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J Devane

Publications and source records attributed to J Devane.

At least 19 recordsLinked to original sources
PubMed2001

In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step.

The purpose of in vivo-in vitro correlation (IVIVC) modeling is described. These models are usually fitted to deconvoluted data rather than the raw plasma drug concentration/time data. Such a two-stage analysis is undesirable because the deconvolution step is unstable and because the fitted model predicts the fraction of a dosage unit dissolved/absorbed in vivo which generally is not the primary focus of our attention. Interest usually centers on the plasma drug concentration or some function of it (e.g., AUC, Cmax). Incorporation of a convolution step into the model overcomes these difficulties. Odds, hazards, and reversed hazards models which include a convolution step are described. The identity model (which states that average in vivo and in vitro dissolution/time curves are coincident or directly superimposable) is a special case of these models. The odds model and the identity model were fitted to data sets for two different products using nonlinear mixed effects model fitting software. Results show that the odds model describes both data sets reasonably well and is a significantly better fit than the identity model in each case.

Cross-Over Studies↗
PubMed1999

A new approach to modelling the relationship between in vitro and in vivo drug dissolution/absorption.

A major goal of the pharmaceutical scientist is finding a relationship between an in vitro characteristic of an oral dosage form and its in vivo performance. One such relationship between drug dissolution (or absorption) in vivo and that in vitro is known as an 'in vitro-in vivo correlation' (IVIVC) whose importance stems from the fact that it may be used to minimize the number of human studies required during product development, assist in setting meaningful in vitro dissolution specifications and justify biowaivers for scale-up and post approval changes. A number of ways of describing an IVIVC have been reported with 'level A' being the most informative and therefore most desirable. In the majority of cases reported to date, both the model and the statistical methods employed for level A IVIVC are very simplistic. The model assumes that the rate and extent of dissolution in vivo are the same as those in vitro. The statistical methods ignore the repeated measures nature of the data and use a response variable as an independent variable without accounting for measurement error. This paper describes some new models which include the simple model as a special case. The modelling approach is based on considering the time at which a drug molecule enters solution (in vitro or in vivo) to be a random variable. The in vitro and in vivo distributions are then related to one another using a proportional odds, proportional hazards or proportional reversed hazards model. The models can be extended by adding a linear time component which describes a time varying relationship. Following the addition of random effects to these structural models in order to account for the repeated measures nature of the data collected, the models may be described as generalized linear mixed effects models. The models were fitted to some data sets using a maximum likelihood based method and the results indicate that these models have potential for describing an in vitro-in vivo relationship which cannot be described using the currently available models.

Absorption↗
PubMed1999

Discussion

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Journal Article↗
PubMed1999

Artificial neural networks applied to the in vitro-in vivo correlation of an extended-release formulation: initial trials and experience.

Artificial neural networks applied to in vitro-in vivo correlations (ANN-IVIVC) have the potential to be a reliable predictive tool that overcomes some of the difficulties associated with classical regression methods, principally, that of providing an a priori specification of the regression equation structure. A number of unique ANN configurations are presented, that have been evaluated for their ability to determine an IVIVC from different formulations of the same product. Configuration variables included a combination of architectural structures, learning algorithms, and input-output association structures. The initial training set consisted of two formulations and included the dissolution from each of the six cells in the dissolution bath as inputs, with associated outputs consisting of 1512 pharmacokinetic time points from nine patients enrolled in a crossover study. A third formulation IVIVC data set was used for predictive validation. Using these data, a total of 29 ANN configurations were evaluated. The ANN structures included the traditional feed forward, recurrent, jump connections, and general regression neural networks, with input-output association types consisting of the direct mapping of the dissolution profiles to the pharmacokinetic observations, mapping the individual dissolution points to the individual observations, and using a "memorative" input-output association. The ANNs were evaluated on the basis of their predictive performance, which was excellent for some of these ANN models. This work provides a basic foundation for ANN-IVIVC modeling and is the basis for continued modeling with other desirable inputs, such as formulation variables and subject demographics.

Computer Simulation↗
PubMed1997

Level A in vivo-in vitro correlation: nonlinear models and statistical methodology.

Some new nonlinear models for the relationship between the fraction of drug dose dissolved (absorbed) in vivo and that dissolved in vitro are described. The models are empirical in nature and are generalizations of the linear model that, at present, is the most commonly used model. The modeling approach is based on considering the time at which a drug molecule goes into solution (in vitro or in vivo) to be a random variable and relating the distribution functions using proportional odds, proportional hazards, and proportional reversed hazards models. The models are further extended by allowing the parameter that relates in vivo and in vitro to be a function of time. A statistical model for the data is developed and used as the basis for a statistical methodology for fitting these models. The methods are shown to be generalized linear mixed effects model (GLMM) methods. The models are fitted to some data sets, and the results demonstrate that these models have potential.

Models, Statistical↗
PubMed1996

Two comparative endoscopic evaluations of Naprelan.

This paper describes the results of two endoscopic studies of Naprelan (Wyeth-Ayerst Laboratories, Philadelphia, Pennsylvania), a controlled-release formulation of naproxen sodium. In one study, 19 healthy male subjects received either 2 controlled release Naprelan 500-mg tablets once daily or 1 Naprosyn (naproxen; Syntex Laboratories, Inc., Palo Alto, California) 500-mg tablet BID. At baseline, all subjects had gastroduodenal endoscopy scores of 0 on both the Lanza and Euler scales. Although the trend favored Naprelan, differences in Lanza and Euler scale elevations were not statistically significant. Eight of 10 subjects in the Naprelan group and 7 of 9 subjects in the Naprosyn group reported a total of 27 adverse events (AEs). In the second study, healthy subjects received Naprelan 1,000 mg once daily, Naprosyn 500 mg BID, and film-coated aspirin 650 mg QID. In the stomach, there was a significant difference in favor of Naprelan over aspirin (P = 0.0001) and in favor of Naprosyn over aspirin (P = 0.0001). Fewer erosions were seen in the duodenum than in the stomach. In the duodenum, there was a significant difference in favor of Naprelan over Naprosyn (P = 0.0236), and in favor of Naprelan over aspirin (P = 0.0086), but the difference between Naprosyn and aspirin was not significant (P = 0.6643). Of the 23 subjects who received medication, 12 reported AEs: 8 while receiving aspirin, 3 while receiving Naprosyn, and 1 while receiving Naprelan. Differences in the number of erosions and ulcers seen following each of the periods of drug administration favored Naprelan and the Intestinal Protective Drug Absorption System.

Adolescent↗