Visualizing the effects of body weight and clearance on the plasma concentration - time profile

Here is an interactive graph that helps you visualize how the volume of distribution (simplistically affected here by the body weight) and the clearance of a drug affect the plasma concentration - time profile of a drug that is orally administered 3 times a day (8 hourly).

Play around with the sliders provided and see how the profile changes. Note how the half-life changes as well as how the steady state plasma concentrations, area under the curve (AUC), minimum and maximum concentrations change. How about the time required to reach steady state? And how much accumulation occurs under the different situations?

Think about how individual profiles may vary because of differences in body weights and the ability of their bodies to clear specific drugs.

[No canvas support]

BW(kgs): 70       

Clearance: 9.1

Dose Interval = 8hrs,

Accumulation Index: 2.0       Halflife: 8.0

Understanding clinical efficacy of drugs (2) - variability in a population

This is a another way of looking at the same plot that was shown in the previous post. A plot representing the chance of a beneficial effect (blue) and a similar plot representing the chance of a detrimental effect (red). The difference here, is that the plots are now a sample of a simulated population with a variation in sensitivity to the drug effect. Likewise, the toxicity profile. In this plot, the therapeutic range is defined as being between an empirical 'average' threshold for the beneficial effect (on the left), and the unacceptable 'average' level of toxicity (on the right). The understanding here, is that potentially you can continue to increase the dose from the left boundary of the therapeutic range, if a stronger drug response is needed. The downside to this is that there will be an increased risk of toxicity. The right boundary to the therapeutic range basically limits the dose increase as any further increase in toxicity risk becomes unacceptable.

As in the previous post, the clinical efficacy plots can be generated from the simulated population. It is shown here with the therapeutic range superimposed. As can be seen, there is an optimal zone where clinical efficacy is maximum. Here is concentration where you can expect maximum benefits with minimum risk of toxicity.

But it should be recognized that this only an expectation of the 'average' response within a population. What should be specifically noted here is the variability and wide scatter of response types within the population sample. For any specific patient within this simulated population, the clinical efficacy is unique, and may look totally unlike the population 'average'

The question is, how do you recognize and deal with this response variability?

Understanding clinical efficacy of drugs (1)

The clinical efficacy of any drug can be understood by visualizing the balance between its risk of producing limiting toxicity as compared to the chance of producing a beneficial clinical response. These two effects may or may not be mediated through the same receptor systems, and toxicity need not necessarily be due to a pharmacological overdose. Simplistically the clinically efficacy can be visualized by comparing two concentration-response curves (not necessarily parallel since they do not necessarily operate through the same receptor system), where one represents the beneficial response, and the other, the toxicity response.

The space between the 2 curves can actually plotted out to visually represent the clinical efficacy profile of the drug. Assuming a threshold of clinical efficacy exists, an empirical therapeutic 'window' may be identified, within which we can try and keep drug concentrations for optimal efficacy. I personally do not like the term window as it suggests the best approach might be to target the middle of that 'window'. I prefer 'therapeutic range' because the therapeutic strategy might actually be to exploit the range of concentrations so as to maximize the efficacy for the individual patient.

The shape of the clinical efficacy plot obviously will depend on the shape of the individual beneficial and toxicity curves. Likewise the therapeutic range will depend on the acceptable threshold of clinical efficacy for that specific drug.