Pharmacogenomics (PGx) investigates the genetic influence on drug response and is an integral part of precision medicine. While PGx testing is becoming more common in clinical practice and may be reimbursed by Medicare/Medicaid and commercial insurance, interpreting PGx testing results for clinical decision support is still a challenge. The Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been designed to tackle the need for transparent, automatic interpretations of patient genetic data. PharmCAT incorporates a patient's genotypes, annotates PGx information (allele, genotype, and phenotype), and generates a report with PGx guideline recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and/or the Dutch Pharmacogenetics Working Group (DPWG). PharmCAT has introduced new features in the last 2 years, including a variant call format (VCF) Preprocessor, the inclusion of DPWG guidelines, and functionalities for PGx research. For example, researchers can use the VCF Preprocessor to prepare biobank-scale data for PharmCAT. In addition, PharmCAT enables the assessment of novel partial and combination alleles that are composed of known PGx variants and can call CYP2D6 genotypes based on single and deletions in the input VCF file. This tutorial provides materials and detailed step-by-step instructions for how to use PharmCAT in a versatile way that can be tailored to users' individual needs.

Community-centric approaches to engage underrepresented populations-including community engagement, community-level consent practices, and capacity development for research-are means to enhance diversity in biomedical research populations in a more ethical way. Low diversity is a known problem in biomedical research that presents challenges in translating the benefits of research to the global population. Through long-term partnerships built on trust and collaboration, communities who would otherwise avoid research may be more willing to participate. When communities are engaged in research as partners and research questions are motivated by community health priorities, research is more meaningful and research methods are more respectful. Conversely, a lack of consultation throughout the research process can further alienate the very communities that these efforts are designed to engage. A number of underserved populations-for example American Indian and Alaska Native peoples-may value the benefits of research to a community equally or more than individual benefits. A community's autonomy must be considered, particularly when that community is not adequately protected by traditional informed consent processes. Opportunities for capacity development to support collaborative partnerships between communities and researchers are required to support engagement and understanding of the research process. Changes to research processes and infrastructure that encourage a higher level of research oversight within the community should be supported. In this paper, we present approaches that may improve diversity and equitable access to research and the delivery of health innovations for people that have historically been left out of biomedical research.

The relationship between race and biology is complex. In contemporary medical science, race is a social construct that is measured via self-identification of study participants. But even though race has no biological essence, it is often used as variable in medical guidelines (e.g., treatment recommendations specific for Black people with hypertension). Such recommendations are based on clinical trials in which there was a significant correlation between self-identified race and actual, but often unmeasured, health-related factors such as (pharmaco)genetics, diet, sun exposure, etc. Many teachers are insufficiently aware of this complexity. In their classes, they (unintentionally) portray self-reported race as having a biological essence. This may cause students to see people of shared race as biologically or genetically homogeneous, and believe that race-based recommendations are true for all individuals (rather than reflecting the average of a heterogeneous group). This medicalizes race and reinforces already existing healthcare disparities. Moreover, students may fail to learn that the relation between race and health is easily biased by factors such as socioeconomic status, racism, ancestry, and environment and that this limits the generalizability of race-based recommendations. We observed that the clinical case vignettes that we use in our teaching contain many stereotypes and biases, and do not generally reflect the diversity of actual patients. This guide, written by clinical pharmacology and therapeutics teachers, aims to help our colleagues and teachers in other health professions to reflect on and improve our teaching on race-based medical guidelines and to make our clinical case vignettes more inclusive and diverse.

Healthcare disparities are a persistent societal problem. One of the contributing factors to this status quo is the lack of diversity and representativeness of research efforts, which result in nongeneralizable evidence that, in turn, provides suboptimal means to enable the best possible outcomes at the individual level. There are several strategies that research teams can adopt to improve the diversity, equity, and inclusion (DEI) of their efforts; these strategies span the totality of the research path, from initial design to the shepherding of clinical data through a potential regulatory process. These strategies include more intentionality and DEI-based goal-setting, more diverse research and leadership teams, better community engagement to set study goals and approaches, better tailored outreach interventions, decentralization of study procedures and incorporation of innovative technology for more flexible data collection, and self-surveillance to identify and prevent biases. Within their remit of overlooking research efforts, regulatory authorities, as stakeholders, also have the potential for a positive effect on the DEI of emerging clinical evidence. All these are implementable tools and mechanisms that can make study participation more approachable to diverse communities, and ultimately generate evidence that is more generalizable and a conduit for better outcomes. The research community has an imperative to make DEI principles key foundational aspects in study conduct in order to pursue better personalized medicine for diverse patient populations.

Although at least 25 million adults are transgender worldwide, few phase III clinical trials have enrolled transgender and gender diverse (TGD) people. HIV is the only therapeutic area to include TGD people intentionally in phase III randomized clinical trials during the development of certain newer HIV pharmacologic prevention interventions. Pharmacologic assessments for HIV prevention efficacy in TGD populations are important, as there may be specific considerations for product use and potential interactions with hormone therapies. Herein, we summarize ongoing and completed phase III HIV trials that included TGD people as part of the study population, we examine investigators' strategies for recruiting and engaging TGD priority populations in these phase III trials, and we comment on the implications of these studies for prioritizing TGD populations in clinical pharmacology research within the phase III clinical trial landscape.

Legislative and technological advancements over the past decade have given rise to the proliferation of healthcare data generated from routine clinical practice, often referred to as real-world data (RWD). These data have piqued the interest of healthcare stakeholders due to their potential utility in generating evidence to support clinical and regulatory decision making. In the oncology setting, studies leveraging RWD offer distinct advantages that are complementary to randomized controlled trials (RCTs). They also permit the conduct of investigations that may not be possible through prospective designs due to ethics or feasibility. Despite its promise, the use of RWD for the generation of clinical evidence remains controversial due to concerns of unmeasured confounding and other sources of bias that must be carefully addressed in the study design and analysis. To facilitate a better understanding of when RWD can provide reliable conclusions on drug effectiveness, we seek to conduct 10 RWD-based studies that emulate RCTs in oncology using a systematic, protocol-driven approach described herein. Results of this investigation will help inform clinical, scientific, and regulatory stakeholders on the applications of RWD in the context of product labeling expansion, drug safety, and comparative effectiveness in oncology.

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K-related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard-principal component adjusted and contemporary-local ancestry adjusted regression models. A total of five variants from vitamin K-related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry-adjusted analysis unveiled 35 associated variants with absolute effects ranging from β = 9.04 ( ±2.23) to 39.18 ( ±10.89) per ancestral allele in the discovery cohort and β = 6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research.

Biosimilar development has a well-documented foundation of product quality and extensive comparative analytics providing the bulk of the "totality of the evidence" that a proposed product is biosimilar to its reference product. This work provides a retrospective evaluation of a single critical quality attribute-high mannose glycans for monoclonal antibody biosimilars. Given the well-established conclusion that high mannose glycans can impact pharmacokinetic (PK) profile, we performed a retrospective evaluation of 21 monoclonal antibody biosimilar programs (those licensed before April 2022), their levels of glycans, and the methods used to study them. We provide herein a summary of the methods used and their relative performance. We also present a subset analysis for seven biosimilar products with levels of high mannose that differ from the corresponding reference product (and where other differences in quality attributes between the two that may influence PK profile were not observed or considered minor) and compared the PK profiles. Critically, this analysis has demonstrated that the measurement of glycan profiles is highly precise, reproducible within and across programs, and can detect differences in mannose levels, even those that do not impact PK. These results provide support that analytics rather than pharmacokinetic data may be sufficient to predict whether differences within a certain magnitude of this attribute are likely to impact PK. This work enhances the Agency's understanding of this issue allowing for better understanding of challenges faced by the biotechnology industry developing biosimilars.

The US Food and Drug Administration (FDA) Biosimilars Guidance describes how biosimilars may be approved based on clinical pharmacokinetic and pharmacodynamic (PD) biomarker data, without comparative clinical studies with efficacy end points. This type of clinical development program, however, has only been implemented for a small number of FDA-approved biosimilar products over the last decade. To encourage the use of PD biomarkers in biosimilar development and approval, the Duke-Margolis Center for Health Policy collaborated with the FDA to host a two-day virtual public workshop entitled "Pharmacodynamic Biomarkers for Biosimilar Development and Approval" on September 20-21, 2021. The public workshop was a forum for global regulators, biopharmaceutical developers, and academic researchers to discuss the current and future role of PD biomarkers in improving the efficiency of biosimilar development and approval. The workshop objectives included: (i) discuss the current and potential future state of leveraging PD biomarkers for biosimilar development and approval; (ii) summarize the FDA's initiatives to advance biosimilar development; (iii) describe stakeholders' experience with PD biomarkers in biosimilar development; and (iv) explain research efforts to promote broader application of PD biomarkers in biosimilar development. This document summarizes presentations and panel discussions from each session of the two-day September 2021 public workshop covering the application of PD biomarkers for biosimilar development.

Most pharmacogenetic research is conducted in adult, non-pregnant populations of European ancestry. Study of more diverse and special populations is necessary to validate findings and improve health equity. However, there are significant barriers to recruitment of diverse populations for genetic studies, such as mistrust of researchers due to a history of unethical research and ongoing social inequities. Engaging communities and understanding community members' perspectives may help to overcome these barriers and improve research quality. Here, we highlight one method for engaging communities, the Community Engagement Studio (CES), a consultative session that allows researchers to obtain guidance and feedback based on community members' lived experiences. We also provide an example of its use in pharmacogenetic studies. In designing a survey study of knowledge and attitudes around pharmacogenetic testing among children with chronic conditions and pregnant individuals, we sought input from diverse community stakeholders through CESs at Vanderbilt University Medical Center. We participated in two CESs with community stakeholders representing study target populations. Our goals were to learn specific concerns about pharmacogenetic testing and preferred recruitment strategies for these communities. Concerns were expressed about how genetic information would be used beyond the immediate study. Participants emphasized the importance of clarity and transparency in communication to overcome participation hesitancy and mistrust of the study team. Recruitment strategy recommendations ranged from informal notices posted in healthcare settings to provider referrals. The CES enabled us to modify our recruitment methods and research materials to better communicate with populations currently under-represented in pharmacogenetics research.

The aging processes alter the body's response to a medicine's pharmacokinetics, pharmacodynamics, and susceptibility to adverse effects. In addition, older adults, especially the oldest age category (85+ years) or those with multiple chronic health conditions, polypharmacy, or frailty, are under-represented in clinical trials of new medicines. Evidence-based prescribing guidelines based on these trials might result in inappropriate prescription, increasing the risk of drug interactions and adverse drug reactions. Regulators face a conundrum between acquiring sufficient data and putting susceptible patients at risk in the early stages of a development program, when little is known about a medicine's effects. Healthcare professionals and patients deserve to have access to clear information on the knowledge and evidence gaps leading to the approval of a new medicinal product. This should also include proper consideration of the population of older patients. In the present article, we outline the approach taken by the European Medicines Agency (EMA) regulators in the assessment of a new medicine's dossier.