Principles and Applications to Forensic Toxicology … · Principles and Applications to Forensic...

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Pharmacokinetics – Some Principles and Applications to Forensic Toxicology Everything (or at Least Some Things) You Wanted to Know but were Afraid to Ask I am a - Pharmatoxicoforensickineticist I am a - Pharmatoxicoforensick ineticist

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Page 1: Principles and Applications to Forensic Toxicology … · Principles and Applications to Forensic Toxicology Everything (or at Least Some Things) You Wanted to Know but were Afraid

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

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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 Unpublished work © 1998 Saguaro Technical Press, Inc.

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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.

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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.

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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.

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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

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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.

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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.”)

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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.

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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.

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(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.

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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

⋅=

⋅=

⋅=

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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

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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

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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

=

=

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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.

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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

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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 -

Page 19: Principles and Applications to Forensic Toxicology … · Principles and Applications to Forensic Toxicology Everything (or at Least Some Things) You Wanted to Know but were Afraid

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

Page 20: Principles and Applications to Forensic Toxicology … · Principles and Applications to Forensic Toxicology Everything (or at Least Some Things) You Wanted to Know but were Afraid

• 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