Medicinal herbs and bioactive compounds overcome the drug resistance to epidermal growth factor receptor inhibitors in non-small cell lung cancer.
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ABSTRACT:
SUBMITTER: Lee HYJ
PROVIDER: S-EPMC8299012 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.
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Project description:In the last five years, the detailed understanding of how to overcome T790M drug resistance in non-small cell lung cancer (NSCLC) has culminated in the development of a third-generation of covalent EGFR inhibitors with excellent clinical outcomes. However, the emergence of a newly discovered acquired drug resistance challenges the concept of small molecule targeted cancer therapy in NSCLC.
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Project description:The goal of this study was to assess the pharmacological effects of black tea (Camellia sinensis var. assamica) water extract on human kinin-forming enzymes in vitro. Tea is a highly consumed beverage in the world. Factor XII (FXII, Hageman factor)-independent- and -dependent activation of prekallikrein to kallikrein leads to the liberation of bradykinin (BK) from high-molecular-weight kininogen (HK). The excessive BK production causes vascular endothelial and nonvascular smooth muscle cell permeability, leading to angioedema. The prevalence of angiotensin-converting enzyme inhibitor (ACEI)-induced angioedema appears to be through BK. Both histamine and BK are potent inflammatory mediators. However, the treatments for histamine-mediated angioedema are unsuitable for BK-mediated angioedema. We hypothesized that long-term consumption of tea would reduce bradykinin-dependent processes within the systemic and pulmonary vasculature, independent of the anti-inflammatory actions of polyphenols. A purified fraction of the black tea water extract inhibited both kallikrein and activated FXII. The black tea water extracts inhibited factor XII-induced cell migration and inhibited the production of kallikrein on the endothelial cell line. We compared the inhibitory effects of the black tea water extract and twenty-three well-known anti-inflammatory medicinal herbs, in inhibiting both kallikrein and FXII. Surprisingly, arjunglucoside II specifically inhibited the activated factor XII (FXIIa), but not the kallikrein and the activated factor XI. Taken together, the black tea water extract exerts its anti-inflammatory effects, in part, by inhibiting kallikrein and activated FXII, which are part of the plasma kallikrein-kinin system (KKS), and by decreasing BK production. The inhibition of kallikrein and activated FXII represents a unique polyphenol-independent anti-inflammatory mechanism of action for the black tea.
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Project description:Epidermal growth factor receptor (EGFR) is a validated target in different human malignancies. EGFR tyrosine kinase inhibitors (TKIs) are known to contribute considerably to the extension of progression-free survival in EGFR-mutant non-small cell lung cancer and monoclonal antibodies (mAbs) targeting EGFR have also improved the efficacy outcomes in KRAS wild-type colorectal cancer. Nevertheless, a significant percentage of lung and colorectal cancer patients do not respond to anti-EGFR agents and secondary resistance after initial benefit is a challenging reality faced by clinicians. Extensive preclinical work on the potential mechanisms of resistance to EGFR inhibitors in different disease settings has guided the development of second-generation irreversible EGFR TKIs, more efficient anti-EGFR mAbs, and combination strategies with agents targeting other receptors and downstream effectors. In order to elucidate the role of the multiple therapeutic strategies under investigation to overcome EGFR inhibitors-resistance, rational drug development based on stringent preclinical data, biomarker validation and proper selection of patients in the ongoing clinical trials are of paramount importance. Preliminary results of clinical trials evaluating these approaches will be discussed in this manuscript, with emphasis on TKIs in lung cancer and mAbs in advanced colorectal cancer.
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Project description:First-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), including gefitinib and erlotinib, have proven to be highly effective agents for advanced non-small cell lung cancer (NSCLC) in patients harboring an activating EGFR mutation such as the exon 19 deletion mutation and L858R. Although those reversible small molecular targeted agents provide a significant response and survival benefit, all responders eventually acquire resistance. Second-generation EGFR-targeting agents, such as irreversible EGFR/HER2 tyrosine kinase inhibitors and pan-HER TKIs, may improve survival further and be useful for patients who acquired resistance to first-generation EGFR-TKIs. This review discusses novel therapeutic strategies for EGFR-mutated advanced NSCLC using first- and second-generation EGFR-TKIs.
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Project description:AimsEpidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have shown dramatic clinical benefits in advanced non-small cell lung cancer (NSCLC); however, resistance remains a serious problem in clinical practice. The present study analyzed mTOR-associated signaling-pathway differences between the EGFR TKI-sensitive and -resistant NSCLC cell lines and investigated the feasibility of targeting mTOR with specific mTOR inhibitor in EGFR TKI resistant NSCLC cells.MethodsWe selected four different types of EGFR TKI-sensitive and -resistant NSCLC cells: PC9, PC9GR, H1650 and H1975 cells as models to detect mTOR-associated signaling-pathway differences by western blot and Immunoprecipitation and evaluated the antiproliferative effect and cell cycle arrest of ku-0063794 by MTT method and flow cytometry.ResultsIn the present study, we observed that mTORC2-associated Akt ser473-FOXO1 signaling pathway in a basal state was highly activated in resistant cells. In vitro mTORC1 and mTORC2 kinase activities assays showed that EGFR TKI-resistant NSCLC cell lines had higher mTORC2 kinase activity, whereas sensitive cells had higher mTORC1 kinase activity in the basal state. The ATP-competitive mTOR inhibitor ku-0063794 showed dramatic antiproliferative effects and G1-cell cycle arrest in both sensitive and resistant cells. Ku-0063794 at the IC50 concentration effectively inhibited both mTOR and p70S6K phosphorylation levels; the latter is an mTORC1 substrate and did not upregulate Akt ser473 phosphorylation which would be induced by rapamycin and resulted in partial inhibition of FOXO1 phosphorylation. We also observed that EGFR TKI-sensitive and -resistant clinical NSCLC tumor specimens had higher total and phosphorylated p70S6K expression levels.ConclusionOur results indicate mTORC2-associated signaling-pathway was hyperactivated in EGFR TKI-resistant cells and targeting mTOR with specific mTOR inhibitors is likely a good strategy for patients with EGFR mutant NSCLC who develop EGFR TKI resistance; the potential specific roles of mTORC2 in EGFR TKI-resistant NSCLC cells were still unknown and should be further investigated.
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Project description:The popularity of small berries has rapidly increased in Western countries given their antioxidant, anti-inflammatory, and antimicrobial activities and health-promoting properties. The aim of this study was to compare the fatty acid (FA) profile, phenolic compounds, and antioxidant capacity of extracts of 11 berries cultivated in the North West of Italy. Berry samples were extracted and evaluated for FA profile and total anthocyanin (TAC), total flavonoid contents (TFC), ferric-reducing antioxidant power (FRAP), and for their radical scavenging activities against 2,2'-diphenyl-1-picrylhydrazyl (DPPH•) radical. The main polyphenols of berry extracts were characterized by HPLC-DAD-UV-ESI HRMS in positive ion mode. Results showed that the highest TAC and TFC contents were recorded in black currants, blackberries, and blueberries. Maximum and minimum DPPH• radical scavenging activities, Trolox Equivalent Antioxidant Capacity, and FRAP measurements confirmed the same trend recorded for TAC and TFC values. HPLC-HRMS analyses highlight how blueberries and blackberries have the highest concentration in polyphenols. Palmitic, stearic, oleic, linoleic, ?-linolenic, and ?-linolenic acids significantly differ between berries, with oleic and ?-linolenic acid representing the most abundant FAs in raspberries. Among the berries investigated, results of phytochemical characterization suggest choosing black currants and blueberries as an excellent source of natural antioxidants for food and health purposes.
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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.
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Project description:Poor quality control of medicinal herbs has led to instances of toxicity, poisoning and even deaths. The fundamental step in quality control of herbal medicine is accurate identification of herbs. Array-based techniques have recently been adapted to authenticate or identify herbal plants. This article reviews the current array-based techniques, eg oligonucleotides microarrays, gene-based probe microarrays, Suppression Subtractive Hybridization (SSH)-based arrays, Diversity Array Technology (DArT) and Subtracted Diversity Array (SDA). We further compare these techniques according to important parameters such as markers, polymorphism rates, restriction enzymes and sample type. The applicability of the array-based methods for fingerprinting depends on the availability of genomics and genetics of the species to be fingerprinted. For the species with few genome sequence information but high polymorphism rates, SDA techniques are particularly recommended because they require less labour and lower material cost.
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