Mechanisms of drug action - we need to rethink our models

When we think about the mechanism of drug action we almost always look to the traditional sigmoidal log dose or log concentration response relationship.

Students are almost always befuddled by the fact that once you go into the clinics, hardly anyone ever refers to this fundamentally important relationship. It seems to be important only at lab benches and do not seem to apply in the clinical context. Part of the reason for this is few drugs exhibit a range of actions that span the entire range of that sigmoidal curve. Many drugs either just operate close to the Emax or are limited in getting close to Emax because of toxicity, or compensatory mechanisms. One other constraint is that the estimates of concentrations are poorly representative of the actual concentrations at the effector site. So often we are reduced to just looking at circulating (fluctuating) plasma concentrations (which are very distant from the effector site) or a very crude estimate of the administered dose.

One other problem is that our ideas of drug response mechanisms are heavily influenced by receptor binding models shaped by earlier studies of G-protein type membrane receptors. The simplistic model assumes easily reversible competitive binding to a receptor with almost immediate effects. More and more drugs nowadays do not operate that way.

Here is a list of the top 50 prescribed drugs (as listed by IMS in 2007).

Of these only a minority can be regarded as operating according to that model of drug action. If you consider that many clinically useful drugs (antiinfectives, anticancer, etc) work through mechanisms more related to irreversible cell kill type models, I think you can readily appreciate the inadequacy of that simplistic traditional concentration-response model of drug action.

Measures of efficacy

In the previous posts we had considered the optimization of warfarin dosages. The genotyping offers an added dimension to the optimization process, but may not be really cost effective. The warfarin problem is probably not a good example for predictive genotyping. This is because it already has a very good biomarker of clinical response - the INR (International Normalized Ratio). Most other drugs do not have good measures of clinical response. The absence of such measures of efficacy makes the optimization much more challenging.

Some drugs like warfarin allow direct measurement of the therapeutic efficacy. So for warfarin, it is the INR. For antihypertensive drugs, it could be the blood pressure response. For an antiasthmatic bronchodilator, it could be the airway relaxation, FEV1 for example. For an antidiabetic drug, it could easily be the blood glucose response. There are many other examples.

But for many therapeutic areas, the clinical response is much less directly quantifiable. For example, in the use of an antiepileptic drug, without a good surrogate measure of response, one is never really sure whether appropriate amounts of drug have been used. Likewise, for an antibiotic or a chemotherapeutic agent, the inappropriateness of the dosing regiment may not be recognized until it is too late.

For such situations, the availability of a 'surrogate' measure of response may be critical in patient care. One such surrogate measure is the measurement of circulating drug concentrations. Crude though it may be, getting the patient into a 'therapeutic range' may provide some guidance as to whether or not appropriate dosages have been used. Such an approach is sometimes referred to as the 'target concentration strategy'. There are however limitations to this method and it is not always applicable.

For the target concentration strategy to work, there must be evidence that circulating concentrations bear a good relationship to therapeutic outcome.