Models and Approaches Describing the Metabolism, Transport, and Toxicity of Drugs Administered by the Ocular Route.

The eye is a complex organ with a series of anatomic barriers that provide protection from physical and chemical injury while maintaining homeostasis and function. The physiology of the eye is multifaceted, with dynamic flows and clearance mechanisms. This review highlights that in vitro ocular transport and metabolism models are confined by the availability of clinically relevant absorption, distribution, metabolism, and excretion (ADME) data. In vitro ocular transport models used for pharmacology and toxicity poorly predict ocular exposure. Although ocular cell lines cannot replicate in vivo conditions, these models can help rank-order new chemical entities in discovery. Historic ocular metabolism of small molecules was assumed to be inconsequential or assessed using authentic standards. While various in vitro models have been cited, no single system is perfect, and many must be used in combination. Several studies document the use of laboratory animals for the prediction of ocular pharmacokinetics in humans. This review focuses on the use of human-relevant and human-derived models which can be utilized in discovery and development to understand ocular disposition of new chemical entities. The benefits and caveats of each model are discussed. Furthermore, ADME case studies are summarized retrospectively and capture the ADME data collected for health authorities in the absence of definitive guidelines. Finally, we discuss the novel technologies and a hypothesis-driven ocular drug classification system to provide a holistic perspective on the ADME properties of drugs administered by the ocular route.

Application of the extended clearance concept classification system (ECCCS) to predict the victim drug-drug interaction potential of statins.

BACKGROUND: During drug development, it is an important safety factor to identify the potential of new molecular entities to become a victim of drug-drug interactions (DDIs). In preclinical development, however, anticipation of clinical DDIs remains challenging due to the lack of in vivo human pharmacokinetic data. METHODS: We applied a recently developed in vitro-in vivo extrapolation method, including hepatic metabolism and transport processes, herein referred to as the Extended Clearance Concept Classification System (ECCCS). The human hepatic clearances and the victim DDI potentials were predicted for atorvastatin, cerivastatin, fluvastatin, lovastatin acid, pitavastatin, pravastatin, rosuvastatin, and simvastatin acid. RESULTS: Hepatic statin clearances were well-predicted by the ECCCS with six out of eight clearances projected within a two-fold deviation to reported values. In addition, worst-case DDI predictions were projected for each statin. Based on the ECCCS class assignment (4 classes), the mechanistic interplay of metabolic and transport processes, resulting in different DDI risks, was well-reflected by our model. Furthermore, predictions of clinically observed statins DDIs in combination with relevant perpetrator drugs showed good quantitative correlations with clinical observations. CONCLUSIONS: The ECCCS represents a powerful tool to anticipate the DDI potential of victim drugs based on in vitro drug metabolism and transport data.