The International Council for Harmonisation (ICH) E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase. ich.org/sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) introduced Quality Tolerance Limits (QTLs) to the industry, and in doing so, modernized quality control for clinical trials. QTLs provide measured feedback on clinical trial parameters previously only used by statistical and clinical functions to track trial progress toward endpoints. Elevating these measures as part of the Quality Management System (QMS) provides greater visibility across clinical trial functions and the enterprise as well as to measures that are important indicators of the state of participant protection and reliability of trial results. In support of this new requirement, TransCelerate developed a framework to guide industry sponsors and their agents in implementing QTLs. This QTL Framework is intended to aid industry's ability to improve the quality of clinical research through the implementation of QTLs in a way that helps protect trial participants and reliability of trial results while meeting Health Authority (HA) expectations. The framework is intended to maximize efficiency and minimize confusion in the implementation of QTLs. The framework includes proposed approaches for implementation of QTLs for a clinical trial as defined in Section 5.0.4 and 5.0.7 of ICH E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase.ich.org/ sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) and considerations for setting thresholds.

Background In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials. Methods This article presents results of historical data analyses for the previously proposed parameters based on data from 294 clinical trials from seven TransCelerate member companies. The differences across therapeutic areas were assessed by comparing Alzheimer’s disease (AD) and oncology trials using a separate dataset provided by Medidata. Results TransCelerate member companies provided historical data on 11 QTL parameters with data sufficient for analysis for parameters. The distribution of values was similar for most parameters with a relatively small number of outlying trials with high parameter values. Medidata provided values for three parameters in a total of 45 AD and oncology trials with no obvious differences between the therapeutic areas. Conclusion Historical parameter values can provide helpful benchmark information for quality control activities in future trials.

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Introduction Population-specific disparities in clinical research are well characterized-with individuals of European ancestry comprising the majority of genetic and clinical data globally. Disease course and treatment response can vary across individuals of different race/ethnicity and ancestral backgrounds. As the population continues to diversify and healthcare evolves toward personalized medicine, it's essential that the biological differences among populations, and how these affect disease pathology, experience and outcomes, are investigated early and throughout the development process. Currently, there is no defined standard for characterizing population differences across diseases. Establishing a methodology to systematically assess and consider medically relevant population specific attributes for understudied populations is a critical enabler for the clinical research enterprise and supports greater inclusive clinical research. We established a methodology to assess and prioritize population specific attributes across disease areas (DA) and a framework to support hypothesis generation and population-driven clinical development considerations. Methods Data sources: NCI SEER, WHO Global Cancer Observatory, Global Health Data Exchange Burden of Disease, and published literature were used to assess population specific differences Attributes included: 1) Incidence and prevalence 2) Clinical outcomes, 3) molecular drivers, and 4) access factors. Population elements of race/ethnicity, genomic ancestry, and geographic origin were used to stratify outputs. A grid ranking framework was established based on relative prevalence and incidence and level of concordance or distinction of the above attributes across populations. Summary Methodology was established that included identification and analysis of key population-specific factors to rank DA's within a grid system. The following diseases were characterized as having disproportionate prevalence as well as biologically plausible population specific differences. Breast Cancer Cervical Cancer Colorectal Cancer Gastric Cancer Hepatocellular Carcinoma Head & Neck SCC Multiple Myeloma NSCLC Prostate Cancer Population specific reports were developed and used to inform business integration process into evidence generation considerations including guidelines for assessments of population level pertinence to study hypothesis, response modification potential, and relevance of biomarker differences. Conclusion The established methodology and framework provides a process and standards to characterize biologically relevant population specific attributes for understudied global populations at the disease level. This approach will support the clinical development environment to systematically approach conduct of scientifically driven inclusion of representative patients in research, ultimately supporting greater inclusion of understudied patient populations. Citation Format: Keith Dawson, Dane Callow, Jimmy Ngueyn, Altovise T. Ewing, Ruma Bhagat, William Boyd, Caroline McCammond, Nicole Richie. Methodology for characterizing clinical differences & disparities for global populations to support disease area prioritization in industry oncology clinical development programs: Insights to inform scientifically driven evidence generation [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-195.

Not all segments of the population have benefited equally from advances in science and medicine. Distinct populations continue to be understudied in science, underrepresented in research, and underserved by medicine. Multiple complex factors, including the lack of diverse representation in research, are contributing to disparate health outcomes. For instance, among both men and women, Black patients have the highest cancer death rates. Without intervention, future medical innovations will not broadly benefit all patients and society. Genentech, a member of the Roche Group, believes data representative of real-world patient populations is required to optimize clinical outcomes for all patients. To make meaningful progress toward this goal, we created a system-level strategy. Last year, we began building a network of clinical trial sites to advance the representation of diverse patient populations in the company's oncology clinical trials, test recruitment and retention approaches, and establish best practices that can be leveraged across the industry to help achieve health equity for people with cancer. The first step in identifying potential partners for this alliance was to develop site criteria. We considered numerous attributes including: operational capabilities and performance in enrolling underrepresented patient populations, D&I reputation and community involvement with these patient populations, attitude and commitment to inclusive research, and scientific reputation. We used both internal and external data sources to stratify sites based on two key attributes. First, we compared sites' historical enrollment of Black, Latinx and other underrepresented patients in oncology trials. Second, we evaluated the size of the Black and Latinx populations within the catchment area of sites. Included in the data analysis were about 20,000 potential oncology sites. Within those sites that had a large number of underrepresented patient populations, we used enrollment and experience with Genentech oncology trials to further refine our search. We refined our list based on study teams' experience with site capability to conduct phase I-IV trials. We then conducted in-depth site interviews to identify those sites that were aligned with our mission and were making significant efforts to reach patients of color. After the interview process was completed, four inaugural sites were selected for the launch of the Advancing Inclusive Research Site Alliance. Each of the centers will focus on enabling historically underrepresented patient groups to participate in Genentech's oncology trials and work collaboratively to share key learnings and explore innovative ways of increasing clinical trial access for every patient who might benefit. The AIR Site Alliance plans to expand to more research centers and broaden its focus into additional disease areas in the future. Together, we will work to enrich science, eliminate disparity in outcomes and provide equitable access to innovative therapies for all patients. Citation Format: Ruma Bhagat, Meghan McKenzie, Melissa Gonzales, Gerren Wilson, Nicole Richie, Quita Highsmith. Building a new clinical trial diversity alliance to transform our ability to reach understudied and underserved patients [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-006.