A robust pharmacokinetic study during drug development is crucial for the success of a candidate pharmaceutical drug. Similar to pharmacokinetic studies, pharmacodynamics approaches are necessary to evaluate the DMPK property of a drug product. Generally, pharmacokinetics and pharmacodynamics studies are independent ventures.
Optimally designed pharmacokinetic (PK) trials generate reliable results. Any discrepancies in PK clinical trial data can affect subsequent downstream analysis. Hence, the current article highlights best practices for pharmacokinetic services to design and analyze PK clinical trial data.
Best Practices for Accurate and Reliable Results
Rational study designs are based on the causal relationship between drug exposure and therapeutic activity. However, such relationships are often complex. Hence, researchers must design robust PK preclinical studies that provide relevant information for subsequent analysis. As more data becomes available, one may refine the initial study models, ultimately providing a powerful iterative tool for understanding drug efficacy.
A core team in a pharma setup will gather literature, data, and reports about the study design and experimental animal models. It is crucial that the interdisciplinary team functions together as early as candidate screening so that a robust collaboration continues to work on the candidate drug. It is highly recommended that the team has reliable data-sharing channels.
A reference tool or compound with available internal and external reports is a good start to establish confidence in PK experiments. If such data is unavailable, investing in a similar data package may be beneficial before beginning PK trials.
Before starting a PK study, it is necessary to define study objectives and identify the gaps and weaknesses in the results obtained from the study. Besides, it is crucial to correlate in vitro and in vivo efficacy data to understand PK properties in animal species. However, the approach may depend on the stages of drug development, with previously generated data guiding subsequent study designs.
Due to animal limitations, collecting samples from the same animal is not always possible. Hence, researchers may employ a satellite group of animals. However, it is vital to match all features of the study design, including strain, species, gender, dose, sample times, dose administration, operator, and disease state. In cases where a satellite group is unavailable, one may conduct bridging experiments in a different strain for PK sampling. This approach instills confidence in the PK data between two groups of animals. Moreover, adding a vehicle-treated control group is also beneficial during PK analysis.
As mentioned earlier, the same samples and animals, or matrix in the case of satellite animals, are desirable in PK analysis. These matrices have several advantages, such as straightforward collection, a proper approach to bioanalysis, and potential transition across animal species. However, blood or plasma is the preferred choice of study samples for PK analysis.
Once scientists have planned and executed a PK study and the sample analysis is complete, the team focuses on examining the PK dataset. Only a thorough analysis of the PK clinical trial data will help understand the mechanism of action. Besides, it will aid in comparing different drug compounds and selecting the most potent compound for subsequent drug development studies.