Effect of Vd change on pharmacokinetic parameters

With the interactive graph provided in the previous post you can vary the body weight by using the slider. Here, simplistically, the body weight directly changes the volume of distribution, so effectively the slider just changes the Vd.

What you will observe is that as you change the body weight, the half-life changes, and correspondingly, the time to reach steady state. The accumulation index also changes, because it is affected by the ratio of half-life/dose interval. The AUC and steady state concentrations remain unchanged because these are affected by Clearance and not Vd. The peaks and troughs changes, and these may affect the efficacy-toxicity balance of the dosage regimen if either efficacy or toxicity is affected by peaks or troughs.

In the display above, the body weight was altered from 70kg to 140kg. The half-life and time to reach steady state both corresponding doubled. While the AUC remained unchanged, the peaks have been lowered while the trough has increased. Whether the dosage regimen needs to be adjusted will depend on the therapeutic ratio of the drug, and the therapeutic objectives.

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.

The issue of clarithromycin and increased cardiac deaths #4 - Where are the potential sources of variability?

1. Bioavailability
Regardless of its touted lipophilicity, clarithromycin has a reported average bioavailability of only about 50%. Generally, as a guiding principle, the lower the bioavailability, the greater the potential for variability in systemic availability.

2. Uncertain target site concentrations
There are two associated problems here.

Firstly, clarithromycin has an elimination half life of about 3-5 hours at low doses and 5-7 at higher doses. At a 12 hourly dosing intervals, there will be significant fluctuations in the plasma concentration profile. Even if it is administered at 8 hourly intervals, and if half-life is assumed to be at the high end of the range, say 8 hours, there will be at least a 2 fold fluctuation between peaks and trough. While this may meet the needs of anti-bacterial efficacy (assuming we keep trough levels above MIC), the levels of the peaks may predispose to cardiac toxicity if it is able to inhibit HERG potassium channels. To some extent, we can mitigate the fluctuations by using extended release formulations, but this may be at the expense of even more variability in bioavailability.

[source:http://medlibrary.org/lib/images-rx/clarithromycin-24/img-0238.jpg]

Comparison between normal formulation and extended release formulations

Secondly, since we do not routinely measure either plasma or tissue concentrations, we have little idea if adequate concentrations are being achieved at the target site. Here, there is some more uncertainty. Tissue and cellular concentrations tend to be higher than plasma unbound concentrations, but concentrations in the extra-cellular fluid (where the bugs are) are variable and may be lower than unbound concentrations of clarithromycin. These are functions of variable protein binding and the variable net activities of specific influx and efflux membrane transporters.

Consequent upon the previous two points, the differential effects of clarithromycin on the bacteria and on HERG channels may be variable between individuals not only because they relate to different effect compartments but the latter may relate to heights of the peak while the former to trough concentrations being above the MIC.

[Source:http://www.fda.gov/ohrms/dockets/ac/03/briefing/Image66.gif]

Relationship between QT prolongation ad clarithromycin concentrations

Although the IC50 for clarithromycin on the HERG channel is about a 100 times higher than the MIC, arrhythmic risk is associated with lower extent of inhibition. Hence cardiac risk is seen at much lower IC10 or IC20 concentrations

Added to all these, is the uncertainty contributed by an active 14-OH metabolite of clarithromycin.

3. Inter-individual variability in pharmacokinetics
Clarithromycin is both a substrate and inhibitor of CYP3A4. This metabolic pathway is also responsible to generating the active 14-OH metabolite. Variable CYP3A4 activity therefore results in a variable mix of clarithromycin and its active 14-OH metabolite.

There is a very high extent of variabilty in CYP3A4 activity in any population studied. There are also significant differences in activity between men and women (women generally higher). While there are genetic polymorphisms associated with CYP3A4, no single genetic variant has been able to account for the variability within a population. On the other hand, CYP3A4 is also vulnerable to many food and drug interactions.

To make matters more complicated, clarithromycin inhibits its own metabolism by CYP3A4, and exhibits a non-linear pharmacokinetic profile.

4. Inter-individual variability in susceptibility to QT prolongation
The HERG potassium channel gene is genetically polymorphic and variants may predispose to variable susceptibility to QT prolongation. Added to this is the uncertainty about appropriate dosing regiments between different ethnic populations, who may have different body weights and distributional volumes, as well as different exposures to CYP3A4 food and drug interactions.

5. Variability in microbial susceptibility
Apart from differences in anti-microbial efficacy due to variability in drug permeation to target sites, bacteria do differ in how susceptible they are to concentrations of clarithromycin. While sensitive bacteria generally have MICs in easily achievable range, resistance genes have become more prevalent and differences in bacterial sensitivity has become more common.

6. Compliance issues
One must never forget the variability that may be caused by failure of the patient to medicate according to instructions, leading to highly irregular dosing intervals and therefore variable degree of fluctuations in circulating drug concentrations.


Taking all these uncertainties into consideration, the question is how to ensure the patient gets optimal dosing? Think about it.

[To be continued]

The issue of clarithromycin and increased cardiac deaths #3 - Does the usual dose or concentration-response relationship apply clinically?

Very often students are taught models of the dose- or concentration response relationship that do not seem to apply clinically. This is because the traditional model of that relationship was derived from in-vitro experiments, and based on the early experiments using G-protein coupled receptor systems. Invariably these receptor systems were superficially sited on the membranes of effector cells. In the simplest of these models the binding of ligand to the receptor is reversible and competitive.

For clarithromycin however, the drug acts by the inhibition of ribosomal RNA within microbial cells. While this binding to the 50S sub-unit of the ribosome may be reversible, the effects are not. Unless the bacteria carries resistance genes, the bacteria either stop growing or dies. So the concentration-response relationship must incorporate elements of growth, death and resistance. Even so, the model will only work if we know the intra-cellular concentrations of clarithromycin. And we do not know that. For clarithromycin, we are ignorant of not only intra-microbial concentrations of the drug, but we are also not even sure of the concentrations of the drug in the fluids bathing the bacterium. We only see plasma concentrations. Unbound drug concentrations in the plasma vary between individuals, and have an unpredictable relationship with interstitial fluid concentrations.

Taking all these into consideration, it is clear that we will not be able to construct any meaningful concentration response relationship according to the traditional model. Instead, we have a model of drug response that is based on the minimum inhibitory concentrations (MIC). For S. pneumoniae, the clarithromycin sensitivity breakpoint occurs at approximately 0.25 ug/ml. Correspondingly, clinical efficacy will depend on how much of the concentration time profile sits above this concentration. More, correctly though, this refers to the concentration in the interstitial fluid, and we can only guess-timate this from plasma concentrations.

Another complication is the fact that, clarithromycin is not the only active molecule against Gram-positive bacteria. The main 14-OH metabolite has activity, albeit lower. Hence, concentrations of clarithromycin alone will under-estimate the anti-bacterial effect.

There is of course, a flip side of this story that has to do with the cardiac toxicity. Macrolides such as clarithromycin have effects of the cardiac HERG potassium channel, leading an inhibition of the delayed rectifier potassium current during the cardiac action potential. This results in a prolongation of the QT interval of the electrocardiogram, which predisposes to potentially fatal ventricular arrhythmias such as torsades de pointes. The concentration response relationship for this effect is quite different from that discussed above for antibacterial effect. The IC50 for clarithromycin on the HERG channel is approximately 30 ug/ml which is about 100 times higher than the MIC.

This therefore sets a therapeutic window for the use of clarithromycin where the physician would need to ensure that clarithromycin concentrations in the inter-cellular space will be higher than the MIC but not so high as to inhibit the HERG channel.

Think about how we best can do this. See this in the context of the Danish study where there was an excess of 37 cardiac deaths per million doses.

(To be continued)

The issue of clarithromycin and increased cardiac deaths #2 - Pharmacology

Clarithromycin is a macrolide bacteriostatic antimicrobial that came onto the market in 1991. It enjoyed considerable success as an orally administrable macrolide, being relatively lipophilic and having a slightly longer elimination half-life. Came off patent about 10 years ago.

It acts by inhibiting bacterial protein synthesis by blocking the ribosomal RNA. Resistance develops as bacteria acquire various resistance genes, such as the plasmid erm (A) gene that confers an ability to methylate the adenine in the binding site.

Clarithromycin can be administered orally with a bioavailability of about 50%. Its permeability across biological membranes is only due in part to its lipophilicity. A significant part of the process depends on a complex interplay between influx and efflux transporters expressed on various membranes. Consequently intra-cellular, and tissue concentrations do not correlate with circulating unbound drug concentrations. Interestingly, tissue interstitial fluid concentrations are lower than free drug concentrations in plasma, but intra-cellular concentrations are to a variably extent much higher than plasma free concentrations.

The protein binding of clarithromycin is about 60-70%. The Volume of Distribution is about 10 L/kg, which is consistent with significant permeability into tissues. Again this increased permeability results not only from lipophilicity but from the complex interplay of influx and efflux transporters, in this case clearly favouring influx.

Clarithromycin is eliminated by both hepatic metabolism and renal elimination. It is extensively metabolized by CYP3A4 (which it also inhibits), to a principal metabolite 14-(R) hydroxyclarithromycin, which is also pharmacologically (less) active. The pharmacokinetics is not linear, and the elimination half-life increases from 3-5 hours at lower doses, to 5-7 hours at higher doses. Tissue concentrations persist for much longer.

Clarithromycin produces a range of adverse reactions, but the one that concerns us for this discussion is with respect to cardiac death. Like many of the macrolides, clarithromycin has an effect on the myocardial delayed potassium rectifier current, leading a prolongation of the QT interval of the ECG. This prolongation of the QT interval is associated with risk of torsades de pointe and a fatal ventricular arrhythmia.

The usual adult dosage is 250-500 mg 12 hourly for 7-14 days.

Clarithromycin is a drug with very interesting pharmacological properties. Give a thought as to how these properties contribute to variability in the clinical response and the risk-benefit ratio particularly with respect to the problem of cardiac death.

(To be continued)

The issue of clarithromycin and increased cardiac deaths

Just this last week I was discussing with my students, the various factors contributing to variable drug response, and possible differences in the risk-benefit ratios for the same drug in different individuals. The recent publicity about increased cardiac death risks associated with clarithromycin provides a useful case study for many of these issues. Follow these posts for a discussion about this interesting issue.

The original publication about this can be found here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138354/

Essentially, this Danish study looked at a large sample (n=160,297) of patients treated with a 7-day course of clarithromycin. The control groups were patients who been treated with roxithromycin and penicillin V. They found an excess of 37 deaths per million doses of clarithromycin compared to penicillinV. This is a very small though statistically significant increase in risk. The risk appeared to be contributed largely by an increased risk primarily among women. No increased risk was observed for roxithromycin.

As in many epidemiological studies of this sort, the design is far from perfect and there are always ways to cast doubt on the significance of the findings. It is not necessary for this discussion to arbitrate on this matter. Rather, this report serve as a context for us to consider the various mechanisms that may contribute to inter-individual variability in the risk-benefit ratio for a drug such as clarithromycin.

(To be continued)

The brain as a protected efficacy and/or toxicity compartment

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Distribution of selected drug transporters at the BBB. ABC transporters are ATP-driven xenobiotic efflux pumps that can be highly polyspecific (P-glycoprotein is an extreme example), with overlapping substrate specificities that are briefly outlined for each in the boxes. There is considerable controversy over the localization of essentially all ABC transporters in brain capillaries [1,6]. It is likely that species differences in protein expression levels and cellular transporter distribution, as well as differences in detection techniques across laboratories, underlie many of the conflicts that permeate the literature. Nevertheless, when expressed on the luminal plasma membrane, ABC transporters provide an active, structure-sensitive barrier to drugs within the vascular space. Other transporters shown are members of the SLC superfamily and handle primarily organic anions and weak organic acids. Substrates for these transporters include some drugs, such as nonsteroidal anti-inflammatory drugs, as well as drug metabolites and waste products of normal CNS metabolism. They, along with luminal ABC transporters, provide a two-stage system for active and efficient excretion of potentially toxic chemicals and metabolites from the CNS.

(Figure taken from Trends in Pharmacological Sciences Vol.31 No.6)

The brain has for a long time been recognized as a very well protected organ. Chemically, the brain is isolated from the circulation by means of a very tight blood-brain barrier (BBB), first described by Stern in 1921. This BBB consists of very tightly stitched together endothelial junctions which restrict free movement of molecules from the circulation into the interstitial fluid of the brain. The capillaries are themselves encased by a thick basement membrane and the enveloping foot processes of the astrocytes.

It was thought for a long time that this barrier allowed small and lipophilic molecules to passively diffuse across, and that specific transporters mediated essential solutes such as glucose etc. This model however has been challenged by the recognition that few molecules, no matter how small or lipophilic, would diffuse across membranes without the assistance of some transporter protein. Even water required the assistance of aquaporins. The BBB is now recognized as a very sophisticated and metabolically dynamic membrane populated by not only transporter proteins but also CYP450 enzymes.

These functions of the BBB have recently been reviewed and it is worthwhile reading up on these. The following one is a recommended as it discusses also the regulation of transporters at the BBB:
Miller, D. Trends in Pharmacological Sciences 31 (2010) 246–254 (from where the above figure has been borrowed).

The article concludes:
It is now clear from studies with animal models and with patient samples that the expression and activity of P-glycoprotein and other ABC transporters at the BBB can be moving targets, affected by genetics, disease, pharmacotherapy and diet. Indeed, we are rapidly adding to maps of the signals and signaling pathways involved with a view to improving both CNS protection and the delivery of small-molecule drugs to the brain. Thus, an understanding of signaling could provide opportunities to both selectively fine tune barrier function up or down and to begin to identify the barrier-based and external factors that contribute to patient-to-patient variability in response to CNS-acting drugs. Although we are rapidly developing a very detailed picture of signaling to ABC transporters in animal models, it is still not clear to what extent these pathways operate in humans. An understanding of transporter function and regulation at the human BBB is critical before we can determine to what extent signaling can be manipulated to improve drug delivery to the CNS and to enhance neuroprotection.

It should however, also be noted that the BBB is not the only barrier that exists in the brain. It is probably the main barrier for drugs which act on surface receptors on neuronal membranes. But for drugs which act intracellularly, there is yet another barrier which exists at the cell (neurones or astrocytes) membranes. Furthermore, it is far from clear if the BBB is a uniform barrier throughout the brain. It is possible that the population of transporters may have regional differences.

Because of the existence of these barriers to the movement of drug molecules, we must be careful of trying to extrapolate too much from plasma concentrations of drugs acting on the CNS. There is often a significant dichotomy between the plasma pharmacokinetics of drug and what is actually happening not only in the brain but more specifically, intracellular mechanisms in the brain.

Deciphering the efficacy-toxicity profiles of CNS drugs become a complex exercise when efficacy is expected in the CNS while toxicity may be manifest in another tissue protected by a different population of receptors. It becomes even more perplexing when one considers that these populations of transporters in efficacy and toxicity compartments can be modulated differently by environmental and epigenetic mechanisms over time as well as between individuals.

Plant alkaloids and chemodefense

Although many alkaloids have toxicity which is immediate and topical so as to discourage predation, many alkaloids do get absorbed into the predator's body and can therefore produce pharmacological and toxicological effects beyond the point of exposure. Some of these effects are extreme and may cause severe reactions in the predator, again discouraging predation.

Animals learn to stay away from such plants. Alternatively, they develop protective mechanisms against the toxicological effects of the alkaloids. Apart from the biological membranes which provide an initial protective barrier against insoluble and hydrophilic chemicals, many organisms also have evolved protective mechanisms such as the cytochrome P450 enzyme systems and the efflux transport proteins to detoxify and repel the more permeable alkaloids which may be able to escape past the biological membranes. In response, plants, over time, evolve even more complex chemicals to overcome animal defense mechanisms. This plant-animal arms-race create the complex environment which now can be seen to determine pharmacokinetic behaviour of the the drugs we use.

Drug metabolism and drug transport must therefore be seen as component parts of an integrated process to protect animals from the toxicity of plant alkaloids.

Read this interesting account of the mustard oil bomb.

A well known groups of alkaloids are the methylxanthines.

Caffeine: R₁ = R₂ = R₃ = CH₃

Theobromine: R₁ = H, R₂ = R₃ = CH₃

Theophylline: R₁ = R₂ = CH₃, R₃ = H

The three main members are caffeine (1,3,7-trimethyxanthine), theobromine (3,7-dimethylxanthine) and theophylline (1,3-diethyxanthine). Caffeine is found in tea and coffee, while theobromine is the main methylxanthine found chocolate. The methylxanthines are phospohodiesterase inhibitors.

The metabolism of caffeine is shown below. Caffeine has been use as a probe substrate to develop metabolic ratios for CYP1A2 and N-acetytransferase 2.

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.

The rofecoxib (Vioxx) story - an interesting case study

The selective COX-2 inhibitor rofecoxib, marketed as Vioxx, tells an interesting story. You can read more about it here at Wikipedia.

Rofecoxib was an initially successful drug that had been used as an anti-inflammatory analgesic for the management of inflammatory joint disease, acute pain and dysmenorrhoea since 1999. The selective inhibition of COX-2 meant that rofecoxib could be used effectively as an anti-inflammatory analgesic but with minimal side effects on the gastric mucosae.

In 2001, rofecoxib was proposed to be used in the prevention of adenomatous polyps in the colon. The 3-year (APPROVe) trial to investigate the prophylactic role of rofecoxib for colorectal polyps was terminated prematurely because rofecoxib was observed to be associated with increased relative risk of thrombotic cardiovascular events beginning after 18 months of treatment. Data in the first 18 months did not reveal any increased risk. In 2004, Merck voluntarily withdrew rofecoxib from the market following the report of this increased risk.

The clinical use of rofecoxib is not without variability issues with respect to its pharmacokinetics. Although it is not substantially metabolized by CYP450 enzymes, it was nevertheless glucuronidated by UGT2B7 and UGT2B15, both of which are genetically polymorphic. It is not clear to what extent these genetic variants are associated with variability in clinical response to rofecoxib.

Although variability in clinical response to NSAIDs are well known, there has not been a major concern about genetic polymorphisms affecting NSAID pharmacokinetics. This has been, I believe, largely related to the the dependence in clinical practice to adjusting dosages and drug choices to the anti-inflammatory clinical drug response. And there is currently a plethora of 'clinical joint scores' that can allow the rheumatologist to adjust for response variability. The toxicities with NSAIDs have also been largely manageable, and with the COX-2 inhibitors, GI toxicity was not perceived to be a significant problem.

The recognition that rofecoxib was associated with cardiovascular toxicity changed all that.

It is interesting that as the use of rofecoxib moved from rheumatology to polyp prevention, the clinical use of rofecoxib also lost its ability to manage any response variability. Rofecoxib use in preventing colorectal polyps was largely based on prophylactic fixed dose regiments. There was an assumption that this was relatively acceptable because rofecoxib was relatively safe.

But this was apparently not so. There was an association with cardiovascular toxicity. Eventually it was the cardiovascular risk that did rofecoxib in, and caused it to be withdrawn. It is not clear to what extent this was due to pharmacokinetic variability. Or individual susceptibilities at the vascular level, but the practical reality was the clinical use of rofecoxib for colorectal polyp prevention did not allow the clinician any means to adjust dosages for any kind of PK or PD variability. Essentially people were flying blind.

It is probably acceptable to fly blind with a drug that had a very high therapeutic index, but not when there is a risk of cardiovascular death for the drug that was being used in a prophylactic setting.

Does body weight matter?

It would appear, not very much. After all drug doses are seldom adjusted for body weight. Only in situations where the therapeutic index is very low, such as for oncologicals, is the drug dose adjusted for body weight. Clearance as a pharmacokinetic parameter, is seldom denominated by body weight.

Often when discussing differences in PK between populations, you can almost hear the sigh of relief when the differences in AUC can be discounted by body weight. Suddenly it's like the differences should not matter any more.

So should body weight matter?

The answer to my mind is, yes.

Pharmacokinetically, body weight does not matter as much to clearance as it does to distribution. For this reason Vd is often denominated by body weight. But not so clearance. The general reluctance to consider body weight as a major determinant of drug effect related to an older line of reasoning where drug response is seen primarily as a function of AUC, steady state concentrations and consequently clearance, rather than Vd. But as pointed out in previous posts, Vd changes can have considerable effects on drug PK, particularly Cmax and trough concentrations. These are less considered mainly because of their relative instability compared to steady state concentrations. It is however possible that variability in drug responses may in fact be more sensitive to changes in Cmax and troughs rather than steady state concentrations. If so, Vd effects may be potentially more profound than have been previously thought.

Why does this reality bother us?

Principally because one of the immediately noticeable differences between our Asian population and Western population is the difference in body weights. Caucasian males may have an average body weight of 85 kg as compared to age matched Chinese males of 65 kg. Chinese females would have an average of about 55 kg. If one recognizes that drug response may be affected by body weight, it would make us serious reconsider if drug dosage regiments developed from studies involving healthy Caucasian males, may be easily applied to Chinese females without adjustments to the average of 35% lesser body weight.

And this is not yet even considering differences in lean body mass, especially since Chinese/Asians have much less lean body mass for a given body weight, when compared to Caucasians.

Direct, surrogates or outcomes?

Students are quite often confused during discussions of these various types of efficacy measures. It is really not surprising, as many clinicians I discuss with also seem quite unclear about these concepts.

But these are to me quite important ideas; ideas which are quite often overlooked during drug development and the design of therapeutic regiments. And we do pay a price for neglecting them.

When we move from the bench to the bedside, we do lose the ability to assess drug response. Often we do not even begin to recognize just how much we have lost in our ability to do this. Yet it is vitally important for us to be able to do this because if we cannot, we will not be able to rationally manage our dosage regiments. This is one of the great difficulties in managing therapeutics.

In a relative small subset of therapeutic situations we do have direct measurements of drug effect - such as in the management of hyper/hypotension or hyper/hypoglycaemia. The blood pressure and blood sugar responses serve us well. A similar possibility exists with the management of the INR using warfarin.

In many therapeutic situations, no clear biological marker of drug response exists; or the therapeutic aims is actually far more complex compared to the immediate aspects of drug response. In these situations the desired drug response may actually be an 'outcome' measure - such as the control of epilepsy or arrhythmia, or even the management of depression and psychoses. In such situations, a surrogate for drug action should be available that can allow real time management of drug dosages. In some situations, measuring drug concentrations, can provide you with a reasonable surrogate for managing dosages. This is referred to as having a 'target concentration strategy', or more popularly called therapeutic drug monitoring.

Admittedly, good surrogate measures are often not even available. This deficiency creates a therapeutic environment where 'therapeutists' are often limited to relatively fixed, or inflexible dosage regiments, and therefore cannot deal effectively with any patient variability in drug response. This could be as comforting as having your pilot fly blind.

The recent problems with rofecoxib (Vioxx) provides an interesting example. While initially developed for the management of inflammatory joint disease, the selective Cox2 inhibitor found a new use in the prevention of intestinal polyps. When used as an anti-inflammatory analgesic, rheumatologists could manage drug dosages through assessing pain relief, joint involvement etc. When it came to the prevention of intestinal polyps, the therapeutist essentially had to 'fly blind' using fixed dose regiments, since there was neither direct nor surrogate measures of drug action. Intestinal polyposis was at best an outcome measure that could only be assessed at the end of treatment periods. Were there patients who over over-dosed or under-dosed? Very likely. Could we have better managed the dosages, and consequently the risks of cardiovascular mortality? Quite likely; but we will never know now. Rofecoxib was eventually withdrawn from the market.

A pity, perhaps.

Further thoughts about distributions.....

For the most part, the volume of distribution Vd is conceptualized as little more than a proportioning factor between the administered dose of a drug and the initial plasma concentrations. While it was important in determining the C0, it had little impact on drug exposure, the AUC or steady state concentrations. These were squarely in the domain of clearance mechanisms.

We expected that while protein binding was inversely related to the Vd, the free drug concentrations would eventually equilibrate across all tissue compartments, regardless of tissue binding. What was observed in the plasma of the central compartment would represent what was happening in all tissues. Furthermore if free drug clearance remained unchanged, free concentrations would remain unchanged, regardless of protein or tissue binding.

According to these ideas, central compartment pharmacokinetics was of prime importance in understanding drug efficacy, or lack of it.

In recent years, the increasing appreciation that few molecules actually permeate across membranes with the involvement of transporter processes, have led many to question the wisdom of an approach that assumed drug molecules existed in equilibrium across membranes and consequently, tissue compartments. Depending on the expression and function of transporters at various membranes, drug concentrations can vary independently of central compartment concentrations. Hence, in some individuals, the plasma concentrations may mirror concentrations in any tissue compartments if the transporters involved are ubiquitous.

Conversely, in some individuals, the concentrations in tissues 'compartments' may be very different if specific transporters are differentially expressed, leading to widely different efficacy-toxicity profiles even though central compartment concentrations appear invariate.