Personal DNA Testing Increases Pharmacy Students' Confidence and Competence in Pharmacogenomics

Mahfoud Assem, PhD; Ulrich Broeckel, MD; George E. MacKinnon, PhD

Disclosures

Am J Pharm Educ. 2021;85(4):8249 

In This Article

Methods

PharmD students (class of 2022) at the MCW School of Pharmacy were required to complete two courses, Principles of Drug Actions and Pharmacogenomics and Patient Care Laboratory during their first professional year. These two courses covered a broad range of lectures and practice techniques including basic pharmacology, medicinal chemistry, pharmacokinetics, principle of pharmacogenomics, applications to pharmacokinetics and pharmacodynamics. Additionally, the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, ethics, and patients counseling with respect to pharmacogenomics were covered.

We designed an intervention to allow students to be active participants in their own learning. The activity allowed students, for a temporary time, make the transition from student-learners to patient-learners. Students were asked to complete a pre-and post-intervention attitudinal survey of pharmacogenomics to determine attitudinal changes from baseline, as has been done by others in pharmacy education.[4]

Participants were asked to complete an anonymous pre-course survey on Qualtrics XM (Qualtrics) prior to starting the course. The survey was administered to assess students' baseline knowledge and attitude towards pharmacogenomics and how they believed they would use pharmacogenomics in their future role as a health care provider. Before completing the pre-course survey, students were provided a consent form that included a detailed description of the purpose, goals, and objectives of the study.

The survey consisted of 27 questions grouped in four categories: assessment of students' knowledge of pharmacogenomics testing, use of the information to counsel patients and other health care providers, application to future clinical pharmacy practices, and comfort with the practice of adapting medication regimens and patient counseling based on genetic information, and their future professional development. The survey items were from the University of Maryland School of Medicine (45% of the questions)[5] and from an article by Coriolan and colleagues (18% of the questions),[6] with the remaining created by the authors (37% of the questions). Students' answers on the pre- and post-survey instruments were paired using anonymous self-selected codes. The scoring was performed according to the Likert 5-point scoring. Each choice was assigned a value from 1 to 5, as follows for the respective questions on understanding, confidence, and usefulness of pharmacogenomics: (1=no understanding/extremely not confident/extremely not useful to 5=thorough understanding/extremely confident/extremely useful). The following scale was used for questions on perceptions of comfort and readiness (1=strongly disagree, to 5=strongly agree). During analysis, the data was merged into two groups for percentage calculations: answers 1 and 2 versus answers 3, 4, and 5. The mean response and standard deviation were also calculated for each question and compared. Student learning outcomes and examinations scores were extracted from ExamSoft (Examsoft Worldwide, Inc).

All of the students were given the opportunity to voluntarily participate in a pharmacogenomics assay performed from a saliva specimen. Following consent, all samples were retained in a locked area and only handled by the investigators until the assays were run by RPRD Diagnostics (Milwaukee, WI). Genomic DNA from saliva was extracted and amplified by multiplex PCR. The PCR products from each student's amplified DNA were pooled, purified, fragmented, labeled, and hybridized to the PharmacoScan Array per the manufacturer's recommendations (ThermoFisher Scientific, Waltham, MA). Arrays were then stained with a fluorescent antibody and scanned on the GeneTitan Multi-Channel Instrument. The data were analyzed using the Axiom Analysis Suite 3.1 (ThermoFisher Scientific). Analysis was performed using the commercially released allele translation table (version R6). Pharmacogenomics data and other relevant demographic data were harvested and anonymously stored.

After the pharmacogenomics testing was conducted, each student who participated was given a copy of their personalized laboratory report. The report provided three sets of data: actionable results with phenotype and gene activity for genes with CPIC guidelines; phenotype and gene activity for genes with known pharmacogenetic functions but without CPIC guidelines; and genotypes for relevant genes that don't yet have any known clinical applications. Students were given two weeks to review their pharmacogenomics results. The genetic testing was 100% focused on genes relevant to pharmacogenomics (ie, mostly drug metabolism genes).

Students were asked to select a few markers of interest based on their and/or their relatives' medication history for a flipped classroom discussion. All students' data were appropriately clustered, assigned to a group based on the data and used to trigger active discussions. Within each cluster, at least one major genetic polymorphism was identified that was later selected as a subject of discussion in small groups of four to six students and in whole class activities using CPIC guidelines and PharmGKB data.

This study was approved by the MCW's institutional review board (IRB) and all participants provided informed consent. According to IRB protocols, genetic data collected from the "pharmacogenomics assay" was maintained by RPRD Diagnostics, and the primary investigators had no direct access to student-specific laboratory results as required by current laws on privacy and confidentiality.

Statistical analyses were performed by R programming software, version 4, using the RStudio platform, version 1.2. All individual questions used a paired Wilcoxon signed-rank test to generate p values and significance was set at a p value under .05.

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