Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach.

Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than 200 samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen and ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients are 0.96, 0.81, and 0.67 for the predictions of emission wavelength, PL quantum yield and radiative decay rate constant, respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.

CSF1R inhibition reprograms tumor-associated macrophages to potentiate anti-PD-1 therapy efficacy against colorectal cancer.

PD-1 blockade therapy has made great breakthroughs in treatment of multiple solid tumors. However, patients with microsatellite-stable (MSS) colorectal cancer (CRC) respond poorly to anti-PD-1 immunotherapy. Although CRC patients with microstatellite instability (MSI) or microsatellite instability-high (MSI-H) can benefit from PD-1 blockade therapy, there are still some problems such as tumor recurrence. Tumor-associated macrophages (TAMs), most abundant immune components in tumor microenvironment (TME), largely limit the therapeutic efficacy of anti-PD-1 against CRC. The CSF1/CSF1R pathway plays a key role in regulating macrophage polarization, and blocking CSF1R signaling transduction may be a potential strategy to effectively reprogram macrophages and remodel TME. Here, we found that increasing expression of CSF1R in macrophages predicted poor prognosis in CRC cohort. Furthermore, we discovered a novel potent CSF1R inhibitor, PXB17, which significantly reprogramed M2 macrophages to M1 phenotype. Mechanically, PXB17 significantly blocked activation of PI3K/AKT/mTORC1 signaling, resulting in inhibition of cholesterol biosynthesis. Results from 3D co-culture system suggested that PXB17-repolarized macrophages could induce infiltration of CD8+ T lymphocytes in tumors and improve the immunosuppressive microenvironment. In vivo, PXB17 significantly halted CRC growth, with a stronger effect than PLX3397. In particular, PXB17 potently enhanced therapeutic activity of PD-1 mAb in CT-26 (MSS) model and prevented tumor recurrence in MC-38 (MSI-H) model by promoting formation of long-term memory immunity. Our study opens a new avenue for CSF1R in tumor innate and adaptive anti-tumor immunomodulatory activity and suggests that PXB17 is a promising immunotherapy molecule for enhancing the efficacy of PD-1 mAb or reducing tumor recurrence of CRC.

Discovery and preclinical evaluations of TQB3616, a novel CDK4-biased inhibitor.

Among small-molecule CDK4/6 inhibitors (palbociclib, ribociclib, and abemaciclib) approved for metastatic breast cancers, abemaciclib has a more tolerable adverse effects in clinic. This is attributable to preferential inhibition of CDK4 over CDK6. In our search for a biased CDK4 inhibitor, we discovered a series of pyrimidine-indazole inhibitors. SAR studies led us to TQB3616 as a preferential CDK4 inhibitor. TQB3616 exhibited improvements in both enzymatic and cellular proliferation inhibitory potency when tested side-by-side with the FDA approved palbociclib and abemaciclib. TQB3616 also possessed favorable PK profile in multiple species. These differentiated properties, together with excellent GLP safety profile warranted TQB3616 moving to clinic. TQB3616 entered into clinical development in 2019 and currently in phase III clinical trials (NCT05375461, NCT05365178).

Discovery of a potent Gilteritinib-based FLT3-PROTAC degrader for the treatment of Acute myeloid leukemia.

Fms-like tyrosine receptor kinase 3 (FLT3) proteolysis targeting chimeras (PROTACs) emerge as a promising approach to overcome the limitations of FLT3 inhibitors, while the development of orally bioavailable FLT3-PROTACs faces great challenges. Here, we report the rational design and evaluation of a series of Gilteritinib-based FLT3-PROTACs. Among them, B3-2 exhibited the strongest antiproliferative activity against FLT3-ITD mutant AML cells, and significantly induced FLT3-ITD protein degradation. Mechanistic investigations demonstrated that B3-2 induced FLT3-ITD degradation in a ubiquitin-proteasome-dependent manner. More importantly, B3-2 exhibited an oral bioavailability of 5.65%, and oral administration of B3-2 showed good antitumor activity in MV-4-11 xenograft models. Furthermore, B3-2 showed strong antiproliferative activity against FLT3 resistant mutations, highlighting its potential in overcoming drug resistance.

Impact of anesthesia-related genes on prognosis and tumor microenvironment in hepatocellular carcinoma: A comprehensive analysis.

Hepatocellular carcinoma (HCC), renowned for its bleak prognosis and high recurrence rates, necessitates innovative strategies for prognosis assessment and therapeutic intervention. In this pursuit, we systematically investigated the influence of anesthesia-related genes (ANARGs) on HCC outcomes. Leveraging data from The Cancer Genome Atlas (TCGA), our study scrutinized RNA sequencing data and clinical profiles from 374 HCC patients alongside 50 non-tumor liver samples to unravel ANARG expression patterns and their clinical relevance. Employing consensus clustering, we segregated HCC samples into two distinct subtypes based on ANARG profiles, unveiling significant survival disparities between them. Further differential expression analysis pinpointed pivotal genes and pathways distinguishing these subtypes, notably implicating lipid metabolism and the MTOR signaling pathway in HCC pathogenesis. A prognostic model, comprising five key ANARGs (DAGLA, CYP26B, HAVCR, G6PD and AKR1B), exhibited robust predictive capability for patient outcomes, validated across independent patient cohorts. Furthermore, immune infiltration analysis uncovered a nuanced interplay between ANARG expression and the tumor immune microenvironment, spotlighting variations in immune cell infiltration and function across the identified HCC subtypes. This comprehensive analysis underscores not only the prognostic significance of ANARGs in HCC but also their potential to modulate the tumor microenvironment, providing novel insights for tailoring anesthetic management and therapeutic strategies in HCC care. Our findings advocate for a more integrative approach to HCC management, amalgamating molecular profiling with traditional clinical parameters to refine patient stratification and personalize treatment strategies.

Lemon-Derived Extracellular Vesicle-like Nanoparticles Block the Progression of Kidney Stones by Antagonizing Endoplasmic Reticulum Stress in Renal Tubular Cells.

Kidney stones, represented by the calcium oxalate (CaOx) type, are highly prevalent and recrudescent. Cumulative evidence shows regular consumption of lemonade intervenes with stone development. However, the detailed mechanism remains obscure. Here, extracellular vesicle-like nanoparticles (LEVNs) isolated from lemonade are demonstrated to traffick from the gut to the kidney, primarily enriched in tubule cells. Oral administration of LEVNs significantly alleviates the progression of kidney stones in rats. Mechanistically, in addition to altering the crystallization of CaOx toward a less stable subtype, LEVNs suppress the CaOx-induced endoplasmic reticulum stress response of tubule cells, as indicated by homeostasis of specific signaling molecules and restoration of subcellular function, thus indirectly inhibiting stone formation. To exercise this regulation, endocytosed LEVNs traffick along the microtubules throughout the cytoplasm and are eventually recruited into lysosomes. In conclusion, this study reveals a LEVNs-mediated mechanism against renal calculi and provides positive evidence for consumption of lemonade preventing stone formation.

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory.

A self-powered sound-driven humidity sensor for wearable intelligent dehydration monitoring system.

Self-powered wearable sensing systems have attracted great attention for their application in continuous health monitoring, which can reveal real-time physiological information on the body. Here, an innovative self-powered sound-driven humidity sensor for wearable intelligent dehydration monitoring system has been proposed. The sensor is primarily comprised of PTFE membrane, ZnO nanoarrays and Ti thin film. The piezoelectric/triboelectric effect of ZnO nanoarrays/PTFE membrane is coupled with the humidity sensing process. Sound wave can drive PTFE membrane to vibrate, and the contact and separation between PTFE and ZnO can generate electrical signals through piezoelectric/triboelectric effect. At the same time, the surface of the nanostructures can absorb the water molecules, which will influence the electrical output of the device. The device can convert sound energy into electrical output without any external electricity power supply, and the outputting voltage decreases with increasing relative humidity, acting as the sensing signal. The sensor has been integrated with data processing unit and wireless transmission module to form a self-powered wearable intelligent dehydration monitoring system, which can actively monitor the humidity of exhaled breath and transmit the information to the mobile phone. The results can open a possible new direction for the development of sound-driven gas sensors and will further expand the scope for self-powered nanosystems.

Design, synthesis, and biological evaluation of a series of indolone derivatives as novel FLT3 inhibitors for the treatment of acute myeloid leukemia.

FLT3-ITD mutant has been extensively studied as a drug discovery target for acute myeloid leukemia. Based on our previous discovered FLT3 inhibitor (2), a series of urea group based indolone derivatives were designed, synthesized, and biological evaluated as novel FLT3 inhibitors for the treatment of FLT3-ITD positive AML. Among them, compound LC-3 exhibited potent inhibitory effects against FLT3 (IC50 = 8.4 nM) and significantly inhibited the proliferation of FLT3-ITD positive AML cells MV-4-11 (IC50 = 5.3 nM). In the cellular context, LC-3 strongly inhibited FLT3-mediated signaling pathways and induced cellular apoptosis by arresting cell cycle in G1 phase. In the in vivo studies, LC-3 significantly suppressed the tumor growth on MV-4-11 xenograft models (10 mg/kg/day, TGI = 92.16%) without exhibiting obvious toxicity. These results suggested that compound LC-3 might be a potential drug candidate for FLT3-ITD positive AML.

Lignin Degradation via Chlorine Dioxide at Room Temperature: Chemical Groups and Structural Characterization.

Lignin degradation is an effective means of achieving the high-value application of lignin, but degradation usually requires the use of high temperatures and harsh reaction-conditions. This study describes a green, mild approach for the degradation of lignin, in which chlorine dioxide (ClO2) was used for the oxidative degradation of lignin (IL) in an acidic aqueous suspension at room temperature. The optimal process conditions were: 30 mL of ClO2 solution (2.5 mg·L-1), pH 4.5 and 3 h. The FT-IR, NMR (1H NMR, 2D-HSQC and 31P NMR), XPS and GPC analyses indicated that lignin could be degraded by ClO2 relatively well at room temperature, to form quinones and muconic acids. Additionally, DIL was reduced to substances with a high phenolic-hydroxyl (OH) content (RDIL) under the presence of NaBH4, which further confirmed the composition of DIL and which can be applied to the development of lignin-based phenolic resins, providing a reference for the further modification as well as the utilization of DIL.

Gypenosides Synergistically Reduce the Extracellular Matrix of Hepatic Stellate Cells and Ameliorate Hepatic Fibrosis in Mice.

Liver fibrosis resulting from chronic liver damage is becoming one of the major threats to health worldwide. Active saponin constituents isolated from Gynostemma pentaphyllum were found to possess a protective effect in liver diseases. Here, we obtained a naturally abundant gypenoside, XLVI, and evaluated its liver protection activity in both animal and cellular models. The results showed that it ameliorated acute and chronic liver injuries and lightened the process of fibrogenesis in vivo. XLVI can inhibit TGF-ß-induced activation of hepatic stellate cells and ECM deposition in vitro. The underlying mechanism study verified that it upregulated the protein expression of protein phosphatase 2C alpha and strengthened the vitality of the phosphatase together with a PP2Cα agonist gypenoside NPLC0393. These results shed new light on the molecular mechanisms and the potential therapeutic function of the traditional herb Gynostemma pentaphyllum in the treatment of liver fibrosis.

Efficient prediction for high precision CO-N2 potential energy surface by stacking ensemble DNN.

High-dimensional potential energy surface (PES) for van der Waals systems with spectroscopic accuracy, is of great importance for quantum dynamics and an extremely challenge job. CO-N2 is a typical van der Waals system and its high-precision PES may help elucidate weak interaction mechanisms. Taking CO-N2 potential energies calculated by CCSD(T)-F12b/aug-cc-pVQZ as the benchmark, we establish an accurate, robust, and efficient machine learning model by using only four molecular structure descriptors based on 7966 benchmark potential energies. The highest accuracy is obtained by a stacking ensemble DNN (SeDNN). Its evaluation parameters MAE, RMSE, and R2 reach 0.096, 0.163, 0.9999 cm-1 , respectively, and the spectroscopic accuracy for vibration spectrum is achieved with predicted PES, which shows SeDNN superior goodness-of-fit and prediction performance. An elaborated PES with the reported global minimum has been predicted with the model, which perfectly reproduces CCSD(T) potential energies and the analytical MLR PES [PCCP, 2018, 20, 2036]. The critical points (global minimum, TSI, TSII, and their barriers), potential curve, and entire PES profile are remarkably consistent with CCSD(T) calculations. To further improve the usability of constructing PESs in practice, the size of the training set (energy points) for the model is reduced to 50%, 30%, and 20% of the database, respectively. The results show that even training with the smallest training set (1593 points), the PES only differs 2.555 cm-1 with the analytic MLR PES. Therefore, the proposed SeDNN is promisingly an alternative efficient tool to construct subtle PES for van der Waals systems.

Discovery and preclinical profile of LX-039, a novel indole-based oral selective estrogen receptor degrader (SERD).

We previously described the discovery of a novel indole series compounds as oral SERD for ER positive breast cancer treatment. Further SAR exploration focusing on substitutions on indole moiety of compound 12 led to the discovery of a clinical candidate LX-039. We report herein its profound anti-tumor activity, desirable ER antagonistic characteristics combined with favorable pharmacokinetic and preliminary safety properties. LX-039 is currently in clinical trial (NCT04097756).

Discovery and preclinical evaluations of WX-0593, a novel ALK inhibitor targeting crizotinib-resistant mutations.

ALK gene rearrangements are oncogenic drivers in approximately 5% of NSCLC. Crizotinib, a first generation ALK inhibitor, is widely prescribed for ALK-positive NSCLC in clinic. Resistance to crizotinib and other ALK inhibitors has been problematic. Addressing resistance, here we describe discovery and development of a novel, proprietary spirocyclic diamine-substituted aryl phosphine oxide series of inhibitors, which led to the identification of WX-0593 (16a) as a potent ALK inhibitor. WX-0593 inhibited the activity of both wild type and resistant mutants of ALK in vitro, showed strong antitumor activity in a crizotinib-resistant mouse PDX model. WX-0593 is currently under development in phase II/III clinical trials.

Discovery and preclinical evaluations of GST-HG131, a novel HBV antigen inhibitor for the treatment of chronic hepatitis B infection.

Chronic hepatitis B (CHB) remains a significant health challenge worldwide. The current treatments for CHB achieve less than 10% cure rates, majority of the patients are on therapy for life. Therefore, cure of CHB is a high unmet medical need. HBV surface antigen (HBsAg) loss and seroconversion are considered as the key for the cure. RG7834 is a novel, orally bioavailable small molecule reported to reduce HBV antigens. Based on RG7834 chemistry, we designed and discovered a series of dihydrobenzopyridooxazepine (DBP) series of HBV antigen inhibitors. Extensive SAR studies led us to GST-HG131 with excellent reduction of HBV antigens (both HBsAg and HBeAg) in vitro and in vivo. GST-HG131 improved safety in rat toxicology studies over RG7834. The promising inhibitory activity, together with animal safety enhancement, merited GST-HG131 progressed into clinical development in 2020 (NCT04499443).

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.

Didymin switches M1-like toward M2-like macrophage to ameliorate ulcerative colitis via fatty acid oxidation.

Inflammatory response by different polarized macrophages has a critical role in a variety of immunological pathophysiology, such as ulcerative colitis (UC). Herein, targeting the paradigm of macrophage phenotypes by small molecular modulators may influence the disease status. In the present study, we firstly demonstrated that didymin, one of the most abundant flavonoid constituents present in the citrus fruits such as oranges and lemons, remarkably attenuated the clinical symptoms of acute and chronic colitis in mice. Mechanistic studies showed that didymin converted pro-inflammatory M1-like to anti-inflammatory M2-like macrophage phenotype, but did not alter the polarization of M2-like macrophages. Metabolic tracing studies revealed that didymin strengthened fatty acid oxidation rather than glycolysis by inducing Hadhb expression. More importantly, in vivo studies verified that promotion of Hadhb expression resulted in the conversion of M1- toward M2-like macrophages and eventually alleviated colitis. Our data highlights the potential of macrophage paradigm in UC inflammation and put forth the stage for considering didymin as a metabolism regulator in reprogramming macrophage polarization, which may serve as a promising therapeutic approach for treatment of inflammation-associated disorders.

Cytotoxic Guaianolide Sesquiterpenoids from Ainsliaea fragrans.

Twelve guaianolide-type sesquiterpene oligomers with diverse structures were isolated from the whole plants of Ainsliaea fragrans, including a novel trimer (1) and two new dimers (2, 3). The chemical structures of the new compounds were elucidated through spectroscopic data interpretation and computational calculations. Ainsfragolide (1) is an unusual guaianolide sesquiterpene trimer generated with a novel C-C linkage at C2'-C15″, which may be biosynthesized prospectively through a further Michael addition. Cytotoxicity results showed that ainsfragolide (1) was the most potent compound against five cancer cell lines with IC50 values in the range of 0.4-8.3 µM.

Rubber Seed Oil-Based UV-Curable Polyurethane Acrylate Resins for Digital Light Processing (DLP) 3D Printing.

Novel UV-curable polyurethane acrylate (PUA) resins were developed from rubber seed oil (RSO). Firstly, hydroxylated rubber seed oil (HRSO) was prepared via an alcoholysis reaction of RSO with glycerol, and then HRSO was reacted with isophorone diisocyanate (IPDI) and hydroxyethyl acrylate (HEA) to produce the RSO-based PUA (RSO-PUA) oligomer. FT-IR and 1H NMR spectra collectively revealed that the obtained RSO-PUA was successfully synthesized, and the calculated C=C functionality of oligomer was 2.27 per fatty acid. Subsequently, a series of UV-curable resins were prepared and their ultimate properties, as well as UV-curing kinetics, were investigated. Notably, the UV-cured materials with 40% trimethylolpropane triacrylate (TMPTA) displayed a tensile strength of 11.7 MPa, an adhesion of 2 grade, a pencil hardness of 3H, a flexibility of 2 mm, and a glass transition temperature up to 109.4 °C. Finally, the optimal resin was used for digital light processing (DLP) 3D printing. The critical exposure energy of RSO-PUA (15.20 mJ/cm2) was lower than a commercial resin. In general, this work offered a simple method to prepare woody plant oil-based high-performance PUA resins that could be applied in the 3D printing industry.

[Structural Chinese medicine: new research field on pharmacodynamic substance basis of traditional Chinese medicine].

The research on the pharmacodynamic substance basis of traditional Chinese medicine(TCM) is a key scientific issue for the inheritance and development of TCM. At present, a large number of remarkable achievements have been made in the field of chemical components in Chinese medicine, however, another important aspect, namely the physical structure and mode of action of the multi-component assembly of TCM, has not been clearly understood and deeply studied. From the bottleneck of restricting material ba-sic research, we objectively analyzed the common cause of the existing problems. Based on the new discoveries and advances of active substances from TCM emerging in recent years, we extracted and summarized the concept of structural Chinese medicine, elaborated the basic ideas, main features and research modes, hoping to provide theoretical and practical references for the study on the pharmacodynamic substance basis and other research fields of TCM.