Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

BACKGROUND

As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote quality assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology.

METHODS

We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 to 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints.

RESULTS

We could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk.

CONCLUSION

Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.

Clinical Quality Considerations when Using Next-Generation Sequencing (NGS) in Clinical Drug Development

Next-generation sequencing (NGS) and decreased costs of genomic testing are changing the paradigm in precision medicine and continue to fuel innovation. Integration of NGS into clinical drug development has the potential to accelerate clinical trial conduct and ultimately will shape the landscape of clinical care by making it easier to identify patients who would benefit from particular therapy(ies) and to monitor treatment outcomes with less invasive tests. This has led to an increased use of NGS service providers by pharmaceutical sponsors: to screen patients for clinical trials eligibility and for patient stratification, expanded Companion Diagnostic (CDx) development for treatment recommendations and Comprehensive Genomic profiling (CGP). These changes are reshaping the face of clinical quality considerations for precision medicine. Although some clinical quality considerations do exist in Health Authorities (HA) guidances and regulations (e.g., International Conference of Harmonization Good Clinical Practices-GCP), there is currently no holistic GxP-like detailed framework for pharmaceutical sponsors using NGS service providers in clinical trials, or for the development of CDx and CGP. In this research, we identified existing and applicable regulations, guidelines and recommendations that could be translated into clinical quality considerations related to technology, data quality, patients and oversight. We propose these considerations as a basis for pharmaceutical sponsors using NGS service providers in clinical drug development to develop a set of guidelines for NGS clinical quality.

Clinical Quality in Cancer Research: Strategy to Assess Data Integrity of Germline Variants Inferred from Tumor-Only Testing Sequencing Data

In the majority of cancers, pathogenic variants are only found at the level of the tumor; however, an unusual number of cancers and/or diagnoses at an early age in a single family may suggest a genetic predisposition. Predisposition plays a major role in about 5-10% of adult cancers and in certain childhood tumors. As access to genomic testing for cancer patients continues to expand, the identification of potential germline pathogenic variants (PGPVs) through tumor-DNA sequencing is also increasing. Statistical methods have been developed to infer the presence of a PGPV without the need of a matched normal sample. These methods are mainly used for exploratory research, for example in real-world clinico-genomic databases/platforms (CGDB). These databases are being developed to support many applications, such as targeted drug development, clinical trial optimization, and postmarketing studies. To ensure the integrity of data used for research, a quality management system should be established, and quality oversight activities should be conducted to assess and mitigate clinical quality risks (for patient safety and data integrity). As opposed to well-defined 'good practice' quality guidelines (GxP) areas such as good clinical practice, there are no comprehensive instructions on how to assess the clinical quality of statistically derived variables from sequencing data such as PGPVs. In this article, we aim to share our strategy and propose a possible set of tactics to assess the PGPV quality and to ensure data integrity in exploratory research.

Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials

Introduction

Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AEs) underreporting remains a recurrent issue. In a recent project, we developed a predictive model that enables oversight of AE reporting for clinical quality program leads (QPLs). However, there were limitations to using solely a machine learning model.

Objective

Our primary objective was to propose a robust method to compute the probability of AE underreporting that could complement our machine learning model. Our model was developed to enhance patients’ safety while reducing the need for on-site and manual QA activities in clinical trials.

Methods

We used a Bayesian hierarchical model to estimate the site reporting rates and assess the risk of underreporting. We designed the model with Project Data Sphere clinical trial data that are public and anonymized.

Results

We built a model that infers the site reporting behavior from patient-level observations and compares them across a study to enable a robust detection of outliers between clinical sites.

Conclusion

The new model will be integrated into the current dashboard designed for clinical QPLs. This approach reduces the need for on-site audits, shifting focus from source data verification to pre-identified, higher risk areas. It will enhance further QA activities for safety reporting from clinical trials and generate quality evidence during pre-approval inspections.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40264-021-01094-8.

Cross-company collaboration to leverage analytics for clinical quality and accelerate drug development: The IMPALA industry group

Quality functions from pharmaceutical sponsor companies aim to increase the use of analytics in their oversight of Good Clinical Practices and Pharmacovigilance activities. To leverage and accelerate progress, several companies decided to establish a collaborative model. The goals of this collaboration span the sharing of knowledge and ideas, the sharing of analytics methods, discussion of talent upskilling and technology adoption strategies, and collaborative discussion on these potential changes with global Health Authorities.

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that is responsible for coronavirus disease 2019 (COVID-19), which has rapidly spread across the world, becoming a pandemic. The "cytokine storm" (CS) in COVID-19 leads to the worst stage of illness, and its timely control through immunomodulators, corticosteroids, and cytokine antagonists may be the key to reducing mortality. After reviewing published studies, we proposed a Cytokine Storm Score (CSs) to identify patients who were in this hyperinflammation state, and at risk of progression and poorer outcomes. We retrospectively analyzed 31 patients admitted to Infectious Disease Department in "St. Maria" Hospital in Terni with confirmed SARS-CoV-2 infections, and analyzed the "CS score" (CSs) and the severity of COVID-19. Then we conducted a prospective study of COVID-19 patients admitted after the definition of the CSscore. This is the first study that proposes and applies a new score to quickly identify COVID-19 patients who are in a hyperinflammation stage, to rapidly treat them in order to reduce the risk of intubation. CSs can accurately identify COVID-19 patients in the early stages of a CS, to conduct timely, safe, and effect administration of immunomodulators, corticosteroids, and cytokine antagonists, to prevent progression and reduce mortality.

COVID-19-associated acute respiratory distress syndrome (CARDS): Current knowledge on pathophysiology and ICU treatment - A narrative review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces coronavirus-19 disease (COVID-19) and is a major health concern. Following two SARS-CoV-2 pandemic “waves,” intensive care unit (ICU) specialists are treating a large number of COVID19-associated acute respiratory distress syndrome (ARDS) patients. From a pathophysiological perspective, prominent mechanisms of COVID19-associated ARDS (CARDS) include severe pulmonary infiltration/edema and inflammation leading to impaired alveolar homeostasis, alteration of pulmonary physiology resulting in pulmonary fibrosis, endothelial inflammation (endotheliitis), vascular thrombosis, and immune cell activation.

Although the syndrome ARDS serves as an umbrella term, distinct, i.e., CARDS-specific pathomechanisms and comorbidities can be noted (e.g., virus-induced endotheliitis associated with thromboembolism) and some aspects of CARDS can be considered ARDS “atypical.” Importantly, specific evidence-based medical interventions for CARDS (with the potential exception of corticosteroid use) are currently unavailable, limiting treatment efforts to mostly supportive ICU care.

In this article, we will discuss the underlying pulmonary pathophysiology and the clinical management of CARDS. In addition, we will outline current and potential future treatment approaches.