For pharma or biotech researchers working in translational oncology, it’s vital to source the right preclinical cancer model to test their hypothesis and fast, to prevent costly delays to their drug development programme.
Here we discuss the three most common challenges researchers encounter when trying to source preclinical cancer models for their drug development studies.
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1. Searching for models is a time-intensive process
There are several approaches for tracking down a model provider or contract research organisation (CRO) which can provide a cancer model with the appropriate genetic and molecular characteristics for a particular study. These include:
- Reviewing online databases of cancer models held by individual CROs
- Approaching academic collaborators to see if they have developed a suitable model
- Conducting literature research to identify providers with relevant models
- Searching online for new CROs that might have a model of interest
However, with hundreds of specialist CROs and model providers worldwide offering thousands of different preclinical cancer models, scattered across individual databases and hidden in PubMed abstracts, it’s a difficult and time-consuming task for pharma and biotech scientists to find the best available model for their preclinical study. In addition, challenges sourcing models from academic providers are often compounded by post-doctoral researchers moving labs or simply no longer maintaining a model used in older research.
2. Comparing models between providers isn’t straightforward
After hours of research and a promising preclinical model in sight, the next hurdle is gathering the necessary information to confirm the model’s characteristics. Often, models are listed with incomplete or even without supporting data (such as genome profiling, RNA sequencing and proteomics analysis) and therefore additional time is needed to obtain this important information. Not only this but often the data provided by one CRO is not comparable to the data provided by another supplier for the same (or very similar) model due to differences in the analytical approaches used by different companies. As a result, biotech and pharma researchers often cannot directly compare models between providers when deciding which one is best for their project.
3. Keeping up with the latest models and technologies is a struggle
Once a project has begun, researchers might look to include another model further down the line. However, with the translational oncology field rapidly advancing, it is hard for pharma and biotech scientists to keep up with the latest models and technologies on offer across the globe, never mind make an informed decision about which to incorporate into the planning of future preclinical programmes. Therefore, the process of finding and comparing preclinical models has to be repeated all over again to ensure the best available model is sourced, threatening to further delay the progression of preclinical studies.
It has previously been a difficult and daunting task for pharma and biotech researchers to source the right preclinical cancer model for their project – yet this is a critical step in ensuring the success of their preclinical studies and the potential future of their cancer therapy. Overwhelmed by the vast number of models and suppliers across the globe and new models and technologies constantly being developed, scientists can end up selecting a model that isn’t the best one available for their project. Luckily there are now a number of initiatives that are helping to overcome the challenges biopharmas face when sourcing preclinical cancer models.
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