I apologize to all my readers for such a long lapse between posts. After a very busy summer and fall, I am back to posting regularly to my blog about PK/PD topics.

When analyzing PK/PD data, one of the most important plots used to visualize the data is to plot time-matched PK/PD data on a scatter plot. The X-axis has the PK concentration and the Y-axis has the PD data. Two examples of these scatter plots are shown below.

No Hysteresis

Hysteresis

The first plot shows a relationship with no hysteresis, and the second shows hysteresis. The easiest way to identify hysteresis is by drawing a vertical line on the concentration-effect plot. If that line crosses the curve in 2 places, indicating 2 different response levels for a single drug concentration, then you have hysteresis. In the first plot (no hysteresis) a vertical line at 40 ng/mL corresponds to a single effect level (20%). However, in the second plot (hysteresis) a vertical line at 40 ng/mL corresponds to both effect levels of 40% and 100%.

A hysteresis is neither good nor bad when reviewing PK/PD data. A hysteresis loop simply means that there is a time delay between the measured concentration and the effect response. Normally this means that the measured effect is indirectly affected by the measured concentration. To properly model this relationship, you would want to use an effect compartment or an indirect PK/PD model.

So a hysteresis loop simply provides information on how to model your PK/PD data.

In May 2011, T.J. Smith and B.E. Hillner published an opinion piece in the New England Journal of Medicine titled “Bending the Cost Curve in Cancer Care” (link). In this opinion piece, Smith and Hillner suggest that the rapidly increasing cost of treating cancer is not sustainable.

“We must find ways to reduce the costs of everyday care to allow more people and advances to be covered without bankrupting the health care system.”

Suggestions to re-balance the cost-effectiveness included limiting chemotherapy based on performance metrics, switching to palliative therapy when chances of success are small, having appropriate end-of-life discussions, and executing comparative-effectiveness and cost effectiveness analyses. These solutions are not novel or unique, but they challenge the standard method of treating patients.

This article made me think about my contributions and how I might contribute to reducing the cost burden on the healthcare system while continuing to provide the best possible therapies to patients. Pharmacokinetic/Pharmacodynamic analysis should provide significant information to optimize therapy for patients, but I don’t think we have achieved that lofty goal. This discipline which we practice uses pharmacostatistical models to relate drug doses to clinical response information. As these models are developed, we include patient demographic information to refine the predictions and customize our models. We also link PK and PD together to create integrated exposure-response models that link dosing to clinical efficacy. Despite all of this effort, many of these PK/PD models never reach clinicians who prescribe the medications nor do they reach the patients who could benefit from our work.

What can we do to change this sad fact? Here are some of my ideas:

• Integrate more clinically relevant features into our models. Focus on demographic measures commonly made in a physician’s office, not those measured in a clinical study.
• Package our models into tools that physicians can use. Provide PK/PD models as web apps, mobile apps, or in conjunction with other physician software packages. Help physicians simplify the process of prescribing medication.
• Provide our models to patients. Provide simplified models to patients as scientific communications, not promotional tools. Today’s patient is educated, curious, and connected to the internet. Let’s recognize that inquisitive nature and provide tools to help patients discuss their medication with their physician
• Simplify our models and target clinical outcomes. Too many models focus on esoteric measures of pharmacodynamic measures. Let’s spend more time integrating clinical outcomes (even those that are categorical) into our models so that they can be more meaningful to physicians and patients.

What do you think we can do to use PK/PD to add value to patient care? Leave your comments below.