IVIVC

Guided by: Dr. Shital Butani

Prepared by: Dhara Patel

14MPH103

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Introduction

Importance

Level of correlation

Parameters

Correlation method

Stages of IVIVC model development

Application

Future aspects

Reference

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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.

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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”.

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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).

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It can be achieved usingo Pharmacological correlations based on clinical observations.

o Semi quantitative correlations based on the drug blood levelsor urinary excretion data.

o Quantitative correlations arising from absorption kinetics andcalculation of in vivo dissolution rate and absorption rateconstants.

Establishing an IVIVC is nothing more complicated thantrying to reproduce all the complex phenomena thatlead to the in vivo release and solubilization of the APIin the gut in a “simple” in vitro system like a vesselagitated with a paddle.

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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

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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.

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Complexity of the delivery

system.

Composition of formulation.

Physicochemical properties of

drug.

Dissolution method

Method of manufacture

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• 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:

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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

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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

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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

oBased on the mass balance among the pharmacokinetic compartments

(e.g. Wagner-Nelson, Loo-Riegelman)

Model-independant

oBased on Theory of Linear System Analysis (Convolution /

Deconvolution)


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Advantages:

1. A point to point correlation is developed. The in vitro dissolution curve serves as asurrogate for in vivo performance. Any change in manufacturing procedure or modificationin 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 predictingdosage form’s performance.


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 MRTvivo15


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.

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A multiple level C correlation relates one or several pharmacokinetic parameters ofinterest (Cmax, AUC, or any other suitable parameters) to the amount of drugdissolved at several time points of the dissolution profile.

A multiple point level C correlation may be used to justify a bio waiver, provided thatthe correlation has been established over the entire dissolution profile with one or morepharmacokinetic parameters of interest.

If such a multiple level C correlation is achievable, then the development of a level Acorrelation is also likely.

A multiple Level C correlation should be based on at least three dissolution time pointscovering the early, middle, and late stages of the dissolution profile.

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Level In vitro In vivo

A Dissolution curve Input (absorption) curves

B Statistical Moments: MDTStatistical 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

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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

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1 . Quantitative correlation: o In vivo parameter-y, in vitro-x, y = mx +c.

oPearson product-moment correlation coefficient, r (-1 to +1) quantify strength of relationship between x & y.

oPearson's correlation reflects the degree of linear relationship between two variables.

PMCC = Product-Moment Correlation Coefficient

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Measurement

of Pearson

PMCC

Symbol

In a Population rho (ρ)

In a Sampler or "Pearson's

r."

Correlation between

variablesLinear relationship

+1 Perfect Positive

-1 Perfect Negative

0 No Linear Relationship

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.

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Correlation Methods

• Simple point type

• Comparison of profiles

• Direct differential equation-based IVIVC

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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.

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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

Any in vitro dissolution method may be used for dissolution characteristics of theER 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 basketand 50-75 rpm for the paddle). In other cases, the dissolution properties ofsome ER formulations may be determine with USP apparatus III (reciprocatingcylinder) or IV (flow through cell). Appropriate review staff in CDER should beconsulted before using any other type of apparatus.

An aqueous medium, either water or a buffered solution preferably notexceeding pH 6.8, is recommended as the initial medium for development of anIVIVC. 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 arediscouraged unless all attempts with aqueous media are unsuccessful.Appropriate review staff in CDER should be consulted before using any othermedia.

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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.

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Model Validation

Model Developmen

t

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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.

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Class SolubilityPermeabilit

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

Class Solubility Permeability IVIVC

IaHigh and site

IndependentHigh and site independent

IVIVC Level A

expected

IbHigh and site

Independent

Dependent on site and

narrow absorption window

IVIVC Level C

expected

IIa

Low and site

IndependentHigh and site independent

IVIVC Level A

expected

IIbLow and site

Independent

Dependent on site and

narrow absorption windowLittle or no IVIVC

Va:

acidicVariable Variable Little or no IVIVC

Vb:

BasicVariable Variable

IVIVC Level A

expected

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The most basic IVIVC models are expressed as a simple linearequation (Equation 1) between the in vivo drug absorbed and in vitrodrug dissolved (released).

In this equation, m is the slope of the relationship, and C is theintercept.

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.

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However, for dosage forms with complicated mechanisms of release(longer duration), in vitro release may not be in the same time scaleas 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 ofsustained-release dosage forms.

In vivo release rate (X’vivo) can also be expressed as a function of invitro release rate (X’rel,vitro) with empirically selected parameters(a, b), as shown in Eq 2.

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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.

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• Convolution is the process ofcombined effect of dissolutionand elimination of drug in thebody to reflect blood drugconcentration-time profile(right to left).

• On the other hand, extractingdissolution profiles from blooddrug concentration-time profileis known as the deconvolutionprocess (left to right).

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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.

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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%.

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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

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

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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

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http://www.fda.gov/cder/guidance/index.htm

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Education

IVIVC