Development of predictive pharmacokinetic simulation models for drug discovery
Introduction
Within the drug discovery environment, drug compounds must be selected based on their potential for success as a new drug product. The selection process, or screening, is classically accomplished using animal and/or in vitro model systems to determine the receptor-based activity profile for each candidate compound. Traditionally, promising drug candidates with acceptable receptor-based activity are promoted to development and screened for ADME properties (oral absorption or permeability, distribution, metabolic stability, and excretion) or toxicity using numerous animal or in vitro models. Compounds which fail to demonstrate satisfactory ADME properties are removed from consideration as drug candidates. Prentis [1] observed, for a 20 year period (1968–1988), the primary reasons that drug candidates were dropped from development were pharmacokinetic difficulties in man and a lack of efficacy. In 1997, this conclusion was confirmed by Kennedy who reported that the termination of the development of drug candidates other than for efficacy was poor pharmacokinetic (or ADME) characteristics [2]. Unfortunately, screening large numbers of compounds for pharmacokinetic properties, especially using animals, is a time and labor intensive process which limits its utility in drug discovery. In addition, although permeability data can be used to predict fraction dose absorbed in humans [3], [4], [5], these methods have broad limitations so that in vitro permeability data alone does not allow prediction of human absorption or activity before clinical data is collected. Numerous similar correlations using in vitro data have also been attempted and reported [6], [7], [8], [9], [10]. Although dissolution tests can be used to create models that will predict oral absorption, Dressman et al. [11] have concluded “there is a real need to develop dissolution tests that better predict in vivo performance of drug products”, indicating current dissolution based models may not be applicable to all situations. Furthermore, bioavailability studies using whole animals do not allow accurate prediction of human bioavailability [12]. A computer simulation model capable of screening compounds for ADME properties prior to promotion for development could drastically reduce the time and expense of total drug discovery and development. This is especially true for potential drugs which have absorption problems, are susceptible to drug–drug interactions or metabolic variability, or have large differences in bioavailability between animals and humans.
This paper describes a physiologically based computer simulation model of the gastrointestinal tract capable of predicting oral absorption of drug compounds in mammals using a limited in vitro data set. The oral absorption properties of ganciclovir, a species and dose dependent low bioavailability drug [13], are used as an example to demonstrate the ability of the simulation model to accurately predict bioavailability in two mammalian species, humans and dogs.
Section snippets
Physiological model development
A physiologically based computer simulation model of the gastrointestinal (GI) tract (IDEA™, NaviCyte, Inc., San Diego, CA), based on a previously published model [13], was designed to account for physiological parameters effecting oral absorption from the mammalian GI tract, such as pH, transit time, and intestinal surface area. The model was also designed to account for drug compound parameters, such as permeability, solubility and dissolution rate. The model was developed by incorporating
Testing the physiological model’s predictive capability
The ability of the physiological model to predict FDp% and t50 was tested using a representative collection of drug compounds exhibiting a wide range of permeabilities, solubilities, and doses (Table 2).
The FDp% calculated from PK drug concentrations were compared to the simulated FDp% from the physiological model for humans (Fig. 1). A reasonably good correlation coefficient is reported (r2>0.92). Even though some variation existed the model was found to accurately distinguish between well and
Discussion
Previous efforts to predict oral absorption in humans have used uptake and transport studies to calculate a permeability through tissue or cell monolayers [3], [5] alone or in conjunction with other physiological or system parameters to predict extent of absorption. Historically, the methods have utilized a two tank perfect mixing model [3] and/or macroscopic mass balance approach [5]. More recently physiologically based models have been described in the literature [13], [14]. The predictive
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