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24 Jun 2014

Crown Reveals Strategy for Combined Drug-IR Therapy to Overcome Resistance to Oncology Therapeutics

Crown Bioscience, Inc. has developed treatment combination strategies with image-guided micro-irradiation (IGMI) to overcome drug resistance. These novel strategies at the pre-clinical stage of drug development will improve the ability of predicting success in clinical trials.

 

In the past, irradiation of tumours in animals was hindered by high doses being delivered to healthy tissue, and inaccurate irradiation dose estimations that do not mimic the modern fractionated clinical radiotherapy dosing which uses complex model-based dose calculation algorithms. The small animal radiation research platform (SARRP) from Xstrahl combines X-ray irradiation with CT imaging allowing IGMI similar to that used in the clinic. Crown has demonstrated how combining this IGMI with targeted oncology therapeutics can re-sensitize tumours which have become resistant to treatment.

 

Dr Jean-Pierre Wery, President at Crown Bioscience, said: “Oncological therapeutics have had limited success in the clinic, largely due to intrinsic resistance or acquired drug resistance resulting from repeated exposure to anti-cancer agents. Current irradiation technology used in the clinic is more advanced than that used in the pre-clinical setting, meaning that pre-clinical testing is not representative of results in the clinic. For drug discovery, crude methods of delivering irradiation are being used, and those crude methods do not translate into the clinic.”

 

He continued: “Recent work testing EGFR kinase inhibitors combined with irradiation on acquired resistance models developed in our labs, demonstrates the significant advancements we have made in pre-clinical combination therapies and their potential for success in testing new combination treatments and identifying biomarkers for drug discovery. This technology is superior to other techniques and platforms currently available for drug discovery.”

 

In previously untreated cases with no intrinsic resistance, the use of either therapeutic strategies or irradiation alone causes tumour regression. However, if a tumour has either intrinsic resistance or has been exposed to previous treatments and developed drug resistance, the tumor shows reduced sensitivity.

 

In the case of non-small cell lung cancer (NSCLC), mutations in EGFR are often associated with cancer and malignant tumour growth. Targeted agents such as Iressa and Tarceva effectively treat this type of cancer. However, it is very common for patients to develop acquired drug resistance resulting in relapse. The mechanisms of resistance are being elucidated and relevant biomarkers being identified. In the EGFR inhibitor resistant NSCLC model generated by Crown Bioscience, there is significant amplification of c-met which is a clinically-relevant mechanism of resistance to treatment. When Crizotinib, a c-met inhibitor, was used in combination with IGMI, significant re-sensitizing of the tumor to the inhibitor was observed and the response to the combination was much better than with either treatment alone. The ability to evaluate new as well as old drugs in a clinically relevant manner at the pre-clinical stage allows researchers to position and even re-position their oncology agents in combination with irradiation.

 

Through the use of live models in a novel human surrogate trial platform, HuTrials, which recruits patient-derived xenografts (PDX) in a Phase II-like trial, Crown Bioscience enables researchers to closely mimic cancer in humans in order to test the efficacy of new treatment strategies and compounds as well as identify genetic signatures of responders versus non-responders and ultimately identify predictive biomarkers. In addition, IGMI can be applied to syngeneic models in immune-competent mice to interrogate the synergy with immunotherapies. Crown's continued commitment to provide clinically-relevant models that are widely accessible for drug discovery research is underpinned by its unique platforms and cutting-edge approach.

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