Very Informative presentation

1. IN VITRO IN VIVO CORRELATION
Presented by
Dr. Neeraj Mishra
Department of Pharmaceutics,
PROFESSOR
ISF College of Pharmacy, Moga, Punjab, 142001
Email:neerajdops@rediffmail.com

2. IN VITRO IN VIVO CORRELATION
correlation could be referred to as the relationship
between appropriate in vitro release characteristics and in
vivo bioavailability parameters
DEVLOPING THE CORRELATION
1)Develop formulation with different release rate such as
slow ,medium, fast or a single release rate if dissolution is
condition independent.
2)Obtain in vitro dissolution profiles &derive in vitro
dissolution parameters to be correlated.
3)obtain in vivo plasma conc. profiles by definitive BA
studies of these formulation & estimate the in vivo
absorption or dissolution time course by proper data
treatment i.e. Wagner-nelson method.

3. In Vitro Studies
1. Quality control procedures
2. Tablet or Capsule disintegration
3. Instrumental methods of analysis
4. Dissolution Rate Test
The rate of drug absorption
Dissolution Profile
Parameters In Vivo Performance
Proper In-Vitro Dissolution Rate –
Correlate the data with the bioavailability

4. In-Vivo Studies
• Definition: In-vivo studies deals with the evolution of
bioavailability and bio equivalence of pharmaceutical dosage
forms using parameters like AUC,Cmax etc..
• PARAMETERS:
1.Drug concentration in plasma at each sampling time
2.Apparent rate constant for elimination
3.Biological half life
4. Urinary excretion rate and amount excreted in urine at infinity

5. Comparison methods
The in vitro dissolution is compared by three way
1. Simply positioning one curve (in-vitro dissolution curve) over
the other (In-vivo input rate curve)
2. by determining intensity factor (I)
(I) = (time for 50%absorption) / (time for 50%dissolution)
This may also be quantified by defining the equation for each
curve & comparing the Corresponding constant such as m & c
Y (In-vivo absorbed) = m X(In vitro dissolved)+ c
(‘m’ is the slope of the relationship, and ‘c’ is the intercept)
3. Plot the graph between fraction absorbed in vivo verses the
fraction released in vitro. Simply positioning one curve over
another:

6. LEVELS(METHODS)OF IN VITRO-IN
VIVO CORRELATION
The concept of correlation is based on its ability to
reflect the entire plasmatic concentration time
curve, obtained after the administration of the
dosage form.
It is the relationship between the entire in vitro
dissolution curve to the entire curve of plasmatic
levels of drug which defines the correlation.
Five levels of correlation may be defined and
classified in a descending order of usefulness.

7. Level In-Vitro In-Vivo
A Dissolution curve In put (Absorption) curve
B Statistical Moment:
MDT
Statistical Moment: MRT,
MAT Etc.
C Disintegration time
T10%, T50%, T90%
Dissolved
Dissolution rate
Dissolution
efficiency
Cmax, Tmax, Ka,
T10%, T50%, T90% Absorption
AUC (Total or Cumulative)

8. Level A CORRELATION
It is highest level of correlation It represents a relation point to
point between the in vitro dissolution, and in vivo input rate
(sometimes referred to as in vivo dissolution) of the drug from
the dosage form.
This level of correlation, the in vitro and in vivo dissolution
curves are directly super imposable, or may be superimposed
using a constant (scale factor).
Level A correlation is usually estimated by a two-stage
procedure:
1) deconvolution
2) followed by comparison of the fraction of drug absorbed to
the fraction of drug dissolved

9. A successful IVIVC model can be
developed if in vitro dissolution
is the rate-limiting step in the
sequence of events leading to
appearance of the drug in the
systemic circulation following
oral or other routes of
administration.
Thus, the dissolution test can be
utilized as a surrogate for
bioequivalence studies
(involving human subjects) if the
developed IVIVC is predictive of
in vivo performance of the
product.

10. The main purpose to conduct a dissolution test is to establish a dissolution
profile and then predict/determine a C-t profile from it to assess potential in vivo
characteristics of the test product.
Therefore, it can be said that in reality the purpose of commonly referred
practices of IVIVC is to transfer a dissolution (in vitro) to a C-t (in vivo) profile, or
simply in vitro-to-in vivo profiling.
The mathematical technique to transfer in vitro profile to in vivo profile is known
as convolution. Convolution is relatively simpler than de-convolution as the
former can be applied using simple spreadsheet software, e.g., MS Excel.

11. Determining plasma blood concentrations (output function), if input function
(dissolution results) is available, the procedure will be called convolution
technique and inverse of it, that is obtaining input function
(absorption/dissolution results) if output function (Plasma drug concentration) is
provided, the procedure will be called deconvolution.
There are computer software available which provide the capability of solving for
a function when the others are available. However, the convolution approach
could be simpler where use of commonly available spreadsheet software may
also be used.

12. Deconvolution
Prediction of plasma drug concentrations
Prediction using convolution integral:
mathematical model based on the convolution integral. For
example, the following convolution integral equation may
be used to predict the plasma concentration (c(t)) resulting
from the absorption rate time course.
C(t) = ᶘ0t C (t-u)X’vitro (u)du
The function C represents the concentration time course
that would result from the instantaneous absorption of a
unit amount of drug and can be estimated from either i.v.
bolus data, oral solution, suspension or rapidly releasing (in
vivo) immediate release dosage forms.

13. Evaluating the Predictability of a
Level A Correlation
The objective of developing an IVIVC is to establish
a predictive mathematical model describing the
relationship between an in vitro property and a
relevant in vivo response.
The evaluation approaches focus on the estimation
of predictive performance or, conversely, prediction
error. It is done by two methods
1)INTERNAL PREDICTIBILITY
2)EXTERNAL PREDICTIBILITY

14. INTERNAL PRADICTIBILITY
:
 It is defined as predictability of data used for model development &is
recommended for all IVIVC model To predict each formulation’s plasma
concentration profile from each respective formulation’s dissolution data.
This is performed for each formulation used to develop the IVIVC model.
The predicted bioavailability is then compared to the observed bioavailability
for each formulation and a determination of prediction error is made.
 %PE= <(observed value-predicted value) observed value>*100
Criteria: 1) Average absolute percent prediction error (% PE) of 10% or less for C max and
AUC establishes the predictability of the IVIVC. In addition, the %PE for each formulation
should not exceed 15%.
2) If these criteria are not met, that is, if the internal predictability of the IVIVC is
inconclusive, evaluation of external predictability of the IVIVC should be performed as a
final determination of the ability of the IVIVC to be used as a surrogate for
bioequivalence.

15. EXTERNAL PREDICTABILITY
It is based on how well the IVIVC predicts additional test data .it provides
more comprehensive analysis of the predictability than internal one.
This approach is used when
1) Internal PE is not conclusive.
2) Only two formulation of diff. release rate are available.
3) Correction is developed for narrow therapeutic index drug.
This involves using the IVIVC to predict the in vivo performance for a
formulation with known bioavailability that was not used in developing
the IVIVC model.
Criteria:
1) % PE of 10% or less for C and AUC establishes the external max predictability of an
IVIVC.
2) 2) % PE between 10 - 20% indicates inconclusive predictability and the need for
further study using additional data sets. Results of estimation of PE from all such
data sets should be evaluated for consistency of predictability. 3)% PE greater than
20% generally indicates inadequate predictability, unless otherwise justified

16. ADVANTAGE
It reflects complete plasma level curve. since a
point to point correlation is developed using every
plasma level & dissolution point.
A change in manufacture site, method of
manufacturing raw material supplies, minor
formulation modification & even product strength
using the same formulation can be justified
without need of additional human study.
The extremes of Q.C. standards can be justified by
convolution or deconvolution procedure

17. Formulation Filler Polymer (%) Mag.
Strearate
(%)
Lubricant
Blending
time (min)
Compression
Force (Kg)
Fast Lactose 15 1.0 2 400
Medium Lactose+ DCP
50:50
32.5 1.5 6 600
Slow DCP 50 2.0 2 800
Develop level a IVIVC model & evaluate internal predictability for three
different release rate (fast, medium, slow) metoprolol tablet.
Step 1:Select at least two formulation with diff. release rate such as fast,
medium & slow by this formulation.
Step 2:determine the dissolution vs. time profile for each formulation. The
USP apparatus 1, pH 6.8 at the speed of 150 rpm used as dissolution testing.
fraction of metoprolol absorbed was determined for each time point &
assume that 100mg dissolved represent complete drug release
CASE STUDY

18. Case Study contd…
Step3:Determine the f2 metric similarity for dissolution data. by
this equation.
F2 = 50 log{[1 + 1/n t=1 (Rt – Tt)2]-0.5 x 100}
The observed f2 for each pair of dissolution was less than 50
(f2: FS=30.9,MS=39.3 ,FM=46.0 )
Step4: Conduct a bioavailability and determine plasma drug conc.
vs. time profile for the ER tablets as well as reference solution.
STEP5:Determine fraction of drug absorbed (FRA) -as the
pharmacokinetic of metoprolol followed a one compartment
model, wagner - nelson method used for determine FRA.

19. Case Study contd…
Step6: Plot the fraction absorbed (FRA) vs. fraction
dissolved (FRD) for each formulation separately. -the
plot provides basic information of the relationship (i.e.
linear, non linear) between two variables. -The
regression line should be significant (i.e. p<0.05) &the
slope should not be significant different from 1.
Step7:Determine how well the correlation predicts
the plasma conc. vs. time profile. The estimated
plasma profile was estimated using Deconvolution
technique.

20. Time(h)
Fast (F) Medium (M) Slow (S)
FRD FRA FRD FRA FRD FRA
0.5 0.289 0.043 0.186 0.032 0.150 0.051
1.0 0.438 0.243 0.299 0.163 0.238 0.132
1.5 0.557 0.445 0.384 0.293 0.309 0.224
2.0 0.672 0.328 0.464 0.424 0.373 0.302
4.0 0.899 0.900 0.596 0.660 0.478 0.478
6.0 0.950 0.972 0.709 0.777 0.568 0.593
8.0 0.962 0.964 0.859 0.904 0.708 0.778
10.0 0.964 0.971 0.939 0.954 0.805 0.874
12.0 0.975 0.958 0.978 0.965 0.877 0.893
IVIVC Models (FRD & FRA) for F, M and S Metoprolol
formulations
FRD: Fraction Drug Dissolved; FRA: Fraction Drug Absorbed

21. IVIVC model predictability
IVIVC model predictability was assessed by percent
Cmax & AUC prediction errors.
Based on FDA guidance, the correlation is valid if the
average PE absolute across formulation is <10%for C
max & AUC &the prediction error for any formulation
is <15% for C max & AUC. So IVIVC was valid & can be
used to predict the in vivo behavior of metoprolol ER
formulation.

22. LEVEL B CORRELATION
It utilizes the principle of STATISTICAL MOMENT ANALYSIS in
which the mean residence time (MRT) of the drug in the
body is related to the mean dissolution time (MDT) in vitro.
It is based on the preliminary assumption that movement of
the individual drug molecules through the body
compartment is governed by probability.
Statistical moments are parameters that describes the
characteristic of the time course of plasma conc. & urinary
excretion rate that follow administration of a single dose of a
drug.

23. Mean Residence Time (MRT)
It is the first moment of the distribution.
It is defined as the mean time for drug molecules
to transit through the body.& involve any kinetic
process.
Mean Dissolution Time (MDT)
It represents the mean time for drug molecules
to completely dissolve.

24. Limitation
level B uses all the in vitro and in vivo data, but it is
not considered a point to point correlation, because it
does not reflect the actual plasma level curve, since a
series of different in vivo curves may produce similar
values of the mean residence time (MRT).
It is not possible to consider only level B correlation
to assess formulation changes, manufacturing site
changes, excipients supplier changes, among others.
The in vitro data of such correlation can not be used
to obtain the extreme limits of the quality control
standard.

25. LEVEL C CORRELATION
It is a single point correlation.
This category relates a dissolution time point (t50%, t90%, etc) to a
pharmacokinetic parameter such as AUC, C max or T max LIMITATION: it is a
weakest level of correlation as partial relationship between absorption &
dissolution established.
Since level C correlation does not utilize all the data, it can not reflect the
complete plasma conc. time curve.
APPLICABILITY:
Since this type of correlation does not allow prediction of the actual performance
of the in vivo product, it is useful only as a guide to the development of
formulations or as a production quality control routine It can useful in the early
stages of formulation development when pilot formulations are being selected.
PREDICTIBILITY:
The methods & criteria for assessing the predictability of level C correlation are
same as those of level A correlation.

26. MULTIPLE LEVEL C CORRELATION
Multiple level C correlation relates one or several
pharmacokinetic parameters of interest to the
amount of drug dissolved at several time point of the
dissolution profile.
A relationship should be demonstrated at each time
point at the same parameter such that the effect on
the in vivo performance of any change in dissolution
can be assessed.
It should be based on at least three dissolution time
point covering the early, middle & later stages of the
dissolution profiles.

27. LEVEL D CORRELATION
Level D correlation is a rank order & qualitative
analysis & is not consider useful for regulatory
purpose.
It is used in the development of a formulation or
processing procedure.

28. Class Solubility Permeability IVIVC
I High High Correlation (If dissolution is
rate limited step)
II Low High IVIVC Expected
III High Low Little or no IVIVC
IV Low Low Little or no IVIVC
Bio-pharmaceutical drug classification and expected IVIVC
for immediate release drug products

29. Class Solubility Permeability IVIVC
Ia High & Site
independent
High & Site
independent
IVIVC Level ‘A’ Expected
1b High & Site
independent
Site dependent
with narrow Abs
window
IVIVC Level ‘C’ Expected
IIa Low & Site
independent
High & Site
independent
IVIVC Level ‘A’ Expected
IIb Low & Site
independent
Site dependent
with narrow Abs
window
Little or no IVIVC
III High Low Little or no IVIVC
IV Low Low Little or no IVIVC
Va
(Acidic)
Variable Variable Little or no IVIVC
Vb
(Basic)
Variable Variable IVIVC Level ‘A’ Expected
Bio-pharmaceutical drug classification and expected IVIVC for
Extended release drug products