Original Research| Volume 67, P213-222, November 2016

Phase i trials in melanoma: A framework to translate preclinical findings to the clinic

Published:September 27, 2016DOI:


      • We present a mathematical model driven framework for virtual clinical trials (phase i trials).
      • The phase i trial paradigm: model development, cohort generation, stratification, and optimizing treatment schedules.
      • Phase i trial in melanoma captured the observed heterogeneity of patient responses to treatment.
      • Phase i trial stratified cohort predicts main differentiators of patient survival.
      • Model predicted optimal treatment schedule not only improved survival but also decreased toxicity.



      One of major issues in clinical trials in oncology is their high failure rate, despite the fact that the trials were designed based on the data from successful equivalent preclinical studies. This is in part due to the intrinsic homogeneity of preclinical model systems and the contrasting heterogeneity of actual patient responses.


      We present a mathematical model-driven framework, phase i (virtual/imaginary) trials, that integrates the heterogeneity of actual patient responses and preclinical studies through a cohort of virtual patients. The framework includes an experimentally calibrated mathematical model, a cohort of heterogeneous virtual patients, an assessment of stratification factors, and treatment optimisation. We show the detailed process through the lens of melanoma combination therapy (chemotherapy and an AKT inhibitor), using both preclinical and clinical data.


      The mathematical model predicts melanoma treatment response and resistance to mono and combination therapies and was calibrated and then validated with in vitro experimental data. The validated model and a genetic algorithm were used to generate virtual patients whose tumour volume responses to the combination therapy matched statistically the actual heterogeneous patient responses in the clinical trial. Analyses on simulated cohorts revealed key model parameters such as a tumour volume doubling rate and a therapy-induced phenotypic switch rate that may have clinical correlates. Finally, our approach predicts optimal AKT inhibitor scheduling suggesting more effective but less toxic treatment strategies.


      Our proposed computational framework to implement phase i trials in cancer can readily capture observed heterogeneous clinical outcomes and predict patient survival. Importantly, phase i trials can be used to optimise future clinical trial design.


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