Phase i trials in cancer: From board to bench to bedside and back again

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• Alexander Anderson (Moffitt Cancer Center)
• Thursday 18 September 2025, 09:30-10:30
• Seminar Room 1, Newton Institute.

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OOEW07 - Mathematical Foundations of Oncological Digital Twins

Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). Cancers are complex evolving systems that adapt to therapeutic intervention through a suite of resistance mechanisms, therefore whilst MTD therapies generally achieve impressive short-term responses, they unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression, metastasis and treatment response is becoming more widely accepted.  However, MTD treatment strategies continue to dominate the precision oncology landscape. Evolutionary therapy is a new evolution inspired treatment paradigm that seeks to exploit how a cancer evolves under treatment through smart drug dosing and sequencing often informed by mathematical modelling. Adaptive therapy is an evolutionary therapy that aims to slow down the emergence of drug resistance by controlling tumor burden through competition between drug sensitive and resistant cell populations. This approach was developed through mathematical model driven insights and has been shown to work in preclinical animal models (prostate, ovarian, melanoma, breast) and in pilot clinical trials (NCT02415621; NCT05189457 ; NCT03543969). In this talk we will discuss how mathematical models based on differential equations and deep reinforcement learning can be used to optimize treatment strategies, including adaptive therapy, and drive Phase i (imaginary) trials. We will highlight how: (i) Mathematical model driven virtual patients can explicitly integrate patient variability both in terms of tumor dynamics and treatment response; (ii) Virtual patients can bridge between bench and bedside; (iii) Virtual patient cohorts can be calibrated from historic clinical data; (iv) Calibrated virtual patient cohorts can drive Phase i trials; (v) Phase i trials can drive treatment stratification and optimization; (vi) Phase i trials can predict novel trial outcomes; (vii) To best optimize the treatment switch threshold in adaptive therapy; (viii) Appointment frequency is critical for some patients; (ix) Robust adaptive therapy is to when patients miss appointments.

This talk is part of the Isaac Newton Institute Seminar Series series.

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