Virtual controls as an alternative to randomized controlled trials for assessing efficacy of interventions

Considerations for master protocols using external controls

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Statistical applications of virtual control groups to nonrodent animal toxicity studies: An initial evaluation

Utilization of data from historical control animals to form virtual control groups (VCGs) is an innovative approach to embody the 3Rs (reduce, refine, and replace use of control animals) principle in research. However, there is no available systematic comparison of statistical performance between concurrent control groups (CCGs) and VCGs in nonrodent safety assessment. The optimal selection criteria and combination of VCGs and CCGs also remain unclear. This study retrospectively evaluated VCGs' statistical performance to detect test article effects on body weight and clinical pathology endpoints in dog and nonhuman primate (NHP) systemic toxicity studies. Body weight and six clinical pathology endpoints were analyzed against the reported study findings from a cohort of 22 previously reported nonrodent 1-month oral gavage toxicity using three different methods of generating VCGs. When the fold change from baseline was used, VCGs yielded a similar or higher statistical sensitivity to detect test article relatedness than CCGs. Compared to simple random sampling or using fixed criteria, the propensity score matching by BW, age, and year of study initiation yielded higher sensitivities. Our analysis supports the hypothesis that VCGs can be a viable instrument in nonrodent toxicity studies.

If it walks like a duck…: Monitored Emergency Use of Unregistered and Experimental Interventions (MEURI) is research

Monitored Emergency Use of Unregistered and Experimental Interventions (MEURI) is an ethical framework developed by the WHO for using unproven interventions in public health emergencies outside the context of medical research. It is mainly intended for use when medical research would be impracticable, but there is still a need to systematically gather data about unproven interventions. As such, it is designed as something of a middle ground between clinical and research ethical frameworks.However, I argue that MEURI does not truly lie at the intersection of clinical care and research. Due to its intent, structure and oversight requirements, it takes on most of the crucial features of research, to the point that it is best understood as a form of research. As a result, cases where MEURI could practicably be applied should instead make use of existing research frameworks. For those circumstances where research is truly impracticable, a more straightforward oversight system than MEURI is needed. While existing practices of compassionate use have some applicability, proposals to make use of clinical ethics committees to oversee unproven interventions may help achieve the right balance in acting in a patient's best interests when the relevant evidence base is weak.

Points to consider regarding the use and implementation of virtual controls in nonclinical general toxicology studies

The replacement of a proportion of concurrent controls by virtual controls in nonclinical safety studies has gained traction over the last few years. This is supported by foundational work, encouraged by regulators, and aligned with societal expectations regarding the use of animals in research. This paper provides an overview of the points to consider for any institution on the verge of implementing this concept, with emphasis given on database creation, risks, and discipline-specific perspectives.

Big data and its impact on the 3Rs: a home cage monitoring oriented review

Undisturbed home cage recording of mouse activity and behavior has received increasing attention in recent years. In parallel, several technologies have been developed in a bid to automate data collection and interpretation. Thanks to these expanding technologies, massive datasets can be recorded and saved in the long term, providing a wealth of information concerning animal wellbeing, clinical status, baseline activity, and subsequent deviations in case of experimental interventions. Such large datasets can also serve as a long-term reservoir of scientific data that can be reanalyzed and repurposed upon need. In this review, we present how the impact of Big Data deriving from home cage monitoring (HCM) data acquisition, particularly through Digital Ventilated Cages (DVCs), can support the application of the 3Rs by enhancing Refinement, Reduction, and even Replacement of research in animals.

A minimal metadata set (MNMS) to repurpose nonclinical in vivo data for biomedical research

Although biomedical research is experiencing a data explosion, the accumulation of vast quantities of data alone does not guarantee a primary objective for science: building upon existing knowledge. Data collected that lack appropriate metadata cannot be fully interrogated or integrated into new research projects, leading to wasted resources and missed opportunities for data repurposing. This issue is particularly acute for research using animals, where concerns regarding data reproducibility and ensuring animal welfare are paramount. Here, to address this problem, we propose a minimal metadata set (MNMS) designed to enable the repurposing of in vivo data. MNMS aligns with an existing validated guideline for reporting in vivo data (ARRIVE 2.0) and contributes to making in vivo data FAIR-compliant. Scenarios where MNMS should be implemented in diverse research environments are presented, highlighting opportunities and challenges for data repurposing at different scales. We conclude with a 'call for action' to key stakeholders in biomedical research to adopt and apply MNMS to accelerate both the advancement of knowledge and the betterment of animal welfare.

Biomarkers of lung cancer for screening and in never-smokers-a narrative review

Background and Objective

Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies.

Methods

Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS).

Key Content and Findings

Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies.

Conclusions

Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Can Historical Control Group Data Be Used to Replace Concurrent Controls in Animal Studies?

The availability of large amounts of high-quality control data from tightly controlled regulated animal safety data has created the idea to re-use these data beyond its classical applications of quality control, identification of treatment-related effects and assessing effect-size relevance for building virtual control groups (VCGs). While the ethical and cost-saving aspects of such a concept are immediately evident, the potential challenges need to be carefully considered to avoid any effect which could lower the sensitivity of an animal study to detect adverse events, safety thresholds, target organs, or biomarkers. In our brief communication, we summarize the current discussion regarding VCGs and propose a path forward how the replacement of concurrent control with VCGs resulting from historical data could be systematically assessed and to come to conclusions regarding the scientific value of the concept.

Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Hurdles and signposts on the road to virtual control groups-A case study illustrating the influence of anesthesia protocols on electrolyte levels in rats

Introduction: Virtual Control Groups (VCGs) represent the concept of using historical control data from legacy animal studies to replace concurrent control group (CCG) animals. Based on the data curation and sharing activities of the Innovative Medicine Initiatives project eTRANSAFE (enhancing TRANSlational SAFEty Assessment through Integrative Knowledge Management) the ViCoG working group was established with the objectives of i) collecting suitable historical control data sets from preclinical toxicity studies, ii) evaluating statistical methodologies for building adequate and regulatory acceptable VCGs from historical control data, and iii) sharing those control-group data across multiple pharmaceutical companies. During the qualification process of VCGs a particular focus was put on the identification of hidden confounders in the data sets, which might impair the adequate matching of VCGs with the CCG. Methods: During our analyses we identified such a hidden confounder, namely, the choice of the anesthetic procedure used in animal experiments before blood withdrawal. Anesthesia using CO₂ may elevate the levels of some electrolytes such as calcium in blood, while the use of isoflurane is known to lower these values. Identification of such hidden confounders is particularly important if the underlying experimental information (e.g., on the anesthetic procedure) is not routinely recorded in the standard raw data files, such as SEND (Standard for Exchange of Non-clinical Data). We therefore analyzed how the replacement of CCGs with VCGs would affect the reproducibility of treatment-related findings regarding electrolyte values (potassium, calcium, sodium, and phosphate). The analyses were performed using a legacy rat systemic toxicity study consisting of a control and three treatment groups conducted according to pertinent OECD guidelines. In the report of this study treatment-related hypercalcemia was reported. The rats in this study were anesthetized with isoflurane. Results: Replacing the CCGs with VCGs derived from studies comprising both anesthetics resulted in a shift of control electrolyte parameters. Instead of the originally reported hypercalcemia the use of VCG led to fallacious conclusions of no observed effect or hypocalcemia. Discussion: Our study highlights the importance of a rigorous statistical analysis including the detection and elimination of hidden confounders prior to the implementation of the VCG concept.

Psychosocial Indicators of Adolescent Alcohol, Cigarette, and Marijuana Use: An Analysis of Normalized, Harmonized, and Pooled Data

We normalized, harmonized, and pooled 344,429 surveys collected from 106,470 research participants from 25 research studies that assessed past 30-day alcohol use, drunkenness, smoking cigarettes, using marijuana, and a host of psychosocial variables. After normalizing and harmonizing psychosocial measures, we completed analyses to examine the ability of psychosocial variables to serve as proxy indicators of use. Intentionality, peer descriptive normative beliefs, and age emerged as being of primary importance in indicating use. Additional variables - peer injunctive norms, beliefs about the positive and negative consequences of use, and attitudes - were also demonstrated to have the potential to serve as proxies in the assessment of substance use risk. There were developmental patterns in how intentionality and descriptive normative beliefs changed with age. Young adolescents had scores that are protective; they have positive intentionality and do not see the prevalence of alcohol and other drug use as widespread. These and other psychosocial variable's mean scores generally erode with age while the distribution of scores widens as youth grow older. The goal of analyses was to define age-related psychosocial profiles that can be used prospectively to estimate substance use risk. These profiles are useful in creating virtual control cases for evaluating disseminated prevention programs.

A Review of Causal Inference for External Comparator Arm Studies

Randomized controlled trials (RCTs) are the gold standard design to establish the efficacy of new drugs and to support regulatory decision making. However, a marked increase in the submission of single-arm trials (SATs) has been observed in recent years, especially in the field of oncology due to the trend towards precision medicine contributing to the rise of new therapeutic interventions for rare diseases. SATs lack results for control patients, and information from external sources can be compiled to provide context for better interpretability of study results. External comparator arm (ECA) studies are defined as a clinical trial (most commonly a SAT) and an ECA of a comparable cohort of patients-commonly derived from real-world settings including registries, natural history studies, or medical records of routine care. This publication aims to provide a methodological overview, to sketch emergent best practice recommendations and to identify future methodological research topics. Specifically, existing scientific and regulatory guidance for ECA studies is reviewed and appropriate causal inference methods are discussed. Further topics include sample size considerations, use of estimands, handling of different data sources regarding differential baseline covariate definitions, differential endpoint measurements and timings. In addition, unique features of ECA studies are highlighted, specifically the opportunity to address bias caused by unmeasured ECA covariates, which are available in the SAT.

Using real world data to support regulatory approval of drugs in rare diseases: A review of opportunities, limitations & a case example

Conducting clinical research in patients with rare diseases presents a variety of challenges. At the same time, rare diseases represent a large area of unmet medical need with a significant burden of morbidity throughout the world. One of the most common issues with designing clinical trials for rare disease populations is that the gold-standard randomized controlled trial design is often not feasible in these small and usually geographically dispersed populations. Real world data therefore has particular relevance in the rare disease setting, where it may be used as a comparator for single-arm treatment trials and in support of submissions to regulatory agencies for drugs to treat these conditions. In this report, we review the potential utility and limitations of external controls for regulatory approval of drugs in rare diseases and present a recent case example of the successful utilization of external controls in the Neurofibromatosis type 1 (NF1) population.