Biorelevant Media l Prediction of plasma profile l Development of IVIVCs

                                         

Biorelevant Media l Prediction of plasma profile   l  Development of IVIVCs

How a medicine should be taken is usually explained in the patient information leaflet provided by the manufacturer. Whether a drug is taken before or after a meal can have a big impact on its absorption because food changes the properties of gastrointestinal fluids a lot. Gut fluids in both fasted and fed states are simulated by Biorelevant Media.

I have already shared the information regarding Fluid composition in the GIT. More details refer link

https://pharmatutor21.blogspot.com/2021/01/biorelevant-dissolution-media.html

This blog will help you to understand other two steps i.e. Prediction of plasma profile and Development of IVIVCs

Biorelevant dissolution medium

Before the development of biorelevant dissolution medium the following steps should be considered

1)         Fluid composition in the GIT

Refer https://pharmatutor21.blogspot.com/2021/01/biorelevant-dissolution-media.html

2)         Hydrodynamics in the GIT

Refer https://pharmatutor21.blogspot.com/2021/01/biorelevant-media-l-hydrodynamics-in.html

3)         API/formulation properties

Refer https://pharmatutor21.blogspot.com/2021/01/biorelevant-media-l-hydrodynamics-in.html

4)         Prediction of plasma profile

5)         Development of IVIVCs

 

4) Prediction of plasma profile

In vitro drug dissolution/release tests are conducted to estimate or predict in vivo drug release characteristics of a product. Direct estimation of in vivo drug dissolution is usually not possible and therefore blood drug concentration-time profiles are used for this purpose.

The prediction of plasma profile can be done by using model dependent or model independent approaches. Wagner-Nelson, Loo-Riegelman and numerical deconvolution are such methods. Wagner-Nelson and Loo-Riegelman are both model dependent methods in which former is used for a one-compartment model and the latter for multi-compartment system. The prediction method using convolution analysis consists of following processes.

i) The drug amount–time profile in each segment is calculated by the convolution method.

ii) The absorption rate–time profile in each segment is calculated by using the drug amount–time profile in each segment, calculated in step (i).

iii) The absorption rate–time profile in the whole GI tract is calculated as the sum of the absorption rate–time profiles of four segments obtained in step (ii).

iv) Prediction of the plasma concentration–time curve of orally administered drug is performed by means of the convolution method.

v) The total absorption rate– time data obtained in step (iii) and pharmacokinetic parameters after intravenous administration correspond to the input function and the weight function, respectively. The inverse Laplace transformation of the obtained equation by the convolution program gives the predicted plasma concentration profile after oral administration without the first-pass metabolism in intestinal epithelium and/or liver.

The pharmacokinetic parameters determined by taking following consideration as study design, population study, study conditions, characteristics investigated during bioavailability study, bioanalytical methodology and statistical evaluation.

5) Development of IVIVCs

The term correlation is frequently employed within the pharmaceutical and related sciences to describe the relationship that exists between variables. Mathematically, the term correlation means interdependence between quantitative or qualitative data or relationship between measurable variables and ranks. From biopharmaceutical standpoint, correlation could be referred to as the relationship between appropriate in vitro release characteristics and in vivo bioavailability parameters. Two definitions of IVIVC have been proposed by the USP and by the FDA.

United State Pharmacopoeia (USP) definition

The establishment of a rational relationship between a biological property and a parameter derived from a biological property produced by a dosage form, and a physicochemical property or characteristic of the same dosage form.

Food and Drug Administration (FDA) definition

IVIVC is a predictive mathematical model that shows relationship between an in vitro property of a dosage form and a relevant in vivo response. Generally, the in vitro property is the rate or extent of drug dissolution or release while the in vivo response is the plasma drug concentration or amount of drug absorbed.

OBJECTIVES OF IVIVC

IVIVC plays an important role in product development which serves as a surrogate of in vivo and assists in supporting biowaivers, supports and / or validates the use of dissolution methods and specifications and assists in quality control during manufacturing and selecting appropriate formulations. The different levels of IVIVC are listed in Table 1.

Table 1: Levels of IVIVCs

Levels	In vitro	In vivo
A	Dissolution curve	Absorption curve
B	Statistical moment MDT	Statistical moments MRT, MAT etc.
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
Multiple level C	One or several pharmacokinetic parameters of interest	Amount of drug dissolved at several time points
D	Not considered useful for regulatory purpose

To develop and validate an IVIVC model, two or three different formulations should be studied in vitro and in vivo. Typically, the qualitative composition of drug products is the same, but the release-controlling variable(s), e.g., the amount of excipients, or a property of the drug substance such as particle size, is varied.

To develop a discriminative in vitro dissolution method, several method variables together with formulation variables are studied, e.g., different pH values, dissolution apparatuses and agitation speeds.

Approaches for development of correlation

Mainly two approaches are used for development of correlation

·       Two step:

Step 1: Estimate the in vivo absorption or dissolution time course using an appropriate technique for each formulation and subject

Step 2: establish link model between in vivo and in vitro variables and predict plasma concentration from in vitro using the link model.

·       One step:

Predict plasma concentration from in vitro using a link model whose parameters are fitted in one step, so here it doesn’t involve deconvolution.

 

The deconvolution technique requires the comparison of in vivo dissolution profile obtained from the blood profiles with in vitro dissolution profiles. It is the most commonly cited and used method in the literature. Perhaps that is the reason for the lack of success of developing IVIVC, since this approach is conceptually weak and difficult to use to derive the necessary parameters for their proper evaluation. For example: (1) Extracting in vivo dissolution data from a blood profile often requires elaborate mathematical and computing expertise. (2) It often requires multiple products having potentially different in vivo release characteristics (slow, medium, fast). These products are then used to define experimental conditions (medium, apparatus etc.) for an appropriate dissolution test to reflect their in vivo behaviour. (3) This technique requires blood data (human study) for the test products to relate it to in vitro results.

An IVIVC should be evaluated to demonstrate that predictability of in vivo performance of a drug product from its in vitro dissolution characteristics is maintained over a range of in vitro dissolution release rates and manufacturing changes. 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. Methodology for the evaluation of IVIVC predictability is an active area of investigation and a variety of methods are possible and potentially acceptable. A correlation should predict in vivo performance accurately and consistently.

 

Ø  Internal predictability is applied to IVIVC established using formulations with three or more release rates for non-narrow therapeutic index drugs exhibiting conclusive prediction error (PE).

% PE = [(Observed parameter – Predicted parameter)/
(Predicted parameter)]*100

According to the IVIVC guidance, the average prediction error across formulations cannot be greater than 10% and a formulation cannot have a prediction error greater than 15%.

Ø  External predictability evaluation is not necessary unless the drug is a narrow therapeutic index, or only two release rates were used to develop the IVIVC, or, if the internal predictability criteria are not met i.e. prediction error internally is inconclusive.

The prediction error for external validation should not exceed 10% where as % PE between 10 - 20% indicates inconclusive predictability and the need for further study using additional data sets.

Ø  Various softwares have been developed such as Simcyp, GastroPlus^TM, PK-Sim^TM, MEDICI-PK^TM, Cloe PK^TM etc. for physiological based pharmacokinetic model (PBPK).

The parameter such as metabolic factors, drug loss in GIT and stereochemistry are to be considered while developing IVIVC