An in vitro – in vivo correlation (IVIVC) is defined by the U.S Food and Drug Administration (FDA) as a predictive mathematical model describing the relationship between the in vitro property of an oral dosage form and relevant in vivo response.

1. IN-VITRO-IN VIVO
CORRELATION (IVIVC)
Submitted By: Rahul Pal, Prachi Pandey Submitted to: Dr. Tejpal Yadav
M. PHARM (PHARMACEUTICS), IIND SEM
Subject: “Advanced Biopharmaceutics & Pharmacokinetics”
Department of Pharmacy, NIMS Institute of Pharmacy, NIMS University Jaipur, Rajasthan India.

2. INTRODUCTION
IVIVC plays an critical role in
drug development and in
optimization of formulation
which is certainly a time
consuming and expensive
process.
In IVIVC, “C” denotes
“Correlation” which means
“The degree of relationship”
between Two variables.
“The term IVIVC, could also be employed to establish dissolution specification
and to support and/ or validate the use of dissolution methods.”

3. IVIVC: DEFINITION
USP (United State Pharmacopoeia) Definition: “The establishment of rational relationship between a
biological property or a parameter derived from a biological property produced by a dosage from and
physicochemical property of same dosage form”.
Conceptually, IVIVC describes a relationship between the in-vitro dissolution/release versus the in-vivo
absorption.
FDA (Food and Drug Administration) Definition: “A predictive mathematical model describing
relationship between in-vitro property of a dosage form and in-vivo response.”

4. IN VITRO-IN VIVO CORRELATION
(IVIVC)
It is defined as “The predictive mathematical model that describes the relationship between in vitro property
(such as rate & extent of dissolution) of a dosage form and in-vivo response (such as plasma drug
concentration or amount of drug absorbed)”.
- The main objectives of developing and evaluating IVIVC is to use dissolution test to serve as alternate for
In-vivo study in human beings.
- Assuring the bioavailability of active ingredients from a dosage form.
- Support and or validates the use of dissolution methods and specification.

5. APPROACHES
There are mainly of two approaches:
 By establishing a relationship between the in-vitro dissolution and the in-vivo bioavailability
parameters.
 By using the data obtained from previous bioavailability studies to modify the dissolution methodology
in order to arrive at meaningful in-vitro in vivo correlation

6. PARAMETERS FOR RELATED
CORRELATION
IN-VITRO
 Dissolution rate.
 Percentage (%) drug dissolved.
 Percent drug dissolved.
IN-VIVO
 Absorption rate (or absorption time)
 Percent of drug absorbed.
 Maximum plasma concentration, Cmax.
 Serum drug concentration, Cp

7. LEVELS OF CORRELATION
IN IVIVC
Correlation
level
LEVEL B
LEVEL C
MULTIPLE C
LEVELA

8. DIFFERENT LEVEL OF CORRELATION
 Highest category of
correlation.
 Linear correlation.
 Superimposable in-vitro and
in-vivo input curve or can be
made superimposable by
user of constant offset value.
 Most informative and useful
from a regulatory
perspective.
 Uses the principle of
statistical moment analysis.
 The mean in-vitro
dissolution time is compared
either to the mean residence
time (MRT) or to the mean
in-vivo dissolution time.
 Is not a point-to-point
correlation.
 Level B correlation are rarely
seen in the NDAs.
 Level C correlation
represents a single point
correlation.
 One dissolution time point
(t50%, t90%) is compared to
one mean pharmacokinetics
parameter such as AUC,
tmax and Cmax.
 Weakest level of correlation
as partial relationship
between absorption and
dissolution is established.
LEVELA LEVEL B LEVEL C

9. MULTIPLE LEVEL C
 Multiple Level C correlation relates one or several pharmacokinetics parameters of interest (Cmax, AUC,
or any other suitable parameters) to the amount of drug dissolved at several time points of the dissolution
profile.
 Its correlation is more meaningful than that of the level C as several time points are considered.

10. DISSOLUTION PROFILE COMPARISON
Definition: “It is a graphically representation in terms of (concentration vs. time) of complete release of API
from a dosage from in a appropriate selected dissolution medium”.
Objectives:
 Development of bioequivalent product.
 To develop in-vitro-in-vivo (IVIVC) correlation which can help to reduce the costs, speed-up product
development and reduced the need of perform costly bioavailability human volunteer studies.
 To stabilize final dissolution specification for the pharmacological.
 For optimizing the dosage formula by comparing the dissolution profiles of various formulas of the same
API.

11. IMPORTANCE OF DISSOLUTION
PROFILE COMPARISON
 Dissolution profile of an API reflects its release pattern under the selected condition sets, i.e. either
sustained release or immediate release of the formulated formulas.
 For optimization the dosage form formula by comparing the dissolution profile of various formulas of
the same API.
 FDA has placed more emphasis on dissolution profile comparison in the field of post approval changes.
 By knowing the dissolution profile of particular of the BRAND®.

12. METHODS TO COMPARE DISSOLUTION
PROFILE

13. GRAPHICAL METHOD
 Graphical method is first step in comparing dissolution profile and it is easy to implement but it is difficult
to make definitive conclusions from the it.
 In this method, plot graph of time vs. concentration of solute (drug) in the dissolution medium or
biological fluids.
 The shape of two curves is compared from comparison of dissolution patterns and the concentration of
drug at each point is compared for extent of dissolution.
 If two or more curves are overlapping then the dissolution profile is comparable.
 If difference is small then it is acceptable but higher differences indicate that the dissolution profile is not
comparable.

14. DISTINGUISH BETWEEN
STUDENT
T-TEST AND ANOVA
 It is a statistical test used to compare the means of
two samples.
 The common types of t-test, are one-sample,
two-sample and paired t-test.
 The test statistical value if t.
 If the t-sore or t-value is small, the group or
samples are similar, whereas if the t-value is
large, the group or samples are different.
 It is a statistical method that compares the means
of more than two samples.
 It having two types such as one-way and two-
way ANOVA.
 The test statistical value if F.
 The higher the F value, there exist significant
variation between sample or groups means, and a
low F value indicates low variability.
ANOVA Test
Student t-test

15. METHODS OF DEPENDENT
METHOD
Zero-order kinetics First-order kinetics Korsmeyer-
Peppas Model
Higuchi Model
Hixon-Crowell Model
Osmotic/
transdermal
system
Water-soluble
drug in polymer
matrix
Erodible matrix
formulation
Diffusion matrix
formulation
The various dependent methods can be used to compare the dissolution profile but selecting the model,
interpretation of model parameters and setting similarity limit is difficult.

16. MODEL DEPENDENT METHODS:
ZERO ORDER KINETICS
Zero order API contributes drug release from dosage form that is independent of amount of drug in delivery system
(constant drug release):
Qt = Qo + Kot
Where, Qt is the amount of drug dissolution in time t, Qo is the amount of drug in the solution and Ko is the zero order
kinetics constant expressed in units of concentration/time.
Plot: The graph plotted between cumulative amount of drug released versus time.
Application: Transdermal system, as well as matrix tablets with low solubility drugs in coated forms, osmotic systems etc.
This release is achieved by making:
 Reservoir diffusion systems.
 Osmotically controlled devices.

17. FIRST ORDER MODEL (WATER SOLUBLE
DRUGS IN MATRIX)
log C = log C0 –Kt/2.303
 Where Co is the initial concentration of drug, K is the first order rate constant and t is the time.
Plot: log cumulative percentage of drug remaining vs. time which would yield a straight line with a slope
of –K/2.303.
Application: The relation describing the drug dissolution in dosage form such as those containing water
soluble drugs in porous matrix.

18. HIXON- CROWELL MODEL (ERODIBLE MATRIX
FORMULATION)
 It model used to evaluate the drug release with the changes in the surface area and the diameter of the
particles/tablets. This model is also known as “Root Law”.
 Hixon and Crowell describing this;
Wo
1/3 –Wt
1/3 = kt
 Where wo is the initial amount of drug, wt is the remaining amount of drug at time t.
Plot: Data is to be plotted cube root of drug percentage remaining in the matrix versus time.
Application: Applied to dosage forms such as tablet, where the dissolution occurs in planes that are
parallel to the drug surface if the tablet dimension diminish proportionally.

19. HIGUCHI MODEL (DIFFUSION MATRIX
FORMULATION)
This method/model used to study the release of water soluble and low soluble drugs incorporated in
semisolid and solid matrix.
This is given by Higuchi;
Q =
𝑲
𝑻
Where, Q is the amount of drug released in time ‘t’ per unit area, k is Higuchi constant and T is
time in hr.
Plot: The data is obtained is to be plotted as cumulative percentage drug release versus square
root of time.
Application: Modified release of dosage forms, transdermal system and matrix tablet with water
soluble drugs.

20. KORSMEYER-PEPPAS MODEL
(SWELLABLE POLYMERIC DEVICES)
This is empirical expression relates the functions of time for diffusion controlled mechanism.
 It is given by the equation;
Mt/Ma = Ktn
Where Mt/Ma is functions of drug released, t is time and k is the constant structural and geomatical
characteristics of the dosage form.
n is the release components which is indicative of drug release mechanism.
If n= 1, the release is zero order.
N = 0.5 the release is best described by the Fickian diffusion.
0.6 <n<1 then release is though amnomalus diffusion or case two diffusion.
This model a plot of present drug release versus time is liner.

21. GRAPHICAL REPRESENTATION OF MODELS
Higuchi-Model Korsemeyar Peppas- Model Higuchi-Model

22. SIMILARITY FACTORS
f1 Factors
 It calculates the percent (%) differences
between the two curves at each point and is a
measurement of the relative error between the
two curves.
f2 Factors
 It is logarithmic reciprocal square root
transformation of the sum of error and is a
measurement of the similarity in the percent
(%) dissolution between the two curves.
The valuers of f1 and f2 are sensitive to the number of dissolution time point used.