Pharmacokinetics and Obesity – Module 4, Session 7

Pharmacokinetics and Obesity – Module 4, Session 7


>>William Douglas Figg:
Our next speaker today is Dr. Amit Pai, who is an Associate Professor of Clinical Pharmacy,
and Deputy Director of the Pharmacokinetic Core Laboratory at the University of Michigan. Dr. Pai’s research focus is on optimizing
drug dose selection in special populations, such as obesity. He earned his Doctorate of Pharmacy degree
from the University of Texas Health Science Center, and completed a pharmacy practice
residency at Bassett Health care followed by an Infectious Disease and Pharmacokinetic
Fellowship at the University of Illinois at Chicago. We know you will enjoy today’s lecture.>>Manjunath Pai:
Hello. My name is Amit Pai. I’m an Associate Professor of Clinical Pharmacy
at the University of Michigan, and the Deputy Director of the Pharmacokinetics Core. It’s my pleasure today to present on the Pharmacokinetics
of obesity. This slide contains my disclosures. What I’d like to cover today is reviewing
the definition and epidemiology of obesity, explain the effects of body size on drug exposure,
with a focus on anti-microbials, demonstrate the similarity and differences in body size
scalars for drug dosing, and explain the rationale for alternate approaches to dose selection
in obese adult patients. This graphic gives a global distribution and
prevalence of obesity in adult females across the world, and in different parts of the world. As you can see from this graph obesity has
more than double since 1980, from almost less than 20 percent to over 35 percent in the
United States, and similar trends are seen in the developing countries, represented by
the brick nations, Brazil, Russia, India, and China. Based on current definitions in 2014, over
600 million adults were classified as obese, and this is really important because the consequences
of overweight and obesity kill more individuals in the word today for the first time in history,
than underweight individuals — than the effects in underweight individuals. This is the prevalence of obesity across the
United States, by county, and clearly there is diversity in the prevalence of obesity
in the United States, with the lowest rate seen in Routt County, and the highest in Greene
County, which — again this area here represents the southeast part of the United States, is
also associated with a high prevalence of diabetes. When we think about obesity and important
point is the definition and prevalence of obesity, and when you think of obesity this
definition — the original definition was derived by Adolphe Quetelet in 1835. He came up with the Body Mass Index definition
which is basically the weight of an individual divide by height in meters squared. And the purpose that Adolphe Quetelet sought
was basically to find a correlation between body size parameters and different events,
such as probability of criminal behavior. And so, when you think of this metric it was
really not designed for a pharmacologic purpose, or for other classification purposes. And not until 1972 was this index transformed
into what we call today as the Body Mass Index, a term defined by Dr. Ancel Keyes, who did
a lot of the initial work on the effects of starvation in humans. Based on this definition that was adopted
by the National Institutes of Health in 1985 body mass index is categorized into three
major categories for obesity, and those are obese class one, between 30 and 34.99 kilograms
per meters squared to obese class three, which is greater than or equal to 40 kilograms per
meters squared. And so again, this classification is broken
down into units of five in a way for simplicity, but these may not necessarily correlate with
predictors or prognostic variables such as incidents of diabetes. So, if you look at specific populations — Asian
versus Caucasian, the risk for diabetes actually increases when the body mass index is greater
than 27 kilograms per meters squared. Irrespective of that current definitions are
based on an obese categorization of 30 kilograms per meters squared or greater. Based on that definition one out of three
adults in the United States meet that definition, and one in six children meet that definition. Now when you think about body mass index it’s
clearly not a perfect index, because it doesn’t represent the true body weight composition
when you’re thinking of extremes of individuals and height. And so, here’s a graphical representation
of two individuals from the movie “Twins,” demonstrating Arnold Schwarzenegger with a
body mass index of 34 kilograms per meters squared, and Danny DeVito with a 35 kilograms
per meters squared, who clearly have very, very different body composition, but have
very similar BMI’s. And this is important because BMI again was
adopted in 1985, but several of our pharmacologic studies were based on another metric known
as the ideal body weight. This is based on height and gender. It’s a very simple rule, and I’m going to
go over that specific equation and explain the origins of that equation. That equation has been used in several pharmacokinetic
studies, and in general terms individuals who are 20 to 30 percent above ideal body
weight are categorized as being obese. Despite these definitions of obesity there
really hasn’t been much progress in modifications of product labels to provide specific dosage
recommendations for obese individuals. This is important because this drives a lot
of dosage selection in our current paradigms, and product labels are based on three scenarios. One is dosing individuals based on their body
surface area. This is a common practice in oncology; weight
based dosing, a common practice in anti-microbial chemotherapy, and when it’s based dosed this
is often based on total body weight dosing, or in some labels as lean body weight dosing. But the predominant approach to dosing drugs
is using a fixed dosing strategy. This is using a dose — for example, 500 milligrams,
irrespective of body size. But this always raises the question that if
you use a fixed dose across a population of adults is using the same dose sufficient for
a 65 versus a 130-kilogram individual, for example. This dosing controversy has also been seen
in oncology, despite body mass index dosing. A study by Griggs and colleagues showed that
37 percent of women with a body mass index over 35 kilograms per meters squared received
lower standard first cycle doses. Patients were also seen to have lower risks
of grade three and four toxicities, despite using higher doses, and this in part may be
related to under dosing these individuals. A survey by Fields and colleagues showed that
80 percent of oncologists dose capped at two meters squared. Again, the idea here is individuals felt that
they were overdosing individuals when they used total body weight to compute body surface
area, and have in the past arbitrarily dose capped at two meters squared. And this is important because this can lead
to under dosing individuals, especially those individuals who are at greater risk for cancers
that are associated with obesity, such as breast cancer and colon cancer. Because of this discrepancy efforts by Griggs
and colleagues and several investigators around the United States has led to issuance of guidance
by the American Society of Clinical Oncology against capping these doses and actually using
total body weight to compute body surface area. And what I will do today is explain the mathematical
rationale for why that is the right approach of computing body surface area. This has also been seen with anti-coagulants. Looking at the CRUSADE trial, which did include
about one out of five individuals over a hundred kilograms, 80 percent of patients over 150
kilograms received below the standard dose, and those individuals that were likely to
receive the standard dose actually were at a higher risk for bleeding. And so, this is an example of a drug that
has been historically dosed on body weight, and a scenario where using body weight may
lead to over dosing the drug and lead to events such as bleeding. Other groups have shown that individualized
dosing lean body weight equation, which I’ll also review, is actually less likely to cause
this problem of bleeding and bruising in obese patients, and so may serve as a better metric
of dosing patients on Enoxaparin. So, the question at hand is really do bigger
adults need bigger doses? And so conceptually over dosing is likely
when you use weight based doses, because using the same milligram per kilogram dose across
a body distribution can lead to computation of much higher doses. The opposite is true with fixed dosing. Using the same dose across a population can
have the opposite effect of leading to lower exposures in larger individuals. And so, it’s really balancing these two dosing
paradigms that we have to consider, really, on a drug by drug basis. So, let’s think of that dose selection that
— from a pharmacokinetic perspective. And when you think of it from a pharmacokinetic
perspective a key concept is that of bi-equivalence. That is, trying to ensure that individuals
across the population have the same or have similar exposures, and that exposure can be
quantified by the area end of the curve, that is assuming having a similar AUC across the
population, or having similar peak concentrations of the Cmax concentration. And so, when this is administered — when
the drug is administered orally we also think of other parameters such as time to peak concentrations
of the Tmax, or in some multiple dose studies we may also consider the minimum concentration
or trough concentration as a metric to assess bi-equivalence. Now I think it’s really important to separate
the concepts of dose and exposure, because they’re certainly related, but can vary across
a population. So, if we give a one-gram dose to three individuals,
for example, those three individuals can have very different concentration time profiles. And so, when we simulate that across a population
we may get a distribution represented here in this histogram as a distribution of AUC’s
for that population. And that’s — that happens because we may
have a lot of inter-individual variability in the volume of distribution that impacts
the peak concentration, and inter-individual variability in the clearance of a drug that
can impact the AUC or area under of the curve. So, when we think of these pharmacokinetic
parameters the volume of distribution is often a term that’s easily confused. It is not physiological space, but rather
a proportionality constant that represents the apparent size of a compartment that the
drug will fill. And when you review the literature for a specific
drug what you may see is a lot of different v-terms [spelled phonetically], and so this
really depends on the method, the mathematical approach that was used to compute the volume
of distribution. So, if you use a non-compartmental method
you may see the value reported as VD, or VDSS. If you use a two-compartment approach you
may see the value reported as VC and VP, which represent the central and peripheral compartment. And if you have larger compartments describing
this you may see this numbered as V1, V2, and V3, and so on and so forth. These volumes essentially are used to help
fit the shape of the concentration curve, and again they do not represent an actual
physical space. They’re helpful because they can help compute
the Cmax or the peak concentration, and here represented by this equation is a very simple
way of thinking of this, which is if a dose was administered as a bolus, that does divide
by the central compartment value gives you some estimate of what that peak concentration
would be. The clearance is then just the mathematical
representation of volume that’s being cleared of drug in unit time. And that parameter is useful because it can
help compute the AUC or area under of the curve, represented again very simply as the
dose divided by clearance. Our objective most often when we’re thinking
about bi-equivalence is to achieve isometric AUC’s across a population. And to do that what we really need to do is
ensure that clearance scales with body size. So, if you’re going to dose a drug based on
body size we want to ensure that that scalar is really representative of clearance. Another perspective when we’re thinking about
optimal dose selection is the pharmacokinetic-pharmacodynamic, or the PK-PD perspective. And what’s done there is essentially looking
at the concentration time curve and breaking it up into different parameters. So often what’s done is to look at the peak
concentration of the Cmax, the area under of the curve, or the AUC, and the trough concentration,
which is the Cmin. These values can then be scaled to a potency
measure, in this case looking at anti-microbials we rely on something knows as the MIC, or
minimum inhibitory concentrations. And we can then index the pharmacokinetic
term with a potency measure such as Cmax or MIC over MIC, or the time above that MIC. Once we have those parameters we can then
look and see which of those parameters best correlates with the effect of the drug, the
safety of the drug, or some other measure. It doesn’t not have to be affected. It could also be emergence of resistance,
for example. Once we do that the parameters that best predict
effect help us classify that drug as either being concentration dependent, or time dependent. And that helps us decide whether we should
dose a drug once a day, or multiple times a day. When we use that principle, we can in essence
bucked drugs into different categories. And so, when we see drugs that are correlated
by their Cmax and AUC/MIC, we see we tend to refer those drugs as being concentration
dependent. When we see drugs that are really predicted
by the time above MIC we refer to those drugs as being time dependent. And when we look at antibiotics specifically
what we see is the AUC to MIC really categorizes most of the drugs. And the reason for this is AUC is really a
mathematical representation of concentration and time. And because it has both of those parameters
this parameter often correlates with effect. Beyond just looking at PK-PD we can also look
at how drugs are currently dosed, and when we look at anti-microbials again they fit
the pattern that we would expect; that is most of them are dosed on a fixed basis, this
is using the same dose across a population. A few of them, like the aminoglycosides, and
polymyxins, and other drugs similar, that are dose on body weight, usually on a milligram
per kilogram basis; and then some drugs actually have recommendations for both a fixed dosing
strategy, and a weigh based strategy. And so, let’s review again why this may or
may not be appropriate. Again, going back to the pharmacokinetic term
of volume of distribution, what you have here is two individuals who are represented roughly
twice in body weight, having very different body compositions. But what volume of distribution really represents
again is nothing physiologic. It simply represents a proportionality constant. The value is then indexed to body weight,
and so is often reported as liter per kilogram. That parameter helps us define what the peak
concentration is, and also as that value gets larger, it impacts the terminal half-life
of the drug. But again, this term does not represent bio-distribution,
but just represents a physical space, which is again very simply represented here, and
does not reflect that physiologic space. Now let’s simulate what would happen in two
individuals based on their body weight if volume increased with weight, but the clearance
of the drug did not increase with weight. So, what you have here in blue is an individual
that’s 60 kilograms versus 120 kilograms, or roughly twice the body weight. What you see in these profiles is because
the volume of distribution is smaller in the smaller individual you see higher concentrations. The effect of this is going to be lower concentrations
of the drug early on, but because the clearance is not changing the area under curve actually
will be identical for these two simulations. What you again will see is a lower Cmax. But you’ll also see over time a higher Cmin. So, when you’re thinking about a drug where
the volume increases with body weight, but clearance does not, this may be beneficial
for drugs that are time dependent, because what you’ll essentially have is increasing
time above a threshold concentration. Where this may be risky is if that time above
a threshold is associated with toxicity or some sort of adverse event. Now the solution of the effect is needed to
be early on — for example, if you’re using a drug for surgical prophylaxis — then this
can be overcome by using a higher initial dose to achieve the same concentration profile
later in the regimen. When we think about clearance this again is
the — is taking that volume and computing how much of that volume is clearing drug over
time, so it’s represented by liter per hour. What we know is this parameter does not really
increase in proportion to body size. So, what you saw is a reduction that is not
proportionate to body size, but often represents about a 50 percent increase as body weight
doubles, for example. The terminal half-life is inversely proportional
to this parameter, and so this parameter also affects the half-life of the drug. Here’s another simulation using the same principles
again, a 120 kilogram versus a 60-kilogram individual. Because the clearance increases, but the volume
does not increase, what essentially happens is you’ll see a lower AUC. You will see a slight lower Cmax over time
because the Cmin and trough concentration’s also decreasing. But the principle change is a reduction in
the AUC. And so, in this scenario what you’ll see is
the potential for failure if lower AUC’s are associated with lower effect, and that’s often
a case that’s seen. You’ll also see a propensity for emergence
of resistance if the dose is not modified with increasing body size. So, the solution in this scenario is the need
for a higher dose, and really the need for a higher maintenance dose. So, when you think about all these different
scenarios that have been presented so far what are the actual physiologic changes that
we’ve seen? One of the things — one of the studies that’s
really done this well is study done by Jeffrey Young and colleagues at the National Toxicology
Center, that have looked at autopsy data in over 300 individuals to sort of quantify what
happens to tissue as body weight increases, and have created mathematical models to explain
this. What you see in this graphical representation
is an expectation that when individuals are over 100 kilograms what you really see a gain
in is a gain in Adipose mass — relative to muscle mass. When you think of the specific organs what
you see is most organs will increase in size, such as the heart, lung, kidneys, and liver,
but they’ll usually reach a max of no more than two-fold change in size, and perhaps
a threefold change when you’re thinking of muscle mass, but again these organs responsible
for clearance of drugs do not increase by more than two-fold in size across an almost
five-fold distribution of body weight. Now this is important because again when we’re
scaling this information we really need to consider how does the change in body weight
impact the change in drug clearance? When you’re thinking about distribution and
metabolism in general there are not a lot of great studies that have done this, that’s
not really well characterized, but we can have general trends when we’re thinking about
drug distribution that can be based on the physiochemical properties of the drug. Typically, what we see is we we’re dealing
with drugs that are more acidic. We tend to see smaller volumes of distributions,
compared to drugs that are more basic that can be sequestered within tissue, and tend
to have larger volumes of distribution. When we’re looking at metabolism, specifically
looking at the cytochrome P450 system, what has been evaluated has been specific probes
of cytochrome P450 3A4, 2C9/19, and CYP2D6. And these studies that have looked at specific
probes of those pathways have seen limited effects of increasing body size and metabolism. An area where there has been a 1.5 to threefold
increase in clearance has been the cytochrome P450 2E1 isoenzyme system, that’s really not
responsible for the metabolism of a lot of drugs. This isoenzyme system is responsible for the
metabolism of more lipophilic compounds that are typically less than 200 daltons in weight. And so, this can impact some anesthetics. It can impact a key probe substrate knows
as chlorzoxazine — chlorzoxazone, sorry. And the metabolic profile of acetaminophen
into its more toxic metabolites. Again, this pathway does not influence the
metabolism of most drugs, so when we think about cyt-metabolism in general we do not
anticipate an increase in the metabolism as individuals increase in body size. Now when we’re accounting for these parameters
clearly weight, that we’ve spent quite of bit of time so far discussing, is really a
parameter that tends to correlate with the volume of distribution. When we look at clearance there are several
factors that are usually accounted for in population PK studies that look at race, height,
weight, age, sex, serum creatinine. There are also several intrinsic variables
that I won’t have an opportunity to cover today, but discuss — that include pharmacogenetic
variation. There could also be extrinsic variables such
as the impact of smoking, or dietary changes that can impact clearance. But typically, these are the parameters that
are looked it, and often these parameters are consolidated into a variable such as kidney
function that incorporates some of these parameters into a compositive parameter that correlates
with clearance. Again, we tend to treat in population PK analyses
these terms to be independent, but they can be inter-related. For example, height clearly correlates with
sex, and so that may not necessarily be an independent parameter when incorporated into
these models. What I did earlier is show changes in body
size, how those body size changes relate to tissues changes, and organ weights. But how do those actually relate to function? And so, this is a — one of the few studies
that’s done this really well. And this is a study done by Avry Chagnac and
colleagues, who have looked at healthy obese individuals. These are — again defining healthy obese
individuals is quite complicated. These are individuals who did not have hypertension
and diabetes, but were very large in body weight. And so, looking at these individuals: similar
age, they looked at glomerular filtration rates. And when you look at these again — although
individuals were almost twice in body size, what you see is only a 62 percent increase
in the glomerular filtration rate. And this is again consistent with several
animal model experiments that have also shown similar trends for increases in GFR relative
to body size. Another study by Friedman and colleagues has
looked at the population of pharmacokinetics of Iohexol, which is another marker that can
be used to compute glomerular filtration. And if you do a simulation using that population
model what you also see is if you look at the change in body weight between 50 and 200
kilograms you would not anticipate the glomerular filtration rate to increase by more than 60
percent. And so, there are several data sets that essentially
show that we do not expect body weight to increase mechanisms that would be associated
with drug clearance by more than 50 to 60 percent on average. Now in the clinical practice we often cannot
measure the creatinine clearance, or estimate the glomerular filtration rate. And so, we often rely on equations. And so, what I’d like to point out here is
that there are really two broad ways of doing this. One is using the EGFR, or Estimated GFR equations,
and this method currently incorporates serum creatinine, age, and race, but does not incorporate
body weight. Instead that parameter is sometimes modified
in individuals by converting this term from a scalar that’s benchmarked to body surface
area, to a non-normalized term. This equation also has been calibrated using
the isotopic dilution mass spectrometry traceable creatinine, which is currently the standard
to ensure the creatinine measurements across institutions are similar. The more classical equation that is actually
incorporated in clinical trials over the last 40 years has been the Cockcroft and Gault
equation. This equation that was introduced in 1976
is a simple equation, and is often taught in many schools, and incorporates the use
of age, weight, serum creatinine. This study actually did not include any females
in its design. And so, this was an assumption that was placed
into the equation with the expectation that women on average have 10 to 20 percent less
lean body weight, and so a term of a 15 percent lower weight was used in females as an expression. Again, this model was never really validated
in females, but has been used over time, and it’s shown to be useful for several drugs. Another equation that’s emerged is the chronic
kidney disease and epidemiology equation. This equation was developed primarily to resolve
the issue of the former equation knows as the modified diet and renal disease equation,
or MDRD equation, that was really restricted to individuals who have GFR estimates less
than 60 mil per 1.37 meters squared. So, what this equation accomplishes is it
permits the calculation of GFR across the GFR distribution, so values below 60 and above
60 mil per 1.37 meters squared. As seen by this equation, again it is much
more complicated because it includes several equations that account for sex, and race of
individuals, but clearly there has been controversy in the use of race as a factor in this equation,
especially in multi-ethnic societies. This equation has not necessarily worked out
in those populations. Now this brings us back to again use of this
term, because clearance often correlates with several kidney function markers, but we often
dose the drug on body weight for certain drugs. And if we’re going to dose the drug on body
weight a scenario that’s expected is that the clearance of the drug should increase
in a linear and proportionate weight. Instead what we see is that the relationship
is really non-linear. And the reason why this error may occur is
early phase clinical trials tend to include individuals within a relatively narrow bandwidth
of body weight. And so, if you include individuals that are
for example between 50 and 80 kilograms you may consider those individuals to have a linear
change in clearance. But when you include the larger body weight
distribution in your pharmacokinetic studies you’re more likely to see this curvature and
non-linearity. Now this phenomenon is not and old phenomenon. This phenomenon has been evaluated in several
disciplines, and so the principles that are used in pharmacology really relate to principles
that were generated in the early understandings of resting metabolism. And so, what we have here are two major paradigms
that develop — that have developed over time. And so, this is what I often refer to as the
battle of the Maxes. This is Max Rubner’s work, from the early
1880’s, to almost 50 years later by work by Max Kleiber. Max Rubner’s work was a pivotal understanding
of the relationship of heat production relative to body surface area. And so, his work included experiments that
obviously would not be conducted today, but involved the use of animals that were placed
in chambers and allowed to starve over time. And what was seen in dogs is heat production
declined as a function of their body weight scaled to 0.6 Max Rubner’s work really looked it scaling
resting metabolism across species, and in so — in those experiments what was shown
is a — when you plot log heat production relative to log body weight production — log
body weight, I’m sorry — what you see is a relationship that has a slow parameter of
0.75. And his original work actually was around
0.73, but again for simplification most literature reports it as .75. Now this is relevant when we’re thinking about
Allometry because we’re thinking about relationships between body size, shape, and physiology. And so often we think of disparate comparisons
as apples and oranges. In this scenario I’m giving you the example
of an apple versus a Romanesco. And this is relevant because when we’re thinking
about computation of surface area we have to think of the world as either being smooth
surfaces, or the true phenomenon which is more rough surfaces. So, when you think of smooth surfaces if we’re
going to compute the surface area of this apple we would have to think of this apple
in three dimensions. And the area would simply be the volume of
this cube to the two-third power, or .67. What’s been shown more eloquently now by West
and colleagues [spelled phonetically] is that our computation of surface and really physiology
are — these relationships are really driven by fractal geometry. And the area is better represented by the
volume of tissue or other spaces to the three-quarters power, or basically .75. And so, the work by Rubner and by Kleiber
basically exists within these two-expected paradigm in science. Now this is again relevant because our approaches
to computation of body surface area that are used for drug dosing also rely on Euclidian
geometry. And so, what I have here are basically graphics
from about a hundred years ago, that used simple computation of body surface area by
— one example here which is — this is an image from the work of DuBois and Du Bois. So, DuBois and Du Bois and colleagues essentially
took a small number of individuals and paper-mâché’d them. And after paper mâché-ing them they removed
the paper mâché placed them on the ground, and took photographs of that paper mâché. After taking those photographs, they then
computed the surface are and derived and equation. So again, that process would have required
a computation based on smooth surfaces, and the Dubois and Dubois equation is represented
here by this equation, which is basically body weight to an exponent, and height to
an exponent. And other individuals over time have thought
this equation to not be representative because again it was based on nine individuals, and
then basically included an additional 23 individuals in their model. So, Gehan and George tried to expand on this
model by studying a larger sample of individuals, but really came up with similar exponents. And I’m going to explain why that’s the case. Haycock and colleagues tried to expand this
to include pediatric populations; and then we have Mosteller, who wrote a simple letter
to the editor in the New England Journal of Medicine that simplified a lot of these equations. And this is the equation, again because of
its simplicity, is included in a lot of textbooks, and simply includes the weight times height
of individual, divided by thirty-six hundred, and is essentially the square root of that. Now I’m going to show you that all these equations
have similar answers because they all rely on Euclidian geometry. So, with Euclidian geometry what you have
is weight is a function of volume times density, and height as you can imagine is just a single
dimension. So, if you take the exponent over weight,
which is a three-dimensional term, and add it to height, which is a one-dimensional term,
what you will see with all equations that have been constructed today is all those values
add up to two. And the reason they add up to two, again,
it’s based on Euclidian geometry that has integer based dimensions, and that’s essentially
what you see with body surface area, which is meters squared — which is a meters squared
term. And so, these body surface equations — essentially
several of them have been developed over time, but really are very similar because they’re
scaling the information the same way. The DuBois and Du Bois equation will lead
to computation of slightly lower values, and the reason that occurs is because the weight
terms has an exponent of .45, so it has a smaller term over weight, and will lead to
computation of a smaller surface area. But again, the scaling is essentially the
same. So, this has dosing implications because if
the dose increases with weight or body surface area, but clearance does not increase with
the parameter, then what you would expect to see is a higher AUC in larger individuals. So, is it really acceptable when we think
about his, when we’re thinking about a milligram per kilogram dose? So, if you’re thinking about a drug, and the
drug’s product label is reported as six milligram per kilogram — if you use that same milligram
per kilogram in 40-kilogram individual you’re going to collect — you’re going to calculate
a dose of 250 milligrams if you use that same — in a 160-kilogram person you would calculate
a dose of a thousand milligrams. On average, you would calculate a dose of
500 milligrams. So, the question often is: Is that okay? Again, this is a common thing that we do. In practice, we address this issue often in
obesity by using another weight parameter. And so now let’s discuss what those other
weight parameters might be. One of those is lean body weight, which we
often think of as a good metric of muscle mass. Now measuring lean body weight is not as simple
as we would think. One: when you think about measurement, even
acquiring total body weight in a very large or morbidly obese individual is not simple. It’s not simple because a very large individual
may not be ambulatory, and so it’s difficult to actually get a body weight. You may also not have the right scales in
your institution to compute body weight for very large individuals. Computation of height also may be compromised
if this is not done correctly. Measurement can also include use of other
modalities such as bi-electric impedance analysis. You can have underwater weighing. You can have dexer [spelled phonetically],
which is an x-ray based method, or you can have a more recent method which relies on
air displacement, plethysmography, which again these systems can be used in healthy individuals,
but often not a system that can be used in acutely ill individuals. Because of this we often rely on estimation,
and that estimation happens with several equations such as the ideal body weight all the way
to the predicted normal weight, and I’m going to review some of those equations with you. When you think about these descriptors total
body weight is often sometimes referred to as actual body weight, so there’s different
rems used in the literature. And this is measured, and as I mentioned not
always easy to do. We have the ideal body weight equation, and
this is an equation that was developed over time, and referenced for the first time in
1974 by Ben Devine without an actual source. And as a resident, this was one of the first
questions I had to tackle, through a literature search, was finding the origin of the ideal
body weight equation. And what I discovered was it was simply a
rule of thumb, a farmer’s rule of thumb, that was based on our principle of fives, because
again what you see in the literature is this love for the number five, because we have
five fingers. We believe that five kilograms for every inch
over five feet would represent an ideal individual. And so, this term again that’s been used in
pharmacology was based on a very simple rule that men start at 110 pounds, and gain five
pounds for every inch, and women also gain the same weight for ever inch, which again
is not a reasonable hypothesis. Because the ideal body weight term did not
work for several drugs a modification was done, and that modification was called the
adjusted body weight. This was tested primarily with the aminoglycosides,
and a correction factor was found. Now that correction factor in the literature
actually ranges between 0.2 and 0.94, but on average is between 0.3 and 0.5. And so, what you see in a lot of text books
is the average of these averages, which is 0.4. And so, the adjusted body weight is simply
saying 40 percent of the difference between total and ideal body weight, plus ideal body
weight, is what we would term the adjusted body weight, and then use that weight to compute
the dose of the drug. Again, whether or not these are truly accurate
ways of representing body weight, what’s been shown is that they help dosing of certain
drugs. We also have other weight descriptors that
have been defined using more scientific methods, such as bi-electrical impedance, and also
based on animal data, and the best representation of that is the most recent equation knows
as the lean body weight 2005, or the Janmahasatian equation. This was based on 300 individuals in Australia
where they relied on body mass — body electrical impedance analysis to compete lean body weight. And this equation has been used in the literature
in more recent times, and computes lean body weight as a function of total body weight,
and a body mass index with slightly different parameters based on males and females. But again, all of these equations are essentially
transforming height and weight in to different metrics. And so that’s an important thing to remember,
that all we’re essentially doing is taking height and weight, and transforming them,
mathematically into another term. So, I want to highlight again what’s been
done in the literature, just to show that there is some harmonization in the principles
that I’ve laid out in this lecture. One of the approaches that’s often used in
pharmacy is using a combination of total body weight, ideal body weight, and adjusted body
weight. Now you can imagine if you’re using total
body weight to dose a drug, as a person increases in body weight you would get a proportionate
increase in the dose of the drug. And clearly that’s not necessarily a good
idea, especially in the extremes of weight. If you were to use that weight distribution
— actually if you were to use the height distribution, because again ideal body weight
is based simply on height, you would get a distribution of weights computed in this manner. If you then used adjusted body weight you
would get another distribution of weight. And then if you used a combination based on
this metric of — that’s often used in the literature you would get this distribution
of dosing weights across a population. And then if you model that data what you essentially
show is using those distribution of weights essentially gives you a dosing weight function
that’s three times total body weight to the .72 power. So, in essence what you see in the literature
are really divergent methods of actually dosing drugs, but in reality — in mathematical reality
these are really congruent approaches of scaling doses, and really the objective again, whether
you use body surface area, or you use an alternate body size descriptor, is you’re essentially
preventing someone from getting twice the dose as would a normal weight individual. So again, this brings, you know, the question
of who is right? Should we be scaling to this power or that
power? And the reality is that there really — the
answer exists somewhere in between that. When you look at this study by McLeay and
colleagues what they’ve demonstrated is it’s really drug dependent. And so, on average what you find is several
drugs will scale based on body surface area, but some drugs where the exponent is closer
to zero would imply that a fixed dosing strategy would be better, and other drugs using actual
body weight or total body weight may actually be beneficial for the dosing of the drug. But on average you would expect most drugs
to be dosed on a parameter such as body surface area. This is relevant because when we think about
drug development current paradigms include more physiologic based pharmacokinetic modeling
systems, and often what’s seen in the literature is the reporting of values based on a kilogram
basis, and that kilogram basis is often used to scale information. And so, it’s important to ensure that the
methods that are being used to scale information in obese individuals are scaling them using
some sort of factor, and not in a linear way. So, essentially what needs to be done is ensuring
that when this physiologic based models are used to derive estimates in obese individuals
that they’re being scaled appropriately based on information that’s actually derived from
the drug in question. So, now that I’ve gone through a lot of theory,
what I’d like to do is actually go over some key examples to illustrate how this impacts
drug dosing. When you think about the source of these different
weight descriptors the aminoglycosides serve as the key example. These are drugs that have a volume of distribution
between .25 and .35 liter per kilogram. What we’ve recognized over the last 70 years
with these class of drugs, where they were first classified as anti-biotics, are that
there’s a higher risk for toxicity when you’re dosing them on total body weight, and that
you can adjust the doses of these drugs based on kidney function. And in this case, we actually also have therapeutic
drug monitoring available that can allow you to modify the maintenance dose. There are several alternate body size descriptors
that have been used. These drugs are relatively small in size,
they have low plasma protein binding. We also know that the clearance of this drug
correlates very well with glomerular filtration rate. The dosing of this drug is based on body weight,
and when you look at the literature there’s a range of doses between 1 and 7 milligram
per kilogram, based on the indication of the drug. And again, in most institutions the dosing
is individualized based on therapeutic drug monitoring. This is a study that I conducted over a decade
ago, looking at almost 20,000 individuals who were dosed on gentamicin and tobramycin,
across a very wide body weight distribution of 30 to almost 206 kilograms. What we showed in this data set is that if
you were to rely on total body weight to scale the volume of distribution of the drug what
happens is you get an unsteady estimate of the volume of distribution; that is the volume
of distribution parameter goes down as the body weight goes up. If you were to use ideal body weight you see
the opposite phenomenon. And again, this is because ideal body weight
as a function of height, and not weight. Instead, if you were to use the equation that
I mentioned, the lean body weight 2005 equation, what you get is similar estimates of lean
body — of volume of distribution across the body distribution. So, the implications of that is if you were
to define a dose based on lean body weight you’re more likely to have a predictable Cmax
concentration for this drug, that is considered to be a concentration dependent or Cmax, or
AUC driven drug, and I’ll show you how this actually also can affect the AUC of the drug. This finding also matches what was seen in
animal models. So, work done by Salazar and colleagues showed
a value that was also similar in rats, when they scaled the information to fat free mass
or lean mass. Now with aminoglycosides, when you think about
the pharmacodynamics, they’re driven both by the peak to MIC and also by the AUC to
MIC. And so, the area under the curve is also important,
and so that’s driven by the clearance of the drug. And so, what we did is we also evaluated all
the different equations that could exist to compute the clearance of the drug, and see
what the correlations are. And when you look at that what we found is
that the cracov [spelled phonetically] — the chronic kidney disease and epidemiology equations
actually gave us the best correlation. But it’s really important to show that even
for a drug class like the aminoglycosides, that are considered to be well correlated
to clearance, that an equation like the CKD equation only explains 50 percent of the inter-individual
variability in the clearance of the aminoglycosides. And so, this is again the rationale for using
therapeutic monitoring to modify the dose of this drug. So, when you think about initial dose selection
of aminoglycosides, where perhaps gearing it to a Cmax to MIC target, or an AUC to MIC
target, but if you’re thinking about it from an AUC target we would consider that milligram
per kilogram dose of this drug, and we may consider different approaches. So, when you’re thinking about this drug use
of tobramycin in cystic fibrosis patients, what’s published in the literature is use
of a higher milligram per kilogram dose, or 10 milligram per kilogram. And the reason this makes sense is cystic
fibrosis individuals tend to be leaner, and because they’re lower in body weight the expectation
is the need for a higher milligram per kilogram dose. In contrast, if you’re going to think about
dosing this drug across a weight distribution, what could be considered is if you’re using
higher — if you have individuals across a higher weight distribution what may be necessary
is using a lower milligram per kilogram term. And so, this again fits within the paradigm
because the paradigm is based on 5 to 7 milligram per kilogram, but you may make the decision
to use a lower milligram per kilogram in a larger individual. And then the third alternative is saying this
may be a confusing algorithm. So instead if you could use a fixed milligram
per kilogram across a population, and then use lean body weight you would then compute
again very similar doses, but have a simpler metric to dose across a weight distribution. Another approach could be consideration of
kidney function as the dosing strategy, and this is — if you believe the AUC to MIC to
be the driver of the relationship. In this scenario we’ve published equations
that demonstrate how this could be done, and essentially you would use the creatinine clearance
estimate using a Cockcroft-Gault equation, to compute an aminoglycoside clearance, to
compute an initial dose based on this target value. This article for reference also relays how
the information can then — if therapeutic drug monitoring is applied, equations are
provided that can be used to compute the AUC of the drug to modify the dosing of the drug. Another drug where this is seen to be quite
relevant is with the dosing of Vancomycin. This dosing historically has been thought
to be reasonable, based on actual or total body weight, because the volume of distribution
of this drug is thought to be very similar to total body water estimates in most individuals. Now there could be multiple approaches that
are used. So, one approach that’s used in current guidelines
is to dose the drug based on total body weight. And so, when you look at that, using total
body weight you were — if you were to use 25 milligram per kilogram you will clearly
compute a much higher dose in a larger individual. And so, for most clinicians this may lead
to consideration of too high of a dose. The alternate in other guidelines when you’re
looking at the methicillin resistant staph OREUS [spelled phonetically] guidelines the
recommendation is to use no more than 2,000 milligrams as the dose. And so, in this scenario in a larger individual
you may end up using a 13 milligram per kilogram dose, and so for some clinicians that may
be too low of a dose. So, what would be the alternative? One of the alternatives would be to use the
same milligram per kilogram dosage, but then use a different body weight descriptor, and
so as the individual gets larger instead of using total body weight, use the adjusted
body weight function, and you would compute much lower doses. A simpler alternative could be to scale the
doses, and so since most individuals have calculators that have square root function. What I’m showing here is if you take the weight
of the individual divided by the average weight, and take the square root of it, you’d basically
be able to replicate the dosing of this drug across a population that would match up to
adjusted body weight dosing. When you’re thinking about Vancomycin, again
this is a drug that also undergoes therapeutic drug monitoring. What has happened over time is guidelines
that have suggested that only trough concentrations are necessary. But a point that needs to be highlighted is
that trough concentrations do correlate with the AUC of this drug, but only predict about
40 percent of the inter-individual variability. And so, the trough concentration is a simple
metric. It’s easy because a single concentration can
be measured, but it may not represent the true AUC in a specific individual. What we’ve shown in a series of studies, is
the importance of getting a peak concentration measurement in obese individuals. And this is really important because basing
approaches that can be used to compute the AUC of a drug like Vancomycin in a population
really needs an accurate estimate of the volume of distribution. And so, you can imagine if you are missing
a peak concentration measurement you don’t actually know which concentration time profile
truly represents the individual. And so, you can have different scenarios represented
here, with the true scenario represented by the actual concentration measurement, and
a two-compartment model. And so again when we think about achieving
the right dose in an obese individual, a common issue is not being able to approximate what
the true volume of distribution is for the specific drug, and so in this scenario what
we demonstrate is the importance of peak concentration measurement. More recently we’ve published a study looking
at the pharmacokinetics of Vancomycin, and looking at alternate metrics. As I mentioned, with the several equations
what we’ve relied on for over a hundred years is using height and weight to compute alternate
body size descriptors. Stewart Wang and colleagues at the University
of Michigan have led a group known as the Morphomics Group, and these individuals have
developed mathematical algorithms that can take existing computer tomography data, and
convert them to different body size metrics. So, this graphical representation is taking
existing data from individuals who are in the hospital who may have had a CT scan done
for some medical reason. And then taking that data to compute parameters
such as body depth, facial area, total solus area, and several other parameters represented
in this slide. What we then did is look retrospectively at
the pharmacokinetic profile of Vancomycin, and assess the correlation of these pharmacokinetic
parameters to these newer body composition metrics. What we were able to clearly demonstrate is
that the volume of distribution of Vancomycin was poorly predicted by body weight, and was
really better predicted by T12 to T4, which is representing again the vertebral columns
T12 to T4 torso area, was a better correlant [spelled phonetically]. But again, looking at this you’d see a very
poor correlation, but in relative terms a better correlation to body weight. We were also able to demonstrate that using
total solus area that — which would be a metric representative of muscle mass, that
this metric was a better predictor of clearance than relying simply on body weight. And so, again this more recent study is clearly
not ready for prime-time use, but just represents a movement away from our simple measures of
height and weight to define drug dosing. For other drugs that are just dosed on a fixed
dosing basis, drugs like Ceftaroline, that — who’s pharmacodynamics are based on time
above MIC, which you’ll see for several drugs and shown earlier, is reductions is the peak
concentration, but really a convergence in the profile. And so, this study by Justo and colleagues
out of Keith Rodvold’s group at the University of Illinois at Chicago, have clearly shown
that for certain drugs like beta lactams you may need higher doses with the first dose,
but really because of this convergence in the profile maintenance doses probably do
not need to be adjusted for most beta lactams. For another drug like Levofloxacin, a drug
that’s concentration dependent, what’s shown in the label is use of a higher milligram
dose that allowed really shortening of the dosage regimen from 10 days to a shorter regimen
in patients with pneumonia. For this drug the AUC to MIC is predictive
of the response, and the observed AC of this drug is between 50 and 150 milligram per hour
liter. This drug also has a really good correlation
to kidney function, and because the clearance of this drug has a good correlation in theory
the dosing of this drug could be improved by computing the clearance of the drug relative
to creatinine clearance. Now in the United States we do not offer therapeutic
drug monitoring for drugs like Levofloxacin, but this study conducted in collaboration
with Dr. Federico Pea at the University of Udine in Italy; what we’re able to show is
therapeutic drug monitoring can be used to improve the dosing of drugs in individuals
across a much larger body weight distribution of 98 to 250 kilograms. What we’re able to show is those individuals
may need doses higher than 750 milligrams to achieve isometric exposures to those that
are smaller in size. But again, the critical piece here is that
therapeutic drug monitoring was available to ensure that we didn’t overdose individuals. And so, this recommendation of using higher
doses is truly off label, and cannot be recommended in clinical practice in the United States,
but for institutions that do have therapeutic drug monitoring it does create a mechanism
to consider uses of higher doses of drugs to achieve the exposures necessary to improve
outcome of a drug like Levofloxacin. Additional examples for other drugs that are
not anti-microbials include work by Dr. Katherine Neib’s [spelled phonetically] group at the
University of Leiden. This group has done several studies looking
at specific probes of CYP-metabolism. And they’ve done a really nice study more
recently with Midazolam, looking at a population of individuals who’ve undergone bariatric
surgery. And so, this is an important study because
this study allows an evaluation of individuals as their own controls. So, this would represent — this figure here
represents individuals who are larger, who are obese, who undergo bariatric surgery,
and over a one-year span lose quite a bit of weight. And because they lose weight, and you can
measure pharmacokinetics before and after weight loss, you can look at changes in drug
clearance. And what they’ve shown with Midazolam, which
is a probe of CYP3A4, is that the systemic clearance increased with this drug with weight
loss. But there’s really no alterations in oral
bioavailability. And so, this expectation is different again,
because we would think in terms of do we need to increase the dose for body size? In this scenario the idea is we perhaps may
need to lower certain doses of certain drugs if this is really true in obese individuals. For other drugs like acetaminophen this is
actually metabolized by multiple pathways, and so it’s not really a good probe for CYP2E1. But this is an important study because this
study shows a shift in the metabolic profile in morbidly obese individuals, represented
by the blue box plot versus the green box plot of non-obese individuals. What you see is an increase in the cystine
[spelled phonetically], and although not statistically significant an increase in the mercaptopurate
[spelled phonetically] metabolite of acetaminophen, metabolites that would be associated with
the hepatotoxicity potential of this drug. And so, in this scenario even though clearance
may increase with the drug like acetaminophen, recommendations cannot be made to increase
the dosage of a drug like acetaminophen, because this may lead to an increase in toxic metabolites
of the drug. And so, studies like this are really important
too, because they can give us good insights on whether doses should be changed in certain
populations, but really if metabolites are also changing in those — in obese individuals. Another important compound that’s been evaluated
is Propofol. And so, this study again be Neib and colleagues
looked at the glucuronidation profile of Propofol, and really were able to show that the clearance
of a compound like Propofol, which is a very low molecular weight, small compound, best
scales allometrically, and this is going to be again a phenomenon that’s seen with several
drugs again, is that for most of these drugs we would expect that the clearance of the
drug to scale to an exponent of .67 or .72. And that is essentially what was seen in this
study, implying that perhaps body surface area or a metric like that may be reasonable
to the dosing of Propofol. Now this has been actually investigated in
much more detail by Eleveld and colleagues, who’ve taken data sets across different weight
distributions, and across different populations of adults, children, and elderly, and have
come up with a much more complex and comprehensive model that not just deals with the issue of
obesity, but really deals with the overall profile. And this is a process that’s being heralded
through the open TCI Initiative, and is an important one to help improve the dosing of
compounds like Propofol that are used in anesthesia. So, what I’ve presented today is a lot of
different paradigms that are currently used to define the dosing of drugs in obesity,
but it’s important to consider that if we have some drugs where we shouldn’t be dosing
them in body weight — rather we should be dosing them on a fixed basis, how do we change
that dosing paradigm? And if we’re going to do that how do we pay
attention to improving the dosing of a drug once a drug is marketed? So, what I’d like to present in the last few
slides here is data with Daptomycins. This is a study that I performed about a decade
ago at the University of New Mexico, where we looked at morbidly obese versus normal
weight individuals, and dosed Daptomycin based on body weight. And what we saw is a very small change in
the volume of distribution of this drug. Even though that the weight of the individual
was almost twice as high in the morbidly obese versus the normal weight individuals the volume
of distribution did not increase proportionately to body weight. We also used bi-electrical impedance analysis
to compute the fat free mass of these individuals. And when we did that we actually were able
to normalize the volume of distribution, implying that total body weight is not the right metric
for this drug. When we looked at clearance of this drug we
were also able to show that the clearance of this drug does not increase proportionately
to body weight, and again this parameter was scaled better with fat free weight. And in this scenario, we also looked at measurement
of clearance because these individuals were matched on their sodium iothalamate glomerular
filtration rate. So, this is a well performed study, and we
were able to show again that neither of these parameters really scaled to body weight. And this is important because this drug is
currently dosed on body weight on a 4 milligram per kilogram and six milligram per kilogram
basis, with specific guidance to not modify the dose for obesity. There are also recent guidance that suggests
that the dose of this drug should be increased to 8 to 10 milligram per kilogram, which would
imply much higher exposures in obese individuals. More recently we published a study in collaboration
with Marco Falcone and colleagues at the University of Rome, and this study was based in individuals
that were critically ill. And in these 50 individuals what we were able
to show again is that body weight does not correlate with the clearance of this drug. Rather, there were certain individuals who
had augmented clearance of this drug. And in fact, what we were able to show is
individuals who had bacteremias, who were sicker individuals, tended to have higher
clearance. And that clearance was not related to body
weight. Again, we were able to discover that through
therapeutic drug monitoring, which is widely not available for this drug. Now if our findings are consistent with the
original findings from over a decade ago. So, if you look — were to look at the original
population PK model for a drug like Daptomycin what you would see is the clearance of this
drug was actually related to body temperature, which is — in most cases when you’re looking
at this you’d say that this is an odd term to incorporate into a population model. But what it really represents is this idea
of illness. If someone is — has a severe infection they’re
more like to be febrile, and if they’re likely to be febrile their temperature will be higher. And so, you would compute a higher clearance. They also incorporated renal clearance in
the term, but when you looked at the function most of that clearance is not really driven
my kidney function. There’s just a value of 0.807, which is the
same value that we saw in our healthy volunteers. Now if you look at this again, tabulate it,
and using a reference population what you would expect is, even if the kidney function
increased and body temperature increased, your maximal expectation for clearance across
that population would be no more than a 70 percent higher clearance, which would imply
that the dosing of the drug, or the absolute dosing of the drug does not need to be increased
by more than two-fold. So again, if you were going to dose a 50-kilogram
person a 150-kilogram person, you would dose that individual, the 150-kilogram person,
three times more if you used total body weight. And clearly the mathematical expectations
are that that would be too high. This has also been shown in febrile neutropenic
patients, when you’re thinking about the effects of — severity of illness, and clearance. Now this is problematic because how do we
solve this issue once a drug is on the market? So, the product label currently recommends
that the drug be dosed on total body weight. And so, some investigators have suggested
in larger individuals — in larger individuals to switch scalars. So instead of using total body weight, because
you would accidentally perhaps calculate too high of a dose, consider switching to lean
body weight. And why this is an issue is if you were to
look at the distribution of lean body weight across a population — this is a representation
of the lean body weight in males, and this is a representation in females — if you were
to use these distributions you would get this phenomenon. You would dose individuals between and 50
as 111 kilograms on total body weight, and so what would happen is you would compute
a dose between 300 and 666 milligrams. And then by switching scalars what you’d essentially
do is drop the dose for individuals that are larger. So, the effect of switching scalars for some
drugs is you would end up giving larger individuals much smaller doses than they need. And so, our group has proposed consideration
of fixed dosing strategy for this drug, to essentially give similar doses across a population,
but that recommendation also needs to be validated through a prospective steady. So, to summarize the key points that I’ve
laid out to day: obesity is associated with the changes in volume of distribution that
might require the use of higher initial doses relative to maintenance doses. Our expectation is obesity has limited changes
in drug clearance, and that for the majority of drugs those changes are likely explained
by Allometry. We also expect that total body weight may
be reasonable for an initial dose, but it’s really unlikely to be a useful metric for
defining the maintenance dose. And the loading dose of a drug could be used
to aid the dosing of drugs that are time dependent — pharmacokinetics. But we should really move toward consideration
of body size stratified or composition stratified dosing regimens for computation of the maintenance
dose of a drug. With that I’d like to thank you for your attention. I’d also like to thank Dr. William Figg, and
Dr. Lisa Cordes, the National Institutes of Health for this opportunity to present on
the principles of clinical pharmacology. If you have any questions, please direct them
to the coordinators of this course.

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