This document provides an overview of in vitro-in vivo correlation (IVIVC) models. It defines IVIVC and discusses the different levels of correlation from A to C. It describes the key parameters used to develop IVIVC models and validate the correlation between in vitro dissolution and in vivo absorption data. The document also addresses the application and future aspects of IVIVC in developing and evaluating drug delivery systems.

1. Guided by: Dr. Shital Butani
Prepared by: Dhara Patel
14MPH103

2. 2
 Introduction
 Importance
 Level of correlation
 Parameters
 Correlation method
 Stages of IVIVC model development
 Application
 Future aspects
 Reference

3. 3
 In IVIVC, "C" denotes "Correlation", which means "the
degree of relationship between two variables".
 Correlation deals with the "tightness" in how two variables
vary together. This term does not limit a relationship to only
the linear type, but allows for non-linear relationships as
well.
 Conceptually, IVIVC describes a relationship between the in
vitro dissolution / release versus the in vivo absorption.

4. 4
 The Food and Drug Administration (FDA) defines
 “A predictive mathematical model describing the relationship
between an in-vitro property of a dosage form and an in-vivo
response”.
 The United States Pharmacopoeia (USP) also defines
 “The establishment of a relationship between a biological
property, or a parameter derived from a biological property
produced from a dosage form, and a physicochemical property of
the same dosage form”.

5. 5
 To serve as a surrogate for in vivo bioavailability.
 To support biowaivers for bioequivalence testing.
 To validate the use of dissolution methods and set the dissolution
specifications.
 IVIVC proves an important research tool in the development of drug delivery
systems.
 The IVIVC model facilitates the rational development & evaluation of
immediate or extended release dosage forms. Hence it acts as a tool for
formulation screening.
 To assist quality control for certain scale-up and post-approval changes
(SUPAC).

6. 6
 It can be achieved using
o Pharmacological correlations based on clinical observations.
o Semi quantitative correlations based on the drug blood levels
or urinary excretion data.
o Quantitative correlations arising from absorption kinetics and
calculation of in vivo dissolution rate and absorption rate
constants.
 Establishing an IVIVC is nothing more complicated than
trying to reproduce all the complex phenomena that
lead to the in vivo release and solubilization of the API
in the gut in a “simple” in vitro system like a vessel
agitated with a paddle.

7. 7
In vitro dissolution parameters In vivo plasma data parameters
Time for specific amount of drug to
dissolve (e.g. 50% of the dose)
Amount dissolved at a specific time point
Mean dissolution time
Parameter estimated after modeling the
dissolution process
AUC, Cmax
Fraction absorbed, absorption rate constant
Ka
Mean residence time, mean dissolution time,
mean absorption time
Concentration at time t, amount absorbed at
time t
Linear relationship between dissolution parameters and plasma level data are established.
Parameters used for correlating
In Vitro Dissolution with Plasma Data

8. 8
Dissolution parameters are correlated to the amount of drug
excreted unchanged in the urine, cumulative amount of drug
excreted as a function of time, etc.
An acute pharmacological effect such as LD50 in animals is
related to any of the dissolution parameters.

9. 9
Complexity of
the delivery
system.
Composition of
formulation.
Physicochemical
properties of
drug.
Dissolution
method
Method of
manufacture

10. 10
• The mean time for which the drug resides in the body. Also known as mean transit
time.
• MRT = AUMC / AUC
• where, AUMC = Area under first moment Curve (Concentration*time Vs time)
• AUC = Area under curve (Concentration Vs time)
• Both AUMC & AUC can be obtained by using Trapezoidal rule.
Mean Residence Time:
• The mean time required for drug to reach systemic circulation from the time of drug
administration.
• MAT = MRT oral – MRT i.v.
Mean Absorption Time:
• It reflects the mean time for drug to dissolve in-vivo. For solid dosage form:
• MDT solid = MRT solid – MRT solution
Mean In-vivo Dissolution Time:
• % PE = [(Observed value – Predicted value) / Observed value] x 100
Percent Prediction Error:

11. 11
Level A
• Most
informative &
recommended
Level B
• Least useful in
regulatory
purpose
Level C
• Useful for early
stages of
formulation
development
Multiple
Level C
• Useful as
Level A

12. 12
 It is defined as a hypothetical model describing the relationship
between a fraction of drug absorbed and fraction of drug
dissolved.
 In order to develop a correlation between two parameters one
variable should be common between them.
 The data available is in vitro dissolution profile and in vivo plasma
drug concentration profile whose direct comparison is not possible.
 To have a comparison between these two data, data transformation
is required.
 It is considered as a predictive model for relationship between the
entire in vitro release time courses.
Level A Level B Level C Multiple C

13. 13
 Most commonly a linear correlation exists but sometimes non-linear In vitro-
in vivo correlation may prove appropriate.
 MATHS TOOL:
o In vivo – deconvolution of plasma profile( wagner- nelson, loo-riegelman,
numeric deconvolution)
o In vitro – weibull, hill or simple interpolation
 Model-dependant
o Based on the mass balance among the pharmacokinetic compartments
(e.g. Wagner-Nelson, Loo-Riegelman)
 Model-independant
o Based on Theory of Linear System Analysis (Convolution /
Deconvolution)
Level A Level B Level C Multiple C

14. 14
Advantages:
1. A point to point correlation is developed. The in vitro dissolution curve serves as a
surrogate for in vivo performance. Any change in manufacturing procedure or modification
in formula can be justified without the need for additional human studies.
2. The in vivo dissolution serves an in vivo indicating quality control procedure for predicting
dosage form’s performance.
Level A Level B Level C Multiple C

15.  Level B IVIVC uses the principles of statistical
moment analysis. The mean in vitro
dissolution time(MDTvitro) is compared
either to the mean residence time (MRT) or
to the mean in vivo dissolution time
(MDTvivo).
 Level B correlation, like a Level A, uses all of
the in vitro and in vivo data, but is not
considered to be a point-to-point correlation.
 Level B correlation does not uniquely reflect
the actual in vivo plasma level curve, because a
number of different in vivo curves will produce
similar mean residence time values.
 MATHS TOOL : MDTvitro VS MDTvivo
or MRTvivo
15
Level A Level B Level C Multiple C

16.  In this level of correlation, one dissolution
time point (t50%, t90%, etc.) is compared
to on mean pharmacokinetic parameter
such as AUC, t max or C max.
 It represents a single point correlation and
doses not reflect the entire shape of the
plasma drug concentration curve.
 Level C correlations can be useful in the early
stages of formulation development when pilot
formulations are being selected.
 While the information may be useful in
formulation development, biowaiver is
generally not possible.
16
Level A Level B Level C Multiple C

17.  A multiple level C correlation relates one or several pharmacokinetic parameters of
interest (Cmax, AUC, or any other suitable parameters) to the amount of drug
dissolved at several time points of the dissolution profile.
 A multiple point level C correlation may be used to justify a bio waiver, provided that
the correlation has been established over the entire dissolution profile with one or more
pharmacokinetic parameters of interest.
 If such a multiple level C correlation is achievable, then the development of a level A
correlation is also likely.
 A multiple Level C correlation should be based on at least three dissolution time points
covering the early, middle, and late stages of the dissolution profile.
17
Level A Level B Level C Multiple C

18. Level In vitro In vivo
A Dissolution curve Input (absorption) curves
B Statistical Moments: MDT
Statistical Moments: MRT,
MAT
C
Disintegration time,
Time to have 10, 50, 90%
Dissolved,
Dissolution rate,
Dissolution efficiency
Cmax,
Tmax,
Ka,
Time to have 10, 50, 90%
absorbed,
AUC (total or cumulative)
18

19. 19
Developed formulation with different release rate like slow, medium ,fast
In-vitro dissolution profile In- vivo plasma concentration
Predict plasma concentration from in
vitro profile using a LINK MODEL
Do not involve DECONVOLUTION
Estimate in vivo absorption /
dissolution time for each formulation
Establish LINK model between in
vivo & in vitro variable
Predict plasma concentration from in
vitro data using LINK model
One step approach
Two step approach

20. 20

21. 1 . Quantitative correlation:
o In vivo parameter-y, in vitro-x, y = mx +c.
o Pearson product-moment correlation coefficient, r (-1 to +1) quantify
strength of relationship between x & y.
o Pearson's correlation reflects the degree of linear relationship
between two variables.
 PMCC = Product-Moment Correlation Coefficient
21
Measurement
of Pearson
PMCC
Symbol
In a Population rho (ρ)
In a Sample
r or "Pearson's
r."
Correlation between
variables
Linear relationship
+1 Perfect Positive
-1 Perfect Negative
0 No Linear Relationship

22. 2 . Rank order correlation:
oSpearman rank correlation
oValues of the two variables are ranked in ascending or descending
order. Rank order correlations are qualitative and are not
considered useful for regulatory purposes.
22

23. Correlation Methods
• Simple point type
• Comparison of profiles
• Direct differential equation-based
IVIVC
23

24. Simple point type:
• The percentage of drug
dissolved in a given time
or the time taken for a
certain percentage of
drug to be dissolved, is
correlated with a certain
parameter of the
bioavailability.
• Since the selection of
these correlative points
usually is arbitrary, the
interpretation of the
results can be misleading.
Comparison of
Profiles:
• The entire in vivo
response time profile
can be correlated to
the complete
dissolution rate time
curve.
• More preferable
method to develop
dissolution tests that
predict reliably the
time course of the in
vivo behavior of the
drug.
Differential Equation-Based IVIVC
• A novel method is proposed that directly
relates the time-profiles of in-vitro
dissolution rates and in-vivo plasma
concentrations by using one- or multi-
compartment pharmacokinetic models and
a corresponding system of differential
equations that allows for time scaling and
time shifting.
• A multiplying factor for the variability of
absorption conditions as the drug moves
along can also incorporated. By avoiding
the integral transforms used in the existing
deconvolution- or convolution-based IVIVC
models, the present method can provide
increased transparency, improved
performance, and greater modelling
flexibility.
24

25.  Human data should be supplied for regulatory consideration of an IVIVC.
 Bioavailability studies for IVIVC development should be performed with
enough subjects to characterize adequately the performance of the drug
product under study. In prior acceptable data sets, the number of subjects
has ranged from 6 to 36.
• Crossover studies are preferred, parallel studies or cross-study analyses
may be acceptable. The latter may involve normalization with a common
reference treatment. The reference product in developing an IVIVC may be
an intravenous solution, an aqueous oral solution, or an immediate release
product.
 IVIVCs are usually developed in the fasted state. When a drug is not tolerated
in the fasted state, studies may be conducted in the fed state. 25

26.  Any in vitro dissolution method may be used for dissolution characteristics of the
ER dosage form. The same system should be used for all formulations tested.
 The preferred dissolution apparatus is USP apparatus I (basket) or II (paddle)
, used at compendially recognized rotation speeds (e.g., 100 rpm for the basket
and 50-75 rpm for the paddle). In other cases, the dissolution properties of
some ER formulations may be determine with USP apparatus III (reciprocating
cylinder) or IV (flow through cell). Appropriate review staff in CDER should be
consulted before using any other type of apparatus.
 An aqueous medium, either water or a buffered solution preferably not
exceeding pH 6.8, is recommended as the initial medium for development of an
IVIVC. Sufficient data should be submitted to justify pH greater than 6.8.
 For poorly soluble drugs, addition of surfactant (e.g., 1% sodium lauryl sulfate)
may be appropriate. In general, non aqueous and hydroalcoholic systems are
discouraged unless all attempts with aqueous media are unsuccessful.
Appropriate review staff in CDER should be consulted before using any other
media.
26

27.  The dissolution profiles of at least 12 individual dosage units from each lot should be
determined. A suitable distribution of sampling points should be selected to define adequately the
profiles. The coefficient of variation (CV) for mean dissolution profiles of a single batch should
be less than 10 %.
 A Level A IVIVC is considered to be the most informative and is recommended, if possible.
 Multiple Level C correlations can be as useful as Level A correlations. However, if a multiple
Level C correlation is possible, then a Level A correlation is also likely and is preferred.
 Level C correlations can be useful in the early stages of formulation development when pilot
formulations are being selected.
 Level B correlations are least useful for regulatory purposes.
 Rank order correlations are qualitative and are not considered useful for regulatory purposes.
27

28. Model
Validation
Model
Developmen
t
28

29.  The principles of IVIVC model development have been successfully applied to oral
dosage forms.
 The rules for developing and validating IVIVC models for novel and non-oral dosage
forms/delivery systems (micro spheres, implants, liposomes, etc) are still unclear today.
 For orally administered drugs, IVIVC is expected for highly permeable drugs or drugs
under dissolution rate-limiting conditions, which is supported by BCS.
 For extended-release formulations following oral administration, modified BCS
containing the three classes (high aqueous solubility, low aqueous solubility, and variable
solubility) is proposed.
29

30. Class Solubility
Permeabilit
y
IVIVC expectations for Possibility of
immediate release product
Predicting
IVIVC from
dissolution
data
I High High
IVIVC expected, if dissolution rate is
slower than gastric emptying rate,
otherwise limited or no correlation
Yes
II Low High
IVIVC expected, if in vitro dissolution
rate is similar to in vivo dissolution rate,
unless dose is very high
yes
III High Low
Absorption is rate determining and
limited or no IVIVC with dissolution.
No
IV Low Low Limited or no IVIVC is expected No
30

31. Class Solubility Permeability IVIVC
Ia
High and site
Independent
High and site independent
IVIVC Level A
expected
Ib
High and site
Independent
Dependent on site and
narrow absorption window
IVIVC Level C
expected
IIa
Low and site
Independent
High and site independent
IVIVC Level A
expected
IIb
Low and site
Independent
Dependent on site and
narrow absorption window
Little or no IVIVC
Va:
acidic
Variable Variable Little or no IVIVC
Vb:
Variable Variable
IVIVC Level A31

32.  The most basic IVIVC models are expressed as a simple linear
equation (Equation 1) between the in vivo drug absorbed and in vitro
drug dissolved (released).
 In this equation, m is the slope of the relationship, and C is the
intercept.
 Ideally, m=1 and C=0, indicating a linear relationship.
 Depending on the nature of the modified-release system, some data
are better fitted using nonlinear models, such as Sigmoid, Higuchi,
or Hixson-Crowell.
32

33.  However, for dosage forms with complicated mechanisms of release
(longer duration), in vitro release may not be in the same time scale
as the in vivo release.
 Thus, in order to model such data, it is necessary to incorporate time-
shifting and time-scaling parameters within the model.
 This kind of data is routinely encountered in the development of
sustained-release dosage forms.
 In vivo release rate (X’vivo) can also be expressed as a function of in
vitro release rate (X’rel,vitro) with empirically selected parameters
(a, b), as shown in Eq 2.
33

34. Model Dependent
methods
Wagner Nelson Equation
Loo-Riegelman Method
Model Independent
methods
Deconvolution
• The numerical deconvolution/convolution
method is more general and thus
preferred because it does not make any
pharmacokinetic model assumptions.
• Using a pharmacokinetic compartmental
analysis approach, the in vivo absorption
rate can be calculated when the
pharmacokinetic parameters of the drug
substance are known.
34

35. • Convolution is the process of
combined effect of dissolution
and elimination of drug in the
body to reflect blood drug
concentration-time profile
(right to left).
• On the other hand, extracting
dissolution profiles from blood
drug concentration-time profile
is known as the deconvolution
process (left to right).
35

36. Internal Validation
• (using data from the formulations used to build the model)
• validation serves the purpose of providing basis for the
acceptability of the model.
External Validation
• (using data obtained from a different (new) formulation)
• External validation is superior and affords greater “confidence”
in the model.
36

37.  Using the IVIVC model, for each formulation, the relevant exposure parameters (C
max and AUC) are predicted and compared to the observed values.
Prediction Error (% PE)
= ( Cmax observed – Cmax predicted) * 100
C max observed
= ( AUC observed – AUC predicted ) * 100
AUC observed
 The criteria set in the FDA guidance on IVIVC are: For C max and AUC, the mean
absolute % PE should not exceed 10%, and the prediction error for individual
formulations should not exceed 15%.
37

38.  For a new formulation the relevant exposure parameters are predicted using its
in vitro dissolution profile and the IVIVC model and are compared to the
observed parameters.
 For C max and AUC, the % PE for the external validation formulation should not
exceed 10%. A prediction error of 10% to 20% indicates inconclusive
predictability and illustrates the need for further study using additional data sets.
 For drugs with narrow therapeutic index, external validation is required despite
acceptable internal validation, whereas internal validation is usually sufficient
with non-narrow therapeutic index drugs.
38

39. 1. Early Stages of Drug Delivery & Development:
• Proof of Concept
2. Formulation Assessment:
• In Vitro Dissolution
3. Dissolution Specifications
4. Future Biowaivers
5 . IVIVC – Parenteral Drug Delivery
• Burst Release
• Potent Drugs & Chronic Therapy
• Limited volume of tissue fluids and Area of absorption
39

40. 1. Guidance for Industry; Extended Release Oral Dosage Forms:
Development, Evaluation, and Application of In Vitro/In Vivo
Correlations. www.fda.gov/cder/guidance/index.htm
2. Dissolution, Bioavailability and Bioequivalence by Hamed M.
Abdou, Mack Publishing House.
3. IVIVC: Methods and Applications in Modified-Release
Product Development; Harald Rettig and Jana Mysicka.
Dissolution Technologies | FEBRUARY 2008
4. International Journal of Generic Drugs ISSN 0793 758X US/
Canada
40

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