Bayesian adaptive methods to incorporate preclinical data into phase I
      clinical trials
Creators: name: Zheng, Haiyan
Publisher: name: Lancaster University
Year: 2019

description: Basing informed decisions on available, relevant information is essential
      in all phases of drug development. This is particularly true in early
      phase clinical trials, when our knowledge about toxicity of a new medicine
      remains limited. Thus, borrowing of information across seemingly disparate
      sources is appealing. Statistical literature has been written about
      augmenting a new clinical trial with data from historical studies designed
      for similar investigational purpose. But very few has looked into
      leveraging preclinical data into phase I first-in-man trials.
      
      The work presented in this thesis attempts to fill the gap by providing
      solutions in the Bayesian paradigm, with purposes of improving the design
      and analysis of adaptive phase I dose-escalation trials. Specifically, our
      focus is on the transition step of early drug development, where phase I
      clinical trials are preceded only with some preclinical information. We
      see preclinical data as a special type of historical data, say, historical
      animal data. This is not an obvious application of the existing approaches
      for data augmentation, since information collected from preclinical
      studies first needs to be translated to account for potential
      physiological differences between animals and humans. Furthermore, due to
      their intrinsic variabilities in drug metabolism, inconsistency between
      the translated preclinical and clinical data may still emerge however
      careful and correct the interspecies translation would be completed. We
      note this thesis will exclusively consider toxicity data, assuming that
      relationship between dose and risk of toxicity can be adequately described
      using a two-parameter logistic regression model.
      
      Grounded in Bayesian statistics, our idea is to represent preclinical data
      into a prior distribution for the dose-toxicity model parameters that
      underpin the human trial(s). Our aim is to propose robust Bayesian
      approaches, keeping in mind the possibility that toxicity in humans could
      be very different from what we have learnt in one or multiple animal
      species even after appropriate translation. The main challenge in
      statistical inferences is essentially to address issues of prior-data
      conflict emerging in a small trial.
      
      This thesis consists of two perspectives on the robust use of preclinical
      animal data. A “sensible” amount of animal data to be leveraged into the
      phase I human trial(s) is determined by either (i) assessing the
      commensurability of the prior predictions of human toxicity, which are
      obtained using animal data alone, with the observed toxicity outcomes from
      the ongoing trial(s), or (ii) fitting a hierarchical model with weakly
      informative priors placed on the variance parameters. Correspondingly, we
      have proposed a Bayesian decision-theoretic approach in Chapter 2 and a
      robust Bayesian hierarchical model in Chapter 3, which build the core of
      this thesis. We have also extended the Bayesian hierarchical model to
      address potential heterogeneity between patient groups in Chapter 4, where
      the methodology has been illustrated in the context of bridging strategies
      considered in phase I clinical trials planned in various geographic
      regions.
      
      Throughout, the proposed Bayesian adaptive methods have been elucidated
      with representative data examples and extensive simulations. Particular
      attention has been paid to balancing the information from different
      sources to draw robust inferences. Numerical results show that our
      proposals have desired properties. More specifically, preclinical data can
      be essentially discounted when they are in fact inconsistent with the
      toxicity in humans. In cases of consistency, benefits are seen as
      increased precision of estimate of the probability of toxicity at a range
      of doses, and higher proportion of patients allocated to the target
      dose(s).