Clinical trial NCT06219720: The Texas Interprofessional Pharmacogenomics (IPGx)
Official title: The Texas Interprofessional Pharmacogenomics (IPGx) PILOT Cohort
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Adverse Drug Reaction
Lead sponsor: Texas A&M University
Sponsor class: OTHER
Collaborators: name: InnovativeGx
name: Texas A&M Health Family Care Clinic
name: iC42 Clinical Research and Development
name: Goldblatt systems
First posted: 2024-01-23
Enrollment: 50
Sex: ALL
Minimum age (years): 45
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study plans to recruit at most 50 volunteers from the Texas A\&M affiliated community health family medicine program.

Brief summary: The investigator's primary aim is to evaluate polypharmacy-associated adverse drug reactions (ADR) in a pilot study of at-risk patients using state-of-the-art pharmacogenomic technology and to use this information to make recommendations for optimization of pharmacotherapy regimens. The data from the pilot cohort will be used to optimize and integrate a customized electronic decision support (clinical semantic network; CSN) dashboard to identify drug regimens that should be modified, replaced, or discontinued. A secondary objective of the pilot study is to evaluate the capacity/saturation of CYP P450 enzymatic pathways in polypharmacy patients. A third objective is to determine the feasibility of the planned informatics workflows between the CLIA lab, the EMR, and the Family Medicine Practice.

Detailed description: The investigators are concerned with patient safety and ADRs as these areas of clinical practice represent significant causes of death, ahead of many of the better recognized acute and chronic causes of mortality. While prescribing medicines can have life-altering benefits, a more precise way of choosing among the options on a formulary continues to lag behind existing technologies. A person's drug response can vary by means of drug-drug or drug-food interactions, as well as by sex, age, and disease status. Large interpersonal variabilities of up to 1000-fold exist in response to the same dose of a given medication. Genetic polymorphisms help define pharmacokinetic and pharmacodynamic profiles, but these insights have not yet been consistently incorporated into clinical practice and standards of care. Several medication management programs have appeared in recent years, but these are mainly geared toward adherence, with only limited incorporation of pharmacogenomics-based medication management. Precision medicine advocates that one size does not fit all medical care. How might the provision of care get closer to the bullseye in polychronic disease management, and the management of polypharmacy?

There exists a polypharmacy crisis in the United States that is large in scope, especially among the older populations who often have diminishing renal and hepatic functions. The prevalence of potential hepatic cytochrome enzyme-mediated drug-drug interactions was estimated to be as high as 80% in one study with elder adults considered to be more susceptible to problematic drug interactions. Conventionally, polypharmacy refers to taking five or more medications concurrently. An estimated 15 million patients 65 or older have been identified as facing the challenge. Polypharmacy patients often have at least two comorbid chronic diseases, and nearly 50% of older adults are using at least one medication that is not necessary. Hospitalized patients average five to eight medications, and the number surpasses nine in 40% of nursing home residents. In a study of patients with cognitive decline or mild Alzheimer's disease, 88% of these patients met polypharmacy criteria, with anticholinergic cognitive burden, drug-drug interactions, and drug-gene interactions all prevalent issues in these populations. In an increasing number of extreme cases, polypharmacy can approximate 20 drugs posing risks for adverse drug outcomes that equal nearly 100%. The greater the number of medications in a regimen, the higher the risk to patient safety and compromised clinical outcomes. One in twenty polypharmacy outpatients seek medical care for ADRs. Polypharmacy has also been associated with hospitalizations among the elderly. Polypharmacy is associated with decreased medication adherence, nutrition, urinary incontinence, reduced activities of daily living, and loss of physical and cognitive functions. Increased falls occur along with accompanying morbidity and mortality. Financially, the impact of polypharmacy has been associated with a 30% increase in health care expenditures, and a major factor in ultrahigh healthcare utilizers. Analysis of the Observational Health Data Sciences and Informatics data set showed that 10% of diabetes patients, 24% of hypertension patients, and 11% of depression patients followed a treatment pathway that was unique among a population of 250 million cases, illustrating the need for electronic decision support to flag drug interactions resulting from patient specific care plans and implement corrective measures.

Electronic health records continue to fall short regarding their level of interoperability, with significant deficiencies enabling a care plan and medication management that can draw data and decision support from across the provider continuum. This is a suboptimal situation in the provision and refinement of precision and personalized medical care. The clinical burden of polypharmacy and medication reconciliation often impacts primary care clinicians who may not have the necessary data at the point of service, a nidus for polypharmacy management problems. Innovative approaches to managing the polypharmacy challenge include the creation of medical management clinics with focused efforts on mitigating the cost and healthcare burden of polypharmacy and to systematically evaluate the incremental clinical changes that accompany medication alterations, modification or discontinuation where indicated.

Utilizing an interprofessional care team that includes physicians, pharmacists, nurses, case coordinators, along with telemedicine and digital tools, the investigators can engage patients and garner information such as phenotypic, functional, and social determinants of disease profiles. These data can be entered as computable actionable data prior to a visit in order to better track what happens in- between visits (adherence to the care plan, or lack thereof). The investigators posit that this information is as important as what happens at an appointment, and this is especially true in clinical cases of polypharmacy. In the end, this information can become more readily available to both patient and provider utilizing the Interprofessional Pharmacogenomics (IPGx) care model.

The investigators are developing a care decision support protocol and pharmacogenomic/ pharmacokinetic dashboards that augment the capacity for primary care clinicians to manage medication more precisely for individual cases and that is minimally disruptive.

The dashboards will be useful to providers and patients, helping to identify clinical cases where there might be benefit from proactive medication management to identify those who may not respond and those at heightened risk of ADRs. The dashboards would be informed by a growing library of clinical cases with a clinical data warehouse. The dashboard would generate and iteratively refine novel care decision trees (algorithms) centered on medication management. The data structure and care protocol are designed to enable concomitant and longitudinal observation (research) of the clinical activities toward validation of the CSN and dashboards as a useful tool for patient-centric clinical research. The ideal databank will include drug blood levels (not relevant to this PILOT), drug list and other relevant modifier data that may impact medication use and effectiveness. This approach would also provide a means to learn more about drug adherence and help to systematically identify patients who may be candidates for a pharmacogenomic evaluation and longer-term participation in a medication management program.

Specific questions that this GENERALIZED approach might inform:

* Does a drug level near zero mean non-adherence, or is the patient metabolizing a drug extensively such that blood or urine levels become undetectable after administration? The phenotypic questionnaire coupled with drug blood or urine level measurements will answer that question.
* Is a protocol needed to determine when to order a pharmacogenomic test? The investigator's data platform, powered by the CSN, can validate the clinical and cost-effectiveness of those decisions.
* What is the best use pharmacogenomic data? If a patient has had a pharmacogenomic test, blood or urine drug levels might help refine knowledge about the metabolic activity for pertinent enzymatic pathways and help craft key questions to identify what constitutes an overloaded CYP P450 pathway in the setting of polypharmacy.

ADRs might be preventable in the psychotropic domain by applying the knowledge derived from a medication management consultation. For instance, in the case of antidepressants, weeks may go by before a clinical response can be evaluated after initiating medication based on standard dosing and trial and error. In a precision medication management scenario, the provider would know at the outset if the patient were an ultra-rapid metabolizer for a relevant CYP P450 enzymatic pathway and be better equipped to identify the drug of choice and to optimize dose titration. If the clinical dashboard reveals a patient that is receiving several medications competing for a common pathway, proper medication adjustments can also be made, as needed.

The CSN to be used here is a proprietary computable health record system containing millions of interrelated medical findings that arc with each other to create a knowledge network. As data is entered, the weighted arcs are used to build clinical decision support and differential diagnoses. This provides a potentially strong environment for a pharmacogenomic profile to create a precision drug and dosing regimen tool while taking advantage of clinical workflows currently in practice. The pharmacogenomic dashboard is contained within the CSN.

Eligibility criteria: Inclusion Criteria:

* People taking 5 or more medications, including over the counter drugs, supplements, natural products, cannabis produces, or other recreational drugs
* Ability to give and comprehend the consent process.
* Consent to donate urine samples, genetic data through buccal swabs, undergo a comprehensive history and physical examination.
* All genders.
* Age 45 and over

Exclusion Criteria:

* Subject has been diagnosed or is being treated for any cancer other than basal cell cancer in the last 5 years. Patients with metastatic melanoma in the last 5 years will be excluded.
* Admitted to hospice.
* Patient has ever been diagnosed with Hepatitis B or C.
* Patient has ever been diagnosed with active liver disease, hepatomegaly, grossly abnormal liver function. Meld score \>10, ALT or AST \>100U/L or an AST/ALT ratio \>2
* Patients taking imidazole antifungal medication.
* Declines to participate or interact with staff/share their medical status.
* A diagnosis of Alzheimer's disease
* Pregnant patients will be excluded
* Unable/unwilling to consent.
* Unable to verbally communicate and comprehend English/Spanish language.

Primary outcomes:
measure: Frequency and nature of ADRs on the Naranjo Scale
timeFrame: 180 days
Secondary outcomes:
measure: Emergency department visits
timeFrame: 180 days
measure: Hospital admissions
timeFrame: 180 days
measure: Serum/plasma drug concentrations
timeFrame: 180 days
measure: Pharmacogenomic genotype with corresponding ADR phenotype
timeFrame: 180 days
measure: Drug-drug interactions, drug gene interactions, drug-drug-gene interactions
timeFrame: 180 days
Other outcomes:
Locations:
city: Bryan
country: United States
facility: The Interprofessional Pharmacogenomics (IPGx) Clinic
state: Texas
Study officials:
affiliation: Texas A&M IBT
name: Kenneth S Ramos, MD
role: PRINCIPAL_INVESTIGATOR
References: