Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy.

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.

Development of an ultrasound-mediated nano-sized drug-delivery system for cancer treatment: from theory to experiment.

Aim: To establish a methodology for understanding how ultrasound (US) induces drug release from nano-sized drug-delivery systems (NSDDSs) and enhances drug penetration and uptake in tumors. This aims to advance cancer treatment strategies. Materials & methods: We developed a multi-physics mathematical model to elucidate and understand the intricate mechanisms governing drug release, transport and delivery. Unique in vitro models (monolayer, multilayer, spheroid) and a tailored US exposure setup were introduced to evaluate drug penetration and uptake. Results: The results highlight the potential advantages of US-mediated NSDDSs over conventional NSDDSs and chemotherapy, notably in enhancing drug release and inducing cell death. Conclusion: Our sophisticated numerical and experimental methods aid in determining and quantifying drug penetration and uptake into solid tumors.

Green synthesis of anti-cancer drug-loaded gold nanoparticles for low-intensity pulsed ultrasound targeted drug release.

In the present work, we have designed a one-pot green protocol in which anti-cancer drugs (curcumin and doxorubicin) can be directly loaded on the surface of gold nanoparticles during their formation. We have further demonstrated that low-intensity pulsed ultrasound (LIPUS) can be used to effectively induce the release of anti-cancer drugs from the surface of gold nanoparticles in an ex vivo tissue model. With this protocol, gold nanoparticles can be easily loaded with different types of anticancer drugs, irrespective of their affinity towards water, and even hydrophobic molecules, like curcumin, can be attached onto the gold nanoparticles in an aqueous medium. The method is very simple and straightforward and does not require stirring or mechanical shaking. The drug molecules interact with the gold seeds formed during the reduction and growth process and modulate the final morphology into a spherical shape. A black-colored colloidal solution of gold nanowire networks is formed in the absence of these anti-cancer drug molecules in the reaction mixture. We used hyperspectral-enhanced dark field microscopy to examine the uptake of gold nanoparticles by breast cancer cells. Upon exposure to LIPUS, the release of the anti-cancer drug from the particle surface can be quantified by fluorescence measurements. This release of drug molecules along with trisodium citrate from the surface of gold nanoparticles by ultrasound resulted in their destabilization and subsequent aggregation, which could be visually observed through the change in the color of colloidal sol. Cancer cell viability was studied by MTT assay to examine the efficacy of this nanoparticle-based drug delivery system. Ultraviolet-visible spectroscopy, dynamic light scattering (DLS), and transmission electron microscope (TEM) analysis were used to characterize the nanoparticles and quantify anti-cancer drug release.

Kinetic modelling of ultrasound-triggered chemotherapeutic drug release from the surface of gold nanoparticles.

Therapeutic ultrasound can be used to trigger the on-demand release of chemotherapeutic drugs from gold nanoparticles (GNPs). In the previous work, our group achieved doxorubicin (DOX) release from the surface of GNPS under low-intensity pulsed ultrasound (LIPUS) exposure. However, the specific release kinetics of ultrasound-triggered DOX release from GNPs is not known. Here, we present a release kinetics study of DOX from GNPs under ultrasound exposure for the first time. A novel dialysis membrane setup was designed to quantify DOX release from LIPUS-activated GNPs at 37.0 °C and 43.4 °C (hyperthermia temperature range). Contributions of thermal and non-thermal mechanisms of LIPUS-triggered DOX release were also quantified. Non-thermal mechanisms accounted for 40 ± 7% and 34 ± 5% of DOX release for 37.0 °C and 43.4 °C trials, respectively. DOX release under LIPUS exposure was found to follow Korsmeyer-Peppas (K-P) kinetics, suggesting a shift from a Fickian (static) to a non-Fickian (dynamic) release profile with the addition of non-thermal interactions. DOX release was attributed to an anomalous diffusion release mechanism from the GNP surface. A finite element model was also developed to quantify the acoustic radiation force, believed to be the driving force of non-thermal DOX release inside the dialysis bag.

Multiphysics Modeling of Low-Intensity Pulsed Ultrasound Induced Chemotherapeutic Drug Release from the Surface of Gold Nanoparticles.

Currently, no numerical model for low-intensity pulsed ultrasound (LIPUS)-triggered anticancer drug release from gold nanoparticle (GNP) drug carriers exists in the literature. In this work, LIPUS-induced doxorubicin (DOX) release from GNPs was achieved in an ex vivo tissue model. Transmission electronic microscopy (TEM) imaging was performed before and after LIPUS exposure, and significant aggregation of the GNPs was observed upon DOX release. Subsequently, GNP surface potential was determined before and after LIPUS-induced DOX release, using a Zetasizer. A numerical model was then created to predict GNP aggregation, and the subsequent DOX release, via combining a thermal field simulation by solving the bioheat transfer equation (in COMSOL) and the Derjaguin, Landau, Verwey, and Overbeek (DLVO) total interaction potential (in MATLAB). The DLVO model was applied to the colloidal DOX-loaded GNPs by summing the attractive van der Waals and electrostatic repulsion interaction potentials for any given GNP pair. DLVO total interaction potential was found before and after LIPUS exposure, and an energy barrier for aggregation was determined. The DLVO interaction potential peak amplitude was found to drop from 1.36 kBT to 0.24 kBT after LIPUS exposure, translating to an 82.4% decrease in peak amplitude value. It was concluded that the interaction potential energy threshold for GNP aggregation (and, as a result, DOX release) was equal to 0.24 kBT.

A spatiotemporal multi-scale computational model for FDG PET imaging at different stages of tumor growth and angiogenesis.

A deeper understanding of the tumor microenvironment (TME) and its role in metabolic activity at different stages of vascularized tumors can provide useful insights into cancer progression and better support clinical assessments. In this study, a robust and comprehensive multi-scale computational model for spatiotemporal transport of F-18 fluorodeoxyglucose (FDG) is developed to incorporate important aspects of the TME, spanning subcellular-, cellular-, and tissue-level scales. Our mathematical model includes biophysiological details, such as radiopharmaceutical transport within interstitial space via convection and diffusion mechanisms, radiopharmaceutical exchange between intracellular and extracellular matrices by glucose transporters, cellular uptake of radiopharmaceutical, as well as its intracellular phosphorylation by the enzyme. Further, to examine the effects of tumor size by varying microvascular densities (MVDs) on FDG dynamics, four different capillary networks are generated by angiogenesis modeling. Results demonstrate that as tumor grows, its MVD increases, and hence, the spatiotemporal distribution of total FDG uptake by tumor tissue changes towards a more homogenous distribution. In addition, spatiotemporal distributions in tumor with lower MVD have relatively smaller magnitudes, due to the lower diffusion rate of FDG as well as lower local intravenous FDG release. Since mean standardized uptake value (SUVmean) differs at various stages of microvascular networks with different tumor sizes, it may be meaningful to normalize the measured values by tumor size and the MVD prior to routine clinical reporting. Overall, the present framework has the potential for more accurate investigation of biological phenomena within TME towards personalized medicine.

Drug delivery through nanoparticles in solid tumors: a mechanistic understanding.

Aim: In this study, the main goal was to apply a multi-scale computational model in evaluating nano-sized drug-delivery systems, following extracellular drug release, into solid tumors in order to predict treatment efficacy. Methods: The impact of several parameters related to tumor (size, shape, vessel-wall pore size, and necrotic core size) and therapeutic agents (size of nanoparticles, binding affinity of drug, drug release rate from nanoparticles) are examined in detail. Results: This study illustrates that achieving a higher treatment efficacy requires smaller nanoparticles (NPs) or a low binding affinity and drug release rate. Long-term analysis finds that a slow release rate in extracellular space does not always improve treatment efficacy compared with a rapid release rate; NP size as well as binding affinity of drug are also highly influential. Conclusion: The presented methodology can be used as a step forward towards optimization of patient-specific nanomedicine plans.

Numerical Methods in Studies of Liquid Crystal Elastomers.

Liquid crystal elastomers (LCEs) are a type of material with specific features of polymers and of liquid crystals. They exhibit interesting behaviors, i.e., they are able to change their physical properties when met with external stimuli, including heat, light, electric, and magnetic fields. This behavior makes LCEs a suitable candidate for a variety of applications, including, but not limited to, artificial muscles, optical devices, microscopy and imaging systems, biosensor devices, and optimization of solar energy collectors. Due to the wide range of applicability, numerical models are needed not only to further our understanding of the underlining mechanics governing LCE behavior, but also to enable the predictive modeling of their behavior under different circumstances for different applications. Given that several mainstream methods are used for LCE modeling, viz. finite element method, Monte Carlo and molecular dynamics, and the growing interest and reliance on computer modeling for predicting the opto-mechanical behavior of complex structures in real world applications, there is a need to gain a better understanding regarding their strengths and weaknesses so that the best method can be utilized for the specific application at hand. Therefore, this investigation aims to not only to present a multitude of examples on numerical studies conducted on LCEs, but also attempts at offering a concise categorization of different methods based on the desired application to act as a guide for current and future research in this field.

Use of microwave ablation for thermal treatment of solid tumors with different shapes and sizes-A computational approach.

Microwave Ablation (MWA) is one of the most recent developments in the field of thermal therapy. This approach is an effective method for thermal tumor ablation by increasing the temperature above the normal physiological threshold to kill cancer cells with minimum side effects to surrounding organs due to rapid heat dispersive tissues. In the present study, the effects of the shape and size of the tumor on MWA are investigated. To obtain the temperature gradient, coupled bio-heat and electromagnetic equations are solved using a three-dimensional finite element method (FEM). To extract cellular response at different temperatures and times, the three-state mathematical model was employed to achieve the ablation zone size. Results show that treatment of larger tumors is more difficult than that of smaller ones. By doubling the diameter of the tumor, the percentage of dead cancer cells is reduced by 20%. For a spherical tumor smaller than 2 cm, applying 50 W input power compared to 25 W has no significant effects on treatment efficiency and only increases the risk of damage to adjacent tissues. However, for tumors larger than 2 cm, it can increase the ablation zone up to 21%. In the spherical and oblate tumors, the mean percentage of dead cells at 6 GHz is nearly 30% higher than that at 2.45GHz, but for prolate tumors, treatment efficacy is reduced by 10% at a higher frequency. Moreover, the distance between two slots in the coaxial double slot antenna is modified based on the best treatment of prolate tumors. The findings of this study can be used to choose the optimum frequency and the best antenna design according to the shape and size of the tumor.

Evaluation of inverse methods for estimation of mechanical parameters in solid tumors.

To treat cancer, knowledge of mechanical parameters can be essential. This study proposes a new approach for estimating hydraulic conductivity (k) and hydraulic conductivity ratio (α) of a living tissue, based on inverse methods, allowing tissue parameter estimation using only a limited set of measurements. First, two population-based algorithms (Levenberg-Marquardt (LM) method and conjugate gradient (CG) method) and two gradient-based algorithms (genetic algorithm (GA) and particle swarm optimization (PSO) algorithm) are considered, and a comparative study between these different inverse methods is performed to determine which methods have a good performance in terms of convergence rate and stability. CG method is shown to perform well in the case of noise-free input data; however, in the case of noisy input data, it fails to converge. The other three methods (LM, GA, and PSO) converge with estimation errors <10% in both noise-free and noisy input data, suggesting their utility to tackle this problem. In the second part, the effectiveness and good accuracy of these robust algorithms (LM, GA, and PSO) are validated with experimental results. The hydraulic conductivity and hydraulic conductivity ratio of a specific living tumor tissue are then estimated for mammary adenocarcinoma (R3230AC). Moreover, assuming measurement of only one-point interstitial pressure inside the tumor, the effect of the location of this one-point on estimation accuracy of hydraulic conductivity is investigated. We show that estimation errors for points measured near the surface and center of the tumor are greater than at other points.

Breast cancer diagnosis with a microwave thermoacoustic imaging technique-a numerical approach.

Microwave-induced thermoacoustic imaging (MITAI) is an imaging technique with great potential for detecting breast cancer at early stages. Thermoacoustic imaging (TAI) combines the advantages of both microwave and ultrasound imaging techniques. In the current study, a three-dimensional novel numerical simulation of TAI phenomenon as a multi-physics problem is investigated. In the computational domain, a biological breast tissue including three different tissue types along with a tumor is placed in a tank containing castor oil and is irradiated by a 2.45-GHz pulsed microwave source from a rectangular waveguide. The generated heat in the biological tissue due to the electromagnetic wave irradiation and its corresponding pressure gradient in the tissue because of the temperature variations are evaluated. Also, capability of the MITAI process with respect to the tumor location and size is investigated. To identify the required power level needed for producing thermoacoustic signals, different power levels of microwave sources are investigated. The study's results demonstrate a minuscule increase in temperature as a result of the absorption of pulsed microwave energy (for example, a maximum of 0.002472 °C temperature increase in tumor with 1 cm diameter which is located in fatty tissue of breast are obtained due to an excitation pulse of 1000 W, 1 ms). This small temperature variation in the tumor produces several kilopascals of pressure variations with maximum of 0.584016 kPa in tumor. This pressure variation will produce acoustic signals, which can be detected with an array of transducers and be used for image construction. Results demonstrate that the location of tumor in breast plays a vital role on the detecting performance of MITAI. Also, it is shown that very small tumors (with the diameter of 0.5 cm) can also be detected using MITAI technique. These simulations and procedures can be used for determining the amount of produced pressure variation, the acoustic pressure magnitude, and other complicated geometries. Graphical abstract A schematic of the thermoacoustic phenomenon.