Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Bioequivalence study of ipratropium bromide inhalation aerosol using PBPK modelling.

Aims: Systemic pharmacokinetic (PK) studies can reflect the overall exposure of orally inhaled drug Products (OIDPs) in the blood after inhalation into the lung and can be used to evaluate the bioequivalence of test and reference products. The aim of this article is: (1) to study the PK characteristics and bioequivalence of ipratropium bromide (IB) inhalation aerosol, reference and test products in healthy Chinese subjects; (2) to establish a physiologically based pharmacokinetic (PBPK) model and verify the accuracy of the model in predicting bioequivalence; (3) attempt to use the model to predict the regional distribution of particles in the lung after inhalation, and discuss the effect of gastrointestinal drug absorption of IB on systemic exposure. Methods: The study involved two clinical studies. Clinical study-1 (registration number: CTR20201284) was used with non-clinical data to construct and validate a PBPK model in the B2O simulator, a web-based virtual drug development platform. This model assessed different test and reference products' bioequivalence. Results were compared to a second clinical study (Clinical study-2: registration number CTR20202291). The particles' regional distribution in the lung and the gastrointestinal absorption effect on systemic exposure were discussed based on the simulation results. Results: The established PBPK model successfully simulated the in vivo PK characteristics of IB inhalation aerosol, with r 2 close to 1. Gastrointestinal absorption had a negligible effect on systemic exposure. Particles accumulated in the alveolar area were cleared within an hour, followed by particles in the bronchioles and bronchi. Conclusion: This model provided a reliable method for exploring the correlation between in vitro and in vivo PK studies of IB inhalation aerosols. According to the simulation results, the test and reference products were bioequivalent.

Predicting the Pharmacokinetics of Orally Administered Drugs across BCS Classes 1-4 by Virtual Bioequivalence Model.

To evaluate the influence of solubility and permeability on the pharmacokinetic prediction performance of orally administered drugs using avirtual bioequivalence (VBE) model, a total of 23 orally administered drugs covering Biopharmaceutics Classification System (BCS) classes 1-4 were selected. A VBE model (i.e., a physiologically based pharmacokinetic model integrated with dissolution data) based on a B2O simulator was applied for pharmacokinetic (PK) prediction in a virtual population. Parameter sensitivity analysis was used for input parameter selection. The predictive performances of PK parameters (i.e., AUC0-t, Cmax, and Tmax), PK profiles, and bioequivalence (BE) results were evaluated using the twofold error, average fold error (AFE), absolute average fold error (AAFE), and BE reassessment metrics. All models successfully simulated the mean PK profiles, with AAFE < 2 and AFE ranging from 0.58 to 1.66. As for the PK parameters, except for the time of peak concentration, Tmax, of isosorbide mononitrate, other simulated PK parameters were all within a twofold error. The simulated PK behaviors were comparable to the observed ones, both for test (T) and reference (R) products, and the simulated T/R arithmetic mean ratios were all within 0.88-1.16 of the observed values. These four evaluation metrics were distributed equally among BCS class 1-4 drugs. The VBE model showed powerful performance to predict the PK behavior of orally administered drugs with various combinations of solubility and permeability, irrespective of the BCS category.

Research Advances in CKLF-like MARVEL Transmembrane Domain-containing Family in Non-small Cell Lung Cancer.

CKLF-like MARVEL transmembrane domain-containing member (CMTM) is a new gene family first cloned and reported in 2001. The CMTM family consists of nine members including CKLF and CMTM1-CMTM8, which are located on different chromosomes. Besides exhibiting extensive chemotactic activity, the CMTM family plays an important role in the hematopoiesis system, the immune system, the cardiovascular system and the male reproductive system. Recent in-depth research has also revealed that CMTM is closely associated with the genesis, development and metastasis of tumors, displaying opposing activities in diverse human tumors. In this review, we discuss the structural and functional characteristics of the CMTM family and summarize latest research findings of the relationship between several CMTM members and non-small cell lung cancer.