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

1. IN VITRO–IN VIVO
CORRELATION (IVIVC)
Submitted to :- Submitted by :-
Dr. Javed Ali Mohd Imran
Department of Pharmaceutics MPharm (Pharmaceutics)
SPER, Jamia Hamdard SPER , Jamia Hamdard
New Delhi New Delhi

2. INTRODUCTION :-
• IVIVC plays an critical role in drug development
and in optimization of formulation which is
certainly a time consuming and expensive
process.
• IVIVC allow dosage form optimization with the
fewest possible trail in man, fixes dissolution
acceptance criteria and also can be used as a
surrogate for further bioequivalence studies.

3. DEFINITION OF IVIVC :-
As per USP :-
The establishment of rational relationship between a biological property,
or parameter derived from biological property produced by dosage form,
and a physiochemical property or characteristic of same dosage form.
As per FDA :-
A predictive mathematical model describing relationship between an
In-vitro property of dosage form and In-vivo response.
• Generally, In-vitro property is rate and extend of drug dissolution or
release while the In-vivo response in plasma drug concentration or amount
of drug absorbed.

4. PURPOSE OF IVIVC
• Reduction of regulatory burden
• Optimization of formulation
• Justification for therapeutic product quality
• SUPAC( Scale Up And Post approval Changes)
• IVIVC to support biowaiver
• To minimize the unnecessary human testing
• Minimizing the cost and time required in the additional bioavailability studies

5. PARAMETERS USED FOR CORRELATING IN
VITRO DISSOLUTION AND IN VIVO DATA
In vitro parameters In vivo parameters
Time for a specific
amount dissolved
Area under the conc. vs
time curve
Amount dissolved at a
specific a time point
Fraction absorbed ,
absorption rate
constant
Mean dissolution time Mean residence time

6. LEVELS OF IVIVC
There are 5 types of levels described in FDA guideline
which include
• Level A
• Level B
• Level C
• Multiple level C
• Level D

7. VARIOUS PARAMETERS USED IN
IVIVC

8. LEVEL A CORRELATION:-
• This correlation is highest category of correlation. It represent a point to point
correlation between In-vitro rate of dissolution and In-vivo response.
• Level A provide linear correlation in which In-vitro dissolution And In-vivo input
curves may be directly superimposable and the mathematical description for the both
curves is same or made to be same.
• Level A correlation is most preferred to achieve such it allows biowaiver for changes
in manufacturing site raw material supplier, minor formulation modification or even
product strength using same formulation can be justified without need of additional
human study.

9. •Generally the % of drug absorbed may be calculated by model dependent
techniques such as:
Wagner nelsons method
Loo Riegelman method
Model independent method ( Numeric deconvolution method)
•The regression analysis of each curve is done to evaluate the strength of
correlation, whether the curve is linear or non linear
•If value of determination coefficient is closer to 1 then the strongest is
correlation and linear is curve.
•The main advantage of a Level A correlation is that the quality control
procedure of the in vitro dissolution test is predictive of the drug product
performance in vivo.

10. • In case of level A correlation , an in vitro dissolution curve can serve as a surrogate
for in vivo performance.
• Therefore change in manufacturing site , manufacturing method , raw material
supply , minor formulation modification , and even change in product strength of the
same formulation can justify without the need of additional human studies.
• Level A IVIV correlation is considered to be the most informative and the
recommended one, when possible, by health authorities.
• When the in vitro and in vivo curves are superimposable then it said to be 1:1
relationship , while if a scaling factor is required to make the superimposable , then
the relationship called point to point relationship.

11. IN LEVEL A GRAPH IS PLOTTED BETWEEN %
DRUG ABSORBED AND % DRUG DISSOLVED.

12. DEVELOPING LEVEL A
CORRELATION
Deconvolution model
• It is the process where the output (plasma concentration profile ) is
converted into the input (in vivo dissolution of dosage form).
• The deconvolution technique requires the comparison of in vivo dissolution
profile which can be obtained from the blood profiles with in vitro
dissolution profiles.
• The plasma or urinary excretion data obtained in the definitive
bioavailability study of MR dosage form are treated by deconvolution.
• The resulting data represent the in vivo input rate of the dosage form.
• It can also be called in vivo dissolution when the rate controlling step is
dissolution rate.

13. DECONVOLUTION METHODS
Model dependent
• Wagner Nelson method
• Loo-reigelman method
Model dependent
• Numeric deconvulution

14. Wagner Nelson method Loo-reigelman method
Used for a one compartment
model.
Used for multi compartment
system
Less complicated More complicated
The cumulative fraction of drug
absorbed at time t is calculated
Fraction absorbed at any time t is
given by
Where ,
CT is plasma conc. at time T & KE
is elimination rate constant
(Xp )T is amount of drug in peripheral
compartment as a function of time Vc is
apparent volume of distribution K10 is
apparent first order elimination rate
constant

15. CONVOLUTION MODEL
• Single step approach .
• Here in vitro dissolution profile(input) is converted into plasma
concentration time profile (output).
• It can be done by model independent or model dependent
approaches, physiology based softwares and simulation can be
applied.
• Then predicted plasma profile is compared with the real plasma
profile.

16. In the development of convolution model, the drug concentration-time
profiles obtained from dissolution results may be evaluated using criteria
for in vivo bioavailability assessment, based on Cmax and AUC
parameters.

18. LEVEL B CORRELATION :-
• It is based on principle of statistical moment analysis.
• In this level of correlation , The mean dissolution time in vitro(MDT
vitro) of the product and mean residence time (MRT ) in the body is
determined and correlated.
• Level B correlation utilize all of the in-vivo in-vitro data but is not
consider point to point correlation.
• Level B correlation does not uniquely reflect the actual in vivo plasma
level curve also in vitro data from such correlation.
• Therefore , one cannot rely on the level B correlation alone to predict
the effects of the formulation modifications and changes.
• In vitro data from this correlation could not be used to justify extremes
of quality control standards .

20. LEVEL C CORRELATION
• In this level of correlation , one dissolution time point (t50% , t90%)
is compared with one mean PK parameter such as AUC , Cmax ,
Tmax.
• Therefore, It is single point correlation establish between
dissolution parameter and does not reflect the entire shape of the
PDC-time curve.
• Level C correlations can be useful in the early stages of
formulation development when pilot formulations are being
selected.
• It is weakest level of the correlation.
• Mathematical model of relationship between the amount of drug
in-vitro at a particular time and summary pharmacokinetic
parameter that characterizes in-vivo time course.
(e.g. Cmax, Tmax, T1/2 or AUC).

21. • Waiver of in vivo BA study (Biowaiver) is not possible in this type of correlation
because it can be useful in the early stages of the formulation development.

22. MULTIPLE LEVEL C
CORRELATION
• This level refer to the relationship between one
or more pharmacokinetic parameter of interest
Cmax, Tmax, AUC or any other suitable
parameter, and the amount of drug dissolved at
several time point of dissolution profile.
• It also used to justify biowaiver.
• Used for quality control purpose.

23. LEVEL D CORRELATION:-
• Level D correlation is not formal but is semi quantitative
and rank order correlation and it is not considered useful
for regulatory purpose.
• This can be serves as aid in development of formulation or
processing procedure.

24. PREDICTABILITY OF
CORRELATION
• After the IVIVC is established, its ability to predict accurately the in
vivo data is verified, in order to assess the predictability. Evaluation of
the predictability can be done by back calculation of the initial data
utilized for the establishment of the IVIVC (internal predictability) or
using a new data set (external predictability).
• The objective of the predictabiltiy correlation evaluation is to estimate
the magnitude of the errors in predicating the in vivo BA results from
in vitro dissolution data .
• Evaluation approaches focus on estimation of predictive performance
or prediction error.

25. • Predictability is calculated by Prediction error that is the error in
prediction of in vivo property from in vitro property of drug product.
• Depending on the intended application of an IVIVC and the
therapeutic index of the drug, evaluation of prediction error
internally and/or externally may be appropriate .
• Predictability is very important mainly when IVIVC is used as a
surrogate of in vivo data (biowaiver).

26. Internal predictability
• Evaluates how well model describes
the data used to define IVIVC
• based on the initial data sets used
to define the IVIVC
• Used for wide therapeutic range
drugs
• Used if formulations with 3 or more
release rates were used
External predictability
• Relates how well the model predicts
when one or more additional data
sets are used
• based on additional data sets
obtained from a different (new)
formulation
• Used for narrow therapeutic range
drugs
• Used if formulations with only 2
release rates were used

27. ACCEPTANCE CRITERIA
Internal predictability External predictability
• Average %PE is 10% or less for
Cmax and AUC
• Average % PE is less than 10%
for Cmax and AUC
• %PE for each formulation
should not exceed 15%
• %PE between 10-20%
demands for additional data
sets.
• If these criteria are not met
external predictability should
be performed.
• %PE greater than 20%
indicates inadequate IVVIC

28. Prediction error
For Cmax
Percentage prediction error (P.E) = Cmaxobserved – CmaxPredicted * 100
Cmax observed

30. BCS
BCS is the scientific framework for the classifying drug substance on the basis of the
solubility and the permeability . When combined with the dissolution of the drug
product , BCS takes into account three major factor that govern the rate and extent of
the absorption from instance release dosage form that are Dissolution , solubility , and
Permeability .
BCS defines three dimensionless numbers to characterize the drug substance that are -
• Dose number (Do) , -- Mass divided by the uptake volume of 250ml and the drug
solubiltiy
• Dissolution number (Dn) – Ratio of MRT to MDT
• Absorption number (An) – Ratio of MRT to Absorption time
• The fraction of dose absorbed can be predicted by these three parameters .

31. Table No 1
IVIVC based on BCS classification:-
Class solubility Permeability IVIVC
Expectations
Examples
1 High High IVIVC level A Metoprolol exhibit
high An & Dn values
2 Low High IVIVC expected
if in vitro DR is
similar to in
vivo DR
Phenytoin exhibits low
Dn and High An
3 High Low Little or no
IVIVC
Absorption
Cimetidine and
ranitidine, permeation
rate is limiting step
4 low low Limited or No
IVIVC
Cyclosporine ,
furosemide , taxol

32. DEVELOPMENT OF IVIVC
Reference – Lachman-Liberman’s “The Theory and Practice of Industrial Pharmacy” ,
fourth edition , page no -208.

34. Predictive errors in IVIVC
Since 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 proposed evaluation approaches focus on the estimation of
predictive performance or, conversely, prediction error. The significant
factor for that is the range of release rates studied.
•The release rates, as measured by percent dissolved, for each
formulation studied, should differ adequately (e.g., by 10%). This should
result in in vivo profiles that show a comparable difference, for example,
a 10% difference in the pharmacokinetic parameters of interest (C or
AUC) max between each formulation.
•Once this relationship has been achieved, in vitro dissolution can be used
confidently as a surrogate for in vivo bioequivalence of ER drug product.

35. Prediction of errors internally :
• If formulations with three or more release rates are used to develop the IVIVC
model, no further evaluation beyond this initial estimation of prediction error
may be necessary for non-narrow therapeutic index drugs.
Prediction of errors externally :-
•This is appropriate in some situations, particularly when only two formulations
with different release rates are used to develop the IVIVC model, when
calculation of prediction error internally is inconclusive, or when a narrow
therapeutic index drug is studied
Criteria :-
• 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%.

36. Role of IVIVC in SUPAC:-
• IVIVC helps in Scale Up Post Approval Changes as following way :-
•When any different change made in composition of formulation.
•If manufacturing site changes take place.
•Change in manufacturing process and process variables.

37. Dissolution could also become a surrogate for bioavailability thus allowing to
evaluate the in vivo performance of a formulation without having to test it in vivo.
•The FDA guidance on this outlines the criteria for granting in vivo
bioequivalence waivers. It is based on the mean predicted plasma concentration–
time profile obtained from the mean in vitro dissolution.
•The difference in both the mean Cmax and AUC should not be more than 20%
between the test and reference.
• If Bioequivalence for reference product and test product is same then they are
same in composition. Hence their dissolution profile is similar.
Role of IVIVC in biowaivers :-

38. IVIVC Guidelines :-
• The guidelines for required of IVIVC is given in 21 CFR 320.24.
•ICH guideline Q8(R2)
Sr No Drug Company Date
1. Ritonavir ROXANE laboratories 15/01/2015
2. Miglitol Orient pharma Co.
Ltd.
24/02/2015
3. olmesartan Mylan
pharmaceuticals inc
26/10/2016
4. locosamide Sun pharma 28/04/2016
5. Carbidopa Edenbridge
pharamceuticals
17/02/2016
6. Zolpidem Par formulations
private limited
08/01/2016
Table No . Recent drug approval based on IVIVC studies

39. REFERENCE
• Guidance for industry, Extended Release Oral Dosage Forms:
Development, Evaluation, and Application of In Vitro/In Vivo
Correlations, U.S. Department of Health and Human Services Food
and Drug Administration, Center for Drug Evaluation and Research
(CDER), September 1997, BP 2.
• Lachman-Liberman’s “The Theory and Practice of Industrial
Pharmacy” , fourth edition.
• S.K Niazi – “Handbook of Bioequivalence testing”

40. THANK YOU