18.10–26.15), but after 28 days of supplementation, this percentage dropped to 62% (median 28.35 ng/mL; IQR 25.78–35.20). The median increase in VD level was 3.09 ng/mL (IQR 1.60–5.68) after 7 days and 8.85 ng/mL (IQR 2.85–13.97F) after 28 days. This study suggests the need for continuing VD supplementation and for measuring target level attainment.     

Optimization of STEM-HAADF Electron Tomography Reconstructions by Parameter Selection in Compressed Sensing Total Variation Minimization-Based Algorithms

A novel procedure to optimize the 3D morphological characterization of nanomaterials by means of high angle annular dark field scanning-transmission electron tomography is reported and is successfully applied to the analysis of a metal- and halogen-free ordered mesoporous carbon material. The new method is based on a selection of the two parameters (μ and β) which are key in the reconstruction of tomographic series by means of total variation minimization (TVM). The parameter-selected TVM reconstructions obtained using this approach clearly reveal the porous structure of the carbon-based material as consisting of a network of parallel, straight channels of ≈6 nm diameter ordered in a honeycomb-type arrangement. Such an unusual structure cannot be retrieved from a TVM 3D reconstruction using default reconstruction values. Moreover, segmentation and further quantification of the optimized 3D tomographic reconstruction provide values for different textural parameters, such as pore size distribution and specific pore volume that match very closely with those determined by macroscopic physisorption techniques. The approach developed can be extended to other reconstruction models in which the final result is influenced by parameter choice.     

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.