ADCdb: the database of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

PDCdb: the biological activity and pharmaceutical information of peptide-drug conjugate (PDC).

Peptide-drug conjugates (PDCs) have emerged as a promising class of targeted therapeutics with substantial pharmaceutical advantages and market potentials, which is a combination of a peptide (selective to the disease-relevant target), a linker (stable in circulation but cleavable at target site) and a cytotoxic/radioactive drug (efficacious/traceable for disease). Among existing PDCs, those based on radiopharmaceuticals (a.k.a. radioactive drugs) are valued due to their accurate imaging and targeted destruction of disease sites. It's demanded to accumulate the biological activity and pharmaceutical information of PDCs. Herein, a database PDCdb was thus constructed to systematically describe these valuable data. Particularly, biological activities for 2036 PDCs were retrieved from literatures, which resulted in 1684, 613 and 2753 activity data generated based on clinical trial, animal model and cell line, respectively. Furthermore, the pharmaceutical information for all 2036 PDCs was collected, which gave the diverse data of (a) ADME property, plasma half-life and administration approach of a PDC and (b) chemical modification, primary target, mode of action, conjugating feature of the constituent peptide/linker/drug. In sum, PDCdb systematically provided the biological activities and pharmaceutical information for the most comprehensive list of PDCs among the available databases, which was expected to attract broad interest from related communities and could be freely accessible at: https://idrblab.org/PDCdb/.

ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening.

Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein-ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein-ligand complexes and cluster the ligands. In ClusterX, the graph was used to represent the protein-ligand complex, and the joint optimisation can be used efficiently for learning the cluster-friendly features. Experiments on the KLIFs database show that the model can distinguish well between the binding modes of different kinase inhibitors. To validate the effectiveness of the model, the clustering results on the virtual screening dataset further demonstrated that ClusterX achieved better or more competitive performance against traditional methods, such as SIFt and extended connectivity fingerprints. This framework may provide a unique tool for clustering analysis and prove to assist computational medicinal chemists in visual decision-making.

TransFoxMol: predicting molecular property with focused attention.

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.

The TRIM28/miR133a/CD47 axis acts as a potential therapeutic target in pancreatic necrosis by impairing efferocytosis.

Efferocytosis, the clearance of apoptotic cells by macrophages, plays a crucial role in inflammatory responses and effectively prevents secondary necrosis. However, the mechanisms underlying efferocytosis in acute pancreatitis (AP) remain unclear. In this study, we demonstrated the presence of efferocytosis in injured human and mouse pancreatic tissues. We also observed significant upregulation of CD47, an efferocytosis-related the "do not eat me" molecule in injured acinar cells. Subsequently, we used CRISPR-Cas9 gene editing, anti-adeno-associated virus (AAV) gene modification, and anti-CD47 antibody to investigate the potential therapeutic role of AP. CD47 expression was negatively regulated by upstream miR133a, which is controlled by the transcription factor TRIM28. To further investigate the regulation of efferocytosis and reduction of pancreatic necrosis in AP, we used miR-133a-agomir and pancreas-specific AAV-shTRIM28 to modulate CD47 expression. Our findings confirmed that CD47-mediated efferocytosis is critical for preventing pancreatic necrosis and suggest that targeting the TRIM28-miR133a-CD47 axis is clinically relevant for the treatment of AP.

Associations of Intrapancreatic Fat Deposition With Incident Diseases of the Exocrine and Endocrine Pancreas: A UK Biobank Prospective Cohort Study.

INTRODUCTION: To investigate whether increased intrapancreatic fat deposition (IPFD) heightens the risk of diseases of the exocrine and endocrine pancreas. METHODS: A prospective cohort study was conducted using data from the UK Biobank. IPFD was quantified using MRI and a deep learning-based framework called nnUNet. The prevalence of fatty change of the pancreas (FP) was determined using sex- and age-specific thresholds. Associations between IPFD and pancreatic diseases were assessed with multivariate Cox-proportional hazard model adjusted for age, sex, ethnicity, body mass index, smoking and drinking status, central obesity, hypertension, dyslipidemia, liver fat content, and spleen fat content. RESULTS: Of the 42,599 participants included in the analysis, the prevalence of FP was 17.86%. Elevated IPFD levels were associated with an increased risk of acute pancreatitis (hazard ratio [HR] per 1 quintile change 1.513, 95% confidence interval [CI] 1.179-1.941), pancreatic cancer (HR per 1 quintile change 1.365, 95% CI 1.058-1.762) and diabetes mellitus (HR per 1 quintile change 1.221, 95% CI 1.132-1.318). FP was also associated with a higher risk of acute pancreatitis (HR 3.982, 95% CI 2.192-7.234), pancreatic cancer (HR 1.976, 95% CI 1.054-3.704), and diabetes mellitus (HR 1.337, 95% CI 1.122-1.593, P = 0.001). DISCUSSION: FP is a common pancreatic disorder. Fat in the pancreas is an independent risk factor for diseases of both the exocrine pancreas and endocrine pancreas.

KnoMol: A Knowledge-Enhanced Graph Transformer for Molecular Property Prediction.

Molecular property prediction (MPP) techniques are pivotal in reducing drug development costs by preemptively predicting bioactivity and ADMET properties. Despite the application of numerous deep learning approaches, enhancing the representational capacity of these models remains a significant challenge. This paper presents a novel knowledge-based Transformer framework, KnoMol, designed to improve the understanding of molecular structures. KnoMol integrates expert chemical knowledge into the Transformer, emulating the analytical methods of medicinal chemists. Additionally, the multiperspective attention mechanism provides a more precise way to represent ring systems. In the evaluation experiments, KnoMol achieved state-of-the-art performance on both MoleculeNet and small-scale data sets, surpassing existing models in terms of accuracy and generalization. Further research indicated that the incorporation of knowledge significantly reduces KnoMol's reliance on data volumes, offering a solution to the challenge of data scarcity. Moreover, KnoMol identified several new inhibitors of HER2 in a case study, demonstrating its value in real-world applications. Overall, this research not only provides a powerful tool for MPP but also serves as a successful precedent for embedding knowledge into Transformers, with positive implications for computer-aided drug discovery and the development of MPP algorithms.

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic target for leukemia treatment. In this study, we introduced a sophisticated virtual screening strategy combined with biological evaluations to discover potent LCK inhibitors. Our initial approach involved utilizing the PLANET algorithm to assess and contrast various scoring methodologies suitable for LCK inhibitor screening. After effectively evaluating PLANET, we progressed to devise a virtual screening workflow that synergistically combines the strengths of PLANET with the capabilities of Schrödinger's suite. This integrative strategy led to the efficient identification of four potential LCK inhibitors. Among them, compound 1232030-35-1 stood out as the most promising candidate with an IC50 of 0.43 nM. Further in vitro bioassays revealed that 1232030-35-1 exhibited robust antiproliferative effects on T-ALL cells, which was attributed to its ability to suppress the phosphorylations of key molecules in the LCK signaling pathway. More importantly, 1232030-35-1 treatment demonstrated profound in vivo antileukemia efficacy in a human T-ALL xenograft model. In addition, complementary molecular dynamics simulations provided deeper insight into the binding kinetics between 1232030-35-1 and LCK, highlighting the formation of a hydrogen bond with Met319. Collectively, our study established a robust and effective screening strategy that integrates AI-driven and conventional methodologies for the identification of LCK inhibitors, positioning 1232030-35-1 as a highly promising and novel drug-like candidate for potential applications in treating T-ALL.

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations.

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

Discovery of N-Benzylpiperidinol derivatives as USP7 inhibitors against Hematology.

USP7 has been recognized as a potential target for the treatment of hematologic malignancies by stabilizing multiple cancer-relevant proteins. Nevertheless, drug-like USP7 inhibitors are still lacking. Herein, compound J21 (USP7 IC50: 41.35 ± 2.16 nM) was discovered based on the structure of L55 and its co-crystal complex with USP7. Additionally, J21 exhibited greater metabolic stability (T1/2: 1.25 h, Cmax: 394.1 ± 48.3 ng/mL, and AUC0-t: 597.8 ± 44.8 ng/mL∙h) compared to L55. These findings may further pave the way for the development of USP7 inhibitors for the treatment of hematologic malignancies.

Discovery of a potent and selective PARP1 degrader promoting cell cycle arrest via intercepting CDC25C-CDK1 axis for treating triple-negative breast cancer.

PARP1 is a multifaceted component of DNA repair and chromatin remodeling, making it an effective therapeutic target for cancer therapy. The recently reported proteolytic targeting chimera (PROTAC) could effectively degrade PARP1 through the ubiquitin-proteasome pathway, expanding the therapeutic application of PARP1 blocking. In this study, a series of nitrogen heterocyclic PROTACs were designed and synthesized through ternary complex simulation analysis based on our previous work. Our efforts have resulted in a potent PARP1 degrader D6 (DC50 = 25.23 nM) with high selectivity due to nitrogen heterocyclic linker generating multiple interactions with the PARP1-CRBN PPI surface, specifically. Moreover, D6 exhibited strong cytotoxicity to triple negative breast cancer cell line MDA-MB-231 (IC50 = 1.04 µM). And the proteomic results showed that the antitumor mechanism of D6 was found that intensifies DNA damage by intercepting the CDC25C-CDK1 axis to halt cell cycle transition in triple-negative breast cancer cells. Furthermore, in vivo study, D6 showed a promising PK property with moderate oral absorption activity. And D6 could effectively inhibit tumor growth (TGI rate = 71.4 % at 40 mg/kg) without other signs of toxicity in MDA-MB-321 tumor-bearing mice. In summary, we have identified an original scaffold and potent PARP1 PROTAC that provided a novel intervention strategy for the treatment of triple-negative breast cancer.

Cpd-A1 alleviates acute kidney injury by inhibiting ferroptosis.

Acute kidney injury (AKI) is defined as sudden loss of renal function characterized by increased serum creatinine levels and reduced urinary output with a duration of 7 days. Ferroptosis, an iron-dependent regulated necrotic pathway, has been implicated in the progression of AKI, while ferrostatin-1 (Fer-1), a selective inhibitor of ferroptosis, inhibited renal damage, oxidative stress and tubular cell death in AKI mouse models. However, the clinical translation of Fer-1 is limited due to its lack of efficacy and metabolic instability. In this study we designed and synthesized four Fer-1 analogs (Cpd-A1, Cpd-B1, Cpd-B2, Cpd-B3) with superior plasma stability, and evaluated their therapeutic potential in the treatment of AKI. Compared with Fer-1, all the four analogs displayed a higher distribution in mouse renal tissue in a pharmacokinetic assay and a more effective ferroptosis inhibition in erastin-treated mouse tubular epithelial cells (mTECs) with Cpd-A1 (N-methyl-substituted-tetrazole-Fer-1 analog) being the most efficacious one. In hypoxia/reoxygenation (H/R)- or LPS-treated mTECs, treatment with Cpd-A1 (0.25 µM) effectively attenuated cell damage, reduced inflammatory responses, and inhibited ferroptosis. In ischemia/reperfusion (I/R)- or cecal ligation and puncture (CLP)-induced AKI mouse models, pre-injection of Cpd-A1 (1.25, 2.5, 5 mg·kg-1·d-1, i.p.) dose-dependently improved kidney function, mitigated renal tubular injury, and abrogated inflammation. We conclude that Cpd-A1 may serve as a promising therapeutic agent for the treatment of AKI.

Discovery of potent CSK inhibitors through integrated virtual screening and molecular dynamic simulation.

Oncogenic overexpression or activation of C-terminal Src kinase (CSK) has been shown to play an important role in triple-negative breast cancer (TNBC) progression, including tumor initiation, growth, metastasis, drug resistance. This revelation has pivoted the focus toward CSK as a potential target for novel treatments. However, until now, there are few inhibitors designed to target the CSK protein. Responding to this, our research has implemented a comprehensive virtual screening protocol. By integrating energy-based screening methods with AI-driven scoring functions, such as Attentive FP, and employing rigorous rescoring methods like Glide docking and molecular mechanics generalized Born surface area (MM/GBSA), we have systematically sought out inhibitors of CSK. This approach led to the discovery of a compound with a potent CSK inhibitory activity, reflected by an IC50 value of 1.6 nM under a homogeneous time-resolved fluorescence (HTRF) bioassay. Subsequently, molecule 2 exhibits strong growth inhibition of MD anderson - metastatic breast (MDA-MB) -231, Hs578T, and SUM159 cells, showing a level of growth inhibition comparable to that observed with dasatinib. Treatment with molecule 2 also induced significant G1 phase accumulation and cell apoptosis. Furthermore, we have explored the explicit binding interactions of the compound with CSK using molecular dynamics simulations, providing valuable insights into its mechanism of action.

Peripheral immune cell profiling of double-hit lymphoma by mass cytometry.

BACKGROUND: Double-hit or Triple-hit lymphoma (DHL/THL) is a subset of high-grade B cell lymphoma harboring rearrangements of MYC and BCL2 and/or BCL6, and usually associate with aggressive profile, while current therapies tend to provide poor clinical outcomes and eventually relapsed. Further explorations of DHL at cellular and molecular levels are in demand to offer guidance for clinical activity. METHODS: We collected the peripheral blood of DHL patients and diffused large B cell lymphoma (DLBCL) patients from single institute and converted them into PBMC samples. Mass cytometry was then performed to characterize these samples by 42 antibody markers with samples of healthy people as control. We divided the immune cell subtypes based on the expression profile of surface antigens, and the proportion of each cell subtype was also analyzed. By comparing the data of the DLBCL group and the healthy group, we figured out the distinguished immune cell subtypes of DHL patients according to their abundance and marker expression level. We further analyzed the heterogeneity of DHL samples by pairwise comparison based on clinical characteristics. RESULTS: We found double-positive T cells (DPT) cells were in a significantly high percentage in DHL patients, whereas the ratio of double-negative T cells (DNT) was largely reduced in patients. Besides, CD38 was uniquely expressed at a high level on some naïve B cells of DHL patients, which could be a marker for the diagnosis of DHL (distinguishing from DLBCL), or even be a drug target for the treatment of DHL. In addition, we illustrated the heterogeneity of DHL patients in terms of immune cell landscape, and highlighted TP53 as a major factor that contributes to the heterogeneity of the T cells profile. CONCLUSION: Our study demonstrated the distinct peripheral immune cell profile of DHL patients by contrast to DLBCL patients and healthy people, as well as the heterogeneity within the DHL group, which could provide valuable guidance for the diagnosis and treatment of DHL.

PROTAC-DB: an online database of PROTACs.

Proteolysis-targeting chimeras (PROTACs), which selectively degrade targeted proteins by the ubiquitin-proteasome system, have emerged as a novel therapeutic technology with potential advantages over traditional inhibition strategies. In the past few years, this technology has achieved substantial progress and two PROTACs have been advanced into phase I clinical trials. However, this technology is still maturing and the design of PROTACs remains a great challenge. In order to promote the rational design of PROTACs, we present PROTAC-DB, a web-based open-access database that integrates structural information and experimental data of PROTACs. Currently, PROTAC-DB consists of 1662 PROTACs, 202 warheads (small molecules that target the proteins of interest), 65 E3 ligands (small molecules capable of recruiting E3 ligases) and 806 linkers, as well as their chemical structures, biological activities, and physicochemical properties. Except the biological activities of warheads and E3 ligands, PROTAC-DB also provides the degradation capacities, binding affinities and cellular activities for PROTACs. PROTAC-DB can be queried with two general searching approaches: text-based (target name, compound name or ID) and structure-based. In addition, for the convenience of users, a filtering tool for the searching results based on the physicochemical properties of compounds is also offered. PROTAC-DB is freely accessible at http://cadd.zju.edu.cn/protacdb/.

Stochastic Stabilization of Dual-Layer Rumor Propagation Model with Multiple Channels and Rumor-Detection Mechanism.

With the development of information technology, individuals are able to receive rumor information through various channels and subsequently act based on their own perceptions. The significance of the disparity between media and individual cognition in the propagation of rumors cannot be underestimated. In this paper, we establish a dual-layer rumor propagation model considering the differences in individual cognition to study the propagation behavior of rumors in multiple channels. Firstly, we obtain the threshold for rumor disappearance or persistence by solving the equilibrium points and their stability. The threshold is related to the number of media outlets and the number of rumor debunkers. Moreover, we have innovatively designed a class of non-periodic intermittent noise stabilization methods to suppress rumor propagation. This method can effectively control rumor propagation based on a flexible control scheme, and we provide specific expressions for the control intensity. Finally, we have validated the accuracy of the theoretical proofs through experimental simulations.

Inhibition of TRIM32 by ibr-7 treatment sensitizes pancreatic cancer cells to gemcitabine via mTOR/p70S6K pathway.

Pancreatic cancer is one of the most notorious diseases for being asymptomatic at early stage and high mortality rate thereafter. However, either chemotherapy or targeted therapy has rarely achieved success in recent clinical trials for pancreatic cancer. Novel therapeutic regimens or agents are urgently in need. Ibr-7 is a novel derivative of ibrutinib, displaying superior antitumour activity in pancreatic cancer cells than ibrutinib. In vitro studies showed that ibr-7 greatly inhibited the proliferation of BxPC-3, SW1990, CFPAC-1 and AsPC-1 cells via the induction of mitochondrial-mediated apoptosis and substantial suppression of mTOR/p70S6K pathway. Moreover, ibr-7 was able to sensitize pancreatic cancer cells to gemcitabine through the efficient repression of TRIM32, which was positively correlated with the proliferation and invasiveness of pancreatic cancer cells. Additionally, knockdown of TRIM32 diminished mTOR/p70S6K activity in pancreatic cancer cells, indicating a positive feedback loop between TRIM32 and mTOR/p70S6K pathway. To conclude, this work preliminarily explored the role of TRIM32 in the malignant properties of pancreatic cancer cells and evaluated the possibility of targeting TRIM32 to enhance effectiveness of gemcitabine, thereby providing a novel therapeutic target for pancreatic cancer.

High efficacy of PD-1 inhibitor after initial failure of PD-L1 inhibitor in Relapsed/Refractory classical Hodgkin Lymphoma.

PURPOSE: We sought to understand the clinical course and molecular phenotype of patients who showed disease progression after programmed cell death ligand 1 (PD-L1) inhibitor treatment but subsequently responded to PD-1 inhibitor treatment. We also explored the response to PD-1-axis targeted therapy of classical Hodgkin lymphoma (cHL) according to genetically driven PD-L1 and programmed cell death ligand 2 (PD-L2) expression. METHODS: Five patients in a phase II clinical trial of CS1001 (PD-L1 inhibitor) for relapsed or refractory (R/R) cHL were retrospectively reviewed. Formalin-fixed, paraffin-embedded whole tissues from the five patients were evaluated for 9p24.1 genetic alterations based on FISH and the expression of PD-L1, PD-L2, PD-1, major histocompatibility complex (MHC) class I-II, and the tumor microenvironment factorsCD163 and FOXP3 in the microenvironmental niche, as revealed by multiplex immunofluorescence. RESULTS: All five patients showed primary refractory disease during first-line treatment. Four patients received PD-1 inhibitor after dropping out of the clinical trial, and all demonstrated at least a partial response. The progression-free survival ranged from 7 to 28 months (median = 18 months), and 9p24.1 amplification was observed in all five patients at the PD-L1/PD-L2 locus. PD-L1 and PD-L2 were colocalized on Hodgkin Reed-Sternberg (HRS) cells in four of the five (80%) patients. There was differential expression of PD-L1 and PD-L2 in cells in the tumor microenvironment in cHL, especially in HRS cells, background cells and tumor-associated macrophages. CONCLUSIONS: PD-L1 monotherapy may not be sufficient to block the PD-1 pathway; PD-L2 was expressed in HRS and background cells in cHL. The immunologic function of the PD-L2 pathway in anti-tumor activity may be underestimated in R/R cHL. Further study is needed to elucidate the anti-tumor mechanism of PD-1 inhibitor and PD-L1 inhibitor treatment.

Design, synthesis and biological evaluation of new dihydropyridine derivatives as PD-L1 degraders for enhancing antitumor immunity.

Immune checkpoint blockade (ICB) by targeting programmed cell death-1/programmed cell death ligand 1 (PD-1/PD-L1) signaling pathway is a promising strategy for tumor immunotherapy. Developing small-molecules inducing PD-L1 protein degradation has been proven as an alternative and useful approach for targeting the immunotherapy pathway. Our previous study showed that Lercanidipine could down-regulate the expression of PD-L1 protein, but its calcium influx antagonistic activity hampers further development. For attenuating the unexpected calcium channel blockade effect, a series of compounds were synthesized and evaluated through structure-activity relationship (SAR) exploration. Amongst, compound F4 exhibited a loss of calcium antagonistic activity, while the PD-L1 degradation activity can still retain. Further studies indicated that F4 degraded PD-L1 dose- and time-dependently, and may function through a lysosomal-dependent manner. Furthermore, compound F4 showed a good bioavailability value of 24.9% in mice. Moreover, the F4-induced PD-L1 degradation strengthened the T cell-mediated killing of tumor cells. Our findings show the discovery of a new PD-L1 degrader, providing a potential strategy for immunotherapy.

Design, synthesis, and biological evaluation of quinazoline derivatives with covalent reversible warheads as potential FGFR4 inhibitors.

Fibroblast growth factor receptor 4 (FGFR4) together with co-receptors modulate the activation of downstream proteins that regulate fundamental processes, and elevated FGFR4 activity is associated with Hepatocellular Carcinoma (HCC). Hence, FGFR4 is a promising therapeutic target for HCC. Based on BLU9931, we designed and synthesized a series of phenylquinazoline derivatives as novel inhibitors of FGFR4 through the covalent reversible strategy. Among them, a novel compound (C3) showed FGFR4 and cell proliferation inhibitory activity. Cellular mechanism studies demonstrated that compound C3 induced apoptosis via the FGFR4 signaling pathway blockage. Further mechanism study showed that C3 has the reversible covalent binding capacity, could be used as a reference for the development of novel FGFR4 covalent reversible inhibitors.