Is DTPA a good competing chelating agent for Th(IV) in human serum and suitable in targeted alpha therapy?

The interaction between thorium and human serum components was studied using difference ultraviolet spectroscopy (DUS), ultrafiltration and high-pressure-anion exchange chromatography (HPAEC) with external inductively conducted plasma mass spectrometry (ICP-MS) analysis. Experimental data are compared with modelling results based on the law of mass action. Human serum transferrin (HSTf) interacts strongly with Th(IV), forming a ternary complex including two synergistic carbonate anions. This complex governs Th(IV) speciation under blood serum conditions. Considering the generally used Langmuir-type model, values of 1033.5 and 1032.5 were obtained for strong and weak sites, respectively. We showed that trace amounts of diethylene triamine pentaacetic acid (DTPA) cannot complex Th(IV) in the blood serum at equilibrium. Unexpectedly this effect is not related to the competition with HSTf but is due to the strong competition with major divalent metal ions for DTPA. However, Th-DTPA complex was shown to be stable for a few hours when it is formed before addition in the biological medium; this is related to the high kinetic stability of the complex. This makes DTPA a potential chelating agent for synthesis of 226Th-labeled biomolecules for application in targeted alpha therapy

Spectroscopic study of the interaction of U(VI) with transferrin and albumin for speciation of U(VI) under blood serum conditions

International audienceThe quantitative description of the interactions of uranium with blood serum components is of high relevance for a rational design of molecules suitable for in vivo chelation of uranium. We have determined the stability constants for the complexation of U(VI) with human serum transferrin and albumin by time-resolved laser-induced fluorescence spectroscopy and difference ultraviolet spectroscopy. Both proteins interact strongly with U(VI), forming ternary complexes with carbonate acting as a synergistic anion. Together with literature data describing the interaction of U(VI) with low molecular weight inorganic and organic serum components, the speciation of U(VI) in blood serum was calculated. In agreement with published experimental data, the model calculation shows that complexation with proteins and carbonate ion governs U(VI) speciation: 35% of U(VI) is bound to proteins and 65% to carbonate. Among the protein pool, albumin is the main protein interacting with U(VI). In addition, the results show that Ca(II) must be considered in the model as a competitive metal ion with respect to U(VI) for binding to albumin surface sites. Based on these findings several promising molecules for in vivo chelation of U-230 could be identified. (C) 2009 Elsevier Inc. All rights reserved

Investigation of para-sulfonatocalix[n]arenes [n = 6, 8] as potential chelates for 230U

International audienceLiterature reports of the efficacy of para-sulfonatocalix[6]- and calix[8]-arenes as U(VI) complexants indicated that they might be useful for in vivo chelation of the novel therapeutic alhpa-emitter 230U. We have studied the complexation of U(VI) with para-sulfonatocalix[6]arene and para-sulfonatocalix[8]arene by time resolved laser induced fluorescence spectroscopy and using competition methods with Chelex resin and 4-(2-pyridylazo)resorcinol in simplified and in biological media. New thermodynamic parameters describing the stability of U(VI)-para-sulfonatocalix[n]arene [n = 6, 8] complexes were obtained. Although the interactions are strong, the complexes do not exhibit sufficient stability to compete with carbonate ions and serum proteins for complexation of U(VI) under physiological conditions

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

In vivo 212Pb/212Bi generator using indium-DTPA-tagged liposomes

International audienceIndium-DTPA-tagged liposomes were studied in the present work as carriers of in vivo 212Pb / 212Bi generator to be used in targeted alpha therapy. The liposomal uptake of 212Pb, into preformed liposomes, was investigated using different lipophilic chelate (DCP, 2,3-dimercapto-1-propanol (BAL), sodium acetate, and A23187), as a function of various parameters (temperature, concentrations of lipids, Pb, DTPA,...) with 212Pb as a tracer. Different formulations of liposomes were tested to evaluate the radiolabeling efficiency. No complexing agent was necessary for the passage of Pb2+ through the membrane. It occurs naturally via a partial permeability of the lipid bilayer which increases with the temperature. A complexing agent (DTPA) appears necessary to concentrate Pb in the internal compartment of the liposomes. Conditions were found (T = 65°C, internal DTPA concentration of 0.025 M, pH 7.4, ...) yielding a high and rapid uptake of 212Pb in liposomes. The protocol established provides a novel method for the efficient entrapment of about 2-3 Pb atoms per liposome with a yield of 75% in conditions relevant for nuclear medicine

Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201

Characterization of At- species in simple and biological media by high performance anion exchange chromatography coupled to gamma detector.

Astatine is a rare radioelement belonging to the halogen group. Considering the trace amounts of astatine produced in cyclotrons, its chemistry cannot be evaluated by spectroscopic tools. Analytical tools, provided that they are coupled with a radioactive detection system, may be an alternative way to study its chemistry. In this research work, High Performance Anion Exchange Chromatography (HPAEC) coupled to a gamma detector (γ) was used to evaluate astatine species under reducing conditions. Also, to strengthen the reliability of the experiments, a quantitative analysis using a reactive transport model has been done. The results confirm the existence of one species bearing one negative charge in the pH range 27.5. With respect to the other halogens, its behavior indicates the existence of negative ion, astatide At-. The methodology was successfully applied to the speciation of the astatine in human serum. Under fixed experimental conditions (pH 7.47.5 and redox potential of 250 mV) astatine exists mainly as astatide At- and does not interact with the major serum components. Also, the method might be useful for the in vitro stability assessment of 211At-labelled molecules potentially applicable in nuclear medicine

Achieving Generalizable Robustness of Deep Neural Networks by Stability Training

We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations including adversarial examples. In our image classification experiments using ImageNet data stability training performs on a par or even outperforms data augmentation for specific transformations, while consistently offering improved robustness against a broader range of distortion strengths and types unseen during training, a considerably smaller hyperparameter dependence and less potentially negative side effects compared to data augmentation.Comment: 18 pages, 25 figures; Camera-ready versio