Design Of Dose-finding Studies Research Articles

Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.

Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.

Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic

BackgroundModern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge.MethodsWe present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments.ResultsWe present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs.ConclusionsThis example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.

Adaptive Optimal Designs for Dose-Finding Studies with Time-to-Event Outcomes

We consider optimal design problems for dose-finding studies with censored Weibull time-to-event outcomes. Locally D-optimal designs are investigated for a quadratic dose–response model for log-transformed data subject to right censoring. Two-stage adaptive D-optimal designs using maximum likelihood estimation (MLE) model updating are explored through simulation for a range of different dose–response scenarios and different amounts of censoring in the model. The adaptive optimal designs are found to be nearly as efficient as the locally D-optimal designs. A popular equal allocation design can be highly inefficient when the amount of censored data is high and when the Weibull model hazard is increasing. The issues of sample size planning/early stopping for an adaptive trial are investigated as well. The adaptive D-optimal design with early stopping can potentially reduce study size while achieving similar estimation precision as the fixed allocation design.

Isotonic designs for phase I trials in partially ordered groups.

Dose-finding trials can be conducted such that patients are first stratified into multiple risk groups before doses are allocated. The risk groups are often completely ordered in that, for a fixed dose, the probability of toxicity is monotonically increasing across groups. In some trials, the groups are only partially ordered. For example, one of several groups in a trial may be known to have the least risk of toxicity for a given dose, but the ordering of the risk among the remaining groups may not be known. The aim of the article is to introduce a method for designing dose-finding trials of cytotoxic agents in completely or partially ordered groups of patients. This article presents a method for dose-finding that combines previously proposed mathematical models, augmented with results using order restricted inference. The resulting method is computationally convenient and allows for dose-finding in trials with completely or partially ordered groups. Extensive simulations are done to evaluate the performance of the method, using randomly generated dose-toxicity curves where, within each group, the risk of toxicity is an increasing function of dose. Our simulations show that the hybrid method, in which order-restricted estimation is applied to parameters of a parsimonious mathematical model, gives results that are similar to previously proposed methods for completely ordered groups. Our method generalizes to a wide range of partial orders among the groups. The problem of dose-finding in partially ordered groups has not been extensively studied in the statistical literature. The proposed method is computationally feasible, and provides a potential solution to the design of dose-finding studies in completely or partially ordered groups.

Designing dose finding studies with an active control for exponential families.

Optimal design of dose-finding studies with an active control has only been considered in the literature for regression models with normally distributed errors and known variances, where the focus is on estimating the smallest dose that achieves the same treatment effect as the active control. This paper discusses such dose-finding studies from a broader perspective. We consider a general class of optimality criteria and models arising from an exponential family. Optimal designs are constructed for several situations and their efficiency is illustrated with examples.

Cancer cells BXPC3 and MCF7 differentially reverse the inhibition of thrombin generation by apixaban, fondaparinux and enoxaparin

IntroductionCancer cells may alter the efficiency of the antithrombotic agents. To explore this possibility, the present study compared the capacity of the LMWH enoxaparin and the specific inhibitors of Xa (apixaban and fondaparinux) to inhibit thrombin generation triggered by pancreas adenocarcinoma cells (BXPC3) and human breast carcinoma cells (MCF7). Materials and methodsSamples of platelet poor (PPP) or platelet rich plasma (PRP) spiked with apixaban, fondaparinux or enoxaparin were added in micro wells carrying cancer cells and assessed for thrombin generation. In the control experiment thrombin generation was triggered with tissue factor reagent. ResultsThe three antithrombotics inhibited thrombin generation in a concentration dependent manner. The BXPC3 and MCF7 cells reversed in a different intensity the effect of the studied agents. According to the histological type of the cancer the antithrombotic efficiency of apixaban was preserved or partially reversed. Fondaparinux, was more vulnerable to the presence of cancer cells as compared to apixaban. The effect of BXCP3 or MCF7 cells on the antithrombotic potency of enoxaparin was of similar magnitude as that on apixaban. ConclusionsThe type of cancer cells is determinant for the antithrombotic efficiency of the specific factor Xa inhibitors. In contrast it does not significantly influence the potency of enoxaparin. The present study shows that the impact of the type of cancer cells on the antithrombotic activity of the specific Xa inhibitors should not be neglected. This has to be taken into consideration for the design of dose-finding studies of the direct orally active FXa inhibitors in patients with different histological types of cancer.

Distinct Roles Of Antithrombin-Dependent Antithrombotic Agents (Lovenox, Fondaparinux) and Direct Anti-Xa Anti-Coagulant (Apixaban) On The Inhibition Of Thrombin Generation Induced By Human Pancreatic (BXPC3) and Human Breast (MCF7) Cells

IntroductionThe optimization of the antithrombotic treatment with LMWH (enoxaparin), direct and indirect anti-Xa ( Apixaban, fondaparinux), in patients with adenocarcinoma of the pancreas, or human breast carcinoma is a challenging issue. The understanding of their mechanism of action in cancer-induced hypercoagulability might provide evidence for treatment optimization. To this aim, we studied the influence of pancreas adenocarcinoma cells (BXPC3), or human breast carcinoma cells (MCF7) on the antithrombotic activity of enoxaparin, fondaparinux and apixaban. Materials and MethodsCells were cultured and adhered in 96-well plates (50cells/µl). Thrombin generation (TG) of normal platelet poor plasma spiked with clinically relevant concentrations of enoxaparin, fondaparinux and apixaban which was added in wells carrying cancer cells was assessed with the Calibrated Automated Thrombogram assay. In the control experiment TG was triggered using PPP-Reagent 5 pM TF. The lag-time of thrombin generation, the maximum concentration of thrombin (Peak), and the mean rate index (MRI) of the propagation phase of thrombin generation were analysed [MRI=Peak/(ttPeak-lag-time)]. The IC50 of the studied compounds were calculated by extrapolation from the concentration-response curve, and compared. ResultsAll studied antithrombotic agents inhibited in a concentration dependent manner the thrombin generation. The three studied agents significantly inhibited TG at plasma concentrations usually achieved at doses for thromboprophylaxis. However the presence of cancer cells and their type was determinant for the antithrombotic potency of the studied drugs. Comparison on the basis of IC50 showed that the presence of either BXPC3 or MCF7 cells did not significantly modify the antithrombotic potency of enoxaparin as compared to the control experiment. In contrast the inhibitory effect of fondaparinux and apixaban on thrombin generation was partially reversed when TG was triggered by BXPC3 since the IC50 were significantly increased as compared to the control experiment. The presence of MCF7 cells did not significantly modify the antithrombotic activity of apixaban and fondaparinux.IC50 for PeakIC50 for MRIPPP(without cells)PPP+ BXPC3PPP+ MCF7PPP (without cells)PPP+ BXPC3PPP+ MCF7Lovenox (anti-Xa IU/ml)0.0500.0510.0460.100.0950.088Arixtra (anti-Xa IU/ml)0.580.780.410.3100.8120.367Apixaban (ng/ml)17.0223.9318.6612.527.28.0 ConclusionsThe LMWH enoxaparin and the specific FXa inhibitors apixaban and fondaparinux demonstrate potent inhibitory capacity on thrombin generation triggered by cancer cells. The type of cancer cells is determinant for the antithrombotic efficiency of the specific factor Xa inhibitors independently of their mechanism of action (i.e. antithrombin dependent or independent inhibition of FXa). In contrast the type of cancer cells does not significantly influence the potency of enoxaparin. This is probably due to the presence of the trace amounts of anti-IIa activity. The present study stresses out that the impact of the type of cancer cells on the antithrombotic activity of the specific Xa inhibitors should not be neglected. These data have to be taken into consideration for the design of dose-finding studies of the direct orally active FXa inhibitors in patients with different histological types of cancer. Disclosures:No relevant conflicts of interest to declare.

Adaptive optimal designs for dose-finding studies based on sigmoid Emax models

The adaptive design strategy is a feasible approach for optimal designs in dose-finding studies, where the information matrices usually depend on the unknown parameters. Focusing on three pharmacodynamics sigmoid Emax models, we derive the corresponding simple formats of the adaptive optimal designs regardless of the optimality criteria or parameters of interest. An algorithm for deriving a specific adaptive optimal design is developed. A simulation study comparing the adaptive optimal designs and the uniform designs is also performed.

Optimal Designs for Dose Finding Studies with an Active Control

SummaryDose finding studies often compare several doses of a new compound with a marketed standard treatment as an active control. In the past, however, research has focused mostly on experimental designs for placebo controlled dose finding studies. To the best of our knowledge, optimal designs for dose finding studies with an active control have not been considered so far. As the statistical analysis for an active controlled dose finding study can be formulated in terms of a mixture of two regression models, the related design problem is different from what has been investigated before in the literature. We present a rigorous approach to the problem of determining optimal designs for estimating the smallest dose achieving the same treatment effect as the active control. We determine explicitly the locally optimal designs for a broad class of models employed in such studies. We also discuss robust design strategies and determine related Bayesian and standardized minimax optimal designs. We illustrate the results by investigating alternative designs for a clinical trial which has recently appeared in a consulting project of one of the authors.

Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

Continual reassessment and related designs in dose-finding studies

The workshop on the continual reassessment method and related Phase I designs for dose-finding studies was held at Memorial Sloan Kettering Cancer Center in New York on October 2nd, 2009. The workshop was the result of several discussions between Alexia Iasonos and John O'Quigley. These discussions centered around the methodological advancements in Phase I designs with a particular focus on the continual reassessment method (CRM). The participants of the workshop included applied statisticians who have practical experience in using the CRM in clinical trials, an experience gained both in academic and cancer centers, as well as in the pharmaceutical industry in the United States, Japan and Europe. The majority of the participants have participated in Institutional Review Boards and have been involved in discussions with the FDA and European Medicines Agency. One goal of the workshop was to provide a forum to share the lessons learned from finished trials and to discuss the operational and logistical issues, as well as, methodological advancements in adaptive Phase I clinical trial design.

Optimal Designs for Dose-Finding Studies

Understanding and properly characterizing the dose–response relationship is a fundamental step in the investigation of a new compound, be it a herbicide or fertilizer, a molecular entity, an environmental toxin, or an industrial chemical. In this article we investigate the problem of deriving efficient designs for the estimation of target doses in the context of clinical dose finding. We propose methods to determine the appropriate number and actual levels of the doses to be administered to patients, as well as their relative sample size allocations. More specifically, we derive local optimal designs that minimize the asymptotic variance of the minimum effective dose estimate under a particular dose–response model. We investigate the small-sample properties of these designs, together with their sensitivity to a misspecification of the true parameter values and of the underlying dose–response model, through simulation. Finally, we demonstrate that the designs derived for a fixed model are rather sensitive with respect to this assumption and construct robust optimal designs that take into account a set of potential dose–response profiles within classes of models commonly used in drug development practice.

Adaptive Designs for Dose-Finding Studies Based on Sigmoid E max Model

We propose an adaptive procedure for dose-finding in clinical trials when the primary efficacy endpoint is continuous. We model the mean of the efficacy endpoint, given the dose, as a four-parameter logistic function. The efficacy endpoint at each dose is distributed according to either a normal or a gamma distribution. We consider the cases of fixed variance and fixed coefficient of variation assuming them to be both known and unknown. The analytic formulae for the Fisher information matrix are obtained, which are used to build the locally and adaptive D -optimal designs.

Experimental designs for phase I and phase I/II dose-finding studies

We review the rationale behind the statistical design of dose-finding studies as used in phase I and phase I/II clinical trials. We underline what the objectives of such dose-finding studies should be and why the widely used standard design fails to meet any of these objectives. The standard design is a ‘memoryless’ design and we discuss how this impacts on practical behaviour. Designs introduced over the last two decades can be viewed as designs with memory and we discuss how these designs are superior to memoryless designs. By superior we mean that they require less patients overall, less patients to attain the maximum tolerated dose (MTD), and concentrate a higher percentage of patients at and near to the MTD. We reanalyse some recently published studies in order to provide support to our contention that markedly better results could have been achieved had a design with memory been used instead of a memoryless design.

In vivo pharmacodynamic activities of two glycylcyclines (GAR-936 and WAY 152,288) against various gram-positive and gram-negative bacteria.

The in vivo pharmacodynamic activities of two glycylcyclines (GAR-936 and WAY 152,288) were assessed in an experimental murine thigh infection model in neutropenic mice. Mice were infected with one of several strains of Streptococcus pneumoniae, Staphylococcus aureus, Escherichia coli, or Klebsiella pneumoniae. Most infections were treated with a twice-daily dosing schedule, with administration of 0.75 to 192 mg of GAR-936 or WAY 152,288 per kg of body weight. A maximum-effect dose-response model was used to calculate the dose that produced a net bacteriostatic effect over 24 h of therapy. This dose was called the bacteriostatic dose. More extensive dosing studies were performed with S. pneumoniae 1199, E. coli ATCC 25922, and K. pneumoniae ATCC 43816, with doses being given as one, two, four, or eight equal doses over a period of 24 h. The dosing schedules were designed in order to minimize the interrelationship between the various pharmacokinetic and pharmacodynamic parameters studied. These parameters were time above 0.03 to 32 times the MIC, area under the concentration-time curve (AUC), and maximum concentration of drug in serum (C(max)). The bacteriostatic dose remained essentially the same, irrespective of the dosing frequency, for S. pneumoniae 1199 (0.3 to 0.9 mg/kg/day). For E. coli ATCC 25922 and K. pneumoniae ATCC 43816, however, more frequent dosing led to lower bacteriostatic doses. Pharmacokinetic studies demonstrated dose-dependent elimination half-lives of 1.05 to 2.34 and 1.65 to 3.36 h and serum protein bindings of 59 and 71% for GAR-936 and WAY 152,288, respectively. GAR-936 and WAY 152,288 were similarly effective against the microorganisms studied, with small differences in maximum effect and 50% effective dose. The glycylcyclines were also similarly effective against tetracycline-sensitive and tetracycline-resistant bacteria. Time above a certain factor (range, 0.5 to 4 times) of the MIC was a better predictor of in vivo efficacy than C(max) or AUC for most organism-drug combinations. The results demonstrate that in order to achieve 80% maximum efficacy, the concentration of unbound drug in serum should be maintained above the MIC for at least 50% of the time for GAR-936 and for at least 75% of the time for WAY 152,288. The results of these experiments will aid in the rational design of dose-finding studies for these glycylcyclines in humans.