Computer-based Evolutionary Search for a Nonlinear Conversion Function for Establishing In Vitro-In Vivo Correlation (IVIVC) of Oral Drug Formulations
Graduate School of Pharmaceutical Sciences, Kyoto University, Japan. Drug Metabolism and Pharmacokinetics
(Impact Factor: 2.57).
12/2011; 27(3):280-5. DOI: 10.2133/dmpk.DMPK-11-RG-075
Establishment of in vitro-in vivo correlation (IVIVC) accelerates optimization of desirable drug formulations and/or modification of the manufacturing processes in the scale-up and post-approval periods. This article presents a method of finding the optimal conversion function for establishing Level A point-to-point IVIVC, based on a computer-based evolutionary search technique. Gene expression programming (GEP) is a technique for optimizing a mathematical expression tree with the help of a genetic algorithm. A parameter optimization routine, which minimizes the number of parameters in the mathematical expression trees and estimates the best-fit parameter values, was implemented in the GEP algorithm. Feasibility of the computer program was investigated using the in vitro and in vivo data for sustained release diltiazem formulations. It provided a mathematical equation that, from their in vitro dissolution profiles, successfully predicts the plasma concentration profiles of three different formulations of diltiazem following oral administration. Because the present approach does not use intravenous injection data like conventional IVIVC analyses, it is widely applicable to the evaluation of various oral formulations.
Available from: Varinder Kumar
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ABSTRACT: The purpose of this study was to utilize IVIVC tool in the development of oral controlled release formulation of a BCS class I model drug Trimetazidine dihydrochloride (TMZ). Commercial products of TMZ are taken two to three times a day to achieve therapeutic benefit. Hence, development of once daily tablet was initiated with the development of "Assumed IVIVC". The assumed IVIVC was developed by obtaining in vivo data of single dose IR formulation (Vastarel® 20 mg) from literature and generating in vitro and in vivo data for Preductal® MR 35 mg modified release tablet (Reference) and TMZ extended release tablet 70 mg (Test) in house. In vitro dissolution ER tablet was conducted by evaluating effect of pH. The in vitro profile as a surrogate to in vivo absorption was generated in 0.1 N HCl medium. The in vivo absorption was calculated using deconvolution approach using the IR data for unit impulse response. A linear model with a time-scaling factor clarified the relationship between the in vitro and the in vivo data. The predictability of the final model was consistent based on internal validation. Average percent prediction errors for pharmacokinetic parameters were within ± 10% and individual values for all formulations were within ± 15%. Same model was used as a target to develop OD tablet using software WinNonlin® IVIVC toolkit™ that would be bioequivalent to 35 mg modified release reference product. The assumed IVIVC was then utilized for "Retrospective IVIVC" development and pharmacokinetic parameters of desired formulations were predicted through the IVIVC model. Predicted results for formulation F4 and F5, projected them as the most suitable for once daily use. In this work, it was demonstrated that the IVIVC can be used in the development of new dosage forms to reduce the number of human studies in product life cycle management.
Available from: J. Szlęk
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ABSTRACT: The purpose of this work was to develop a mathematical model of the drug dissolution () from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of versus extrudate diameter () and the time variable () and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution: and . The extrudates length () was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of versus and resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.
Available from: Mohammad Hassan Khalid
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ABSTRACT: IVIVR as an extension of IVIVC beyond the domain of linear modeling is a predictive model that binds the in vivo PK profile with the in vitro dissolution profile of a particular drug. Several computational intelligence-modeling tools for IVIVR were chosen and tested in this study: decision trees (randomForest), artificial neural networks (monmlp), genetic programming (rgp), and a recently published tool, RIVIVR. R statistical environment was used for numerical experiments. All of the above-mentioned tools succeeded in the creation of empirical relationships between in vivo and in vitro profiles without the need of the additional impulse curve (intravenous [iv] profile). The best results were found for genetic programming and decision trees. RIVIVR achieved a superior cost–effectiveness ratio, namely, short time of execution and high level of automation.
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