Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Methods. 2021 Jan;185:82-93. doi: 10.1016/j.ymeth.2020.02.010. Epub 2020 Mar 5.
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.
计算机癌症模型已被证明是一种很有前途的工具，可以改进药物设计，优化药物在宿主组织中的靶向部位的传递，从而提高治疗效果和患者预后。然而，将计算机技术从实验室成功转化为临床应用还存在重大障碍。更确切地说，未知模型参数的规范、模型必须充分反映体内条件以及评估模型准确性和可靠性所需的相关验证数据的有限数量，这些都是其临床转化的主要障碍。本文旨在综述血管化实体瘤生长的计算机癌症建模的最新进展，并确定这些模型在肿瘤临床中的重要进展和成功障碍。本文特别强调了基于连续体的癌症模型，因为在机制时空建模方法中，它们在模拟传输现象和组织生物力学方面具有良好的基础，并且已经显示出了临床转化的潜力。本文考虑了计算机建模的三个重要方面：首先，由于系统治疗是癌症的主要治疗方法，因此我们首先概述了肿瘤灌注和血管生成的计算机模型。接下来，我们介绍了目前通过血流将化学治疗药物递送至癌症纳米药物的计算机工作的最新进展，然后回顾了基于连续体的建模方法，这些方法为成功的临床转化展示了巨大的潜力。最后，我们讨论了我们认为个性化和精准医学中计算机建模的关键挑战和机遇。
