Modelling drug efficacy: capturing the target engagement of heterogeneous cancer cells

Tumour heterogeneity is well known, leading to therapeutic limitations and failure as cancer cells that exhibit some degree of resistance prevail in the face of treatment, as highlighted by the ultimate failure of non-small cell lung carcinoma therapies with multiple tyrosine kinase inhibitors. These inhibitors antagonise numerous aberrant signalling pathways that have induced comprehensive changes in cell behaviour, including tumour formation. At the level of signalling pathways, the capabilities of a cell to buffer changes induced by external perturbations, such as the (partial) inhibition of tyrosine kinases, is generally complex as highlighted by a study of homeostasis in biochemical cellular networks, in the context of metabolism [1,2]. Thus, using diverse techniques in the mathematical modelling and simulation of dynamical systems together with parameter reduction [3], parameter estimation and model selection studies, our aim will be to generalise theoretical investigations of how cells maintain robust homeostasis for cancer relevant signalling pathways. In particular our objectives will include exploring how biochemical motifs within the signalling pathway of an individual cell may buffer antagonistic perturbations of aberrant pathways and investigating the impact of such mechanisms at the population level, including the parameterisation and predictions of population level Pharmacokinetic-Pharmacodynamic models. For example, to date we have refined a mathematical model capturing the EGFR signaling pathway [4]. A systematic sensitivity analysis has been implemented to explore how model outputs depend on parameters that are of interest. In addition, MATCONT (a graphical MATLAB software package) has been utilized to perform bifurcation and stability analysis of the steady states of the system and there is extensive further interest in signalling via the G protein coupled receptor [5]. More generally, the potential impact of such studies lies in understanding how an insensitivity to treatment, that is resistance, may arise together with the resulting predictions for single and multiple targets aimed at normalising signalling pathway responses. In addition, the novelty of this approach concerns the application of concepts from homeostasis to cancer relevant pathways and their perturbation. The industrial partner of this project is Dr James Yates of GlaxoSmithKline (GSK) and its theoretical study of relatively large signalling pathways falls within the remit of the EPSRC Mathematical Biology and Non-linear Systems research areas. [1] Reed, M., Best, J., Golubitsky, M., Stewart, I., & Nijhout, H. F. (2017). Analysis of Homeostatic Mechanisms in Biochemical Networks. Bulletin of Mathematical Biology, 79(11), 2534-2557. https://doi.org/10.1007/s11538-017-0340-z [2] Watson, E., Chappell, M., Ducrozet, F., Poucher, S., & Yates, J. (2009). A New General Glucose Homeostatic Model using a Proportional-Integral-Derivative Controller. IFAC Proceedings Volumes, 42(12), 79-84. https://doi.org/10.3182/20090812-3-dk-2006.0027 [3] Cheung, S. A., Majid, O., Yates, J. W., & Aarons, L. (2012). Structural identifiability analysis and reparameterisation (parameter reduction) of a cardiovascular feedback model. European Journal of Pharmaceutical Sciences, 46(4), 259-271. https://doi.org/10.1016/j.ejps.2011.12.017 [4] Shvartsman, S. Y., Hagan, M. P., Yacoub, A., Dent, P., Wiley, H. S., & Lauffenburger, D. A. (2002). Autocrine loops with positive feedback enable context-dependent cell signaling. American Journal of Physiology-Cell Physiology, 282(3), C545-C559. https://doi.org/10.1152/ajpcell.00260.2001 [5] Bridge, L., Mead, J., Frattini, E., Winfield, I., & Ladds, G. (2018). Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor. Journal of Theoretical Biology, 442, 44-65. https://doi.org/10.1016/j.jtbi.2018.01.01

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.