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

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.This research has been supported by grants awarded to VMPG by James S. Mc. Donnell Foundation, United States of America, 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (collaborative award 220020560) and Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/17/180501/000154). VMPG and GFC thank the funding from Ministerio de Ciencia e Innovacion, Spain (grant number PID2019-110895RB-I00). This research has also been supported by a grant awarded to GFC and JBB by the Junta de Comunidades de Castilla-La Mancha, Spain (grant number SBPLY/19/180501/000211). AMR received support from Asociacion Pablo Ugarte (http://www.asociacionpablougarte.es). JJS received support from Universidad de Castilla-La Mancha (grant number 2020-PREDUCLM-15634). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Whither Magnetic Hyperthermia? A Tentative Roadmap

The scientific community has made great efforts in advancing magnetic hyperthermia for the last two decades after going through a sizeable research lapse from its establishment. All the progress made in various topics ranging from nanoparticle synthesis to biocompatibilization and in vivo testing have been seeking to push the forefront towards some new clinical trials. As many, they did not go at the expected pace. Today, fruitful international cooperation and the wisdom gain after a careful analysis of the lessons learned from seminal clinical trials allow us to have a future with better guarantees for a more definitive takeoff of this genuine nanotherapy against cancer. Deliberately giving prominence to a number of critical aspects, this opinion review offers a blend of state-of-the-art hints and glimpses into the future of the therapy, considering the expected evolution of science and technology behind magnetic hyperthermia

Whither Magnetic Hyperthermia? A Tentative Roadmap

The scientific community has made great efforts in advancing magnetic hyperthermia for the last two decades after going through a sizeable research lapse from its establishment. All the progress made in various topics ranging from nanoparticle synthesis to biocompatibilization and in vivo testing have been seeking to push the forefront towards some new clinical trials. As many, they did not go at the expected pace. Today, fruitful international cooperation and the wisdom gain after a careful analysis of the lessons learned from seminal clinical trials allow us to have a future with better guarantees for a more definitive takeoff of this genuine nanotherapy against cancer. Deliberately giving prominence to a number of critical aspects, this opinion review offers a blend of state-of-the-art hints and glimpses into the future of the therapy, considering the expected evolution of science and technology behind magnetic hyperthermia.This work was supported by the NoCanTher project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 685795. The authors acknowledge support from the COST Association through the COST actions "RADIOMAG" (TD1402) and "MyWAVE" (CA17115). D.O., A.S.-O. and I.R.-R. acknowledge financial support from the Community of Madrid under Contracts No. PEJD-2017-PRE/IND-3663 and PEJ-2018-AI/IND-11069, from the Spanish Ministry of Science through the Ramon y Cajal grant RYC2018-025253-I and Research Networks RED2018-102626-T, as well as the Ministry of Economy and Competitiveness through the grants MAT2017-85617-R, MAT2017-88148R and the "Severo Ochoa" Program for Centers of Excellence in R&D (SEV-2016-0686). M.B. and N.T.K.T. would like to thank EPSRC for funding (grant EP/K038656/1 and EP/M015157/1) and AOARD (FA2386-171-4042) award. This work was additionally supported by the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program, grant no. 16NRM04 "MagNaStand". The work was further supported by the DFG grant CRC "Matrix in Vision" (SFB 1340/1 2018, no 372486779, project A02)

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?

The Oxford English Dictionary includes 17 definitions for the word “model” as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, “model railways” refer to replicas of railways and trains at a smaller scale and a “model student” refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, “model” can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different “models” of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word “model” related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used “models”, the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use