Predicting the impact of physiological and biochemical processes on oral drug bioavailability
Introduction
Application of computational technology during drug discovery and development has the potential to decrease the length of time prior to NDA submission, and reduce the number of experimental procedures required for compound selection and development. Oral bioavailability can be broken down into components that reflect delivery to the intestine (gastric emptying, intestinal transit, local pH, and nutritional status), absorption from the lumen (dissolution, permeability, particle size, intestinal efflux and carrier-mediated transport), first-pass metabolism in the gut, and subsequent first-pass hepatic extraction [1]. Both experimental in vitro and estimated in silico biopharmaceutical properties can be used to predict drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). This review will focus on in silico approaches that have the potential to save valuable resources in drug discovery and development. We review methods that have been developed to build models of individual biopharmaceutical properties, followed by a review of theoretical methods employed to simulate gastrointestinal absorption, hepatic and intestinal metabolism, and pharmacokinetics. Finally, we present our results in predicting bioavailability by estimating biopharmaceutical properties and simulating gastrointestinal absorption and metabolism by extending the advanced compartmental absorption and transit (ACAT) model to account for nonlinear saturable processes.
Section snippets
Computer models of biopharmaceutical properties
In order to lessen the expense and decrease the time associated with experimental determination of in vitro biopharmaceutical properties, existing data has been used to build computational models of octanol–water partition coefficient (log P), effective human jejunal permeability (Peff), cell culture permeability (Caco-2 or MDCK), aqueous solubility, and molecular diffusivity. These models have been developed by application of statistical methods such as linear and partial least squares (PLS)
Simulation of oral drug absorption
Absorption of drugs from the gastrointestinal (GI) tract is very complex and can be influenced by many factors that fall into three classes [2]. The first class represents physicochemical factors including pKa, solubility, stability, diffusivity, lipophilicity, and salt forms. The second class comprises physiological factors including GI pH, gastric emptying, small and large bowel transit times, active transport and efflux, and gut wall metabolism. The third class comprises formulation factors
Metabolism and first pass extraction
The various steps involved in oral drug absorption – dissolution, transit, drug transport and clearance have been traditionally assumed to be first-order non-saturable processes [17]. In reality, some processes are enzyme- or transporter-catalyzed, and they exhibit saturable, nonlinear kinetics. These processes include hepatic and intestinal metabolism, active efflux, and carrier-mediated transport. Apart from strongly influencing drug bioavailability, these nonlinear pharmacokinetic processes,
Bioavailability
Bioavailability is defined as the fraction of administered drug that reaches systemic circulation. Any loss could be due to any of the processes listed above – incomplete absorption, gut metabolism, liver metabolism, etc. If Fi is defined as the fraction of drug that escapes extraction at an organ (or process) i, then the overall bioavailability F is defined asIn practice, bioavailability is defined as the ratio of the AUC’s corrected for dose, after extra- and intravascular
Carrier-mediated transport
In order to facilitate absorption of polar nutrients, numerous membrane-bound transport proteins exist in the gastrointestinal tract. Prominent examples include facilitative transporters, the secondary active symporters and antiporters driven by ion gradients, and active ABC (ATP binding cassette) transporters involved in multiple-drug resistance and targeting of antigenic peptides to MHC Class I molecules [61]. Transported substrates range from nutrients and ions to a broad variety of drugs,
Transport, metabolism, and efflux modeling procedures
While many of these processes – gastrointestinal absorption coupled with efflux and transport, liver metabolism, and pharmacokinetics – have been individually modeled, there is still a need to understand all the processes in tandem. Thus, a mathematical model, which incorporates all the nonlinear processes in a systematic manner, will be invaluable in predicting dose–response relationships, and designing optimum dosage regimens for nonlinearly extracted drugs. We have developed a mathematical
Hepatically eliminated drugs (propranolol)
Propranolol is a widely used β-adrenoceptor blocking agent. The conjugate acid form of propranolol is highly soluble in the pH range that exists in the GI tract and it is also rapidly and completely absorbed from the proximal small intestine. Due to high hepatic clearance, the plasma half-life is short – varying from 1.5 to 3 h [73], [74]. After administration of single oral doses, hepatic extraction remains high and much of the dose is eliminated from hepatic portal blood during transfer from
Liver metabolism
Based on literature biopharmaceutical properties and optimized liver Vmax and Km, we predicted the plasma concentration profile, fraction absorbed, and the bioavailability of two different doses of propranolol (IR and ER doses). The predicted quantities agreed well with literature values. Based on steady state plasma concentrations, Wagner et al. [75] estimated a pooled Vmax of approximately 450 mg/day and an apparent Km of 0.05 mg/l [75] while our estimates were 3888 mg/day and 0.05 mg/l (true
Conclusions
- 1.
We have developed a new model of saturable processes in oral drug absorption that simulates nonlinear responses in drug bioavailability and pharmacokinetics.
- 2.
We have validated the model against experimental data for drugs that undergo liver metabolism alone, gut metabolism and liver metabolism, and efflux and metabolism.
- 3.
We used in vitro kinetic constants obtained from homogenate or whole cell experiments under controlled conditions and scaled the constants to the in vivo scenario using
Acknowledgements
We gratefully acknowledge the work of Robert Fraczkiewicz, Grace Fraczkiewicz, and Boyd Steere who helped to collect literature data and developed the application QMPRPlus™ used for estimation of some of the biopharmaceutical properties in this article.
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