How to Analyze Pharmacokinetic Data in Phase 0 Microdosing Trials

Introduction: Why Pharmacokinetics (PK) Matters in Phase 0

The main objective of most Phase 0 trials is to gather pharmacokinetic (PK) data in humans using microdose levels. These early insights help answer crucial questions such as: Is the drug absorbed? How long does it stay in circulation? Does it behave as expected?

Despite the low dose, the methods used to analyze and interpret PK data are nearly the same as in later-phase trials—but with added sensitivity, precision, and ethical implications.

Key PK Parameters to Calculate in Microdose Studies

Once plasma or blood concentration data is obtained, you can calculate the following PK parameters:

• Cmax: Maximum concentration observed
• Tmax: Time at which Cmax is observed
• AUC (Area Under Curve): Total drug exposure over time
• t½ (Half-life): Time required for the plasma concentration to fall by 50%
• CL (Clearance): Volume of plasma cleared per unit time
• Vd (Volume of distribution): Theoretical volume required to contain the drug at observed concentration

These metrics inform dose scaling and determine whether full development is warranted.

Noncompartmental vs. Compartmental Analysis

1. Noncompartmental Analysis (NCA)

• Uses observed concentration-time data without assuming any model
• Suitable for linear PK and simpler microdose studies
• Tools: WinNonlin, PKSolver, R packages

2. Compartmental Modeling

• Assumes drug fits into one or more compartments (e.g., central, peripheral)
• Used in non-linear or complex ADME profiles
• Helps predict PK at therapeutic doses using simulation

Phase 0 studies often start with NCA and progress to modeling if needed.

Sampling Strategy in Microdosing Trials

To accurately capture PK data from a microdose, a well-designed sampling schedule is critical:

• 0 (pre-dose), 0.25h, 0.5h, 1h, 2h, 4h, 6h, 8h, 12h, 24h
• More frequent early sampling helps define Cmax and Tmax
• Later samples are needed to estimate t½ and AUC∞

Parallel group or crossover designs can also affect sampling points.

Extrapolating Microdose PK to Therapeutic Dose

One goal of Phase 0 is to determine whether microdose PK can predict full-dose PK. This is only possible when:

• The drug has linear pharmacokinetics
• There is no saturation of absorption, metabolism, or elimination pathways
• Validated PBPK (physiologically-based pharmacokinetic) models are available

If microdose data deviates significantly at higher doses, predictive value may be limited.

Handling Data Below the Limit of Quantification (BLQ)

Due to the low dose, BLQ values are common. Strategies to handle them include:

• Assigning a value of zero
• Using LLOQ/2 (half the lower limit of quantification)
• Excluding early/later BLQ points from AUC calculations

Regulatory bodies prefer transparent handling and justification of BLQ data.

Software and Tools for PK Analysis

• WinNonlin® – Industry standard for both NCA and modeling
• PKSolver (Excel-based) – Ideal for students and simple analyses
• R packages – Flexible and powerful for academic users

Ensure software outputs are supported by validation and version control documentation.

Real-World Example: PK Profiling in a Phase 0 Oncology Study

In a microdosing trial for a kinase inhibitor, plasma samples collected at 0.5 to 24 hours showed a Cmax of 50 pg/mL and a half-life of 5 hours. NCA revealed an AUC of 300 pg·h/mL. This data aligned with PBPK models and supported Phase 1 dose predictions, streamlining development timelines.

Common Mistakes in Microdose PK Analysis

• Under-sampling or missed time points
• Incorrect handling of BLQ or missing values
• Failure to validate models against real data
• Ignoring matrix effects or assay variability in interpretation

Collaborating with experienced PK scientists and statisticians helps avoid these pitfalls.

Summary for Clinical Research Students

Pharmacokinetics is the backbone of Phase 0 trials. As a student or early-career professional in clinical pharmacology, biostatistics, or drug development, learning to analyze and interpret PK data is a core skill. Microdosing doesn’t mean micro-importance—these tiny data points can have a big impact on which drug moves forward and which one doesn’t.

Use the right tools, the right models, and the right mindset—and your PK data will speak volumes, even at microgram doses.