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Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
Published on: September 30, 2016
Harnessing the predictive power of preclinical models for oncology drug development
Alexander Honkala1, Sanjay V Malhotra1,2, Shivaani Kummar3,4
1Department of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
Abstract:
Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.
Insights
Developing effective oncology drugs requires better preclinical models. Aligning these models with patient tumor biology and using both forward and reverse translation can improve cancer treatment development.
Area of Science:
- Oncology Drug Development
- Translational Cancer Research
Background:
- Advances in molecular oncology and drug development are rapid.
- High costs, long timelines, and attrition rates plague oncology drug development.
- Lack of predictive preclinical models is a major cause of attrition.
Purpose of the Study:
- To review challenges in preclinical-to-clinical translatability of targeted cancer therapies.
- To present concepts and examples of successful reverse translation in oncology.
- To emphasize aligning preclinical models with patient tumor biology.
Main Methods:
- Review of existing literature on preclinical models and translational oncology.
- Analysis of successful reverse translation case studies.
- Discussion of strategies for improving preclinical model relevance.
Main Results:
- Preclinical models often fail to accurately predict clinical outcomes for targeted therapies.
- Successful reverse translation elucidates mechanisms behind clinical observations.
- Better alignment of preclinical models with patient tumor biology is crucial.
Conclusions:
- Improving preclinical model translatability is essential for reducing attrition in oncology drug development.
- Integrating forward and reverse translation enhances the development of effective anticancer treatments.
- Systematic evaluation of preclinical model strengths and weaknesses is needed.
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