Predicting Oral Drug Absorption: Mini Review on Physiologically-Based Pharmacokinetic Models

Abstract

Most marketed drugs are administered orally, despite the complex process of oral absorption that is difficult to predict. Oral bioavailability is dependent on the interplay between many processes that are dependent on both compound and physiological properties. Because of this complexity, computational oral physiologically-based pharmacokinetic (PBPK) models have emerged as a tool to integrate these factors in an attempt to mechanistically capture the process of oral absorption. These models use inputs from in vitro assays to predict the pharmacokinetic behavior of drugs in the human body. The most common oral PBPK models are compartmental approaches, in which the gastrointestinal tract is characterized as a series of compartments through which the drug transits. The focus of this review is on the development of oral absorption PBPK models, followed by a brief discussion of the major applications of oral PBPK models in the pharmaceutical industry.

Keywords: food-effect; formulation simulation; oral absorption; pH effect; physiologically-based pharmacokinetic modeling.

Figure 1

Comparison of an empirical classical compartmental model and a mechanistic physiologically-based pharmacokinetic (PBPK) model. (A) In the classical compartment model, a drug is inputted into the gut compartment, and absorption into the systemic circulation compartment is governed by the absorption rate constant (k_a). Elimination is described by the elimination rate constant (k_e); (B) In the whole-body PBPK model, major organs/tissues are represented by compartments, connected by blood flows (Q). Specific organ blood flows are described by subscripts. Intravenous (IV) dosing inputs drugs directly into venous blood, whereas oral dosing inputs drug into the gut compartment. In this illustration, the liver is the major eliminating organ.

Figure 2

Diagram of the mixing tank model which represents the gastrointestinal (GI) tract as a single well-stirred compartment. k_a is the absorption rate constant, X_diss is the amount of drug dissolved in the GI tract. k_diss is the dissolution rate constant, and X_solid is the dose that has been placed into the GI tract. The oral absorption rate is governed by k_a and X_diss. The dissolution rate is governed by X_solid and k_diss.

Figure 3

The compartmental absorption and transit (CAT) model extends the mixing tank model to characterize drug transit through the gastrointestinal tract (GIT). Seven well-stirred compartments are used to describe absorption and transit through the small intestine.

Figure 4

The representative Advanced Compartment Absorption and Transit (ACAT) model pictured here is an extension of the CAT model. Shown in this representative ACAT model, additional compartments are added to characterize features such as stomach and colon absorption, drug release from formulation, and first-pass metabolism from the liver and the gut (shown by the Clearance arrows).

Figure 5

Modeling of transporters and intestinal metabolism is achieved by separately compartmentalizing the enterocytes. Rates of transport and metabolizing enzyme activity are described by Michaelis-Menten kinetics using parameters derived from in vitro enzyme activity assays (V_maxE, V_maxI, and V_maxM are the maximum rate for efflux transporters, influx transporters, and metabolic enzymes respectively; K_mE, K_mI, and K_mM are the Michaelis-Menten constants for efflux transporters, influx transporters, and metabolic enzymes, respectively). In this example, it is assumed that substrate concentrations are far below saturation (Equation (7)).

Figure 6

Sources of PK data required for model verification.