AUTHOR=Ogilvie Lesley A. , Kovachev Aleksandra , Wierling Christoph , Lange Bodo M. H. , Lehrach Hans 

TITLE=Models of Models: A Translational Route for Cancer Treatment and Drug Development

JOURNAL=Frontiers in Oncology

VOLUME=Volume 7 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2017.00219

DOI=10.3389/fonc.2017.00219

ISSN=2234-943X

ABSTRACT=Every patient and every disease is different. Each patient therefore requires a personalised treatment approach. For technical reasons, a personalised approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalised drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro and in vivo models in preclinical development could lead to significant gains in efficiency and cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using ‘models of models’ has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way highly optimised computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer treatment and drug development, from enhancing experimental design, reducing costs and improving animal welfare, to increasing the translational value of results generated.