TY - GEN A1 - Hofman, Jakub A1 - Sorf, Ales A1 - Vagiannis, Dimitrios A1 - Sucha, Simona A1 - Kammerer, Sarah A1 - Küpper, Jan-Heiner A1 - Chen, Si A1 - Guo, Lei A1 - Ceckova, Martina A1 - Staud, Frantisek T1 - Brivanib Exhibits Potential for Pharmacokinetic Drug-Drug Interactions and the Modulation of Multidrug Resistance through the Inhibition of Human ABCG2 Drug Efflux Transporter and CYP450 Biotransformation Enzymes T2 - Molecular Pharmaceutics Y1 - 2019 U6 - https://doi.org/10.1021/acs.molpharmaceut.9b00361 VL - 16 IS - 11 SP - 4436 EP - 4450 ER - TY - GEN A1 - Vagiannis, Dimitrios A1 - Novotna, Eva A1 - Skarka, Adam A1 - Kammerer, Sarah A1 - Küpper, Jan-Heiner A1 - Chen, Si A1 - Guo, Lei A1 - Staud, Frantisek A1 - Hofman, Jakub T1 - Ensartinib (X-396) Effectively Modulates Pharmacokinetic Resistance Mediated by ABCB1 and ABCG2 Drug Efflux Transporters and CYP3A4 Biotransformation Enzyme T2 - Cancers N2 - Ensartinib (X-396) is a promising tyrosine kinase inhibitor currently undergoing advanced clinical evaluation for the treatment of non-small cell lung cancer. In this work, we investigate possible interactions of this promising drug candidate with ATP-binding cassette (ABC) drug efflux transporters and cytochrome P450 biotransformation enzymes (CYPs), which play major roles in multidrug resistance (MDR) and pharmacokinetic drug-drug interactions (DDIs). Accumulation studies showed that ensartinib is a potent inhibitor of ABCB1 and ABCG2 transporters. Additionally, incubation experiments with recombinant CYPs showed that ensartinib significantly inhibits CYP3A4 and CYP2C9. Subsequent molecular docking studies confirmed these findings. Drug combination experiments demonstrated that ensartinib synergistically potentiates the antiproliferative effects of daunorubicin, mitoxantrone, and docetaxel in ABCB1, ABCG2, and CYP3A4-overexpressing cellular models, respectively. Advantageously, ensartinib’s antitumor efficiency was not compromised by the presence of MDR-associated ABC transporters, although it acted as a substrate of ABCB1 in Madin-Darby Canine Kidney II (MDCKII) monolayer transport assays. Finally, we demonstrated that ensartinib had no significant effect on the mRNA-level expression of examined transporters and enzymes in physiological and lung tumor cellular models. In conclusion, ensartinib may perpetrate clinically relevant pharmacokinetic DDIs and modulate ABCB1-, ABCG2-, and CYP3A4-mediated MDR. The in vitro findings presented here will provide a valuable foundation for future in vivo investigations. KW - ensartinib KW - cancer KW - multidrug resistance KW - drug-drug interaction KW - ABC transporter KW - cytochrome P450 Y1 - 2020 UR - https://www.mdpi.com/2072-6694/12/4/813 U6 - https://doi.org/10.3390/cancers12040813 SN - 2072-6694 VL - 12 IS - 4 ER - TY - GEN A1 - Gao, Yunlong A1 - Luo, Si-Zhe A1 - Pan, Jin-Yan A1 - Chen, Bai-Hua A1 - Zhang, Yi-Song T1 - Robust PCA Using Adaptive Probability Weighting T2 - Acta Automatica Sinica N2 - Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms. KW - Principle component analysis (PCA) KW - weighted principal component analysis (WPCA) KW - dimensionality reduction KW - robustness Y1 - 2021 U6 - https://doi.org/10.16383/j.aas.c180743 SN - 0254-4156 VL - 47 IS - 4 SP - 825 EP - 838 ER - TY - GEN A1 - Cheng, Si A1 - Chen, Siyi A1 - Glasauer, Stefan A1 - Keeser, Daniel A1 - Shi, Zhuanghua T1 - Neural mechanisms of sequential dependence in time perception: the impact of prior task and memory processing T2 - Cerebral Cortex N2 - Our perception and decision-making are susceptible to prior context. Such sequential dependence has been extensively studied in the visual domain, but less is known about its impact on time perception. Moreover, there are ongoing debates about whether these sequential biases occur at the perceptual stage or during subsequent post-perceptual processing. Using functional magnetic resonance imaging, we investigated neural mechanisms underlying temporal sequential dependence and the role of action in time judgments across trials. Participants performed a timing task where they had to remember the duration of green coherent motion and were cued to either actively reproduce its duration or simply view it passively. We found that sequential biases in time perception were only evident when the preceding task involved active duration reproduction. Merely encoding a prior duration without reproduction failed to induce such biases. Neurally, we observed activation in networks associated with timing, such as striato-thalamo-cortical circuits, and performance monitoring networks, particularly when a “Response” trial was anticipated. Importantly, the hippocampus showed sensitivity to these sequential biases, and its activation negatively correlated with the individual’s sequential bias following active reproduction trials. These findings highlight the significant role of memory networks in shaping time-related sequential biases at the post-perceptual stages. Y1 - 2023 U6 - https://doi.org/10.1093/cercor/bhad453 SN - 1460-2199 VL - 34(2024) IS - 1 SP - 1 EP - 14 ER -