Conventional & Mechanistic IVIVC: Complementary or Clashing Methods?

Conventional & Mechanistic IVIVC: Complementary or Clashing Methods?

An IVIVC (in vitro-in vivo correlation) is a mathematical relationship that predicts key pharmacokinetic parameters (Cmax, AUC) from in vitro dissolution data. Drug developers use IVIVCs for 3 major reasons:

  1. To serve as a surrogate for human bioequivalence (BE) studies
  2. To support and/or validate the use of dissolution methods and specifications
  3. To assist in quality control during manufacturing and selecting appropriate formulations

My colleague, Nikunj Patel, has previously written on the Certara blog about mechanistic IVIVC models. In this blog post, I’ll discuss how to develop IVIVC models using the conventional approach.

How to develop an IVIVC model

An IVIVC model has been defined by the 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.” Generally, the in vitro data that is used in an IVIVC is the rate of drug dissolution over time. The in vivo dissolution is plotted against the in vitro dissolution. Deconvolution is used to evaluate in vivo release and delivery when data from a known drug input (UIR) from IV or oral solution is available. Convolution is the opposite process where the drug’s in vivo input and elimination profiles are combined to reflect the plasma drug concentration-time profile.

Conventional methods of fraction absorbed estimation

Conventional IVIVC generally uses one of three deconvolution methods:

  • Wagner-Nelson
  • Loo-Riegelman
  • Numerical

These methods use several important assumptions. The first two approaches deconvolute the systemic input rate which is a composite function: dissolution + gastrointestinal (GI) transit + GI permeation + first pass metabolism. These two methods are also restricted to being applied to drugs that undergo linear elimination. Also, the Wagner-Nelson method treats the body as a single compartment. Thus, this method is not appropriate for drugs that follow multiple compartment characteristics. Likewise, it does not assume that the absorption follows zero- or first-order kinetics. Finally, it has the advantage of being able to calculate the fraction of drug absorbed over time without requiring IV plasma drug concentration-time data.

By contrast, the Loo-Riegelman Method takes a compartmental modeling approach. This method requires concentration-time data from both extravascular and intravenous administration of the drug to the same subject.

When would you want to use numerical methods of deconvolution? These methods are model independent. They make no assumptions on the number of compartments or kinetics of absorption. Like the Loo-Riegelman method, numerical approaches require both extravascular and reference data from oral solution/immediate release formulation or IV data. In addition, these methods assume that the drug undergoes linear distribution and elimination and is time-invariant. Numerical methods also assume that the input site is the same for all formulations and that the input rate is constant (similar to infusion) between two time points. Depending on which unit impulse response (UIR) you are using, numerical methods deconvolute a composite function of dissolution, GI transit, GI permeation, and first pass metabolism.

When to use mechanistic IVIVC

All of these conventional methods are sufficient for IVIVC models where no complex ADME processes are involved. Sometimes, you may want to separately estimate the different processes that are involved in drug systemic absorption (dissolution, GI transit time, permeation, gut wall metabolism, and first pass metabolism). In this case, you’ll want to use a mechanistic IVIVC approach. This approach can separate in vivo dissolution from systemic input to be correlated against in vitro dissolution and provide a better IVIVC.

Steps involved in IVIVC model development

Let’s briefly cover the process you would undertake to develop a conventional IVIVC model:

  1. Look at the molecule that you are investigating and determine when an IVIVC is likely to succeed or fail. Drugs are divided into different classes based on their permeability and solubility. If the drug is BCS class I or II (Biopharmaceutics Classification System), IVIVC is likely to succeed when dissolution is the rate limiting step in absorption.
  2. Understand the data requirements. You will need in vitro data from all the formulations that you are investigating. Ideally, you should have fast, intermediate, and slow release formulations with at least a 10% difference in dissolution profiles between the formulations. Then, you will need your corresponding in vivo data from a crossover study. In addition, you will need in vivo data from the innovator product if you want to determine bioequivalence.
  3. Model the dissolution data to be able to predict the dissolution at any given time point. An IVIVC that correlates the entire in vitro and in vivo profiles has regulatory relevance and is called a Level A correlation. This level of correlation is the highest category of correlation and represents a point-to-point relationship between the in vitro dissolution rate and the in vivo input rate of the drug from the dosage form.
    Level A correlation is the most preferred since it may allow a 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.
  4. Estimate UIR from the reference formulation (IV/oral solution/immediate release formulation) plasma concentration data. The Unit Impulse Response can be described as disposition functions in terms of exponents with constant parameters corresponding to a unit dose. For example, for a drug that can be described best by a one compartment model, the parameters for UIR are A1 and alpha1. When oral data is used as a reference, the absorption components are removed after modeling and only the disposition parameters are used as the UIR.
  5. Use deconvolution to estimate the fraction absorbed using the UIR.
  6. Learn to use plots. There are two major plots used: Fraction absorbed vs. fraction dissolved and the Levy Plot which plots time in vivo vs time in vitro. These plots are very helpful in determining which IVIVC models are most appropriate.
  7. Account for Tscale, Tshift, AbsScale, and AbsBase if required. Tscale refers to time scale differences between in vitro and in vivo data. Tshift refers to any lag time in the in vivo data. If you have any differences in the bioavailability between formulations, this is accounted for by AbsScale. AbsBase is not used very often. It is only applicable for drugs that are also present as endogenous molecules. Then, AbsBase is applied for a baseline correction if there is a Y-intercept of the regression line of fraction absorbed vs. fraction dissolved.
  8. Once you have the IVIVC model developed, you can predict the plasma concentration-time profiles for each of the internal formulations used in IVIVC development. Estimate Cmax and AUC from predicted plasma concentration-time profiles. Estimate prediction error using observed Cmax and AUC data as references. If the average prediction error is less than 10% and 15% or less for these parameters for any one of the formulations, your IVIVC model is validated.

For more information on comparing conventional and mechanistic approaches with example case studies, I encourage you to watch a webinar that I recently gave with my colleague Nikunj Patel.

*This post received editorial support from Suzanne Minton

Venkateswari Muthukrishnan

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Venkateswari Muthukrishnan

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Dr. Venkateswari Muthukrishnan joined Certara in 2012. She is responsible for Training and Pre-sales Support in South Asia Pacific and Middle East. In addition, she provides Training and Support on Phoenix Products: Phoenix WinNonlin, NLME, Connect and IVIVC Toolkit. Prior to joining Certara, she held positions at a number of organizations including: AAI International, FibroGen Inc., Biocon Limited, Nektar Therapeutics India Pvt. Ltd., PharMantra Consulting Services, and BITS-Pilani (HYD).