A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages

Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori

A Bayesian adaptive design for dual-agent phase I-II cancer clinical trials combining efficacy data across stages

Integrated phase I-II clinical trial designs are efficient approaches to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, two-stage approaches are usually employed. When different patient populations are involved across stages, it is worth of discussion about the use of efficacy data collected from both stages. In this paper, we focus on a two-stage design that aims to estimate safe dose combinations with a certain level of efficacy. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. The maximum tolerated dose (MTD) curve is estimated based on a Bayesian dose-toxicity model. In stage II, we consider an adaptive allocation of patients to drug combinations that have a high probability of being efficacious along the obtained MTD curve. A robust Bayesian hierarchical model is proposed to allow sharing of information on the efficacy parameters across stages assuming the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The proposed methodology is assessed with extensive simulations motivated by a real phase I-II drug combination trial using continuous doses

A robust Bayesian meta-analytic approach to incorporate animal data into phase I oncology trials

Before a first-in-man trial is conducted, preclinical studies are performed in animals to help characterise the safety profile of the new medicine. We propose a robust Bayesian hierarchical model to synthesise animal and human toxicity data, using scaling factors to translate doses administered to different animal species onto an equivalent human scale. After scaling doses, the parameters of dose-toxicity models intrinsic to different animal species can be interpreted on a common scale. A prior distribution is specified for each translation factor to capture uncertainty about differences between toxicity of the drug in animals and humans. Information from animals can then be leveraged to learn about the relationship between dose and risk of toxicity in a new phase I trial in humans. The model allows human dose-toxicity parameters to be exchangeable with the study-specific parameters of animal species studied so far or non-exchangeable with any of them. This leads to robust inferences, enabling the model to give greatest weight to the animal data with parameters most consistent with human parameters or discount all animal data in the case of non exchangeability. The proposed model is illustrated using a case study and simulations. Numerical results suggest that our proposal improves the precision of estimates of the toxicity rates when animal and human data are consistent, while it discounts animal data in cases of inconsistency

Induction of release and up-regulated gene expression of interleukin (IL)-8 in A549 cells by serine proteinases

BACKGROUND: Hypersecretion of cytokines and serine proteinases has been observed in asthma. Since protease-activated receptors (PARs) are receptors of several serine proteinases and airway epithelial cells are a major source of cytokines, the influence of serine proteinases and PARs on interleukin (IL)-8 secretion and gene expression in cultured A549 cells was examined. RESULTS: A549 cells express all four PARs at both protein and mRNA levels as assessed by flow cytometry, immunofluorescence microscopy and reverse transcription polymerase chain reaction (PCR). Thrombin, tryptase, elastase and trypsin induce a up to 8, 4.3, 4.4 and 5.1 fold increase in IL-8 release from A549 cells, respectively following 16 h incubation period. The thrombin, elastase and trypsin induced secretion of IL-8 can be abolished by their specific inhibitors. Agonist peptides of PAR-1, PAR-2 and PAR-4 stimulate up to 15.6, 6.6 and 3.5 fold increase in IL-8 secretion, respectively. Real time PCR shows that IL-8 mRNA is up-regulated by the serine proteinases tested and by agonist peptides of PAR-1 and PAR-2. CONCLUSION: The proteinases, possibly through activation of PARs can stimulate IL-8 release from A549 cells, suggesting that they are likely to contribute to IL-8 related airway inflammatory disorders in man

Bayesian adaptive methods to incorporate preclinical data into phase I clinical trials

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)

Coherent manipulation of spin wave vector for polarization of photons in an atomic ensemble

We experimentally demonstrate the manipulation of two-orthogonal components of a spin wave in an atomic ensemble. Based on Raman two-photon transition and Larmor spin precession induced by magnetic field pulses, the coherent rotations between the two components of the spin wave is controllably achieved. Successively, the two manipulated spin-wave components are mapped into two orthogonal polarized optical emissions, respectively. By measuring Ramsey fringes of the retrieved optical signals, the \pi/2-pulse fidelity of ~96% is obtained. The presented manipulation scheme can be used to build an arbitrary rotation for qubit operations in quantum information processing based on atomic ensembles