Lack of Correlation between In Vitro and In Vivo Studies on the Inhibitory Effects of (‒)-Sophoranone on CYP2C9 Is Attributable to Low Oral Absorption and Extensive Plasma Protein Binding of (‒)-Sophoranone

Abstract

(‒)-Sophoranone (SPN) is a bioactive component of Sophora tonkinensis with various pharmacological activities. This study aims to evaluate its in vitro and in vivo inhibitory potential against the nine major CYP enzymes. Of the nine tested CYPs, it exerted the strongest inhibitory effect on CYP2C9-mediated tolbutamide 4-hydroxylation with the lowest IC50 (Ki) value of 0.966 ± 0.149 μM (0.503 ± 0.0383 μM), in a competitive manner. Additionally, it strongly inhibited other CYP2C9-catalyzed diclofenac 4′-hydroxylation and losartan oxidation activities. Upon 30 min pre-incubation of human liver microsomes with SPN in the presence of NADPH, no obvious shift in IC50 was observed, suggesting that SPN is not a time-dependent inactivator of the nine CYPs. However, oral co-administration of SPN had no significant effect on the pharmacokinetics of diclofenac and 4′-hydroxydiclofenac in rats. Overall, SPN is a potent inhibitor of CYP2C9 in vitro but not in vivo. The very low permeability of SPN in Caco-2 cells (Papp value of 0.115 × 10−6 cm/s), which suggests poor absorption in vivo, and its high degree of plasma protein binding (>99.9%) may lead to the lack of in vitro–in vivo correlation. These findings will be helpful for the safe and effective clinical use of SPN.

Prediction of CYP‐mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator

“…Reasons for the differences between in vivo and in vitro K i values have been discussed in the literature previously and include possible inhibition by metabolites, general environmental differences between in vitro and in vivo enzyme systems, partitioning into organelles (e.g., lysosomal distribution) or cellular membranes, and active uptake processes altering local concentrations. 19 , 20 , 21 As it is not always possible to identify these mechanisms a priori, we suggest conducting appropriate sensitivity analyses for compounds in development to assess the impact of a range of K i values on the magnitude of interaction. Thus, when in vitro K i values of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP3A4 perpetrators indicate negligible DDI liability with a sensitive substrate, we recommend predicting the impact of K i values up to 10‐fold lower.…”

“…Among the 37% of inhibitors where an optimized K i value was used, with the exception of two weak inhibitors, the median difference between the optimized and in vitro K i values was about 10‐fold. Reasons for the differences between in vivo and in vitro K i values have been discussed in the literature previously and include possible inhibition by metabolites, general environmental differences between in vitro and in vivo enzyme systems, partitioning into organelles (e.g., lysosomal distribution) or cellular membranes, and active uptake processes altering local concentrations 19‐21 . As it is not always possible to identify these mechanisms a priori, we suggest conducting appropriate sensitivity analyses for compounds in development to assess the impact of a range of K i values on the magnitude of interaction.…”

mRNA-Based Nanomedicinal Products to Address Corneal Inflammation by Interleukin-10 Supplementation

“…The intensity of fluorescence of GFP observed in the HCE-2 cells in vitro, representative of the protein production, was similar for all formulations, and it seems to correlate better with the in vivo results than with the percentage of transfected cells in vitro. Nevertheless, the lack of a strict correlation between in vitro and in vivo studies [62][63][64][65] highlights the necessity to perform the latter ones at the earliest phases of the pharmaceutical development process, in order to perform adequate selection and optimization of candidate formulations.…”

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

“…Therefore, AI algorithms may struggle to accurately capture the nonlinear relationships of absorption, leading to inaccurate predictions. It is also challenging to develop accurate AI models for predicting drug absorption due to lack of in vitro and in vivo correlation where conditions in the lab may not accurately reflect the conditions in the human body. , Furthermore, absorption is just one aspect of the overall ADMET profile of a drug. AI algorithms must be able to integrate information on absorption with information on other properties, such as distribution, metabolism, excretion, and toxicity, to accurately predict the overall ADMET profile.…”