Efficient full Monte Carlo modelling and multi-energy generative model development of an advanced X-ray device.

Monte Carlo (MC) simulations of X-ray image devices require splitting the simulation into two parts (i.e. the generation of x-rays and the actual imaging). The X-ray production remains unchanged for repeated imaging and can thus be stored in phase space (PhS) files and used for subsequent MC simulations. Especially for medical images these dedicated PhS files require a large amount of data storage, which is partly why Generative Adversarial Networks (GANs) were recently introduced. We enhanced the approach by a conditional GAN to model multiple energies using one network. This study compares the use of PhSs, GANs, and conditional GANs as photon source with measurements. An X-ray -based imaging system (i.e. ImagingRing) was modelled in this study. half-value layers (HVLs), focal spot, and Heel effect were measured for subsequent comparison. MC simulations were performed with GATE-RTion v1.0 considering the geometry and materials of the imaging system with vendor specific schematics. A traditional GAN model as well as the favourable conditional GAN was implemented for PhS generation. Results of the MC simulation were in agreement with the measurements regarding HVL, focal spot, and Heel effect. The conditional GAN performed best with a non-saturated loss function with R1 regularisation and gave similarly results as the traditional GAN approach. GANs proved to be superior to the PhS approach in terms of data storage and calculation overhead. Moreover, a conditional GAN enabled an energy interpolation to separate the network training process from the final required X-ray energies.

New approaches for the reliable in vitro assessment of binding affinity based on high-resolution real-time data acquisition of radioligand-receptor binding kinetics.

BACKGROUND: Resolving the kinetic mechanisms of biomolecular interactions have become increasingly important in early-phase drug development. Since traditional in vitro methods belong to dose-dependent assessments, binding kinetics is usually overlooked. The present study aimed at the establishment of two novel experimental approaches for the assessment of binding affinity of both, radiolabelled and non-labelled compounds targeting the A3R, based on high-resolution real-time data acquisition of radioligand-receptor binding kinetics. A novel time-resolved competition assay was developed and applied to determine the Ki of eight different A3R antagonists, using CHO-K1 cells stably expressing the hA3R. In addition, a new kinetic real-time cell-binding approach was established to quantify the rate constants k on and k off, as well as the dedicated K d of the A3R agonist [125I]-AB-MECA. Furthermore, lipophilicity measurements were conducted to control influences due to physicochemical properties of the used compounds. RESULTS: Two novel real-time cell-binding approaches were successfully developed and established. Both experimental procedures were found to visualize the kinetic binding characteristics with high spatial and temporal resolution, resulting in reliable affinity values, which are in good agreement with values previously reported with traditional methods. Taking into account the lipophilicity of the A3R antagonists, no influences on the experimental performance and the resulting affinity were investigated. CONCLUSIONS: Both kinetic binding approaches comprise tracer administration and subsequent binding to living cells, expressing the dedicated target protein. Therefore, the experiments resemble better the true in vivo physiological conditions and provide important markers of cellular feedback and biological response.