SYNTHETIC PATIENTS
Cancer (mCSPC), enzalutamide, apalutamide, abiraterone and docetaxel. Interestingly, for some mCSPC patients, docetaxel is given before they become castration-resistant. All of these prior treatments may affect the subsequent outcome of docetaxel treatment for mCRPC. In the nine Phase III docetaxel RCT’s dataset, virtually no patients had been treated with the newer prostate cancer drugs (or docetaxel for mCSPC). Thus, patients from this data resource may be much less comparable/compatible with our needs for building a docetaxel SCA in our study.
Do you feel that synthetic patients will play a pivotal role in future oncology-based trials? Absolutely. I believe that the availability and quality of clinical data repositories – both from clinical trial databases and e-health records – will increase. In addition, the artificial intelligence (AI) tools to construct SCA patients will get better and become more sophisticated. For example, a company like The start-up Unlearn takes this one step further by using its digital twin technology to create fully virtual SCAs composed of digital twin participants ie, they do not use external RWE data sets. And, not
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to forget, our understanding of the biology of the disease is growing and there will be less unknown confounding factors to deal with when composing a virtual patient data set that matches the active trial arm. Therefore, using a SCA in an RCT should be considered in Phase III development planning. In other words, think of designing a single arm trial and replacing the historical control comparison with a robust and validated SCA.
Outside of synthetic patients, is there anything else exciting happening that you feel will bring a big impact to the oncology industry? In my view, that also has to do with the selection of trial patients, but from a different perspective. New phenotyping screening tools – for example organ-on-a-chip technologies – offer the possibility to improve the selection of viable drug candidates in pre-clinical development. These technologies generate patient-derived tumour models that capture the original heterogeneity and micro- environment. Such ‘phenotypic biomarkers’ could subsequently be used to enrich the patient population in clinical trials, enabling truly personalised medicine strategies.
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