In order to improve patient outcomes, factors that can reduce variability in treatment must be identified and quantified. For drug therapies, individual differences in pharmacokinetics (PK) and pharmacodynamics (PD) are major sources of variability. Population PK/PD studies have been proven to be very useful to identify these factors, but are underutilized. A major barrier to this approach is the difficulty of obtaining data from a large number of patients, which requires precise time information and accurate values for dosing and drug plasma concentration or response measurements appropriately formatted for PK/PD studies. Electronic health records (EHRs) are potentially an excellent source for such data, but standardized methods for data extraction and preprocessing for PK/PD modeling are lacking. We will work to develop methods for abstraction, validation and preprocessing of EHR data to construct data for PK/PD studies.
In Aim 1, we will develop programs for data preprocessing and building PK/PD datasets, and validate the developed programs using the test datasets.
In Aim 2, we will evaluate the robustness of data construction system using EHRs by performing tipping point analyses by intentionally introducing errors into test datasets. This approach is highly recommended by the Food and Drug Administration to determine the sensitivity of analysis to methods of handling missing data.
In Aim 3, we will develop a preliminary dose optimization algorithm for tacrolimus based on a Bayesian PK/PD prediction model, which will serve as a basis for a clinical support decision tool for future study. A more efficient and standardized system for data construction will promote population based PK/PD studies by providing a PK/PD data pipeline. The ultimate product of this work will be a generalizable and validated data pipeline for analysis of drug exposures and responses using EHR data, which will be widely applicable to many population studies for an array of medications. Our validated system will extend opportunities to a wider research community to perform population based PK/PD studies and facilitate the chance of finding factors affecting PK/PD profiles. This research will be utilized to advance precision medicine across a wide array of therapies.
It is well known that there is high patient-to-patient variability in response to drug treatment; identifying factors that contribute variability in pharmacokinetics (PK) and pharmacodynamics (PD) is a first step toward precision medicine. Population PK/PD studies have been proven to be very useful to identify these factors using datasets from a large number of individuals with few data points per individual, but are underutilized due to difficulties in constructing the necessary datasets. We will develop and validate programs that will comprise a standardized system for building reliable PK/PD datasets from electronic health records data, which would be the basis of dose optimization agorithm via real-time clinical support systems to improve patient care.