What is NCA?

Background: Understanding Non-Compartmental Analysis (NCA) and the Role of aNCA

This document will help you understand the fundamentals of Non-Compartmental Analysis (NCA) in pharmacokinetics (PK) and the challenges analysts face when performing NCA. It also explains why the aNCA application was developed and how it aims to streamline and enhance the NCA workflow. For a video-content referring to all these concepts you can also visit the R Pharma aNCA video (yet not available).

What is Pharmacokinetics and Why Does NCA Matter?

Drug development is not just about studying how drugs act on the body (pharmacodynamics); it’s equally important to understand how the body interacts with drugs. This is what we call pharmacokinetics (PK)—the study of how drugs are absorbed, distributed, metabolized, and excreted over time.

To visualize this, we use PK curves: graphs showing drug concentration plotted against time after dosing. These curves tell us how long a drug stays in the patient’s system, helping us determine the optimal dose that balances efficacy and safety. The sweet spot—where drug levels are high enough to have an effect but not so high as to cause toxicity—is called the therapeutic window.

One of the main goals of PK analysis is to calculate the dose that achieves this window for the longest and safest period.

What Is Non-Compartmental Analysis (NCA)?

When it comes to evaluating PK data, analysis approaches can range from simple to highly complex. On one end, you have compartmental models, where drug movement between body compartments (like blood, liver, or muscle) is described mathematically using differential equations. On the other end, you have Non-Compartmental Analysis (NCA), which simplifies the process by treating the body as a “black box.”

In NCA, instead of modeling how a drug moves between compartments, we focus only on the macroparameters—high-level PK metrics that describe overall drug behavior. These include: - AUC (Area Under the Curve): A measure of total drug exposure over time.
- Cmax: The peak concentration of the drug in the blood.
- Tmax: The time it takes to reach peak concentration.
- Half-life ( $t_{1/2}$ ): The time required for the drug concentration to drop by half, which helps calculate elimination rates.

Because NCA calculations are model-independent, they make fewer assumptions about drug distribution and can often provide quick, robust results.

Key Calculations in NCA

1. Area Under the Curve (AUC):

AUC represents the total drug exposure in the body.
It’s calculated by summing up the areas under the concentration-time curve using methods like the trapezoidal rule.
Two common AUC calculations are:
- AUClast: Exposure up to the last measurable concentration (the one before concentrations falling below quantifiable levels, BLQ).
- AUCinf: Total exposure to infinity, including extrapolated data beyond the last measurable point (requires estimating a slope from the terminal phase).

2. Half-Life ( $t_{1/2}$ ):

Derived from the terminal slope of the concentration-time curve.
Half-life impacts key PK calculations, including AUCinf and clearance, making it critical to get this value right.
Analysts may allow customization of $t_{1/2}$ using methods like manual slope selection to exclude outliers or focus on more reliable data points.

3. Other Calculations:

Partial AUCs: Focused on specific time intervals (e.g., 0-8 hours) to evaluate absorption or distribution phases.
Parameter Ratios: Comparison of exposure levels (e.g., metabolite-to-parent ratios) for deeper insights into the drug’s behavior.
Clearance and Volume of Distribution: Derived from AUC and half-life, these parameters help understand how efficiently the body eliminates the drug and how widely it distributes.

Challenges and Common Actions in NCA Analysis

Despite its mathematical simplicity, performing NCA can be operationally complex, especially in a corporate or regulatory setting. Here is why:

1. Pre-Analysis Steps:

Exclusions: Identifying which data points (outliers, missing data, or post-dose anomalies) should be excluded from the analysis.
Method Selection: Analysts decide which slope estimation, extrapolation techniques, or AUC intervals are most appropriate.
Customized Intervals: Tailoring intervals for partial AUCs or adding coefficient ratios.

2. Data Preparation:

PK analysis requires significant data cleaning, preparation, and mapping. The data follows strict standards, such as CDISC SDTM, to ensure it can be used directly in regulatory workflows.
Here’s where roles emerge: A data collector works on SDTM datasets, a statistical programmer processes the data to make it analysis-ready, and a PK analyst performs the NCA calculations.

3. Post-Analysis Steps (CDISC Outputs and Reporting):

Output from NCA must meet CDISC compliance standards for submission to regulatory agencies like the FDA or EMA. Typical datasets include:
- PP (Pharmacokinetic Parameters): Lists macroparameters (e.g., AUC, Tmax, Cmax, half-life) for each individual.
- ADPP (Analysis Dataset for Pharmacokinetic Parameters): A more detailed, CDISC-compliant dataset linked to analysis-ready inputs.
Outputs are used to generate Tables, Listings, and Graphs (TLGs), which are essential in dose-escalation studies, bioequivalence trials, and drug approval submissions. These provide visual, tabular, and narrative summaries of the NCA results, allowing stakeholders and regulators to understand findings at a glance.

Why Do We Need aNCA?

Despite its simplicity, NCA workflows often become a bottleneck in drug development due to inefficiencies and heavy manual interventions: - Fragmented workflows require coordination between multiple roles (data collectors, programmers, analysts). - Many NCA tools are proprietary, expensive, and lack flexibility for custom needs. - Errors introduced during manual data handling lead to reproducibility challenges and delays.

This is why we created aNCA: - To simplify and unify the entire NCA process, from data preparation to generating CDISC-compliant outputs. - To empower analysts, even those without extensive programming experience, by offering a user-friendly interface built on PKNCA, an open-source R package for NCA in pharmacokinetics. - To increase automation and reduce errors, ensuring a faster and reproducible workflow.

Do you want to know more?

Here is a list of recommended links to deepen your understanding of NCA and related concepts: - R Pharma aNCA video - yet not available - Here you can find a video explaining NCA main concepts and how aNCA works.