Five Data-Informed Principles for Advancing Inclusive Research in Clinical Trials: A Pharma Perspective

Abstract

Advancing inclusive research (AIR) in clinical trials requires frameworks and metrics for assessing real-world data and measuring population science. Because different factors drive health inequities and variables in measuring population science, relying on one metric for measuring progress may have limitations. Five principles (5Ps) are proposed for AIR globally that form the basis for a data-informed framework to measure and systematically define inclusive research to ensure rigor and benchmarking within organizations and across the broader sector. The first principle addresses biological, genetic, and population science considerations and their responsible use as data elements. The second principle pertains to using data to inform global region, country, and site placement, which includes geographical proportionality in trial enrollment, enabled access and commercialization strategies, and representative real-world demographic representation. The third principle is developing a data-informed and user-informed approach to end-to-end inclusive trial design. The fourth principle integrates patient-reported data collection standards and initiatives supporting complete and consistent clinical trial collection. The fifth principle enables trial access by demonstrating trustworthiness, improving patient navigation, and providing assistance programs. These 5Ps can be used as an end-to-end measurable framework using reference metrics, reproducible data, and methodologies for AIR in clinical development.

Infographic available for this article.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-025-03283-8.

Key Summary Points

Diversity in clinical trials is critical, and each study has unique considerations to determine the diversity and representativeness of its baseline population, as well as the biomedical problem being investigated.

However, this has been difficult to achieve and scale as advancing inclusive research requires metrics and systematized frameworks that have yet to be established.

The 5 principles for advancing inclusive research framework standardizes processes by which inclusive research and clinical trials can be conducted in an organization.

The 5 principles that form the basis for the framework are (1) population science (biological, genetic, demographic) considerations, (2) data-informed site placement for global and country-specific enrollment, (3) data-driven and user-informed inclusive trial design, (4) patient-reported data collection standards, and (5) enabling access to trials.

Use of these 5 principles will allow for benchmarking, performance assessments, and integration of health equity and inclusive research at a population level.

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Introduction

In life science research and development, including diverse populations is critical for ensuring scientific rigor and generalizability of research findings. Historically, this has not been accomplished for many reasons, including racial and gender biases, differential access to medical care, and varied enrollment in clinical trials across different populations, geographical regions, and countries [1, 2].

In clinical research, diversity, including representativeness, is prioritized through larger, additional studies on health disparities and government recommendations/legislation for inclusive research. However, standard metrics and systematized frameworks have yet to be established. These foundations are integral to ensure robust, transparent benchmarking and performance tracking over time. Practices were reviewed for how clinical study teams are currently embedding inclusive research concepts through their development program as well as qualitative feedback was received from US health systems, data warehouses, and advocacy groups that are engaged in clinical research to propose the 5 Principles (5Ps), a conceptualized framework designed to ensure a study is representative to the extent possible—based on data availability, operational constraints, and regulatory requirements—and adheres to biomedical ethical principles.

This article is based on previously conducted studies and contains no new studies with human participants or animals performed by any of the authors.

Principle 1: Population Science (Biological, Genetic, Demographic) Considerations

The first principle anchors on the “Who,” i.e., who is most affected by the disease or condition (Supplemental Fig. 1)? Collecting diverse biological, genetic, and demographic data is essential for understanding patient outcomes, disease risk, and enrollment rates. Race and ethnicity are social constructs, with different interpretations that have led to the exclusion of certain populations in clinical research. Genetic ancestry is often oversimplified to broad continental categories, undermining scientific objectivity and potentially perpetuating harmful stereotypes linked to racial genetic determination. Genetic ancestry has been shown to regulate the biology of cancers. For example, in breast cancer, there is a correlation between the percentage of Western African ancestry and the biology of the tumor where an African American with greater West African ancestry has a higher chance of developing triple-negative breast cancer (TNBC), thus making a case for the inclusion of genetic ancestry determination. Social demographic factors may affect not only the trajectory of the patient’s disease but can also regulate the epigenetic makeup of the tumor, and thus both factors are important.

The National Academies of Sciences, Engineering, and Medicine (NASEM) recommends the appropriate use of population descriptors in genetics and genomics research. The US Food and Drug Administration (FDA) has updated guidelines to standardize race and ethnicity data collection in clinical studies [3, 4]. Roche has established the Race, Ethnicity, and Ancestry position statement to affirm its commitment to the responsible collection, use, and application of race, ethnicity, and genetic ancestry data [5]. This initiative may enhance the understanding and enrollment of the diverse patient populations in Roche clinical trials.

Disease burden should be measured by demographic-specific variables using standardized metrics, and by identifying demographic groups with granularity in geographical areas to enable precise benchmarking [6–9], recognizing the limitations in that most existing sources of data on disease prevalence are biased in the same direction as the problem. That is, groups that are underrepresented in trials also tend to be underrepresented in available data sources, reflecting structural biases in health system interactions. To evaluate disease burden, a disease area population analysis should be conducted with a literature review of the therapy area to summarize the epidemiology, risk factors, disparities in diagnosis, disease characteristics and progression, genetics, biomarkers, treatment patterns and outcomes, and quality of care in global patient populations with a focus on health equity (Figs. 1, 2, and 3). Searches and summaries can be done manually or with natural language processing or augmented intelligence tools, keeping biases and limitations in mind. Inclusion and exclusion criteria should be defined for the literature search for population, disease stage(s), diagnosis, biomarkers, existing treatments, study design, outcomes, countries, publication dates, publication type (abstracts, manuscripts), and language restrictions. Studies that meet the criteria and are included in the report should be graded using a quality assessment tool for the strength of evidence [10]. Roche has generated disease area population analysis reports across oncology, neurology, immunology, ophthalmology, pulmonology, and cardio-metabolic-renal therapy areas that have subsequently informed Diversity Action Plans (DAPs) for the FDA [11].

Fig. 1.

5P AIR framework for multiple myeloma. AIR advancing inclusive research, eCRF electronic case report file, MM multiple myeloma, OMB United States Office of Management and Budget, SDOH social determinants of health, SOGI sexual orientation and gender identity. Demographics data are sourced from US Cancer Statistics [13]; incidence maps are sourced from Globocan 2022 [41] and the Institute for Health Metrics and Evaluation (IHME). Used with permission. All rights reserved. For terms and conditions of use, please visit http://www.healthdata.org/about/terms-and-conditions. For any usage that falls outside of these license restrictions, please contact IHME Client Services at services@healthdata.org

Fig. 2.

5P AIR framework for breast cancer. AIR advancing inclusive research, ANC absolute neutrophil count, eCRF electronic case report file, HBV hepatitis B virus, HCV hepatitis C virus, HIV human immunodeficiency virus, OMB United States Office of Management and Budget, SDOH social determinants of health, SOGI sexual orientation and gender identity. Incidence maps are sourced from Globocan 2022 [41] and the Institute for Health Metrics and Evaluation (IHME). Used with permission. All rights reserved. For terms and conditions of use, please visit http://www.healthdata.org/about/terms-and-conditions. For any usage that falls outside of these license restrictions, please contact IHME Client Services at services@healthdata.org

Fig. 3.

5P AIR framework for COPD. AIR advancing inclusive research, COPD chronic obstructive pulmonary disease, eCRF electronic case report file, HBV hepatitis B virus, HCV hepatitis C virus, HIV human immunodeficiency virus, OMB United States Office of Management and Budget, SDOH social determinants of health, SOGI sexual orientation and gender identity

Principle 2: Data-Informed Country and Site Placement

Once the patient population (biology, genetics, demographics) with the disease is understood, Principle 2 anchors on “Where” these patients are (Supplemental Fig. 2). The FDA issued draft guidance for the creation of DAPs by sponsors of pivotal studies to increase enrollment of historically underrepresented populations to improve the strength of the evidence for the intended-use population [11]. Roche has submitted over 30 DAPs to the FDA for 23 therapies across oncology, ophthalmology, neurology, autoimmune, and infectious diseases. Processes, procedures, and platforms have also been developed to monitor and track diverse enrollment.

Global Trial Enrollment

Not all countries have adequate infrastructure and expertise to conduct clinical trials. Therefore, established regional frameworks can be used to advance diverse trial enrollment globally. The International Council for Harmonisation (ICH) Guideline on General Principles for Planning and Design of Multi-Regional Clinical Trials (E17) provides a framework for multiregional clinical trials to support global development and regulatory approvals. It highlights that enrollment should be proportional to the geographical burden of disease, to maximize regional representativeness within operational constraints [12].

Preferred sources, such as the US Cancer Statistics (USCS) and the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease study, should be identified based on data quality, representativeness, and disease area coverage to provide epidemiology estimates at the indication or disease area level [6, 8, 9, 13]. These sources should provide incidence or prevalence of the disease by region to determine the global burden of disease, as well as age and sex distribution to set overall global targets, and race and ethnicity distribution in the USA to set the US targets. A limitation of these sources is that they provide information about epidemiology estimates at the indication level with limited information about subpopulations, e.g., biomarker-driven subsets, stage, and evolution beyond the initial diagnosis. Different epidemiology estimates may be needed if the diversity metric varies by these factors. These estimates can come from real-world data sources or literature reviews. Therefore, augmenting multiple data sources may be required to generate epidemiologic estimates that closely approach the trial population, or the development plan may require triangulation, using two disparate data sources to generate an epidemiology estimate.

To measure representativeness, Roche uses the participation-to-prevalence ratio by dividing the percentage of a particular population among the study participants (e.g., percentage of women among participants in a lung cancer trial) by the percentage of the particular population in the disease population (e.g., percentage of women in the USA with lung cancer). A range of 80–120% has been suggested as adequate representation, where each study should aim for zero error between these proportions [14, 15].

Country-Specific Enrollment

Country-specific enrollment goals should adhere to standardized local requirements and frameworks. Diversity outside the USA cannot be captured or measured by US-specific demographic frameworks alone, such as the Office of Management and Budget’s (OMB’s) standards on race and ethnicity [16], as ex-US patients may not identify to the OMB categories and/or concepts of race/ethnicities are not globally relevant. The use of regional and/or country-specific enrollment by demographic variables ensures that trial results are reflective of countries’ diversity, as well as how these concepts are recorded per national statistical offices or census bureaus. For the USA, population health data licensed from the IHME or USCS can be used to set within-country enrollment targets for demographic groups [6, 13]. Assessments of accessibility should be formalized to ensure efficient and diverse patient enrollment as well as codifying data-informed tactics for enrolling representative populations. Outside of the USA, the site selection process is guided by country-specific disease prevalence estimates. For example, phase 3 studies for chronic obstructive pulmonary disease (COPD) replicated the COPD global disease burden and epidemiology data, and 35 countries were selected as study sites accordingly (Fig. 3). This included low- and middle-income countries (LMICs), such as China, Kenya, and the Philippines, to enroll patients according to unmet needs and with the greatest burden (NCT05595642).

Country-specific needs are also very different in LMICs versus high-income countries, and this framework may not be clearly defined. To have a global impact/implications, metrics need to be defined to assess gaps between epidemiological data and operational feasibility amongst countries. Building capability before clinical trials in partnership with local stakeholders (such as patient networks, non-governmental organizations, ministries of health, and community navigators) can help enable future clinical trial access and inclusive research, as was done with the Ghana Women’s Health Project [17]. When considering these countries, not for profit, but for health equity, a regulatory path for drug approval and sustainable access must also exist.

Site Selections Should Be Representative of the Real-World Population

There are data-informed opportunities to ensure clinical trials are representative of real-world patient populations. Data sources such as the US Census, USCS, the Surveillance, Epidemiology, and End Results Program, IHME, electronic health record-derived real-world data (e.g., TrinetX and Flatiron), and genomic databases are available for epidemiological estimates of US and global populations [18–22]. The representativeness index (R-index) has been proposed as a standardized metric for reporting representativeness of race, ethnicity, and other diversity-related attributes of clinical trial participants, regardless of therapeutic area and trial phase [23]. While no one data source is perfect, key factors to consider are data access, population and geographic coverage, demographic characteristics, and availability of clinical and genomic details.

Study site placements should also be based on operational capabilities, regulatory considerations, demographic data, and disease burden. Methods include using health systems, small area demographics, disease burden, and operational performance data to optimize representativeness and efficiency. Pre-site activation surveys can be coded with questions that assess the baseline state of sites’ advancing inclusive research (AIR) commitments and involvements and guide site selection decisions. This approach allows discrepancies in enrollment metrics to drive local insights and hypothesis generation. Following data-driven processes for site placement ensures that representative trials are not managed at the expense of efficiency and allows discrepancies in enrollment metrics to drive local insights and hypothesis generation. For instance, all else being equal in terms of population and disease burden data, a study site that enrolls slowly compared with a peer must have factors that may merit investigation, such as socioeconomic barriers that have yet to be identified, organizational limitations, or otherwise. If data suggest that a site takes more time to start up or is slow to enroll, one must uncover if it relates to site-based inefficiencies, such as less infrastructural support, or enrollment challenges with historically under-represented patients because of justified mistrust due to systemic racism or limited English proficiency. The former might be a site that would not be selected, the latter should be chosen and provided with appropriate resources, such as the Roche Site Academy, a sponsor program that offers core training to sites that are new to research and provides access to clinical trial equipment and inclusive research training for established sites [24, 25]. Sixteen global sites have utilized this program, including in Africa and India. Data-driven site placement also provides quantitative evidence of efforts by a sponsor if study targets are not met at the time of the last patient enrolled and can lead to the activation of additional resources to address barriers and trial support.

Even if not all countries have adequate infrastructure and expertise to conduct clinical trials, the aim is for enrollment to be proportional to the geographical burden of disease and to maximize regional representativeness within operational constraints. There is also the ethical obligation to only conduct clinical trials in countries with a path to regulatory approvals. To increase the operational capacity of LMIC countries to participate in clinical trials, and accounting for this later policy, Roche-Genentech developed a program called AIR Site Alliance in sub-Saharan Africa and India, intending to expand to other geographies. The AIR Site Alliance program enabled a significant increase in the number of clinical trials conducted in selected LMIC countries [26].

Principle 3: End-to-End Inclusive Trial Design (Data-Driven and User-Informed)

Principle 3 focuses on the “What” of designing a trial. A large portion of trial design is defining the eligibility criteria, which need to be as inclusive as possible while balancing safety risks (Fig. 1, Supplemental Fig. 3). When clinical trial inclusion and exclusion criteria are overly strict, the eligibility and generalizability of results can be limited. Data-driven algorithms combined with real-world data can potentially improve several aspects of clinical trials [27]. Several studies have introduced approaches to quantify the difference between the study samples of a clinical trial and the target population that can use the treatment. An American Society of Clinical Oncology (ASCO) working group found that laboratory value trial criteria rarely change over time in oncology trials, are carried over from protocol to protocol, and may account for the exclusion of a large proportion of patients [28]. They developed evidence-based, consensus recommendations that laboratory tests should be used as exclusion criteria only when necessary.

Another critical aspect of AIR is to include input from providers, patient advocacy groups, and investigators, particularly those from diverse backgrounds, on the study design. When investigators, clinical research staff, and patients were asked for recommendations for future clinical study protocols, themes emerged that included: the use of lay terms in patient materials; allowing flexibility in protocol inclusion/exclusion criteria; incorporating flexibility for sites to enable the fastest decision(s) for patients; and show commitment to diversity in words and actions, such as including target enrollment goals and incorporating patient input on protocols [29]. In the evERA Breast Cancer trial (NCT05306340), specific visit procedures are required for screening, and day 1 can be performed at a single visit to minimize a patient’s time burden (Fig. 2). Patient-facing study resources were co-created with patients and advocates to ensure that resources reflected cultural sensitivity in language and imagery. For multiple myeloma trials, inclusion and exclusion criteria were assessed for late-stage studies and optimized for patients and the healthcare system (e.g., blood volumes, visits) (Fig. 1) with input provided by an external global advisory board with operational expertise. Participants included study nurses and coordinators, who provided feedback on protocols, DAPs, and consent forms. Patient councils and Global Patient Advisory Networks also contributed to the multiple myeloma development plan, protocol design, and study operations, which included the diversity action plan. For COPD trials, input from the Global COPD Focus Group on study design and execution led to understanding the importance of consistent collection of insights, co-creation of materials, patient need for continuous education, solutions on patient retention, and bringing trials closer to patients (Fig. 3). Solutions such as mobile nursing offerings were adapted to the needs of the site staff and the patient community, including developing study materials in 74 different languages. The outcome of offering mobile nursing was an enhanced and flexible decentralized approach, with two-thirds of visits customized according to patient and caregiver needs.

Principle 4: Patient-Reported Data Collection Standards

Principle 4 anchors on the “Who”—who are the individual patients? Standardizing categorical collection would further interpret results meaningfully and allow for a fuller understanding of all patient populations being treated. Currently, there is no global standardization for collecting race, ethnicity, and identity-related variables, and it is challenging to collect and synthesize data across US and global studies, and many times even within the same study, to advance inclusive research. While data collection should respect local definitions and patient-reported data, standardizing categories that support the collection, access, and exchange of patient-reported data for all researchers could increase awareness of the patient populations they are treating and improve clinical trials. Standards can be implemented in collecting patient-reported identification on race/ethnicity categories in electronic case report forms (eCRF), social determinants of health (SDOH), and sexual orientation and gender identity (SOGI) data while respecting local data privacy regulations.

Roche has developed a reconciliation of a standardized eCRF to FDA/OMB Race Categories (Supplemental Fig. 4A) that contains additional race subcategories, such as Middle Eastern and North African, that can be embedded in multiregional clinical trials to ensure global inclusivity, FDA compliance, and appropriate tracking and characterization of patient cohort diversity.

The collection of SDOH data is critical as this information can activate resources during the trial to address and resolve barriers to inclusive research and offer additional enrollment and trial support. The SDOH questionnaire was developed using a multistep collaborative approach that included a literature review, adaptation, feasibility assessment, and compliance checks as well as protocol and informed consent updates. The questionnaire captures patient- and area-level SDOH data, such as education, income, employment status, marital status, insurance status, and 5-digit zip code. Six Genentech-sponsored US clinical studies have the SDOH questionnaires in their protocols, including the evERA study (Supplemental Fig. 4B) [30].

For SOGI data, despite recommendations by the National Institutes of Health, NASEM, and National Science and Technology Council, most clinical trials have not defined sex and gender, or there was inconsistent use of these terms [31–34]. In 2022, Roche-Genentech developed a SOGI questionnaire, which is being piloted in three US clinical studies. These surveys were developed by internal working groups that conducted literature reviews and discussions with clinical research centers and organizations to address current practices and gaps in SOGI data collection. SOGI eCRF will be implemented into the clinical data standards, leveraging the Clinical Data Interchange Standards Consortium terms (Supplemental Fig. 4C).

To ensure the appropriate terms are chosen and consistently collected across the clinical trial process, researchers should: (1) choose the most appropriate term for consistent use based on the objectives of the research, (2) ensure race, ethnicity, ancestry, sex, gender, and SDOH data are self-reported vs observer assigned, (3) obtain self-reported race, ethnicity, ancestry, sex, gender, and SDOH data at each assessment in prospective studies, (4) use self-reported ethnicity with genetic ancestry in genetic association studies, and (5) contextualize the clinical trial data in a manner that maximizes the benefit for all study participants, with careful considerations of any potential harms. A master informed consent form and data privacy agreements can be deployed that can be amended per local laws and government. There are country regulatory managers who review and amend language locally to meet local privacy laws and regulations. Similarly, for eCRF, there is a need to understand which countries will allow incorporation of patient data.

Principle 5: Enabling Access to Trials

Principle 5 anchors on the “How”—how patients will access trials and treatment beyond trial completion (Supplemental Fig. 5). A fundamental and ethical aspect of Roche’s study placement and access plan is that Roche will only conduct trials in countries where a sustainable path to regulatory and access approval exists.

Enabling Access During Trials

A critical component of AIR is demonstrating trustworthiness to participants, caregivers, and healthcare professionals so that they feel the motives of the clinical trial are beneficial and ethical for society. Participants also need to feel informed about the risks of the trial and participate voluntarily. Results from focus groups showed that willingness to participate in clinical research is in part due to how trustworthy a patient finds the researchers, the reputation of the institution and/or sponsor, and whether the researchers and institution or sponsor have previously conducted ethical and transparent research [35, 36].

The Clinical Trial Modernization Act has been proposed by the US Congress, with advocacy led by the American Cancer Society Cancer Action Network, to address economic barriers that may prevent participation in clinical trials [37]. The act would allow trial sponsors to use digital technology for remote participation and cover medical and non-medical costs to help all patients participate in clinical trials, including those who are underrepresented including older adults, people who live in rural areas, and certain racial and ethnic groups. Enhanced Patient Support Services (EPSS) can facilitate access for patients in clinical trials, as well as address language, cultural, literacy, and communication barriers, and provide better quality of care through the elimination of existing health inequities. Activities provided by EPSS include making referrals and contacts on behalf of patients, arranging transportation, financial support, medication, and equipment services, and offering proactive navigation through the process [38]. EPSS has been embedded in all US study contracts since November 2022 and is being expanded to additional countries (Figs. 1, 2, and 3) [39]. EPSS includes a well-defined catalog of invoiceable services that are built into the study budget for consistency and sustainability, where the sites determine who is best suited to provide each service [40]. The evERA study piloted a community oncology patient navigation program to facilitate access to information and resources essential to understanding and mitigating the impact of breast cancer on daily life while educating patients about clinical trial participation (Fig. 2). The evERA trial provides per-visit stipends as allowed by clinical sites to support costs associated with clinical trial participation, patient reimbursements for meals and transportation, and options for transportation and lodging that require no out-of-pocket expenses for patients. This reinforces community engagement and builds trustworthiness with participants. The phase 3 COPD program also included support and reimbursement of caregiver expenses, community education, health system navigation, site staffing, and capability upskilling, such as clinical trial execution.

Post-Trial Access and Patient Assistance Programs

If patients have received treatment via participation in a clinical trial, they must still have access to it once the trial is completed, even while awaiting regulatory approval. These considerations are critical for patients who have life-threatening conditions that require continued administration of the treatment, and for patients who do not have appropriate treatment alternatives. The goal is to ensure that treatment is not interrupted between the completion of the clinical trial and drug commercialization. Each country participating in the clinical trial must also have the treatment available once approved.

Post-trial patient assistance programs can help obtain medication, provide financial help with insurance premiums and co-pays, assist with diagnostic testing, provide travel assistance, provide caregiver respite, and provide disease education. These programs are available globally and are valuable in assisting those without health insurance and those who are underinsured to afford medications by either covering the full cost or providing a discount, per local regulations.

Conclusions

Inclusion and representation of all patient populations with disease globally are critical for responsible drug development, and meaningful improvement is predicated on the ability to measure progress. Precision medicine is fundamentally connected to inclusive research as it aims to target the right treatments to the right patients at the right time. Principle 1 starts with collecting a range of epidemiological, biological, genetic, and demographic data to understand a given disease’s prevalence, presentation, and potential outcomes in specific subpopulations. These data are accounted for in designing the protocols (Principle 3), to set the diversity enrollment goals (Principles 1 and 2), and to implement the studies in ways that will facilitate diverse patients’ participation (Principle 4), accounting for their different needs and preferences. At the end, the outcomes will be analyzed and reported for the different subpopulations defined in the diversity plan. While the sample size of a given study will often not be sufficient to answer all questions to target the right treatments to the right patients, the systematic framework provided by the 5Ps should contribute to precision medicine significantly by the totality of evidence it will bring to the development of new treatments.

The framework, and specifically Principle 3, supports and enables innovative trial design. This principle is a two-dimensional approach. First, data-driven trial design is applied to expand eligibility criteria to enable more diverse patient recruitment: study designer tools using AI, leveraging a library of previous protocols, and applying dynamic eligibility criteria to impact real-world data sets are under development to become our new standard for designing protocols. The next aspect of the design is to challenge the type and schedule of assessments carefully and to use elements of pragmatic trials design to represent the diversity of clinical care better and facilitate patient enrollment and retention. Real-time monitoring of data on screening failures and real-time analysis of the demography of enrolled patients enable study teams to identify and correct study design limitations through additional support interventions (Principles 2, 3, and 5) or protocol amendments if needed. Second, user-informed trial design activates a process to ensure the needs of patients are met, such as collaboration with focus groups and patient councils to understand the patient voice during study design as well as during study conduct. The latter, with data monitoring, ensures the design is operational and efficient. The 5P AIR framework is intended to provide processes by which inclusive research can be conducted in an organization and adhere to regulatory requirements. Future frameworks may focus on real-world data principles for observational or interventional studies and to leverage real-world data as an engine for measuring impact and outcomes.

The 5P framework advances the goal of ensuring life science research is inclusive, representative, and efficient. Establishing a framework for AIR requires demographic, biological, and access considerations, provider and patient advocacy feedback into protocol development, expanding race and ethnic subcategories, and SDOH data collection, all of which collectively can help achieve the best next steps in science to narrow health inequities and improve patient outcomes.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

Shalini Hede: conceptualization and writing. Spencer L James: conceptualization and writing. Altovise T Ewing-Crawford: conceptualization and writing. Ruma Bhagat: conceptualization and writing. Nicole Richie: conceptualization and writing. Mitchell D’Rozario: conceptualization and writing. Pierre Theodore: conceptualization and writing. Bea Lavery: conceptualization and writing. Sarah Bentouati: conceptualization and writing. Assaf Oron: conceptualization and writing. Kate W Gillespie: conceptualization and writing. Cleo A Ryals: conceptualization and writing. Megan Bair-Merritt: conceptualization and writing. Johanna Chesley: conceptualization and writing. Evelyn Jiagge: conceptualization and writing. Bruno Jolain: conceptualization and writing.

Funding

This report, Rapid Service Fee, and Open Access fees were funded by F. Hoffmann-La Roche Ltd/Genentech, Inc.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Declarations

Conflict of Interest

All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: Shalini Hede: employee of Roche, Spencer L James: former employee of Roche, Altovise T Ewing-Crawford: nothing to declare, Ruma Bhagat: nothing to declare, Nicole Richie: nothing to declare, Mitchell D’Rozario: owns stock in Roche, Pierre Theodore: employee of Roche/Genentech Inc, and owns stock in Roche, Bea Lavery: employee of Genentech Inc., Sarah Bentouati: nothing to declare, Assaf Oron: nothing to declare, Kate W Gillespie: employee of the University of Washington. No payments were made to myself or my institution for the present manuscript, Cleo A Ryals: employee of Flatiron, a member of the Roche group and owns Roche stock, Megan Bair-Merritt: nothing to declare, Johanna Chesley: nothing to declare, Evelyn Jiagge: received the Genentech Health Equity Innovation Grant and Pfizer Global Medical Grant; has received other grants from Pfizer, Bruno Jolain: employee of and owns stock in Roche.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.