European Journal of Pharmaceutical Sciences 17 (2002) 51–61 www.elsevier.nl / locate / ejps Prediction of intestinal absorption: comparative assessment of GASTROPLUS and IDEA * ´ Neil Parrott , Thierry Lave F. Hoffmann-La Roche AG, Pharmaceuticals Division, CH-4070 Basel, Switzerland Received 17 April 2002; received in revised form 9 July 2002; accepted 17 July 2002 Abstract We have assessed two commercial software tools employing physiologically based models for prediction of intestinal absorption in human. IDEA 2.0 and GASTROPLUS 3.1.0 were compared both in their ability to predict fraction absorbed for a set of 28 drugs and in terms of the functionality offered. The emphasis was placed on the practical usefulness to pharmaceutical drug discovery. Predictions were assessed for three levels of input data (i) pure in silico input, (ii) thermodynamic solubility and in silico permeability, (iii) thermodynamic solubility and human colon carcinoma cell line (CACO-2) permeability. We found the pure in silico prediction ability of the tools to be comparable with 70% correct classification rate. With measured input data the IDEA prediction rate improved to 79% while GASTROPLUS stayed at 70%. In terms of functionality GASTROPLUS is a powerful system for the trained user. Open access to model parameters, diagnostic tools and the ability to integrate data make it particularly suitable for the later stages of discovery and development. IDEA is web based and presents a simple interface suitable for widespread use with minimal training. However the limited functionality and inconvenient handling of multiple compound batches currently restrict the usefulness of version 2.0 for drug discovery.  2002 Elsevier Science B.V. All rights reserved. Keywords: ADME; Oral absorption; Physiologically based pharmacokinetics; Simulation; Modeling 1. Introduction challenges. In particular a need for reliable models of oral absorption exists. The ability to be administered by the oral route is a The prediction of in vivo absorption is complex and the highly desirable property for new pharmaceutical drugs number of factors to be considered large. Absorption of because it is the safest, most convenient and economical drugs from the gastrointestinal tract can be influenced by method (Goodman et al., 1999). For this reason good oral physicochemical, physiological and formulation factors. availability is a required property for drug candidate The physicochemical factors include pK , solubility, a molecules in a large percentage of pharmaceutical discov- stability, lipophilicity, and salt forms. The physiological ery projects. It has been observed that drug development factors include gastrointestinal pH, gastric emptying, small often failed for reasons of poor pharmacokinetics (Prentis and large bowel transit times, active transport and efflux, et al., 1988). To avoid the high costs associated with such and gut wall metabolism. The formulation factors are failures the current practice is to consider metabolism and related to drug particle size, crystal form and dosage forms pharmacokinetic properties in parallel with pharmacologi- such as solution, tablet, capsule or suspension. cal tests during the discovery phase. High throughput in Considering the complexity of absorption and the num- vitro technology now allows properties of importance for ber of processes involved, an integrated approach taking oral absorption like solubility, permeability, lipophilicity most of the data into account is highly desirable. Physio- and pK to be measured early and for many compounds. logically based models provide a rational basis for integra- a Consequently the discovery scientist is presented with tion of data and can predict both extent and rate of large volumes of multivariate data and is faced with absorption. considerable data integration and information generation A further step forward is the pure in silico approach where measured in vitro data is replaced with properties predicted from chemical structure alone. Input of in silico *Corresponding author. Tel.: 141-61-688-0813. E-mail address: neil john.parrott@roche.com (N. Parrott). predictions into validated predictive models then gives the ] 0928-0987 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved. PII: S0928-0987(02)00132-X