The impact of dose escalation and resistance modulation in older patients with acute myeloid leukaemia and high risk myelodysplastic syndrome: the results of the LRF AML14 trial.:the results of the LRF AML14 trial

The acute myeloid leukaemia (AML)14 trial addressed four therapeutic questions in patients predominantly aged over 60 years with AML and High Risk Myelodysplastic Syndrome: (i) Daunorubicin 50 mg/m(2) vs. 35 mg/m(2); (ii) Cytarabine 200 mg/m(2) vs. 400 mg/m(2) in two courses of DA induction; (iii) for part of the trial, patients allocated Daunorubicin 35 mg/m(2) were also randomized to receive, or not, the multidrug resistance modulator PSC-833 in a 1:1:1 randomization; and (iv) a total of three versus four courses of treatment. A total of 1273 patients were recruited. The response rate was 62% (complete remission 54%, complete remission without platelet/neutrophil recovery 8%); 5-year survival was 12%. No benefits were observed in either dose escalation randomization, or from a fourth course of treatment. There was a trend for inferior response in the PSC-833 arm due to deaths in induction. Multivariable analysis identified cytogenetics, presenting white blood count, age and secondary disease as the main predictors of outcome. Although patients with high Pgp expression and function had worse response and survival, this was not an independent prognostic factor, and was not modified by PSC-833. In conclusion, these four interventions have not improved outcomes in older patients. New agents need to be explored and novel trial designs are required to maximise prospects of achieving timely progress.

Choosing the number of doses and the cohort size for phase I dose-escalation studies

This paper reviews Bayesian procedures for phase 1 dose-escalation studies and compares different dose schedules and cohort sizes. The methodology described is motivated by the situation of phase 1 dose-escalation studiesin oncology, that is, a single dose administered to each patient, with a single binary response ("toxicity"' or "no toxicity") observed. It is likely that a wider range of applications of the methodology is possible. In this paper, results from 10000-fold simulation runs conducted using the software package Bayesian ADEPT are presented. Four designs were compared under six scenarios. The simulation results indicate that there are slight advantages of having more dose levels and smaller cohort sizes.

A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints

Bayesian decision procedures have already been proposed for and implemented in Phase I dose-escalation studies in healthy volunteers. The procedures have been based on pharmacokinetic responses reflecting the concentration of the drug in blood plasma and are conducted to learn about the dose-response relationship while avoiding excessive concentrations. However, in many dose-escalation studies, pharmacodynamic endpoints such as heart rate or blood pressure are observed, and it is these that should be used to control dose-escalation. These endpoints introduce additional complexity into the modeling of the problem relative to pharmacokinetic responses. Firstly, there are responses available following placebo administrations. Secondly, the pharmacodynamic responses are related directly to measurable plasma concentrations, which in turn are related to dose. Motivated by experience of data from a real study conducted in a conventional manner, this paper presents and evaluates a Bayesian procedure devised for the simultaneous monitoring of pharmacodynamic and pharmacokinetic responses. Account is also taken of the incidence of adverse events. Following logarithmic transformations, a linear model is used to relate dose to the pharmacokinetic endpoint and a quadratic model to relate the latter to the pharmacodynamic endpoint. A logistic model is used to relate the pharmacokinetic endpoint to the risk of an adverse event.

Potential improvements in the therapeutic ratio of prostate cancer irradiation: dose escalation of pathologically identified tumour nodules using intensity modulated radiotherapy.

The potential of intensity modulated radiotherapy (IMRT) to improve the therapeutic ratio in prostate cancer by dose escalation of intraprostatic tumour nodules (IPTNs) was investigated using a simultaneous integrated boost technique. The prostate and organs-at-risk were outlined on CT images from six prostate cancer patients. Positions of IPTNs were transferred onto the CT images from prostate maps derived from sequential large block sections of whole prostatectomy specimens. Inverse planned IMRT dose distributions were created to irradiate the prostate to 70 Gy and all the IPTNs to 90 Gy. A second plan was produced to escalate only the dominant IPTN (DIPTN) to 90 Gy, mimicking current imaging techniques. These plans were compared with homogeneous prostate irradiation to 70 Gy using dose–volume histograms, tumour control probability (TCP) and normal tissue complication probability (NTCP) for the rectum. The mean dose to IPTNs was increased from 69.8 Gy to 89.1 Gy if all the IPTNs were dose escalated (p=0.0003). This corresponded to a mean increase in TCP of 8.7–31.2% depending on the /ß ratio of prostate cancer (p

Investigating the Potential Impact of Four-dimensional Computed Tomography (4DCT) on Toxicity, Outcomes and Dose Escalation for Radical Lung Cancer Radiotherapy

AIMS: To investigate the potential dosimetric and clinical benefits predicted by using four-dimensional computed tomography (4DCT) compared with 3DCT in the planning of radical radiotherapy for non-small cell lung cancer.

MATERIALS AND METHODS:
Twenty patients were planned using free breathing 4DCT then retrospectively delineated on three-dimensional helical scan sets (3DCT). Beam arrangement and total dose (55 Gy in 20 fractions) were matched for 3D and 4D plans. Plans were compared for differences in planning target volume (PTV) geometrics and normal tissue complication probability (NTCP) for organs at risk using dose volume histograms. Tumour control probability and NTCP were modelled using the Lyman-Kutcher-Burman (LKB) model. This was compared with a predictive clinical algorithm (Maastro), which is based on patient characteristics, including: age, performance status, smoking history, lung function, tumour staging and concomitant chemotherapy, to predict survival and toxicity outcomes. Potential therapeutic gains were investigated by applying isotoxic dose escalation to both plans using constraints for mean lung dose (18 Gy), oesophageal maximum (70 Gy) and spinal cord maximum (48 Gy).

RESULTS:
4DCT based plans had lower PTV volumes, a lower dose to organs at risk and lower predicted NTCP rates on LKB modelling (P < 0.006). The clinical algorithm showed no difference for predicted 2-year survival and dyspnoea rates between the groups, but did predict for lower oesophageal toxicity with 4DCT plans (P = 0.001). There was no correlation between LKB modelling and the clinical algorithm for lung toxicity or survival. Dose escalation was possible in 15/20 cases, with a mean increase in dose by a factor of 1.19 (10.45 Gy) using 4DCT compared with 3DCT plans.

CONCLUSIONS:
4DCT can theoretically improve therapeutic ratio and dose escalation based on dosimetric parameters and mathematical modelling. However, when individual characteristics are incorporated, this gain may be less evident in terms of survival and dyspnoea rates. 4DCT allows potential for isotoxic dose escalation, which may lead to improved local control and better overall survival.

Gaining acceptability for the Bayesian decision-theoretic approach in dose-escalation studies

There has recently been increasing demand for better designs to conduct first-into-man dose-escalation studies more efficiently, more accurately and more quickly. The authors look into the Bayesian decision-theoretic approach and use simulation as a tool to investigate the impact of compromises with conventional practice that might make the procedures more acceptable for implementation. Copyright © 2005 John Wiley & Sons, Ltd.

Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.

An evaluation of a Bayesian method of dose escalation based on bivariate binary responses

Recently, various approaches have been suggested for dose escalation studies based on observations of both undesirable events and evidence of therapeutic benefit. This article concerns a Bayesian approach to dose escalation that requires the user to make numerous design decisions relating to the number of doses to make available, the choice of the prior distribution, the imposition of safety constraints and stopping rules, and the criteria by which the design is to be optimized. Results are presented of a substantial simulation study conducted to investigate the influence of some of these factors on the safety and the accuracy of the procedure with a view toward providing general guidance for investigators conducting such studies. The Bayesian procedures evaluated use logistic regression to model the two responses, which are both assumed to be binary. The simulation study is based on features of a recently completed study of a compound with potential benefit to patients suffering from inflammatory diseases of the lung.

An evaluation of Bayesian designs for dose-escalation studies in healthy volunteers

In this paper, Bayesian decision procedures previously proposed for dose-escalation studies in healthy volunteers are reviewed and evaluated. Modifications are made to the expression of the prior distribution in order to make the procedure simpler to implement and a more relevant criterion for optimality is introduced. The results of an extensive simulation exercise to establish the proper-ties of the procedure and to aid choice between designs are summarized, and the way in which readers can use simulation to choose a design for their own trials is described. The influence of the value of the within-subject correlation on the procedure is investigated and the use of a simple prior to reflect uncertainty about the correlation is explored. Copyright (c) 2005 John Wiley & Sons, Ltd.

Implementation of a Bayesian design in a dose-escalation study of an experimental agent in healthy volunteers

Bayesian decision procedures have recently been developed for dose escalation in phase I clinical trials concerning pharmacokinetic responses observed in healthy volunteers. This article describes how that general methodology was extended and evaluated for implementation in a specific phase I trial of a novel compound. At the time of writing, the study is ongoing, and it will be some time before the sponsor will wish to put the results into the public domain. This article is an account of how the study was designed in a way that should prove to be safe, accurate, and efficient whatever the true nature of the compound. The study involves the observation of two pharmacokinetic endpoints relating to the plasma concentration of the compound itself and of a metabolite as well as a safety endpoint relating to the occurrence of adverse events. Construction of the design and its evaluation via simulation are presented.