This document discusses in-vitro in-vivo correlation (IVIVC), which relates an in-vitro property like dissolution to an in-vivo response. It defines IVIVC and outlines its importance for predicting bioavailability changes and minimizing human testing. The document describes parameters and methods used to generate correlation data, including different levels of IVIVC and approaches to modeling the in-vitro and in-vivo release profiles. It also addresses applications like biowaivers and setting dissolution specifications, as well as challenges in reliably establishing IVIVC models.

1. In-Vitro-In-Vivo Correlation (IVIVC)
Guru Gobind Singh College of Pharmacy, Yamunanagar
By Dr. Rameshwar Dass

2. Contents
• Definition of IVIVC
• Why require IVIVC
• Parameters and methods of correlation
• Levels of IVIVC
• Generation of in-vitro release profile
• Generation of in-vivo release profile
• Predictability Error and Issues
• In-vitro in silico in-vivo Correlation
• Applications

3. Definition of IVIVC
• In-vitro in-vivo correlations (IVIVC)
• It is the inter-relationship b/w an in-vitro property (such as
dissolution) and an in-vivo response.
• Valid in-vitro and in-vivo methods valid IVIVC

4. Why require IVIVC
• To find change in process effects
• Effect of site change
• Effect of formulation and
• For biowaiver of BA/BE testing
• To minimize unnecessary human testing
• To setup meaningful in-vitro release specifications
• Decreased regulatory burdens
• To minimize the product cost & time required in additional BA studies

5. Parameters in IVIVC level
Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.
Level In-vitro In-vivo
A Dissolution curve Input (absorption) curves
B MDT MRT, MAT, MDT
C
Disintegration time, Time to have
10,50,90% dissolution, Dissolution
rate, Dissolution efficiency
Cmax, Tmax, Ka, Time to have 10,
50, 90% absorption, AUC (total or
cumulative)

6. Methods of IVIVC
Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.
Convolution Deconvolution
Joins together three signals: input and output , as well as
the signal characterizing the system (subject of our
studies)
Determine an unknown input signal
Dissolution data (C) may be evaluated using criteria for
in-vivo BA/BE assessment, based on Cmax and AUC
parameters
A numerical method used to estimate the time
course of drug input using a mathematical model
based on the convolution importance
NONMEM can be fitted to the data, model linking the
in-vitro and in-vivo components
Drug absorbed is estimated using Wagner-Nelson
method or Loo Riegelman
The relationship between in-vitro release and Cp is
modeled directly in a single stage rather than via an
indirect two stage approach.
Difficult to calculate in-vivo dissolution data from a
blood profile and often requires mathematical and
computing expertise.

7. Levels of IVIVC
Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.
Level A –
It is Point to point relationship
First de-convolution to get in-vivo %
drug absorbed, then compare with
%dissolved
The in-vitro dissolution and in-vivo
input curves may be directly super-
imposable

8. Levels of IVIVC
Level B –
 Statistical moments analysis
 MRT or MDT in-vivo vs. MDT in-
vitro
 MRT=AUMC/AUC
C*t
Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.
t
AUMC

9. Levels of IVIVC
Level C –
 Single point
 PK parameter vs. % dissolved
 Weakest level of correlation
Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.

10. A
B
C
Flow chart of IVIVC
Wang et al (2009) Diss Tech, 8, 6-12
IVIVC
API –
Physicochemical
Properties
BCS Class
PK Data Cmax
AUC, Tmax
Dosage Form
Properties
Biorelevent
Dissolution Profile
(MDT)
NONMEM Computer Modelling (Using Convolution including, PK Models,
and PK Parameters, API properties or Drug Release Data)
IVIVR

11. Generation of In-Vitro Release Profile
• Dissolution apparatus 1 (Basket, 100rpm) or 2 (Paddle, 50 &75rpm)
– Aqueous medium: 900 ml
– pH: 1to 7.4
– Temperature 37±0.20C
– Simulated gastric fluid (class 1) and simulated intestinal fluid (class 3)
– In-vitro food effect: Effects of oils, enzymes and pH
• Rotating dialysis cell method

12. Generation of In-Vitro Release Profile
 In-vitro dissolution time profile: formulations data were fitted to
– where Fdiss, vitro -fraction dissolved at time t,
– Finf is the fraction dissolved at time infinity—fixed to 1,
– MDT is the Mean Dissolution Time (hours), and b is the slope factor.
1
t
+
MDT
t
*
F
=
(t)
F b
b
b
inf
vitro
diss, 




2
)
MDT
t
(
-
exp
-
(1
=
(t)
F vitro
diss, 









 b
Weibull functions
Hill functions

13. Generation of In-Vitro Release Profile
• The similarity factor: the similarity in the % dissolution between the two
curves.
• Wt – optimal weighing factor, Rt & Tt – Reference & test dissolution value,
n- No. of dissolution time points
• Note: f2 values ˃50 (50-100) mean equivalence of the two curves.
2
100
*
)
(
1
1
log
*
50
=
f
5
.
0
1
2 

























n
t
t
t
t T
R
W
n

14. Dissolution Specifications
• Without IVIVC
– ± 10% of the label claim from mean dissolution profile of the bio or clinical batch
– Can be >10% but range not >25% in certain cases
• With IVIVC
– All batches should have dissolution profiles with upper and lower predicted
bioequivalence
• Proper or Biorelevant Dissolution conditions
– Consider medium, volume, duration, apparatus
– pH 1 – 7.4
• Predictive of bioavailability
– Similar conditions, similar dissolution and similar bioavailability

15. Generation of In-Vivo Release Profile
• Compartmental Models
– Wagner-Nelson (Ke, Ka)
– Loo-Riegelman
• Linear Systems Models
– Deconvolution
– Convolution
Note: mathematically they all yield the same result

16. Other Methods of Generating
In-Vivo Release Profiles
• Non-linear relationships between fraction dissolved and fraction absorbed
was also observed using following equation:
• where, α is the ratio of a first order permeation rate constant to the first order dissolution
rate constant,
• Finf, is the fraction of the dose absorbed at time infinity, and
• D, is a fraction of the total amount of drug absorbed at time t.
• For high values of α, dissolution is rate limiting step in absorption process and a linear
level A IVIVC
• Small values of α give rise to a sort of parabolic relationship, non-linear (1.92) show in
vitro rapid initial dissolution rate as compared to that of in vivo.
(1)
-
-
-
)
1
(
1
1
)
1
(
1
1
1
A
inf












 D
D
F 



17. IVIVC Bench Issues
• Reliable and bio-relevant dissolution method and apparatus suitability
– Qualification and calibration of equipment, sink conditions
– Ability to discriminate non-BE lots
– Apparatus and media for continuous IVIVC (minimum 3 lots) and tuning with GI conditions
• Accurate deconvolution of the plasma concentration-time profile
– %absorbed in-vivo may be reflective than release; absorption rate limitation is common for CR
products
• Dissolution Specifications
– Based on biological findings rather than pharmacopeia

18. IVIVC Modeling Issues
• Intra- and Inter-subject variation: High variations can alter the mean data
and in rotate the deconvolution
– Enterohepatic recycling or second peak
– Reproducibility of reference profiles
• Modeling
– Smoothness of input and response functions
– Stability of numerical methods
– Jumps in input rate e.g., delayed release or gastric emptying
– Statistical properties of the models (Cmax, AUC)

19. In-vitro- in silico- in-vivo Correlation
• In silico is an expression performed on computer or via computer simulation.
• Miramontes used the term “in silico” to characterize biological experiments carried
out entirely on a computer.
• in silico studies predict how drugs interact with the body and with pathogens.
• For example: software emulations to predict how certain drugs already in the
market could treat multiple-drug-resistant and extensively drug-resistant strains of
tuberculosis.

20. In-vitro- in silico- in-vivo Correlation
• This approach is used in drug discovery and early preclinical phases where PK
data is not available.
• simulation of structural properties of a molecule
• To generate experimental data
• There are two in silico approaches for prediction of in vivo oral absorption:
– Statistical models
– Mechanism-based models

21. In-vitro- in silico- in-vivo Correlation
• There is a variety of in silico techniques are discuss:
• Bacterial sequencing techniques – As an alternative to in vitro methods for
identifying bacteria,
• In this the sequence of bacterial DNA and RNA have been developed.
• Polymerase Chain Reaction (PCR).
– PCR takes a single or few copies of a piece of DNA and generating millions or more copies of a
particular DNA sequence.
– It allow to detect bacteria associated with a variety of conditions with increasingly high
sensitivity.

22. In-vitro- in silico- in-vivo Correlation
• Molecular modeling – in silico work, demonstrating how drugs and other
substances interact with the nuclear receptors of cells.
• The computer-based emulations show that 25-D, one of the vitamin D metabolites,
and Capnine, a substance produced by bacteria, turn off the Vitamin D Receptor
• These results have since been validated by clinical observations.
• Whole cell simulations –built a computer model of the crowded interior of a
bacterial cell
– Find its response to sugar in its environment
– accurately reproduce the behavior of living cells.

23. In-vitro- in silico- in-vivo Correlation
• Mechanism based model used GastroPlusTM .
• Inputs to software include:
– Oral dose
– Physiochemical properties (pH-solubility profile, permeability etc.)
– Physiological properties (species, GI transit, GI pH, food status etc.)
– Formulation properties (release profile, particle size etc.)
– PK parameters (optional)
• The output includes:
– Fraction of oral dose absorbed
– Plasma Concentration time profiles (if PK parameters are given)

24. In-vitro- in silico- in-vivo Correlation
CASE STUDY by GastroPlus TM
• whether or not the mean particle size requirement of Compound I (aqueous
solubility>100 mg/mL) may be relaxed from 35µm to approximately 100µm
without affecting its oral bioavailability.
• A simulation suggested that the extent of absorption is not sensitive to changes in
particle size in the range of 35–250 µm.
• This helps in decision making with respect to dosage form design.

25. Failure of Level A IVIVC
• For Level A analysis, Fa is plotted against Fd (requires linear regression)
• IR products is less successful as they do not show dissolution limited absorption. A
reason for this lack of success and acceptance
• Controlled release products, rather than IR products, are the focuses in the IVIVC
• Indicate that dissolution from such products as an alternate for bioavailability.

26. Acceptance criteria
IVIVC
According to FDA guidance
• 1) ≤15% for absolute prediction error (%P.E.) of each formulation.
• 2) ≤ 10% for mean absolute prediction error (%P.E.).
Prediction error
For Cmax
For AUC:
(5)
(6)
100
%
max
max
max
x
C
C
C
PE
observed
predicted
observed
absolute 




 

100
% x
AUC
AUC
AUC
PE
observed
predicted
observed
absolute 




 


27. Applications of IVIVC
Biowaivers
This is main role of establishing IVIVC and dissolution test as a surrogate
for human studies
Establishment of dissolution specifications
Dissolution specifications may be used to minimize the possibility of difference
between in-vitro & in-vivo performance.

28. Applications of IVIVC
 Product development of new formulations, pre-formulation studies, laboratory
scale trials.
 Optimization of the formulation/process predicted from the IVIVC validated.
 Scale-up and post-approval changes (SUPAC): the dissolution data are used to
judge the impact of process changes
 Design and analysis of clinical studies possibly needed for generating the IVIVC.
 Optimization of in-vitro dissolution system to be a predictor of in-vivo
performance.
 Development and validation of Level A & C, including linear and nonlinear
models.

29. IVIVC Software
WinNonlin- IVIVC Toolkit
GastroPlus v. 6.1 IVIVCPlus
PDx-IVIVC
DDDPlus v. 3.0
ivivc for R

30. Assignment Problem
• Distinguish the convolution and deconvolution model?
• What do you understand by in-vitro in silico in-vivo
method and write Case study on it?
• Discuss in detail the IVIVC?

31. For further reading
• https://mpkb.org/home/patients/assessing_literature/in_vitro_studies
• J Emami, In vitro - In vivo Correlation: From Theory to Applications.
JPharm Pharmaceut Sci (www.cspscanada.org) 9(2):169-189, 2006
• Biopharmaceutics & pharmacokinetics by D.M.Brahmankar & Sunil B.
Jaiswal.
• Biopharmaceutics & pharmacokinetics by P.L.Madan.
• Applied Biopharmaceutics and Pharmacokinetics, 7th edition by Leon Shargel