Set NCA preferences

The core of the NCA workflow involves configuring NCA preferences to align with the data and study protocol.

Settings for Calculation of Area Under the Curve (AUC)

All AUC Parameters are derived values, computed using observed concentration-time data. The linear trapezoidal rule is applied when assuming a linear relationship between adjacent data points (c1,t1) and (c2,t2), while the logarithmic trapezoidal rule is used when assuming an exponential relationship. The equations used in the above scenario are shown below

• Linear trapezoidal
  $AUC_{int} = (t_{2} - t_{1}) \times \frac{{c_{1} + c_{2}}}{2}$

• Log trapezoidal
  $AUC_{int} = \frac{(t_2 - t_1) \cdot (c_2 - c_1)}{\ln\left(\frac{c_2}{c_1}\right)}$

Regression Method

Two primary regression methods are commonly used to characterize the pharmacokinetic profile of a substance:

• linear-up-linear-down
• linear-up-log-down

Linear up – Linear down

The linear-up-linear-down method calculates the area under the curve (AUC) using what is known as the linear trapezoidal rule. This rule is used to estimate the area between pairs of data points in the dataset, provided these points have valid (non-missing) values. To get the total AUC, the areas estimated between each pair of points are added together.

If the method needs to calculate a part of the area where the end point does not exist in the data, it uses linear interpolation. This means it estimates a concentration value for the missing endpoint by drawing a straight line between the known points on either side. This helps to complete the calculation accurately even when some data points are missing or not directly provided in the dataset.

Linear up – Log down

In the linear-up-log-down method, the area under the curve (AUC) is calculated differently depending on whether the drug concentration in the body is increasing or decreasing. When the concentration increases, the method uses the linear trapezoidal rule to compute the AUC, which simply connects the data points with straight lines to form trapezoids and then calculates the area of these trapezoids.

However, when the concentration is decreasing, the logarithmic trapezoidal rule is applied. This rule first transforms the concentration data into a logarithmic scale (which makes the decreasing trend appear as a straight line) before forming trapezoids and calculating their area, which provides a more accurate measure of the AUC during elimination phases.

For parts of the graph where data points are missing, the method fills in the gaps with estimated points. If the concentration at these points is increasing, linear interpolation (drawing a straight line between the known points) is used to estimate the missing values. Conversely, if the concentration is decreasing, logarithmic interpolation (which accounts for the rate of decrease in a logarithmic scale) is used to estimate the missing points. This ensures a more accurate calculation of the AUC by closely following the actual trend of the concentration data.

PumasCP facilitates the selection of the most appropriate regression approach.

Threshold

Threshold sets the number for the maximum number of points that can be used for lambdaz calculation using one of the regression methods. It is requires that this number be greater than 2. Users don't need to set this unless they want to limit the number of points used for lambdaz calculation across all subjects.

Rules

Handling BLQ

Dealing with Below Limit of Quantitation (BLQ) values is crucial for accurate results. BLQ values are measurements that fall below the sensitivity threshold of the assay used in the study. The "Handle BLQ" feature in PumasCP provides three options for managing these values:

First

In this option users to choose how to handle BLQ values that occur at the first few data points in the dataset, usually before Tmax. The options are:

• Keep: Select this option to retain BLQ values as they are in the dataset. This is useful when the BLQ values carry important information for early or late time points.
• Drop: This option removes any BLQ values from the dataset. Use this if BLQ values might introduce bias or if they are not required for the analysis.
• Replace with: Choose this to substitute BLQ values with a specific numerical value. It is beneficial when a conservative estimate of concentration is needed for non-detects.

Middle

• The same options apply to BLQ values that occur in the middle of the dataset, slightly before and after Tmax.

Last

• Similarly, options for handling BLQ values at the last data points, towards the terminal phase are provided. This is particularly important for accurately estimating the terminal elimination phase.

Notes

• The text box at the bottom allows users to add any specific notes or rationale for the chosen BLQ handling method. This is crucial for traceability and understanding the context during later stages of the analysis or by other team members.

Remember to click "Save" after making your selections to apply the changes to your dataset. It is recommended to consider the impact of each option on the PK analysis and to consult with the business rules and experts of the organization.

Adjusted R²

The Adjusted R² threshold feature is designed to assess the goodness-of-fit for log-linear regression for the λz estimation. Here's how to use this feature in your analysis:

Setting the Threshold

• Enter a value in the "Value" field to set the minimum acceptable threshold for the adjusted R² statistic.
• The subject's data will pass this quality check if its adjusted R² value is greater than or equal to the threshold you set.

Notes Section

• Utilize the "Notes" area to document any justifications or specific considerations for the chosen threshold. This could include explanations for the threshold level, its relevance to the study, or any other contextual details.

Saving Your Settings

• After inputting the desired value and any notes, click "Save" to apply the threshold to your dataset.

AUC% Extrapolation

The AUC % Extrapolation feature in PumasCP allows users to define quality control criteria based on the extent of extrapolation used in calculating the AUC for PK analysis.

Setting the Rule

• Input a percentage value in the "Value" field to set the maximum allowable limit for AUC extrapolation. This limit determines the acceptability of the PK analysis for a subject. Typically, a value of 20% is considered a reasonable threshold for AUC extrapolation.
• A subject's data will meet the quality criteria if the AUC % Extrapolation does not exceed this predefined threshold.

Notes

• The "Notes" field is available for users to record any explanations, justifications, or specific considerations related to the chosen AUC % Extrapolation threshold. It's important for ensuring traceability and providing context for the set threshold.

Applying the Settings

• Once the desired AUC % Extrapolation threshold is entered, and any relevant notes are added, click the "Save" button to enforce the rule within the PK analysis.

This feature ensures that the AUC calculation remains within a scientifically acceptable range, enhancing the integrity of the PK analysis. It is particularly important in studies where the tail of the plasma concentration-time profile is not fully characterized, and extrapolation is necessary to estimate the total AUC.

Parameters to Summarize

In PumasCP, users have the ability to tailor the PK analysis by selecting specific parameters to summarize. The interface provides a list of standard parameters, from which you can choose the ones relevant to your analysis for summarization.

How to Use:

• Standard Parameters: Check the boxes next to the PK parameters you wish to include in your summary report. tmax, cmax, half_life, auclast, and aucinf_obs, are selected by default, but you can add or remove parameters based on your study requirements.

• Custom Parameters: You can add more specialized measurements by clicking on the "Click here to add new partial AUC" link under the Custom Parameters section.

• Selected Parameters Panel: On the right, you'll see a summary of the parameters you've chosen. You can review this list to ensure it includes all the parameters you need. If you change your mind or make a selection by mistake, you can remove any parameter by clicking the "x" next to it or clear the entire selection with the "Remove all" option.

• Partial AUC: For a more detailed analysis, you can calculate partial areas under the curve by clicking the "Partial AUC" button. This feature allows for the evaluation of AUC within specified time intervals, which can be particularly insightful for certain studies.

Once you have made your selections, make sure to save your settings. This will update the analysis configuration, ensuring the resulting summary report includes all the chosen parameters. This customizable approach allows you to focus on the most important data points for your specific research needs.