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SUMMARY:Phase i trials in cancer: From board to bench to bedside and back 
 again - Alexander Anderson (Moffitt Cancer Center)
DTSTART:20250918T083000Z
DTEND:20250918T093000Z
UID:TALK235183@talks.cam.ac.uk
DESCRIPTION:Our current approach to cancer treatment has been largely driv
 en by finding molecular targets\, those patients fortunate enough to&nbsp\
 ;have a targetable mutation will receive a fixed treatment schedule design
 ed to deliver the maximum tolerated dose (MTD). Cancers are complex evolvi
 ng systems that adapt to&nbsp\;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 tum
 or progression\, metastasis and treatment response is becoming more widely
  accepted. &nbsp\;However\,&nbsp\;MTD treatment strategies continue to dom
 inate the precision oncology landscape. Evolutionary therapy is a new evol
 ution inspired treatment paradigm that seeks to exploit how a cancer evolv
 es under treatment through smart drug dosing and sequencing often informed
  by mathematical modelling. Adaptive therapy is an evolutionary therapy th
 at aims to slow down the emergence of drug resistance by controlling tumor
  burden through competition between drug sensitive and resistant cell popu
 lations. This approach was developed through mathematical model driven ins
 ights and has been shown to work in preclinical animal models (prostate\, 
 ovarian\, melanoma\, breast) and in pilot clinical trials (NCT02415621\; N
 CT05189457\; NCT03543969).\nIn 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\, a
 nd drive Phase i (imaginary) trials. We will highlight how: (i) Mathematic
 al model driven virtual patients can explicitly integrate patient variabil
 ity both in terms of tumor dynamics and treatment response\; (ii) Virtual 
 patients can bridge between bench and bedside\; (iii) Virtual patient coho
 rts can be calibrated from historic clinical data\; (iv) Calibrated virtua
 l patient cohorts can drive Phase i trials\; (v) Phase i trials can drive 
 treatment stratification and optimization\; (vi) Phase i trials can predic
 t novel trial outcomes\; (vii) To best optimize the treatment switch thres
 hold in adaptive therapy\; (viii) Appointment frequency is critical for so
 me patients\; (ix) Robust adaptive therapy is to when patients miss appoin
 tments.
LOCATION:Seminar Room 1\, Newton Institute
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