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.

Pharmacokinetic variability

Most doctors do not fully appreciate just how much variability there exists in the population with respect to how their patients respond to a standard dose of the drug they use. Even without considering the variability in terms of the pharmacodynamics of the response, there is already a lot of variability. Here is a simple simulation of what a 'typical' plasma concentration profile might look like after a 8 hourly dosing regimen.

Looks fairly 'normal' doesn't it? But here (below), I have simulated a set of random profiles of a theoretical drug from a fictitious set of 20 patients with a very reasonable range of pharmacokinetic properties (body weight, clearance, volumes of distribution, bioavailability). These are values that one can reasonably expect from any patient. Just look at the wide range of plasma concentration profiles that can be found in a set of 20 'patients'. Whether these are eventually clinically important or not will of course, depend on the specific nature and the toxicity profile of the drug in question, but recognizing that such variability exists, is the first step to being able to personalize therapy.

The issue of clarithromycin and increased cardiac deaths #5 - How can we deal with the variability?

Clinical efficacy is never only about therapeutic efficacy. It is always a balance between benefits and risks. Drug response variability shapes the clinical efficacy curve by altering the relative distance between benefits and risks along the dose or concentration range. Good therapeutics therefore is always about being able to manage the variability in drug response, so that the optimal dose or concentration for the patient can be selected that will maximize benefits and minimize risk.

If we just consider the anti-microbial effects.... we actually manage the variability rather badly. Theoretically, there is a therapeutic target that is based on maintaining drug concentrations at the target site (where the bugs are actually growing) above the minimum inhibitory concentrations (MIC). By drug concentrations we refer mainly to the trough concentrations. Therapeutically, the assumption is that if we dose according to published guidelines, we will achieve target concentrations at the site of action. In reality, we do not know that for certain. In fact, we have absolutely no idea whether we are dosing too much or too little, and basically act on faith that a certain dose (quantum and frequency) will allow trough concentrations at the site of action to exceed the MIC.

Therapeutic drug monitoring of clarithromycin had been proposed, but has never ever been taken up seriously for it to be included in routine patient management.

This is fine, if there were no serious toxicities, because you can administer more drug than really necessary just to make sure you have adequate concentrations at the target site. However, clarithromycin use does carry a serious risk of toxicity....namely sudden death. Therefore the correct dosing schedule is important to deliver only adequate levels of clarithromycin so that the troughs are over the MIC and the peaks do not approach the IC10 of IC20 of HERG channel blockade.

Currently we have no means to doing this.

On the flip side of the coin, there is the problem of cardiac toxicity. Although there is a considerable gap between routinely achieved levels of clarithromycin and the IC10 or IC20 of HERG channel blockade, obviously this gap can sometimes, though rarely, be crossed. The Danish study suggests this might be happening at least in 37 out of 1 million dosing regiments. How do we monitor and manage this? Routine ECG to look for QT prolongation would certainly be helpful. But this is almost never done during clarithromycin use.

Effectively therefore we have no means to manage variability where clarithromycin use is concerned. There is an over-dependence on published dosage guidelines, and faith in the adequacy and safety of these guidelines. Am I surprised by the association with cardiac deaths? Definitely not. Understanding the pharmacology of clarithromycin, this risk is predictable. The risk is not high. But one sudden death occurring in a relatively healthy individual, no matter how infrequent, is one death too many.

Can we do better? Yes.

Pharmacogenetics-Pharmacogenomics-Personalized Medicine

The latest plot of numbers of publications suing the terms 'Pharmacogenetics', 'Pharmacogenomics', and 'Personalized Medicine'. (see previous plot) Interestingly the term 'Pharmacogenomics' isn't replacing the term 'Pharmacogenetics' at all. This is despite what has often been expressed that the two terms are used interchangeably. Quite obviously the scientific community does make a distinction between the two terms and continue to use them in distinct ways. The number of times the P'genetics term is used still remains greater than that for P'genomics.

Perhaps P'genomics should be considered a subset of P'genetics rather than what has often conversely been suggested.

Optimization? What's that?

Optimization refers to the situation where you can adjust the inputs into a system according to output functions, and eventually arrive at the best solution. In therapeutic terms, it is the process of calibrating the dosage of a drug according to the clinical response so that the best dosage for the patient can be arrived at depending on the therapeutic targets and the patient's individualized response.

This is really no different from the engineering concept of a control system.

When there is an input into a particular process, and no feedback is received about the outcome....this is called an 'open loop control system'. This is probably the least ideal of all control systems. It fundamentally assumes you know everything there is to know and that the decision taken about the initial input is already adequate. This happens in therapeutics when you have fixed dose regiments, and there is no feedback about the outcome. Consider the situation in most cancer chemotherapeutic regiments. Dosage regiments are pretty much determined at the outset, and the only feedback received is if the patient has obvious toxicity which requires cessation of therapy, i.e. switch off the system! This is essentially the pharmacogenomic approach towards 'personalized medicine'.

In other scenarios, there is possibility for the operator to make adjustments to the original input based on feedback received, but this takes place independent of the original model which determined the input. This is called an "open-loop feedback control system'. Most therapeutic scenarios are of this sort, where an initial decision about a starting dosage regiment is made based on starting knowledge and assumptions about the patient. Subsequently minor adjustments to the dosage regiment can be made by the physician depending on feedback received about the patient's clinical drug response. Where there is poor ability to receive feedback about clinical drug response, the system starts to flounder and approximates the simple open-loop control system.

The open-loop feedback control system can operate with varying degrees of sophistication. It can be empirical, where the response to feedback received is relatively intuitive and based on clinical judgement. Or it can be highly deterministic where the response is determined by precise mathematical (PK or PK-PD) models.

A more sophisticated model takes into consideration the uncertainty in the system. This is called a stochastic approach.

The ideal system is that of a closed loop system where the original models determining the original input is linked to, and continually modified by new feedback received. The following diagram represents a closed loop control system with warfarin as an example.

Modified from Applied pharmacokinetics & pharmacodynamics: principles of therapeutic drug monitoring. 1992 Michael E. Burton et al.

At top-right there is a population PK-PD model of warfarin. This represents what is known about how warfarin behaves in the population in which the patient exists. Together with the clinical model at top-left, of what the desired or target INR response is, a decision about the starting dosage regiment can be made.

Subsequently, feedback is regularly received about warfarin pharmacokinetics (bottom right) and INR response (bottom left). These feedback into the original models at top right and left, and continually adjust the model so that decisions are continually taken about how the dosage regiment can be adjusted.

This is optimization... and represents what personalized medicine ought to be.

Can it be done for all drugs? Yes, it can. But it requires that there are good population PK-PD models for the drug, and good biomarkers of response that can be used as feedback. It requires resources and effort.

Above all, it will require that physicians be prepared to put in the extra effort to optimize their therapy according the the patient's real requirements.

Predictive pharmacogenetics

Many people agonize about the predictivity of the science of pharmacogenetics. Somehow there is an expectation that pharmacogenetics should somehow lead to position where we can stop thinking.

Sounds a bit harsh but true.

Pharmacogenetics began as a science to understand the genetic basis for outlier behaviour. In much earlier experiences, outliers were characterized principally by phenotypic behaviour. As it is now, phenotypes tended to be classified in binary fashion - rapid/slow, fast/slow, extensive/poor. Such binary depictions of reality can only be predictive when the reaction or process in point is singular, critical or both. In some situations drug response can be described binarily as 'at risk for toxicity', or 'not at risk', e.g. G6PD deficiencies or HLA B*1502 for carbamazepine-SJS. For the most part however, pharmacogenetics data only helped explain genetic bases for limited aspects of drug response.

FDA classification: +, for information only; ++, recommended; +++, required

Gervasini et al, Eur J Clin Pharmacol (2010) 66:755–774

In highly controlled experiments, it can be easily shown that genetic variants can result in either loss or gain in function in specific processes related to drug response, e.g. drug metabolism and clearances. However since drug response/metabolism/

pharmacokinetics is seldom the result of a singular process, genotyping a genetic variant almost never provides a clear prediction of what the final drug response would be like.

Unfortunately the market place has misled many to believe that we can somehow construct a genetic testing panel that will predict with some degree of finality, what the patient's drug response and hence his drug dosage requirements will be. Hooray.... and we can therefore stop thinking! This line of thinking is clearly fallacious. The concept of 'personalized medicine' has been hijacked (biotech commercialism?) to refer to a one step genotyping approach to therapeutics when correctly it should refer to the ability to look at the patient in totality, i.e. the entire person - not just from the perspective of his constitutive make-up, but the totality of contributions of his altered physiology and environmental effects.

The more correct and rational approach is that of 'optimization', but the term sadly is far more mundane and less commercially sexy compared to 'personalized medicine'.