A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours

Effect of Surface Properties of Chitosan-Based Nanoparticles in the Skin-Diffusion Rate

Skin diseases may cause rash, inflammation, itchiness, and other important skin changes, including dysplasia. Some skin conditions may be due to genetic and lifestyle factors and immune-mediated factors. The current skin disease treatment can include oral medication, topical cream, or ointments. Nanotechnology is revolutionizing the drug delivery systems, increasing the time life of active therapeutic compounds and improving the treatment efficiency. This work hypothesizes that varying the surface properties of chitosan nanoparticles (Ch-NPs) can modulate their diffusion through dermal tissue. Thus, Ch-NPs were synthesized, and their surface was modified with polyethylene glycol, oxalic acid, and linoleic acid for transdermal therapy. The different Ch-NPs were labeled with a fluorophore, and the dermal diffusion was measured on human skin by histological preparations and fluorescent microscopy. The surface properties of nanoparticles were shown to play an essential role in skin diffusion rate. Surface modification with a lipophilic moiety such as linoleic fatty acid showed a diffusion rate of 7.23 mm2/h in human full-thickness abdominal flap, which is 2.7 times faster nanoparticle diffusion through dermal tissue when compared with the unmodified Ch-NPs (2.92 mm2/h). The positive (zeta potential +27.5 mV) or negative (zeta potential -2.2 mV) surface charge does not affect the chitosan nanoparticle diffusion. Polyethylene glycol surface modification slightly improved the nanoparticle diffusion rate (3.63 mm2/h). Thus, modulating the nanoparticle surface properties can control the skin diffusion rate. The implications of this finding on dermic drug delivery are discussed.

In-silico tool based on Boolean networks and meshless simulations for prediction of reaction and transport mechanisms in the systemic administration of chemotherapeutic drugs

Using in-house computational tools, this work focuses on investigating how the combination of the electric field magnitude (E), bloodstream velocity (λinl) and pharmaco-kinetic profile (PK) impacts the reaction and transport mechanisms of drug (RTMs) arising in electro-chemotherapeutic treatments. The first step implies retrieving the ratios between extracellular, free intracellular, and bound intracellular concentrations from numerical simulations, employing a meshless code developed, calibrated and validated in a previous work. Subsequently, a Boolean model is developed to determine the presence, interaction and rates of RTMs based on the comparison of the spatio-temporal evolution of the drug concentration ratios, being this the main contribution of the present work to the comprehension of the phenomena involved in the systemic administration of chemotherapeutic drugs in cancer tumors. Different combinations of E (0 kV/m, 46 kV/m, 70 kV/m), λinl (1x10-4m/s, 1x10-3m/s, 1x10-2m/s) and PK (One-short tri-exponential, mono-exponential) are examined. In general, results show that both the presence and relative importance of RTMs can differ between both PKs for a given combination of E and λinl. Additionally, for a given PK, radial uniformity of transmembrane transport rate is aversively affected by the increase of E and λinl, whereas radial homogeneity of association/dissociation rate is monotonously affected only by E. Regarding the axial uniformity of transmembrane transport rate, this is benefited by the increase of λinl and, in a lower extent, by the reduction of E.

Influence of electroporation parameters on the reaction and transport mechanisms in electro-chemotherapeutic treatments using Boolean modeling and the Method of Fundamental Solutions

The systemic administration of chemotherapeutic drugs involves some reaction and transport mechanisms (RTMs), including perfusion along the blood vessels, extravasation, lymphatic drainage, interstitial and transmembrane transport, and protein association and dissociation, among others. When tissue is subjected to the controlled application of electric pulses (electroporation), the vessel wall and cell membrane are permeabilized, capillaries are vasoconstricted and tissue porosity is modified, affecting the RTMs during electro-chemotherapeutic treatments. This study is a theoretical investigation about the influence of the electric field magnitude (E), number of electroporation treatments (Nep) and duration of each electroporation protocol (Tep) on the presence, interaction and rates of the RTMs using in-house computational tools. Firstly, the ratios between the extracellular, free intracellular and bound intracellular concentrations are calculated by solving the species conservation equations of a tumor cord domain by the Method of Fundamental Solutions (MFS), which was implemented, calibrated and validated in a previous work. Then, a Boolean model, which is founded on the comparison of the spatio-temporal evolution of concentration ratios, is proposed here to explore the interaction between RTMs. Different combinations of E=[0kV/m,46kV/m,70kV/m], Nep=[6,8,12] and Tep=[5min,10min,15min] are tested here. The MFS results indicate that Nep and Tep do not have a relevant influence on the types and relative importance of RTMs, but only on the rates of these mechanisms. In general, increasing E reduces the radial uniformity of transmembrane transport and association rates regarding the non-electroporated tissue, whereas the axial uniformity is affected in a lower extent.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Numerical investigation of the pharmacokinetics and pharmacodynamics of the chemotherapeutic drug in avascular and vascular stages of a brain tumor

One of the most commonly used approaches for treating solid tumors is the systemic delivery of chemotherapeutic drugs. However, our understanding of the factors influencing treatment efficacy through this method is still limited. This study presents a comprehensive and realistic mathematical model that incorporates the dynamics of tumor growth, capillary network extension, and drug delivery in a coupled and simultaneous manner. The model covers two stages of tumor growth: avascular and vascular. For tumor growth, a continuum model is employed using the phase field interface-capturing method. The neo-vascularization process is modeled using a hybrid discrete-continuum approach. Additionally, a multi-scale model is used to describe the pharmacokinetics of doxorubicin, considering various agents. The study investigates the effect of haptotaxis and reveals that a higher haptotaxis coefficient leads to faster tumor growth (up to 2.6 times) and a quicker progression to angiogenesis. The impact of tumor-related and drug-related parameters is also examined, including tumor size, tumor sensitivity to the drug, chemotherapy initialization, treatment cycle duration, drug affinity to cells, and drug dose. The findings indicate that chemotherapy is more effective during the angiogenesis stage when active loops have formed. Other clinical methods such as radiotherapy and surgery may be more appropriate during the avascular stage or the transition period between angiogenesis initialization and loop formation. The penetration depth of the drug decreases by approximately 50% with an increase in the drug binding rate to surface-cell receptors. As a result, high-associate-rate drugs are preferred for chemotherapy after active loops have formed, while low-associate-rate drugs are suitable for earlier stages.

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

Every sixth person in the world dies due to cancer, making it the second leading severe cause of death after cardiovascular diseases. According to WHO, cancer claimed nearly 10 million deaths in 2020. The most common types of cancers reported have been breast (lung, colon and rectum, prostate cases), skin (non-melanoma) and stomach. In addition to surgery, the most widely used traditional types of anti-cancer treatment are radio- and chemotherapy. However, these do not distinguish between normal and malignant cells. Additional treatment methods have evolved over time for early detection and targeted therapy of cancer. However, each method has its limitations and the associated treatment costs are quite high with adverse effects on the quality of life of patients. Use of individual atoms or a cluster of atoms (nanoparticles) can cause a paradigm shift by virtue of providing point of sight sensing and diagnosis of cancer. Nanoparticles (1-100 nm in size) are 1000 times smaller in size than the human cell and endowed with safer relocation capability to attack mechanically and chemically at a precise location which is one avenue that can be used to destroy cancer cells precisely. This review summarises the extant understanding and the work done in this area to pave the way for physicians to accelerate the use of hybrid mode of treatments by leveraging the use of various nanoparticles.

Computational Simulation of Tumor-Induced Angiogenesis

Cancer cells require higher oxygen levels and nutrition than normal cells. Cancer cells induce angiogenesis (the development of new blood vessels) from preexisting vessels. This biological process depends on the special, chemical, and physical properties of the microenvironment surrounding tumor tissues. The complexity of these properties hinders an understanding of their mechanisms. Various mathematical models have been developed to describe quantitative relationships related to angiogenesis. We developed a three-dimensional mathematical model that incorporates angiogenesis and tumor growth. We examined angiopoietin, which regulates the spouting and branching events in angiogenesis. The simulation successfully reproduced the transient decrease in new vessels during vascular network formation. This chapter describes the protocol used to perform the simulations.

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking, DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.

Combined anti-angiogenic and cytotoxic treatment of a solid tumour: In silico investigation of a xenograft animal model's digital twin

Anti-angiogenic (AA) treatments have received significant research interest due to the key role of angiogenesis in cancer progression. AA agents can have a strong effect on cancer regression, by blocking new vessels and reducing the density of the existing vasculature. Moreover, in a process termed vascular normalisation, AA drugs can improve the abnormal structure and function of the tumour vasculature, enhancing the delivery of chemotherapeutics to the tumour site. Despite their promising potential, an improved understanding of AA treatments is necessary to optimise their administration as a monotherapy or in combination with other cancer treatments. In this work we present an in silico multiscale cancer model which is used to systematically interrogate the role of individual mechanisms of action of AA drugs in tumour regression. Focus is placed on the reduction of vascular density and on vascular normalisation through a parametric study, which are considered either as monotherapies or in combination with conventional/metronomic chemotherapy. The model is specified to data from a mammary carcinoma xenograft in immunodeficient mice, to enhance the physiological relevance of model predictions. Our results suggest that conventional chemotherapy might be more beneficial when combined with AA treatments, hindering tumour growth without causing excessive damage on healthy tissue. Notably, metronomic chemotherapy has shown significant potential in stopping tumour growth with minimal toxicity, even as a monotherapy. Our findings underpin the potential of our in silico framework for non-invasive and cost-effective evaluation of treatment strategies, which can enhance our understanding of combined therapeutic strategies and contribute towards improving cancer treatment management.

Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment

Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element formulation is utilized which employs a brain model representation from clinical imaging data and is used to predict the drug deposition in CED regimes. The model encompasses biofluid dynamics and the transport of drugs in the brain parenchyma. Drug distribution is studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Through a parametric study, our contribution reports the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.

Microdissected Tissue vs Tissue Slices-A Comparative Study of Tumor Explant Models Cultured On-Chip and Off-Chip

Predicting patient responses to anticancer drugs is a major challenge both at the drug development stage and during cancer treatment. Tumor explant culture platforms (TECPs) preserve the native tissue architecture and are well-suited for drug response assays. However, tissue longevity in these models is relatively low. Several methodologies have been developed to address this issue, although no study has compared their efficacy in a controlled fashion. We investigated the effect of two variables in TECPs, specifically, the tissue size and culture vessel on tissue survival using micro-dissected tumor tissue (MDT) and tissue slices which were cultured in microfluidic chips and plastic well plates. Tumor models were produced from ovarian and prostate cancer cell line xenografts and were matched in terms of the specimen, total volume of tissue, and respective volume of medium in each culture system. We examined morphology, viability, and hypoxia in the various tumor models. Our observations suggest that the viability and proliferative capacity of MDTs were not affected during the time course of the experiments. In contrast, tissue slices had reduced proliferation and showed increased cell death and hypoxia under both culture conditions. Tissue slices cultured in microfluidic devices had a lower degree of hypoxia compared to those in 96-well plates. Globally, our results show that tissue slices have lower survival rates compared to MDTs due to inherent diffusion limitations, and that microfluidic devices may decrease hypoxia in tumor models.

Enhanced Drug Delivery to Solid Tumors via Drug-Loaded Nanocarriers: An Image-Based Computational Framework

Objective

Nano-sized drug delivery systems (NSDDSs) offer a promising therapeutic technology with sufficient biocompatibility, stability, and drug-loading rates towards efficient drug delivery to solid tumors. We aim to apply a multi-scale computational model for evaluating drug delivery to predict treatment efficacy.

Methodology

Three strategies for drug delivery, namely conventional chemotherapy (one-stage), as well as chemotherapy through two- and three-stage NSDDSs, were simulated and compared. A geometric model of the tumor and the capillary network was obtained by processing a real image. Subsequently, equations related to intravascular and interstitial flows as well as drug transport in tissue were solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. Finally, the role of periodic treatments was investigated considering tumor recurrence between treatments. The impact of different parameters, nanoparticle (NP) size, binding affinity of drug, and the kinetics of release rate, were additionally investigated to determine their therapeutic efficacy.

Results

Using NPs considerably increases the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy. Tumoral FKCs for two-stage DDS with smaller NP size (20nm) is higher than that of larger NPs (100nm), in all investigate release rates. Slower and continuous release of the chemotherapeutic agents from NPs have better treatment outcomes in comparison with faster release rate. In three-stage DDS, for intermediate and higher binding affinities, it is desirable for the secondary particle to be released at a faster rate, and the drug with slower rate. In lower binding affinities, high release rates have better performance. Results also demonstrate that after 5 treatments with three-stage DDS, 99.6% of tumor cells (TCs) are killed, while two-stage DDS and conventional chemotherapy kill 95.6% and 88.5% of tumor cells in the same period, respectively.

Conclusion

The presented framework has the potential to enable decision making for new drugs via computational modeling of treatment responses and has the potential to aid oncologists with personalized treatment plans towards more optimal treatment outcomes.

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

Graphene Oxide Nanosheets Interact and Interfere with SARS-CoV-2 Surface Proteins and Cell Receptors to Inhibit Infectivity

Nanotechnology can offer a number of options against coronavirus disease 2019 (COVID-19) acting both extracellularly and intracellularly to the host cells. Here, the aim is to explore graphene oxide (GO), the most studied 2D nanomaterial in biomedical applications, as a nanoscale platform for interaction with SARS-CoV-2. Molecular docking analyses of GO sheets on interaction with three different structures: SARS-CoV-2 viral spike (open state - 6VYB or closed state - 6VXX), ACE2 (1R42), and the ACE2-bound spike complex (6M0J) are performed. GO shows high affinity for the surface of all three structures (6M0J, 6VYB and 6VXX). When binding affinities and involved bonding types are compared, GO interacts more strongly with the spike or ACE2, compared to 6M0J. Infection experiments using infectious viral particles from four different clades as classified by Global Initiative on Sharing all Influenza Data (GISAID), are performed for validation purposes. Thin, biological-grade GO nanoscale (few hundred nanometers in lateral dimension) sheets are able to significantly reduce copies for three different viral clades. This data has demonstrated that GO sheets have the capacity to interact with SARS-CoV-2 surface components and disrupt infectivity even in the presence of any mutations on the viral spike. GO nanosheets are proposed to be further explored as a nanoscale platform for development of antiviral strategies against COVID-19.

State-of-the-Art of Nanodiagnostics and Nanotherapeutics against SARS-CoV-2

The pandemic outbreak of SARS-CoV-2, with millions of infected patients worldwide, has severely challenged all aspects of public health. In this regard, early and rapid detection of infected cases and providing effective therapeutics against the virus are in urgent demand. Along with conventional clinical protocols, nanomaterial-based diagnostics and therapeutics hold a great potential against coronavirus disease 2019 (COVID-19). Indeed, nanoparticles with their outstanding characteristics would render additional advantages to the current approaches for rapid and accurate diagnosis and also developing prophylactic vaccines or antiviral therapeutics. In this review, besides presenting an overview of the coronaviruses and SARS-CoV-2, we discuss the introduced nanomaterial-based detection assays and devices and also antiviral formulations and vaccines for coronaviruses.

Mathematical simulation of tumour angiogenesis: angiopoietin balance is a key factor in vessel growth and regression

Excessive tumour growth results in a hypoxic environment around cancer cells, thus inducing tumour angiogenesis, which refers to the generation of new blood vessels from pre-existing vessels. This mechanism is biologically and physically complex, with various mathematical simulation models proposing to reproduce its formation. However, although temporary vessel regression is clinically known, few models succeed in reproducing this phenomenon. Here, we developed a three-dimensional simulation model encompassing both angiogenesis and tumour growth, specifically including angiopoietin. Angiopoietin regulates both adhesion and migration between vascular endothelial cells and wall cells, thus inhibiting the cell-to-cell adhesion required for angiogenesis initiation. Simulation results showed a regression, i.e. transient decrease, in the overall length of new vessels during vascular network formation. Using our model, we also evaluated the efficacy of administering the drug bevacizumab. The results highlighted differences in treatment efficacy: (1) earlier administration showed higher efficacy in inhibiting tumour growth, and (2) efficacy depended on the treatment interval even with the administration of the same dose. After thorough validation in the future, these results will contribute to the design of angiogenesis treatment protocols.

Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic

The COVID-19 outbreak has fueled a global demand for effective diagnosis and treatment as well as mitigation of the spread of infection, all through large-scale approaches such as specific alternative antiviral methods and classical disinfection protocols. Based on an abundance of engineered materials identifiable by their useful physicochemical properties through versatile chemical functionalization, nanotechnology offers a number of approaches to cope with this emergency. Here, through a multidisciplinary Perspective encompassing diverse fields such as virology, biology, medicine, engineering, chemistry, materials science, and computational science, we outline how nanotechnology-based strategies can support the fight against COVID-19, as well as infectious diseases in general, including future pandemics. Considering what we know so far about the life cycle of the virus, we envision key steps where nanotechnology could counter the disease. First, nanoparticles (NPs) can offer alternative methods to classical disinfection protocols used in healthcare settings, thanks to their intrinsic antipathogenic properties and/or their ability to inactivate viruses, bacteria, fungi, or yeasts either photothermally or via photocatalysis-induced reactive oxygen species (ROS) generation. Nanotechnology tools to inactivate SARS-CoV-2 in patients could also be explored. In this case, nanomaterials could be used to deliver drugs to the pulmonary system to inhibit interaction between angiotensin-converting enzyme 2 (ACE2) receptors and viral S protein. Moreover, the concept of "nanoimmunity by design" can help us to design materials for immune modulation, either stimulating or suppressing the immune response, which would find applications in the context of vaccine development for SARS-CoV-2 or in counteracting the cytokine storm, respectively. In addition to disease prevention and therapeutic potential, nanotechnology has important roles in diagnostics, with potential to support the development of simple, fast, and cost-effective nanotechnology-based assays to monitor the presence of SARS-CoV-2 and related biomarkers. In summary, nanotechnology is critical in counteracting COVID-19 and will be vital when preparing for future pandemics.

Drug delivery to solid tumors with heterogeneous microvascular networks: Novel insights from image-based numerical modeling

The present study examines chemotherapy by incorporating multi-scale mathematical modeling to predict drug delivery and its effects. This approach leads to a more-realistic physiological tumor model than is possible with previous approaches, as it obtains the capillary network geometry from an image, and also considers the tumor's necrotic core, drug binding, and cellular uptake. Modeling of the fluid flow and drug transport is then performed in the extracellular matrix. The results demonstrate a 10% drop in the fraction of killed cancer cells 69% rather than the 79% reported earlier for a tumor of similar geometry a more-accurate value. This study examines how tumor-related parameters including the necrotic core size and tumor size, and also drug-related parameters drug dosage, binding affinity of drug, and drug degradation can affect the delivery of the drug to solid tumors. Results indicate that concentration of drug are high in the tumor, low in normal tissue, and remarkably low in the necrotic core. Results also offer a treatment of tumors with smaller necrotic core. Tumor size, which implies the tumor progression, has a considerable impact on treatment outcomes, so to be more effective, treatment should be applied at a specific size of tumor. It is demonstrated that binding affinity of drugs to cell-surface receptors and drug dosage have significant impact on treatment efficacy, so they should be regulated based on a balanced quantification between maximum treatment efficacy and minimum side effects. On the other hand, considering the effects of drug degradation in the model has not significant effect on treatment efficacy. The findings of the present study provide insight into the mechanism of drug delivery to solid tumors based on analyzing the effective parameters and modeling how their behavior in the tumor microenvironment affects treatment efficacy.

From tumour perfusion to drug delivery and clinical translation of in silico cancer models

In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.

An in silico hybrid continuum-/agent-based procedure to modelling cancer development: Interrogating the interplay amongst glioma invasion, vascularity and necrosis

This paper develops a three-dimensional in silico hybrid model of cancer, which describes the multi-variate phenotypic behaviour of tumour and host cells. The model encompasses the role of cell migration and adhesion, the influence of the extracellular matrix, the effects of oxygen and nutrient availability, and the signalling triggered by chemical cues and growth factors. The proposed in silico hybrid modelling framework combines successfully the advantages of continuum-based and discrete methods, namely the finite element and agent-based method respectively. The framework is thus used to realistically model cancer mechano-biology in a multiscale fashion while maintaining the resolution power of each method in a computationally cost-effective manner. The model is tailored to simulate glioma progression, and is subsequently used to interrogate the balance between the host cells and small sized gliomas, while the go-or-grow phenotype characteristic in glioblastomas is also investigated. Also, cell-cell and cell-matrix interactions are examined with respect to their effect in (macroscopic) tumour growth, brain tissue perfusion and tumour necrosis. Finally, we use the in silico framework to assess differences between low-grade and high-grade glioma growth, demonstrating significant differences in the distribution of cancer as well as host cells, in accordance with reported experimental findings.

Multi-resolution simulations of intracellular processes

This is an introduction to the Thematic Issue of Interface Focus containing papers by speakers of the Theo Murphy International Scientific Meeting on ‘Multi-resolution simulations of intracellular processes’