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INTRODUCTION
The measurement of drug concentrations in blood samples provides useful clinical information
on the relationship between drug administration and drug efficacy (or) toxicity1. In order to
ensure the efficacy and safety of drug therapy, the outcome of therapy must be monitored.
Therapeutic drug monitoring is useful to identify the causes of unwanted (or) unexpected
responses, prevent unnecessary diagnostic testing, improve clinical outcomes and even save
lives. Information obtained can be used to determine (or) adjust a dosage regimen, evaluate a
drug response, aid in the assessment of toxicity, check compliance, minimize the cost of
hospitalization and decrease the risk associated with medical-legal problems2.
THERAPEUTIC DRUG MONITORING (TDM):
Definition3:
It is the process of quantifying drug concentrations in patients and using these
measurements to design individualized dosing regimen (dose, formulation, route and frequency
of administration.
The aim of TDM is to obtain4:
(1) An increased proportion of serum drug concentrations (or assays) with in the therapeutic
range.
(2) Better therapeutic responses and
(3) A reduction in toxic effects.
TDM refers to the individualization of dosage by maintaining plasma (or) blood drug
concentrations with in a target range.
There are two major sources of variability between individual patients in drug response.
These are variation in the relation ship between:
(1) Dose and Plasma concentration (Pharmacokinetic variability)
(2) Drug concentration at the receptor and the response (Pharmacodynamic variability)
By adjusting doses to maintain plasma drug concentration with in a target range, variability in
the pharmacokinetic phase of drug action is greatly reduced.
A more appropriate description for the optimum use of drug concentration in clinical practice
is Target concentration intervention5.
The rational series of steps involved in achieving the desired outcome in an individual is known
as the target concentration strategy. These steps are
1. Select a target concentration.
2. Predict clearance and volume of distribution values for the patient based on population
pharmacokinetic parameters and observable individual characteristics.
3. Calculate loading dose and maintenance dose to achieve the target concentration.
4. Administer the doses and measure drug concentrations.
5. Use the measured concentrations to predict individualized values of clearance and volume of
distribution for the patient.
6. If appropriate, revise the target concentration for the individual based on the clinical
assessment.
The target concentration should be considered as a surrogate effect, i.e. a convenient substitute
for the desired therapeutic outcome. The target concentration can provide some assurance that
treatment is adequate even if the therapeutic benefit cannot be observed. By intervening with a
dose adjustment to achieve a specific target concentration, a major source of the variability in the
dose response relationship can be reduced, i.e. the variability in concentration when the same
dose is given to different people due to interindividual differences in pharmacokinetics. The
collaborative team approach encourages optimum use of the therapeutic skills of all health
professionals involved.
CONCEPT OF TDM
If there is a concentration-effect relationship, then the measurement of plasma or blood drug
concentration, appropriately sampled, with adequate dosing and clinical history available, allows
rational interpretation of results and subsequent dosage adjustment
WHEN SHOULD TDM BE USED AND FOR WHICH DRUGS
Therapeutic drug monitoring is indicated in clinical situations in which an expected
therapeutic effect of a drug has not been observed or in cases where drug toxicity related to high
toxic plasma drug concentration is suspected. The characteristics of drugs, which make them
suitable for, or make them require, therapeutic drug monitoring are
Marked pharmacokinetic variability
Compliance
Age- neonates, children, elderly
Physiology-gender, pregnancy
Disease- hepatic, renal, cardiovascular, respiratory
Drug interactions
Environmental influences on drug metabolism
Genetic polymorphism of drug metabolism
Concentration related therapeutic and adverse effects
Narrow therapeutic index
Defined therapeutic (target) concentration range
Desired therapeutic effect difficult to monitor
FACTORS AFFECTING THE THERAPEUTIC RESPONSE OF A PATIENT TO A CERTAIN
DRUG REGIMEN6
A number of factors may affect serum drug concentrations and need to be considered when
interpreting TDM results
Patient demographics
The Patient’s age, sex, body weight and ethnicity should be considered when interpreting TDM
results. Age, sex and lean body weight are particularly important for renally cleared drugs as
knowledge of these allows calculation of creatinine clearance. Ethnicity may be an important
consideration for TDM of some hepatically cleared drugs.
Dosage regimen and duration of therapy
For a drug, which has recently been commenced, sufficient time should elapse to allow steady
state to be achieved before TDM is performed. If loading dose has not been given, this means at
least 5 half - lives of the drug should elapse to allow steady- state to be achieved before TDM is
performed.
Sampling Time
The serum concentration of a drug depends on the time when the blood drawn for a TDM assay
was sampled in relation to the last dose.
The time and date of last dose, and the time and date of blood sampling therefore need to be
known for drugs with a short half - life. Samples should be drawn immediately before the next
dose i.e., a trough level. For drugs with long half-life, samples may be drawn at any time during
the post distribution phase once steady state has been achieved. As the time to reach peak
concentrations shows great variability, peak levels are not performed routinely in clinical
practice.
Patient compliance
If the concentration of the drug is lower than expected, the possibility
Of non-compliance should be considered before a dose increase is recommended. The simplest
way to check for non- compliance is to ask the Patient in a non-judgmental way about their
compliance.
Individual capacity to distribute/metabolize/excrete the drug
Patients with renal impairment have a reduced ability to excrete renally cleared drugs, and the
interpretation of TDM for renally cleared drugs such as digoxin and aminoglycosides should
always be made in the context of the patient’s renal function.
Altered protein binding
Conditions such as malnutrition or nephropathy may reduce the concentration of plasma
proteins. For drugs, which are strongly bound to plasma proteins, such as phenytoin, a reduced
albumin level may result in higher concentration of unbound (free) drug. The measurement of
both total drug concentration and free drug concentration can be useful in those situations.
Drug interactions
TDM results should be interpreted in the light of the patient’s concomitant drug therapy. For
example, patients on digoxin may have unexpectedly high digoxin serum concentrations and
develop digoxin toxicity if drugs such as amiodarone, Quinidine or verapamil are commenced
with out reduction in the digoxin dose. The serum concentrations of some hepatically cleared
drugs may be affected by the commencement or cessation of drugs, which either induce or
inhibit hepatic cytochrome P450 isoenzymes.
Pathological factors
The patient’s comorbidites should be taken in to consideration when interpreting TDM results.
Conditions such as vomiting, diarrhea or inflammatory bowel disease can alter the absorption of
drugs, which in turn can alter serum drug concentrations. In patients having hepatic cirrhosis and
tuberculosis, administration of normal doses of rifampicin and isoniazid can lead to elevated
concentrations of the drugs along with increased hepatotoxicity. Malabsorption leads to
decreased serum concentrations.
Alcohol and Tobacco use
Chronic use of alcohol has been shown to cause non-specific hepatic microsomal serum
concentration resulting increased clearance and decreased serum concentrations of hepatically
cleared drugs such as phenytoin. Cigarette smoking increases the hepatic clearance of
theophylline and patients who have recently stopped smoking may be unexpectedly high
theophylline concentrations.
Medication or Sampling errors
In cases where the TDM result is incompatible with drug administration records, the possibility
of a medication or sampling error should be considered.
Laboratory errors
If a laboratory error is suspected, the laboratory should be contacted and asked to repeat the
assay.
CONCEPT OF TARGET CONCENTRATION INTERVENTION
Target concentration intervention (TCI) is proposed as an alternative conceptual strategy to
therapeutic drug monitoring (TDM). It is argued that the idea of a therapeutic range has limited
the interpretation of measured drug concentrations and diminished the anticipated clinical benefit
to patients by use of an oversimplified pharmacodynamic model. TCI on the other hand
embraces pharmacokinetic and pharmacodynamic concepts and uses the idea of a target effect
and associated target concentration to make rational individual dose decisions.
Rational therapeutics
Pharmacokinetics Pharmacodynamics
Target concentration intervention
Dose Effect Concentration
The aim of rational therapeutics is to achieve the correct effect with the correct dose. The
foundation of decision-making is based on the pharmacokinetics and pharmacodynamics, which
provide the rational principles to link dose and effect through drug concentration.
Target concentration intervention can now be placed at the Centre of the therapeutic triangle.
This strategy takes information about doses, concentrations and effects in an individual and
integrates them to estimate more precisely the pharmacokinetic and pharmacodynamic
parameters in that individual. These new values can then be used to predict the consequences of
future dosage decisions and lead to the selection of an appropriate dose to achieve the desired
effect, i.e. rational therapeutics.
Pharmacodynamics: The Concentration-Effect Relationship
Pharmacodynamics is the science linking concentration to effect by defining the maximum effect
of the drug (Emax) and the sensitivity of the target organ (EC50) .The selection of a target
concentration for a drug is based upon what is known of the relationship between concentration
and effects, both desired (therapeutic effects) and undesired (adverse effects).
Guidance in establishing the target concentration can be obtained from knowledge of Emax
and EC50 because these define the extent of the therapeutic response that can be expected and the
steepest part of the concentration- response curve where the most gain can be expected for the
smallest increase in concentration. The target concentration can only be defined on the basis of
pharmacodynamics; it is quite independent of the determinants of concentration, i.e. dose and
pharmacokinetics.
Pharmacokinetics: The Dose-Concentration Relationship
Pharmacokinetics is the science that links dose and concentration by defining the process of drug
distribution (volume of distribution) and elimination (clearance). The selection of dose needed to
achieve the target concentration rests upon pharmacokinetics. The elementary principles (loading
dose, maintenance dose rate) of pharmacokinetics relating dose to concentration emphasize that a
major goal of target concentration intervention is to estimate the volume of distribution and
clearance in an individual. Once this has been done, other useful derived parameters such as the
half-life can be obtained to assist in determining appropriate dosage intervals or the time needed
to reach steady state.
Therapeutic Range and Target Concentration7
The concentration of drug required in the plasma to produce adequate therapeutic response has
been established for several drugs and is referred to as therapeutic range of drug. The
concentration below this range is referred to as sub therapeutic range and the concentration
higher are referred to as being in the toxic range.
Sheiner & Tozer proposed the target concentration strategy (TCS) as an algorithm for rational
dose individualization but this idea has rarely been recognized. The essential feature of the
algorithm is to use a target concentration rather than a range. The goal is to achieve this target
using initial doses based on typical pharmacokinetic parameters for the individual predicted from
patient specific factors (covariates) such as body size and renal function. Drug concentration
measurements are used solely to individualize the pharmacokinetic parameters in order to predict
future dosing. TCS does not aim to have the measured concentration equal to the target
concentration. This is a key distinction from TDM, which aims to have the measured
concentration within the therapeutic range. TCS explicitly uses a PK model to understand what
makes the patient an individual while TDM offers no direct guidance and leaves the prescriber to
use any method to get the concentration within range.
Drugs for which TDM is used commonly
Amiodarone Gentamicin
Amikacin Impiramine
Amytryptylline Phenytoin
Carbamazepine Sodium Valproate
Cyclosporine Theophylline
Digoxin Vancomycin
Fluoxetin
ANALYTICAL METHODS USED TO CARRY OUT TDM
The methods used for the estimation of drugs in a TDM can be classified into three groups.
1) CHROMATOGRAPHY METHODS
a) High Performance Liquid Chromatography (HPLC)
b) Gas Chromatography (GC)
2) IMMUNOLOGICAL METHODS
a) Enzyme Immunoassay (EIA)
b) Cloned Enzyme Donor Immunoassay (CEDIA)
c) Enzyme Linked Immunosorbent Assay (ELISA)
d) Enzyme Multiplied Immunoassay (EMIT)
e) Solid Phase Enzyme Immunoassay (SPEIA)
f) Fluorescence Polarization Immunoassay (FPIA)
g) Substrate Linked Fluorescence Immunoassay (SLFIA)
h) Radial Partition Immunoassay (RPIA)
3) ONE STEP- DRY CHEMISTRY
This uses dry membrane antibody-coated cellular strips.
POPULATION PHARMACOKINETICS8
Population pharmacokinetics is the study of the sources and correlates of variability in plasma
drug concentrations between individuals representative of those in whom drug will be used
clinically when relevant dosage regimens are administered. Certain patient path physiological
features can regularly alter dose concentration relationships. Population pharmacokinetics seeks
to identify the measurable pathophysiological factors that cause changes in the dose-
concentration relationship and to what degree so that the appropriate dosage can be
recommended.
A conceptual framework within which we can provide a more formal definition of population
kinetics is provided by a so-called hierarchical population model (also called a population model,
a mixed effects model, or a random-effects model). At the first level of the hierarchy, such a
model views pharmacokinetic observations in an individual (such as concentrations of drug
species in biological fluids) as arising from an individual probability model, whose mean is
given by a pharmacokinetic model (e.g., a biexponential) quantified by individual-specific
parameters, which may vary according to the value of individual-specific time-varying
covariates. The variance of the individual PK observations is also modeled, and the parameters
of this model are additional individual-specific PK parameters.
At the second stage of the hierarchy, the individual parameters are regarded as random variables
and the probability distribution of these (often only the mean and variance) is modeled as a
function of individual-specific covariates. These models and their parameter values are what we
mean by a the population kinetics of a given drug, while the use of study designs and data
analysis methods designed to elucidate population PK models and their parameter values is what
is meant by the population pharmacokinetic approach.
The population pharmacokinetic approach offers the possibility of gaining integrated information
on pharmacokinetics not only from relatively sparse data, but also from dense data (or from a
combination of dense and sparse data) obtained from subjects. The approach allows the analysis
of data from a variety of unbalanced designs as well as from studies that are normally excluded
because they do not lend themselves to the usual forms of pharmacokinetic analysis, such as
concentration data obtained from pediatric and elderly patients, or from data obtained during the
evaluation of the relationships between dose or concentration and efficacy or safety.
The subjects of pharmacokinetic studies are usually healthy volunteers or highly selected
patients. Traditionally, the average behavior of a group (i.e., the mean plasma concentration-time
profile) has been the main focus of interest. Interindividual variability in pharmacokinetics is
viewed by many incorrectly as a nuisance factor that has to be overcome, often through complex
study designs and control schemes, and reduced through restrictive inclusion criteria. Study
design and selection of volunteers that are rigidly standardized so that they are as homogeneous
as possible are typical features of pharmacokinetic investigations. These studies, therefore, are
often performed under artificial conditions that do not represent the intended clinical use of the
drug.
The population pharmacokinetic approach encompasses some or all of the following features
It seeks to obtain relevant pharmacokinetic information in patients who are representative of
the target population to be treated with the drug.
It recognizes variability as an important feature that should be identified and measured
during drug development or evaluation.
It seeks to explain variability by identifying factors of demographic, path physiological,
environmental, or drug-related origin that may influence the pharmacokinetic behavior of a drug.
It seeks to quantitatively estimate the magnitude of the unexplained part of the variability in
the patient population.
The magnitude of the unexplained (random) variability is important because the efficacy and
safety of a drug may decrease as unexplainable variability increases. Drug levels outside the
target range become more likely, the greater the uncompensated variability in the relationship of
dosage to steady state drug concentration. In addition to interindividual variability, the degree to
which steady state drug concentrations in individuals typically vary about their long-term
average is also important. Concentrations appear to vary due to inexplicable day-to-day or week-
to-week kinetic variability and due to errors in concentration measurement. Estimates of this
kind of variability (residual intrasubject, interoccasion variability) are important for therapeutic
drug monitoring using the empiric Bayes approach. The knowledge of the relationship between
concentrations, response, and physiology is essential to design dosing strategies for rational
therapeutics that may not necessarily require therapeutic drug monitoring.
II. POPULATION METHODS
This discussion of population methods focuses on methods that provide estimates of some or all
of the components of variability.
A. The Two-Stage Approach
The usual method of pharmacokinetic data analysis is the two-stage approach. The first stage is
the estimation of pharmacokinetic parameters through nonlinear regression using an individual’s
experimental data (data rich situation). Individual estimates obtained in the first stage serve as
input data for the second-stage calculation of descriptive summary statistics on the sample,
typically mean vector and variance-covariance matrix. Analysis of dependencies between
parameters and covariates using classical statistical approaches (linear stepwise regression,
covariance analysis, cluster analysis) may be included in the second stage. The standard two-
stage approach, when applicable, can yield adequate estimates of population characteristics.
Mean estimates of parameters are usually unbiased, but the random effects (variance and
covariance) are likely to be overestimated in all realistic situations. Refinements have been
proposed to improve the standard two-stage approach by bias correction for the random effects
covariance and differential "weighting" of individual data according to its quality and quantity.
B. The Population Approach
The population approach in the context of drug evaluation developed from a recognition that, if
pharmacokinetics and pharmacodynamics were to be investigated in patients, pragmatic
considerations dictated that data should be collected under less stringent and restrictive design
conditions. Considering the complete sample, rather than the individual as a unit of analysis, the
population method of analysis (i.e., analysis according to a hierarchical random effect model)
aims to estimate the distribution of the parameters and their relationships with covariates. The
approach uses individual pharmacokinetic data of the observational (experimental) type, which
are unbalanced and fragmentary, in addition to or instead of conventional pharmacokinetic data
from traditional pharmacokinetic studies characterized by rigid and extensive design. Analysis
according to a hierarchical (non-linear) random effects model9 provides estimates of population
characteristics that define the population distribution of the pharmacokinetic (and/or
pharmacodynamics) parameters. In the mixed-effects modeling context, the collection of
population characteristics is composed of population typical values and population variability
values (generally the variance-covariance matrix). In sparse data situations where estimates of
individual parameters are, a priori, out of reach, an original one-stage or population estimation
approach is required. A population analysis of pharmacokinetic data, therefore, consists of
estimating directly the parameters of the population from the full set of individual concentration
values. The individuality of each subject is maintained and accounted for, even when raw data
are sparse.
III. WHEN TO PERFORM A POPULATION PHARMACOKINETIC STUDY AND
ANALYSIS
The population approach can help increase knowledge of the quantitative relationships between
drug input patterns, patient characteristics, drug disposition, and responses. The population
approach may be used to estimate population parameters of a response surface model in phases 1
and 2B of clinical drug development, where information is gathered on how the drug will be
used in subsequent stages of drug development and after release. The population approach may
increase the efficiency and specificity of drug development by suggesting more informative
designs and analyses of experiments. Application of the population approach to phase 1 and
perhaps much of phase 2B, where patients are sampled extensively, does not necessarily involve
complex methods of data analysis. The two-stage methods can be used to analyze the data, and
standard regression methods can be used to model dependence of parameters on covariates.
Alternatively, the data from individual studies can be pooled and analyzed using the nonlinear
mixed-effects modeling approach.
The population approach can also be applied to phases 2A and 3 of drug development to gain
information on drug safety (efficacy) and to gather additional information on drug
pharmacokinetics (and pharmacodynamics) in special populations, such as the elderly. It is also
useful in post marketing surveillance (phase 4) studies. Studies performed in phase 3 and 4 of
clinical drug development lend them to the use of the full pharmacokinetic screen study design.
V. STUDY DESIGN AND EXECUTION
Certain preliminary pharmacokinetic information should be known before any population
pharmacokinetics study is undertaken. The drugs major elimination pathways in humans should
be known. Preliminary studies should have established the basic model describing the
pharmacokinetics of the drug. The latter is important because the sparse data collected during
population pharmacokinetic studies may not provide adequate information for the deduction of a
pharmacokinetic model. In addition, a sensitive and specific assay (see Assay section) capable of
measuring all species (parent drug and metabolites) of clinical relevance should be available
before a population pharmacokinetic study is undertaken. When properly performed, population
pharmacokinetic studies in patients combined with suitable mathematical/statistical analysis
(e.g., using nonlinear mixed-effects modeling) is a valid approach and, on some occasions, an
alternative to extensive studies.
A. Study Design
In the population pharmacokinetics context, there are two broad approaches for obtaining
information about pharmacokinetic variability: (a) trough screen (single or multiple) studies and
(b) full pharmacokinetic screen (experimental population pharmacokinetic) studies. They yield
an increasing amount of information.
1. Single-Trough Screen
A single blood sample is obtained from each patient at or close to the trough of drug
concentrations, shortly before the next dose, and a frequency distribution of plasma or serum
levels in the sample of patients are calculated. Provided that the sample size is large, that assay
and sampling errors are small, and that the dosing regimen and sampling times are identical for
all patients, a histogram of such a trough screen gives a fairly accurate picture of the variability
in trough concentrations in a target population. If these conditions are not met, such histograms
do not represent strict pharmacokinetic variability because the data include many other sources
of random fluctuation with significant contribution to the observed spread. When related with
therapeutic outcome and occurrence of side effects, such histograms can be useful to improve the
knowledge of the optimal concentration range of a given drug.
The relationships of patient characteristics to the trough levels can be explored by simple
statistical procedures such as multiple linear regressions. Although simple, the trough
(pharmacokinetic) screen will only yield information about oral clearance and no other
parameters of interest (e.g., apparent volume of distribution, half-life). Only qualitative, not
quantitative, information will be obtained. Components of variability C interindividual and
residual variability C cannot be separated. This method will identify, qualitatively,
pharmacokinetically relevant factors and their differences among subgroups (subpopulations).
When implementing this sampling strategy, the difficulty of getting patients and physicians to
adhere to the sampling strategy should be kept in mind. Compliance with at least the last two
doses before trough level measurement is adequate for this type of study, but the drug should be
dosed to steady state. Because of uncertainty in doses and samples, the method can only
reasonably be applied to drugs dosed at intervals less than or equal to one elimination half-life
unless timing of dose and of level can be assured, as in inpatient studies. Large numbers of
subjects would be needed for this type of study because the data would be noisy.
With this design, it is not advisable to contemplate measuring peak observations unless the drug
is given intravenously or is a certain type of sustained release formulation. The time for
achieving maximum concentration depends on rates of all processes of drug disposition and may
vary among subjects. Thus, the simple estimation of peak levels is subject to large uncertainty.
Sampling peak levels also yields information on variability of largely irrelevant kinetic processes
for drugs for which effects relate to steady-state mean concentrations, or the area under the
concentration curve.
2. Multiple-Trough Screen
In this design, two or more blood samples are obtained near the trough of steady-state
concentrations from most or all patients. In addition to relating blood concentrations to patient
characteristics, it is possible now to separate interindividual and residual variabilities. Since
patients are studied in greater detail, this design requires fewer subjects, and the relationships to
patient characteristics can be evaluated with higher precision. To estimate interindividual
variability of the oral clearance, nonlinear mixed-effects modeling should be used. When using
pharmacokinetic models for parameter estimation, a sensitivity analysis10 should be required to
fix a parameter such as absorption rate constant to estimate other parameters and to determine
the fixed parameter value that has the least effect on the estimation of the remaining parameters.
The drawbacks of the single-trough screen design apply here. Although the estimates of
intersubject and residual variability may or may not be biased, they may not be precise unless a
large number of patients are studied.
3. Full Pharmacokinetic Screen
With this approach, blood samples are drawn from subjects at various times (typically 1 to 6 time
points) following drug administration .The objective is to obtain, where feasible, multiple drug
levels per patient at different times. This approach permits an estimation of pharmacokinetic
parameters of the drug in the study population and an explanation of variability using the
nonlinear mixed-effects modeling approach. The full pharmacokinetic screen (experimental
population pharmacokinetic) study should be designed to explore the relationship between the
pharmacokinetics of a drug and demographic/pathophysiological features of the target population
(with its subgroups) for which the drug is being developed. A full pharmacokinetic screen study
requires careful design considerations. Population pharmacokinetic analysis is useful for looking
at influences of pathophysiological conditions on parameters of a model with a well-established
structure. The qualitative aspects of the model should be well known before embarking on a
population study.
The objective for carrying out a population pharmacokinetic study should be clearly defined
since this will determine the study design. When designing a population pharmacokinetic study,
the practical limitations of sampling times, number of samples/subject, and number of subjects
should be considered. Preliminary information on variability from pilot studies make it possible,
through simulation, to anticipate certain fatal study designs as well as informative ones.
Simulation studies can optimize design features for accurate and precise estimation of population
pharmacokinetic parameters. Optimization of sampling design is critical to efficient experimental
design when there are severe limitations on the number of subjects and/or samples/subject, as in
pediatrics and the elderly .The use of informative study designs for population pharmacokinetic
studies that yield informative data is encouraged .The use of Bayesian designs for pediatric
patients where adult data may serve as informative priors may be appropriate. Such a study
should include enough patients in important subgroups to ensure accurate and precise parameter
estimation and the detection of any subgroup differences.
B. Importance of Sampling Individuals on More Than One Occasion
The variance of an individuals PK observations about the individual-specific PK model on a
given occasion (i.e., the intra-individual variability; see Introduction) can conceptually be
factored into two components: variability of PK observations due to variability of the PK model
from occasion to occasion (inter-occasion variability), and variability of PK observations about
the individual PK model appropriate for the particular occasion (noise; PK model
misspecification). To be sure, some inter-occasion variability may be explained by inter-occasion
variation in individual time-varying covariates, but to the extent that it is not, it represents, along
with the noise, the irreducible uncertainty in predicting, and hence controlling drug
concentrations. Drugs with narrow therapeutic indices and large inter-occasion variability, for
example, will be very difficult to control. If a population PK study consists of PK observations
solely from individuals each studied on only a single occasion, the inter-occasion variability will
appear incorrectly in the inter-individual variability term and not in the intra-individual
variability term. This may lead to inappropriate optimism about the ability to control individual
therapy within the therapeutic range by using feedback (e.g., therapeutic drug monitoring, or
simply adjusting dose according to observed drug effects), and also to a fruitless search for inter-
individual covariates that might explain the spuriously inflated) inter-individual variability. It is
of utmost importance to avoid this artifact by ensuring that at least a moderate subset of subjects
contributing data to a population PK study contributes data from more than one occasion. Indeed
if this is done, one may hope to separately estimate the components of intra-individual
variability.
C. Simulation
Simulation of a planned study offers a powerful tool for evaluating and understanding the
consequences of different study designs. Shortcomings in study design result in the collection of
uninformative data. Simulation can reveal the effect of input variables and assumptions on the
results of a planned population pharmacokinetic study. Simulation allows study designers to
assess the consequences of the design factors chosen and the assumptions made. Thus,
simulation enables the pharmacometrician to better predict the results of a population study and
to choose the study design that will best meet the study objectives. It is important to simulate a
population pharmacokinetic study using alternative study designs to determine the most
informative design, given the study objectives, before initiating such studies. Simulation is a
useful tool to provide convincing objective evidence of the merits of a proposed study design and
analysis.
D. Study Protocol
Two types of protocol may be considered depending on the setting in which a population
pharmacokinetic study is to be performed. If it is added on to a clinical trial (as can be envisaged
in most situations), the population study should be carefully interwoven with the existing clinical
protocol to ensure that it does not compromise the primary objectives of the study. Every effort
should be made to convince investigators of the relevance of including a population
pharmacokinetic study. Graphical displays of simulation results may help to achieve this
objective. In addition, a population pharmacokinetic study protocol should be written since a
population study can also be defined as evaluating data from existing data and/or data coming
from more than one study. When a population study is a stand-alone study, a comprehensive
protocol should be prepared. The population pharmacokinetic study as part of the clinical
protocol and the population pharmacokinetic study protocol are discussed briefly.
i) Clinical Protocol
The objectives of the population pharmacokinetic study should be clearly defined. These
objectives should be secondary to the primary clinical study objectives or primary when they
would not compromise the study in question. The criteria for sampling subjects and methods for
data analysis (described in the population pharmacokinetic study protocol) should be clearly
stated. The data to be used for population analysis should be defined, including patients and
subgroups to be used and covariates to be measured. The sampling design should be specified
and any subgroup stratification should be defined. In a multicenter trial, it may sometimes be
necessary to obtain extensive data from some centers and sparse data from others. This type of
data collection can be useful for informative data analysis protecting against model
misspecification and it should be specified in the protocol. The data analysis plan should be
described in advance in the protocol as accurately as possible. Statements such as a
pharmacokinetic screen will be performed or a data will be analyzed using NONMEM are
inappropriate because they do not convey information on the study objective or how the analysis
will be carried out.
If possible, special case report forms that can be easily understood by investigators should be
designed to meet the needs of the pharmacokinetic evaluation.
ii) Population Pharmacokinetic Study Protocol
The practical details of the pharmacokinetic evaluation should be described in a population
pharmacokinetic study protocol, although the principles may be specified in the clinical study
protocol in a general way. The primary (same as that in the clinical protocol) and secondary
objectives should be clearly stated. The secondary objectives should be those that enable the data
analyst to search for the unexpected, after the primary objectives have been addressed. The
sampling design, data assembly, data checking procedure, and procedures for handling missing
data and data anomalies should be clearly spelled out in the protocol. The data to be used for
population analysis should be defined, including patients and subgroups to be used and
covariates to be measured. The sampling design should be specified and any subgroup
stratification should be defined11. Real-time data assembly would permit population
pharmacokinetic data analysis to be performed before the end of a clinical trial and would make
it possible to include the results in the filing of the new drug application (NDA). If drug-drug
interactions are to be characterized, the protocol should prespecify whether to determine the
effect of the presence or absence of a specific concomitant medication or the total daily dose of
the concomitant medication or the plasma concentration of the potentially interacting medication.
If food effect is to be evaluated, the time of sampling in relation to food intake, and the
composition of food, should be specified in the protocol. Also, the procedure for analyzing the
data (and validation when appropriate) should be specified.
Distinguishing between clinically relevant and statistically significant covariates is important.
Time variant covariates represent particular problems. In this case, several measurements should
be made during the course of the study and, if this information is found to be incomplete, model-
based techniques may be used for imputation between available data (See Handling of Missing
Data). This also applies to time invariant covariates. Sensible methods of dealing with missing
data should be predefined in the data analysis plan of the protocol. The issue of interoccasion
variability should be recognized and addressed in long-term studies. It is understandable that
population pharmacokinetic data analysis, as a modeling exercise, cannot be planned to the
fullest detail. However, the protocol should include study objectives; patient inclusion/exclusion
criteria and pharmacokinetic evaluability criteria; sampling design; data handling and checking
procedures; initial assumptions for modeling; a list of possible covariates to be investigated and
the rationale for choosing them; and whether a sensitivity analysis and a validation procedure is
envisaged.
E. Study Execution
A population pharmacokinetic study should be conducted according to current good clinical
practice and good laboratory practice standards. The sampling strategy and the recording of
samples should be part of good clinical practice and the handling of samples part of good
laboratory practice. Error in recording sampling times relative to dosing history could result in
biased and imprecise parameter estimates, depending on the nature and degree of the error. Every
effort should be made to ensure that study subjects and clinical investigators comply with study
protocol. To improve compliance, the protocol should not be overly complicated and blood-
sampling times should be convenient to both clinical staff and patients. The necessity of blood
sampling should be carefully explained to patients and investigators. Instructions provided to the
investigators should be clear and concise. Adequate monitoring by the sponsor while the study is
ongoing should back up these measures. Adequate resources should be available for optimal
sample preparation, storage at the investigator site, and transportation and storage of biological
samples prior to analysis.
Noncompliance with drug intake can be a source of confounding and lead to inappropriate
interpretation of study results. Special care should be taken to use methodologies that are as
objective as possible to reconstruct dosage history. Communication between all parties involved
is essential for the successful conduct of a population study, especially if the study is part of a
large-scale clinical trial.
VI. ASSAY
Correct evaluation of pharmacokinetic data depends on the accuracy of the analytic data
obtained. Clinical investigators and their staff should be educated on the importance of sample
timing, recording, proper labeling, and handling of samples. The accuracy of analytical data
depends on the criteria used to validate the method. Consequently, drug and/or metabolite(s)
stability, assay sensitivity, selectivity, recovery, linearity, precision, and accuracy should be
carefully scrutinized before samples are analyzed. The importance of using validated assay
methods for analyzing pharmacokinetic data cannot be over emphasized.
VII. DATA HANDLING
A. Data Assembly and Editing
Real-time data assembly prevents the problems that generally arise when population
pharmacokinetic data are stored until the end of a clinical trial. Real-time data assembly permits
an ongoing evaluation of site compliance with the study protocol and provides the opportunity to
correct violations of study procedures and policy. Evaluation of pharmacokinetic data can
provide the data safety monitoring board with insights into drug exposure safety evaluations and
drug-drug interactions. Adequate policies and procedures should be in place for studying blind
maintenance. Real-time data assembly creates the opportunity for editing the concentration-time
data, drug dosing history, and covariate data necessary to meet the pharmacokinetic objectives of
a clinical trial Data assembly to create a population pharmacokinetic database after study
completion may result in delays that are incompatible with the time course of the development
program. It is important, therefore, to specify in the study protocol the use of real-time data
analysis.
Data editing means using a set of procedures for detecting and correcting errors in the data. The
procedures should be planned before study initiation and predefined in the study protocol.
Criteria for declaring data usable or unusable (e.g., time of blood sampling missing, dosing
information with no associated concentrations, concentrations with missing dosing information)
should be spelled out in the study protocol.
B. Handling of Missing Data
After assembling data for population analysis, the issue of any missing covariate data should be
addressed. Missing data represent a potential source of bias. Thus, every effort should be made to
fulfill the protocol requirements concerning the collection and management of data. Deletion of
subjects with missing covariates can yield biased and imprecise parameter estimates. Although
caution should be taken when imputing missing values, it is usually better to estimate and impute
a subject missing covariate data values than to delete that subject from the data set. Simple
methods of imputation include the use of median, mean, or mode for missing values. These
methods are biased and inefficient when predictors are correlated. Using maximum likelihood
procedures (i.e., deriving regression models) for predicting each predictor from all other
predictors is a better method. Alternatively, where imputation across a time series is possible,
such a method should be used12. Imputation procedures should be described, including a detailed
explanation of how such imputations were done and of the underlying assumptions made. The
sensitivity of the results of the analysis to the method of imputation of missing data should be
tested, especially if the number of missing values is substantial. So-called multiple imputation, in
which several imputed data sets are analyzed, can be used, where conclusions are of borderline
significance, yet of clinical importance, to remove the optimistic bias from estimates of precision
caused by imputing data and treating it as though it were actually observed.
C. Outliers
The statistical definition of an outlier is, to some extent, arbitrary. The reasons for declaring a
data point to be an outlier should be statistically convincing and prespecified in the protocol. Any
physiological or study-related event that renders the data unusable should be explained in the
study report. Because of the exploratory nature of population analysis, it may be that the study
protocol did not specify a procedure for dealing with outliers. In such a situation, model building
should be performed on the reduced data set (i.e., data set without the outliers) as long as the
conclusions are appropriately restricted to the limited population defined by the outlier-removal
procedure. Including extreme outliers is not a good practice when using least squares or normal-
theory type estimation methods, as these inevitably have a disproportionate effect on estimates.
Also, it is well known that for most biological phenomena, outlying observations are far more
frequent than suggested by the normal distribution (i.e., biological distributions are heavy-tailed).
Some robust methods of population analysis have recently been suggested, and these may allow
outliers to be retained, without giving them undue weight.
D. Data Type
Two types of data can be used in population analysis: experimental data and observational (or
population) pharmacokinetic data. Experimental data arise from traditional pharmacokinetic
studies characterized by controlled conditions of drug dosing and extensive blood sampling.
Population pharmacokinetic data are collected, most often, as a supplement in a study designed
and carried out for another purpose. The data are characterized by minimal control and few
design restrictions: the dosing history is subject specific, the amount of pharmacokinetic data
collected from each subject varies, the timing of blood sampling in relation to drug
administration differs, and the number of samples per patient, typically 1 to 6, is small.
E. Data Integrity and Computer Software
Data management activities should be based on established standard operating procedures. The
validity of the data analysis results depends on the quality and validity of methods and software
used for data management (data entry, storage, verification, correction, and retrieval), and
pharmacostatistical processing. Documentation of testing procedures for the computer software
used for data management should be available. It is crucial that the software used for population
analysis be adequately supported and maintained.
VIII. DATA ANALYSIS
Population modeling can potentially be used in several phases of new drug development,
including the planning, design, and analysis of studies in the exploratory and confirmatory stages
of new drug development. Thus, the protocol should describe the pharmacokinetic models to be
tested. Careful documentation of modeling efforts, data visualization, model validation (when
appropriate), and data listing should be provided.
Population pharmacokinetic data analysis can be carried out in three interwoven steps: (a)
exploratory data analysis, (b) population pharmacokinetic model development, and (c) model
validation. The data analysis plan should be clearly defined in the study protocol.
A. Exploratory Data Analysis
Exploratory data analysis isolates and reveals patterns and features in the population data set
using graphical and statistical techniques. It also serves to uncover unexpected departures from
familiar models. An important element of the exploratory approach is its flexibility, both in
tailoring the analysis to the structure of the data and in responding to patterns that successive
analysis steps uncover.
Most population analysis procedures are based on explicit assumptions about the data, and the
validity of the analyses depends upon the validity of assumptions. Exploratory data analysis
techniques provide powerful diagnostic tools for confirming assumptions or, when the
assumptions are not met, for suggesting corrective actions. Without such tools, confirmation of
assumptions can be replaced only by hope. Exploratory data analysis should be coupled with
more sophisticated population modeling techniques in the analysis of population
pharmacokinetic data13. Exploratory data analysis performed should be well described in the
population report.
B. Population Pharmacokinetic Model Development
1. Objectives, Hypotheses, and Assumptions
The objectives of the analyses should be clearly stated. The hypotheses being investigated should
be clearly articulated. It is recommended that all known assumptions inherent in the population
analysis be explicitly expressed.
2. Population Model Development
The steps taken (i.e., sequence of models tested) to develop a population model should be clearly
outlined to permit the reproducibility of the analysis. The criteria and rationale for model
building procedures dealing with confounding, covariate, and parameter redundancy should be
clearly stated. The criteria and rationale for model reduction to arrive at the final population
model should also be clearly explained.
3. Reliability of Results
The reliability of the analysis results should be checked using diagnostic plots; confidence
intervals (standard errors) for key parameters should be checked using nonparametric techniques
(such as the jackknife and the profile likelihood plot (mapping the objective function. This is
appropriate because the possibility of biased results is increased by the complexity of the
population models and by the sparse individual data available. The nonlinearity of the statistical
model and ill conditioning of a given problem can produce numerical difficulties and force the
estimation algorithm into a false minimum. Because the maximum likelihood procedure is
sensitive to bizarre observations, it may be necessary to check the stability of the model .It is
important to evaluate the quality of the results of a population study/analysis for robustness.
Evaluation for robustness may be approached by sensitivity analysis. The use of case deletion
diagnostics is also encouraged. Evidence of robustness renders the results reasonable and
independent of the analyst.
C. Model Validation
Validation can be defined as the evaluation of the predictability of the model developed (i.e., the
model form together with the model parameter estimates) with a learning or index data set on a
validation data set not used for model building and estimation. A model may be valid for one
purpose and not valid for another. The objective of validation is to examine whether the model is
a good description of the validation data set in terms of its behavior and of the application
proposed.
Validation depends on the objective of the analysis. Not all population models may need to be
validated. Population models developed for explaining variability (which does not require dosage
adjustment recommendation) and for providing descriptive information for labeling may be
tested for stability only14. Also, population pharmacokinetic models developed as part of a
population pharmacokinetic /pharmacodynamics model may not need to be validated. However,
the predictive performance of a population pharmacokinetic model developed for dosage
recommendation as part of labeling should be tested. In such a situation, model validation
procedure should be an integral part of the protocol.
1. Types of Validation
There are two types of validation: external and internal. External validation is the application of
the developed model to a new data set (validation data set) from another study. Internal
validation refers to the use of data-splitting and resampling techniques (cross-validation and
bootstrapping) for validation purposes. External validation provides the most stringent method
for testing a developed model.
Data-splitting
For testing predictive performance, data splitting is an effective method of model validation
when it is not practical to collect a new set of data to test the model. The disadvantage of this
method is that, in the area of linear regression, the predictive accuracy of the model is a function
of the sample size resulting from the data-splitting .To maximize the predictive accuracy, it is
recommended that the entire sample be used for model development and assessment. Data
splitting may not validate the final model if one desires to recombine the index and validation
data sets to derive a refined model for predictive purposes. However, if data splitting is to be
used, a random subset of the data (two-thirds, i.e., the index data set) should be used for model
building and the remaining data should be used for model validation. At the end of the exercise,
the index and validation data sets should be pooled and the final population model fitted to the
data to determine the appropriateness of each covariate retained in the final model.
Cross-validation
Cross-validation is repeated data-splitting. The benefits of cross-validation over data-splitting are
that (1) the size of the model development database can be much larger so that less data are
discarded from the estimation process and (2) not relying on a single sample split reduces
variability. Due to high variation of accuracy estimates, cross-validation is inefficient when the
entire validation process is repeated.
Bootstrapping
Bootstrapping, an alternative method of internal validation, has the advantage of using the entire
data set for model development. Sample size is critical in pediatric settings where ethical and
medical concerns limit recruitment into studies. The bootstrap resampling procedure can be
useful for evaluating the performance of a population model when there is no test data set.
2. Validation Methods
The issue of validation of population models remains unresolved. The advantages and
disadvantages of methods addressed in the literature and of methods used in applications have
been discussed above. The data analyst should justify the method he/she chooses. Although the
science of validation of population models is still evolving, consideration will be given to well-
described innovative methods of model validation.
Prediction Errors on Concentrations
This is calculated as the difference between observed and model-predicted concentrations. The
mean prediction error is calculated and used as a measure of accuracy and the mean absolute
error (or root mean square error) is used as a measure of precision.
This method can be used when only one sample per subject is obtained. When more than one
observation is obtained per subject, the method is inadequate because prediction errors are not
independent if several concentrations per subject are available .The method does not take into
account the correlation of observations within subjects.
Standardized Prediction Errors
This method15 takes into account variability and correlation of observations within an individual.
The mean standardized prediction error and the variance are calculated, and a t-test
(appropriately a z-test) performed to determine whether the mean is significantly different from
zero and the standard deviation approximates 1. Confidence intervals about the standard
deviation of the standardized prediction errors can be constructed. This test performed on the
mean of the standardized prediction errors incorrectly assumes that the estimates for the
population parameter values are given without error. The use of the approach is discouraged.
Validation through Parameters
This method avoids the problems encountered in prediction error of concentrations by
performing validation with model parameters. Model parameters are predicted from the
validation data set with or without covariates and bias and precision are calculated for the
predictions.
Plot of Residuals against Covariates
A simple plot of residuals obtained by freezing the final model and predicting into a validation
data set against covariates can yield information on the clinical significance of the model in
terms of a covariate or subpopulation.
A Plot of Residuals against Covariates and Validation through Parameters.
These methods are useful approaches for examining the predictive performance of population
models. When there is no test data set, the bootstrap approach should be used: the mean
parameter values obtained by repeatedly fitting the final population model to at least 200
bootstrap replicate data sets are compared to the final population model parameter estimates
obtained without bootstrap replication.
Posterior Predictive Check
A new technique, the so-called Posterior Predictive Check, may prove useful in determining
whether important clinical features of present and future data sets are faithfully reproduced by
the model.
IX. POPULATION ANALYSIS REPORT
The report should contain the following: (a) introduction, (b) objectives, hypotheses, and
assumptions (c) assay, (d) data, (e) data analysis methods, (f) results, (g) discussion,
(h) application, (I) appendix, and (j) electronic format files.
A. Introduction
The introduction should briefly state the general intent of the analysis. It should include enough
background information to place the analysis in its proper context within the drug=s clinical
development and to indicate any special features of a population pharmacokinetic study.
B. Objectives, Hypotheses, and Assumptions
The objectives of the analysis, and study where applicable, should be stated. In addition to the
primary objective, any secondary objectives should be explicitly stated. If the analysis was
performed as a result of the implementation of a study protocol, the report should note whether
the objectives were preplanned or were formulated during or after study completion. This is not
necessary for the analysis of pooled data. The assumptions made and the hypotheses tested
should be clearly stated in the report.
C. Assay
This section should contain a description of the assay method(s) used in quantitating drug
concentrations. Assay performance (quality control samples) should also be included. The
validity of the method(s) should also be described.
D. Data
Where data are pooled for analysis, the report should state the studies from which the data were
pooled. The data set should be part of the appendix to the report. The report should contain the
response variable and all covariate information and explain how they were obtained. The report
should include a description of the sampling design used to collect the plasma samples and a
description of the covariate, including their distributions and the accuracy and precision with
which they were measured. An electronic copy of the data set should be submitted. Data quality
control and editing procedures should be described in this section.
E. Data Analysis Methods
This section should contain a description of the treatment of outliers and missing data (where
applicable), and a detailed description of the criteria and procedures for model building and
reduction incorporating exploratory data analysis. A flow diagram(s) of the analysis performed
and representative control/command files for each significant model building/reduction step
should be provided
F. Results
The key results of the analysis should be compiled into comprehensible tables and plots. To aid
interpretation and application, a thorough description of the results should be provided. Complete
output of results obtained for the final population model and key intermediate steps should be
included.
G. Discussion
The report should include a comprehensive statement of the rationale for the model building and
reduction procedures, interpretation of the results, protocol violations, and any other relevant
information.
H. Application of Results
A discussion of how the results of the analysis will be used (e.g., to support labeling,
individualize dosage, support safety, or define additional studies) should be provided. In
addition, the use of graphics to communicate the application of a population model (e.g., for
dosage adjustment) is recommended.
I. Appendix
The appendix should contain the data set(s) used in population analysis. The output table from
the final model should be in this section, as well as any additional plots that are deemed
important. Where the analysis was performed as a result of a clinical study or a population
pharmacokinetic study, the study protocol should be included in the appendix.
J. Electronic Format Files
Data set and representative command files used for population analyses may be submitted as
ASCII files and/or PDF files with the filing of a new drug application. It is understood that data
format may be software specific. The Agency may, on some occasions, request that the data be
formatted in a manner that is compatible with another type of software. An electronic copy of the
report may also be a part of the submission. However, the submission of these data and reports in
electronic form does not eliminate the need to submit a paper copy.
X. LABEL
Where population model parameter estimates are included in the label, the total number of
subjects used for the analysis and the precision with which the parameters were estimated should
be included in the report. Where the results of the population analysis provide descriptive
information for the label, it should be stated that the information was obtained from a population
analysis. Information from population analyses used to characterize subpopulations should
include the total sample size used for the analysis and the proportion of subjects belonging to the
subpopulation.
ASTHMA:
DEFINITION:
An expert panel of the National Institutes of Health National Asthma Education and
Prevention Program (NAEPP) has defined asthma as a chronic inflammatory disorder of the
airways in which many cells and cellular elements play a role. In susceptible individuals,
inflammation causes recurrent episodes of wheezing, breathlessness, chest tightness and
coughing. Air flow obstruction often reversible either spontaneously or with treatment.
CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD):
The American Thoracic Society (ATS) defines COPD as a disease process involving
progressive chronic airflow obstruction because of chronic bronchitis,
emphysema (or) both.
Chronic bronchitis is defined clinically as excessive cough and sputum production on most days
for at least three months during at least two consecutive years.
Emphysema is characterized by chronic dyspnea resulting from the destruction of lung tissue
and the enlargement of air spaces.
Asthma and COPD Treatment with Theophylline:
Theophylline has been used in the treatment of asthma and chronic obstructive pulmonary
disease (COPD) for over 60 years and remains one of the most widely prescribed drugs for the
treatment of airway disease.
ASTHMA: Theophylline is used as bronchodilator in the symptomatic treatment of asthma. The
drug relieves the primary manifestation of asthma, including shortness of breath, wheezing and
dyspnea, and improves pulmonary function as measured by increased flow rates and vital
capacity. Theophylline also suppresses exercise- induced bronchospasm.
CHRONIC OBSTRUCTIVE PULMONARY DISEASE:
In the stepped–care approach to drug therapy for chronic obstructive pulmonary disease
(COPD), theophylline may be administered orally (as an extended- release preparation) as
alternative therapy in patients with mild COPD when inhaled β2-agonist bronchodilator are
unavailable (or) as an adjunct to short (or) long –β2 acting bronchodilator (e.g. Ipratropium, a
selective inhaled β2 agonist in patients with moderate to severe COPD who require additional
therapy because of in adequate response.
THEOPHYLLINE
Theophylline is a methyl xanthine used to treat bronchospasm including asthma and obstructive
airway disease.
Theophylline occurs as a white, odourless crystalline powder. The theophylline is slightly
soluble in water and in chloroform, very slightly soluble in ether.
PHARMACOLOGY
BRONCHODILATOR ACTION
Theophylline is a weak and non-selective inhibitor of Phosphodiesterase enzymes that break
down cyclic nucleotides in the cell, thereby leading to an increase intracellular
cAMP and cyclic guanosine 3¹, 5¹- monophosphate concentrations. It relaxes airway smooth
muscle by inhibition of PDE activity (PDE3, PDE4, and PDE5), but relatively high
concentrations are needed for maximal relaxation15. It is a potent inhibitor of adenosine receptors
in the bronchi resulting in relaxation of the smooth muscle.
ANTIINFLAMMATORY EFFECT
Theophylline has anti-inflammatory effects in asthma. In patients with nocturnal asthma, low-
dose theophylline inhibits the influx of neutrophils and to a lesser extent, eosinophils in the early
morning. In patients with asthma, low dose of theophylline reduce the number of eosinophils in
bronchial biopsies, bronchoalveolar lavage and induced sputum. Interleukin-10 has a broad
spectrum of anti-inflammatory effects, and its secretion is reduced in asthma. interleukin-10
release is increased by theophylline and this effect may be mediated via PDE inhibition.
OTHERS EFFECTS
Theophylline also increases sensitivity of the medullary respiratory centre to carbon dioxide, to
reduce apneic episodes. It prevents muscle fatigue, especially that of the diaphragm. It also
causes diuresis and cardiac and CNS stimulant16.
PHARMACOKINETICS
ABSORPTION
Theophylline is well absorbed from oral liquids and uncoated plain tablets, maximal plasma
concentrations are reached in 2 hrs for immediate release oral preparations and between 4 and 12
hrs after ingestion of sustained release preparations. Food may alter rate of absorption, especially
of some extended release preparations.
DISTRIBUTION17
Distributed throughout the extracellular fluids. Equilibrium between fluid and tissues occurs
with in an hour of an I.V loading dose. Average volume of distribution is 0.45L/kg
(Range 0.3 to 0.7 L/kg). Theophylline does not distribute in to fatty tissue but readily crosses the
placenta and is excreted in to breast milk. Approximately 40% is bound to plasma protein.
Therapeutic serum levels generally range from 10 to 20 mcg /ml.
METABOLISM
Metabolized in the liver to inactive compounds. Half-life is 7 to 9 hours in adults,
4 to 5 hours in smokers, 20 to 30hours in premature infants, and 3 to 5 hours in children.
EXCRETION
Theophylline is primarily eliminated by metabolism via the hepatic cytochrome P-450 mixed
function oxidase microsomal enzymes primarily the CYP1A2 Isoenzyme, with 10% of dose is
excreted in the urine unchanged. The other metabolites include 1.3-dimethyl uric acid, 1-
methyluricacid and 3-methylxanthine. But in neonates around 50% is excreted un changed, and a
large proportion is excreted as caffeine.
CONTRAINDICATIONS AND PRECAUTION
Contraindicated in patients with hypersensitivity to xanthine compounds, such as caffeine and
theobromine, and in those with active peptic ulcer and seizure disorders.
Use cautiously in the elderly; in neonates, infants under age 1,and young children; and in
patients with COPD, cardiac failure, corpulmonale, renal or hepatic disease, peptic ulcer,
hyperthyroidism, diabetes mellitus, glaucoma, severe hypoxemia, hypertension, compromised
cardiac or circulatory function, angina, acute MI, or sulfite sensitivity.
INTERACTIONS
DRUG-DRUG:
Allopurinol (high dose), calcium channel blockers, cimetidine, corticosteroids, erythromycin,
interferon, mexiletine, oral contraceptives, propranolol, quinolones, troleandomycin – May
increase serum levels of theophylline. Dose adjustment may be needed if use together can’t be
avoided.
Activated charcoal, barbiturates, ketoconazole, phenytoin, rifampicin – Decreased plasma
theophylline levels. Dose adjustment may be needed if use together can’t be avoided.
Beta blockers: Exert an antagonist pharmacological effect. Avoid use together.
carbamazepine, isoniazid, loop diuretics: May increase or decrease theophylline levels.
Dose adjustment may be needed if use together can’t be avoided.
Lithium: Increased excretion of lithium. Lithium dose may require dose adjustment. Monitor
patients carefully.
Drug- Herb- Cocoa tree: Possible inhibition of theophylline metabolism. Patient should avoid
ingesting large amounts of cocoa when using theophylline.
Guarana, caffeine: Additive CNS and CV effects. Avoid use together.
Drug–Food- Any food: Accelerates absorption. Advise patient to take drug on an empty
stomach.
Drug-Lifestyle: Smoking (cigarettes, marijuana): Increases elimination of theophylline. Monitor
theophylline response and serum levels. Dose adjustment may be required.
