Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome

A Bayesian latent-subgroup platform design for dose optimization

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Modified isotonic regression based phase I/II clinical trial design identifying optimal biological dose

Conventional phase I/II clinical trial designs often use complicated parametric models to characterize the dose-response relationships and conduct the trials. However, the parametric models are hard to justify in practice, and the misspecification of parametric models can lead to substantially undesirable performances in phase I/II trials. Moreover, it is difficult for the physicians conducting phase I/II trials to clinically interpret the parameters of these complicated models, and such significant learning costs impede the translation of novel statistical designs into practical trial implementation. To solve these issues, we propose a transparent and efficient phase I/II clinical trial design, referred to as the modified isotonic regression-based design (mISO), to identify the optimal biological doses for molecularly targeted agents and immunotherapy. The mISO design makes no parametric model assumptions on the dose-response relationship and yields desirable performances under any clinically meaningful dose-response curves. The concise, clinically interpretable dose-response models and dose-finding algorithm make the proposed designs highly translational from the statistical community to the clinical community. We further extend the mISO design and develop the mISO-B design to handle the delayed outcomes. Our comprehensive simulation studies show that the mISO and mISO-B designs are highly efficient in optimal biological dose selection and patients allocation and outperform many existing phase I/II clinical trial designs. We also provide a trial example to illustrate the practical implementation of the proposed designs. The software for simulation and trial implementation are available for free download.

SCI: A Bayesian adaptive phase I/II dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials

An immunotherapy trial often uses the phase I/II design to identify the optimal biological dose, which monitors the efficacy and toxicity outcomes simultaneously in a single trial. The progression‐free survival rate is often used as the efficacy outcome in phase I/II immunotherapy trials. As a result, patients developing disease progression in phase I/II immunotherapy trials are generally seriously ill and are often treated off the trial for ethical consideration. Consequently, the happening of disease progression will terminate the toxicity event but not vice versa, so the issue of the semi‐competing risks arises. Moreover, this issue can become more intractable with the late‐onset outcomes, which happens when a relatively long follow‐up time is required to ascertain progression‐free survival. This paper proposes a novel Bayesian adaptive phase I/II design accounting for semi‐competing risks outcomes for immunotherapy trials, referred to as the dose‐finding design accounting for semi‐competing risks outcomes for immunotherapy trials (SCI) design. To tackle the issue of the semi‐competing risks in the presence of late‐onset outcomes, we re‐construct the likelihood function based on each patient's actual follow‐up time and develop a data augmentation method to efficiently draw posterior samples from a series of Beta‐binomial distributions. We propose a concise curve‐free dose‐finding algorithm to adaptively identify the optimal biological dose using accumulated data without making any parametric dose–response assumptions. Numerical studies show that the proposed SCI design yields good operating characteristics in dose selection, patient allocation, and trial duration.

A Bayesian phase I/II platform design for co-developing drug combination therapies for multiple indications

There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.

A Bayesian adaptive phase I/II platform trial design for pediatric immunotherapy trials

Immunotherapy is the most promising new cancer treatment for various pediatric tumors and has resulted in an unprecedented surge in the number of novel immunotherapeutic treatments that need to be evaluated in clinical trials. Most phase I/II trial designs have been developed for evaluating only one candidate treatment at a time, and are thus not optimal for this task. To address these issues, we propose a Bayesian phase I/II platform trial design, which accounts for the unique features of immunotherapy, thereby allowing investigators to continuously screen a large number of immunotherapeutic treatments in an efficient and seamless manner. The elicited numerical utility is adopted to account for the risk-benefit trade-off and to quantify the desirability of the dose. During the trial, inefficacious or overly toxic treatments are adaptively dropped from the trial and the promising treatments are graduated from the trial to the next stage of development. Once an experimental treatment is dropped or graduated, the next available new treatment can be immediately added and tested. Extensive simulation studies have demonstrated the desirable operating characteristics of the proposed design.

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.

Comparison of Phase I-II designs with parametric or semi-parametric models using two different risk-benefit trade-off criteria

A semi-parametric stochastic ordering model (SPSO) is introduced to characterize functional relationships between dose level and the probabilities of binary Efficacy and Toxicity events. This model is used to implement a Bayesian adaptive phase I-II clinical trial using one of two different optimality criteria, either dose desirability defined as a function of the marginal Efficacy and Toxicity probabilities, or mean utility based on numerical scores of the four possible (Efficacy, Toxicity) events. A simulation study is conducted to compare designs using the SPSO model to the parametric EffTox model described in Thall and Cook, with each (model, optimality criterion) combination. Each of these four designs adaptively assigns patient cohorts to estimated optimal dose levels after restricting assignments to dose levels that are acceptably efficacious and safe. The simulation study shows that different design configurations may have superior performance depending on the assumed true dose-outcome scenario.

Bayesian cancer clinical trial designs with subgroup-specific decisions

Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model.

Bayesian Utility-Based Designs for Subgroup-Specific Treatment Comparison and Early-Phase Dose Optimization in Oncology Clinical Trials

PURPOSE

Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. This article reviews two clinical trial designs that make subgroup-specific decisions and compares each to a simpler design that ignores patient heterogeneity. The purpose is to illustrate the benefits of accounting prospectively for treatment-subgroup interactions and how utilities may be used to quantify risk-benefit trade-offs.

METHODS

Two Bayesian clinical trial designs that perform subgroup-specific decision making and inference based on elicited utilities of patient outcomes are reviewed. The first is a randomized comparative trial of nutritional prehabilitation for patients undergoing esophageal resection that has two prognostic subgroups and is based on postoperative morbidity score. The second is a sequentially adaptive trial of natural killer cells for treating hematologic malignancies that is based on five time-to-event outcomes and that performs safety monitoring and optimizes cell dose within six disease subgroups. Computer simulations under a range of different scenarios are presented for each design to establish its operating characteristics and compare it to a more conventional design that ignores patient heterogeneity.

RESULTS

Each design has attractive operating characteristics, is greatly superior to a simplified design that ignores patient subgroups, is robust to deviations from its assumed statistical model, and is feasible to use for conducting trials.

CONCLUSION

Bayesian designs that make subgroup-specific decisions in randomized comparative trials or sequentially adaptive early-phase dose-finding trials are superior to designs that ignore patient heterogeneity. Using elicited utilities of complex patient outcomes to quantify risk-benefit trade-offs provides a practical and ethical basis for decision making and treatment evaluation in clinical trials.

Evaluation of an Intubation Readiness Score to Assess Neonatal Sedation before Intubation

BACKGROUND

Premedication for neonatal intubation facilitates the procedure and reduces stress and physiological disturbances. However, no validated scoring system to assess the effect of premedication prior to intubation is available.

OBJECTIVE

To evaluate the usefulness of an Intubation Readiness Score (IRS) to assess the effect of premedication prior to intubation in newborn infants.

METHODS

Two-center prospective study in neonates who needed endotracheal intubation. Intubation was performed using a standardized procedure with propofol 1-2 mg/kg as premedication. The level of sedation was assessed with the IRS by evaluating the motor response to a firm stimulus (1 = spontaneous movement; 2 = movement on slight touch; 3 = movement on firm stimulus; 4 = no movement). Intubation was proceeded if an adequate effect, defined as an IRS of 3 or 4, was reached. IRS was compared to the quality of intubation measured with the Viby-Mogensen intubation score.

RESULTS

A total of 115 patients, with a median gestational age of 27.7 weeks (interquartile range 5.3) and a median birth weight of 1,005 g (interquartile range 940), were included. An adequate IRS was achieved in 105 patients, 89 (85%) of whom also had a good Viby-Mogensen intubation score and 16 (15%) had an inadequate Viby-Mogensen intubation score. The positive predictive value of the IRS was 85%.

CONCLUSIONS

Preintubation sedation assessment using the IRS can adequately predict optimal conditions during intubation in the majority of neonates. We suggest using the IRS in routine clinical care. Further research combining the IRS with other parameters could further improve the predictability of adequate sedation during intubation.

A Bayesian adaptive phase II clinical trial design accounting for spatial variation

Conventional phase II clinical trials evaluate the treatment effects under the assumption of patient homogeneity. However, due to inter-patient heterogeneity, the effect of a treatment may differ remarkably among subgroups of patients. Besides patient’s individual characteristics such as age, gender, and biomarker status, a substantial amount of this heterogeneity could be due to the spatial variation across geographic regions because of unmeasured or unknown spatially varying environmental and social exposures. In this article, we propose a hierarchical Bayesian adaptive design for two-arm randomized phase II clinical trials that accounts for the spatial variation as well as patient’s individual characteristics. We treat the treatment efficacy as an ordinal outcome and quantify the desirability of each possible category of the ordinal efficacy using a utility function. A cumulative probit mixed model is used to relate efficacy to patient-specific covariates and geographic region spatial effects. Spatial dependence between regions is induced through the conditional autoregressive priors on the spatial effects. A two-stage design is proposed to adaptively assign patients to desirable treatments according to each patient’s spatial information and individual covariates and make treatment recommendations at the end of the trial based on the overall treatment effect. Simulation studies show that our proposed design has good operating characteristics and significantly outperforms an alternative phase II trial design that ignores the spatial variation.

A Bayesian Phase I/II Trial Design for Immunotherapy

Immunotherapy is an innovative treatment approach that stimulates a patient's immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dosefinding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response, toxicity and efficacy. The objective is to identify the biologically optimal dose, defined as the dose with the highest desirability in the risk-benefit tradeoff. An Emax model is utilized to describe the marginal distribution of the immune response. Conditional on the immune response, we jointly model toxicity and efficacy using a latent variable approach. Using the accumulating data, we adaptively randomize patients to experimental doses based on the continuously updated model estimates. A simulation study shows that our proposed design has good operating characteristics in terms of selecting the target dose and allocating patients to the target dose.

Phase I-II clinical trial design: a state-of-the-art paradigm for dose finding

Background

Conventional phase I algorithms for finding a phase-2 recommended dose (P2RD) based on toxicity alone is problematic because the maximum tolerated dose (MTD) is not necessarily the optimal dose with the most desirable risk-benefit trade-off. Moreover, the increasingly common practice of treating an expansion cohort at a chosen MTD has undesirable consequences that may not be obvious.

Patients and methods

We review the phase I-II paradigm and the EffTox design, which utilizes both efficacy and toxicity to choose optimal doses for successive patient cohorts and find the optimal P2RD. We conduct a computer simulation study to compare the performance of the EffTox design with the traditional 3 + 3 design and the continuous reassessment method.

Results

By accounting for the risk-benefit trade-off, the EffTox phase I-II design overcomes the limitations of conventional toxicity-based phase I designs. Numerical simulations show that the EffTox design has higher probabilities of identifying the optimal dose and treats more patients at the optimal dose.

Conclusions

Phase I-II designs, such as the EffTox design, provide a coherent and efficient approach to finding the optimal P2RD by explicitly accounting for risk-benefit trade-offs underlying medical decisions.

Assessment of sedation level prior to neonatal intubation: A systematic review

BACKGROUND

Adequate premedication before neonatal endotracheal intubation reduces pain, stress, and adverse physiological responses, diminishes duration and number of attempts at intubation, and prevents traumatic airway injury. Therefore, intubation should not be started until an adequate level of sedation is reached. It is not clear how this should be measured in the clinical situation.

OBJECTIVES

The aim of this study is to provide a systematic review of the usability and validity of scoring systems or other objective parameters to evaluate the level of sedation before intubation in neonates. Secondary aims were to describe parameters that are used to determine the level of sedation and criteria on which the decision to proceed with intubation is based.

METHODS

Literature was searched (January 2017) in the following electronic databases: Embase, Medline, Web of Science, Cochrane Central Registrar of Controlled Trials, Pubmed Publisher, and Google Scholar.

RESULTS

From 1653 hits, 20 studies were finally included in the systematic review. In 7 studies, intubation was started after a predefined time period; in 1 study, preoxygenation was the criterion to start with intubation; and in 12 studies, intubation was started in case of adequate sedation and/or relaxation. Only 4 studies described the use of 3 different objective scoring system, all in the neonatal intensive care unit, which are not validated.

CONCLUSION

No validated scoring systems to assess the level of sedation prior to intubation in newborns are available in the literature. Three objective sedation assessment tools seem promising but need further validation before they can be implemented in research and clinical settings.

Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies

The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose–toxicity and dose–efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children.

Parametric Dose Standardization for Optimizing Two-Agent Combinations in a Phase I-II Trial with Ordinal Outcomes

A Bayesian model and design are described for a phase I-II trial to jointly optimise the doses of a targeted agent and a chemotherapy agent for solid tumors. A challenge in designing the trial was that both the efficacy and toxicity outcomes were defined as four-level ordinal variables. To reflect possibly complex joint effects of the two doses on each of the two outcomes, for each marginal distribution a generalised continuation ratio model was assumed, with each agent's dose parametrically standardised in the linear term. A copula was assumed to obtain a bivariate distribution. Elicited outcome probabilities were used to construct a prior, with variances calibrated to obtain small prior effective sample size. Elicited numerical utilities of the 16 elementary outcomes were used to compute posterior mean utilities as criteria for selecting dose pairs, with adaptive randomisation to reduce the risk of getting stuck at a suboptimal pair. A simulation study showed that parametric dose standardisation with additive dose effects provides a robust, reliable model for dose pair optimisation in this setting, and it compares favourably with designs based on alternative models that include dose-dose interaction terms. The proposed model and method are applicable generally to other clinical trial settings with similar dose and outcome structures.

Neonatal medicines research: challenges and opportunities

INTRODUCTION

The key feature of the newborn is its fast age-dependent maturation, resulting in extensive variability in pharmacokinetics and -dynamics, further aggravated by newly emerging covariates like treatment modalities, environmental issues or pharmacogenetics. This makes clinical research in neonates relevant and needed, but also challenging.

AREAS COVERED

To improve this knowledge, tailoring research tools as well as building research networks and clinical research skills for neonates are urgently needed. Tailoring of research tools is illustrated using the development of dried blood spot techniques and the introduction of micro-dosing and -tracer methodology in neonatal drug studies. Both techniques can be combined with sparse sampling techniques through population modeling. Building research networks and clinical research skills is illustrated by the initiatives of agencies to build and integrate knowledge on neonatal pharmacotherapy through dedicated working groups.

EXPERT OPINION

Challenges relating to neonatal medicine research can largely be overcome. Tailored tools and legal initiatives, combined with clever trial design will result in more robust information on neonatal pharmacotherapy. This necessitates collaborative efforts between clinical researchers, sponsors, regulatory authorities, and last but not least patient representatives and society.