Drug Metabolism and Pharmacokinetics最新文献

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and C_max for drug candidates identified as a “victims” of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also C_max ratio (C_maxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (f_m), the intestinal bioavailability (F_g), and the hepatic intrinsic clearance (CL_int) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and C_maxR. The result showed that 65–69% and 65–67% of predictions were within 2-fold of observed AUCR and C_maxR, respectively. A simulation using multiple parameter combinations suggested that the variability of f_m and F_g within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and C_maxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Cytochrome P450 3A4 (CYP3A4) enzyme is involved in the metabolism of about 30 % of clinically used drugs, including the antimalarials artemether and lumefantrine. CYP3A4 polymorphisms yield enzymatic variants that contribute to inter-individual variation in drug metabolism. Here, we examined CYP3A4 polymorphisms in populations from malaria-endemic islands in Lake Victoria, Kenya, and Vanuatu, to expand on the limited data sets. We used archived dried blood spots collected from 142 Kenyan and 263 ni-Vanuatu adults during cross-sectional malaria surveys in 2013 and 2005–13, respectively, to detect CYP3A4 variation by polymerase chain reaction (PCR) and sequencing. In Kenya, we identified 14 CYP3A4 single nucleotide polymorphisms (SNPs), including the 4713G (CYP3A4∗1B; allele frequency 83.9 %) and 19382A (CYP3A4∗15; 0.7 %) variants that were previously linked to altered metabolism of antimalarials. In Vanuatu, we detected 15 SNPs, including the 4713A (CYP3A4∗1A; 88.6 %) and 25183C (CYP3A4∗18; 0.6 %) variants. Additionally, we detected a rare and novel SNP C4614T (0.8 %) in the 5′ untranslated region. A higher proportion of CYP3A4 genetic variance was found among ni-Vanuatu populations (16 %) than among Lake Victoria Kenyan populations (8 %). Our work augments the scarce data sets and contributes to improved precision medicine approaches, particularly to anti-malarial chemotherapy, in East African and Pacific Islander populations.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Nintedanib is used to treat idiopathic pulmonary fibrosis, systemic sclerosis, interstitial lung disease, and progressive fibrotic interstitial lung disease. It is primarily cleared via hepatic metabolism, hydrolysis, and glucuronidation. In addition, formation of the iminium ion, a possible reactive metabolite, was predicted based on the chemical structure of nintedanib. To obtain a hint which may help to clarify the cause of nintedanib-induced liver injury, we investigated whether iminium ions were formed in the human liver. To detect unstable iminium ions using liquid chromatography-tandem mass spectrometry (LC-MS/MS), potassium cyanide was added to the reaction mixture as a trapping agent. Human liver and intestinal microsomes were incubated with nintedanib in the presence of NADPH to form two iminium ion metabolites on the piperazine ring. Their formation is strongly inhibited by ketoconazole, a potent cytochrome P450 (CYP) 3A4 inhibitor. Among the recombinant P450s, only CYP3A4 formed cyanide adducts. The role of CYP3A4 was supported by the positive correlation between CYP3A4 protein abundance, as determined by LC-MS-based proteomics, and the formation of cyanide adducts in 25 individual human liver microsomes. In conclusion, we have demonstrated that iminium ion metabolites are formed from nintedanib by CYP3A4 as potential reactive metabolites.

Substance use disorders (SUDs) are complex mental health conditions involving a problematic pattern of substance use. Challenges remain in understanding their neural mechanisms, which are likely to lead to improved SUD treatments. Human brain organoids, brain-like 3D in vitro cultures derived from human stem cells, show unique potential in recapitulating the response of a developing human brain to substances. Here, we review the recent progress in understanding SUDs using human brain organoid models focusing on neurodevelopmental perspectives. We first summarize the background of SUDs in humans. Moreover, we introduce the development of various human brain organoid models and then discuss current progress and findings underlying the abuse of substances like nicotine, alcohol, and other addictive drugs using organoid models. Furthermore, we review efforts to develop organ chips and microphysiological systems to engineer better human brain organoids for advancing SUD studies. Lastly, we conclude by elaborating on the current challenges and future directions of SUD studies using human brain organoids.

Cyclic peptides have attracted attention as new modalities for drug development owing to their unique pharmacokinetic and pharmacodynamic properties. Destruxin E, a 19-membered cyclodepsipeptide, is a promising candidate drug for cancer therapy. The purpose of the present study was to clarify the molecular mechanisms underlying membrane transport, metabolism, and the binding for target molecules of destruxin E in human cervical carcinoma HeLa cells used as a model of cancer cells. The influx transport and the intracellular metabolism of destruxin E were non-saturable and saturable, respectively, at up to 10 μM. The intracellular amounts of destruxin E and destruxin E-diol after incubation of destruxin E with the cells significantly decreased at 4 °C compared to those at 37 °C. Destruxin E-diol, but not destruxin E, undergoes efflux transport out of cells via P-gp/MDR1/ABCB1 and BCRP/ABCG2. The epoxide hydrolase EPHX2 functions as a potent metabolizing enzyme that can convert the epoxide of destruxin E to the destruxin E-diol. Treatment with an EPHX2 inhibitor increased the intracellular destruxin E levels and enhanced the inhibitory activity of vacuolar type-H⁺ ATPase. These results suggest that epoxide hydrolase could be a regulatory factor for intracellular destruxin E levels and its pharmacological activity.

Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms – the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method – using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.

Chimeric antigen receptor (CAR) cells are genetically engineered immune cells that specifically target tumor-associated antigens and have revolutionized cancer treatment, particularly in hematological malignancies, with ongoing investigations into their potential applications in solid tumors. This review provides a comprehensive overview of the current status and challenges in drug metabolism and pharmacokinetics (DMPK) for CAR cell therapy, specifically emphasizing on quantitative modeling and simulation (M&S). Furthermore, the recent advances in quantitative model analysis have been reviewed, ranging from clinical data characterization to mechanism-based modeling that connects in vitro and in vivo nonclinical and clinical study data. Additionally, the future perspectives and areas for improvement in CAR cell therapy translation have been reviewed. This includes using formulation quality considerations, characterization of appropriate animal models, refinement of in vitro models for bottom-up approaches, and enhancement of quantitative bioanalytical methodology. Addressing these challenges within a DMPK framework is pivotal in facilitating the translation of CAR cell therapy, ultimately enhancing the patients’ lives through efficient CAR cell therapies.