Gastrointestinal Acute Radiation Syndrome: Mechanisms, Models, Markers, and Medical Countermeasures.

There have been a number of reported human exposures to high dose radiation, resulting from accidents at nuclear power plants (e.g., Chernobyl), atomic bombings (Hiroshima and Nagasaki), and mishaps in industrial and medical settings. If absorbed radiation doses are high enough, evolution of acute radiation syndromes (ARS) will likely impact both the bone marrow as well as the gastrointestinal (GI) tract. Damage incurred in the latter can lead to nutrient malabsorption, dehydration, electrolyte imbalance, altered microbiome and metabolites, and impaired barrier function, which can lead to septicemia and death. To prepare for a medical response should such an incident arise, the National Institute of Allergy and Infectious Diseases (NIAID) funds basic and translational research to address radiation-induced GI-ARS, which remains a critical and prioritized unmet need. Areas of interest include identification of targets for damage and mitigation, animal model development, and testing of medical countermeasures (MCMs) to address GI complications resulting from radiation exposure. To appropriately model expected human responses, it is helpful to study analogous disease states in the clinic that resemble GI-ARS, to inform on best practices for diagnosis and treatment, and translate them back to inform nonclinical drug efficacy models. For these reasons, the NIAID partnered with two other U.S. government agencies (the Biomedical Advanced Research and Development Authority, and the Food and Drug Administration), to explore models, biomarkers, and diagnostics to improve understanding of the complexities of GI-ARS and investigate promising treatment approaches. A two-day workshop was convened in August 2022 that comprised presentations from academia, industry, healthcare, and government, and highlighted talks from 26 subject matter experts across five scientific sessions. This report provides an overview of information that was presented during the conference, and important discussions surrounding a broad range of topics that are critical for the research, development, licensure, and use of MCMs for GI-ARS.

Quantitative Brain Amyloid PET.

Since the development of amyloid tracers for PET imaging, there has been interest in quantifying amyloid burden in the brains of patients with Alzheimer disease. Quantitative amyloid PET imaging is poised to become a valuable approach in disease staging, theranostics, monitoring, and as an outcome measure for interventional studies. Yet, there are significant challenges and hurdles to overcome before it can be implemented into widespread clinical practice. On November 17, 2022, the U.S. Food and Drug Administration, Society of Nuclear Medicine and Molecular Imaging, and Medical Imaging and Technology Alliance cosponsored a public workshop comprising experts from academia, industry, and government agencies to discuss the role of quantitative brain amyloid PET imaging in staging, prognosis, and longitudinal assessment of Alzheimer disease. The workshop discussed a range of topics, including available radiopharmaceuticals for amyloid imaging; the methodology, metrics, and analytic validity of quantitative amyloid PET imaging; its use in disease staging, prognosis, and monitoring of progression; and challenges facing the field. This report provides a high-level summary of the presentations and the discussion.

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

US FDA public meeting: identification of concepts and terminology for multicomponent biomarkers.

The US FDA convened a virtual public workshop with the goals of obtaining feedback on the terminology needed for effective communication of multicomponent biomarkers and discussing the diverse use of biomarkers observed across the FDA and identifying common issues. The workshop included keynote and background presentations addressing the stated goals, followed by a series of case studies highlighting FDA-wide and external experience regarding the use of multicomponent biomarkers, which provided context for panel discussions focused on common themes, challenges and preferred terminology. The final panel discussion integrated the main concepts from the keynote, background presentations and case studies, laying a preliminary foundation to build consensus around the use and terminology of multicomponent biomarkers.

Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials.

PURPOSE: Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials. METHODS: BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error. RESULTS: Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates. CONCLUSION: BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.

A Trans-Agency Workshop on the Pathophysiology of Radiation-Induced Lung Injury.

Research and development of medical countermeasures (MCMs) for radiation-induced lung injury relies on the availability of animal models with well-characterized pathophysiology, allowing effective bridging to humans. To develop useful animal models, it is important to understand the clinical condition, advantages and limitations of individual models, and how to properly apply these models to demonstrate MCM efficacy. On March 20, 2019, a meeting sponsored by the Radiation and Nuclear Countermeasures Program (RNCP) within the National Institute of Allergy and Infectious Diseases (NIAID) brought together medical, scientific and regulatory communities, including academic and industry subject matter experts, and government stakeholders from the Food and Drug Administration (FDA) and the Biomedical Advanced Research and Development Authority (BARDA), to identify critical research gaps, discuss current clinical practices for various forms of pulmonary damage, and consider available animal models for radiation-induced lung injury.

A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine.

New technologies for novel biomarkers have transformed the field of precision medicine. However, in applications such as liquid biopsy for early tumor detection, the misclassification rates of next generation sequencing and other technologies have become an unavoidable feature of biomarker development. Because initial experiments are usually confined to specific technology choices and application settings, a statistical method that can project the performance metrics of other scenarios with different misclassification rates would be very helpful for planning further biomarker development and future trials. In this article, we describe an approach based on an extended version of simulation extrapolation (SIMEX) to project the performance of biomarkers measured with varying misclassification rates due to different technological or application settings when experimental results are only available from one specific setting. Through simulation studies for logistic regression and proportional hazards models, we show that our proposed method can be used to project the biomarker performance with good precision when switching from one to anther technology or application setting. Similar to the original SIMEX model, the proposed method can be implemented with existing software in a straightforward manner. A data analysis example is also presented using a lung cancer data set and performance metrics for two gene panel based biomarkers. Results demonstrate that it is feasible to infer the potential implications of using a range of technologies or application scenarios for biomarkers with limited human trial data.

Perspectives on statistical strategies for the regulatory biomarker qualification process.

Qualification of a biomarker for use in a medical product development program requires a statistical strategy that aligns available evidence with the proposed context of use (COU), identifies any data gaps to be filled and plans any additional research required to support the qualification. Accumulating, interpreting and analyzing available data is outlined, step-by-step, illustrated by a qualified enrichment biomarker example and a safety biomarker in the process of qualification. The detailed steps aid requestors seeking qualification of biomarkers, allowing them to organize the available evidence and identify potential gaps. This provides a statistical perspective for assessing evidence that parallels clinical considerations and is intended to guide the overall evaluation of evidentiary criteria to support a specific biomarker COU.

Hi3 + 3: A model-assisted dose-finding design borrowing historical data.

BACKGROUND: In phase I clinical trials, historical data may be available through multi-regional programs, reformulation of the same drug, or previous trials for a drug under the same class. Statistical designs that borrow information from historical data can reduce cost, speed up drug development, and maintain safety. PURPOSE: Based on a hybrid design that partly uses probability models and partly uses algorithmic rules for decision making, we aim to improve the efficiency of the dose-finding trials in the presence of historical data, maintain safety for patients, and achieve a level of simplicity for practical applications. METHODS: We propose the Hi3+3 design, in which the letter "H" represents "historical data". We apply the idea in power prior to borrow historical data and define the effective sample size (ESS) of the prior. Dose-finding decision rules follow the idea in the i3+3 design (Liu et al., 2020 [1]) while incorporating the historical data via the power prior and ESS. The proposed Hi3+3 design pretabulates the dosing decisions before the trial starts, a desirable feature for ease of application in practice. RESULTS: In most cases we investigated, the Hi3+3 design is superior than the i3+3 design due to information borrow from historical data. Even when the historical data is incompatible with the current data, it is capable of maintaining a high level of safety for trial patients and comparable performances without sacrificing the ability to identify the correct MTD too much. Ilustration of this feature are found in the simulation results. CONCLUSION: With the demonstrated safety, efficiency, and simplicity, the Hi3+3 design could be a desirable choice for dose-finding trials borrowing historical data.

Cutaneous Radiation Injuries: Models, Assessment and Treatments.

Many cases of human exposures to high-dose radiation have been documented, including individuals exposed during the detonation of atomic bombs in Hiroshima and Nagasaki, nuclear power plant disasters (e.g., Chernobyl), as well as industrial and medical accidents. For many of these exposures, injuries to the skin have been present and have played a significant role in the progression of the injuries and survivability from the radiation exposure. There are also instances of radiation-induced skin complications in routine clinical radiotherapy and radiation diagnostic imaging procedures. In response to the threat of a radiological or nuclear mass casualty incident, the U.S. Department of Health and Human Services tasked the National Institute of Allergy and Infectious Diseases (NIAID) with identifying and funding early- to mid-stage medical countermeasure (MCM) development to treat radiation-induced injuries, including those to the skin. To appropriately assess the severity of radiation-induced skin injuries and determine efficacy of different approaches to mitigate/treat them, it is necessary to develop animal models that appropriately simulate what is seen in humans who have been exposed. In addition, it is important to understand the techniques that are used in other clinical indications (e.g., thermal burns, diabetic ulcers, etc.) to accurately assess the extent of skin injury and progression of healing. For these reasons, the NIAID partnered with two other U.S. Government funding and regulatory agencies, the Biomedical Advanced Research and Development Authority (BARDA) and the Food and Drug Administration (FDA), to identify state-of-the-art methods in assessment of skin injuries, explore animal models to better understand radiation-induced cutaneous damage and investigate treatment approaches. A two-day workshop was convened in May 2019 highlighting talks from 28 subject matter experts across five scientific sessions. This report provides an overview of information that was presented and the subsequent guided discussions.

Recent advances in clinical trial design considerations in Thera"nostics".

Theranostics in drug development is an evolving framework, known as combining 'thera' (a therapeutic drug) with 'nostics' (a diagnostic imaging drug) and with the latter being mostly used to select patient for evaluation of safety and efficacy of an investigational therapeutics. However, when a diagnostic imaging drug is still investigational, patient selection performance of a nostics imaging has not been demonstrated. Clinical trials conducted to assess the effect of an investigational therapeutics in a theranostics setting may focus only on the therapeutics development and not necessarily require definitive truth standard or reference standard to also assess patient selection performance of an investigational diagnostic imaging drug. We propose an In-Parallel with Leveraging development pathway in view of current practice of theranostics for a nostics imaging development. We rationalize minimum statistical metrics necessary for patient selection to allow for rigors of a nostics or diagnostics imaging drug development. We highlight tangible benefits with newer design considerations. We articulate potential indications of a nostics development including prognostic, predictive and treatment response monitoring in addition to patient selection. We further articulate potential additional clinical utilities of risk stratification and clinical management. To take full advantage and the likely payoff in the benefit of leveraging, a group sequential design or an adaptive design for the therapeutic trial is highly encouraged.

The i3+3 design for phase I clinical trials.

The traditional rule-based design, 3 + 3, has been shown to be less likely to achieve the objectives of dose-finding trials when compared with model-based designs. We propose a new rule-based design called i3 + 3, which is based on simple but more advanced rules that account for the variabilities in the observed data. We compare the operating characteristics for the proposed i3 + 3 design with other popular phase I designs by simulation. The i3 + 3 design is far superior than the 3 + 3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3 + 3 also demonstrates comparable performances.

Commentary on "Applying CHW method to 2-in-1 design: gain or lose".

While 2-in-1 designs give a flexibility to make a clinical trial either an information generation Phase 2 trial or a full scale confirmatory Phase 3 trial, flexible sample size designs can naturally fit into the 2-in-1 design framework. This study is to show that the CHW design can be blended into a 2-in-1 design to improve the adaptive performance of the design. Commenting on the usual 2-in-1 design, we demonstrated that the CHW design can achieve the goal of a 2-in-1 design with satisfactory statistical power and efficient average sample size for a targeted range of the treatment effect.

Evaluation of the extent of adaptation to sample size in clinical trials for cardiovascular and CNS diseases.

A planned adaptation to sample size in an ongoing trial aims at providing an opportunity to modify design assumptions made at the trial planning stage. Reassessment of sample size in an ongoing trial may be performed in a non-comparative or a comparative fashion, either with or without use of external data that surface. We review the completed new drug applications (NDAs) and biologic license applications (BLAs) submitted since 2000 to cardio-renal, neurology and psychiatry drug products divisions of Center for Drug Evaluation and Research, U.S. Food and Drug Administration. Interestingly, it was found that the maximal sample size increase across the identified confirmatory clinical trials was less than 2-fold the originally planned sample size. Additionally, as a result of sample size increase, precision in treatment effect estimation was often improved for the primary endpoint and the key secondary endpoints.

A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2.

There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.