Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

BACKGROUND

The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally.

OBJECTIVE

We aimed to optimize AD trial design using simulations.

METHODS

We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens.

RESULTS

We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population.

CONCLUSIONS

This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Multiplex PCR Detection of Respiratory Tract Infections in SARS-CoV-2-Negative Patients Admitted to the Emergency Department: an International Multicenter Study during the COVID-19 Pandemic

Respiratory tract infection (RTI) is a common cause of visits to the hospital emergency department. During the ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), nonpharmaceutical intervention has influenced the rates of circulating respiratory viruses. In this study, we sought to detect RTI etiological agents other than SARS-CoV-2 in emergency department patients from 13 countries in Europe, the Middle East, and Africa from December 2020 to March 2021. We sought to measure the impact of patient characteristics and national-level behavioral restrictions on the positivity rate for RTI agents. Using the BioFire Respiratory Panel 2.0 Plus, 1,334 nasopharyngeal swabs from patients with RTI symptoms who were negative for SARS-CoV-2 were tested. The rate of positivity for viral or bacterial targets was 36.3%. Regarding viral targets, human rhinovirus or enterovirus was the most prevalent (56.5%), followed by human coronaviruses (11.0%) and adenoviruses (9.9%). Interestingly, age stratification showed that the positivity rate was significantly higher in the children's group than in the adults' group (68.8% versus 28.2%). In particular, human rhinovirus or enterovirus, the respiratory syncytial virus, and other viruses, such as the human metapneumovirus, were more frequently detected in children than in adults. A logistic regression model was also used to determine an association between the rate of positivity for viral agents with each country's behavioral restrictions or with patients' age and sex. Despite the impact of behavioral restrictions, various RTI pathogens were actively circulating, particularly in children, across the 13 countries. IMPORTANCE As SARS-CoV-2 has dominated the diagnostic strategies for RTIs during the current COVID-19 pandemic situation, our data provide evidence that a variety of RTI pathogens may be circulating in each of the 13 countries included in the study. It is now plausible that the COVID-19 pandemic will one day move forward to endemicity. Our study illustrates the potential utility of detecting respiratory pathogens other than SARS-CoV-2 in patients who are admitted to the emergency department for RTI symptoms. Knowing if a symptomatic patient is solely infected by an RTI pathogen or coinfected with SARS-CoV-2 may drive timely and appropriate clinical decision-making, especially in the emergency department setting.