Written by:
Executive Director of Immuno-Oncology Translational Medicine, AstraZeneca
Director, Tumor Immunobiology, Clinical ICC Discovery Group, Oncology R&D, AstraZeneca
Director, Head of Spatial Science, Translational Medicine, AstraZeneca
At AstraZeneca, we are aiming to advance a diverse oncology pipeline designed to tackle cancer from multiple angles, with immunotherapy representing one important area of focus.
Realising the full potential of immunotherapy requires translational approaches that can better predict how these medicines might work in patients. This includes developing models that better reflect human biology, methods to generate mechanistic insights and tools that can simulate treatment responses. These approaches can help build a more detailed view of how treatments work
How can patient-derived preclinical models potentially improve immunotherapy response prediction?
The tumour microenvironment (TME) is complex and hostile. It contains cells and molecules that actively inhibit immune responses, which can limit the effectiveness of immunotherapies in some patients. Many traditional preclinical models do not fully capture the complexity of this environment and therefore may have limited ability to predict how well a patient may respond to immunotherapy.
At AstraZeneca, we are developing advanced patient-derived preclinical models that better reflect human biology and the complexity of the TME. To create these models, tumour samples collected during routine surgical procedures are processed and cultured in a three-dimensional matrix that helps preserve key features of the tumour architecture. Potential treatments can then be tested and their effects analysed using a range of techniques.
These models can support more personalised treatment strategies by helping to identify potential biomarkers associated with response to immunotherapy. They can also provide insights into the mechanism of action of potential treatments and how they are differentiated. We are using these high-throughput models across our portfolio.
Creating patient-derived ex vivo models
What can Phase 0 studies reveal about potential immunotherapies?
Phase 0 studies are exploratory, mechanistic studies that enable researchers to gather early data about how potential new medicines work in humans. This involves delivering very small doses of a potential therapy directly into a sample of tumour tissue and then analysing how that tissue responds using spatial biology techniques. We’re advancing our Phase 0 capabilities through technology-focused collaborations that enable simultaneous dosing of multiple molecules followed by single-cell analysis of the tumour microenvironment. Phase 0 studies can generate new quantitative insights into mechanisms of action and help identify potential combination partners for immunotherapy. A Phase 0 study can be used to compare multiple novel agents with each other or against the standard of care in a single patient tumour sample, helping generate earlier insight into which approaches have the potential to progress into clinical trials.
Phase 0 studies
Using ‘digital twins’ to help optimise clinical trials
Digital twins are virtual representations of patients built using real-world data, AI and advanced analytics. Imaging, pathology and spatial data are integrated to build models that help simulate how patients with similar characteristics may respond to treatments, including immunotherapies. Digital twins could be used to assess if a new investigational treatment is differentiated from the standard of care, supporting more informed treatment decisions.
Building a digital twin requires access to high-quality, multimodal real-world data that reflect actual patient populations. We’re integrating our own data with external data sets, including through our collaboration with Tempus,1 to support digital twin development. This approach also depends on sophisticated AI models, an area we are further advancing through our strategic partnership with Stanford University.2
Digital twins in oncology
How can translational insights guide immunotherapy clinical development?
At AstraZeneca, generating translational insights across our portfolio to help ensure preclinical findings inform clinical development decisions
Each approach provides different, complementary insights. Together, they can help deepen our understanding of how immunotherapies work, how they are differentiated, and which treatments have the greatest potential to advance into clinical development.
As the science evolves, we aim to extend these translational insights beyond immunotherapy to help design rational combinations where diverse modalities target different aspects of cancer biology. They may also support patient selection by helping identify those most likely to respond.
Our ambition is to use these insights to shape treatment strategies for people living with cancer. This supports our broader Oncology R&D strategy of attacking cancer from multiple angles, treating earlier and smarter, and leading with transformational technologies to potentially improve drug discovery, development and patient care. By generating deeper insights earlier, we hope to advance science into the clinic more efficiently and ultimately help improve outcomes for patients.
