Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with limited response to chemotherapy and immune checkpoint inhibitors (ICIs). This study evaluates the efficacy and safety of combining Albumin-Bound Paclitaxel (nab-PTX) with an anti-PD-1 antibody and aims to identify potential biomarkers to optimize therapeutic outcomes. The murine model of PDAC is established by subcutaneously injecting the murine pancreatic cancer cell line Panc02 into C57BL/6J mice. The mice are treated with either an anti-PD-1 antibody, nab-PTX, or nab-PTX plus anti-PD-1 antibody, with untreated mice serving as the control. Tumor growth, immune cell infiltration, cytokine levels, overall survival, organ damage, and gene expression profiles are analyzed. The combination therapy shows superior efficacy compared to nab-PTX and non-inferior efficacy compared to the anti-PD-1 antibody. Moreover, this strategy significantly reduces the risk of irAEs and hyperprogression caused by the anti-PD-1 antibody. In addition, screening identifies WNT9a as a potential gene associated with improved efficacy and ATF3 with enhanced safety, providing valuable insights for optimizing therapeutic strategies in PDAC.

OBJECTIVE:This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs). METHODS:An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models. RESULTS:Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 μM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 μM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure. CONCLUSIONS:Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Overdose of Acetaminophen (APAP) is a major contributor to acute liver injury (ALI), a complex pathological process with limited effective treatments. Emerging evidence links lipid peroxidation to APAP-induced ALI. Cynarin (Cyn), a hydroxycinnamic acid derivative, exhibits liver protective effects, but whether it mitigates APAP-induced ALI is unclear. Our aim was to verify the protective impact of Cyn on APAP-induced ALI and elucidate the molecular mechanisms governing this process. Herein, the regulation of the Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2) interaction was determined to be a novel mechanism underlying this protective impact of Cyn against APAP-induced ALI. Nrf2 deficiency increased the severity of APAP-induced ALI and lipid peroxidation and counteracted the protective effect of Cyn against this pathology. Additionally, Cyn promoted the dissociation of Nrf2 from Keap1, enhancing the nuclear translocation of Nrf2 and the transcription of downstream antioxidant proteins, thereby inhibiting lipid peroxidation. Molecular docking demonstrated that Cyn bound competitively to Keap1, and overexpression of Keap1 reversed Nrf2-activated anti-lipid peroxidation. Additionally, Cyn activated the adenosine monophosphate-activated protein kinase (AMPK)/sirtuin (SIRT)3 signaling pathway, which exhibits a protective effect on APAP-induced ALI. These findings propose that Cyn alleviates APAP-induced ALI by enhancing the Keap1/Nrf2-mediated lipid peroxidation defense via activation of the AMPK/SIRT3 signaling pathway.

Acetaminophen (APAP) overdose has long been considered a major cause of drug-induced liver injury. Ferroptosis is a type of programmed cell death mediated by iron-dependent lipid peroxidation. Endoplasmic reticulum (ER) stress is a systemic response triggered by the accumulation of unfolded or misfolded proteins in the ER. Ferroptosis and ER stress have been proven to contribute to the progression of APAP-induced acute liver injury (ALI). It was reported that salidroside protects against APAP-induced ALI, but the potential mechanism remain unknown. In this study, male C57BL/6 J mice were intraperitoneally (i.p.) injected APAP (500 mg/kg) to induce an ALI model. Salidroside was i.p. injected at a dose of 100 mg/kg 2 h prior to APAP administration. Mice were sacrificed 12 h after APAP injection and the liver and serum of the mice were obtained for histological and biochemistry analysis. AML12 cells were used in in vitro assays. The results indicated that salidroside mitigated glutathione degradation via inhibiting cation transport regulator homolog 1 (CHAC1) to attenuate ferroptosis, and simultaneously suppressing PERK-eIF2α-ATF4 axis-mediated ER stress, thus alleviating APAP-induced ALI. However, PERK activator CCT020312 and overexpression of ATF4 inhibited the protective function of salidroside on CHAC1-mediated ferroptosis. Besides this, activation of the AMPK/SIRT1 signaling pathway by salidroside was demonstrated to have a protective effect against APAP-induced ALI. Interestingly, selective inhibition of SIRT1 ameliorated the protective effects of salidroside on ER stress and ferroptosis. Overall, salidroside plays a significant part in the mitigation of APAP-induced ALI by activating the AMPK/SIRT1 signaling to inhibit ER stress-mediated ferroptosis in the ATF4-CHAC1 axis.

BACKGROUND:Recent studies have reported a higher risk of bleeding among patients that are co-administrated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) and anticoagulant, which raises our concern about the possible TKIs-warfarin pharmacokinetic and pharmacodynamic interaction that could be life-threatening to tumor patients who take warfarin for preventing deep vein thrombosis (DVT). METHODS:Influences of anlotinib and fruquintinib on the pharmacokinetic and dynamic behavior of warfarin were estimated. Influence on the activity of cytochrome P450 (CYP450) enzymes was detected in vitro through rat liver microsomes. Quantitative analysis of blood concentration in rats was finished by a validated UHPLC-MS/MS method. Furthermore, pharmacodynamic interactions were studied in rats by monitoring prothrombin time (PT) and activated partial thromboplastin time (APTT), while Inferior vena cava (IVC) stenosis-induced DVT model was built to further investigate the antithrombotic effect after co-administration. RESULTS:Anlotinib inhibited the activity of cyp2c6, cyp3a1/2 and cyp1a2 in rat liver microsomes in a dose-dependent manner, meanwhile enhanced the AUC0∼t and AUC0∼∞ of R-warfarin. However, fruquintinib showed no effects on pharmacokinetics of warfarin. Anlotinib and fruquintinib co-administrated with warfarin was found to exert more significant increase on PT and APTT values than that taking warfarin alone. In IVC stenosis-induced DVT model rats, the co-administration groups significantly reduced the length of thrombus compared with the single warfarin group. CONCLUSIONS:Anlotinib and fruquintinib enhanced the anticoagulated and antithrombotic effect of warfarin. The anlotinib-induced interaction may due to the inhibition of the metabolism of warfarin. The mechanism of the pharmacodynamic interaction between fruquintinib and warfarin should be further investigated.

Background Diabetes mellitus, especially type 2 diabetes mellitus (T2DM), is regarded as highly heterogeneous. Novel diabetes phenotypes by cluster analysis have been proposed in Europeans but may show different cluster features in Asians. The applicability of cluster analysis in middle-aged and elderly Chinese community T2DM patients needs further investigation. Methods Participants were recruited from Jiading community in Shanghai, China. We adopted k-means cluster analysis in 1130 patients (aged ≥ 40 years) with newly-diagnosed T2DM at baseline. Cluster analysis was performed based on seven variables, including fasting plasma glucose, 2 hours postprandial blood glucose, age at diagnosis, body mass index, hemoglobin A1c, homoeostatic model assessment estimates of β-cell function and insulin resistance. All subjects were re-examined at 4.4 years later. Metabolic associated fatty liver disease was diagnosed using B-ultrasound, hepatic fibrosis by non-invasive scores, renal and cardiovascular status by subclinical biomarkers. Multivariable logistic regression models were used to compare the risks of complications between clusters. Results Patients were classified into 4 clusters. 381 (33.7%), 456 (40.4%), 87 (7.7%), and 206 (18.2%) patients were separately assigned to mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), severe insulin-deficient and insulin-resistant diabetes (SIDRD), or severe obesity-related and insulin-resistant diabetes (SOIRD), respectively. Participants in MARD, SOIRD, and SIDRD clusters were associated with significantly increased risks of different complications. SOIRD and SIDRD showed novel features in Chinese T2DM patients that were different from those in Europeans. Conclusions The refined diabetes phenotypic approach was applicable to Chinese middle-aged and elderly T2DM patients. Patients in different clusters presented significantly different characteristics, progression of metabolic features, and risks of diabetic complications.

Objective: We aimed to establish a population pharmacokinetic (PPK) model for isoniazid (INH) and its major metabolite Acetylisoniazid (AcINH) in healthy Chinese participants and tuberculosis patients and assess the role of the NAT2 genotype on the transformation of INH to AcINH. We also sought to estimate the INH exposure that would achieve a 90% effective concentration (EC90) efficiency for patients with various NAT2 genotypes.Method: A total of 45 healthy participants and 157 tuberculosis patients were recruited. For healthy subjects, blood samples were collected 0–14 h after administration of 300 mg or 320 mg of the oral dose of INH; for tuberculosis patients who received at least seven days therapy with INH, blood samples were collected two and/or six hours after administration. The plasma concentration of INH and AcINH was determined by the reverse-phase HPLC method. NAT2 genotypes were determined by allele-specific amplification. The integrated PPK model of INH and AcINH was established through nonlinear mixed-effect modeling (NONMEM). The effect of NAT2 genotype and other covariates on INH and AcINH disposition was evaluated. Monte Carlo simulation was performed for estimating EC90 of INH in patients with various NAT2 genotypes.Results: The estimated absorption rate constant (Ka), oral clearance (CL/F), and apparent volume of distribution (V2/F) for INH were 3.94 ± 0.44 h−1, 18.2 ± 2.45 L⋅h−1, and 56.8 ± 5.53 L, respectively. The constant of clearance (K30) and the volume of distribution (V3/F) of AcINH were 0.33 ± 0.11 h−1 and 25.7 ± 1.30 L, respectively. The fraction of AcINH formation (FM) was 0.81 ± 0.076. NAT2 genotypes had different effects on the CL/F and FM. In subjects with only one copy of NAT2 *5, *6, and *7 alleles, the CL/F values were approximately 46.3%, 54.9%, and 74.8% of *4/*4 subjects, respectively. The FM values were approximately 48.7%, 63.8%, and 86.9% of *4/*4 subjects, respectively. The probability of target attainment of INH EC90 in patients with various NAT2 genotypes was different.Conclusion: The integrated parent-metabolite PPK model accurately characterized the disposition of INH and AcINH in the Chinese population sampled, which may be useful in the individualized therapy of INH.

In order to improve the benefit–risk ratio of pharmacokinetic (PK) research in the early development of new drugs, in silico and in vitro methods were constructed and improved. Models of intrinsic clearance rate (CLint) were constructed based on the quantitative structure–activity relationship (QSAR) of 7882 collected compounds. Moreover, a novel in vitro metabolic method, the Bio-PK dynamic metabolic system, was constructed and combined with a physiology-based pharmacokinetic model (PBPK) model to predict the metabolism and the drug–drug interaction (DDI) of azidothymidine (AZT) and fluconazole (FCZ) mediated by the phase II metabolic enzyme UDP-glycosyltransferase (UGT) in humans. Compared with the QSAR models reported previously, the goodness of fit of our CLint model was slightly improved (determination coefficient (R2) = 0.58 vs. 0.25–0.45). Meanwhile, compared with the predicted clearance of 61.96 L/h (fold error: 2.95–3.13) using CLint (8 µL/min/mg) from traditional microsomal experiment, the predicted clearance using CLint (25 μL/min/mg) from Bio-PK system was increased to 143.26 L/h (fold error: 1.27–1.36). The predicted Cmax and AUC (the area under the concentration–time curve) ratio were 1.32 and 1.84 (fold error: 1.36 and 1.05) in a DDI study with an inhibition coefficient (Ki) of 13.97 μM from the Bio-PK system. The results indicate that the Bio-PK system more truly reflects the dynamic metabolism and DDI of AZT and FCZ in the body. In summary, the novel in silico and in vitro method may provide new ideas for the optimization of drug metabolism and DDI research methods in early drug development.

Sorafenib was suggested to cause drug-drug interaction (DDI) with the common anticoagulant, warfarin based on published studies. The inhibition on CYP2C9 enzyme was thought to be the mechanism, but further studies are warranted. Thus, a mechanistic PBPK/PD model for warfarin enantiomers was developed to predict DDI potential with sorafenib, aiming at providing reference for the rational use of both drugs. PBPK models of warfarin enantiomers were constructed by Simcyp software. A mechanistic PK/PD model was built in NONMEM software. PBPK model of sorafenib was fitted via a top-down method. The final PBPK/PD model of warfarin enantiomers was verified and validated by different dosing regimens, ethnicities and genetic polymorphisms, and used to perform DDI simulations between warfarin racemate and sorafenib among general populations and sub-populations with various CYP2C9 and VKORC1 genotypes. Results suggested low DDI risk between warfarin and sorafenib for general populations. Potentially serious consequence was seen for those carrying both CYP2C9 ∗2 and ∗3 and VKORC1 A/A genotypes. This PBPK/PD modeling approach for warfarin enantiomers enabled DDI evaluation with sorafenib. Close monitoring and warfarin dosage adjustment were recommended for patients carrying mutant genotypes. The novel model could be applied to investigate other drugs that may interact with warfarin.

To elucidate current domestic factors influencing pharmacogenomics (PGx) implementation and its future in China, we conducted a questionnaire survey on PGx applications and testing. A questionnaire-based survey was created on the popular online professional survey platform “Wenjuanxing” ( www.wjx.cn ) and performed via the social media platform WeChat. Among 422 participants, there were physicians (27.7%), pharmacists (31.3%), and researchers (41.0%). We found that less than 50% of physicians were aware of the importance of PGx in drug therapy, while over 50% of pharmacists and researchers recognized the importance. Only 38.5% of physicians, 40.9% of pharmacists, and 55.5% of researchers concurred that PGx analysis could lower the economic burdens for patients. However, most of the responders affirmed that PGx should be effectively implemented in clinical practices. A lack of sector standards, a lack of clinical research, and a lack of guidelines were found to be the major factors for hindering PGx clinical application. Among drugs associated with PGx assays, the most common were warfarin and clopidogrel. Although PGx research has advanced rapidly in recent years in mainland China, the clinical implementation of PGx has a long way to go.

Background and Objective Hypertension is a common comorbidity of patients with COVID-19, SARS or HIV infection. Those patients are often treated with commonly used antiviral and antihypertensive agents concomitantly, such as ritonavir-containing regimens and nifedipine. Since ritonavir is a strong inhibitor of CYP3A, when nifedipine is combined with ritonavir-containing antiviral drugs, there is a potential risk of drug-drug interaction. This study aimed to provide guidance on nifedipine treatment during and after co-administration with ritonavir-containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) analysis. Methods A PBPK/PD model was developed for nifedipine by the software of Simcyp, and the model was verified using published data. The effects of ritonavir on nifedipine exposures and systolic blood pressure were assessed for instant-release, sustained-release and controlled-release formulations. Moreover, various nifedipine regimens were investigated when co-administrated with and withdrawing ritonavir. Results PBPK/PD models for three formulations of nifedipine were successfully established. The model predicted pharmacokinetic profiles of nifedipine were comparable to the published data. Ratios of predicted versus observed AUCDDI/AUCNifedipine of nifedipine were within 0.70- to 1.83-fold. Model simulations showed that the inhibitory effect of ritonavir on CYP3A4 increased the Cmax of nifedipine by 9.82-34.35 times and the AUC by 44.94-50.77 times at steady state. Moreover, nifedipine dose reduced to 1/16 of the regular dose during ritonavir co-administration could lead to severe hypotension. Conclusions Ritonavir had a pronounced influence on the pharmacokinetics and antihypertensive effect of nifedipine. It is not recommended for patients to take nifedipine and ritonavir-containing regimens simultaneously.

Background and Objective Hypertension is a common comorbidity of patients with COVID-19, SARS or HIV infection. Those patients are often treated with commonly used antiviral and antihypertensive agents concomitantly, such as ritonavir-containing regimens and nifedipine. Since ritonavir is a strong inhibitor of CYP3A, when nifedipine is combined with ritonavir-containing antiviral drugs, there is a potential risk of drug-drug interaction. This study aimed to provide guidance on nifedipine treatment during and after co-administration with ritonavir-containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) analysis. Methods A PBPK/PD model was developed for nifedipine by the software of Simcyp, and the model was verified using published data. The effects of ritonavir on nifedipine exposures and systolic blood pressure were assessed for instant-release, sustained-release and controlled-release formulations. Moreover, various nifedipine regimens were investigated when co-administrated with and withdrawing ritonavir. Results PBPK/PD models for three formulations of nifedipine were successfully established. The model predicted pharmacokinetic profiles of nifedipine were comparable to the published data. Ratios of predicted versus observed AUCDDI/AUCNifedipine of nifedipine were within 0.70- to 1.83-fold. Model simulations showed that the inhibitory effect of ritonavir on CYP3A4 increased the Cmax of nifedipine by 9.82-34.35 times and the AUC by 44.94-50.77 times at steady state. Moreover, nifedipine dose reduced to 1/16 of the regular dose during ritonavir co-administration could lead to severe hypotension. Conclusions Ritonavir had a pronounced influence on the pharmacokinetics and antihypertensive effect of nifedipine. It is not recommended for patients to take nifedipine and ritonavir-containing regimens simultaneously.

The individual and ethnic differences of drug effects are very important issues in clinical drug therapy. They may be caused by genetic variations which mainly come from polymorphisms of genes encoding metabolic enzymes, transporters and drug targets that affect the in vivo pharmacokinetic and pharmacodynamics of drugs. With the development and huge successes of HGP project, one of its major applications is emerge of a new research area of pharmacogenomics, which is used in standardization and individualization of drug therapy. In order to fulfill its goal, precision medicine is the key to solve the problem.

[This corrects the article DOI: 10.3389/fphar.2020.591854.].

Studies on the efficacy evaluation of UDP-glucuronosyltransferases (UGTs) substrates often ignore the existence of active metabolites. However, the present study aims to establish an in-vitro Phase II metabolism system to predict their pharmacological effects after metabolism. Rat liver microsomes (RLMs) encapsulated in a F1270-Acr-Bis (FAB) hydrogel were placed in the incubation system. Baicalein (BA) was chosen as a model drug and the metabolic activity was investigated by quantitating the metabolite Baicalin (BG). The 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay was used to measure the cell viability in Traditional cell culture system (TCCS) and Microsome-hydrogel added to cell culture system for Phase II metabolism (MHCCS-II). Finally, MHCCS-II was applied to predict the metabolic effects of Oroxylin A (OA) and Wogonin (W). Compared to TCCS group, for HepG2 and MCF-7 cells, BA in MHCCS-II led to lower survival ratios of cells (P < 0.05), while for PC12 cells it led to higher survival ratios of cells (P < 0.01). For HepG2 cells, OA and W showed obviously enhanced tumor inhibition after metabolism with the IC50 of 32.7 +/- 2.9 mu M and 76.1 +/- 5.1 mu M, respectively (P < 0.01). In conclusion, the MHCCS-II could be a useful tool for studying the pharmacokinetics and pharmacodynamics of UGTs substrates. (C) 2019 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.