Abstract
Non-compartmental analysis (NCA) remains a foundational method in pharmacokinetics for deriving key parameters directly from concentration-time data. However, the evolving landscape of drug development requires a more critical look at which pharmacokinetic (PK) parameters truly matter and how they should be reported. This article explores the strengths and limitations of NCA, recommends an evidence-based reporting strategy, and highlights the need to move beyond outdated assumptions that may compromise analytical integrity.
Introduction
Change in pharmacokinetic (PK) workflows is hard, and understandably so. Many scientists are trained with traditional methods and parameters that have been passed down for decades. But in today’s fast-moving drug development landscape, it's worth asking: are we still using the right tools and metrics?
As the founder of Aplos Analytics and a long-time advocate for better, faster, and more accurate PK analysis, I want to challenge some long-standing conventions around non-compartmental analysis (NCA). In this article, I’ll share what I believe matters most in NCA, which parameters are useful (and which aren't), and how scientists can design better studies with fewer assumptions and more reliable outcomes.
The Case for NCA — Simpler, Faster, Less Bias
NCA is a method used to calculate PK parameters directly from observed concentration-time data, no model fitting, no regression. That’s a major advantage.
Compared to nonlinear or population PK modeling, NCA requires fewer assumptions, which reduces the risk of injecting bias into your results. It also simplifies analysis: you get actionable outputs without spending hours validating model fits. But that simplicity comes at a cost, predictive power. NCA tells you what happened, not what might happen.
Cmax and AUC: The Most Valuable Parameters
Among the many parameters derived from NCA, two stand out: Cmax and AUC.
Cmax, the peak concentration observed in a dosing interval, is essential for understanding safety and acute effects. It's often correlated with adverse outcomes and efficacy thresholds.
AUC (Area Under the Curve) gives a measure of total drug exposure. Whether it’s AUClast, AUCall, or partial AUCs, this parameter is highly relevant to understanding the relationship between drug concentration and both therapeutic and toxic effects.
These values are directly observable, easy to calculate, and don't require model assumptions, making them the cornerstone of reliable NCA.
Parameters of Questionable Value
Now let’s talk about the controversial ones: half-life, clearance, volume of distribution, and AUCinfinity. These rely on estimating a terminal slope, and here’s the problem: ten scientists can get five different values from the same data, all using valid methods. That’s a red flag.
The variation in results isn’t due to bad science; it's due to inherent ambiguity in defining the “terminal phase.” Without a consistent method, these parameters become unreliable for decision-making. They’re also based on a one-compartment model assumption, which rarely reflects today’s multi-compartment drugs.
So, should we still report them? Maybe, but only as appendices or modeling inputs, not as the basis for key conclusions.
Better Study Design Starts with Better Parameters
One of the most overlooked aspects of good PK analysis is the sampling schedule. Ideally, you want to collect data long enough to confidently observe the terminal phase, usually 5 to 7 times the half-life, but this varies.
Even for drugs with long terminal slopes and multi-compartment kinetics, you don’t always need to sample until the concentration hits zero, just far enough to get a clear terminal segment. Proper sampling ensures that your AUC and Cmax values are accurate, which in turn supports reliable decision-making.
My Recommendations for NCA Reporting
For non-clinical and richly sampled clinical studies, I recommend reporting:
Primary parameters: Cmax, AUCall or AUClast
Time-based parameters: Tmax, Tlag, Tmin, Tlast
Steady-state parameters: Cmin, Cavg, Ctau
Optional: Partial AUCs when relevant
Parameters that depend on the terminal slope, half-life, clearance, volume can be reported in appendices but should not be highlighted in main conclusions or executive summaries.
What Regulatory Bodies Actually Use
It’s worth noting: when the FDA, EMA, or other regulatory agencies make critical decisions (like food effects, drug-drug interactions, or bioequivalence), they rely on Cmax, AUClast, and AUCinfinity. Not clearance. Not half-life. Not volume.
Even AUCinfinity has its flaws. In most well-designed studies, AUClast captures 95% of the profile. If that’s the case, I’d argue AUClast should replace AUCinfinity entirely in regulatory comparisons.
Conclusion
Non-compartmental analysis remains a powerful tool, but only when used wisely. Focus on what matters: Cmax and AUC. Use terminal-slope-derived parameters cautiously and always assess your sampling schedule to ensure valid conclusions.
This perspective may differ from what you’ve been taught or what your department currently does, and that’s okay. If you agree, disagree, or want to discuss further, I invite you to connect. Let’s improve the science of drug development, together.
And if you're looking for a faster, automated, cloud-based way to perform NCA, try Aplos NCA. You can run it from your browser or integrate with R, SAS, Python, or any API-driven workflow. Take a free trial and see what it can do for you.