Level A in vitro-in vivo Correlation (IVIVC) Model with Bayesian Approach to Formulation Series

Applications of bio-predictive dissolution tools for the development of solid oral dosage forms: Current industry experience

Development and optimization of orally administered drug products often require bio-predictive tools to help with informing formulation and manufacturing decisions. Reliable bio-predictive dissolution toolkits not only allow rational development of target formulations without having to conduct excessive in vivo studies but also help in detecting critical material attributes (CMAs), critical formulation variables (CFVs), or critical process parameters (CPPs) that could impact a drug's in vivo performance. To provide early insights for scientists on the development of a bio-predictive method for drug product development, this review summarizes current phase-appropriate bio-predictive dissolution approaches applicable to address typical concerns on solubility-limited absorption, food effect, achlorhydria, development of extended-release formulation, clinically relevant specification, and biowaiver. The selection of an in vitro method which can capture the key rate-limiting step(s) of the in vivo dissolution and/or absorption is considered to have a better chance to produce a meaningful in vitro-in vivo correlation (IVIVC) or in vitro-in vivo relationship (IVIVR).

In vitro and in vivo correlation for lipid-based formulations: Current status and future perspectives

Lipid-based formulations (LBFs) have demonstrated a great potential in enhancing the oral absorption of poorly water-soluble drugs. However, construction of in vitro and in vivo correlations (IVIVCs) for LBFs is quite challenging, owing to a complex in vivo processing of these formulations. In this paper, we start with a brief introduction on the gastrointestinal digestion of lipid/LBFs and its relation to enhanced oral drug absorption; based on the concept of IVIVCs, the current status of in vitro models to establish IVIVCs for LBFs is reviewed, while future perspectives in this field are discussed. In vitro tests, which facilitate the understanding and prediction of the in vivo performance of solid dosage forms, frequently fail to mimic the in vivo processing of LBFs, leading to inconsistent results. In vitro digestion models, which more closely simulate gastrointestinal physiology, are a more promising option. Despite some successes in IVIVC modeling, the accuracy and consistency of these models are yet to be validated, particularly for human data. A reliable IVIVC model can not only reduce the risk, time, and cost of formulation development but can also contribute to the formulation design and optimization, thus promoting the clinical translation of LBFs.

Bayesian knowledge integration for an in vitro-in vivo correlation model

The primary goal of "in vitro-in vivo correlation" (IVIVC) is the reliable prediction of the in vivo serum concentration-time course, based on the in vitro drug dissolution or release profiles. IVIVC methods are particularly appropriate for formulations that are released over an extended period of time or with a lag in absorption and may support approving a change in formulation of a drug without additional bioequivalence trials in human subjects. Most of the current IVIVC models are assessed using frequentist methods, such as linear regression, based on averaged data and entail complex and potentially unstable mathematical deconvolution. The proposed IVIVC approach includes (a) a nonlinear-mixed effects model for the in vitro release data; (b) a population pharmacokinetic (PK) compartment model for the in vivo immediate release (IR) data; and (c) a system of ordinal differential equations (ODEs), containing the submodels (a) and (b), which approximates and predicts the in vivo controlled release (CR) data. The innovation in this paper consists of splitting the parameter space between submodels (a) and (b) versus (c). Subsequently, the uncertainty on these parameters is accounted for using a Bayesian framework, that is estimates from the first two submodels serve as priors for the Bayesian hierarchical third submodel. As such, the Bayesian method explained ensures a natural integration and transfer of knowledge between various sources of information, balancing possible differences in sample size and parameter uncertainty of in vitro and in vivo studies. Consequently, it is a very flexible approach yielding results for a broad range of data situations. The application of the method is demonstrated for a transdermal patch (TD).

Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach

A Bayesian approach with frequentist validity has been developed to support inferences derived from a "Level A" in vivo-in vitro correlation (IVIVC). Irrespective of whether the in vivo data reflect in vivo dissolution or absorption, the IVIVC is typically assessed using a linear regression model. Confidence intervals are generally used to describe the uncertainty around the model. While the confidence intervals can describe population-level variability, it does not address the individual-level variability. Thus, there remains an inability to define a range of individual-level drug concentration-time profiles across a population based upon the "Level A" predictions. This individual-level prediction is distinct from what can be accomplished by a traditional linear regression approach where the focus of the statistical assessment is at a marginal rather than an individual level. The objective of this study is to develop a hierarchical Bayesian method for evaluation of IVIVC, incorporating both the individual- and population-level variability, and to use this method to derive Bayesian tolerance intervals with matching priors that have frequentist validity in evaluating an IVIVC. In so doing, we can now generate population profiles that incorporate not only variability in subject pharmacokinetics but also the variability in the in vivo product performance.

In vitro- in vivo correlation's dissolution limits setting

PURPOSE

In vitro in vivo correlation (IVIVC) is a biopharmaceutical tool recommended for use in formulation development. When validated, IVIVC can be used to set dissolution limits and, based on the dissolution limits, as a surrogate for an in vivo study. The purpose of this paper is to study the various methods used to fix dissolution limits.

METHODS

Fixing dissolution limits is not a straightforward process; various approaches exist. The classical ±10% of dissolution limits was compared to the recommended ±10% of Cmax and AUC and to an innovative back calculation of the 90% CI. Based on simulated values the influence of the calculation method as well as of the variability of the results and pharmacokinetic processes was investigated.

RESULTS

Depending upon the method, the results are different and their comparison leads to possible rules. It appears that the usage of a back calculation of a 90% CI is an accurate and advantageous method when intra-individual variability associated with the drug is low. Those findings are in accordance with the current practice of IVIVC, which is not recommended for highly variable drugs.

CONCLUSIONS

The approach of using a 90% CI allows the intra-subject variability to be taken into account and fixes limits that ensure a greater chance to show acceptable BE, in case of reasonable intra-subject variability, leading to setting broader in vitro dissolution limits compared to classical solutions.

Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks

BACKGROUND

The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR.

METHODS

Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique.

RESULTS

The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations.

CONCLUSION

It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2-4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.

In vitro-in vivo correlations: tricks and traps

In vitro-in vivo correlation (IVIVC) is a biopharmaceutical tool recommended to be used in development of formulation. When validated, it can speed up development of formulation, be used to fix dissolution limits and also as surrogate of in vivo study. However, as do all tools, it presents limitations and traps. The aim of the present paper is to investigate five common traps which could limit either the setting or use of IVIVC (1) using mean or individual values; (2) correction of absolute bioavailability; (3) correction of lag time and time scaling; (4) flip-flop model; and (5) predictability corrections.

A 1-step Bayesian predictive approach for evaluating in vitro in vivo correlation (IVIVC)

IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled-release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between- and within-subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, C(max) and T(max) can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly.

Pharmacokinetics in drug discovery

The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.