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Quantitative Translation Immuno-Oncology Research Development CIC

Quantitative Translation in Immuno-Oncology Research and Development


Summary

  • Exploring the therapeutic potential of the rapidly expanding field of immuno-oncology (IO) drug targets and diverse therapeutic modalities.
  • Recognizing the need for principled decisions based on biologically sound quantitative translation in a competitive clinical research environment.
  • Highlighting the untapped opportunity in model-informed multidimensional optimization of dose, schedule, combination, and patient population.
  • Emphasizing a Bayesian mindset to model-informed decision-making in early clinical development, utilizing the totality of evidence.

Differentiating features of IO drug development include increased diversity of modalities, greater emphasis on combinations,including optimal sequencing, and patient selection strategies that require multi-dimensional characterization of the tumor micro environment. Additionally, the complexity of human tumor immunology and questionable translatability of exposure response relationships for anti tumor activity from pre-clinical in vivo models (e.g., synergistic mice) to the clinical setting poses challenges for dose/schedule selection for early clinical development. This and other challenges are listed in Figure 1.Although there are many opportunities for quantitative disciplines to address the key translational challenges in IO drug discovery and development, in this Perspective,we highlight two major themes: fit for purpose mechanism-informed modeling of the cancer immunity cycle (CIC), including the relevant mechanisms of action (MoAs)of the investigational treatment and potential combination partners, and novel phase I study designs that are best-suited to the challenges of IO early development. Importantly,we emphasize the need to quantify both efficacy and safety to maximize the therapeutic index of the investigational treatment through optimal selection of dose, schedule,and combinations in context of the underlying mechanisms and patient population.

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Quantitative Translation, Immuno-Oncology, Research, Development, CIC