D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data.

Doublets formed during single-cell RNA sequencing (scRNA-seq) severely affect downstream studies, such as differentially expressed gene analysis and cell trajectory inference, and limit the cellular throughput of scRNA-seq. Several doublet detection algorithms are currently available, but their generalization performance could be further improved due to the lack of effective feature-embedding strategies with suitable model architectures. Therefore, SoCube, a novel deep learning algorithm, was developed to precisely detect doublets in various types of scRNA-seq data. SoCube (i) proposed a novel 3D composite feature-embedding strategy that embedded latent gene information and (ii) constructed a multikernel, multichannel CNN-ensembled architecture in conjunction with the feature-embedding strategy. With its excellent performance on benchmark evaluation and several downstream tasks, it is expected to be a powerful algorithm to detect and remove doublets in scRNA-seq data. SoCube is freely provided as an end-to-end tool on the Python official package site PyPi (https://pypi.org/project/socube/) and open-source on GitHub (https://github.com/idrblab/socube/).

Assessing the efficiency of eligibility criteria for low-dose computed tomography lung screening in China according to current guidelines.

BACKGROUND: Evidence from observational studies indicates that lung cancer screening (LCS) guidelines with high rates of lung cancer (LC) underdiagnosis, and although current screening guidelines have been updated and eligibility criteria for screening have been expanded, there are no studies comparing the efficiency of LCS guidelines in Chinese population. METHODS: Between 2005 and 2022, 31,394 asymptomatic individuals were screened using low-dose computed tomography (LDCT) at our institution. Demographic data and relevant LC risk factors were collected. The efficiency of the LCS for each guideline criteria was expressed as the efficiency ratio (ER). The inclusion rates, eligibility rates, LC detection rates, and ER based on the different eligibility criteria of the four guidelines were comparatively analyzed. The four guidelines were as follows: China guideline for the screening and early detection of lung cancer (CGSL), the National Comprehensive Cancer Network (NCCN), the United States Preventive Services Task Force (USPSTF), and International Early Lung Cancer Action Program (I-ELCAP). RESULTS: Of 31,394 participants, 298 (155 women, 143 men) were diagnosed with LC. For CGSL, NCCN, USPSTF, and I-ELCAP guidelines, the eligibility rates for guidelines were 13.92%, 6.97%, 6.81%, and 53.46%; ERe for eligibility criteria were 1.46%, 1.64%, 1.51%, and 1.13%, respectively; and for the inclusion rates, they were 19.0%, 9.5%, 9.3%, and 73.0%, respectively. LCs which met the screening criteria of CGSL, NCCN, USPSTF, and I-ELCAP guidelines were 29.2%, 16.4%, 14.8%, and 86.6%, respectively. The age and smoking criteria for CGSL were stricter, hence resulting in lower rates of LC meeting the screening criteria. The CGSL, NCCN, and USPSTF guidelines showed the highest underdiagnosis in the 45-49 age group (17.4%), while the I-ELCAP guideline displayed the highest missed diagnosis rate (3.0%) in the 35-39 age group. Males and females significantly differed in eligibility based on the criteria of the four guidelines (P < 0.001). CONCLUSIONS: The I-ELCAP guideline has the highest eligibility rate for both males and females. But its actual efficiency ratio for those deemed eligible by the guideline was the lowest. Whereas the NCCN guideline has the highest ERe value for those deemed eligible by the guideline.

Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations.

Although the current coronavirus disease 2019 (COVID-19) vaccines have been used worldwide to halt spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the emergence of new SARS-CoV-2 variants with E484K mutation shows significant resistance to the neutralization of vaccine sera. To better understand the resistant mechanism, we calculated the binding affinities of 26 antibodies to wild-type (WT) spike protein and to the protein harboring E484K mutation, respectively. The results showed that most antibodies (~85%) have weaker binding affinities to the E484K mutated spike protein than to the WT, indicating the high risk of immune evasion of the mutated virus from most of current antibodies. Binding free energy decomposition revealed that the residue E484 forms attraction with most antibodies, while the K484 has repulsion from most antibodies, which should be the main reason of the weaker binding affinities of E484K mutant to most antibodies. Impressively, a monoclonal antibody (mAb) combination was found to have much stronger binding affinity with E484K mutant than WT, which may work well against the mutated virus. Based on binding free energy decomposition, we predicted that the mutation of four more residues on receptor-binding domain (RBD) of spike protein, viz., F490, V483, G485 and S494, may have high risk of immune evasion, which we should pay close attention on during the development of new mAb therapeutics.

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

Development of an FAP-Targeted PET Probe Based on a Novel Quinolinium Molecular Scaffold.

Fibroblast activation protein (FAP) has recently gained significant attention as a promising tumor biomarker for both diagnosis and therapeutic applications. A series of radiopharmaceuticals based on fibroblast activation protein inhibitors (FAPIs) have been developed and translated into the clinic. Though some of them such as radiolabeled FAPI-04 probes have achieved favorable in vivo imaging performance, further improvement is still highly desired for obtaining radiopharmaceuticals with a high theranostics potential. In this study, we innovatively designed an FAPI ligand SMIC-3002 by changing the core quinoline motif of FAPI-04 to the quinolinium scaffold. The engineered molecule was further radiolabeled with 68Ga to generate a positron emission tomography (PET) probe, [68Ga]Ga-SMIC-3002, which was then evaluated in vitro and in vivo. [68Ga]Ga-SMIC-3002 demonstrated high in vitro stability, nanomolar affinity for FAP (8 nM for protein, 23 nM for U87MG cells), and specific uptake in FAP-expressing tumors, with a tumor/muscle ratio of 19.1 and a tumor uptake of 1.48 ± 0.03 ID/g% at 0.5 h in U87MG tumor-bearing mice. In summary, the quinolinium scaffold can be successfully used for the development of the FAP-targeted tracer. [68Ga]Ga-SMIC-3002 not only shows high potential for clinical translation but also offers insights into designing a new generation of FAPI tracers.

Ribosomal protein S3 mediates drug resistance of proteasome inhibitor: potential therapeutic application in multiple myeloma.

Multiple myeloma (MM) remains incurable due to drug resistance. Ribosomal protein S3 (RPS3) has been identified as a non-Rel subunit of NF-κB. However, the detailed biological roles of RPS3 remain unclear. Here, we report for the first time that RPS3 is necessary for MM survival and drug resistance. RPS3 was highly expressed in MM, and knockout of RPS3 in MM inhibited cell growth and induced cell apoptosis both in vitro and in vivo. Overexpression of RPS3 mediated the proteasome inhibitor resistance of MM and shortened the survival of MM tumor-bearing animals. Moreover, our present study found an interaction between RPS3 and the thyroid hormone receptor interactor 13 (TRIP13), an oncogene related to MM tumorigenesis and drug resistance. We demonstrated that the phosphorylation of RPS3 was mediated by TRIP13 via PKCδ, which played an important role in activating the canonical NF-κB signaling and inducing cell survival and drug resistance in MM. Notably, the inhibition of NF-κB signaling by the small-molecule inhibitor targeting TRIP13, DCZ0415, was capable of triggering synergistic cytotoxicity when combined with bortezomib in drug-resistant MM. This study identifies RPS3 as a novel biomarker and therapeutic target in MM.

Dihydrocelastrol induces cell death and suppresses angiogenesis through BCR/AP-1/junb signalling in diffuse large B cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Although treatment options have improved, a large proportion of patients show low survival rates, highlighting an urgent need for novel therapeutic strategies. The aim of this study was to investigate the efficacy of the new small-molecule compound dihydrocelastrol (DHCE), acquired through the structural modification of celastrol (CE), in the treatment of DLBCL. DHCE showed potent anti-lymphoma efficacy and synergistic effects with doxorubicin. DHCE triggered DLBCL cell apoptosis and G0/G1-phase blockade, thereby hindering angiogenesis. DHCE inhibited B-cell receptor cascade signalling and Jun B and p65 nuclear translocation, thereby suppressing pro-tumourigenic signalling. Finally, DHCE exerted lower toxicity than CE, which showed severe hepatic, renal, and reproductive toxicity in vivo. Our findings support further investigation of the clinical efficacy of DHCE against DLBCL.

Design and synthesis of cantharidin derivative DCZ5418 as a TRIP13 inhibitor with anti-multiple myeloma activity in vitro and in vivo.

Natural product cantharidin can inhibit multiple myeloma cell growth in vitro, while serious adverse effects limited its clinical application. Therefore, the structural modification of cantharidin is needed. Herein, inspired by the structural similarity of the aliphatic endocyclic moiety in cantharidin and TRIP13 inhibitor DCZ0415, we designed and synthesized DCZ5418 and its nineteen derivatives. The molecular docking study indicated that DCZ5418 had a similar binding mode to TRIP13 protein as DCZ0415 while with a stronger docking score. Moreover, the bioassay studies of the MM-cells viability inhibition, TRIP13 protein binding affinity and enzyme inhibiting activity showed that DCZ5418 had good anti-MM activity in vitro and definite interaction with TRIP13 protein. The acute toxicity test of DCZ5418 showed less toxicity in vivo than cantharidin. Furthermore, DCZ5418 showed good anti-MM effects in vivo with a lower dose administration than DCZ0415 (15 mg/kg vs 25 mg/kg) on the tumor xenograft models. Thus, we obtained a new TRIP13 inhibitor DCZ5418 with improved safety and good activity in vivo, which provides a new example of lead optimization by using the structural fragments of natural products.

Design, synthesis and structure-activity relationship of 1,8-naphthalimide derivatives as highly potent hCYP1B1 inhibitors.

Human cytochrome P450 1B1 enzyme (hCYP1B1), a member of hCYP1 subfamily, plays a crucial role in multiple diseases by participating in many metabolic pathways. Although a suite of potent hCYP1B1 inhibitors have been previously reported, most of them also act as aryl hydrocarbon receptor (AhR) agonists that can up-regulate the expression of hCYP1B1 and then counteract their inhibitory potential in living systems. This study aimed to develop novel efficacious hCYP1B1 inhibitors that worked well in living cells but without AhR agonist effects. For these purposes, a series of 1,8-naphthalimide derivatives were designed and synthesized, and their structure-activity relationships (SAR) as hCYP1B1 inhibitors were analyzed. Following three rounds SAR studies, several potent hCYP1B1 inhibitors were discovered, among which compound 3n was selected for further investigations owing to its extremely potent anti-hCYP1B1 activity (IC50 = 0.040 nM) and its blocking AhR transcription activity in living cells. Inhibition kinetic analyses showed that 3n potently inhibited hCYP1B1 via a mix inhibition manner, showing a Ki value of 21.71 pM. Docking simulations suggested that introducing a pyrimidine moiety to the hit compound (1d) facilitated 3n to form two strong interactions with hCYP1B1/heme, viz., the C-Br⋯π halogen bond and the N-Fe coordination bond. Further investigations demonstrated that 3n (5 µM) could significantly reverse the paclitaxel (PTX) resistance in H460/PTX cells, evidenced by the dramatically reduced IC50 values, from 632.6 nM (PTX alone) to 100.8 nM (PTX plus 3n). Collectively, this study devised a highly potent hCYP1B1 inhibitor (3n) without AhR agonist effect, which offered a promising drug candidate for overcoming hCYP1B1-associated drug resistance.

D3Rings: A Fast and Accurate Method for Ring System Identification and Deep Generation of Drug-Like Cyclic Compounds.

Continuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds, as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in three different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, and fused and bridged rings. Based on this, three deep generative models, namely, VAE, AAE, and CharRNN, were trained and used to construct two data sets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged data sets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like data set against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.

Equivalent analysis model of a GaN LED used as a receiver.

In general, visible light communication (VLC) uses LEDs as transmitters. However, LEDs can serve as receivers to construct a simple duplex VLC system that uses only two LEDs instead of one LED and one photo-diode (PD). There is a lack of effective equivalent analysis models for characterizing and evaluating the inherent behavioral characteristics of LEDs used as receivers. This paper presents an equivalent analysis model for GaN LEDs as receivers. First, based on the proposed receiving equivalent circuit model, a third-order signal transmission mathematical analysis model is established, revealing the transmission relationship between the photocurrent and output voltage. Further research is conducted on the impact of parameter changes on the bandwidth, and the model can be simplified into a first-order low-pass mathematical analysis model under specific conditions, providing theoretical support for improving the bandwidth of LED receiving applications. The experimental results also confirm the theoretical predictions. This research result holds significant importance for revealing the intrinsic mechanisms and the improved optical communication performance of LEDs for effective reception.

Lost genome segments associate with trait diversity during rice domestication.

BACKGROUND: DNA mutations of diverse types provide the raw material required for phenotypic variation and evolution. In the case of crop species, previous research aimed to elucidate the changing patterns of repetitive sequences, single-nucleotide polymorphisms (SNPs), and small InDels during domestication to explain morphological evolution and adaptation to different environments. Additionally, structural variations (SVs) encompassing larger stretches of DNA are more likely to alter gene expression levels leading to phenotypic variation affecting plant phenotypes and stress resistance. Previous studies on SVs in rice were hampered by reliance on short-read sequencing limiting the quantity and quality of SV identification, while SV data are currently only available for cultivated rice, with wild rice largely uncharacterized. Here, we generated two genome assemblies for O. rufipogon using long-read sequencing and provide insights on the evolutionary pattern and effect of SVs on morphological traits during rice domestication. RESULTS: In this study, we identified 318,589 SVs in cultivated and wild rice populations through a comprehensive analysis of 13 high-quality rice genomes and found that wild rice genomes contain 49% of unique SVs and an average of 1.76% of genes were lost during rice domestication. These SVs were further genotyped for 649 rice accessions, their evolutionary pattern during rice domestication and potential association with the diversity of important agronomic traits were examined. Genome-wide association studies between these SVs and nine agronomic traits identified 413 candidate causal variants, which together affect 361 genes. An 824-bp deletion in japonica rice, which encodes a serine carboxypeptidase family protein, is shown to be associated with grain length. CONCLUSIONS: We provide relatively accurate and complete SV datasets for cultivated and wild rice accessions, especially in TE-rich regions, by comparing long-read sequencing data for 13 representative varieties. The integrated rice SV map and the identified candidate genes and variants represent valuable resources for future genomic research and breeding in rice.

A Bioengineered Nanovesicle Vaccine Boosts T-B cell Interaction for Immunotherapy of Echinococcus multilocularis.

Alveolar echinococcosis (AE) is a zoonotic parasitic disease, resulting from being infected with the metacestode larvae of the tapeworm Echinococcus multilocularis (E. multilocularis). Novel prophylactic and therapeutic interventions are urgently needed since the current chemotherapy displays limited efficiency in AE treatment. Bioengineered nano cellular membrane vesicles are widely used for displaying the native conformational epitope peptides because of their unique structure and biocompatibility. In this study, four T-cells and four B-cells dominant epitope peptides of E. multilocularis with high immunogenicity were engineered into the Vero cell surface to construct a membrane vesicle nanovaccine for the treatment of AE. The results showed that the nanovesicle vaccine can efficiently activate dendritic cells, induce specific T/B cells to form a mutually activated circuit, and inhibit E. multilocularis infection. This study presents for the first time a nanovaccine strategy that can completely eliminate the burden of E. multilocularis.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19.

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Inspired by the multi-hub characteristic, we design 3 billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Based on the representation vectors and transcriptomics data, we predict 22 drugs that bind to tumor necrosis factor-α or interleukin-6, whose therapeutic associations with the inflammation storm in COVID-19 patients, and molecular binding model are further validated via data from PubMed publications, ongoing clinical trials and a docking program. In addition, the results on five biomedical applications suggest that DeepR2cov significantly outperforms five existing representation approaches. In summary, DeepR2cov is a powerful network representation approach and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients. The source code and data can be downloaded from https://github.com/pengsl-lab/DeepR2cov.git.

The novel norcantharidin derivative DCZ5417 suppresses multiple myeloma progression by targeting the TRIP13-MAPK-YWHAE signaling pathway.

BACKGROUND: Multiple myeloma (MM), an incurable disease owing to drug resistance, requires safe and effective therapies. Norcantharidin (NCTD), an active ingredient in traditional Chinese medicines, possesses activity against different cancers. However, its toxicity and narrow treatment window limit its clinical application. In this study, we synthesized a series of derivatives of NCTD to address this. Among these compounds, DCZ5417 demonstrated the greatest anti-MM effect and fewest side effects. Its anti-myeloma effects and  the mechanism were further tested. METHODS: Molecular docking, pull-down, surface plasmon resonance-binding, cellular thermal shift, and ATPase assays were used to study the targets of DCZ5417. Bioinformatic, genetic, and pharmacological approaches were used to elucidate the mechanisms associated with DCZ5417 activity. RESULTS: We confirmed a highly potent interaction between DCZ5417 and TRIP13. DCZ5417 inhibited the ATPase activity of TRIP13, and its anti-MM activity was found to depend on TRIP13. A mechanistic study verified that DCZ5417 suppressed cell proliferation by targeting TRIP13, disturbing the TRIP13/YWHAE complex and inhibiting the ERK/MAPK signaling axis. DCZ5417 also showed a combined lethal effect with traditional anti-MM drugs. Furthermore, the tumor growth-inhibitory effect of DCZ5417 was demonstrated using in vivo tumor xenograft models. CONCLUSIONS: DCZ5417 suppresses MM progression in vitro, in vivo, and in primary cells from drug-resistant patients, affecting cell proliferation by targeting TRIP13, destroying the TRIP13/YWHAE complex, and inhibiting ERK/MAPK signaling. These results imply a new and effective therapeutic strategy for MM treatment.

Molecular mechanism by which RRM2-inhibitor (cholagogue osalmid) plus bafilomycin A1 cause autophagic cell death in multiple myeloma.

Despite significant improvement in the prognosis of multiple myeloma (MM), the disease remains incurable; thus, more effective therapies are required. Ribonucleoside-diphosphate reductase subunit M2 (RRM2) is significantly associated with drug resistance, rapid relapse, and poor prognosis. Previously, we found that 4-hydroxysalicylanilide (osalmid), a specific inhibitor of RRM2, exhibits anti-MM activity in vitro, in vivo, and in human patients; however, the mechanism remains unclear. Osalmid inhibits the translocation of RRM2 to the nucleus and stimulates autophagosome synthesis but inhibits subsequent autophagosome-lysosome fusion. We confirm that RRM2 binds to receptor-interacting protein kinase 3 (RIPK3) and reduces RIPK3, inhibiting autophagosome-lysosome fusion. Interestingly, the combination of osalmid and bafilomycin A1 (an autophagy inhibitor) depletes RIPK3 and aggravates p62 and autophagosome accumulation, leading to autophagic cell death. Combination therapy demonstrates synergistic cytotoxicity both in vitro and in vivo. Therefore, we propose that combining osalmid and bafilomycin A1(BafA1) may have clinical benefits against MM.

Discovery of Novel SIRT1/2 Inhibitors with Effective Cytotoxicity against Human Leukemia Cells.

The sirtuin enzyme family members, SIRT1 and SIRT2, play both tumor-promoting and tumor-suppressing roles, depending on the context and experimental conditions. Compounds that inhibit either SIRT1 or SIRT2 show promising antitumor effects in several types of cancer models, both in vitro and in vivo. The simultaneous inhibition of SIRT1 and SIRT2 is helpful in treating cancer by completely blocking p53 deacetylation, leading to cell death. However, only a few SIRT1/2 dual inhibitors have been developed. Here, we report the discovery of a novel series of SIRT1/2 dual inhibitors via a rational drug design that involved virtual screening and a substructure search. Eleven of the derived compounds exhibited high inhibitory activities, with IC50 < 5 µM and high specificity for both SIRT1 and SIRT2. Compounds hsa55 and PS9 strongly induced apoptosis and showed antiproliferative effects against human leukemia cell lines, which could be due to their ability to increase of p53 and α-tubulin acetylation, as we observed in MOLM-13 cells. Therefore, the new scaffolds of these compounds and their efficacy in leukemia cell lines provide important clues for the further development of novel anti-leukemia drugs.