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The Importance of Clinical Trial Monitoring and Data Management for Population PK Modeling

What are some of the most common and costly mistakes sponsors make when it comes to collection of pharmacokinetic (PK) samples in clinical studies?

In this post, we will discuss some of the preventable issues we have observed, with a particular focus on impacts on population pharmacokinetic (population PK) analyses.

Getting the Most Out of Your Clinical Trial Data

Regardless of the purpose of a clinical trial, appropriate study design is critical for getting the most out of your clinical trial data. It is not uncommon for sponsors to run an entire clinical study only to realize when it comes time to analyze the PK data that the sampling schedule was suboptimal or the data management procedures were lacking. Oversights such as these can prove costly in both time and money and reduce the usefulness of your data.

If you are collecting PK data in your clinical study and want to keep PK analysis costs as low as possible (and who doesn’t?), then avoiding preventable pitfalls in study set-up, study conduct, data collection, and data management is critical.

In addition, it is important to consider that there are many nuances to the collection and management of clinical data that can themselves impact PK analyses and be a significant cost driver for those analyses. This is especially true when employing population PK analysis.

Common Mistakes and Their Impact on Population PK Modeling

Developing a population PK model is a multi-step process that builds upon source data collected from (often multiple) clinical trials. A final population PK model is the result of many model runs, statistical tests, and visual diagnostic outputs from modeling software. As such, a population PK model can take a significant amount of time to complete.

Because population PK modeling is data-driven and all models have to be rerun several times, any problems with the clinical data can cause a domino-effect, prolonging the modeling process and increasing cost.

Many problems related to PK data collection and analysis stem from inadequate study design or suboptimal data management practices. Some of the more common preventable problems that affect population PK model development include:

  • Ambiguous data
  • Missing or erroneous data
  • Physiologically implausible data
  • Drug concentration profiles that are illogical
  • Abnormalities in covariates (body sizes, laboratory measurements, race, gender, etc.)
  • Inconsistent subject IDs between studies (e.g., Subject #100-01 in Study 1 and Subject #100-A in Study 2).
  • Inconsistencies in naming adverse events or concomitant medications between studies (e.g., use of branded vs. generic drug names)
  • Dates and times for study drug dosing not matching with sampling times
  • Missing or incorrect dosing times
  • Inconsistent coding (e.g., “plasma” vs. “Plasma”, nM vs. ng/ml, free drug vs. salt form)

Techniques to Save Time & Money on Population PK Modeling

We have identified some of the more common mistakes Nuventra’s modeling experts have observed with regard to PK data collection and data management. Now we will provide some effective strategies to prevent those mistakes.

1) Clinical Trial Monitoring

Clinical trial monitoring is a vital part of the clinical trial process. A clinical monitor ensures, among other things, that the clinical site is conducting the clinical trial according to the protocol. This includes verifying that the clinical site is following all drug administration, sample collection, and data documentation procedures outlined in the protocol.

To facilitate the clinical monitoring process, sponsors develop a clinical monitoring plan to establish guidelines for clinical monitoring visits and related tasks. This lays the groundwork for the entire monitoring program.

By including a population PK-specific addendum to the clinical monitoring plan, sponsors can instruct the clinical monitor to look for common errors specific to population PK at the clinical trial site. Catching and remedying these errors early can help prevent these errors from becoming much larger problems when it comes time to analyze the data.

Nuventra can help prepare your clinical trial monitors to look for population PK-specific errors.

2) Data Management Plan

A data management plan formally defines procedures for handling data both during and after a clinical trial. This includes procedures for data collection, data entry (e.g., into case report forms [CRFs] and/or databases), data annotation/coding, dataset generation, and data archiving.

One way to minimize the impact of data management issues on the collection of PK data and subsequent population PK analysis is to include a population PK-specific addendum with the overall data management plan.

Nuventra can assist with developing a data management plan for data that will be used for population PK analysis and help you avoid common pitfalls.

3) Case Report Form and Database Structure

Case report forms allow a clinical site to collect all protocol-defined data in an organized fashion. Case report forms may be in paper form or, as is becoming much more common, in electronic (eCRF) form.

Clinical trial databases are structured arrangements of clinical trial data that allow for the storage, retrieval, analysis, and reporting of study data. Information collected in CRFs/eCRFs are ultimately entered into a database to facilitate generation of datasets and subsequent analyses.

Data collected in the CRF/eCRF is mapped to the database using an annotated CRF. An annotated CRF is a blank CRF with annotations that document the location of the data with the corresponding names of the datasets and the names of variables included in the datasets.

Intelligent CRF development and database setup with an eye towards how the data will ultimately be used (i.e., for population PK analysis) is crucial. Having a pharmacometrician (i.e., population PK analyst) assist with the creation of the annotated CRF is important to ensure data integrity and to help keep the cost of PK/PD modeling as low as possible.

4) Covariates

Covariates include variables such as weight, gender, age, BMI, concomitant medications, and biomarkers. Covariates are critical considerations when developing population PK models. As such, all protocol-defined covariates MUST have a value captured in the clinical database. While this may seem obvious, missing covariate data is a common problem in our experience.

Nuventra has techniques to ensure that sites and clinical monitors understand the importance of covariates for population PK modeling in a way that will help facilitate assembly of a robust dataset.

5) Dosing History

The most common problem faced when building a population PK model is not having an adequate dosing history for every study subject. The dosing history (i.e., dosing times, amounts, and infusion rates, if applicable) is just as important to understanding the time course of plasma concentrations as the observed values. Nonetheless, in routine clinical conduct and monitoring, the quality of these data is often overlooked.

Dosing history from 5 half-lives prior to collection of any PK concentration must be included. Ideally, these are doses administered by Investigational staff at the clinical site. While patient diaries and patient reported dose times are commonly used, they are prone to errors.

Nuventra has multiple techniques to ensure that adequate dosing history is collected during clinical studies to support population PK analysis.

Conclusions

Population PK modeling is a complex and often time-consuming activity. The time- and cost-efficiency of population PK modeling is tied in large measure to the quality and completeness of the underlying clinical trial data. Mistakes related to study design and data management can negatively impact population PK analysis timelines and budget and may affect the utility of the clinical trial data.

In this post, we have highlighted some of the more common mistakes that we have observed, especially those that can negatively affect population PK modeling. We have also provided some strategies for preventing these mistakes, with a particular focus on clinical trial monitoring and data management.

Remember, when it comes to clinical trials, the best mistake is one you never make. If you want to take full advantage of Nuventra’s industry-leading experience, make the most out of your clinicals trial data, and save time and money on your next population PK analysis. Contact Nuventra before your next clinical trial.

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