Abstract: Adverse drug events are common in critically ill children and often result from systemic or target organ drug exposure. Methods of drug dosing and titration that consider pharmacokinetic alterations may improve our ability to optimally dose critically ill patients and reduce the risk for drug-related adverse events. To demonstrate this possibility, we explored the exposure-response relationship between midazolam and delirium in critically ill children. We retrospectively examined electronic health records (EHRs) of critically ill children <18 years of age hospitalized in the pediatric intensive care unit at Duke University; these children were administered midazolam during mechanical ventilation and had >/=1 Cornell Assessment of Pediatric Delirium (CAPD) score. We used individual-level data extracted from the EHR and a previously published population pharmacokinetic (PK) model developed in critically ill children to simulate plasma concentrations at the time of CAPD scores in 1,000 representative datasets. We used multilevel repeated measures models, with clustering at patient and simulation levels, to evaluate the associations between measures of drug exposure (e.g., concentration and area under concentration time curve) and delirium scores. We included 61 children, median age 1.5 years (range = 0.1-16.3), with 181 CAPD assessments. We identified similarities between simulated Empirical Bayesian parameter estimates from the EHR cohort and those from the PK model population. We identified a stronger association between drug concentration at the time of score and CAPD scores (coefficient 1.78; 95% confidence interval: 1.66-1.90) compared with cumulative dose per kilogram and CAPD scores (coefficient -0.01; 95% confidence interval: -0.01 to -0.01). EHR and PK models can be leveraged to investigate exposure-response relationships in critically ill children.

Abstract: BACKGROUND: Cancer is a second leading cause of death in the world, killing approximately 3500 per million people each year. Therefore, the drugs with multitarget pharmacology based on biological networks are crucial to investigate the molecular mechanisms of cancer drugs and repurpose the existing drugs to reduce adverse effects. Clerodendrum is a diversified genus with a wide range of economic and pharmacological properties. Limited studies were conducted on the genus's putative anticancer properties and the mechanisms of action based on biological networks remains unknown. This study was aimed to construct the possible compound/target/pathway biological networks for anticancer effect of Clerodendrum sp. using docking weighted network pharmacological approach and to investigate its potential mechanism of action. METHODS: A total of 194 natural Clerodendrum sp. Compounds were retrieved from public databases and screened using eight molecular descriptors. The cancer-associated gene targets were retrieved from databases and the function of the target genes with related pathways were examined. Cytoscape v3.7.2 was used to build three major networks: compound-target network, target-target pathway network, and compound-target-pathway network. RESULTS: Our finding indicates that the anticancer activity of Clerodendrum sp. involves 6 compounds, 9 targets, and 63 signaling pathways, resulting in multicompounds, multitargets, and multipathways networks. Additionally, molecular dynamics (MD) simulations were used to estimate the binding affinity of the best hit protein-ligand complexes. Conclusion. This study suggests the potential anticancer activity of Clerodendrum sp. which could further contribute to scavenger novel compounds for the development of new alternative anticancer drugs.

Abstract: Protein tyrosine phosphatases function in dephosphorylating target proteins to regulate signaling pathways that control a broad spectrum of fundamental physiological and pathological processes. Detailed knowledge concerning the roles of classical PTPs in human cancer merits in-depth investigation. We comprehensively analyzed the regulatory mechanisms and clinical relevance of classical PTPs in more than 9000 tumor patients across 33 types of cancer. The independent datasets and functional experiments were employed to validate our findings. We exhibited the extensive dysregulation of classical PTPs and constructed the gene regulatory network in human cancer. Moreover, we characterized the correlation of classical PTPs with both drug-resistant and drug-sensitive responses to anti-cancer drugs. To evaluate the PTP activity in cancer prognosis, we generated a PTPscore based on the expression and hazard ratio of classical PTPs. Our study highlights the notable role of classical PTPs in cancer biology and provides novel intelligence to improve potential therapeutic strategies based on pTyr regulation.

Abstract: Overview: In treating pulmonary fibrosis (PF), traditional Chinese medicine (TCM) has received much attention, but its mechanism is unclear. The pharmacological mechanisms of TCM can be explored through network pharmacology. However, due to its virtual screening properties, it still needs to be verified by in vitro or in vivo experiments. Therefore, we investigated the anti-PF mechanism of Yiqi Huayu Decoction (YHD) by combining network pharmacology with in vivo experiments. Methods: Firstly, we used classical bleomycin (BLM)-induced rat model of PF and administrated fibrotic rats with YHD (low-, medium-, and high-dose). We comprehensively assessed the treatment effect of YHD according to body weight, lung coefficient, lung function, and histopathologic examination. Second, we predict the potential targets by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) combined with network pharmacology. In brief, we obtained the chemical ingredients of YHD based on the UHPLC-MS/MS and TCMSP database. We collected drug targets from TCMSP, HERB, and Swiss target prediction databases based on active ingredients. Disease targets were acquired from drug libraries, Genecards, HERB, and TTD databases. The intersecting targets of drugs and disease were screened out. The STRING database can obtain protein-protein interaction (PPI) networks and hub target proteins. Molecular Complex Detection (MCODE) clustering analysis combined with enrichment analysis can explore the possible biological mechanisms of YHD. Enrichment analyses were conducted through the R package and the David database, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Reactome. Then, we further validated the target genes and target proteins predicted by network pharmacology. Protein and gene expression detection by immunohistochemistry, Western blot (WB), and real-time quantitative PCR (rt-qPCR). Results: The results showed that high-dose YHD effectively attenuated BLM-induced lung injury and fibrosis in rats, as evidenced by improved lung function, relief of inflammatory response, and reduced collagen deposition. We screened nine core targets and cellular senescence pathways by UHPLC-MS/MS analysis and network pharmacology. We subsequently validated key targets of cellular senescence signaling pathways. WB and rt-qPCR indicated that high-dose YHD decreased protein and gene expression of senescence-related markers, including p53 (TP53), p21 (CDKN1A), and p16 (CDKN2A). Increased reactive oxygen species (ROS) are upstream triggers of the senescence program. The senescence-associated secretory phenotypes (SASPs), containing interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and transforming growth factor-beta1 (TGF-beta1), can further exacerbate the progression of senescence. High-dose YHD inhibited ROS production in lung tissue and consistently reduced the SASPs expression in serum. Conclusion: Our study suggests that YHD improves lung pathological injury and lung function in PF rats. This protective effect may be related to the ability of YHD to inhibit cellular senescence.

Abstract: INTRODUCTION: Approximately 15%~30% of breast cancers have gene amplification or overexpression of the human epidermal growth factor receptor 2 (HER2), resulting in the chemotherapy resistance, a more-aggressive phenotype and poor prognosis. METHODS: We propose a strategy of nanocarriers co-loaded with docetaxel (DTX) and pictilisib (PIC) at a synergistic ratio and non-covalently bound with dual anti-HER2 epitopes bispecific antibodies (BsAbs: anti-HER2-IV/methoxy-polyethylene glycol (mPEG) and anti-HER2-II/methoxy-PEG) for synergistic targeting to overcome the therapeutic dilemmas of the resistance for HER2-targetable chemodrugs. DTX/PIC-loaded nanocarriers (D/P_NCs) were prepared with single emulsion methods and characterized using dynamic light scattering analysis, and the drug content was assayed by high-performance liquid chromatographic method. The integrity and function of BsABs were evaluated using sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) and enzyme-linked immunosorbent assay (ELISA). The in vitro cell studies and in vivo breast tumor-bearing mice model were used to evaluate the anti-cancer effect and biosafety of formulations. RESULTS: D/P_NCs optimally prepared exhibited a spherical morphology with small particle sizes (~140 nm), high drug loading (~5.5%), and good colloidal stability. The synergistic tumor cytotoxicity of loading DTX and PIC at 2:1 ratio in D/P_NCs was discovered. The BsAbs are successfully decorated on mPEGylated DTX/PIC-loaded nanocarriers via anti-mPEG moiety. In vitro studies revealed that non-covalent decoration with dual BsAbs on D_P-NCs significantly and synergistically increased cellular uptake, while with loading DTX and PIC at a synergistic ratio of 2:1 in D/P_NCs further resulted in synergistic cytotoxicity. In vivo tumor inhibition studies showed the comparable results for synergistic antitumor efficacy while minimizing systemic toxicity of chemodrugs. CONCLUSION: Non-covalent modification with dual distinct epitopes BsAbs on the nanocarriers loaded with dual chemodrugs at a synergistic ratio was expected to be a promising therapeutic platform to overcome the chemoresistance of various cancers and warrants further development for future therapy in the clinical.

Abstract: OBJECTIVE: This work studied the molecular mechanism of the Saposhnikovia divaricata-Angelica dahurica herb pair (SAHP) in migraine treatment. METHODS: The active ingredients of drugs were screened, and potential targets were predicted by the Traditional Chinese Medicine Systems Pharmacology (TCMSP), TCMID, ETCM, and other databases. Migraine-related targets were obtained by harnessing the GeneCards, DrugBank, OMIM, TTD, and other databases. The protein-protein interaction (PPI) network was constructed with STRING software by performing a Venn analysis with bioinformatics. Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the Metascape platform. The component-target-pathway (C-T-P) network was constructed with Cytoscape 3.7.2 software, and molecular docking was assessed with AutoDockVina software. RESULTS: A total of 183 relevant targets and 39 active ingredients in migraine therapy were obtained from SAHP. The active ingredients and targets were screened according to topological parameters: wogonin, anomalin, imperatorin, prangenin, 2-linoleoylglycerol, and methylenetanshinquinone were identified as key active ingredients. PTGS2, PIK3CA, PIK3CB, PIK3CD, F2, and AR were identified as key targets. The molecular docking results demonstrated high binding activity between the key active ingredients and key targets. A total of 20 important signaling pathways, including neural signaling pathways, calcium signaling pathways, pathways in cancer, cAMP signaling pathways, and PI3K-Akt signaling pathways, were obtained through enrichment analysis. CONCLUSION: Migraine with SAHP is mainly treated through anti-inflammatory and analgesic effects. The herb pair can be used for migraine using "multicomponent, multitarget, and multipathway" approaches.

Abstract: Introduction: Antibody-drug conjugates (ADCs) produce unparalleled efficacy in refractory neoplasms but can also lead to serious toxicities. Although ADC-related sepsis has been reported, the clinical features are not well characterized in real-world studies. Objective: The aim of this study was to identify the association between ADCs and sepsis using FAERS data and uncover the clinical characteristics of ADC-related sepsis. Methods: We performed disproportionality analysis using FAERS data and compared rates of sepsis in cancer patients receiving ADCs vs. other regimens. Associations between ADCs and sepsis were assessed using reporting odds ratios (RORs) and information component (IC). For each treatment group, we detected drug interaction signals, and conducted subgroup analyses (age, gender, and regimens) and sensitivity analyses. Results: A total of 24,618 cases were reported with ADCs between Q1, 2004 and Q3, 2021. Sepsis, septic shock, multiple organ dysfunction syndrome, and other sepsis-related toxicities were significantly associated with ADCs than other drugs in this database. Sepsis and multiple organ dysfunction syndrome have the highest safety concerns with ADCs compared with other anticancer monotherapies. Gemtuzumab ozogamicin and inotuzumab ozogamicin showed increased safety risks than other ADCs. For the top nine ADC-related sepsis, males showed higher sepsis safety concern than females (p <0.001); however, age did not exert influence on the risk of sepsis. We identified that 973 of 2,441 (39.9%) cases had acute myeloid leukemia (AML), and 766 of 2613 (29.3%) cases on ADCs died during therapy. Time-to-onset analysis indicated ADC-related sepsis is prone to occur within a month after administration. Co-administration of ADCs with colony-stimulating factors, proton pump inhibitors, H2-receptor antagonists, or CYP3A4/5 inhibitors showed to synergistically increase the risk of sepsis-related toxicities. Conclusion: Antibody-drug conjugates may increase the risk of sepsis in cancer patients, leading to high mortality. Further studies are warranted to characterize the underlying mechanisms and design preventive measures for ADC-related sepsis.

Abstract: We report on the case of a 46-year-old woman with generalized anxiety disorder, paranoid personality disorder, and mild reduction in glomerular filtration rate (GFR). She was treated with pregabalin, trazodone, hydroxyzine, and clonazepam before hospital admission. Pharmacotherapy for the patient was changed during her first week in the hospital. Dosing of hydroxyzine and clonazepam was gradually decreased, and then these two drugs were withdrawn. Treatment with amisulpride was started on the fourth day after admission, and amisulpride serum levels were then measured three times as a part of therapeutic drug monitoring (TDM). The serum concentration of amisulpride detected during concurrent use of trazodone and pregabalin was approximately twice the therapeutic range for amisulpride. When the dose of pregabalin was reduced by half, the serum concentration of amisulpride decreased to therapeutic serum levels. We hypothesize that an interaction between amisulpride and pregabalin was responsible for the increased amisulpride concentration since both drugs are almost exclusively excreted from the body by the renal route. Pregabalin-amisulpride interaction might also be influenced by concomitant therapy with trazodone or a mild reduction in GFR. However, we only have clinical evidence for an interaction between amisulpride and pregabalin because after we halved the dose of pregabalin, the amisulpride concentration decreased, and the C/D ratio normalized. This could be helpful information for psychiatrists in order to avoid drug-drug interactions between amisulpride and pregabalin. We recommend TDM of amisulpride in patients treated concomitantly with other drugs eliminated mainly by the kidneys.

Abstract: Pyroptosis has been proved to significantly influence the development of lung squamous cell carcinoma (LUSC). To better predict overall survival (OS) and provide guidance on the selection of therapy for LUSC patients, we constructed a novel prognostic biomarker based on pyroptosis-related genes. The dataset for model construction were obtained from The Cancer Genome Atlas and the validation dataset were obtained from Gene Expression Omnibus. Differential expression genes between different pyroptosis expression patterns were identified. These genes were then used to construct pyroptosis expression pattern score (PEPScore) through weighted gene co-expression network analysis, univariate and multivariate cox regression analysis. Afterward, the differences in molecule and immune characteristics and the effect of different therapies were explored between the subgroups divided by the model. The PEPScore was constructed based on six pyroptosis-related genes (CSF2, FGA, AKAP12, CYP2C18, IRS4, TSLP). Compared with the high-PEPScore subgroup, the low-PEPScore subgroup had significantly better OS, higher TP53 and TTN mutation rate, higher infiltration of T follicular helper cells and CD8 T cells, and may benefit more from chemotherapeutic drugs, immunotherapy and radiotherapy. PEPScore is a prospective prognostic model to differentiate prognosis, molecular and immune microenvironmental features, as well as provide significant guidance for selecting clinical therapies.

Abstract: Background: Biomedical named entity recognition is one of the important tasks of biomedical literature mining. With the development of natural language processing technology, many deep learning models are used to extract valuable information from the biomedical literature, which promotes the development of effective BioNER models. However, for specialized domains with diverse and complex contexts and a richer set of semantically related entity types (e.g., drug molecules, targets, pathways, etc., in the biomedical domain), whether the dependencies of these drugs, diseases, and targets can be helpful still needs to be explored. Method: Providing additional dependency information beyond context, a method based on the graph attention network and BERT pre-training model named MKGAT is proposed to improve BioNER performance in the biomedical domain. To enhance BioNER by using external dependency knowledge, we integrate BERT-processed text embeddings and entity dependencies to construct better entity embedding representations for biomedical named entity recognition. Results: The proposed method obtains competitive accuracy and higher efficiency than the state-of-the-art method on three datasets, namely, NCBI-disease corpus, BC2GM, and BC5CDR-chem, with a precision of 90.71%, 88.19%, and 95.71%, recall of 92.52%, 88.05%, and 95.62%, and F1-scores of 91.61%, 88.12%, and 95.66%, respectively, which performs better than existing methods. Conclusion: Drug, disease, and protein dependencies can allow entities to be better represented in neural networks, thereby improving the performance of BioNER.