Part 1: Exposure Metrics

Introduction

Model-informed drug development uses dose-exposure-response models to make more informed decisions about dosing. The first element of these models is using information about drug dosages to predict drug exposure.

Dose vs. Exposure: What’s the Difference?

Dose is the amount of drug given to the patient, usually given as mass units (such as mg) or mass units by patient weight (such as mg/kg). Exposure is the amount of drug actually available in the body after dosing, and may be represented as concentration in plasma (such as mg/L) or as area under the concentration-time curve (such as mg/L*hr). Exposure is a closer approximation of the amount of drug that is actually active at the mechanism of action (e.g., the amount of an oncology drug acting on the tumor).

Defining Exposure Metrics

Population pharmacokinetic (PopPK) models, estimate a patient’s concentration in plasma over time, given their drug dosing schedule and covariates such as patient characteristics or concomitant medications. The concentration-time profile can be estimated even more specifically for individual patients if their drug concentration has been sampled.

Read more about population PK modeling here: https://a2-ai.com/blog/modeling-monday-part-2/

Some models, such as tumor-growth inhibition models, use the concentration-time profile. Many exposure-response analyses summarize the concentration-time profile into single values such as the maximum concentration. These summary values are referred to as exposure metrics.

Exposure Metrics: From Concentration Over Time to a Single Value

Exposure in an interval

Concentration over a certain time period can be summarized as the average concentration (Cavg), the maximum concentration (Cmax), and the trough concentration (Ctrough - the minimum concentration before the next dose is administered).

In the figure below, Cmax and Ctrough during the first dosing interval are marked by the red and blue dashed lines. Cavg is indicated by the black dashed line, and would be calculated by dividing the area of the blue shaded region (AUC) by the length of the dosing interval (12 hours).

Figure 1: Average, maximum, and trough concentration over one dosing interval (0-12 hours).

Exposure at steady-state

Exposure metrics are also commonly calculated over a dosing interval at steady state. A patient receiving repeated doses of a drug on a consistent schedule has reached steady state when the concentration-time profile in one dosing interval is the same as the previous interval, indicating that the rate of drug administration and elimination are in equilibrium.

Figure 2. Steady-state is reached when the difference in area under the concentration-time curve (Cauc) is minimal between dosing intervals. The blue shaded region highlights the first interval at which this patient is at steady-state.

Steady-state exposure metrics are useful because they can be meaningfully compared between different lengths of dosing intervals. It may not be possible to calculate steady-state exposure metrics in datasets with many dose modifications or irregular dosing patterns.

When timing matters: Exposure up to event time

The impact of dose modifications or irregular dosing on a certain event may be summarized by Cavg up to the event of interest. This may be useful when exploring the exposure-response relationship pertaining to adverse events, drug discontinuation, or efficacy assessments. The event time may be the same across all patients (i.e. assessment of tumor volume at 6 weeks post-dose), or different for each patient (i.e. individual’s first occurrence of a dermatological adverse event after dosing begins). In cases where many patients experience dose modifications and do not receive exactly their assigned drug regimen, exposure up to event time captures important information that is not available from Cycle 1 or steady state exposure metrics.

Figure 3: Cavg is calculated up to the time of the event, encompassing any dose modifications that occurred up to that point. For this patient, Cavg up to the event time would be calculated by dividing the blue shaded area by 64 hours.

Conclusions

Multiple different exposure metrics can summarize the concentration-time profile of individual patients. The most appropriate exposure metric to explain a response may differ depending on the true underlying mechanism of action and other factors. Model development procedures can help select the best exposure metric, even if those factors are unknown.

As we continue this series, we will dive deeper into consideration and selections of representative exposure metrics that best connect the exposure-response relationships to relevant clinical endpoints to de-risk decisions at each step of the drug development process

If you’d like to explore how this could support your program, our world-class PK and exposure-response teams would be glad to learn more about your needs and help inform the best path forward.

Exposure Metrics, Part 1