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.

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.

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 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.

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.

Clopidogrel - variability in response

Indian Heart Journal. 2008 Nov-Dec; 60(6): 543-7

The use of clopidogrel presents another interesting challenge with respect to the variability in drug response.

Clopidogrel is a a platelet inhibitor, acting through irreversible binding to the P2Y12 purinergic receptor on the platelet membrane; though it is not clopidogrel itself that binds, but the active metabolite. The PK of clopidogrel itself is quite complex. Upon oral administration about 90% of clopidogrel is removed through the action of circulating and hepatic esterases to inactive metabolites. Only about 10-15% gets activated by CYP2C19 and CYP3A4 to the final metabolite that binds to the P2Y12 receptor. As the receptor inactivation is irreversible, the recovery of function is dependent on fresh platelet regeneration from megakaryocytes.

The way clopidogrel produces its action is therefore fraught with all kinds of problems which clearly contributes to the observed variability in therapeutic response. These are potential sources of variability:

a) high first pass and low active metabolite bioavailability

b) variability of CYP3A4 and CYP2C19 activities due to pharmacogenetics and food/drug interactions

c) irreversible binding to receptor

d) temporal delay in onset, as well as in recovery of platelet function

e] variability in rate of platelet recovery.

This extent of variability really points to a crying need for dosages of clopidogrel to be optimized according to some clinical measure of drug response. Unlike the situation with warfarin however, there isn't a universally accepted way of monitoring plate function. Nevertheless, platelet function test is shaping up to become a standard bedside test for this very reason. A recent review by Williams et al (Thromb Haemost 2010; 103: 29–33) is worth a read.

Drug level testing would clearly not be useful as it is not clopidogrel itself but the metabolite that is active. Furthermore the irreversible binding to the platelet purinergic receptor would not allow concentrations of the active metabolite to be useful in predicting the level of platelet inhibition.

Ibn Sina and clinical pharmacology

It is probably an auspicious occasion to recognize that the preceding discussions on efficacy are not anything new, and that their very foundations were laid a thousand years ago by an amazing Persian Shia physician called Abū ‘Alī al-Ḥusayn ibn ‘Abd Allāh ibn Sīnā, or just "ibn Sina" or Avicenna.

In the second volume of his famous treatise "The Canon of Medicine", he identified 7 important principles which are still valid today, and establish the foundations of modern clinical pharmacology. He writes:

“Experimentation will bring us complete understanding of the strength of drugs; however,only if the conditions below are followed.”:

"The drug must be free from any extraneous accidental quality."

"It must be used on a simple, not a composite, disease."

"The drug must be tested with two contrary types of diseases, because sometimes a drug cures one disease by Its essential qualities and another by its accidental ones."

"The quality of the drug must correspond to the strength of the disease. For example, there are some drugs whose heat is less than the coldness of certain diseases, so that they would have no effect on them."

"The time of action must be observed, so that essence and accident are not confused."

"The effect of the drug must be seen to occur constantly or in many cases, for if this did not happen, it was an accidental effect."

"The experimentation must be done with the human body, for testing a drug on a lion or a horse might not prove anything about its effect on man."

Biomarkers of efficacy

Worthwhile pulling out for a read. Useful perspectives re approach for development of biomarkers for efficacy and disease progression.

The Pharmacogenomics Journal , (8 December 2009)

A multistep validation process of biomarkers for preclinical drug development
W M Freeman, G V Bixler, R M Brucklacher, C-M Lin, K M Patel, H D VanGuilder, K F LaNoue, S R Kimball, A J Barber, D A Antonetti, T W Gardner and S K Bronson

Abstract
Biomarkers that can be measured in preclinical models in a high-throughput, reproducible manner offer the potential to increase the speed and efficacy of drug development. Development of therapeutic agents for many conditions is hampered by the limited number of validated preclinical biomarkers available to gauge pharmacoefficacy and disease progression, but the validation process for preclinical biomarkers has received limited attention. This report defines a five-step preclinical biomarker validation process and applies the process to a case study of diabetic retinopathy. By showing that a gene expression panel is highly reproducible, coincides with disease manifestation, accurately classifies individual animals and identifies animals treated with a known therapeutic agent, a biomarker panel can be considered validated. This particular biomarker panel consisting of 14 genes (C1inh, C1s, Carhsp1, Chi3l1, Gat3, Gbp2, Hspb1, Icam1, Jak3, Kcne2, Lama5, Lgals3, Nppa, Timp1) can be used in diabetic retinopathy pharmacotherapeutic research, and the biomarker development process outlined here is applicable to drug development efforts for other diseases.

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.

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.