A Clinical Pharmacology and Therapeutics Teacher's Guide to Race-Based Medicine, Inclusivity, and Diversity

Understanding factors that influence the drug choice of prescribers: A Q-methodology study

INTRODUCTION

Therapeutic decision-making often involves weighing several treatment options on the basis of, among other things, the disease profile, patient characteristics but also prescriber-specific factors. This study investigated the factors influencing therapeutic decision-making among prescribers and explores how these factors differ between healthcare professionals.

METHODS

This Q-methodology study involved 37 participants from different medical backgrounds, including physicians, dentists, midwives, physician assistants and advanced nurse practitioners from various intra- and extramural settings in the Netherlands. Participants ranked 55 factors on their importance for medication prescribing, and factor analysis was used to identify distinct prescriber profiles. These profiles were subsequently enriched using qualitative data derived from post-sorting interviews.

RESULTS

Four prescriber profiles were identified that explained 59% of the study variance: pragmatic contextual, guideline oriented, experience driven and vulnerability focused. Several factors were ranked differently in each profile. The pragmatic contextual profile emphasized patient context and risk prevention, the guideline-oriented profile adhered to clinical guidelines, the experience-driven profile relied on clinical experience and patient-centred communication, and the vulnerability-focused profile prioritized personalized care for specific patient groups.

CONCLUSION

This study sheds light on the diverse factors influencing prescriber decision-making in medical practice. By identifying four prescriber profiles, it reveals the complexity and diversity in how prescribers balance guidelines, clinical experience and patient-specific considerations in their practice. These findings offer direction for developing educational and policy initiatives to enhance the quality of prescribing, ultimately improving patient outcomes.

Addressing Ethnicity in the Design and Evaluation of an Educational Intervention on Interindividual Variation in Pharmacokinetics

Interindividual variation in pharmacokinetics can occur due to diet, environmental or lifestyle factors, underlying pathology, and gene variants, typically single nucleotide polymorphisms (SNPs). Genetic mechanisms have received the most attention in research and education about ethnic differences in pharmacokinetics. Making this connection between genetics and ethnicity is problematic because it could reinforce the erroneous idea that there is a biological basis to ethnicity. The aim of this work was to design an educational intervention about interindividual variation in pharmacokinetics, explore how students perceive ethnicity and genetic differences prior to the educational intervention, and then assess the impact of the intervention and whether it could influence any misconceptions students might have about ethnicity and genetic similarity. Through the use of questionnaires and focus groups, we found that students typically refer to ethnicity to mean culture and place of origin, whereas in the pharmacological literature, ethnicity is synonymous with racial groups, that is, Black, White, and Asian. Prior to the educational intervention, students tended to expect a genetic mechanism for ethnic differences in drug metabolism and this was reduced after the intervention when a range of other nongenetic mechanisms were presented for interindividual variation. However, students' views about possible underlying mechanisms for ethnic differences in hypertension and about ethnicity more generally were unaffected by the intervention. This highlights the importance of reevaluating the way ethnicity is presented across the medical and medical sciences curriculums to be clear that ethnicity is socially constructed and avoid implying a biological basis.

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Publication Recommendations to Report Laboratory Data of Neonates - a Modified Delphi Approach

BACKGROUND

Clinical and analytical information on laboratory data of neonates in scientific publications is sparse and incomplete. Furthermore, interpreting neonatal laboratory data can be complex due to their time-dependent and developmental physiology, and paucity of well-established age-appropriate reference ranges for neonates. This study aims to develop publication recommendations to report laboratory data of neonates to enhance the quality of these data in research and clinical care.

METHODS

A modified Delphi approach was used to develop recommendations in cooperation with the International Neonatal Consortium. A Core Group, including different stakeholders, was responsible for developing the recommendations, in collaboration with a Reflection Group, responsible for providing additional input.

RESULTS

The recommendations were classified into three categories: 'Clinical Characteristics', 'Bio-analytical Information' and 'Data-analytical Information'. These were each divided into 'Core Data' (always to be reported) and 'Supplemental Considerations' (to be reported when considered relevant to the study).

CONCLUSION

Our recommendations provide guidance on standardization of neonatal laboratory data in publications. This will enhance the comparison, replication, and application of study results in research initiatives and clinical practice. Furthermore, these recommendations also serve as foundational work to develop reference ranges for neonatal laboratory values by standardizing the quality of information needed for such efforts.

IMPACT

Standardized reporting of neonatal laboratory data in scientific publications will enhance the comparison, replication, and application of study results in research initiatives and clinical practice, as well as improve reporting to regulatory agencies. To integrate multistakeholder perspectives, a modified Delphi approach was used to develop publication recommendations which strengthens the applicability of the recommendations. Implementation of standardization will likely improve the overall quality of neonatal clinical research and neonatal healthcare. In addition, these recommendations are foundational to develop reference ranges for neonatal laboratory values by standardizing the quality of information needed for such efforts.

Using artificial intelligence to create diverse and inclusive medical case vignettes for education

AIMS

Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)-assisted computer-generated clinical cases to expedite case creation and enhance diversity, along with AI-generated patient photographs for more lifelike portrayal.

METHODS

In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation.

RESULTS

Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set.

CONCLUSIONS

Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.

The frequency of rs2231142 in ABCG2 among Native Hawaiian and Pacific Islander subgroups: implications for personalized rosuvastatin dosing

Statins are among the most commonly prescribed medications worldwide. Rosuvastatin is a moderate- to high-intensity statin depending on the prescribed dose. Statin-associated muscle symptoms are the main side effects, contributing to low adherence to statins. The missense variant rs2231142 in ABCG2 affects the functionality of the ABCG2 transporter, altering the pharmacokinetics and pharmacodynamics of rosuvastatin. This special report aims to accentuate the importance of considering the ABCG2 genotype upon prescribing rosuvastatin in high cardiovascular disease risk subgroups, specifically Native Hawaiian and Pacific Islander populations. Based on the reported frequencies of rs2231142 in ABCG2, it may be justifiable to initiate low-dose rosuvastatin in Samoans relative to Marshallese or Native Hawaiians. Interpopulation differences in pharmacogenetic allele frequencies underscore the need to disaggregate broad population categories to achieve health equity in treatment outcomes.