Oral nanotherapeutic formulation of insulin with reduced episodes of hypoglycaemia

Injectable insulin is an extensively used medication with potential life-threatening hypoglycaemic events. Here we report on insulin-conjugated silver sulfide quantum dots coated with a chitosan/glucose polymer to produce a responsive oral insulin nanoformulation. This formulation is pH responsive, is insoluble in acidic environments and shows increased absorption in human duodenum explants and Caenorhabditis elegans at neutral pH. The formulation is sensitive to glucosidase enzymes to trigger insulin release. It is found that the formulation distributes to the liver in mice and rats after oral administration and promotes a dose-dependent reduction in blood glucose without promoting hypoglycaemia or weight gain in diabetic rodents. Non-diabetic baboons also show a dose-dependent reduction in blood glucose. No biochemical or haematological toxicity or adverse events were observed in mice, rats and non-human primates. The formulation demonstrates the potential to orally control blood glucose without hypoglycaemic episodes.

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

Neuromorphic learning, working memory, and metaplasticity in nanowire networks

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed "seven plus or minus two" rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to "synaptic metaplasticity" in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

Nanoparticles for MRI-guided radiation therapy: a review

The development of nanoparticle agents for MRI-guided radiotherapy is growing at an increasing pace, with clinical trials now underway and many pre-clinical evaluation studies ongoing. Gadolinium and iron-oxide-based nanoparticles remain the most clinically advanced nanoparticles to date, although several promising candidates are currently under varying stages of development. Goals of current and future generation nanoparticle-based contrast agents for MRI-guided radiotherapy include achieving positive signal contrast on T1-weighted MRI scans, local radiation enhancement at clinically relevant concentrations and, where applicable, avoidance of uptake by the reticuloendothelial system. Exploiting the enhanced permeability and retention effect or the use of active targeting ligands on nanoparticle surfaces is utilised to promote tumour uptake. This review outlines the current status of promising nanoparticle agents for MRI-guided radiation therapy, including several platforms currently undergoing clinical evaluation or at various stages of the pre-clinical development process. Challenges facing nanoparticle agents and possible avenues for current and future development are discussed.

Opportunities and Challenges for Nanotherapeutics for the Aging Population

Nanotherapeutics utilize the properties of nanomaterials to alter the pharmacology of the drugs and therapies being transported, leading to changes in their biological disposition (absorption, distribution, cellular uptake, metabolism and elimination) and ultimately, their pharmacological effect. This provides an opportunity to optimize the pharmacology of drugs, particularly for those that are dependent on hepatic action. Old age is associated with changes in many pharmacokinetic processes which tend to impair drug efficacy and increase risk of toxicity. While these age-related changes are drug-specific they could be directly addressed using nanotechnology and precision targeting. The benefits of nanotherapeutics needs to be balanced against toxicity, with future use in humans dependent upon the gathering of information about the clearance and long-term safety of nanomaterials.

Avalanches and edge-of-chaos learning in neuromorphic nanowire networks

The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Information dynamics in neuromorphic nanowire networks

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Quantum Dot Nanomedicine Formulations Dramatically Improve Pharmacological Properties and Alter Uptake Pathways of Metformin and Nicotinamide Mononucleotide in Aging Mice

Orally administered Ag₂S quantum dots (QDs) rapidly cross the small intestine and are taken up by the liver. Metformin and nicotinamide mononucleotide (NMN) target metabolic and aging processes within the liver. This study examined the pharmacology and toxicology of QD-based nanomedicines as carriers of metformin and NMN in young and old mice, determining if their therapeutic potency and reduced effects associated with aging could be improved. Pharmacokinetic studies demonstrated that QD-conjugated metformin and NMN have greater bioavailability, with selective accumulation in the liver following oral administration compared to unconjugated formulations. Pharmacodynamic data showed that the QD-conjugated medicines had increased physiological, metabolic, and cellular potency compared to unconjugated formulations (25× metformin; 100× NMN) and highlighted a shift in the peak induction of, and greater metabolic response to, glucose tolerance testing. Two weeks of treatment with low-dose QD-NMN (0.8 mg/kg/day) improved glucose tolerance tests in young (3 months) mice, whereas old (18 and 24 months) mice demonstrated improved fasting and fed insulin levels and insulin resistance. High-dose unconjugated NMN (80 mg/kg/day) demonstrated improvements in young mice but not in old mice. After 100 days of QD (320 μg/kg/day) treatment, there was no evidence of cellular necrosis, fibrosis, inflammation, or accumulation. Ag₂S QD nanomedicines improved the pharmacokinetic and pharmacodynamic properties of metformin and NMN by increasing their therapeutic potency, bypassing classical cellular uptake pathways, and demonstrated efficacy when drug alone was ineffective in aging mice.

Roadmap for metal nanoparticles in radiation therapy: current status, translational challenges, and future directions

This roadmap outlines the potential roles of metallic nanoparticles (MNPs) in the field of radiation therapy. MNPs made up of a wide range of materials (from Titanium, Z = 22, to Bismuth, Z = 83) and a similarly wide spectrum of potential clinical applications, including diagnostic, therapeutic (radiation dose enhancers, hyperthermia inducers, drug delivery vehicles, vaccine adjuvants, photosensitizers, enhancers of immunotherapy) and theranostic (combining both diagnostic and therapeutic), are being fabricated and evaluated. This roadmap covers contributions from experts in these topics summarizing their view of the current status and challenges, as well as expected advancements in technology to address these challenges.

Enhanced MRI-guided radiotherapy with gadolinium-based nanoparticles: preclinical evaluation with an MRI-linac

Background

The AGuIX® (NH TherAguix) nanoparticle has been developed to enhance radiotherapy treatment and provide strong MR contrast. These two properties have previously been investigated separately and progressed to clinical trial following a clinical workflow of separate MR imaging followed some time later by radiotherapy treatment. The recent development of MRI-linacs (combined Magnetic Resonance Imaging–linear accelerator systems enabling MRI-guided radiotherapy) opens up a new workflow where MR confirmation of nanoparticle uptake can be carried out at the time of treatment. A preclinical study was carried out to assess the suitability of a gadolinium-containing nanoparticle AGuIX® (NH TherAguix) for nano-enhanced image-guided radiotherapy on an MRI-linac.

Methods

Treatments were carried out on F344 Fischer rats bearing a 9L glioma brain tumour. Animals received either: (A) no treatment; (B) injection of nanoparticles followed by MRI; (C) radiotherapy with MRI; or (D) injection of nanoparticles followed by radiotherapy with MRI. Pre-clinical irradiations were carried out on the 1.0 T, 6 MV in-line Australian MRI-linac. Imaging used a custom head coil specially designed to minimise interference from the radiotherapy beam. Anaesthetised rats were not restrained during treatment but were monitored with a cine-MRI sequence. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis was used to quantify residual gadolinium in the brain in normal and tumour tissue.

Results

A preclinical evaluation of nano-enhanced radiation treatment has been carried out on a 1.0 T MRI-linac, establishing a workflow on these novel systems. Extension of life when combining radiotherapy with nanoparticles was not statistically different from that for rats receiving radiotherapy only. However, there was no detrimental effect for animals receiving nanoparticles and radiation treatment in the magnetic field compared with control branches. Cine-MR imaging was sufficient to carry out monitoring of anaesthetised animals during treatment. AGuIX nanoparticles demonstrated good positive contrast on the MRI-linac system allowing confirmation of tumour extent and nanoparticle uptake at the time of treatment.

Conclusions

Novel nano-enhanced radiotherapy with gadolinium-containing nanoparticles is ideally suited for implementation on an MRI-linac, allowing a workflow with time-of-treatment imaging. Live irradiations using this treatment workflow, carried out for the first time at the Australian MRI-linac, confirm the safety and feasibility of performing MRI-guided radiotherapy with AGuIX® nanoparticles. Follow-up studies are needed to demonstrate on an MRI-linac the radiation enhancement effects previously shown with conventional radiotherapy.

High-sensitivity in vivo contrast for ultra-low field magnetic resonance imaging using superparamagnetic iron oxide nanoparticles

Magnetic resonance imaging (MRI) scanners operating at ultra-low magnetic fields (ULF; <10 mT) are uniquely positioned to reduce the cost and expand the clinical accessibility of MRI. A fundamental challenge for ULF MRI is obtaining high-contrast images without compromising acquisition sensitivity to the point that scan times become clinically unacceptable. Here, we demonstrate that the high magnetization of superparamagnetic iron oxide nanoparticles (SPIONs) at ULF makes possible relaxivity- and susceptibility-based effects unachievable with conventional contrast agents (CAs). We leverage these effects to acquire high-contrast images of SPIONs in a rat model with ULF MRI using short scan times. This work overcomes a key limitation of ULF MRI by enabling in vivo imaging of biocompatible CAs. These results open a new clinical translation pathway for ULF MRI and have broader implications for disease detection with low-field portable MRI scanners.

Topological Properties of Neuromorphic Nanowire Networks

Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire–node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small–world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.

Rapid Intestinal Uptake and Targeted Delivery to the Liver Endothelium Using Orally Administered Silver Sulfide Quantum Dots

Quantum dots (QDs) are used for imaging and transport of therapeutics. Here we demonstrate rapid absorption across the small intestine and targeted delivery of QDs with bound materials to the liver sinusoidal endothelial cells (LSECs) or hepatocytes in vitro and in vivo following oral administration. QDs were radiolabeled with ³H-oleic acid, with a fluorescent tag or ¹⁴C-metformin placed within a drug binding site. Three different biopolymer shell coatings were compared (formaldehyde-treated serum albumin (FSA), gelatin, heparin). Passage across the small intestine into mesenteric veins is mediated by clathrin endocytosis and micropinocytosis. 60% of an oral dose of QDs was rapidly distributed to the liver within 30 min, and this increased to 85% with FSA biopolymer coating. Uptake into LSECs also increased 3-fold with FSA coating, while uptake into hepatocytes was increased from 40% to 85% with gelatin biopolymer coating. Localization of QDs to LSECs was confirmed with immunofluorescence and transmission electron microscopy. 85% of QDs were cleared within 24 h of administration. The bioavailability of ¹⁴C-metformin 2 h post-ingestion was increased 5-fold by conjugation with QD-FSA, while uptake of metformin into LSECs was improved 50-fold by using these QDs. Endocytosis of QDs by SK-Hep1 cells (an LSEC immortal cell line) was via clathrin- and caveolae-mediated pathways with QDs taken up into lysosomes. In conclusion, we have shown high specificity targeting of the LSEC or hepatocytes after oral administration of QDs coated with a biopolymer layer of FSA or gelatin, which improved the bioavailability and delivery of metformin to LSECs.

A Radio-Nano-Platform for T1/T2 Dual-Mode PET-MR Imaging

Purpose

This study aimed to develop a chelate-free radiolabeled nanoparticle platform for simultaneous positron emission tomography (PET) and magnetic resonance (MR) imaging that provides contrast-enhanced diagnostic imaging and significant image quality gain by integrating the high spatial resolution of MR with the high sensitivity of PET.

Methods

A commercially available super-paramagnetic iron oxide nanoparticle (SPION) (Feraheme^®, FH) was labeled with the [⁸⁹Zr]Zr using a novel chelate-free radiolabeling technique, heat-induced radiolabeling (HIR). Radiochemical yield (RCY) and purity (RCP) were measured using size exclusion chromatography (SEC) and radio-thin layer chromatography (radio-TLC). Characterization of the non-radioactive isotope ⁹⁰Zr-labeled FH was performed by transmission electron microscopy (TEM). Simultaneous PET-MR phantom imaging was performed with different ⁸⁹Zr-FH concentrations. The MR quantitative image analysis determined the contrast-enhancing properties of FH. The signal-to-noise ratio (SNR) and full-width half-maximum (FWHM) of the line spread function (LSF) were calculated before and after co-registering the PET and MR image data.

Results

High RCY (92%) and RCP (98%) of the [⁸⁹Zr]Zr-FH product was achieved. TEM analysis confirmed the ⁹⁰Zr atoms adsorption onto the SPION surface (≈ 10% average radial increase). Simultaneous PET-MR scans confirmed the capability of the [⁸⁹Zr]Zr-FH nano-platform for this multi-modal imaging technique. Relative contrast image analysis showed that [⁸⁹Zr]Zr-FH can act as a dual-mode T1/T2 contrast agent. For co-registered PET-MR images, higher spatial resolution (FWHM enhancement ≈ 3) and SNR (enhancement ≈ 8) was achieved at a clinical dose of radio-isotope and Fe.

Conclusion

Our results demonstrate FH is a highly suitable SPION-based platform for chelate-free labeling of PET tracers for hybrid PET-MR. The high RCY and RCP confirmed the robustness of the chelate-free HIR technique. An overall image quality gain was achieved compared to PET- or MR-alone imaging with a relatively low dosage of [⁸⁹Zr]Zr-FH. Additionally, FH is suitable as a dual-mode T1/T2 MR image contrast agent.

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A Chelate-Free Nano-Platform for Incorporation of Diagnostic and Therapeutic Isotopes

PURPOSE

Using our chelate-free, heat-induced radiolabeling (HIR) method, we show that a wide range of metals, including those with radioactive isotopologues used for diagnostic imaging and radionuclide therapy, bind to the Feraheme (FH) nanoparticle (NP), a drug approved for the treatment of iron anemia.

MATERIAL AND METHODS

FH NPs were heated (120°C) with nonradioactive metals, the resulting metal-FH NPs were characterized by inductively coupled plasma mass spectrometry (ICP-MS), dynamic light scattering (DLS), and r1 and r2 relaxivities obtained by nuclear magnetic relaxation spectrometry (NMRS). In addition, the HIR method was performed with [90Y]Y3+, [177Lu]Lu3+, and [64Cu]Cu2+, the latter with an HIR technique optimized for this isotope. Optimization included modifying reaction time, temperature, and vortex technique. Radiochemical yield (RCY) and purity (RCP) were measured using size exclusion chromatography (SEC) and thin-layer chromatography (TLC).

RESULTS

With ICP-MS, metals incorporated into FH at high efficiency were bismuth, indium, yttrium, lutetium, samarium, terbium and europium (>75% @ 120 oC). Incorporation occurred with a small (less than 20%) but statistically significant increases in size and the r2 relaxivity. An improved HIR technique (faster heating rate and improved vortexing) was developed specifically for copper and used with the HIR technique and [64Cu]Cu2+. Using SEC and TLC analyses with [90Y]Y3+, [177Lu]Lu3+ and [64Cu]Cu2+, RCYs were greater than 85% and RCPs were greater than 95% in all cases.

CONCLUSION

The chelate-free HIR technique for binding metals to FH NPs has been extended to a range of metals with radioisotopes used in therapeutic and diagnostic applications. Cations with f-orbital electrons, more empty d-orbitals, larger radii, and higher positive charges achieved higher values of RCY and RCP in the HIR reaction. The ability to use a simple heating step to bind a wide range of metals to the FH NP, a widely available approved drug, may allow this NP to become a platform for obtaining radiolabeled nanoparticles in many settings.

Emergent dynamics of neuromorphic nanowire networks

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Radio-enhancement effects by radiolabeled nanoparticles

In cancer radiation therapy, dose enhancement by nanoparticles has to date been investigated only for external beam radiotherapy (EBRT). Here, we report on an in silico study of nanoparticle-enhanced radiation damage in the context of internal radionuclide therapy. We demonstrate the proof-of-principle that clinically relevant radiotherapeutic isotopes (i.e. 213Bi, 223Ra, 90Y, 177Lu, 67Cu, 64Cu and 89Zr) labeled to clinically relevant superparamagnetic iron oxide nanoparticles results in enhanced radiation damage effects localized to sub-micron scales. We find that radiation dose can be enhanced by up to 20%, vastly outperforming nanoparticle dose enhancement in conventional EBRT. Our results demonstrate that in addition to the favorable spectral characteristics of the isotopes and their proximity to the nanoparticles, clustering of the nanoparticles results in a nonlinear collective effect that amplifies nanoscale radiation damage effects by electron-mediated inter-nanoparticle interactions. In this way, optimal radio-enhancement is achieved when the inter-nanoparticle distance is less than the mean range of the secondary electrons. For the radioisotopes studied here, this corresponds to inter-nanoparticle distances <50 nm, with the strongest effects within 20 nm. The results of this study suggest that radiolabeled nanoparticles offer a novel and potentially highly effective platform for developing next-generation theranostic strategies for cancer medicine.

Radio-enhancement by gold nanoparticles and their impact on water radiolysis for x-ray, proton and carbon-ion beams

Gold nanoparticle (GNP) radio-enhancement is a promising technique to increase the dose deposition in a tumor while sparing neighboring healthy tissue. Previous experimental studies showed effects on cell survival and tumor control for keV x-rays but surprisingly also for MV-photons, proton and carbon-ion beams. In a systematic study, we use the Monte Carlo simulation tool TOPAS-nBio to model the GNP radio-enhancement within a cell as a function of GNP concentration, size and clustering for a wide range of energies for photons, protons and, for the first time, carbon-ions. Moreover, we include water radiolysis, which has been recognized as a major pathway of GNP mediated radio-enhancement. At a GNP concentration of 0.5% and a GNP diameter of 10 nm, the dose enhancement ratio was highest for 50 keV x-rays (1.36) and decreased in the orthovoltage (1.04 at 250 keV) and megavoltage range (1.01 at 1 MeV). The dose enhancement linearly increased with GNP concentration and decreased with GNP size and degree of clustering for all radiation modalities. While the highest physical dose enhancement at 5% concentrations was only 1.003 for 10 MeV protons and 1.004 for 100 MeV carbon-ions, we find the number of hydroxyl ([Formula: see text]) altered by 23% and 3% after 1 [Formula: see text]s at low, clinically-relevant concentrations. For the same concentration and proton-impact, the G-value is most sensitive to the nanoparticle size with 46 times more radical interactions at GNPs for 2 nm than for 50 nm GNP diameter within 1 [Formula: see text]s. Nanoparticle clustering was found to decrease the number of interactions at GNPs, e.g. for a cluster of 25 GNPs by a factor of 3.4. The changes in G-value correlate to the average distance between the chemical species and the GNPs. While the radiochemistry of GNP-loaded water has yet to be fully understood, this work offers a first relative quantification of radiolysis products for a broad parameter-set.

IMPACT OF NANOPARTICLE CLUSTERING ON DOSE RADIO-ENHANCEMENT

High atomic number nanoparticles (NPs) have been shown to enhance the effects of radiation in vitro and in vivo. However, NPs are often observed to cluster together, leading to inhomogeneous distribution within the tissue and within cells themselves. The effect of this clustering on the capability of NPs to enhance radiation dose has not yet been fully investigated. In this Monte Carlo simulation study, the dependence of radio-enhancement on a separation parameter characterising NP clustering was investigated. A target water cube of side length 100 μm was simulated containing gold NPs constituting ~1% by mass. The NPs were placed in a cubic grid pattern and the separation distance between nanoparticles was varied. For NPs of 100 nm radius widely separated 2 μm apart, 91% of the total energy deposit was found to occur in the surrounding water, compared to only 56% when the NPs were moved closer together to 0.2 μm. The remaining energy deposit was absorbed by the NPs themselves. A similar trend was observed for NPs of radius 50 nm. The clustering effect was found to persist to greater separations for the larger NPs. The proportion of energy deposit in the available water of the target impacts the potential for cellular damage. Energy deposited within nanoparticles is unlikely to cause biological damage, as ionisations in the surrounding water are required to create radical oxygen species which then progress to cause the biological response to radiation. Clustering of nanoparticles is therefore expected to decrease their effectiveness for enhancing radiotherapy.

A New Standard DNA Damage (SDD) Data Format

Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

Comparison of radiobiological parameters for 90Y radionuclide therapy (RNT) and external beam radiotherapy (EBRT) in vitro

Background

Dose rate variation is a critical factor affecting radionuclide therapy (RNT) efficacy. Relatively few studies to date have investigated the dose rate effect in RNT. Therefore, the aim of this study was to benchmark ⁹⁰Y RNT (at different dose rates) against external beam radiotherapy (EBRT) in vitro and compare cell kill responses between the two irradiation processes.

Results

Three human colorectal carcinoma (CRC) cell lines (HT29, HCT116, SW48) were exposed to ⁹⁰Y doses in the ranges 1–10.4 and 6.2–62.3 Gy with initial dose rates of 0.013–0.13 Gy/hr (low dose rate, LDR) and 0.077–0.77 Gy/hr (high dose rate, HDR), respectively. Results were compared to a 6-MV photon beam doses in the range from 1–9 Gy with constant dose rate of 277 Gy/hr. The cell survival parameters from the linear quadratic (LQ) model were determined. Additionally, Monte Carlo simulations were performed to calculate the average dose, dose rate and the number of hits in the cell nucleus.

For the HT29 cell line, which was the most radioresistant, the α/β ratio was found to be ≈ 31 for HDR–⁹⁰Y and ≈ 3.5 for EBRT. LDR–⁹⁰Y resulting in insignificant cell death compared to HDR–⁹⁰Y and EBRT. Simulation results also showed for LDR–⁹⁰Y, for doses ≲ 3 Gy, the average number of hits per cell nucleus is ≲ 2 indicating insufficiently delivered lethal dose. For ⁹⁰Y doses \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\gtrsim $\end{document}≳ 3 Gy the number of hits per nucleus decreases rapidly and falls below ≈ 2 after ≈ 5 days of incubation time. Therefore, our results demonstrate that LDR–⁹⁰Y is radiobiologically less effective than EBRT. However, HDR–⁹⁰Y at ≈ 56 Gy was found to be radiobiologically as effective as acute ≈ 8 Gy EBRT.

Conclusion

These results demonstrate that the efficacy of RNT is dependent on the initial dose rate at which radiation is delivered. Therefore, for a relatively long half-life radionuclide such as ⁹⁰Y, a higher initial activity is required to achieve an outcome as effective as EBRT.

A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes

Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found > 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level ‘trans-omic’ approach when studying time-resolved responses to cellular perturbations.

Nanoparticle radio-enhancement: principles, progress and application to cancer treatment

Enhancement of radiation effects by high-atomic number nanoparticles (NPs) has been increasingly studied for its potential to improve radiotherapeutic efficacy. The underlying principle of NP radio-enhancement is the potential to release copious electrons into a nanoscale volume, thereby amplifying radiation-induced biological damage. While the vast majority of studies to date have focused on gold nanoparticles with photon radiation, an increasing number of experimental, theoretical and simulation studies have explored opportunities offered by other NPs (e.g. gadolinium, platinum, iron oxide, hafnium) and other therapeutic radiation sources such as ion beams. It is thus of interest to the research community to consolidate findings from the different studies and summarise progress to date, as well as to identify strategies that offer promising opportunities for clinical translation. This is the purpose of this Topical Review.

ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data

Transcription factors (TFs) play a fundamental role in coordinating biological processes in response to stimuli. Consequently, we often seek to determine the key TFs and their regulated target genes (TGs) amidst gene expression data. This requires a knowledge-base of TF-TG interactions, which would enable us to determine the topology of the transcriptional network and predict novel regulatory interactions. To address this, we generated an Open-access Repository of Transcriptional Interactions, ORTI, by integrating available TF-TG interaction databases. These databases rely on different types of experimental evidence, including low-throughput assays, high-throughput screens, and bioinformatics predictions. We have subsequently categorised TF-TG interactions in ORTI according to the quality of this evidence. To demonstrate its capabilities, we applied ORTI to gene expression data and identified modulated TFs using an enrichment analysis. Combining this with pairwise TF-TG interactions enabled us to visualise temporal regulation of a transcriptional network. Additionally, ORTI enables the prediction of novel TF-TG interactions, based on how well candidate genes co-express with known TGs of the target TF. By filtering out known TF-TG interactions that are unlikely to occur within the experimental context, this analysis predicts context-specific TF-TG interactions. We show that this can be applied to experimental designs of varying complexities. In conclusion, ORTI is a rich and publicly available database of experimentally validated mammalian transcriptional interactions which is accompanied with tools that can identify and predict transcriptional interactions, serving as a useful resource for unravelling the topology of transcriptional networks.

Elucidating the Activation Mechanism of the Insulin-Family Proteins with Molecular Dynamics Simulations

The insulin-family proteins bind to their own receptors, but insulin-like growth factor II (IGF-II) can also bind to the A isoform of the insulin receptor (IR-A), activating unique and alternative signaling pathways from those of insulin. Although extensive studies of insulin have revealed that its activation is associated with the opening of the B chain-C terminal (BC-CT), the activation mechanism of the insulin-like growth factors (IGFs) still remains unknown. Here, we present the first comprehensive study of the insulin-family proteins comparing their activation process and mechanism using molecular dynamics simulations to reveal new insights into their specificity to the insulin receptor. We have found that all the proteins appear to exhibit similar stochastic dynamics in their conformational change to an active state. For the IGFs, our simulations show that activation involves two opening locations: the opening of the BC-CT section away from the core, similar to insulin; and the additional opening of the BC-CT section away from the C domain. Furthermore, we have found that these two openings occur simultaneously in IGF-I, but not in IGF-II, where they can occur independently. This suggests that the BC-CT section and the C domain behave as a unified domain in IGF-I, but as two independent domains in IGF-II during the activation process, implying that the IGFs undergo different activation mechanisms for receptor binding. The probabilities of the active and inactive states of the proteins suggest that IGF-II is hyperactive compared to IGF-I. The hinge residue and the hydrophobic interactions in the core are found to play a critical role in the stability and activity of IGFs. Overall, our simulations have elucidated the crucial differences and similarities in the activation mechanisms of the insulin-family proteins, providing new insights into the molecular mechanisms responsible for the observed differences between IGF-I and IGF-II in receptor binding.

Dose enhancement effects to the nucleus and mitochondria from gold nanoparticles in the cytosol

Gold nanoparticles (GNPs) have shown potential as dose enhancers for radiation therapy. Since damage to the genome affects the viability of a cell, it is generally assumed that GNPs have to localise within the cell nucleus. In practice, however, GNPs tend to localise in the cytoplasm yet still appear to have a dose enhancing effect on the cell. Whether this effect can be attributed to stress-induced biological mechanisms or to physical damage to extra-nuclear cellular targets is still unclear. There is however growing evidence to suggest that the cellular response to radiation can also be influenced by indirect processes induced when the nucleus is not directly targeted by radiation. The mitochondrion in particular may be an effective extra-nuclear radiation target given its many important functional roles in the cell. To more accurately predict the physical effect of radiation within different cell organelles, we measured the full chemical composition of a whole human lymphocytic JURKAT cell as well as two separate organelles; the cell nucleus and the mitochondrion. The experimental measurements found that all three biological materials had similar ionisation energies  ∼70 eV, substantially lower than that of liquid water  ∼78 eV. Monte Carlo simulations for 10-50 keV incident photons showed higher energy deposition and ionisation numbers in the cell and organelle materials compared to liquid water. Adding a 1% mass fraction of gold to each material increased the energy deposition by a factor of  ∼1.8 when averaged over all incident photon energies. Simulations of a realistic compartmentalised cell show that the presence of gold in the cytosol increases the energy deposition in the mitochondrial volume more than within the nuclear volume. We find this is due to sub-micron delocalisation of energy by photoelectrons, making the mitochondria a potentially viable indirect radiation target for GNPs that localise to the cytosol.

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

A1 Highlights from the eleventh ISCB Student Council Symposium 2015

Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman

O1 Prioritizing a drug’s targets using both gene expression and structural similarity

Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau

O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms

Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha

O3 Detection of Heterogeneity in Single Particle Tracking Trajectories

Paddy J Slator, Nigel J Burroughs

O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture

Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski

O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification

Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici

O6 A Systems Biology Compendium for Leishmania donovani

Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens

O7 Unravelling signal coordination from large scale phosphorylation kinetic data

Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

Correction for human head motion in helical x-ray CT

Correction for rigid object motion in helical CT can be achieved by reconstructing from a modified source-detector orbit, determined by the object motion during the scan. This ensures that all projections are consistent, but it does not guarantee that the projections are complete in the sense of being sufficient for exact reconstruction. We have previously shown with phantom measurements that motion-corrected helical CT scans can suffer from data-insufficiency, in particular for severe motions and at high pitch. To study whether such data-insufficiency artefacts could also affect the motion-corrected CT images of patients undergoing head CT scans, we used an optical motion tracking system to record the head movements of 10 healthy volunteers while they executed each of the 4 different types of motion ('no', slight, moderate and severe) for 60 s. From these data we simulated 354 motion-affected CT scans of a voxelized human head phantom and reconstructed them with and without motion correction. For each simulation, motion-corrected (MC) images were compared with the motion-free reference, by visual inspection and with quantitative similarity metrics. Motion correction improved similarity metrics in all simulations. Of the 270 simulations performed with moderate or less motion, only 2 resulted in visible residual artefacts in the MC images. The maximum range of motion in these simulations would encompass that encountered in the vast majority of clinical scans. With severe motion, residual artefacts were observed in about 60% of the simulations. We also evaluated a new method of mapping local data sufficiency based on the degree to which Tuy's condition is locally satisfied, and observed that areas with high Tuy values corresponded to the locations of residual artefacts in the MC images. We conclude that our method can provide accurate and artefact-free MC images with most types of head motion likely to be encountered in CT imaging, provided that the motion can be accurately determined.

Unraveling the mechanistic complexity of Alzheimer's disease through systems biology

Alzheimer's disease (AD) is a complex, multifactorial disease that has reached global epidemic proportions. The challenge remains to fully identify its underlying molecular mechanisms that will enable development of accurate diagnostic tools and therapeutics. Conventional experimental approaches that target individual or small sets of genes or proteins may overlook important parts of the regulatory network, which limits the opportunity of identifying multitarget interventions. Our perspective is that a more complete insight into potential treatment options for AD will only be made possible through studying the disease as a system. We propose an integrative systems biology approach that we argue has been largely untapped in AD research. We present key publications to demonstrate the value of this approach and discuss the potential to intensify research efforts in AD through transdisciplinary collaboration. We highlight challenges and opportunities for significant breakthroughs that could be made if a systems biology approach is fully exploited.

Molecular Dynamics Simulations of Insulin: Elucidating the Conformational Changes that Enable Its Binding

A sequence of complex conformational changes is required for insulin to bind to the insulin receptor. Recent experimental evidence points to the B chain C-terminal (BC-CT) as the location of these changes in insulin. Here, we present molecular dynamics simulations of insulin that reveal new insights into the structural changes occurring in the BC-CT. We find three key results: 1) The opening of the BC-CT is inherently stochastic and progresses through an open and then a “wide-open” conformation—the wide-open conformation is essential for receptor binding, but occurs only rarely. 2) The BC-CT opens with a zipper-like mechanism, with a hinge at the Phe24 residue, and is maintained in the dominant closed/inactive state by hydrophobic interactions of the neighboring Tyr26, the critical residue where opening of the BC-CT (activation of insulin) is initiated. 3) The mutation Y26N is a potential candidate as a therapeutic insulin analogue. Overall, our results suggest that the binding of insulin to its receptor is a highly dynamic and stochastic process, where initial docking occurs in an open conformation and full binding is facilitated through interactions of insulin receptor residues with insulin in its wide-open conformation.

Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy

The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2-9.9 mm, which was 0.4-2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.

Advances in Computational Radiation Biophysics for Cancer Therapy: Simulating Nano-Scale Damage by Low-Energy Electrons

Computational radiation biophysics is a rapidly growing area that is contributing, alongside new hardware technologies, to ongoing developments in cancer imaging and therapy. Recent advances in theoretical and computational modeling have enabled the simulation of discrete, event-by-event interactions of very low energy (≪ 100 eV) electrons with water in its liquid thermodynamic phase. This represents a significant advance in our ability to investigate the initial stages of radiation induced biological damage at the molecular level. Such studies are important for the development of novel cancer treatment strategies, an example of which is given by microbeam radiation therapy (MRT). Here, new results are shown demonstrating that when excitations and ionizations are resolved down to nano-scales, their distribution extends well outside the primary microbeam path, into regions that are not directly irradiated. This suggests that radiation dose alone is insufficient to fully quantify biological damage. These results also suggest that the radiation cross-fire may be an important clue to understanding the different observed responses of healthy cells and tumor cells to MRT.

A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems

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We propose the dQSSA model as a novel way of modelling complex biological networks.

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No low enzyme concentration assumption, covering more biological settings.

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Reduces the number of parameters, which simplifies optimisation.

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dQSSA was validated both in silico and in vitro.

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Both biochemical and signalling pathways can be modelled accurately and simply.

Quasi steady-state enzyme kinetic models are increasingly used in systems modelling. The Michaelis Menten model is popular due to its reduced parameter dimensionality, but its low-enzyme and irreversibility assumption may not always be valid in the in vivo context. Whilst the total quasi-steady state assumption (tQSSA) model eliminates the reactant stationary assumptions, its mathematical complexity is increased. Here, we propose the differential quasi-steady state approximation (dQSSA) kinetic model, which expresses the differential equations as a linear algebraic equation. It eliminates the reactant stationary assumptions of the Michaelis Menten model without increasing model dimensionality. The dQSSA was found to be easily adaptable for reversible enzyme kinetic systems with complex topologies and to predict behaviour consistent with mass action kinetics in silico. Additionally, the dQSSA was able to predict coenzyme inhibition in the reversible lactate dehydrogenase enzyme, which the Michaelis Menten model failed to do. Whilst the dQSSA does not account for the physical and thermodynamic interactions of all intermediate enzyme-substrate complex states, it is proposed to be suitable for modelling complex enzyme mediated biochemical systems. This is due to its simpler application, reduced parameter dimensionality and improved accuracy.

The cytoplasm as a radiation target: an in silico study of microbeam cell irradiation

We performed in silico microbeam cell irradiation modelling to quantitatively investigate ionisations resulting from soft x-ray and alpha particle microbeams targeting the cytoplasm of a realistic cell model. Our results on the spatial distribution of ionisations show that as x-rays are susceptible to scatter within a cell that can lead to ionisations in the nucleus, soft x-ray microbeams may not be suitable for investigating the DNA damage response to radiation targeting the cytoplasm alone. In contrast, ionisations from an ideal alpha microbeam are tightly confined to the cytoplasm, but a realistic alpha microbeam degrades upon interaction with components upstream of the cellular target. Thus it is difficult to completely rule out a contribution from alpha particle hits to the nucleus when investigating DNA damage response to cytoplasmic irradiation. We find that although the cytoplasm targeting efficiency of an alpha microbeam is better than that of a soft x-ray microbeam (the probability of stray alphas hitting the nucleus is 0.2% compared to 3.6% for x-rays), stray alphas produce more ionisations in the nucleus and thus have greater potential for initiating damage responses therein. Our results suggest that observed biological responses to cytoplasmic irradiation include a small component that can be attributed to stray ionisations in the nucleus resulting from the stochastic nature of particle interactions that cause out-of-beam scatter. This contribution is difficult to isolate experimentally, thus demonstrating the value of the in silico approach.

Issues involved in the quantitative 3D imaging of proton doses using optical CT and chemical dosimeters

Dosimetry of proton beams using 3D imaging of chemical dosimeters is complicated by a variation with proton linear energy transfer (LET) of the dose-response (the so-called 'quenching effect'). Simple theoretical arguments lead to the conclusion that the total absorbed dose from multiple irradiations with different LETs cannot be uniquely determined from post-irradiation imaging measurements on the dosimeter. Thus, a direct inversion of the imaging data is not possible and the proposition is made to use a forward model based on appropriate output from a planning system to predict the 3D response of the dosimeter. In addition to the quenching effect, it is well known that chemical dosimeters have a non-linear response at high doses. To the best of our knowledge it has not yet been determined how this phenomenon is affected by LET. The implications for dosimetry of a number of potential scenarios are examined.Dosimeter response as a function of depth (and hence LET) was measured for four samples of the radiochromic plastic PRESAGE(®), using an optical computed tomography readout and entrance doses of 2.0 Gy, 4.0 Gy, 7.8 Gy and 14.7 Gy, respectively. The dosimeter response was separated into two components, a single-exponential low-LET response and a LET-dependent quenching. For the particular formulation of PRESAGE(®) used, deviations from linearity of the dosimeter response became significant for doses above approximately 16 Gy. In a second experiment, three samples were each irradiated with two separate beams of 4 Gy in various different configurations. On the basis of the previous characterizations, two different models were tested for the calculation of the combined quenching effect from two contributions with different LETs. It was concluded that a linear superposition model with separate calculation of the quenching for each irradiation did not match the measured result where two beams overlapped. A second model, which used the concept of an 'effective dose' matched the experimental results more closely. An attempt was made to measure directly the quench function for two proton beams as a function of all four variables of interest (two physical doses and two LET values). However, this approach was not successful because of limitations in the response of the scanner.