Application of in Silico Tools in Clinical Practice using Ketoconazole as a Model Drug

Abstract

Hypochlorhydria is a condition where the production of hydrochloric acid in the stomach is decreased. As a result, the intragastric pH is elevated. This condition can be due to a series of causes, such as disease (gastric mucosal infection caused by Helicobacter pylori and is prominent in AIDS patients), ethnicity, age and also the use of antisecretory agents. This may significantly impact the absorption of other drugs that have pH-dependent solubility, such as ketoconazole, a weak base. Within this context, the purpose of this study was to demonstrate how GastroPlusTM – a physiological based software program- can be used to predict clinical pharmacokinetics of ketoconazole in a normal physiological state vs. elevated gastric pH. A simple physiologically based pharmacokinetic model was built and validated to explore the impact that different physiologic conditions in the stomach (hypochlorhydria, drug administered with water and Coca Cola®) had on ketoconazole’s bioavailability. The developed model was able to accurately predict the impact of increased pH and beverage co-administration on dissolution and absorption of the drug, and confirmed that complete gastric dissolution is essential. Particle size only mattered in hypochlorhydric conditions due to the incomplete gastric dissolution, as its absorption would depend on intestinal dissolution, which corroborates with previous studies. Therefore, in silico approaches are a potential tool to assess a pharmaceutical product’s performance and efficacy under different physiological and pathophysiological states supporting the assessment of different dosing strategies in clinical practice.

Impact of Composition and Morphology of Ketoconazole-Loaded Solid Lipid Nanoparticles on Intestinal Permeation and Gastroplus-Based Prediction Studies

“…This may be correlated to the weak dibasic nature of ketoconazole (two p K a values of 2.91 and 6.5), and one can anticipate the maximum dissolution at a lower pH range (pH 2–3) as compared to pH > 6.0. 24 The drug released at pH 7.4 was approximately 18%, which may be due to drug precipitation (exceeding pH 5.5), and the result is in close agreement with the previous reported value wherein only 10% KTZ was dissolved at pH 6.0 over a period of 60 min. 25 Comparing all formulations against control KSUS, these formulations followed the same release pattern due to the obvious reasons shown in Figure 2 A–C.…”

“…It is clear that the selected K-SLN1, K-SLN13, K-SLN2, K-SLN7, and K-SLN4 formulations exhibited improved drug release at the explored pH-based mediums as compared to the suspension (KSUS). 24 The drug release was extended over 24 h at all pH values. However, KTZ release from the suspension was significantly increased with a decrease in pH value.…”

“…Using input parameters ( Table 3 ), the program was run to predict plasma drug concentration in rats and compared between both formulations. 11 , 24 …”

“…However, the model predicted contradictory results for the stomach which may be due to perfusion-limited kinetics of KTZ in humans (as default data in the input tab). 24 In the literature, KTZ is reported to be highly lipophilic and maximally absorbed from these major sites and cases. 48 , 49 However, any free drug causes several intestinal side effects and hepatic disease on oral delivery.…”

In Silico Prediction of Plasma Concentrations of Fluconazole Capsules with Different Dissolution Profiles and Bioequivalence Study Using Population Simulation

“…Currently, the prediction of intestinal absorption of drugs based on computer simulations using advanced compartmental absorption and transit (ACAT) and physiologically-based pharmacokinetic (PBPK) models is a reality [13,14,15,16,17,18,19,20,21]. Computer software can be used to predict oral absorption of drugs from IR products containing different dissolution profiles, helping formulation scientists to decide on the best dissolution test conditions or formulation, gaining time, and reducing costs in drug development [17].…”