Clinical Implementation Pilot of Preemptive Pharmacogenetic Testing in Medically Underserved Patient Populations
Phase: NA
Conditions: Pharmacogenetic Testing
Sponsor: University of Florida

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Protocol 
1. Project Title: Clinical implementation pilot of preemptive pharmacogenetic testing in medically 
underserved patient populations 
2. Investigators: 
 
Principal Investigator: 
 
Julio Duarte, PharmD, PhD 
Assistant Professor 
Department of Pharmacotherapy and Translational Research 
UF College of Pharmacy 
 
Co-Investigators: 
  
Larisa Cavallari, Pharm.D. 
Associate Professor 
Department of Pharmacotherapy and Translational Research 
UF College of Pharmacy 
 
Ramzi Salloum, Ph.D. 
Assistant Professor 
Department of Health Outcomes & Biomedical Informatics 
UF College of Medicine 
 
Carla L. Fisher, Ph.D. 
Associate Professor  
Department of Advertising 
UF Health Cancer Center 
UF College of Journalism and Communications 
 
Elvira S. Mercado, M.D.  
Assistant Professor 
Department of Community Health and Family Medicine 
UF College of Medicine 
 
John Z. Malaty, M.D. 
Associate Professor 
Department of Community Health and Family Medicine 
UF College of Medicine 
3. Abstract: 
Little information exists regarding clinical implementation of pharmacogenetics (PGx) in medically 
underserved patient populations. Our preliminary data indicate that underserved patients are prescribed a 
higher rate of drugs associated with PGx guidelines (PGx drugs). Thus, an important knowledge gap 
exists regarding the use of PGx in a patient population that may be the most likely to clinically benefit. The 
objective of this project is to develop key feasibility data to equitably advance preemptive PGx testing 
within University of Florida Health, and to generate important preliminary data to support future larger 

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studies. We plan to accomplish this objective by pursuing three specific aims: (1) assess the feasibility of 
preemptive PGx clinical implementation in primary care clinics predominantly serving medically 
underserved patients; (2) understand perspectives about preemptive PGx among key stakeholders in the 
primary care clinics predominantly serving medically underserved patients; and (3) identify specific 
socioeconomic characteristics most strongly associated with PGx drug prescription rate. To achieve these 
aims, we will recruit patients for preemptive PGx testing from primary care clinics that primarily serve our 
target patient population, and use patient-reported outcomes, feedback from semi-structured interviews, 
and data collected from the electronic health record to assess testing feasibility and pilot outcome data. 
Upon successful completion of this project, we expect to have generated important preliminary data that 
can be used to support wider implementation of PGx testing to underserved patients and a variety of 
current and larger studies designed to further examine implementation of precision medicine 
technologies. This line of research will positively impact drug-related outcomes by reducing healthcare 
disparities in the field of precision medicine.  
4.  Background: 
While, for over 30 years, we have known that genetic polymorphisms affect drug response, clinical use of 
pharmacogenetic (PGx) testing outside of cancer is still not widespread. PGx testing is primarily limited to 
large academic medical centers, which greatly limits the number of patients who have access to this 
technology and precision of drug therapy that it allows. In addition, with healthcare costs rising rapidly, 
there appears to be little appetite for additional reimbursements in our current fee-for-service healthcare 
model.1 Thus, many health insurers often refuse to reimburse for PGx tests, especially those done 
preemptively, requiring the patients to self-pay. This practice excludes low-income patients who are 
unable or unwilling to incur the out-of-pocket costs. Lastly, many PGx tests have been developed using 
data primarily from subjects of European descent, making their results less clinically useful for patients of 
Latino or African descent. 
Current implementation patterns of PGx testing, as with other innovative technologies, have the potential 
to increase disparities in health care quality and outcomes, especially if there is greater preexisting need 
in the populations unable to access them. This phenomenon, known as the Inverse Equity Hypothesis,2 
will continue to occur unless specific access barriers are overcome when a new technology is 
implemented. 
PGx testing may be particularly beneficial in medically underserved populations by reducing the number 
of medical encounters required to optimize drug therapy and preserving use of less expensive off-patent 
drugs – the type drugs most often with PGx guidelines available – for patients predicted to benefit based 
on their genotype results. This is supported by preliminary data from the University of Florida (UF) Health, 
which indicate that patients with poor geographic access to healthcare providers are prescribed a higher 
rate of drugs with PGx guidelines available (PGx drugs). Race compounds this disparity, with 
underserved black patients having both significantly fewer encounters with healthcare providers and more 
PGx drug prescriptions than underserved non-black patients. Because medically underserved patients 
use more PGx medications and visit their provider less often, these patients should benefit the most from 
preemptive PGx testing, where trial-and-error drug selection could be minimized. Thus, there is a critical 
need to gather data to inform clinical implementation of PGx testing in medically underserved populations 
to improve healthcare quality and assure equitable distribution of innovative healthcare technologies. 
Most experts agree that clinical PGx implementation will eventually evolve to a preemptive testing model 
where patients are tested for multiple PGx variants at one time, with these data stored in their electronic 
health record (EHR) to inform future prescribing.3,4 Pre-emptive testing is more efficient than “reactive” 
testing (i.e. at the time of drug prescribing) because most PGx tests have the most clinical benefit when 
genotype is known before a medication is administered, reducing the risk of treatment failure or adverse 

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effects. In addition, preemptive testing allows genotyping samples from many patients to be batched and 
run in high volume, which substantially decreases the genotyping cost per patient. Thus, the most 
feasible/sustainable way to facilitate the clinical implementation of PGx in underserved patients will likely 
involve preemptive genotyping. 
To our knowledge, there have been no studies reporting implementation data of preemptive PGx testing 
in medically underserved patients. Further, there are few data in the literature regarding the perceptions 
and attitudes of black and Latino patients toward preemptive PGx testing. This work is significant because 
it is expected to contribute valuable data toward both the feasibility of preemptive PGx testing in medically 
underserved patients, an area of PGx research where little information is available. We anticipate these 
data will inform future multi-site clinical trials of preemptive PGx implementation. 
5. Specific Aims: 
Motivated by the critical need to expand access of preemptive PGx testing to underserved populations, 
the overall objective for this application is to develop key feasibility data to equitably advance preemptive 
PGx testing within UF Health, and to generate important preliminary data to support future funding 
applications.  The rationale for this project is that generating these feasibility data should facilitate 
equitable clinical implementation that may reduce medication treatment disparities, as well as inform 
future larger clinical studies. We plan to accomplish the overall objective of this application by pursuing 
three specific aims:  
1. Assess the feasibility of preemptive PGx clinical implementation in primary care clinics 
predominantly serving medically underserved patients. We will provide clinical preemptive PGx 
testing for 100 patients from specific UF Health clinics serving mostly medically underserved patients, 
including clinics with a high rate of black and Latino patients. We will assess feasibility of this 
implementation effort by comparing patient-reported outcomes data abstracted from electronic health 
records before and 6 months after PGx testing.  
2. Understand perspectives on preemptive PGx testing among key stakeholders in the primary 
care clinics predominantly serving medically underserved patients. We will conduct in-depth, semi-
structured interviews with a subsample of recruited patients from Aim 1 and clinicians within the clinic 
sites to identify influential factors critical to implementing PGx testing, including motivations, barriers, and 
best practices for the delivery of results.  
3. Identify specific socioeconomic characteristics most strongly associated with PGx drug 
prescription rates. Our preliminary data using EHR data from UF Health show strong correlations 
between zip code-derived healthcare geographic access scores and rate of PGx drug prescriptions. We 
aim to use the results of a validated socioeconomic estimation tool as well as individual measures of 
socioeconomic status in a single predictive model to determine which social determinants most associate 
with PGx drug prescription rate within the 100 patients from Aim 1. 
6. Research Plan: 
Study population and setting. This study will include UF Health patients recruited from clinics that 
serve mostly medically underserved areas. Primarily, this will include the UF Health Family Medicine 
Eastside and Main St. Clinics, which serve a large number of low-income, black and Latino patients. 
Overall, we plan to recruit 100 patients from targeted clinics. Inclusion criteria are designed to identify 
patients who have a high possibility of benefitting from PGx testing. 
 

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Inclusion Criteria: 
1. Adults (18 years or older) with active prescriptions for at least 3 medications documented within 
the EHR. 
2. At least 1 drug/drug class that could be informed by the PGx test panel (See Appendix A for 
complete list). 
3. A medication change within the past 6 months (associated with a healthcare provider 
encounter) 
4. Self-identify as black or Latino.  
Exclusion Criteria: 
1. Patients with any history of PGx testing within the EHR. 
Participant Recruitment Methods.  
Patients may be approached for the study in various way including clinic intercepts, study flyers and clinic 
referrals. The electronic health record will be queried to identify potentially eligible participants using the 
inclusion/exclusion criteria. Providers (including but not limited to physicians, nurse practitioners, and 
pharmacists) who care for patients at UF Health will be notified when their patients qualify for the study 
and will be asked to discuss the study with their patients.  
Patients can be recruited by study staff via two methods: 
1. Clinic providers or staff ask patients if they are interested in participating and they acknowledge that 
they are interested. 
2. Clinic provider or staff will provide patients an approved flyer and they contact study staff if 
interested. 
If recruitment occurs during a clinic visit, patients meeting eligibility criteria will be approached by a 
research coordinator about study participation. Patients may also be contacted via phone after their clinic 
visit if in-person contact is not possible (for example, but not limited to, space limitations due to COVID-19 
restrictions).  
A research coordinator will review the consent document with the patient prior to enrollment. The 
consenting process can be in-person or remote via telephone or through electronic means. Prospective 
research participants will have the opportunity to ask questions before providing consent and will be 
provided with a signed copy of the consent form.  
After providing written informed consent, a DNA sample will be collected from all patients by saliva (via 
mouthwash swish and expectorate collection) or buccal cell (via buccal brush/swab). If the genotyping 
fails with initial sample collection, the patients may be mailed a DNA collection kit for saliva or buccal cell 
sample collection for retesting. 
Study design.  After eligible participants provide written informed consent and are enrolled, a DNA 
sample will be obtained for PGx testing and questionnaires (as described below) will be administered at 
baseline, and then again at 3 months and 6 months after PGx results are entered into the EHR. 
Information will be abstracted from the EHR including PGx test results, medication changes, and 
healthcare provider encounters over the 6-month study duration (Table 1). A 1-month window will be 
permitted for each follow-up study interaction (± 15 days of exact due date). 
PGx testing will be completed using the GatorPGx panel, which is already available for clinical use in the 
CAP/CLIA certified UF Molecular Pathology Laboratory. Once results are entered into the EHR, Clinical 
Decision Support Best Practice Advisories already built into Epic will alert providers if a PGx interaction 

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might occur with a medication they prescribe. All clinical medication decisions will be made at the 
discretion of the healthcare 
provider. 
Feasibility data collection: 
The primary feasibility 
outcome will be change in 
patient treatment satisfaction 
between baseline and 6 
months after PGx testing. 
This patient reported 
outcome will be measured via 
the Treatment Satisfaction 
Questionnaire for Medication 
(TSQM). The TSQM  is a 
validated tool that assesses 
three medication-related 
domains (effectiveness, side 
effects, and convenience) to 
synthesize a global satisfaction score.5,6 Both global satisfaction and scores from individual domains will 
be analyzed.  
An important secondary outcome assessing efficacy will include the number of medication changes within 
the 6-month follow-up period. Medication changes will be defined as the addition, removal, or dosage 
change to any PGx medication or medication within the same drug class. Comparisons of this outcome 
should estimate differences in trial-and-error drug dosing that is traditionally used in practice. These 
outcomes are important because if the results suggest that preemptive PGx testing is associated with 
increased patient treatment satisfaction, a reduction in medication changes, or increased perceived value 
of PGx testing, these findings would support expanded clinical implementation in medically underserved 
patients. This might also suggest that PGx could be further used as a tool to minimize disparities in 
pharmacotherapy. Other secondary outcomes that will be analyzed include: The prescriber acceptance 
rate of PGx EHR alert recommendations, number of encounters with a healthcare provider over the study 
period, genotype turnaround time, and estimated time spent discussing medications with a healthcare 
provider. These outcomes will be used to assess feasibility and will inform design of a large clinical trial to 
be funded by future grants. 
Socioeconomic data collection: We have previously observed significant associations between calculated 
geographic access scores and rate of PGx drug prescriptions, which considerably strengthened in 
subanalyses by race. Whether this association is exclusively related to geography and race or whether it 
is also related to socioeconomic determinants is unknown. Thus, we will test whether socioeconomic 
status is a social determinant of health related to PGx drug usage. Patients will answer survey questions 
related to their education, employment, and income (Table 1).   
Qualitative data collection: An estimated sample size of 20 patients from the 100 recruited participants will 
be recruited for this aim. Patient sampling will be aimed at achieving an approximately even distribution of 
sex (male and female) and race (Latino and black).  
Qualitative data collection will follow a sequential explanatory approach8,9 to expand on findings from the 
quantitative analyses of patient survey data in Aim 1. Following the initial study visit, patients will be asked 
to provide additional consent for participation in an in-depth, semi-structured, audio-recorded phone 
interview expected to take 20-30 minutes. They will receive additional compensation for participation.  A 
semi-structured script will be developed with interviewers trained by a qualitative method expert. 
Table 1.  Proposed outcomes/measures to be tested 
Outcome/Measure 
Data 
source/Instrument 
Ascertainment 
method 
Baseline 
3 
mo. 
6 
mo. 
Implementation Outcomes 
Treatment satisfaction 
Treatment Satisfaction 
Questionnaire for 
Medication (TSQM) 
Online or by phone 
X 
X 
X 
Medication adherence 
Adherence 
Questionnaire 
Online or by phone 
X 
X 
X 
Perceived value 
Patient reported value 
Online or by phone 
X 
X 
X 
Time discussing meds 
w/ healthcare provider 
Patient reported 
estimate 
Online or by phone 
X 
X 
X 
Effectiveness Outcomes 
Medication changes 
EHR data 
EHR data pull 
X 
 
X 
Provider encounters 
EHR data 
EHR data pull 
X 
 
X 
Socioeconomic Measures 
SEI 
Patient report 
Online or by phone 
X 
 
 
Occupation 
Patient report 
Online or by phone 
X 
 
 
Education level 
Patient report 
Online or by phone 
X 
 
 
Household Income 
Patient report 
Online or by phone 
X 
 
 
Household zip code 
Patient report 
Online or by phone 
X 
 
 

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Interviews will explore patients’ perspectives on implementation outcomes associated with the 
intervention to identify best practices in three key areas: 1) factors (barriers and facilitators) impacting 
willingness to undergo PGx testing; 2) preferences for the overall testing process (to identify strategies to 
improve upon intervention adoption and sustainability); and 3) preferences for the delivery of results, 
including best provider-patient communication approaches (and related challenges) as well as practices 
for sharing results (e.g., with other providers or family). Thematic findings have emerged in samples of 
15-20 participants.10,11 An estimated sample of 20 participants will be recruited, but recruitment will end 
once thematic saturation has been attained. Data analysis of transcribed interviews and data collection 
will be concurrent to ensure thematic saturation can be met.12  
Subject Compensation:  Subjects will receive compensation for the time required for questionnaire 
completion. They will receive $20 (cash or gift card) upon completion of the baseline survey and another 
$20 upon completion of the 6-month survey. Subjects who complete a semi-structured interview will 
receive another $20 upon interview completion. 
Analysis plan. For Aim 1, implementation outcomes such as prescriber acceptance, turnaround time, 
and cost will be assessed using descriptive statistics. Differences in patient reported outcomes such as 
treatment satisfaction, perceived value, and effectiveness outcomes (Table 1) will be initially compared 
between baseline and 6 months using Student’s t-test or Mann-Whitney U test, depending on the data 
distribution. In addition to univariate analyses, multiple regression models will also be completed for each 
outcome, adjusted for potential covariates, such as age, race, sex, clinic site, and Charlson Comorbidity 
Index. In all analyses, a two-sided P ≤ 0.05 will be considered statistically significant, and all statistical 
analyses will be completed in R ver. 3.6.3. 
For Aim 2, interview transcripts will be managed using data management software (e.g., Nvivo or Atlas.ti). 
Data will be thematically analyzed using the widely used constant comparative method.13 This systematic 
approach involves several coding steps including identifying concepts and assigning codes, 2) grouping 
categories of emergent themes, and 3) axial coding to identify thematic properties. To ensure similarities 
and differences between patients’ experiences can be captured, data analysis will be segmented by 
group and triangulated with survey data collected from healthcare providers (via separate protocol). 
Triangulation of data between sources also enhances the validity of the findings.14 Multiple coders, trained 
in the CCM approach, will conduct the analysis to ensure rigor. Meetings will be held to compare 
independent coding, discuss differences in interpretation, collapses analyses, and continuously refine the 
codebooks. Codebooks will be developed with each stakeholder group dataset. Once codebooks are 
finalized and analyses complete, each analysis will be validated by an additional coder analyzing a subset 
of the transcripts from each group.12 Operational and thematic memos will be maintained across data 
collection and analysis to ensure researcher reflexivity, thematic saturation, and rich description of 
themes. 
For Aim 3, total scores and scores within individual domains will be compared with number of medications 
prescribed, number of healthcare encounters, and PGx drug prescription rate. Comparisons will be 
completed in univariate analyses via Spearman’s correlation test. In addition, multiple regression models 
will be developed to allow the ability to adjust for potential confounders such as age, and Charlston 
Comorbidity index. In addition, we will estimate the proportion of variation in PGx prescription rate that 
can be explained by each socioeconomic measure by comparing coefficients of determination (r2) in each 
model. 
7. Possible Discomforts and Risks: 
 
Risks involved with participating in this study are minimal, and include accidental disclosure of PHI. To 
safeguard against this, electronic data will be stored in a custom-designed REDCap database, which is 
encrypted and password-protected. Paper documents and other physical media will be stored in a 

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locked cabinet within the PI’s locked, private office or the secure Precision Medicine Program research 
office. Due to the nature of the study, adverse events are not anticipated.  However, if any events are 
observed, they will be reported to the IRB in a manner consistent with UF regulations. 
8. Possible Benefits: 
 
Subjects may not directly benefit from participating in this study. There is a possibility of direct benefit to 
individual subjects, as genotype information could lead to more effective drug prescribing. 
Nevertheless, in the absence of a direct benefit, the study may advance the field by providing an 
increased understanding of the utility of preemptive PGx testing in traditionally underserved patient 
populations.  Such understanding is deemed essential for broader dissemination of PGx into clinical 
care. 
9. Conflict of Interest: 
The investigators have no conflicts of interest to report. Publications that may result from such research 
could enhance the reputation of the investigators, but are not expected to affect the conduct of this 
research. 
 

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10. References: 
 
 
 
1. 
Keeling NJ, Rosenthal MM, West-Strum D, Patel AS, Haidar CE, Hoffman JM. 
Preemptive pharmacogenetic testing: exploring the knowledge and perspectives of US 
payers. Genet Med. 2019;21(5):1224-1232. 
2. 
Victora CG, Vaughan JP, Barros FC, Silva AC, Tomasi E. Explaining trends in 
inequities: evidence from Brazilian child health studies. Lancet. 2000;356(9235):1093-
1098. 
3. 
Weitzel KW, Cavallari LH, Lesko LJ. Preemptive Panel-Based Pharmacogenetic 
Testing: The Time is Now. Pharm Res. 2017;34(8):1551-1555. 
4. 
Haidar CE, Relling MV, Hoffman JM. Preemptively Precise: Returning and Updating 
Pharmacogenetic Test Results to Realize the Benefits of Preemptive Testing. Clin 
Pharmacol Ther. 2019;106(5):942-944. 
5. 
Atkinson MJ, Sinha A, Hass SL, et al. Validation of a general measure of treatment 
satisfaction, the Treatment Satisfaction Questionnaire for Medication (TSQM), using a 
national panel study of chronic disease. Health Qual Life Outcomes. 2004;2:12. 
6. 
Bharmal M, Payne K, Atkinson MJ, Desrosiers MP, Morisky DE, Gemmen E. Validation 
of an abbreviated Treatment Satisfaction Questionnaire for Medication (TSQM-9) 
among patients on antihypertensive medications. Health Qual Life Outcomes. 
2009;7:36. 
7. 
Oakes JM, Rossi PH. The measurement of SES in health research: current practice and 
steps toward a new approach. Soc Sci Med. 2003;56(4):769-784. 
8. 
Creswell JW, Klassen AC, Plano Clark VL, Smith KC. Best practices for mixed methods 
research in the health sciences. Bethesda (Maryland): National Institutes of Health. 
2011;2013:541-545. 
9. 
Palinkas LA, Aarons GA, Horwitz S, Chamberlain P, Hurlburt M, Landsverk J. Mixed 
Method Designs in Implementation Research. Adm Policy Ment Hlth. 2011;38(1):44-53. 
10. 
Kvale S. Inter Views. An introduction to qualitative research interviewing. Thousand 
Oaks, CA: Sage; 1996. 
11. 
Conrad P. The experience and management of chronic illness. Greenwich, CT: JAI 
Press; 1987. 
12. 
Barrett M, Mayan M, Olson K, Spiers J. Verification Strategies for Establishing 
Reliability and Validity in Qualitative Research. International Journal of Qualitative 
Methods. 2002;1(2):10p. 
13. 
Corbin JM, Strauss AL. Basics of qualitative research : techniques and procedures for 
developing grounded theory. Fourth edition. ed. Los Angeles: SAGE; 2015. 
14. 
Miller DL. Determining Validity in Qualitative Inquiry. Theory Into Practice. 
2000;39(3):7p. 

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Appendix A: List of medications that could potentially be informed by preemptive 
pharmacogenetic (GatorPGx) panel  
 
Medication 
Gene 
Selected SSRIs (escitalopram, citalopram, sertraline) 
CYP2C19 
PPIs (pantoprazole, dexlansoprazole esomeprazole, lansoprazole, 
omeprazole) 
CYP2C19 
Clopidogrel  
CYP2C19 
Voriconazole 
CYP2C19 
High dose TCAs (amitriptyline, nortriptyline, clomipramine, 
doxepin) 
CYP2C19, 
CYP2D6 
Certain opioids (codeine, tramadol, hydrocodone, oxycodone)  
CYP2D6 
Certain SSRIs (paroxetine, fluvoxamine) 
CYP2D6 
Ondansetron 
CYP2D6 
Atomoxetine 
CYP2D6 
Tamoxifen 
CYP2D6 
Phenytoin 
CYP2C9 
Warfarin 
CYP2C9, 
VKORC1, 
CYP4F2 
Selected NSAIDs (celecoxib, ibuprofen, meloxicam, flurbiprofen, 
lornoxicam, piroxicam, tenoxicam) 
CYP2C9 
Tacrolimus 
CYP3A5 
Simvastatin 
SLCO1B1 
Thiopurines (azathioprine, mercaptopurine, thioguanine) 
TPMT