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Pharmacokinetics – Some Principles and Applications to
Forensic ToxicologyEverything (or at Least Some
Things) You Wanted to Know but were Afraid to Ask
I am a -Pharmatoxicoforensickineticist
I am a -
Pharmatoxicoforensickineticist
PHARMACOKINETICS
pharmakon- substance (from the Greek)kinetics- rate processes
pharmacokinetics is the specialty which studies the rate processes associated with drug disposition and absorption in the body (ADME).
Absorption, Distribution, Metabolism and Excretion
Pharmacokinetics- what the body does to the drug.
Pharmacology – study of the effects of the drug on the body
Pharmacodynamics – the time course of the pharmacological (or toxicological) or clinical effect in relationship to the plasma concentrations of the drug (or poison)
Schematic representation of drug dosing, plasma concentrations and response. Unpublished work © 1998 Saguaro Technical Press, Inc.
Confused yet? How about now?
Well, I have had enough!! CHARACTERIZING THE ELIMINATION PROCESSFIRST-ORDER KINETICS
First order kinetics describes the behavior of virtually all drugs (fortunately, as discussed later).
If drug behavior is not described by first-order kinetics, there are significant clinical and toxicological implications.
Principle of first-order kinetics is very simple:
rate concentration1
rate = rate constant x concentration1
rate = K x concentration = K x C1 = K x C
The term first-order arises from the fact that concentration is raised to the power of one.The symbol, C, refers to plasma (or serum or blood) concentration
following dosing of drug.
Assume for now that:
.an intravenous (iv) bolus dose is given (i.e., instantaneousadministration)
and
.drug distributes instantaneously throughout the body (i.e., “1-compartment model”)
then, the correct form of the relationship is,
rate = -K x C = rate of elimination
where, the minus sign indicates loss of drug from the body through elimination mechanisms and,
CrateK =
K is often referred to as the apparent overall first-order elimination rate constant
and it simply relates rate of change (in this case, rate of elimination) with plasma
concentration (C).
Since rate is measured in units of concentration/time, the units of K in any first-order
kinetic process will be reciprocal time (viz., concentration/time divided by concentration),
1/t or t-1. The meaning of this will become clear.
The problem with the above rate equation is that it is generally not very
useful (with some exceptions) and what we really want is a relationship
between plasma concentration and time (not concentration and rate).
Whenever this situation arises, we resort to integration to obtain a useful
expression. Integrating the rate equation gives the exponential relationship,
where, C is plasma concentration at any time, t, after dosing, C0 is plasma
concentration at time zero immediately after dosing and K is the first-order
elimination rate constant.
tKeCC ⋅−⋅= 0
The above equation may be written in several different formats, remembering that a scientist will do anything to get a straight line relationship,
tKeCC ⋅−⋅= 0
Taking the natural logarithms, ln, of both sides gives a linear relationship,
tKCC ⋅−= 0lnln
Since we generally deal with base 10 logarithms,
320
.loglog tKCC ⋅
−=
The above expression is now in the form of a straight line equation,
Y = b - m ⋅ X
tKCC ⋅−=32
0
.loglog
where, log C is the value of plasma concentration on the Y-axis which is plotted against t on the X-axis, b is the plasma concentration intercept on the Y-axis andm is the slope of the line.
What is the meaning of the apparent overall first-order elimination rate
constant, K? The following is not correct, but it is good enough to
understand the meaning of any first-order rate constant.
The units of K are reciprocal time and the value for K represents the
fractional rate of loss of material from the body. Thus, if K is numerically
equal to 0.1/hr or 0.1hr-1, it is approximately correct to say that every hour
about 10% of what was there at the beginning of the hour is lost during
the next hour.
If K is numerically equal to 0.05/day, then it is about correct to say that
5% of what was there at the beginning of the day is lost during that day.
Examine the following table.
Table 1. Decline in the concentration of a drug whose elimination rate constant is 0.1 hr-1
Time, hours Concentration0 1001 ≈90 2 ≈813 ≈734 ≈66
What is not correct about my definition is that one really needs to consider
an instantaneous rate of change (remember the tangent to a curved
surface!?) and not a change over a relatively long time, as I have selected.
The larger the value for K, the more rapidly a drug is removed from the
body. The value for K is analogous to a valve in a tank; the larger the valve
the more rapidly fluid flows out of the tank.
A term which is more useful than K and is easier to understand is half-life, t1/2. The rate
constant, K, and half-life, t1/2, not surprisingly, are simply related,
21
6930/
.t
K =
and,
Kt 6930
21
./ =
While this does not let us understand the meaning of half-life, as we will discuss later, it
does offer a simpler way to express drug loss from the body compared to values of K.
One can determine the value of a slope from a plot of log C vs. time and calculate K,
from which t1/2 is estimated or, alternatively, estimate half-life from a graph and then
calculate K. Of course, in practice, you will probably use a program such as Excel® for
plotting the data and have a linear regression analysis performed from which an
estimate of K is obtained.
Half-life is defined as the time necessary for concentration to decline by 50%. I will
modify that definition here, but it will only have meaning later; half-life is the time
needed for plasma concentration to decline by 50% in the terminal, log-linear
phase. This is the terminal half-life. Take any pair of plasma concentration values in
the ratio of 2 to 1 and the corresponding difference in times on the X-axis is, by
definition, half-life.
It is important to notice that t1/2 is independent of dose and plasma concentration and,
as a result, first-order kinetics is referred to as being dose-independent (or linear
kinetics). Therefore, everything about first-order kinetics is predictable; double the dose
and you will double the plasma concentration. As noted on the next slide.
A consequence of the above behavior gives rise to what has been called the “principle
of superposition”, a characteristic of all first-order kinetic processes. This principle is
illustrated in a semi-log plot of plasma concentration divided by dose as a function of
time. A single log-linear line will be obtained if the drug disposition is characterized by
first-order kinetics. This is seen in the graph shown below.
Cefazedone serum concentrations as a function of time following an intravenous bolusdose to human subjects. The data in the graph on the left are plotted on a regular or Cartesiany-axis while the same data are plotted on the right using a semi-logarithmic scale on the y-axis. Taken from Mayersohn, 1982
There are many terms used to modify the word half-life, but only one is the correct
usage under all circumstances, terminal half-life, which describes what it is; the half-life
determined from the terminal (log-linear) data.
Some times you hear the (older) term, biological half-life, but this can be misleading
as it may refer to a half-life associated with some measured clinical or pharmacological
value.
The most commonly used term is elimination half-life, but this term, as I will illustrate
later, is not correct, since it suggests that half-life is a function of or governed by
elimination (or clearance) processes only, which is incorrect.
Finally, the term disposition half-life is often used and, in general, this is a good descriptor,
except when a non-intravenous dose (e.g., oral dose) is given and absorption is slow or
prolonged, in which case the term is not correct.
NON-FIRST-ORDER KINETICS
This type of kinetic process will be discussed in detail later as it can describe the absorption and disposition kinetics of some drugs. Briefly, the rate (of absorption, elimination, etc.) is not strictly a function of plasma concentration or dose, but rather beyond a certain concentration, the rate process becomes constant (i.e., approaches a “zero-order system”; plasma concentration raised to a zero power, which is a constant). This is a non-linear or dose-dependent system and it has significant clinical consequences. The process is no longer simply predictable; double the dose and you do not double plasmaconcentration (it may more than double or less than double).
A classic example with regard to elimination is ethanol. The quantity of ethanol ingested can exceed the ability of the body’s enzymes (alcohol dehydrogenase) to metabolize; the rate of metabolism cannot keep up with the dose presented.
In such an instance half-life increases with dose. Other examples are salicylate and phenytoin (for treating epilepsy). This condition is most likely to occur with an overt overdose, where the quantity of drug (or toxin) presented to the body swamps the ability to be eliminated.
In the following graph, half-life is represented as “t50%” (since t1/2 strictly applies only to
first-order kinetics) as a function of (log) dose. The top line represents the situation
where there is only one route of elimination and it is saturable (e.g., ethanol). The
bottom line is for a compound that undergoes saturable elimination, but there are
parallel first-order processes. The symbol, KM, refers to the Michaelis constant.
THE CONCEPT OF MODELS
The purpose of any science is to make predictions. In order to do so one
needs to develop quantitative methods of data analysis. This need exists in
the biological sciences whether one deals with a unicellular organism or an
entire animal body. This is certainly true in pharmacokinetics where we are
interested in being able to quantitatively describe the rates at which a
given drug undergoes a variety of processes (e.g., rate of absorption or
elimination). In order to describe such data and better understand these
processes and then to make necessary predictions we generally resort to
the development of a biological or physiological model.
The model is an artificial construct or abstract notion of our concept of what the real system is.
The body could be represented by one large ("black") box or by several boxes connected together
(as discussed below). The choice of the model is dictated by the behavior of the data. Every model
must have two characteristics that are in opposition to each other. The model must be simple,
after all, this is the only reason for developing the model; to simplify the system (i.e., the body) so
that we can rigorously quantitate data and better understand its behavior. This is analogous to the
construction of a model airplane, which is a simplification of the real, complex airplane.
In contrast, however, to this need to simplify, is the demand that the model must adequately
reflect the data and the behavior of the system. If the model is too simple and does not explain our
data then the model is useless. On the other hand, however, if the model is too complex (i.e., a
replicate and not a model), we will not be able to quantitatively treat the data. We need to walk this
narrow line between simplicity on the one hand and reality on the other.
The rule of parsimony applies here; given the choice between two alternatives, one
simple and the other complex, chose the simpler approach unless there is information to
suggest the more complex. In modern parlance this can be referred to as the K.I.S.S.
principle (Keep It Simple, Stupid!). It is sometimes referred to as “Ocham’s Razor”. In
practice, the decision between models is usually made on the basis of a statistical
judgment. This balance between simplicity and reality is illustrated below.
Illustration of the relationship between a model-predicted line and real data for several possible models, illustrating the need to balance simplicity and reality. Unpublished work © 1998 Saguaro Technical Press, Inc..
There are basically three approaches to modeling pharmacokinetic data:
1. model or compartmental or parametric (or “classical”) analysis
2. model-independent or non-compartmental (or non-parametric) analysis
3. physiologically-based models
These will be illustrated below. One caution is contained in a quotation from the
statistician George E.P. Box, an idea to which I completely subscribe,
ALL MODELS ARE WRONG; SOME MODELS ARE USEFUL!
(Also, according to M. Eigen, “A theory has only the alternative of being right or
wrong. A model has a third possibility; it may be right, but irrelevant.”)
1. model-dependent or compartmental or parametric (or “classical”) analysis:
The technique most often used in biological modeling is referred to as
compartmentalization. One views the body as one or more regions or compartments
which are interconnected. These compartments represent regions of the body that
behave in a certain way with regard to a specific drug and they have certain
characteristics in common. In compartmental analysis the compartment does not
represent a real anatomical body space but rather a lumping of regions into this
hypothetical area. These regions are often characterized and distinguished on the basis
of blood flow (i.e., well-perfused or poorly-perfused). This abstract, non-physiological
approach to modeling is often unappealing to some investigators and, while it is a useful
and practical approach, there are alternative methods of analyses (e.g., non-
compartmental analysis and physiologically-based pharmacokinetic models).
The simplest possible compartmental model is referred to as the one-compartment
model where one conceives of the body to be represented by a single box. Drug enters
the box and exits the box. This model assumes that drug is instantaneously distributed
throughout the body once it enters the blood stream. Thus, the drug achieves an
immediate equilibrium between the blood and all other tissues in the body (distribution
equilibrium). Essentially we are saying that the movement of the drug from the blood
stream to all tissues in the body occurs with such speed that we cannot measure that
movement; it is instantaneous. Of course this is not really true, it is just that we cannot
sample fluids rapidly enough to see the distribution process. However, this assumption
is sufficiently correct in practice that it applies to many drugs. This assumption also
considerably simplifies the analysis of the data.
The following figure illustrates the plasma concentration-time data that leads to this
model.
A one-compartment model following an iv bolus dose illustrating an ‘anatomical’ view (A) and the more usual compartment version (B). The resulting (log) concentration-time data
for several tissues are shown in (C). Note that the lines are parallel but do not superimpose. Unpublished work © 1998 Saguaro Technical Press, Inc..
Many tissues have been scrunched into one box, a single compartment, since all tissues of
the body behave the same kinetically and, therefore, they can be lumped together. The
plasma concentration-time data for several tissues are at the right. Note that the scale of
the y-axis is logarithmic since this will give a straight line for a first-order elimination process.
There are several aspects of that figure that should be recognized.
First, all concentrations start at some maximum value and then decline; they do not increase
and then decline. This is consistent with our assumption of instantaneous distribution. The
fact that all of the lines are parallel to each other is an expression of kinetic homogeneity;
all the tissues behave the same in a kinetic sense.
Second, all of the lines are parallel but not superimposable, which leads to the distinction
between kinetic homogeneity and concentrational homogeneity. While all tissues
including the blood are kinetically homogeneous, there is no reason to believe that the
tissue concentrations are equal. As a consequence, there is no concentrational
homogeneity and the lines do not superimpose. The reason for this lack of equality in
concentrations is a result of differences in tissue binding of drug. In the example shown the
order of binding is: muscle>plasma>kidney. Generally, blood and tissue protein binding are
not of the same magnitude. Many drugs may be characterized as behaving according to a
one-compartment model.
Keep in mind that all analytical methods quantitate total drug (i.e., bound + unbound) in
the tissue sample being sampled. Other techniques must be applied to determine
unbound concentrations (e.g., dialysis). It is expected, however, that the unbound
concentrations of drug are the same in all tissues; a basic assumption made in
pharmacology and, while difficult to prove experimentally, microdialysis data support this
presumption. Thus, it is the unbound plasma concentration of drug that drives the
pharmacological response.
For many drugs the one-compartment model is too simple and, therefore, it will not
explain the concentration-time profile. The primary difference, which requires a more
complex model, is with respect to the rate of drug distribution or movement out of the
blood stream into other tissues. In more complex models we cannot lump together all
tissues with respect to drug distribution because, unlike the one-compartment model,
distribution is not instantaneous. In terms of compartmental models, the next more
complex situation is referred to as the two-compartment model, which is illustrated
below.
Tissues are now scrunched into two connected boxes. The box on the left, which
includes blood, contains other organs and tissues that receive drug rapidly from the
blood. We often assume that the organs of elimination reside here (i.e., liver and kidney).
The figure illustrates the typical representation of the two-compartment model, devoid
of tissues. Drug enters the blood stream, which is part of the so-called "central" (or first)
compartment, following intravenous bolus dosing. The latter is attached to a "peripheral"
(or second) compartment into which drug distributes. Connecting these regions are the
first-order inter-compartmental transfer rate constants, k12 and k21 as well as the
elimination rate constant from the central compartment, k10 or kel.
(A) An “anatomical” version of a two-compartment model illustrating the presence of specific organs and tissues in each of the two compartments connected by the blood stream. Tissues lumped together in the compartment on the left receive drug rapidly, whereas, tissues lumped into the compartment on the right receive drug slowly. (B) Hypothetical two-compartment model devoid of any consideration of tissues. Compartment 1 (“central”) receives and eliminates drug, whereas, compartment 2 (“peripheral”) receives drug slowly. The inter-compartmental first-order rate constants are shown (i.e., k12 and k21) as well as the elimination rate constant (k10 or kel). (C)
Concentration-time profiles (semi-log axes) of plasma and tissues in rapid equilibrium with plasma (e.g., kidney; compartment 1) and tissues in slow equilibrium with plasma (e.g., muscle; compartment 2). Unpublished work ©1998 Saguaro Technical Press,
Inc..
These compartments are strictly hypothetical in terms of real anatomical spaces.
Tissues are lumped together as a result of similar drug distribution patterns. Thus, the
central compartment contains tissues into which the drug very rapidly distributes;
whereas, the “peripheral” compartment contains regions that receive drug slowly and at
a measurable rate. Drug exits from the central compartment. A hydrodynamic analogy is
also shown.
Plasma and representative tissue concentration-time profiles are illustrated in the
Figure. Drug enters the tissue until an equilibrium-type condition is reached (maximum
concentration in the tissue curve for muscle) and thereafter concentrations decline in
parallel with the blood curve. As seen, the kidney is part of the central compartment,
which rapidly receives drug from the blood and, therefore, its profile is parallel to that of
plasma at all times. In contrast, the muscle is part of the “peripheral” compartment that
receives drug more slowly. Muscle concentrations initially rise as drug moves from the
blood to the muscle, reaches a maximum (distribution equilibrium) and then decline in
parallel with plasma.
Mathematically, we have seen that a one compartment model can be described by a
single exponential equation assuming iv bolus dosing. For every compartment added to
the model, another exponential term is added, thus the two compartment model
equation is written in two common, but not the only, forms.tt BeAeC βα −− +=
tt
eAeAC 22
11
λλ −−
+=The exponential symbols represent hybrid first-order rate constants (i.e., they are a
function of all constants in the model). Since, by definition, the first rate constant is
numerically larger than the next one in the series (i.e., α>>β), at some time the first term
becomes numerically insignificant compared to the second term. When that happens,
the data take on the appearance of a single exponential, which translates into a log-
linear decline in concentration (i.e., the terminal phase, from which the terminal half-life
is obtained).
We need to reexamine the cefazedone plasma concentration-time data presented
previously, but now I illustrate all of the data following the iv dose. Notice that prior to 2
hours the data decline more rapidly due to distribution from the blood into other tissues
and then it becomes log-linear. These data are consistent with first-order kinetics and a
multi-compartmental model; at least a 2-compartment model.
Cefazedone serum concentrations vs. time following an iv bolus dose to human subjects. These are the same data as shown in Figure 4 except that the earlier samples (before 2 hour) are shown here. Taken from Mayersohn (1982).
Meaning of Half-life
• Half-life gives an idea of how long the drug (or any other compound) will remain in the body.
• In general, it takes about 6-half-lives for a drug to be completely eliminated from the body.
• Most important is the fact that half-life is dependent upon two other parameters:
Half-Life Depends Upon:
• V= apparent volume of distribution• CLs=systemic or total body clearance
1 20 693
/. VTCLs
⋅=
To understand the meaning of half-life, the previous equation must be viewed in its functional form, as it was written, although it is mathematically correct in any rearranged form.
1 2
1 2
1 2
0 693
0 693
0 693
/
/
/
.
.
.
VTCLs
VCLsT
T CLsV
⋅=
⋅=
⋅=
Half-Life Value Will Depend Upon:
•Drugs with a short half-life have:small V orlarge CLs orboth
•Drugs with a long half-life have:large V orsmall CLs orboth
Some Examples
SHORT HALF-LIFE LONG HALF-LIFEremifentanil 0.2 hr phenobarbital 4 dayomeprazole 0.7 hr amiodarone 25 daynitroglycerin 2.0 hr dioxin 6 yrmorphine 2.0 hrmeperidine 2.0 hr
Other Factors That May Affect Half-life
• Half-life depends upon both distribution (V) and elimination (CLs).
• However, half-life may be dependent upon other processes that precede distribution and elimination.
• This is especially true of absorption or input into the body.
• Thus, SLOW absorption, such as seen with poorly water-soluble drugs, on overdose or for controlled release products, will result in estimates of half-life that reflect the slowest step, absorption.
• This is referred to as a ‘flip-flop’ model.
Rate-Limiting Step• In any sequence of events, only one of those
events will rate-limit the overall process.• In the sequence:
absorption into body—elimination from bodyEither the absorption rate constant (ka) or the
elimination rate constant (K) will be the slowest step.
The terminal slope of the plasma concentration-time data will always reflect the slowest step.
If, ka >> K, the terminal slope will be given by KIf, ka << K, the terminal slope will be given by ka
When, ka >> K
Drug at absorption site Drug in body eliminationka K
When, ka << K
Drug at absorption site Drug in body eliminationka K
Metabolite Plasma Concentrations-Another Example of a Rate-Limiting
Step• The relative values of rate constants and
rate limiting steps in sequential processes also have great meaning when discussing parent drug and metabolite formation.
• Some metabolites are formed rapidly and then eliminated slowly
• Some metabolites are formed slowly and then eliminated rapidly
Metabolic Scheme
Drug Metabolite Metabolitekm kmu Elimination
When, kmu >> km (or K)Drug Metabolite Metabolite
km kmu Elimination
When, kmu << km (or K)Drug Metabolite Metabolite
km kmu Elimination
CLEARANCE (CL)
The volume of blood that must be acted upon to irreversibly remove all drug per unit of time in order to account for the rate of drug elimination.
NB: units of flow
Systemic or total body clearance, CLS (TBC)
CLEARANCE
lim
( / )( / )( / )
S
S
rate of e inationCLplasma concentration
rate amount timeCL vol timeC amt vol
=
=
Creatinine Clearance, CLCr• In normal healthy adults, CLCr is about 100
ml/min. In a normal adult human, the kidneys are able to irreversibly remove all of the creatinine contained in 100 ml of blood in 1 minute.
• In a renal diseased human, CLCr may have a value of about 20 ml/min. The kidneys in that human is only able to remove all of the creatinine contained in 20 ml of blood in 1 minute.
• Which is more efficient?
Drug Clearance
• The values of CLs for drugs has the same meaning as CLCr.
• CLs may be metabolic or hepatic (CLH) and/or renal (CLR) or any other eliminating route.
• CLs=CLH + CLR + CLother
• Clearances are additive and independent• The maximum value for clearance by any route
of elimination is the value of blood flow to that organ.
Drug Clearance (continued)
• Thus, in the normal adult human, CLH can’t exceed liver or hepatic blood flow (QH) and CLRcan’t exceed kidney blood flow (QR).
• This is another example of a rate-limiting step; the drug can’t be cleared by an organ any faster than it gets delivered there by the blood flow.
• QH ≈ 1,500 ml/min (on average in adult)• QR ≈ 1,200 ml/min (on average in adult)
EXTRACTION RATIO (ER)
• The efficiency of drug removal by an organ system is often expressed as an extraction ratio, ER.
• The ER is the value of organ clearance for a drug divided (or normalized) by blood flow to that organ.
EXTRACTION RATIO
120 0 11 200
0 1 10
/ min ., / min
exp.
. %
min .
HH
H
RR
R
CLERQ
CLERQ
mlCLCrml
ER resses a fractional rateof lossAvalueof indicates thatof creatinineis removed fromblood bythekidney every ute
=
=
= =
EXTRACTION RATIOS• Elimination Low (<0.3) Intermediate (0.3-0.7) High (>0.7)• HEPATIC carbamazepine aspirin alprenolol• diazepam quinidine cocaine• indomethacin codeine desipramine• naproxen nifedipine lidocaine• nitrazepam nortriptyline meperidine• phenobarbital morphine• phenytoin nicotine• procainamide nitroglycerin• salicylic acid pentazocine• theophylline propoxyphene• valproic acid verapamil• warfarin
• RENAL amoxicillin amiloride glucuronideatenolol cimetidine hippurates
• cefazolin cephalothin penicillins• digoxin ranitidine sulfates
Methamphetamine Excretion and Urine pH Renal Clearance and Urine Flow
PHENCYCLIDINE (PCP):A Good Example of Not Understanding
Pharmacokinetic Principles
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30• CLnon-renal 350•• CLR, as % CLS 8%• % increase in CLR -• % increase in CLS -•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30• CLnon-renal 350 350•• CLR, as % CLS 8%• % increase in CLR -• % increase in CLS -•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135 (485-350)
• CLnon-renal 350 350•• CLR, as % CLS 8%• % increase in CLR -• % increase in CLS -•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135• CLnon-renal 350 350•• CLR, as % CLS 8% 28% (135/485)x100
• % increase in CLR -• % increase in CLS -•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135• CLnon-renal 350 350•• CLR, as % CLS 8% 28% (135/485)x100
• % increase in CLR - 350% (135-30/30)x100
• % increase in CLS -•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135• CLnon-renal 350 350•• CLR, as % CLS 8% 28% (135/485)x100
• % increase in CLR - 350% (135-30/30)x100
• % increase in CLS - 28% (485-380/380)x100
•• T1/2, hr 13• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135• CLnon-renal 350 350•• CLR, as % CLS 8% 28% (135/485)x100
• % increase in CLR - 350% (135-30/30)x100
• % increase in CLS -•• T1/2, hr 13 10• % decrease in T1/2 -
PHENCYCLIDINE (PCP):A Good Example of Not Understanding Pharmacokinetic Principles (continued)
• Urine pH• Parameter Uncontrolled Acidic (<pH 5.0)• Clearance, ml/min:• CLs 380 485• CLrenal 30 135• CLnon-renal 350 350•• CLR, as % CLS 8% 28% (135/485)x100
• % increase in CLR - 350% (135-30/30)x100
• % increase in CLS -•• T1/2, hr 13 10• % decrease in T1/2 - 23% (10-13/13)x100
• Does urine pH effect the kinetics of elimination of PCP?
• Yes.• Is the change in renal clearance
important?• Yes.• Is the effect meaningful overall?• Probably not, since there is not a dramatic
increase in CLs or decrease in half-life.
IMPORTANT TOPICS NEEDING RESEARCH
• Post-mortem redistribution: How can we predict the occurrence and extent?
• Time of drug ingestion (overdose): How can we predict this time from 1 or 2 plasma or urine samples?
• Markers of drug exposure: Are there metabolites that can serve as surrogates of exposure (e.g., ethanol glucuronide).
PREDICTION IS THE PURPOSE OF ALL
SCIENCES
Not all predictions are painless