Frances Brightman, Eric Fernandez, David Orrell, Christophe Chassagnole
Physiomics plc
Objectives: Drug resistance is a major cause of treatment failure in cancer, and understanding and overcoming mechanisms of resistance is a key challenge in advancing cancer therapy[1]. Although the progression from cytotoxic chemotherapy to drugs aimed at specific molecular targets has improved response rates and reduced adverse effects, in the majority of cases there is still no effective treatment for metastatic disease[2]. Resistance arises from mutations in the genome of cancer cells and/or epigenetic changes[3]. The problem is compounded by considerable intra- and inter-tumour genetic heterogeneity, dictated by the genetic background and history of each cancer cell[2,3]. It is therefore becoming increasingly clear that cancer should be managed through personalized medicine[4], although this is unlikely to be widespread in clinical practice in the immediate future. In the interim, recent studies have shown that the emergence of drug-resistant disease can at least be delayed through treatment with novel dosing regimens[5,6].
Methods: Physiomics has developed a ‘Virtual Tumour’ (VT) technology that can predict how a tumour will respond to drug exposure. The VT technology integrates pharmacokinetic and pharmacodynamic effects, and models the way individual cells behave within a tumour population. These agent-based methods are particularly suitable for modelling multiple cell populations, and representing the heterogeneity of a clinical tumour. Given the significance of cancer drug resistance, and the form that future cancer therapy is likely to take, Physiomics is actively engaged in developing personalized medicine solutions. As a first step, we have incorporated chemotherapeutic resistance into our VT platform.
Results: The VT has been extended by the addition of a resistance module, which has been developed, calibrated and qualified using data taken from the literature[6]. This module captures the fundamental mechanism by which resistance arises. Through a case study also derived from the literature, we demonstrate that the extended VT can be applied to model the emergence of resistance in patient-derived xenografts. Furthermore, we show that the VT can be used to identify and optimize therapeutic strategies for delaying the emergence of drug resistance.
Conclusions: Our enhanced VT capability represents the first step towards a ground-breaking tool for developing personalized treatment, which is set to revolutionize cancer therapy in the near future, especially for patients with resistant disease.
References:
[1] Farrell, A. A close look at cancer. Nat. Med. 17, 262–265 (2011).
[2] Gottesman, M. M. Mechanisms of Cancer Drug Resistance. Annu. Rev. Med. 53, 615–627 (2002).
[3] Rebucci, M. & Michiels, C. Molecular aspects of cancer cell resistance to chemotherapy. Biochem. Pharmacol. 85, 1219–1226 (2013).
[4] Gonzalez de Castro, D., Clarke, P. A., Al-Lazikani, B. & Workman, P. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance. Clin. Pharmacol. Ther. 93, 252–259 (2013).
[5] Chmielecki, J. et al. Optimization of Dosing for EGFR-Mutant Non-Small Cell Lung Cancer with Evolutionary Cancer Modeling. Sci. Transl. Med. 3, 90ra59 (2011).
[6] Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).
Reference: PAGE 25 () Abstr 5964 [www.page-meeting.org/?abstract=5964]
Poster: Drug/Disease modeling - Oncology