As defined by F.H. Dost in 1953, Pharmacokinetics is a
science dealing with study of biological fate of drug &/or its
metabolite(s) during its sojourn within the body of a man or
animal, with the help of mathematical modeling.
In simple words it is the study of what body does to the drug.
The term Pharmacokinetics was coined by Torston Teorell.
It involves the study of ADME.
SCHEMATIC REPRESENTATION ADME
It refers to the relationship between drug concentration at the
site of action and the resulting effect, including the time
course and intensity of therapeutic and adverse effects.
In simple words it is the study of what drug does to the body.
IUPAC definition : Branch of pharmacology concerned with
pharmacological actions on living systems, including
reactions with and binding to cell constituents, and the
biochemical and physiological consequences of these
RECEPTOR OCCUPANCY MODEL
Given by Langley, hill and Clarke.
Based on law of Mass Action.
Drug effect is related to proportion of receptors occupied.
[DRUG] + [RECEPTOR] [DRUG][RECEPTOR]
Any drug that binds to a receptor and stimulates the
Has both affinity as well as intrinsic activity.
It has affinity to receptor but no intrinsic activity.
It prevents binding of agonist to receptor.
in my way!
Any drug that binds to a receptor and produces an
opposite effect as that of an agonist.
to that of
the true agonist
Produces a sub maximal response.
Affinity is there but intrinsic activity is less than agonist.
Oh!!!, I should
Have been here
Pharmacological response is not dependent on drug-receptor
complex concentration but rather depends upon rate of
association of drug and receptor.
LOCKAND KEY MODEL
Only a drug of specific chemical structure can bind with the
INDUCED FIT MODEL
When the drug binds to the receptor, it produces some
conformational change in the receptor which helps in better
fitting of the drug inside active site of receptor.
PK/PD modeling is a scientific mathematical tool which
integrates PK model to that of PD model.
PK model - describes the time course of drug concentration
in the plasma or blood.
PD model - describes the relationship between drug
concentration at site of action and effect.
PK/PD models use data derived from plasma drug
concentration vs. time profile and from the time course of
pharmacological effect to predict the Pharmacodynamics of
Result is summation of Pharmacodynamics and
Simple direct effect
Nonsteady-state & time-dependent
Effect of drug is direct.
Fast mechanism of action.
Rapid equilibrium exists
between site of action and
the sampling biofluids.
PD parameters are time
Drug effect is directly proportional to drug concentration.
Pharmacodynamically it is explained as:
E ∝ C …..(1)
E = S×C …..(2)
E = Effect of drug
C = Drug concentration
S = Slope obtained from E vs C graph
In case of baseline effect (E0), when the drug is absent, model
may be represented as:
E = E0 + S*C …..(3)
slope = S
E = E0 + S*C
y = c + mx
Model is simple and parameter estimation can be easily
performed by linear regression.
Applicable at low drug concentrations only
excludes the prediction of maximum effect
Relationship between central activity of diazepam and
plasma drug concentration
When the effect of drug is measured over a large range, the
relationship between concentration and effect is not linear
and may be curvilinear and log transformation is needed.
The log concentration-Effect is roughly linear in
concentration range of 20-80% of maximum Effect.
It is given by:
E = E0 + S*log C …(4)
E = effect
S = slope
It expands the initial part of the curve where response is
slowly making progression before it accelerates
It contracts the latter part of the curve where a large change
in concentration produces a slight change in response.
In middle part relationship is linear.
Unlike linear model it is applicable over large concentration
Pharmacological effect cannot be estimated when the
concentration is zero because of the logarithmic function.
Maximum effect cannot be predicted.
This model has been successfully used in predicting the
pharmacological activities of beta blockers and
This model incorporates the observation known as the law
of diminishing returns.
This law shows that an increase in drug concentration near
the maximum pharmacological response produces a
disproportionately smaller increase in the pharmacological
This model describes the drug action in the terms of :
E max (maximum effect)
EC50 ( the drug concentration that produces 50%
maximum pharmacological effect)
It mimics the hyperbolic shape of pharmacologic response
vs. drug concentration curve.
After maximum response (Emax) has reached, no further
increase in pharmacologic response is seen on increase in
concentration of the drug.
EC50 is useful for determining drug concentration that lies
within the therapeutic range.
In case, there is a baseline effect i.e. the measured
pharmacologic effect has some value in absence of drug (e.g.
blood pressure, heart rate, respiratory rate) then the equation
E0 = Pharmacologic effect (baseline activity) at zero
drug concentration in the body
It is a saturable process and resembles the Michaelis-Menton
A double-reciprocal plot of equation is used to linearize the
relation, similar to Lineweaver-Burke equation.
50 1 1
E C E
-1/ EC50 1/C
slope = EC50 / Emax
Maximum pharmacological response can be found out.
EC50 can be calculated (i.e., concentration needed to
produce half maximum response).
In case of highly potent drugs it is not possible to find the
maximum effect because test organisms die long before the
maximum effect is attained.
The method can be time consuming if maximum effect is
obtained at a very high concentration.
Bronchodilator activity of Theophylline is studied by this
Given by Hill.
It describes the pharmacologic response versus drug
concentration curve for many drugs that appear to be S-shaped
(i.e. Sigmoidal) rather than hyperbolic as
described by more simple Emaxmodel.
The equation for the sigmoid Emax Model is an extension
of the Emax Model:
n is an exponent describing the number of drug molecules
that combine with each receptor molecule.
When n=1, the Sigmoid Emax Model reduces to the Emax Model
A value of n>1 influences the slope of the curve and the
In the Sigmoid Emax Model, the slope is influenced by the
number of drug molecules bound to the receptor.
A very large n value may indicate allosteric or cooperative
effects in the interaction of the drug molecules with the
Cooperativity is the case when binding of substrate at on
binding site affects the affinity of other sites to their
n > 1
n = 1
E n < 1
Indirect effect of the drug.
The effect is not immediate.
Distribution of the drug is the rate limiting step.
Slow association and dissociation of drug with the
For some drugs, the pharmacologic response produced by
the drug may be observed before or after the plasma drug
concentration has peaked. Such drugs may produce
indirect or delayed response.
Drug distribution to the effect site may represent a rate-limiting
step for drugs in exerting their pharmacological
To account for this indirect or delayed response, a
hypothetical effect compartment has been postulated by
Holford and Sheiner.
It is not part of the pharmacokinetic model but is a
hypothetical pharmacodynamic compartment that links to the
plasma compartment containing drug.
It is because amount of drug entering this compartment is
considered to be negligible and is therefore not reflected in
pharmacokinetics of the drug.
V C1 Ve Ce Effect
Drug transfer from plasma to hypothetical effect compartment
takes place with first order rate constant.
Only free drug can diffuse into the effect compartment.
The pharmacological response of the drug depends on the rate
constant ke0 and the drug concentration in the effect
The amount of drug in the effect compartment after i.v. bolus
dose may be given by:
dDe k1eD1 ke0De
De = amount of drug in effect compartment
D1 = amount of drug in central compartment
ke0 = rate constant for drug transfer out of the effect
K1e = rate constant for drug transfer from plasma to effect
Integrating the equation we get:
0 1 kt k t
e e e e
Dividing by Ve ,
0 1 kt k t
e e e
V k k
The above equation is not very useful as parameters Ve and
k1e are both unknown and cannot be obtained from plasma
drug concentrations. Therefore assumptions are made.
Even though an effect compartment is present in addition
to the plasma compartment, this hypothetical effect
compartment takes up only a negligible amount of the
So plasma drug level still follows a one-compartment
After an IV bolus dose, the rate of drug entering and
leaving the effect compartment is controlled by k1e and
At steady state,
input = output
k1eD1 = keoDe …(12)
Dividing by VD yields the steady state plasma drug concentration
0 1 kt k t
substituting De in equation (14)
e e e
k D k
e e e e e
0 0 1
k V k k
kt k t
e D e
k D k
e e e e
V k k
kt k t
At steady state, C1 is unaffected by k1e but depends on
k and ke0.
C1 is the steady state concentration and has been used
to relate the pharmacokinetic effect of many drugs,
including some of delayed equilibrium between plasma
and effect compartment.
k and ke0 jointly determine the pharmacodynamic
profile of the drug.
Dynamic flexibility and adaptability.
The model accommodates the aggregate effects of drug
elimination, binding, partitioning and distribution.
Model represent in vivo pharmacologic event relating to
plasma drug concentration that clinician can monitor and
This model has been used to characterize the PK/PD of several
drugs (e.g. midazolam, pancuronium, alprazolam, etc.) whose
plasma concentrations could not be correlated with the effect
The indirect response model is based on the premise that the
drug response is indirectly mediated by either inhibition or
stimulation of the factors controlling either the production
(Kin) or the dissipation of response (Kout).
Indirect response modeling was first introduced by
Nagashima et al. for the anticoagulant effect of warfarin.
These models may be appropriate for various classes of
drugs, including histamine H2-receptor antagonists (such
as cimetidine) and oral hypoglycemic agents (such as
In the absence of drug, the rate of change in response over
time (dR/dt) can be described by a differential equation as
k k R
R = response
kin = zero-order rate constant for the production of
kout = first order rate constant for the dissipation of
Used in cases where endogenous mediators are involved in
the expression of the response.
TYPES OF INDIRECT RESPONSE MODELS
I. Inhibition of Kin
(Inhibition of production)
II. Inhibition of Kout
(Stimulation of response)
III. Stimulation of Kin
(Stimulation of production)
IV. Stimulation of Kout
(Dissipation of response)
K I t K R
K K I t R
K St K R
K K St R
S(t), I(t) – Stimulation and inhibition functions
1. H2-receptor antagonist: Inhibition of gastric secretion.
SIGNAL TRANSDUCTION MODEL
The pharmacological effects
of drugs may be mediated by a
transduction process, in
which the response measured
clinically involves multiple
steps removed from the initial
biochemical effect of the drug.
There are two major classes of receptors involved in signal
1.cell membrane receptors
Since cascade of steps is involved in signal transduction,
theoretically there should be delay between each step.
Owing to technical and research limitations at cellular and
molecular level, PD response vs. time relationship for every
step is difficult to obtain.
To characterize such delayed effects stochastic models with
transit compartments and transit times are employed.
This model has been used to characterize the
parasympathomimetic activity of scopolamine and atropine
Tolerance is characterized by a reduction in pharmacological
response after repeated or continuous drug exposure.
For some drugs, pharmacodynamic parameters like Emax and
EC50 may appear to vary over time, resulting in changes in
pharmacological response despite the presence of constant
concentrations at the effect site.
The complex mechanism of tolerance may involve:
receptor pool depletion
decrease in receptor affinity
The development of tolerance can have a significant impact
on the exposure-response relationship and, if not
recognized, can contribute to poor clinical outcome.
Pharmacokinetic/ pharmacodynamic modeling can be a
very useful tool to characterize the time course and
magnitude of tolerance development.
An increase in EC50 over time for Terbutaline which is
likely attributed to a decrease in the receptor number’
Development of tolerance to the acid inhibitory effect of
ranitidine. The derived model indicated that ranitidine
developed tolerance with increased EC50 by 100% within 6 –
10 hr after prolonged IV administration.
Many pharmacological responses are complex and do not
show a direct relationship between pharmacologic effect
and plasma drug concentration.
Some drugs have a plasma drug concentration and response
that resembles hysteresis loop.
Hysteresis is defined as ‘the retardation or lagging of an
effect behind the cause of the effect’.
An alternative definition would be ‘failure of one of two
related phenomena to keep pace with the other’.
Identical drug concentration can result in different
pharmacological response, depending on whether the plasma
drug concentration is on ascending or descending phase of the
Here response decreases with time.
If we take a concentration say (C1), it can be clearly seen
that the response at this concentration decreases from E2 to
E1 with passage of time
1.Fentanyl and Alfentanil
Explanation: These are opioid analgesics and have
high lipid solubility. Initially, with increase in plasma
concentration effect is increasing proportionally but
after some times effect decreases due to redistribution
Explanation: The diminished response is due to result
of cellular response and physiologic adaptation to
intense stimulation of drug.
Explanation: physical adaptation.
Explanation: Exhaustion of mediators.
Explanation: Increased metabolism.
Explanation: Loss of modulator binding site.
In the counterclockwise hysteresis loop, response increases
If we take a concentration say (C1), it can be clearly seen
that the response at this concentration increases from E1to
E2 with passage of time.
Explanation: Drug is highly bound to α1-AGP and
initial diffusion of drug into effect compartment is
Explanation: Slow movement of ionized compound
from capillaries to NMJ.
Explanation: Slow entry into CNS due to low lipid
POPULATION PK/PD MODELLING
OBJECTIVE : Characterisation of interindividual variability
in PK/PD parameters.
This includes the search for covariates such as patient weight,
age, renal function & disease status which contribute to
interindividual variability, affecting PK/PD relationship.
The detection and quantification of covariate effects may
influence the dosage regimen design.
It is a useful tool during drug development.
METHODS USED IN PK/PD MODELING
Two Stage Approach
Naive Pooled Approach
Hierarchical Non-linear Mixed
TWO STAGE APPROACH
The standard two-stage approach can be used to estimate
population model parameters:
STAGE 1: Individual parameters are estimated
for each subject.
STAGE 2: Using these estimates, in the second
stage, population mean values and
interindividual variability of parameters are
• Requires extensive sampling for each individual in order to
estimate individual parameters.
• It has been shown from simulation studies that the standard
two stage approach tends to overestimate parameter
Naive Pooled Approach
It was proposed by Sheiner and Beal.
Method involves pooling all the data from all individuals
as if they were from a single individual to obtain population
Generally, the naïve pooled approach performs well in
estimating population pharmacokinetic parameters from
balanced pharmacokinetic data with small between-subject
Tends to confound individual differences and diverse sources
of variability, and it generally performs poorly when dealing
with imbalanced data.
Caution is warranted when applying the naïve pooled
approach for PD data analysis because it may produce a
distorted picture of the exposure–response relationship and
thereby could have safety implications when applied to the
treatment of individual patients.
HIERARCHICAL NON-LINEAR MIXED-EFFECT
Can handle both sparse and intensive sampled
data, making it a powerful tool to study PK/PD
in special populations, such as neonates, the
elderly, and AIDS patients, where the number
of samples to be collected per subject is
limited due to ethical and/or medical concerns.
Analyzes the data of all individuals at once, estimating
individual and population parameters, as well as the
interindividual, intraindividual residual, and interoccasion
It also allows the evaluation and quantification of potential
sources of variability in pharmacokinetics and
pharmacodynamics in the target population.
Influence of patient demographics (e.g., weight, gender,
age, etc.) and pathophysiological factors (e.g., hepatic
function, renal function, disease status, etc.) on drug PK
and PD disposition may be assessed.
Useful in the design of dosing regimens and
therapeutic drug monitoring.
The non-linear mixed-effects model is the most
widely used method and has proven to be very useful
for continuous measures of drug effect, categorical
response data, and survival-type data.
The non-linear mixed-effects modeling software
(NONMEM) introduced by Sheiner and Beal is one
of the most commonly used programs for population
NIH (National Institute of Health) defines biomarkers as,
an indicator of a biological state.
It is a characteristic that is measured and evaluated as an
indicator of normal biological processes, pathogenic
processes or pharmacologic responses to a therapeutic
Detection of biomarker
a link between quantity of the marker and disease .
a link between existence of a marker and disease.
An Ideal Marker should have great sensitivity, specificity, and
accuracy in reflecting total disease burden. A tumor marker
should also be prognostic of outcome and treatment
ANTECEDENT BIOMARKERS : Identifying the risk of
developing an illness. e.g. amyloidal plaques start forming
before the symptoms of AD appear.
SCREENING BIOMARKERS: Screening for subclinical
disease. E.g. abnormal lipid profile is a screening marker of
DIAGNOSTIC BIOMARKERS: Recognizing overt
disease. E.g. Diagnostic kits for various diseases.
STAGING BIOMARKERS : Categorizing disease
PROGNOSTIC BIOMARKERS: Predicting future
disease course, including recurrence and response to
therapy and monitoring efficacy of therapy.
APPLICATIONS OF BIOMARKERS
• Use in early-phase clinical trials to establish “proof of
• Diagnostic tools for identifying patients with a specific
• As tools for characterizing or staging disease processes.
• As an indicator of disease progress.
• For predicting and monitoring the clinical response to
1.PK/PD STUDIES IN DRUG DEVELOPMENT
• Pharmacokinetic (PK) and pharmacodynamic (PD) modelling
and simulation (M&S) are well-recognized powerful tools
that enable effective implementation of the learn-and confirm
paradigm in drug development.
• M&S methodologies can be used to capture uncertainty and
use the expected variability in PK/PD data generated in
preclinical species for projection of the plausible range of
Clinical trial simulation can be used to forecast the
probability of achieving a target response in patients
based on information obtained in early phases of
Framing the right question and capturing the key
assumptions are critical components of the learn-and-confirm
paradigm in the drug development process and
are essential to delivering high-value PK/PD M&S
LEARN AND CONFIRM DRUG-DEVELOPMENT
• Demonstration of biologic activity in experimental models.
• Accrual of toxicology data to support initial dosing in
• Identify the lead candidates based on desired attributes.
• Efficacy and safety of NCE?
• Dose range to be studied in early clinical trials given
the uncertainty in the predicted dose required for
efficacy and safety?
MODELING AND SIMULATION TASKS
To understand mechanism of action PK/PD assist in the
identification of potential surrogates or biomarkers.
PK/PD assists in identification of the appropriate animal
Development of mechanism-based PK/PD models for
efficacy and toxicity early in the drug development process is
very useful and preferred over the development of empirical
Unlike empirical models, mechanism-based PK/PD models
take into account the physiological processes behind the
observed pharmacological response, likely making it more
‘‘predictive’’ for future study outcome.
Understanding and developing the PK/PD relationship early in the
discovery stage can also provide a quantitative way of selecting
the best candidate. In the anticancer area, a typical way of
selecting the most potent candidate within a series of anticancer
drug candidates is to measure tumor volumes from in vivo
evaluation of the antitumor effect.
For initial dose selection and the subsequent escalation scheme in
Phase 1 studies, there are many examples in which PK/PD models
enabled the successful extrapolation of preclinical results in order
to predict the effective and toxicologic drug concentrations for
Assessing and predicting drug–drug interaction potential as well
as formulation development.
Combination of M&S approaches, including population analysis
of sparse preclinical PK data, allometric scaling to predict human
PK, and empirical efficacy scaling, can be used to project the
anticipated human dose and/or dosing regimen.
This can be explained by a case study:
A NCE, possessing a high amount of prior information from
other drugs in the therapeutic class, was to be evaluated as a
treatment for hypertension. The main M&S objective was to
project the clinical dose range based on the preclinical PK/PD
properties of the NCE. The preclinical and clinical PK/PD
properties of a comparator drug were well known.
The main assumptions of these analyses were as follows:
The relative efficacy and potency observed in the rat
hypertension model between the comparator and the NCE
were predictive of the relative efficacy and potency in
Allometric scaling provided a reasonable estimate of the
clearance of the NCE in humans.
The concentration-response parameters for the NCE in clinical
hypertension were calculated using an empirical scaling
approach by combining the results of the rat hypertension Emax
model parameters and the clinical Emax model parameters of the
CLINICAL DRUG DEVELOPMENT:
In clinical drug development, PK/PD modeling and simulation
can potentially impact both internal and regulatory decisions in
•Assist in characterizing PK, safety, and tolerability of the
•Provide information for the rational design of all
subsequent clinical trials.
Phase 1 starts with dose escalating studies in normal
volunteers with rigorous sampling. In addition, one may
establish an initial dose–concentration–effect relationship
that offers the opportunity to predict and assess drug
tolerance and safety in early clinical development.
Quantitative dose–concentration–effect relationships
generated from PK/PD modeling in Phase1 can be utilized in
Phase 2 study design.
PK/PD modeling is an important tool in assessing drug-drug
Dosage form improvements often occur based on the PK
properties of the drug candidate.
Phase 2 trials are typically divided into two stages, each with
some specific objectives.
Phase 2A : is to test the efficacy hypothesis of a drug
candidate, demonstrating the proof of concept.
Phase 2B : is to develop the concentration–response
relationship in efficacy and safety by exploring a large
range of doses in the target patient population.
The PK/PD relationship that has evolved from the
preclinical phase up to Phase 2B is used to assist in
designing the Phase 3 trial.
To provide confirmatory evidence that demonstrates
an acceptable benefit/risk in a large target patient
This period provides the ideal condition for final
characterization of the PK/PD in patients as well as for
explaining the sources of interindividual variability in
response, using population PK/PD approaches.
PK/PD modeling plays an important role during the NDA
submission and review phase by integrating information from
the preclinical and development phases.
Existence of a well defined PK/PD model furthermore
enables the reviewer to perform PK/PD simulations for various
This ability helps the reviewer gain a deeper understanding of
the compound and provides a quantitative basis for dose
Thus, PK/PD modeling can facilitate the NDA review
process and help resolve regulatory issues.
POST MARKETING PHASE:
Post-marketing strategy, population modeling approaches
can provide the clinician with relevant information regarding
dose individualization by:
Characterizing the variability associated with PK and
Identifying subpopulations with special needs.
PK/PD STUDIES IN DOSAGE REGIMEN
PK/PD modeling is a scientific tool to help developers
select a rational dosage regimen for confirmatory clinical
Applied to individual dose optimization.
Time course and variability in the effect versus time
relationship can be predicted for different dosage-regimen
FOR DEVELOPMENT OF A NEW ANTIMICROBIAL
• Serial concentration-time data were available from 19 healthy,
male and female subjects administered NCE in doses ranging
1 to 200 mg in the first single-dose-multiple-dose study in
A 2-compartmental population PK model best described the
• For the first efficacy trial in patients, the target concentration
was defined based on the concentration required to kill
90% of the susceptible bacterial strains, or IC90, determined
from an Emax model fit of in vitro exposure-kill data.
The clinical target concentration was 1.7 mcg (mcg)/mL
(calculated by dividing in vitro IC90, or 0.05 mcg/mL, by
plasma bound fraction of 0.03).
Given the target exposure, the population PK model, and
margin of safety based on preliminary preclinical safety the
objective of M&S for the first efficacy trial was to select one
dose level to be studied as a once-a-day regimen that would
maintain concentrations >1.7 mcg/mL for the entire dosing
period in 85% of the patients.
Based on historical information on comparator compounds, it
is known that disease and protein binding can contribute to
differences in PK properties of an NCE between healthy
subjects and patients.
To minimize the risk of underpredicting the dose, a 20%
higher clearance (lower exposure) was assumed, and an
additional 10% variability was added to the between-subject
clearance and volume for patients. Concentration-time data
were simulated for 500 patients administered daily doses
ranging from 100 to 300 mg for 14 days. Eighty-five percent
of patients maintained the 24-hour trough concentrations
above the target at doses >200mg.
The 200-mg dose, therefore, met the criteria as the lowest
dose, which maintains persistent drug exposure for the entire
dosing interval in 85% of the patient population.
3.PK/PD MODELING IN INTERSPECIES
Primary source of between-species variability is often
attributable to variability that is mainly of PK origin.
Drug plasma concentration required to elicit a given
response is rather similar between species, whereas the
corresponding dose for eliciting the same effect can differ
4. EXTRAPOLATION FROM in vitro to in vivo:
If an efficacious concentration (EC for stimulation, IC for
inhibition) is obtained on the basis of an in vitro assay, then
a dose can be proposed by incorporating the in vitro EC
directly into equation:
ED 50 = Cl x EC 50/Bioavailability
As in vitro concentrations are generally equivalent to free
drug concentrations, corrections for drug binding to plasma
protein might be needed to estimate the corresponding in-vivo
EC or IC.
4. SELECTION OF ANTIBACTERIAL
PK/PD parameters correlate the bacteriological and clinical
outcome in animal models and in humans.
PK/PD parameters (AUC/MIC, Cmax/MIC) can be used to
select agents with maximum potential for bacterial
5. APPLICATIONS OF PK/PD METHODS
STUDY DRUG INTERACTIONS:
Drug interactions study protocols often incorporate
pharmacodynamic endpoints to allow estimating the clinical
consequences of drug interactions along with the usual
pharmacokinetic outcome measures.
Co-administration of triazolam and erythromycin produced a
large increase in plasma concentration of triazolam.
Drug Development process
Preclinical (3.5 years)
Phase 1 (1 year)
Phase 2 (2 years)
Phase 3 (3 years)
Thus it takes a molecule around 12-13 years to come
into market where it further faces the challenge of
Phase 4 trials.
CTS refers to computer modeling approaches that replicate
actual human trials using predictive equations and virtual
It is relatively fast and inexpensive as compared to cost of
actual clinical trials.
It can provide insight into both efficacy and cost
effectiveness, even with limited data.
Project team members from various disciplines utilize the
CTS to explore a series of scenarios, from different trial
designs, to alternative ways of analyzing the generated
Optimize design of Phase 2 to phase 4 human trials (set
inclusion and exclusion criteria, give statistically significant
results by accounting for variation in compliance and inter-patient
Help in making in-licensing decisions based on predictions
Optimize target selection for a therapeutic indication.
Formulating strategies for competitive differentiation of
novel drugs based on predicted effectiveness in clinical and
post market populations.
SOFTWARES USED IN PK/PD MODELING
• JGuiB (Java Graphic User Interface for Boomer)