Analysis Of Drug Effects Research Articles

Adverse Effects Associated With Multimodal Analgesic Regimens in Critically Ill, Nonintubated Patients: A Systematic Review and Meta-Analysis.

Objective: To evaluate the safety of utilizing multimodal analgesic regimens in critically ill, nonintubated patients. Data Sources: A systematic review was conducted using Embase, MEDLINE, Cochrane, SciELO, Web of Science, and the Korean Journal Index. Clinical trials of critically ill, nonintubated patients that contained complete safety outcomes date, including the incidence of specific adverse drug effects (ADE) associated with a multimodal analgesic medication or regimen, were included. Study Selection and Data Extraction: The primary outcome was the incidence of adverse drug effects associated with multimodal analgesics in comparison to standard-of-care analgesic strategies in our patient population. The secondary outcome was a subgroup analysis of adverse drug effect by type and by medication. Data Synthesis: 10 trials were included in this systematic review, of which 6 were randomized control trials that were evaluated in the meta-analysis. There was no statistically significant difference with respect to the primary outcome (mean difference = -0.11, P = 0.31, 95% CI = [-0.35, 0.13]). The subgroup analysis, which was conducted on randomized clinical control trials that documented a single adjunctive analgesic rather than a multimodal regimen, was stratified by the type of adverse effect encountered and the medication in question. There were no statistically significant findings regarding the incidence of specific ADE. Nonrandomized data included in this study support these findings. Conclusions: While concerns of the additive deleterious adverse effects commonly encountered in polypharmacy are valid, our findings support the use of multimodal analgesic regimens in the provision of analgesic in critically ill, nonintubated patients.

Corrections to “Spectroscopic Analysis of Candida Species, Viability, and Antifungal Drug Effects with a Microwave Flow Cytometer”

Corrections to “Spectroscopic Analysis of Candida Species, Viability, and Antifungal Drug Effects with a Microwave Flow Cytometer”

Joint analysis of vaccination effectiveness and antiviral drug effectiveness for COVID-19: a causal inference approach

ObjectivesThis study aims to estimate the causal effects of oral antivirals and vaccinations in the prevention of all-cause mortality and progression to severe COVID-19 in an integrative setting with both antivirals and vaccinations considered as interventions. MethodsWe identified hospitalized adult patients (i.e. aged 18 or above) in Hong Kong with confirmed SARS-CoV-2 infection between March 16, 2022, and December 31, 2022. An inverse probability-weighted (IPW) Andersen-Gill model with time-dependent predictors was used to address immortal time bias and produce causal estimates for the protection effects of oral antivirals and vaccinations against severe COVID-19. ResultsGiven prescription is made within 5 days of confirmed infection, nirmatrelvir-ritonavir is more effective in providing protection against all-cause mortality and development into severe COVID-19 than molnupiravir. There was no significant difference between CoronaVac and Comirnaty in the effectiveness of reducing all-cause mortality and progression to severe COVID-19. ConclusionsThe use of oral antivirals and vaccinations causes lower risks of all-cause mortality and progression to severe COVID-19 for hospitalized SARS-CoV-2 patients.

Topiramate inhibits adjuvant-induced chronic orofacial inflammatory allodynia in the rat.

Chronic orofacial pain disorders are common debilitating conditions, affecting the trigeminal system. Its underlying pathophysiological mechanisms are still unclear and the therapy is often unsatisfactory, therefore, preclinical models are crucial to identify the key mediators and novel treatment options. Complete Freund's adjuvant (CFA)-induced orofacial inflammatory allodynia/hyperalgesia is commonly used in rodents, but it has not been validated with currently used drugs. Here we tested the effects of the adjuvant analgesic/antiepileptic voltage-gated Na+ channel blocker complex mechanism of action topiramate in comparison with the gold standard antimigraine serotonin 5-HT1B/D receptor agonist sumatriptan in this model. CFA was injected subcutaneously into the right whisker pad of male Sprague-Dawley rats (250-300g), then mechanonociceptive threshold values were investigated with von Frey filaments (3, 5, and 7 days after CFA injection). Effects of topiramate (30mg/kg per os) and sumatriptan (1mg/kg subcutaneous) on the adjuvant-induced chronic inflammatory orofacial allodynia were investigated 60, 120, and 180min after the treatments each day. To determine the optimal concentration for drug effect analysis, we tested the effects of two different CFA-concentrations (1 and 0.5mg/mL) on mechanonociceptive thresholds. Both concentrations of CFA induced a chronic orofacial allodynia in 60% of all rats. Although, higher CFA concentration induced greater allodynia, much more stable threshold reduction was observed with the lower CFA concentration: on day 3 the thresholds decreased from 18.30g to approximately 11g (low) and 5g (high), respectively, however a slight increase was observed in the case of higher CFA concentration (on days 5, 7, and 11). In all investigation days, topiramate showed significant anti-allodynic effect comparing the pre and post drug dose and comparing the vehicle treated to the drug treated groups. Sumatriptan also caused a significant threshold increase compared to pre dose thresholds (day 3) and also showed a slight anti-allodynic effect compared to the vehicle-treated group (day 3 and 5). In the present study CFA-induced chronic orofacial allodynia was reversed by topiramate in rats validating the model with the adjuvant analgesic. Other than establishing a validated orofacial pain-related syndrome model in rats, new ways are opened for the repurposing of topiramate.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction arepromising strategiesthat could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Predictive Analysis of Adverse Drug effects using Machine Learning

Abstract: The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using databases like SIDER (Side Effect Research) and OFFSIDES (Government medical records). In this paper, three Machine Learning algorithms SVM (Support Vector Machine), Random Forest, and Gradient boosted trees are implemented on the datasets to predict various disorders caused due to adverse effects, and the performance is evaluated based on performance metrics such as average precision, recall, accuracy, f1 binary, f1 macro, and f1 micro. Finally, the two datasets were merged to understand and compare the performance of a combined dataset to a single dataset.

Importance of beating rate control for the analysis of drug effects on contractility in human induced pluripotent stem cell-derived cardiomyocytes

Cardiac contractility evaluation using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has recently attracted much attention as a clinical cardiotoxicity predictive model. Most studies on this were conducted under spontaneous beating conditions and involved video-based analyses. Cardiac contractility is known to be influenced by beating rates; accordingly, beating rate control is recommended to accurately analyze the effects of drugs on cardiac contractility. Therefore, we investigated the relationship between contraction parameters and beating rates of cardiac cell sheet tissues by directly measuring the contraction force and compared the effects of ion channel drugs (mexiletine, ranolazine, and dofetilide) on contraction parameters under spontaneous beating conditions with those under pacing (1 Hz) conditions. To characterize the contraction/relaxation kinetics, we introduced a novel analysis tool, called a “C-V loop,” a plot of contraction force versus force-changing rate (“velocity”). When we increased the beating rate, the contraction force, force-changing rate, and relaxation time markedly decreased. The occurrence frequencies of beating arrest and irregular beats at high concentration ranges of mexiletine and ranolazine were more suppressed in paced samples than in spontaneously beating ones. We also found that relaxation time increased by treatment with dofetilide and contraction amplitude decreased in a concentration-dependent manner by mexiletine treatment only in the samples under pacing. These drug responses were consistent with the previous reports using human samples. These results indicated that beating rate control is necessary to stably evaluate the effects of drugs on contractility and that tests under 1-Hz pacing are more relevant to clinical settings.

An integrated microfluidics platform with high-throughput single-cell cloning array and concentration gradient generator for efficient cancer drug effect screening

BackgroundTumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment. Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy. However, current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.MethodsWe developed a new microfluidics-based “SMART” platform that is Simple to operate, able to generate a Massive single-cell array and Multiplex drug concentrations, capable of keeping cells Alive, Retainable and Trackable in the microchambers. These features are achieved by integrating a Microfluidic chamber Array (4320 units) and a six-Concentration gradient generator (MAC), which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.ResultsA simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity. The statistic results reveal that Imatinib (Ima) and Resveratrol (Res) combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment, indicated by the markedly reduced half maximal inhibitory concentration (IC50). Additionally, single-cell derived clones demonstrate a higher IC50 in each drug treatment compared to single cells. Moreover, primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.ConclusionThis microfluidics-based “SMART” platform allows high-throughput single-cell capture and culture, dynamic drug-gradient treatment and cell response monitoring, which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.

Drug library screening identifies histone deacetylase inhibition as a novel therapeutic strategy for choriocarcinoma.

Choriocarcinoma is a rare and aggressive gynecological malignancy. The standard treatment is systemic chemotherapy as choriocarcinoma exhibits high chemosensitivity. However, refractory choriocarcinoma exhibits chemoresistance; thus, the prognosis remains very poor. This study aimed to identify novel therapeutic agents for choriocarcinoma by utilizing a drug repositioning strategy. Three choriocarcinoma cell lines (JAR, JEG-3, and BeWo) and a human extravillous trophoblast cell line (HTR-8/SVneo) were used for the analyses. The growth inhibitory effects of 1,271 FDA-approved compounds were evaluated in vitro screening assays and selected drugs were tested in tumor-bearing mice. Functional analyses of drug effects were performed based on RNA sequencing. Muti-step screening identified vorinostat, camptothecin (S, +), topotecan, proscillaridin A, and digoxin as exhibiting an anti-cancer effect in choriocarcinoma cells. Vorinostat, a histone deacetylase inhibitor, was selected as a promising candidate for validation and the IC50 values for choriocarcinoma cells were approximately 1 μM. RNA sequencing and subsequent pathway analysis revealed that the ferroptosis pathway was likely implicated, and key ferroptosis-related genes (i.e., GPX4, NRF2, and SLC3A2) were downregulated following vorinostat treatment. Furthermore, vorinostat repressed tumor growth and downregulated the expression of GPX4 and NRF2 in JAR cell-bearing mice model. Vorinostat, a clinically approved drug for the treatment of advanced primary cutaneous T-cell lymphoma, showed a remarkable anticancer effect both in vitro and in vivo by regulating the expression of ferroptosis-related genes. Therefore, vorinostat may be an effective therapeutic candidate for patients with choriocarcinoma.

SiCoDEA: A Simple, Fast and Complete App for Analyzing the Effect of Individual Drugs and Their Combinations.

The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of single drugs. For these reasons, it is of interest to study combinations of drugs and to determine whether a specific combination has a synergistic, antagonistic or additive effect. Various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. We have developed an open access and easy to use app that allows different models to be explored and the most fitting to be chosen for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis). Despite the existence of other tools for drug combination analysis, SiCoDEA remains the most complete and flexible since it offers options such as outlier removal or the ability to choose between different models for analysis. SiCoDEA is an easy to use tool for analyzing drug combination data and to have a view of the various steps and offer different results based on the model chosen.

Abstract 6269: A cell double barcoding system for quantitative evaluation of primary tumors & metastasis in animals: Uncovers clonal-specific anti-cancer drug effects

Abstract Imaging in monitoring metastasis in mouse models have low sensitivity and is not quantitative. Cell DNA barcoding, demonstrating high sensitivity and resolution, allows monitoring effects of drugs on the number of tumor and metastatic clones. However, this technology is not suitable for comparison of sizes of metastatic clones in different animals, e.g. drug treated and untreated, due to high biological and technical variability upon tumor and metastatic growth and isolation of barcodes from tissue DNA. Yet, both numbers of clones and their sizes are critical parameters for analysis of drug effects. Here we developed a modification of the barcoding approach for monitoring drug effects on tumors and metastasis that is quantitative, highly sensitive and highly reproducible. This novel cell double barcoding system allows simultaneously following the fate of two or more cell variants or cell lines in xenograph models in vivo, and also following the fates of individual clones within each of these populations. This system allows comparing effects of drugs on different cell populations, and thus normalizing drug effects by drug-resistant lines, which corrects for both biological and technical variabilities and significantly increases the reproducibility of results. Using this barcoding system, we uncovered that effects of a novel DYRK1B kinase inhibitor FX9847 on primary tumors and metastasis is clone-dependent, while a distinct drug osimertinib demonstrated clone-independent effects on cancer cell populations. Overall cell double barcoding approach can significantly enrich our understanding of drug effects in basic research and preclinical studies. Citation Format: Arkadi Hesin, Santosh Kumar, Valid Gahramanov, Maria Becker, Maria Vilenchik, Ilya Alexandrov, Julia Yaglom, Michael Sherman. A cell double barcoding system for quantitative evaluation of primary tumors & metastasis in animals: Uncovers clonal-specific anti-cancer drug effects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6269.

DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

Dose-dependent interaction of two heavy metals with amiodarone toxicity in Saccharomyces cerevisiae.

Amiodarone (AMD) is an antiarrhythmic drug that induces idiosyncratic toxicity. Environmental pollutants, including heavy metals, could interact with its toxicity by affecting pharmacokinetics and pharmacodynamics. Other levels of interaction could exist in yeast, such as oxidative stress and the general stress response. In this study, we investigated the interaction of mercury chloride (HgCl2) and cadmium chloride (CdCl2) with AMD toxicity on Saccharomyces cerevisiae. Interaction type - synergistic, additive, or antagonistic - was determined by median drug effect analysis using "CompuSyn". HgCl2 potentiated AMD toxicity at high doses (≥ 71.4μm, which yielded more than 60% inhibition). CdCl2 acted similarly at high doses (≥ 57.9μm). An antagonistic effect appeared at lower doses with both heavy metals (≤ 49.4μm for HgCl2 and AMD; ≤ 18.9μm for CdCl2 and AMD). The threshold concentrations (HgCl2 or CdCl2 combined with AMD) that switched the interaction from antagonistic to additive, and then to synergistic, were decreased in the yeast strain mutant in catalase (CTT1), suggesting an important role for this enzyme. Moreover, mutation of the nutrient sensing receptor gene GPR1 caused the synergistic interaction of CdCl2, but not HgCl2, with AMD to occur at the lowest tested concentrations (1.2μm). The reverse was obtained with the mutant strain in calcium-manganese transporter gene PMR1, where the synergistic interaction of HgCl2 with AMD occurred at concentrations (20.7μm) lower than that of the wild type (71.4μm). These results demonstrated a dose-dependent interaction between the two heavy metals with AMD toxicity, and the involvement of oxidative stress, calcium homeostasis, and nutrient sensing in the observed interaction.

A Cell Double-Barcoding System for Quantitative Evaluation of Primary Tumors and Metastasis in Animals That Uncovers Clonal-Specific Anti-Cancer Drug Effects.

Simple SummaryThe main problem in treating advanced cancers is a metastatic spread when individual cancer cells leave the primary tumor and colonize to distant organs. In drug development, it is important to quantitatively assess effects of novel drug candidates on both primary tumors and metastasis. Unfortunately, current methods of monitoring metastasis in mouse models have low sensitivity and are not quantitative. Here, we developed a methodology to monitor drug effects on metastasis that is quantitative and has a very high sensitivity and resolution. In fact, it allows monitoring effects of drugs on individual cancer cells in animals.Imaging in monitoring metastasis in mouse models has low sensitivity and is not quantitative. Cell DNA barcoding, demonstrating high sensitivity and resolution, allows monitoring effects of drugs on the number of tumor and metastatic clones. However, this technology is not suitable for comparison of sizes of metastatic clones in different animals, for example, drug treated and untreated, due to high biological and technical variability upon tumor and metastatic growth and isolation of barcodes from tissue DNA. However, both numbers of clones and their sizes are critical parameters for analysis of drug effects. Here we developed a modification of the barcoding approach for monitoring drug effects on tumors and metastasis that is quantitative, highly sensitive and highly reproducible. This novel cell double-barcoding system allows simultaneously following the fate of two or more cell variants or cell lines in xenograft models in vivo, and also following the fates of individual clones within each of these populations. This system allows comparing effects of drugs on different cell populations and thus normalizing drug effects by drug-resistant lines, which corrects for both biological and technical variabilities and significantly increases the reproducibility of results. Using this barcoding system, we uncovered that effects of a novel DYRK1B kinase inhibitor FX9847 on primary tumors and metastasis is clone-dependent, while a distinct drug osimertinib demonstrated clone-independent effects on cancer cell populations. Overall, a cell double-barcoding approach can significantly enrich our understanding of drug effects in basic research and preclinical studies.

Semiconducting Carbon Nanotube-Based Nanodevices for Monitoring the Effects of Chlorphenamine on the Activities of Intracellular Ca2+ Stores.

We report a flexible and noninvasive method based on field-effect transistors hybridizing semiconducting single-walled carbon nanotubes for monitoring the effects of histamine on Ca2+ release from the intracellular stores of a nonexcitable cell. These nanodevices allowed us to evaluate the real-time electrophysiological activities of HeLa cells under the stimulation of histamine via the recording of the conductance changes of the devices. These changes resulted from the binding of histamine to its receptor type 1 on the HeLa cell membrane. Moreover, the effects of chlorphenamine, an antihistamine, on the electrophysiological activities of a single HeLa cell were also evaluated, indicating that the pretreatment of the cell with chlorpheniramine decreased intracellular Ca2+ release. Significantly, we only utilized a single nanodevice to perform the measurements for multiple cells pretreated with various concentrations of chlorphenamine. This enabled the statistically meaningful analysis of drug effects on cells without errors from device variations. Obtained results indicated the novel advantages of our method such as real-time monitoring and quantitative capability. Our devices, therefore, can be efficient tools for biomedical applications such as electrophysiology research and drug screening.

A high-content platform for physiological profiling and unbiased classification of individual neurons

SummaryHigh-throughput physiological assays lose single-cell resolution, precluding subtype-specific analyses of activation mechanism and drug effects. We demonstrate APPOINT (automated physiological phenotyping of individual neuronal types), a physiological assay platform combining calcium imaging, robotic liquid handling, and automated analysis to generate physiological activation profiles of single neurons at large scale. Using unbiased techniques, we quantify responses to sequential stimuli, enabling subgroup identification by physiology and probing of distinct mechanisms of neuronal activation within subgroups. Using APPOINT, we quantify primary sensory neuron activation by metabotropic receptor agonists and identify potential contributors to pain signaling. We expand the role of neuroimmune interactions by showing that human serum directly activates sensory neurons, elucidating a new potential pain mechanism. Finally, we apply APPOINT to develop a high-throughput, all-optical approach for quantification of activation threshold and pharmacologically validate contributions of ion channel families to optical activation.

Analysis of Drug Effectiveness and Efficiency of Use of Amlodipine-Captopril Combination Compared to Amlodipine-Valsartan in the X Hospital Patients of Bogor

Prevalence of people with hypertension are increased with the increasingly cost of health, especially the cost of drug and hypertension including disease with long life medication therefore, we need cost effectiveness analysis for hypertension in order to assist in making effectiveness the drug selection decisions by benefit and cost. This study aim to find out difference of cost-effectiveness between amlodipine-captopril combination and amlodipine-valsartan on hypertension inpatient with BPJS in X Hospital of Bogor. Cost effectiveness analysis was conducted troughpayers perspective namely BPJS from direct medical cost. The output is patient blood pleasure after medication with antihipertensi. Cost effectiveness analysis using value of ACER based difference on ratio between cost with output. Based on sociodemography hypertension inpatient most widely male sex (57,8%), age 56-65 years old (33,3%), housewife profession (36,7%), and last education in elementary school (33,3%). Result revealed theres difference of effectivity therapy and theres difference of cost effectiveness between amlodipine-captopril combination and amlodipine-valsartan combination, based on ACER value of amlodipine-captopril combination was Rp.19.361,9 and it is smaller than value ACER of amlodipine-valsartan combination was Rp 27.655,8.Indicating that amlodipine-captopril combination more effective and more cost-effective than amlodipine-valsartan.

Evaluation of hepatic drug-metabolism for glioblastoma using liver-brain chip.

Glioma is one of the most aggressive and highly fatal diseases with an extremely poor prognosis. Considering the poor clinical response to therapy in glioma, it is urgent to establish an in vitro model to facilitate the screening and assessment of anti-brain-tumor drugs. The blood-brain barrier (BBB), as well as liver metabolism plays an important role in determining the pharmacological activity of many anti-brain-tumor drugs. In this work, we designed a multi-interface liver-brain chip integrating co-culture system to assess hepatic metabolism dependent cytotoxicity of anti-brain-tumor drug in vitro. This microdevice composed of three microchannels which were separated by porous membrane and collagen. HepG2 and U87 cells were cultured in separated channels as mimics of liver and glioblastoma. Brain microvascular endothelial cells (BMECS) and cerebral astrocytes were co-cultured on collagen to mimic the brain microvascular endothelial barrier. Three common anti-tumor drugs, paclitaxel (PTX), capecitabine (CAP) and temozolomide (TMZ), were evaluated on this chip. In integrated liver-brain chip, liver enhanced the cytotoxicity of CAP on U87 cells by 30%, but having no significant effect on TMZ. The BBB decreased the cytotoxicity of PTX by 20%, while no significant effects were observed on TMZ and CAP, indicating the importance of liver metabolism and blood-brain barrier on the evaluation of anti-brain-tumor drugs. This work provides a biomimetic liver-brain model to mimic the physiological and pharmacological processes in vitro and presents a simple platform for long-term cell co-culture, drug delivery and metabolism, and real-time analysis of drug effects on brain cancer.

Analysis of the mechanism of Shufeng Jiedu capsule prevention and treatment for COVID-19 by network pharmacology tools

IntroductionThe novel coronavirus pneumonia that broke out in 2019 has become a global epidemic. According to the diagnosis and treatment plan issued in China and the existing clinical data, Shufeng Jiedu (SFJD) Capsule can be effectively used in the treatment of COVID-19 patients. This study aimed to explore its mechanism of action by network pharmacology and molecular docking technology. MethodsThe Chinese Medicine System Pharmacology Analysis Platform (TCMSP), a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM), the Encyclopedia of Traditional Chinese Medicine (ETCM) and related literature records were used to search the composition and main active compounds of SFJD, and to screen out the targets of drug components. Disease-associated genes were obtained by the Human Gene Database (GeneCards), the Human Online Mendelian Inheritance Platform (OMIM) and the DisGeNET database, and the co-targeted genes/proteins as targets of both SFJD and COVID-19 were selected by the Comparative Toxicogenomics Database (CTD). Co-targeted genes/proteins were analyzed by STRING, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and Reactome for proteins to protein interaction (PPI), pathway and GO (gene ontology) enrichment, and predicted by AutoDock for their high-precision docking simulation. In addition, the therapeutic effect for SFJD treatment on COVID-19 was validated by the Chinese medicine anti-novel coronavirus pneumonia drug effect prediction and analysis platform (TCMCOVID). ResultsScreening resulted in 163 compounds and 463 targeted genes. The PPI core network contains 76 co-targeted proteins. The Reactome pathways were enriched in signaling by interleukins, immune system, etc. Finally, 6 key proteins of TNF, IL-10, IL-2, IL-6, STAT1 and CCL2 were selected and successfully docked with 4 active ingredients of quercetin, luteolin, wogonin and kaempferol. ConclusionSFJD may play a role in the prevention and treatment of COVID-19 through multiple active compounds acting on multiple targets and then multiple pathways.

Decomposition profile data analysis of multiple drug effects identifies endoplasmic reticulum stress-inducing ability as an unrecognized factor

Chemicals have multiple effects in biological systems. Because their on-target effects dominate the output, their off-target effects are often overlooked and can sometimes cause dangerous adverse events. Recently, we developed a novel decomposition profile data analysis method, orthogonal linear separation analysis (OLSA), to analyse multiple effects. In this study, we tested whether OLSA identified the ability of drugs to induce endoplasmic reticulum (ER) stress as a previously unrecognized factor. After analysing the transcriptome profiles of MCF7 cells treated with different chemicals, we focused on a vector characterized by well-known ER stress inducers, such as ciclosporin A. We selected five drugs predicted to be unrecognized ER stress inducers, based on their inducing ability scores derived from OLSA. These drugs actually induced X-box binding protein 1 splicing, an indicator of ER stress, in MCF7 cells in a concentration-dependent manner. Two structurally different representatives of the five test compounds exhibited similar results in HepG2 and HuH7 cells, but not in PXB primary hepatocytes derived from human-liver chimeric mice. These results indicate that our decomposition strategy using OLSA uncovered the ER stress-inducing ability of drugs as an unrecognized effect, the manifestation of which depended on the background of the cells.