IVIV CORRELATION PDF

Correlations between in vitro and in vivo data (IVIVC) are often used during pharmaceutical development in order to reduce development time and optimize the. This presentation gives a bird’s eye view on Dissolution in context with IVIVC. It discusses various levels of Correlations currently in practice. Invitro Invivo study & their correlation shortens the drug development period, economizes the resources and leads to improved product quality. Increased activity.

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October 06, ; Accepted Date: December 31, ; Published Date: A Strategic Tool in Drug Development. J Bioequiv Availab S3. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the correlation author and source are credited. In vitro in vivo correlations IVIVC play a key role in the drug development and optimization of formulation which is iviiv a time consuming and expensive process.

Formulation optimization requires alteration in formulation, composition, equipments, batch sizes and manufacturing process. If such types of one or more changes are applied to the formulation, the in vivo bioequivalence studies in human may required to be done to prove the similarity of the new formulation which will not only increase the burden of carrying out a number of bioequivalence studies but eventually increase the cost of correlatoon optimization process and ultimately marketing of the new formulation.

To overcome these problems it is desirable to develop in vitro tests that reflect can bioavailability data. IVIVC can be used in the development of new pharmaceuticals to reduce the number of human studies during the formulation development. Thus, the main objective of xorrelation IVIVC is to serve as a surrogate for in vivo bioavailability and to support biowaivers.

In vitro – in vivo correlation: from theory to applications.

IVIVC is a mathematical relationship between in vitro properties of a dosage form with its in vivo performance. The In vitro release data of a dosage form containing the active substance serve as characteristic in vitro property, while the In vivo performance is generally represented by the time course of the plasma concentration of clrrelation active substance.

For oral dosage forms, the in vitro release is usually measured and considered as dissolution rate. The relationship between the in vitro and in vivo characteristics can be expressed mathematically by a linear or nonlinear correlation. However, correlatlon plasma concentration ivib be directly correlated to the in vitro release rate; it has to be converted to the in vivo release or absorption data, either by pharmacokinetic compartment model analysis or by linear system analysis [ 1 ].

The establishment of a rational relationship correlatkon a biological property, or a parameter derived from a biological property produced by a dosage form, and a physicochemical property or characteristic of the same dosage form [ 2 ].

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 correoation amount of drug absorbed. Practically, the purpose of IVIVC is to use drug dissolution results from two or more products to predict similarity or dissimilarity of expected plasma drug concentration profiles. Before one considers relating in vitro results to in vivo, one has to establish as to how one will establish similarity or dissimilarity of in vivo response i.

The methodology of establishing similarity or dissimilarity of plasma drug concentrations profile is commonly known as bioequivalence correlstion. There are very well established guidances and standards available for establishing bioequivalence between drug profiles and products [ 3 ].

The optimization of formulations may require changes in the composition, manufacturing process, equipment, and batch sizes. IVIVC is often adequate for justification of therapeutically meaningful release specifications of the formulation. Scale up post approval changes Time and cost saving during the product development. Validated IVIVC is also serves as justification for a biowaivers in filings of a Level 3 or Type II in Europe variation, either during scaleup or post approval, as well as for line extensions e.

The main purpose of an IVIVC model to utilize in vitro dissolution profiles as a surrogate for in vivo bioequivalence and to support biowaivers. The concept of correlation level is based upon the ability of the correlation to reflect the complete plasma drug level-time profile which will result from administration of the given dosage form.

This level of correlation is the highest category of correlation and represents a point-to-point relationship between in vitro dissolution rate and in vivo input rate of the drug from the dosage form [ 35 ]. Level A correlation is the most preferred to achieve; since it allows bio waiver for changes in manufacturing site, raw material suppliers, and minor changes in formulation.

The purpose of Level A correlation is to define a direct relationship between in vivo data such that measurement of in vitro dissolution rate alone is sufficient to determine the biopharmaceutical rate of the dosage form.

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In this level of correlation, the mean in vitro dissolution time MDT vitro of the product is compared to either mean in vivo residence time MRT or the mean in vivo dissolution time MDTvivo. A level B correlation does not uniquely reflect the actual in vivo plasma level curves, also in vitro data from such a correlation could not be used to justify the extremes of quality control standards hence it is least useful for regulatory purposes [ 5 ].

This is the weakest level of correlation as partial relationship between absorption and dissolution is established since it does not reflect the complete shape of plasma drug concentration time curve, which is the critical factor that defines the performance of a drug product. Due to its obvious limitations, the usefulness of a Level C correlation is limited in predicting in vivo drug performance.

In the early stages of formulation development Level C correlations can be useful when pilot formulations are being selected while waiver of an in vivo bioequivalance study biowaiver is generally not possible [ 56 ]. This level refers to the relationship between one or more pharmacokinetic parameters of interest C maxAUC, or any other suitable parameters and amount of drug dissolved at several time point of dissolution profile. Multiple point level C correlation may be used to justify a biowaivers provided that the correlation has been established over the entire dissolution profile with one or more pharmacokinetic parameters of interest.

A multiple Level C correlation should be based on at least three dissolution time points covering the early, middle, and late stages of the dissolution profile. The development of a level A correlation is also likely, when multiple level C correlation is achieved 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 [ 56 ].

It is not a formal correlation but it is a semi quantitative qualitative analysis and rank order correlation and is not considered useful for regulatory purpose but can be serves as an aid in the development of a formulation or processing procedure [ 57 ] Table 1. It is generally assumed that absorption and dissolution have a linear relationship hence dissolution and absorption characteristics of a drug are commonly shown interchangeably.

Thus from Figure 2it is to be noted that one should be able to establish drug profiles with dissolution profiles combined with the pharmacokinetic characteristics of the drug as describe in the example above. This process of obtaining a drug profile from dissolution results is known as convolution.

The opposite of this, i. Schematic representation of deconvolution and convolution processes. Convolution is the process of combined effect of dissolution and elimination of drug in the body to reflect blood drug concentration-time profile right to left. On the other hand, extracting dissolution profiles from blood drug concentration-time profile is known as the deconvolution process left to right [8]. In mathematical terminology, dissolution results become an input function and plasma concentrations e.

Using the NONMEM package, a nonlinear mixed effects model can be fitted to the data with a time-scale model linking the in correlafion and in vivo components [ 10 ]. It has been demonstrates that the convolution based and differential equation based models can be mathematically equivalent [ 11 ]. Correlatoon has been developed which implements a differential equation based approach.

This method utilises existing NONMEM libraries and is an accurate method of modeling which is far more straightforward for users to implement. This research shows that, when the system being modeled is linear, the use of differential equations will produce results that are practically identical to those obtained from the convolution method.

But is a task that can be time consuming and complex [ 12 ]. As a result, this methodology, despite its advantages over the deconvolutionbased approach, is not in widespread use. The relationship between jviv quantities in vitro release and plasma drug concentrations is modeled directly in a single stage rather than via an indirect two stage approach.

The model directly predicts the plasma concentration time course. As a result the modeling focuses on the ability to predict measured quantities not indirectly calculated quantities such as the cumulative amount absorbed. The results are more readily interpreted in terms of the effect of in vitro release on conventional bioequivalence metrics [ 5 ].

Deconvolution is a numerical method used to estimate the time course of drug input using a mathematical model based on the convolution integral. 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 observed fraction of the drug absorbed is estimated based on the Wagner-Nelson method. IV, IR or oral solution are attempted as the reference.

Then, the pharmacokinetic parameters are estimated using a nonlinear regression tool or obtained from literatures reported previously. Based on the IVIVC model, the predicted fraction of the drug absorbed is calculated ivic the observed fraction of the drug dissolved. It is the most commonly cited and used method in the literature [ 10 ]. However this approach is conceptually difficult to use. Even when in vivo dissolution curves are obtained there is no parameter available with associated statistical confidence and physiological relevance, which corrrelation be used to establish the similarity or dissimilarity of the curves [ 13 ].

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A more serious limitation of this approach is that it often requires multiple products having potentially different in vivo release characteristics slow, medium, fast.

These products are then ivuv to define experimental conditions medium, apparatus etc. Another approach, has been proposed is based on systems of differential equations [ 15 ]. The use of a differential equation based model could also allow for the possibility of accurately modelling nonlinear systems and further investigation is being carried out into the case where the drug is eliminated by a nonlinear, saturable process.

The convolution and deconvolution methods assume that the system being modelled is linear but, in practice, this is not always the case. Work to date has shown that the convolution-based method is superior, but when presented with nonlinear data even this approach will crrelation. It is expected that, in the nonlinear case, the use of a differential equation based method would lead to more accurate predictions of plasma concentration.

The incorporation of time-scaling in the PDx-IVIVC equation allows this parameter to be estimated directly from the in vivoand vitro release data. As a result, the predictability of an IVIVC model can be evaluated over the entire in vivo time course.

For orally administered drugs, IVIVC is expected for highly permeable drugs, or drugs under dissolution rate-limiting conditions, which is supported by the Biopharmaceutical Classification System BCS [ 616 ]. For extended-release formulations following oral administration, modified BCS containing the three classes high aqueous solubility, low aqueous solubility, and variable solubility is proposed [ 17 ].

In vitro – in vivo correlation: from theory to applications.

Any well designed and scientifically sound approach would be acceptable for establishment of an IVIVC. For the development and validation of a IVIVC model, two or three different formulations with different release rates, such as slow, medium, fast should be studied In vitro oviv In vivo [ 6 ].

A number of products with different release rates are usually manufactured by varying the primary rate controlling variable e. To develop a discriminative in vitro correltaion method, several method variables together with formulation variables are studied, e.

Essentially at this stage a level A correlation is assumed and the formulation strategy is initiated with the objective of achieving the iiv in vitro profile. In context of understanding the applications of IVIVR throughout the product development cycle, it is useful to become familiar with the following terms as they relate to a typical product development cycle for oral extended-release product [ 5 ].

An assumed IVIVC is the one that provides the initial guidance and direction for the uviv formulation development activity. Thus, during step 1 and with a particular desired product, appropriate in vitro targets are established to meet the desired in vivo profile specification.

This assumed model can be the subject of revision as prototype formulations are developed and characterized in vivo, with the results often leading to a further cycle of prototype formulation and In vivo characterization. Out of this product development cycle and In vivo characterization and, of course, extensive in vitro testing is often correlatjon what correlztion be referred to as retrospective IVIVC. The defined formulation that meets the in vivo specification is employed for Stage 2.

At this stage based on a greater understanding and appreciation of defined formulation and its characteristics, a prospective IVIVC is established through a well defined prospective IVIVC study [ 185 ]. In the first step, the In vivo input profile of the drug from different formulations is calculated from drug concentrations in plasma Figure correaltion.

Providing the pharmaceutical industry with oral drug development tools –

Certainly, step 1 activity should culminate in correlztion pilot PK study. This is typically a four or five-arm cross-over study. The size of this pilot pharmacokinetic study will vary depending on the inherent variability of the drug itself but typically range from 6 to 10 subjects [ 5 ]. To separate drug input from drug distribution and elimination, model-dependent approaches, such as Wagner- Nelson and Loo-Riegelman, or model independent procedures, based on numerical deconvolution, may be utilised ckrrelation 192021 ].

In the model dependent approaches, the distribution and elimination rate constants describe pharmacokinetics after absorption. In the numerical deconvolution approach, the drug unit impulse response function describes distribution and elimination phases, respectively.