Modelling the Emergence of Resistance to Chemotherapeutics with Virtual Tumour


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Frances Brightman1,Eric Fernandez1,David Orrell1,Christophe Chassganole1
1Physiomics plc

Abstract

Background

Understanding and overcoming mechanisms of drug resistance is a key challenge in advancing cancer therapy: in the majority of cases there is still no effective treatment for metastatic disease. Resistance arises from mutations in the genome of cancer cells and/or epigenetic changes. The problem is compounded by intra- and inter-tumour genetic heterogeneity. It is therefore becoming increasingly clear that cancer should be managed through personalized medicine, and recent studies have shown that the emergence of drug-resistant disease can at least be delayed through treatment with novel dosing regimens.

Method

Physiomics has developed a ‘Virtual Tumour’ (VT) technology that can predict how a tumour will respond to drug exposure. The VT integrates pharmacokinetic and pharmacodynamic effects, and models the way individual cells behave within a tumour population. These agent-based methods are particularly suitable for 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. 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 melanoma xenografts. Furthermore, we show that the VT can be used to identify and optimize therapeutic strategies for delaying the emergence of drug resistance.

Conclusion

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.