What can we learn from a human mass balance study?

November 27, 2012 By 1 Comment

Mass balance studies are also called “C-14 studies” or “Absorption, Metabolism, and Excretion (AME) studies”. It is important to understand what you are trying to learn from the experiment. The primary objectives of a mass balance study are generally:

  1. To determine the mass balance of drug-related material following dose administration
  2. To determine the ratio of parent drug to metabolite(s) in circulation
  3. To determine the primary route of excretion of drug-related material

Let’s discuss each of these in order … First, mass balance is a term that refers to balancing the amount of drug administered as a dose to the amount of drug-related material collected in human excreta (normally feces and urine, but also could include expired air and sweat). One would expect if we administered 100 drug molecules to a human subject, we should collect 100 drug-related molecules in the excreta to achieve mass balance. Because there are always errors in any measurement technique, we normally use recovery of >90% as reference for nearly complete recovery. The difference between the theoretical 100% and the actual 90% could be due to measurement errors, sample processing errors, or missing samples. Since the experiment requires accurate collection and measurement of all feces and urine for up to 14 days, there are many opportunities for errors.

To calculate mass balance, we need an accurate method for measuring drug-related material in the various human excreta. While LC/MS-MS methods are sensitive, they can only be used to quantify the amount of an analyte with a known structure. In this situation, we need to measure parent drug AND all metabolites (even ones that are previously unknown). Thus, a different tool is used. The most common tool is “labeling” the parent drug molecule with a Carbon-14 atom. Carbon-14 (C-14) only represents 0.1% of carbon in the world, so it is not commonly found in any molecule. But, we can make a drug product with extra C-14 to “label” it in a way that we can follow it with sensitive radiometric detection methods (liquid scintillation or accelerated mass spectroscopy). Thus we can compare the amount of “radioactivity” in the original dose to the amount of radioactivity in the excreta to calculate the mass balance. Radioactivity measurements are independent of chemical structure, thus total radioactivity measurements can be thought of as “parent + all metabolites”.

Second, we want to learn how much of the circulating drug is parent drug versus metabolites. This is important to evaluate the safety of each metabolite, and identify unique human metabolites. The blood or plasma can be analzyed for parent drug concentrations using standard techniques (e.g. LC/MS-MS) to allow for estimates of total exposure (AUCparent). Then the blood or plasma can be analyzed for total drug product (parent + metabolites) using radiometric detection methods to allow for estimates of total exposure (AUCparent+metabolites). The ratio of the two AUC measurements gives the proportion of total exposure represented by parent drug. Similarly, if specific assays are available for some metabolites, the proportion of each metabolite relative to total drug exposure can be calculated. These ratios are important for addressing development questions around safety metabolite testing and drug-drug interaction studies. Further, the presence of the “label” allows for identification of metabolites using LC/MS-MS methods combined with radiometric detection.

Third, the primary route of excretion (feces or urine) can be determined in a mass balance study. Normally only feces and urine are collected as human excreta, but in certain situations expired air and sweat might be obtained if excretion by those routes is expected. Depending on the specific excretion profile of the drug, the majority of radioactivity will normally end up in the urine or the feces. Radioactivity can only appear in the urine after systemic absorption, suggesting that the bioavailability is at least equal to the fraction of drug appearing in the urine. The amount of drug in the feces is a mixture of unabsorbed drug (assuming oral administration), drug excreted in the GI tract, and drug excreted in the bile.

A properly designed human mass balance study will allow you to address these three main objectives with a small number of healthy volunteers.

Filed Under: pharmacokinetics, study design Tagged With: , ,

Designing a clinical drug-drug interaction study

October 29, 2012 By Leave a Comment

After a much longer delay that I expected, I am back to blogging on a regular basis. Today I want to discuss a common topic among clinical pharmacologists. How do you properly design a drug-drug interaction study?

Defining Drug-Drug Interactions

While these studies may appear complicated, they can be simplified very quickly to make the study design straightforward. In all cases, there are 2 drugs involved in a drug-drug interaction study, and they always interact in the same way. One drug is considered the probe substrate, and the other is the interacting drug. The probe substrate is metabolized or transported by the biological enzyme being studied (e.g. cytochrome P450 3A4, OATP1, etc.). The interacting drug affects the biological enzyme being studied by either inhibition or stimulation. This can be illustrated as shown below:

Drug-drug interaction

To make the example more specific, let’s assume you are evaluating the potential for a candidate drug to inhibit cytochrome P450 3A4. This would make the candidate drug the “interacting drug” (shown in red text) and you would select a probe substrate that is sensitive to changes in CYP3A4 activity (e.g. midazolam). A second example might be the evaluation of inhibition of CYP2C8 on the metabolism of a candidate drug. In this second example, the candidate drug would be the probe substrate (shown in black text), and you would select an interacting drug that is a strong inhibitor of CYP2C8 (e.g. gemfibrozil).

The first step to designing the correct study is to draw an image like the one shown above and list the names of the probe substrate and the interacting drug. In addition, you will need to identify if the interacting drug is expected to inhibit or stimulate the metabolism (or transport) process.Before we move to study design, let’s consider the analysis that needs to be conducted at the end of the study.

Analysis of Drug-Drug Interaction Studies

The basic study involves evaluating the clearance of the probe substrate under two conditions:

 Condition  Treatment  Common term
 1  Probe substrate alone  Reference
 2  Probe substrate + interacting drug  Test

The most common way to evaluate changes in clearance is to measure the area under the curve (AUC) since CL = Dose/AUC. Thus increases in AUC represent decreases in clearance, and vice-versa. You can calculate the relative exposure between the Test and Reference using a ratio (Test/Reference). If the ratio is close to 100%, then the interacting drug did not affect the clearance of the probe substrate. If the ratio is >100%, then the interacting drug inhibited the clearance of the probe substrate. And a ratio <100% suggests that the interacting drug stimulates the clearance of the probe substrate.

Study Design

Now that we have the basics, we can design the study. We need to administer 2 separate treatments to each subject. Since we are testing a biological enzyme that is thought to be free from influence of the study subject, blinding is not necessary in these studies. Since it is an open label study, a fixed sequence design is the easiest to conduct. In this design, a subject receives treatment 1 (probe substrate alone) in the first period, followed by an appropriate washout. Then the same subject receives treatment 2 (probe substrate + interacting drug) in the second period. By comparing period 2 (Test) to period 1 (Reference) you can determine the effect of the interacting drug.

Easy right! … Well there are some additional considerations that may affect your study design.

Washout Period

First, if the washout period for the probe substrate is long (e.g. >14 days), then you may have difficulty running the fixed sequence design. If the probe substrate has a long washout period, you will want to run a parallel study. In this study design, one group of subjects will receive treatment 1 (probe substrate alone) and a second group of subjects will receive treatment 2 (probe substrate + interacting drug). The comparison can then be made across the treatment groups. If you conduct a parallel study try to make sure that you match subject demographics across treatment groups to minimize variability.

Steady-State

Second, you may need to give multiple doses of the interacting drug to achieve maximal inhibition or stimulation. In general the interacting drug is dosed to steady-state levels. This can be achieved by administering doses for multiple days, or giving an initial “loading” dose followed by standard doses. The duration of dosing for the interacting drug varies based on the drug being used.

This should give you a good idea of how to design clinical drug-drug interaction studies for anything you encounter.

Filed Under: drug-interaction, study design

Trial designs – Non-inferiority vs. Superiority vs. Equivalence

January 1, 2011 By 1 Comment

The primary purpose of a clinical trial is to address a scientific hypothesis. To address a hypothesis, different statistical methods are used depending on the type of question to be answered. Most often the hypothesis is related to the effect of one treatment as compared to another. For example, one trial could compare the effectiveness of a new antibiotic to that of an older antibiotic. Yet the specific comparison to be used will depend on the hypothesis to be addressed. Let’s use this two antibiotic example for this discussion.

We can construct 3 possible hypothesis to be addressed when comparing these two antibiotics. The three hypothesis are:

  1. The New Antibiotic is at least as good as the Old Antibiotic.
  2. The New Antibiotic is better than the Old Antibiotic.
  3. The New Antibioitic is equivalent to the Old Antibiotic.

While each of these hypothesis may seem similar, they are slightly different scientific questions, thus each requires a slightly different statistical test. For the first hypothesis (“at least as good as”), the New Antibiotic must be as good as the Old Antibiotic, but it can also be better than the Old Antibioitic. In mathematical terms:

1. New Antibiotic \ge Old Antibiotic

For the second hypothesis (“better than”), the New Antibiotic can no longer be equivalent to the Old Antibiotic. In mathematical terms:

2. New Antibiotic \textgreater Old Antibiotic

The last hypothesis (“equivalent to”) indicates that the New Antibiotic cannot be worse than or better than the Old Antibiotic. In mathematical terms:

3. \frac{New Antibiotic}{Old Antibiotic} = 1\pm \alpha

As you can see, each hypothesis contains a slightly different mathematical arrangement. These mathematical expressions have been given “lay names” by satisticians in an effort to describe the math to non-statisticians; these names are:

  1. Non-inferiority
    • New Antibiotic \ge Old Antibiotic
  2. Superiority
    • New Antibiotic \textgreater Old Antibiotic
  3. Bioequivalence
    • \frac{New Antibiotic}{Old Antibiotic} = 1\pm \alpha

Of these three comparisons, the non-inferiority has the largest range of successful trial outcomes (equivalence or superiority). Thus a calculated sample size for a non-inferiority trial is usually the smallest of the three hypothesis. The superiority comparison is a subset of the non-inferiority and will have a sample size that is similar to the non-inferiority or a sample size that is much larger. As the expected difference between the two treatments decreases, the sample size will increase, often dramatically. Finally, the bioequivalence comparison is the most restrictive because it requires that the two treatments be identical within some acceptable range defined by α (normally ±20%). In general a bioequivalence trial will have a sample size that is larger than a non-inferiority trial.

So which is best to use? It all depends on which scientific question you are trying to answer. All three study types are useful in the development of drugs. Non-inferiority studies are used to show that a minimum level of efficacy has been achieved. In comparison studies with a current therapy, non-inferiority is used to demonstrate that the new therapy provides at least the same benefit to the patient. Superiority trials are always used when comparisons are made to placebo or vehicle treatments. In these studies, it is critical that the effect in the treatment group be clearly superior to any effects in the placebo groups. Failure to demonstrate superiority over vehicle suggests that the drug is not effective. Superiority trials are also used for marketing purposes (“our drug is better than your drug” studies). Bioequivalence trials are used to show that a new treatment is identical (within an acceptable range) to a current treatment. This is used in the registration and approval of generic drugs that are shown to be bioequivalent to their branded reference drugs.

In the end, ask yourself which hypothesis am I trying to address, then use the appropriate study design. Best of luck!

Filed Under: statistics, study design Tagged With: , , , ,