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PRESENTED BY 
Jaspreet Singh 
Deepika 
(M.Pharm I)
 As defined by F.H. Dost in 1953, Pharmacokinetics is a 
science dealing with study of biological fate of drug &/or its 
...
SCHEMATIC REPRESENTATION ADME 
DOSE 
DRUG IN 
TISSUES 
ABSORPTION 
DRUG IN 
SYSTEMIC 
CIRCULATION 
ELIMINATION 
EXCRETION ...
 It refers to the relationship between drug concentration at the 
site of action and the resulting effect, including the ...
 RECEPTOR OCCUPANCY MODEL 
 Given by Langley, hill and Clarke. 
 Based on law of Mass Action. 
 Drug effect is related...
 Any drug that binds to a receptor and stimulates the 
functional activities 
 Has both affinity as well as intrinsic ac...
 It has affinity to receptor but no intrinsic activity. 
 It prevents binding of agonist to receptor. 
 e.g. atropine 
...
 Any drug that binds to a receptor and produces an 
opposite effect as that of an agonist. 
Receptor 
Inverse agonist 
Ef...
 Produces a sub maximal response. 
 Affinity is there but intrinsic activity is less than agonist. 
Partial agonist 
Tru...
 RATE THEORY 
 Pharmacological response is not dependent on drug-receptor 
complex concentration but rather depends upon...
 PK/PD modeling is a scientific mathematical tool which 
integrates PK model to that of PD model. 
 PK model - describes...
ADVANCED/NON 
STEADY-STATE/TIME 
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 
m...
Linear 
model 
Log-Linear 
Model 
Emax Model 
Sigmoidal 
Emax Model 
Effect of drug is direct. 
Fast mechanism of action...
 Drug effect is directly proportional to drug concentration. 
 Pharmacodynamically it is explained as: 
E ∝ C …..(1) 
E ...
so, 
slope = S 
intercept= E0 
Effect 
E0 
Concentration 
S 
E = E0 + S*C 
y = c + mx
Advantages 
 Model is simple and parameter estimation can be easily 
performed by linear regression. 
Limitations 
 Appl...
 When the effect of drug is measured over a large range, the 
relationship between concentration and effect is not linear...
E 
Log C 
 It expands the initial part of the curve where response is 
slowly making progression before it accelerates 
...
Advantage 
 Unlike linear model it is applicable over large concentration 
range. 
Limitations 
 Pharmacological effect ...
 This model incorporates the observation known as the law 
of diminishing returns. 
 This law shows that an increase in ...
 It mimics the hyperbolic shape of pharmacologic response 
vs. drug concentration curve. 
 After maximum response (Emax)...
Contd… 
In case, there is a baseline effect i.e. the measured 
pharmacologic effect has some value in absence of drug (e.g...
 A double-reciprocal plot of equation is used to linearize the 
relation, similar to Lineweaver-Burke equation. 
…(7) 
EC...
Advantages 
 Maximum pharmacological response can be found out. 
 EC50 can be calculated (i.e., concentration needed to ...
 Given by Hill. 
 It describes the pharmacologic response versus drug 
concentration curve for many drugs that appear to...
 A value of n>1 influences the slope of the curve and the 
model fit. 
 In the Sigmoid Emax Model, the slope is influenc...
E 
EMAX 
Graphical representation 
EC50 
CONCENTRATION 
n > 1 
n = 1 
E n < 1 
CONCENTRATION
Biophase 
distribution model 
Mechanism-based 
indirect response 
model 
Signal 
transduction 
model 
Tolerance model
 Indirect effect of the drug. 
 The effect is not immediate. 
 Distribution of the drug is the rate limiting step. 
 S...
 For some drugs, the pharmacologic response produced by 
the drug may be observed before or after the plasma drug 
concen...
EFFECT COMPARTMENT 
It is not part of the pharmacokinetic model but is a 
hypothetical pharmacodynamic compartment that li...
k1e keo 
V C1 Ve Ce Effect 
k1 
Plasma 
Compartment 
Effect 
Compartment 
Drug transfer from plasma to hypothetical effect...
The amount of drug in the effect compartment after i.v. bolus 
dose may be given by: 
...(9) 
dDe  k1eD1 ke0De 
dt 
wher...
Integrating the equation we get: 
…(10) 
D k 
0 1 kt k t 
De    
e e e e 
k k 
( ) 
 
Dividing by Ve , 
…(11) 
( 0 
) ...
 Even though an effect compartment is present in addition 
to the plasma compartment, this hypothetical effect 
compartme...
Dividing by VD yields the steady state plasma drug concentration 
C1 
…(14) 
from eq.…(10) 
k D 
e 0 
e 
k V 
D k 
0 1 kt ...
 At steady state, C1 is unaffected by k1e but depends on 
k and ke0. 
 C1 is the steady state concentration and has been...
 Dynamic flexibility and adaptability. 
 The model accommodates the aggregate effects of drug 
elimination, binding, par...
The indirect response model is based on the premise that the 
drug response is indirectly mediated by either inhibition or...
Response 
Kin 
Kout 
Stimulation 
Or 
Inhibition 
Stimulation 
Or 
Inhibition 
[DRUG] [DRUG]
In the absence of drug, the rate of change in response over 
time (dR/dt) can be described by a differential equation as 
...
TYPES OF INDIRECT RESPONSE MODELS 
I. Inhibition of Kin 
(Inhibition of production) 
II. Inhibition of Kout 
(Stimulation ...
1. H2-receptor antagonist: Inhibition of gastric secretion.
which MODEL is it 
representing? 
Model I
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...
CONTD… 
 There are two major classes of receptors involved in signal 
transduction process: 
1.cell membrane receptors 
2...
D + R DR 
τ τ τ 
1 2 3 N
TOLERANCE MODEL 
 Tolerance is characterized by a reduction in pharmacological 
response after repeated or continuous dru...
 The development of tolerance can have a significant impact 
on the exposure-response relationship and, if not 
recognize...
An increase in EC50 over time for Terbutaline which is 
likely attributed to a decrease in the receptor number’ 
Develop...
 Many pharmacological responses are complex and do not 
show a direct relationship between pharmacologic effect 
and plas...
Identical drug concentration can result in different 
pharmacological response, depending on whether the plasma 
drug conc...
 Here response decreases with time. 
C 
E 
C1 
E2 
E1 
 If we take a concentration say (C1), it can be clearly seen 
tha...
1.Fentanyl and Alfentanil 
Explanation: These are opioid analgesics and have 
high lipid solubility. Initially, with incre...
 4.Amphetamine 
Explanation: Exhaustion of mediators. 
 5. Anticonvulsants 
Explanation: Increased metabolism. 
 6. Ben...
 In the counterclockwise hysteresis loop, response increases 
with time. 
E 
C 
E2 
E1 
C1 
 If we take a concentration ...
1.Ajmaline 
Explanation: Drug is highly bound to α1-AGP and 
initial diffusion of drug into effect compartment is 
slow. 
...
POPULATION PK/PD MODELLING 
OBJECTIVE : Characterisation of interindividual variability 
in PK/PD parameters. 
This includ...
METHODS USED IN PK/PD MODELING 
Two Stage Approach 
Naive Pooled Approach 
Hierarchical Non-linear Mixed 
Effects Modeling...
TWO STAGE APPROACH 
The standard two-stage approach can be used to estimate 
population model parameters: 
STAGE 1: Indivi...
CONTD…. 
ADVANTAGE : 
• Simplicity 
LIMITATIONS : 
• Requires extensive sampling for each individual in order to 
estimate...
Naive Pooled Approach 
It was proposed by Sheiner and Beal. 
Method involves pooling all the data from all individuals 
as...
 Tends to confound individual differences and diverse sources 
of variability, and it generally performs poorly when deal...
HIERARCHICAL NON-LINEAR MIXED-EFFECT 
MODELLING 
Can handle both sparse and intensive sampled 
data, making it a powerful ...
Contd… 
Analyzes the data of all individuals at once, estimating 
individual and population parameters, as well as the 
in...
Contd… 
Useful in the design of dosing regimens and 
therapeutic drug monitoring. 
The non-linear mixed-effects model is t...
 NIH (National Institute of Health) defines biomarkers as, 
an indicator of a biological state. 
 It is a characteristic...
ANTECEDENT BIOMARKERS : Identifying the risk of 
developing an illness. e.g. amyloidal plaques start forming 
before the s...
APPLICATIONS OF BIOMARKERS 
• Use in early-phase clinical trials to establish “proof of 
concept”. 
• Diagnostic tools for...
APPLICATIONS 
OF PK/PD 
MODELING
1.PK/PD STUDIES IN DRUG DEVELOPMENT 
• Pharmacokinetic (PK) and pharmacodynamic (PD) modelling 
and simulation (M&S) are w...
Contd… 
Clinical trial simulation can be used to forecast the 
probability of achieving a target response in patients 
bas...
Contd… 
LEARN AND CONFIRM DRUG-DEVELOPMENT 
PARADIGM
Contd… 
PRECLINICAL PHASE: 
OVERALL OBJECTIVE: 
• Demonstration of biologic activity in experimental models. 
• Accrual of...
MODELING AND SIMULATION TASKS 
 To understand mechanism of action PK/PD assist in the 
identification of potential surrog...
Contd… 
 Understanding and developing the PK/PD relationship early in the 
discovery stage can also provide a quantitativ...
Contd… 
Combination of M&S approaches, including population analysis 
of sparse preclinical PK data, allometric scaling to...
Contd… 
The main assumptions of these analyses were as follows: 
 The relative efficacy and potency observed in the rat 
...
Contd… 
The concentration-response parameters for the NCE in clinical 
hypertension were calculated using an empirical sca...
CLINICAL DRUG DEVELOPMENT: 
In clinical drug development, PK/PD modeling and simulation 
can potentially impact both inter...
Contd… 
 Phase 1 starts with dose escalating studies in normal 
volunteers with rigorous sampling. In addition, one may 
...
Contd… 
Phase 2 
Phase 2 trials are typically divided into two stages, each with 
some specific objectives. 
Phase 2A : i...
Contd… 
PHASE 3: 
OBJECTIVE: 
 To provide confirmatory evidence that demonstrates 
an acceptable benefit/risk in a large ...
Contd… 
NDA REVIEW: 
 PK/PD modeling plays an important role during the NDA 
submission and review phase by integrating i...
Contd… 
POST MARKETING PHASE: 
Post-marketing strategy, population modeling approaches 
can provide the clinician with rel...
PK/PD STUDIES IN DOSAGE REGIMEN 
OPTIMISATION: 
PK/PD modeling is a scientific tool to help developers 
select a rational ...
Contd… 
EXAMPLE: 
FOR DEVELOPMENT OF A NEW ANTIMICROBIAL 
AGENT: 
• Serial concentration-time data were available from 19 ...
Contd… 
The clinical target concentration was 1.7 mcg (mcg)/mL 
(calculated by dividing in vitro IC90, or 0.05 mcg/mL, by ...
Contd… 
To minimize the risk of underpredicting the dose, a 20% 
higher clearance (lower exposure) was assumed, and an 
ad...
3.PK/PD MODELING IN INTERSPECIES 
EXTRAPOLATION: 
 Primary source of between-species variability is often 
attributable t...
4. EXTRAPOLATION FROM in vitro to in vivo: 
If an efficacious concentration (EC for stimulation, IC for 
inhibition) is ob...
4. SELECTION OF ANTIBACTERIAL 
AGENT: 
PK/PD parameters correlate the bacteriological and clinical 
outcome in animal mode...
5. APPLICATIONS OF PK/PD METHODS 
STUDY DRUG INTERACTIONS: 
Drug interactions study protocols often incorporate 
pharmacod...
Drug Development process 
 Discovery (3years) 
 Preclinical (3.5 years) 
 Phase 1 (1 year) 
 Phase 2 (2 years) 
 Phas...
 CTS refers to computer modeling approaches that replicate 
actual human trials using predictive equations and virtual 
s...
 Optimize design of Phase 2 to phase 4 human trials (set 
inclusion and exclusion criteria, give statistically significan...
SOFTWARES USED IN PK/PD MODELING 
•WinNonlin 
•NONMEM 
•XLMEM 
•Boomer 
• JGuiB (Java Graphic User Interface for Boomer) 
...
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
Pharmacokinetic and Pharmacodynamic Modeling
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Pharmacokinetic and Pharmacodynamic Modeling

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The presentation gives you a bird eye's view regarding basics of PK-PD modeling, its applications, types, limitations and various softwares used for the same.

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Pharmacokinetic and Pharmacodynamic Modeling

  1. 1. PRESENTED BY Jaspreet Singh Deepika (M.Pharm I)
  2. 2.  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.
  3. 3. SCHEMATIC REPRESENTATION ADME DOSE DRUG IN TISSUES ABSORPTION DRUG IN SYSTEMIC CIRCULATION ELIMINATION EXCRETION AND METABOLISM DISTRIBUTION
  4. 4.  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.
  5. 5.  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
  6. 6.  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 Some Effect A Cell
  7. 7.  It has affinity to receptor but no intrinsic activity.  It prevents binding of agonist to receptor.  e.g. atropine Atropine Dude, you’re in my way! Acetylcholine
  8. 8.  Any drug that binds to a receptor and produces an opposite effect as that of an agonist. Receptor Inverse agonist Effect opposite to that of the true agonist A Cell
  9. 9.  Produces a sub maximal response.  Affinity is there but intrinsic activity is less than agonist. Partial agonist True agonist Oh!!!, I should Have been here Submaximal effect
  10. 10.  RATE THEORY  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 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.
  11. 11.  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.
  12. 12. ADVANCED/NON STEADY-STATE/TIME DEPENDENT MODELS SIMPLE DIRECT EFFECT/STEADY-STATE/ TIME INVARIANT MODELS
  13. 13. 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
  14. 14. Linear model Log-Linear Model Emax Model Sigmoidal 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.
  15. 15.  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)
  16. 16. so, slope = S intercept= E0 Effect E0 Concentration S E = E0 + S*C y = c + mx
  17. 17. 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
  18. 18.  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
  19. 19. 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.
  20. 20. 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.
  21. 21.  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) E C max EC C E   50
  22. 22.  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 EC50 C Emax
  23. 23. Contd… 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, E C max E Eo  EC C   50 E0 = Pharmacologic effect (baseline activity) at zero drug concentration in the body It is a saturable process and resembles the Michaelis-Menton equation.
  24. 24.  A double-reciprocal plot of equation is used to linearize the relation, similar to Lineweaver-Burke equation. …(7) EC 50 1 1 E C E max max E   1/ Emax -1/ EC50 1/C slope = EC50 / Emax Contd… 1/E
  25. 25. 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.
  26. 26.  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: …(8) n n E C max EC C E   50 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
  27. 27.  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.
  28. 28. E EMAX Graphical representation EC50 CONCENTRATION n > 1 n = 1 E n < 1 CONCENTRATION
  29. 29. Biophase distribution model Mechanism-based indirect response model Signal transduction model Tolerance model
  30. 30.  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.
  31. 31.  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.
  32. 32. 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.
  33. 33. k1e keo V C1 Ve Ce Effect k1 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.
  34. 34. The amount of drug in the effect compartment after i.v. bolus dose may be given by: ...(9) dDe  k1eD1 ke0De dt where, De = amount of drug in effect compartment D1 = amount of drug in central compartment ke0 = rate constant for drug transfer out of the effect compartment K1e = rate constant for drug transfer from plasma to effect compartment
  35. 35. Integrating the equation we get: …(10) D k 0 1 kt k t De    e e e e k k ( )  Dividing by Ve , …(11) ( 0 ) 0 e  D k 0 1 kt k t C    e e e ( ) e V k k ( ) 0 0 e e e   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.
  36. 36.  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) k D e e k e D 0 1 1  Assumptions
  37. 37. Dividing by VD yields the steady state plasma drug concentration C1 …(14) from eq.…(10) k D e 0 e k V D k 0 1 kt k t D    substituting De in equation (14) …(16) …(17) e D C 1 1  ( ) ( ) 0 0 e e e e e e k  k  k D k C    e e e e e ( ) 0 0 1 k V k k ( ) 0 1 0 1 kt k t e D e   k D k e e e e C    ( ) 0 0 V k k ( ) 0 0 1 kt k t D e  
  38. 38.  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.
  39. 39.  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.
  40. 40. 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).
  41. 41. Response Kin Kout Stimulation Or Inhibition Stimulation Or Inhibition [DRUG] [DRUG]
  42. 42. 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, k k R dR dt in out   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.
  43. 43. 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 St  K R dR dR dR dt  in   out  dt  in   out  K K St  R dR dt  in  out   dt  in  out   S(t), I(t) – Stimulation and inhibition functions
  44. 44. 1. H2-receptor antagonist: Inhibition of gastric secretion.
  45. 45. which MODEL is it representing? Model I
  46. 46. 2. Induction of MX protein synthesis: Interferon α-2a
  47. 47. Now which model is it? MODEL III
  48. 48. 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.
  49. 49. 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.
  50. 50. D + R DR τ τ τ 1 2 3 N
  51. 51. 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 depletion decrease in receptor affinity
  52. 52.  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. 53
  53. 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
  54. 54.  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’. .
  55. 55. 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
  56. 56.  Here response decreases with time. C E C1 E2 E1  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
  57. 57. 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.
  58. 58.  4.Amphetamine Explanation: Exhaustion of mediators.  5. Anticonvulsants Explanation: Increased metabolism.  6. Benzodiazepenes Explanation: Loss of modulator binding site.
  59. 59.  In the counterclockwise hysteresis loop, response increases with time. E C E2 E1 C1  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.
  60. 60. 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 .
  61. 61. 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.
  62. 62. METHODS USED IN PK/PD MODELING Two Stage Approach Naive Pooled Approach Hierarchical Non-linear Mixed Effects Modeling 1. 2. 3.
  63. 63. 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
  64. 64. CONTD…. 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. 65
  65. 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.
  66. 66.  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.
  67. 67. 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.
  68. 68. Contd… 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. 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.
  69. 69. 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.
  70. 70.  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
  71. 71. 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 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.
  72. 72. 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.
  73. 73. APPLICATIONS OF PK/PD MODELING
  74. 74. 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.
  75. 75. Contd… 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. 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.
  76. 76. Contd… LEARN AND CONFIRM DRUG-DEVELOPMENT PARADIGM
  77. 77. Contd… PRECLINICAL PHASE: 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?
  78. 78. 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.
  79. 79. 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.
  80. 80. 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.
  81. 81. 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.
  82. 82. Contd… 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.
  83. 83. 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.
  84. 84. 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.
  85. 85. Contd… Phase 2 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.
  86. 86. 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.
  87. 87. 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.
  88. 88. 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.
  89. 89. 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.
  90. 90. 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 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.
  91. 91. 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.
  92. 92. 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.
  93. 93. 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.
  94. 94. 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.
  95. 95. 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.
  96. 96. 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.
  97. 97. 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.
  98. 98.  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.
  99. 99.  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.
  100. 100. SOFTWARES USED IN PK/PD MODELING •WinNonlin •NONMEM •XLMEM •Boomer • JGuiB (Java Graphic User Interface for Boomer) •TOPFIT •ADAPT II •BIOPAK •MULTI

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