Strategies and Mechanism in Reversing Intestinal Drug Efflux in Oral Drug Delivery

Lü, Rong; Zhou, Yun; Ma, Jinqian; Wang, Yuchen; Miao, Xiao

doi:10.3390/pharmaceutics14061131

Cited by 9 publications

(3 citation statements)

References 120 publications

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“…However, the mechanism of absorption is too complex and is related to the diffusion of the compound for solubility, 7,8 membrane permeability, 9,10 and active efflux transport by transporters, such as Pgp. 11 Therefore, it is difficult to predict the human Fa with high accuracy, even if any of these in vitro experiments are combined. In ex vivo experiments, in situ perfusion through isolated intestinal segments in rats is well known; however, it is not a perfect predictor of absorption.…”

Section: Introductionmentioning

confidence: 99%

Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage

Handa,

Sugiyama,

Kageyama

et al. 2023

Digital Discovery

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Section: Introductionmentioning

confidence: 99%

Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage

Handa,

Sugiyama,

Kageyama

et al. 2023

Digital Discovery

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“…The mechanisms of these interactions are not fully determined. Some excipients affect cellular connections, e.g., tight junctions [ 16 ], others increase drug solubility [ 12 ] or serve as surfactants [ 17 ], and others interact with protein transporters like efflux pumps [ 18 , 19 ]. Thus, excipients can improve the oral bioavailability of drugs [ 15 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Tigecycline Absorption Improved by Selected Excipients

et al. 2023

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To investigate the effects of (2,6-di-O-methyl)-β-cyclodextrin (DM-β-CD), (2-hydroxypropyl)-β-cyclodextrin (HP-β-CD), tocopherol polyethylene glycol 1000 succinate (TPGS), sodium desoxycholate (SDOCH), trimethyl chitosan (TMC), and sodium caprate (C10) on the plasma concentration and the oral bioavailability of tigecycline in broiler chickens. To test the effects of the excipients on absorption of tigecycline, a tetracycline that is poorly absorbed from the gastrointestinal tract, broiler chickens were used as an animal model. Tigecycline (10 mg/kg body weight) was administered intravenously, orally, and orally with one of the excipients. Plasma samples were taken after administration. To measure tigecycline concentrations, high-performance liquid chromatography coupled with tandem mass spectrometry was used. Compartmental and non-compartmental analyses were used for pharmacokinetic analyses of mean plasma concentrations versus time. With the exception of sodium caprate, all the excipients significantly increased the area under the curve and bioavailability of tigecycline (p < 0.05). These parameters were approximately doubled by HP-β-CD, TPGS, and SDOCH, with 95% confidence intervals (95% CIs) for the difference that included only increases of 1.5-fold or higher (bioavailability: control, 1.67%; HP-β-CD, 3.24%; TPGS, 3.30%; and SDOCH, 3.24%). The increases in these parameters were smaller with DM-β-CD and TMC (DM-β-CD, 2.41%; TMC, 2.55%), and the 95% CIs ranged from close to no difference to nearly double the values in the control group. These results indicate that HP-β-CD, TPGS, and SDOCH substantially increase the area under the curve and oral bioavailability of tigecycline. They suggest that DM-β-CD and TMC may also substantially increase these parameters, but more research is needed for more precise estimates of their effects.

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“…Regarding in vitro systems, JP2 solubility 4 , PAMPA 5 , and Caco-2 6 are well-known methods for predicting drug absorption in humans at the non-clinical stage. However, the mechanism of absorption is too complex and is related to the diffusion of the compound for solubility 7 , 8 , membrane permeability 9,10 , and active efflux transport by transporters, such as P-gp 11 . Therefore, it is difficult to predict the human Fa with high accuracy, even if any of these in vitro experiments are combined.…”

Section: Introductionmentioning

confidence: 99%

Combined Data-driven and Mechanism-based Approaches for Human-Intestinal-Absorption Prediction in Early Drug-Discovery Stage

Handa¹,

Sugiyama²,

Kageyama³

et al. 2023

Preprint

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It is important to precisely predict the intestinal absorption ratio (Fa) at an early stage in the discovery of orally available drugs because it directly influences drug efficacy. Gastrointestinal unified theoretical framework (GUTFW) and machine learning (ML) are commonly used to predict the percentage of Fa. In GUTFW, the Fa of a drug is estimated using an equation based on the mechanism of human intestinal absorption, dose, solubility, membrane permeability, and dispersion of the drug. The experimental values of these in vitro parameters are required to accurately predict Fa. However, most of these values are unavailable at early stages of development. ML uses a dataset of the observed Fa values of many drugs in humans. In most previously published ML approaches, the dose information for each drug has been ignored. However, Fa can vary in a dose-dependent manner through changes in solubility, membrane permeability, and dispersion. To overcome these problems, we combined GUTFW and ML to compensate for each defect. We collected published data on the chemical structures of 460 drugs, including Fa and dose amounts. The key parameters of the GUTFW (Do, dose number; Dn, dispersion number; Pn, permeation number), solubility, membrane permeability, and structural descriptors were calculated and used as explanatory variables for ML. ML algorithms, namely, the random forest (RF) and message-passing neural network (MPNN; Chemprop), were investigated. The GUTFW model was compared to the conventional ML method, which uses only structural descriptors, and combined ML method, which uses both structural descriptors and GUTFW parameters. In addition, using the Chemprop framework, we investigated important substructures of Fa. Our result suggested that combinational ML produced higher predictivity than the GUTFW model and conventional ML model in the test dataset (20% of the dataset) [R2 value and RMSE in combinational ML method: 0.611 and 19.7 (RF), 0.520 and 21.6 (Chemprop); in conventional ML: 0.339 and 25.4 (RF), 0.497 and 22.1 (Chemprop); in GUTFW: 0.353 and 31.9]. Additionally, most of the substructures indicated by the Chemprop framework were consistent with the common knowledge of medicinal chemistry. We developed an accurate prediction method for human Fa using a combination of data-driven ML and mechanism-based GUTFW, where the parameters could be calculated without experimental data, enabling the model to efficiently promote early drug discovery. Furthermore, some of the important substructures identified here were previously unknown and require further investigation.

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Strategies and Mechanism in Reversing Intestinal Drug Efflux in Oral Drug Delivery

Cited by 9 publications

References 120 publications

Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage

Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage

Tigecycline Absorption Improved by Selected Excipients

Combined Data-driven and Mechanism-based Approaches for Human-Intestinal-Absorption Prediction in Early Drug-Discovery Stage

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