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

An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.

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 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.

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.