PHARMACOKINETIC/

PHARMACODYNAMIC

MODELINGPRESENTED BYJaspreet SinghDeepika(M.Pharm I)

PHARMACOKINETICS

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.

DOSE

DRUG IN TISSUES

DRUG IN SYSTEMIC

CIRCULATION

EXCRETION AND

METABOLISM

SCHEMATIC REPRESENTATION ADME

ABSORPTION

ELIMINATION

DISTRIBUTION

PHARMACODYNAMICS

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 actions.

DRUG - RECEPTOR BINDING

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] RESPONSE

K1

K2

WHAT IS A RECEPTOR AGONIST?

Any drug that binds to a receptor and stimulates the functional activities

Has both affinity as well as intrinsic activity. e.g. Ach

Receptor

Acetylcholine

A Cell

Some Effect

WHAT IS A RECEPTOR ANTAGONIST?

It has affinity to receptor but no intrinsic activity. It prevents binding of agonist to receptor. e.g. atropine

Acetylcholine

Atropine

Dude, you’rein my way!

WHAT IS AN INVERSE AGONIST?

Any drug that binds to a receptor and produces an opposite effect as that of an agonist.

Receptor

Inverse agonist

A Cell

Effect opposite to that of the true agonist

WHAT IS A PARTIAL AGONIST?

Produces a sub maximal response. Affinity is there but intrinsic activity is less than agonist.

True agonist

Partial agonist

Oh!!!, I shouldHave been here

Submaximal effect

RATE THEORY Pharmacological response is not dependent on drug-receptor

complex concentration but rather depends upon rate of association of drug and receptor.

LOCK AND KEY MODEL Only a drug of specific chemical structure can bind with the

receptor.

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.

WHAT IS PK/PD MODELING

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 the drug.

Result is summation of Pharmacodynamics and pharmacokinetics effect.

TYPES OF PK/PD MODELS

ADVANCED/NON STEADY-STATE/TIM

E DEPENDENT MODELS

SIMPLE DIRECT EFFECT/STEADY- STATE/TIME INVARIANT MODELS

Linear model

Log-linear model

Emax model

Sigmoidal Emax model

Biophase distribution

model

Signal transduction

model

Tolerance model

Mechanism based indirect

response model

Simple direct effect models

Nonsteady-state & time- dependent models

SIMPLE DIRECT EFFECT MODELS

Linear model

Log-Linear Model

Emax ModelSigmoidal Emax Model

Effect of drug is direct.

Fast mechanism of action.

Rapid equilibrium exists between site of action and the sampling biofluids.

PD parameters are time invariant.

LINEAR MODEL

Drug effect is directly proportional to drug concentration. Pharmacodynamically it is explained as:

E ∝ C …..(1)

E = S×C …..(2)

where,

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)

GRAPHICAL REPRESENTATION

so,

slope = S

intercept= E0

Eff

ect

E0

Concentration

S E = E0 + S*C

y = c + mx

Contd… Advantages

Model is simple and parameter estimation can be easily performed by linear regression.

Limitations Applicable at low drug concentrations only excludes the prediction of maximum effect

Example Relationship between central activity of diazepam and

plasma drug concentration

LOG-LINEAR MODEL

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)

where,

E = effect

E0=Baseline effect

S = slope

C= concentration

GRAPHICAL REPRESENTATION

E

Log C 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.

Contd…

Advantage Unlike linear model it is applicable over large concentration

range.

Limitations Pharmacological effect cannot be estimated when the

concentration is zero because of the logarithmic function. Maximum effect cannot be predicted.

Example This model has been successfully used in predicting the

pharmacological activities of beta blockers and anticoagulants.

MAXIMUM EFFECT (EMAX) MODEL

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 response.

This model describes the drug action in the terms of :E max (maximum effect)EC50 ( the drug concentration that produces 50%

maximum pharmacological effect)

….(5)

CEC

CEE

50

max

Contd…

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.

E

C

EC50

Emax

It is a saturable process and resembles the Michaelis-Menton equation.

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 becomes:

….(6)

where, E0 = Pharmacologic effect (baseline activity) at zero drug concentration in the body

CEC

CEEE o

50

max

Contd…

A double-reciprocal plot of equation is used to linearize the

relation, similar to Lineweaver-Burke equation.

…(7)

maxmax

50 11

ECE

EC

E

-1/ EC50 1/C

1/ Emax

slope = EC50 / Emax

Contd…

1/E

Contd…

Advantages Maximum pharmacological response can be found out. EC50 can be calculated (i.e., concentration needed to

produce half maximum response).

Limitations 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.

Example Bronchodilator activity of Theophylline is studied by this

model.

SIGMOIDAL Emax MODEL

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 Emax model.

The equation for the sigmoid Emax Model is an extension of the Emax Model:

…(8) n

n

CEC

CEE

50

max

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

Contd…

A value of n>1 influences the slope of the curve and the model fit.

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 receptor.

Cooperativity is the case when binding of substrate at on binding site affects the affinity of other sites to their substrates.

E

CONCENTRATION

EMAX

EC50

Graphical representation

n > 1

n = 1

n < 1E

CONCENTRATION

NON-STEADY TIME-DEPENDENT MODEL

Biophase distribution

model

Mechanism-based indirect

response model

Signal transduction

model

Tolerance model

BASIC CHARACTERS

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 receptors.

BIOPHASE DISTRIBUTION MODEL

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 effect.

To account for this indirect or delayed response, a hypothetical effect compartment has been postulated by Holford and Sheiner.

EFFECT COMPARTMENT

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 k1e keo

Plasma Compartment

Effect Compartment

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 compartment.

k1

The amount of drug in the effect compartment after i.v. bolus dose may be given by:

...(9)

where, De = amount of drug in effect compartmentD1 = amount of drug in central compartmentke0 = rate constant for drug transfer out of the effect compartment K1e = rate constant for drug transfer from plasma to effect compartment

dt

DkDkdD eeee 011

Contd…

Contd…

Integrating the equation we get:

…(10)

Dividing by Ve ,

…(11)

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.

)()(

0

0

10 tkkt

e

e eeekk

kDDe

)()(

0

0

10 tkkt

ee

ee

eeekkV

kDC

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 drug dose.

So plasma drug level still follows a one-compartment equation.

After an IV bolus dose, the rate of drug entering and leaving the effect compartment is controlled by k1e and ke0.

At steady state,

input = output

k1eD1 = keoDe …(12)

Rearranging,

…(13)

e

ee

k

DkD

1

01

Assumptions

Dividing by VD yields the steady state plasma drug concentration C1

…(14)

from eq.…(10)

substituting De in equation (14)

…(16)

…(17)

De

ee

Vk

DkC

1

01

)()(

0

0

10 tkkt

e

ee

eeekk

kDD

)()(

0

01

1001

tkkt

eDe

ee eeekkVk

kDkC

)()(

0

0

001

tkkt

eD

e eeekkV

kDkC

Contd…

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.

ADVANTAGES

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 adjust.

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 being produced.

INDIRECT RESPONSE MODEL

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).

EXAMPLES: 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 tolbutamide).

SCHEMATIC REPRESENTATION OF THE MODEL

Response

[DRUG] [DRUG]

KinKout

Stimulation OrInhibition

Stimulation OrInhibition

MATHEMATICAL REPRESENTATION

In the absence of drug, the rate of change in response over time (dR/dt) can be described by a differential equation as follows:

…(18)

where,

R = response

kin = zero-order rate constant for the production of response

kout = first order rate constant for the dissipation of response

Used in cases where endogenous mediators are involved in the expression of the response.

Rkkdt

dRoutin

TYPES OF INDIRECT RESPONSE MODELS

II. Inhibition of Kout

III. Stimulation of Kin

(Stimulation of production)

IV. Stimulation of Kout

(Dissipation of response)

Inhibition of Kout -

RKtSKdt

dRoutin

RKtIKdt

dRoutin

RtSKKdt

dRoutin

RtIKKdt

dRoutin

S(t), I(t) – Stimulation and inhibition functions

 I. Inhibition of Kin (Inhibition of production)

(Stimulation of response)

EXAMPLES1. H2-receptor antagonist: Inhibition of gastric secretion.

which MODEL is it representing?

Model I

Contd…

2. Induction of MX protein synthesis: Interferon α-2a

Now which model is it?

MODEL III

SIGNAL TRANSDUCTION MODEL

The pharmacological effects of drugs may be mediated by a time-dependent signal transduction process, in which the response measured clinically involves multiple steps removed from the initial biochemical effect of the drug.

CONTD…

There are two major classes of receptors involved in signal transduction process:

1.cell membrane receptors 2.cytosolic/nuclear 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 in rats.

1 2 3 Nτ τ τ

D + R DR

TOLERANCE MODEL

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 depletiondecrease 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.

CONTD…

53

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.

54

EXAMPLES

HYSTERESIS OF PHARMACOLOGICAL RESPONSE

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 loop.

Hysteresis

Clockwise Anticlockwise

CLOCKWISE HYSTERESIS

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

C

E

C1

E2

E1

EXAMPLES

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 of drug.

2.Isoprterenol

Explanation: The diminished response is due to result of cellular response and physiologic adaptation to intense stimulation of drug.

3.Acetazolamide

Explanation: physical adaptation.

CONTD…

4.Amphetamine

Explanation: Exhaustion of mediators.

5. Anticonvulsants

Explanation: Increased metabolism.

6. Benzodiazepenes

Explanation: Loss of modulator binding site.

COUNTER CLOCKWISE HYSTERESIS

In the counterclockwise hysteresis loop, response increases with time.

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.

E

C

E2

E1

C1

EXAMPLES

1.Ajmaline

Explanation: Drug is highly bound to α1-AGP and initial diffusion of drug into effect compartment is slow.

2.Pancuronium

Explanation: Slow movement of ionized compound from capillaries to NMJ.

3. Morphine

Explanation: Slow entry into CNS due to low lipid solubility .

POPULATION PK/PD MODELLING

This includes the search for covariates such as patient weight, age, renal function & disease status which contribute to interindividual variability, affecting PK/PD relationship.

It is a useful tool during drug development.

OBJECTIVE : Characterisation of interindividual variability in PK/PD parameters.

The detection and quantification of covariate effects may influence the dosage regimen design.

METHODS USED IN PK/PD MODELING

Two Stage Approach

Naive Pooled Approach

Hierarchical Non-linear Mixed

Effects Modeling

1.

2.

3.

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 calculated

ADVANTAGE :

• Simplicity

LIMITATIONS :

• 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 dispersion.

CONTD….

65

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 parameter estimates.

Generally, the naïve pooled approach performs well in estimating population pharmacokinetic parameters from balanced pharmacokinetic data with small between-subject variations.

LIMITATIONS

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 MODELLING

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.

Contd…

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.

Analyzes the data of all individuals at once, estimating individual and population parameters, as well as the interindividual, intraindividual residual, and interoccasion variabilities.

It also allows the evaluation and quantification of potential sources of variability in pharmacokinetics and pharmacodynamics in the target population.

Contd…

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 analysis.

BIOMARKERS

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 intervention. 

Detection of biomarker

Quantitative a link between quantity of the marker and disease .Qualitative 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

CLASSIFICATION OF BIOMARKERSANTECEDENT 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 heart disease.

DIAGNOSTIC BIOMARKERS: Recognizing overt disease. E.g. Diagnostic kits for various diseases.

STAGING BIOMARKERS : Categorizing disease severity.

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 concept”.

• Diagnostic tools for identifying patients with a specific disease.

• As tools for characterizing or staging disease processes.

• As an indicator of disease progress.

• For predicting and monitoring the clinical response to therapeutic intervention.

APPLICATIONS

OF PK/PD

MODELING

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 dose.

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 development.

Contd…

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 results.

LEARN AND CONFIRM DRUG-DEVELOPMENT PARADIGM

Contd…

PRECLINICAL PHASE:

Contd…

OVERALL OBJECTIVE:• Demonstration of biologic activity in experimental models.• Accrual of toxicology data to support initial dosing in humans.• Identify the lead candidates based on desired attributes.

QUESTIONS:• 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 model.

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 models.

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.

Contd…

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 clinical investigations.

Assessing and predicting drug–drug interaction potential as well as formulation development.

Contd…

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.

Contd…

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 humans. 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 comparator.

Contd…

CLINICAL DRUG DEVELOPMENT:

In clinical drug development, PK/PD modeling and simulation can potentially impact both internal and regulatory decisions in drug development.

PHASE 1:

•Assist in characterizing PK, safety, and tolerability of the drug candidate.

•Provide information for the rational design of all subsequent clinical trials.

Contd…

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 interaction potential.

Dosage form improvements often occur based on the PK properties of the drug candidate.

Contd…

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.

Phase 2

Contd…

PHASE 3:

OBJECTIVE: To provide confirmatory evidence that demonstrates an acceptable benefit/risk in a large target patient population.

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.

Contd…

NDA REVIEW:

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 scenarios.

This ability helps the reviewer gain a deeper understanding of the compound and provides a quantitative basis for dose selection.

Thus, PK/PD modeling can facilitate the NDA review process and help resolve regulatory issues.

Contd…

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 PD.

Identifying subpopulations with special needs.

PK/PD STUDIES IN DOSAGE REGIMEN OPTIMISATION:

PK/PD modeling is a scientific tool to help developers select a rational dosage regimen for confirmatory clinical testing.

Applied to individual dose optimization.

Time course and variability in the effect versus time relationship can be predicted for different dosage-regimen scenarios.

Contd…

EXAMPLE:FOR DEVELOPMENT OF A NEW ANTIMICROBIAL

AGENT:• Serial concentration-time data were available from 19 healthy, male and female subjects administered NCE in doses ranging

from 1 to 200 mg in the first single-dose-multiple-dose study in

humans. A 2-compartmental population PK model best described the

data.

• For the first efficacy trial in patients, the target concentrationwas defined based on the concentration required to kill90% of the susceptible bacterial strains, or IC90, determinedfrom an Emax model fit of in vitro exposure-kill data.

Contd…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.

Contd…

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 variability in 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 EXTRAPOLATION:

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 widely.

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 plasma EC or IC.

4. SELECTION OF ANTIBACTERIAL AGENT:

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 eradication.

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.

Example:Co-administration of triazolam and erythromycin produced a large increase in plasma concentration of triazolam.

CLINICAL TRIALS SIMULATIONS

Drug Development process Discovery (3years) 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 subject.

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 data.

WHERE IT IS USED 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 variability.

Help in making in-licensing decisions based on predictions of effectiveness.

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

•WinNonlin•NONMEM•XLMEM•Boomer• JGuiB (Java Graphic User Interface for Boomer)•TOPFIT•ADAPT II•BIOPAK•MULTI