Discovery of 2‑Acylaminothiophene-3-Carboxamides as Multitarget Inhibitors for BCR-ABL Kinase and Microtubules

The emergence of drug resistance of the BCR-ABL kinase inhibitor imatinib, especially toward the T315I gatekeeper mutation, poses a great challenge to targeted therapy in treating chronic myeloid leukemia (CML) patients. To discover novel inhibitors against drug-resistant CML bearing T315I mutation, we applied a physics-based hierarchical virtual screening approach to dock a large chemical library against ATP binding pockets of both wild-type (WT) and T315I mutant ABL kinases in a combinatorial fashion. This strategy automatically resulted in 87 compounds satisfying structural and energetic criteria of both WT and T315I mutant kinases. Among them, nine compounds, which share a common thiophene-based scaffold and adopt similar binding poses, were chosen for experimental testing and one of them was shown to have low micromolar inhibition activities against both WT and mutant ABL kinases. Structure–activity relationship analysis with a series of structural modifications based on 2-acylaminothiophene-3-carboxamide scaffold supports our predicted binding mode. Interestingly, the same chemical scaffold was also enriched in our previous virtual screening campaign against colchicine site of microtubules using the same computational protocol, which suggests our virtual screening strategy is capable of discovering small-molecule ligands targeting distinct protein binding sites without sharing any sequential and structural similarity. Furthermore, the multitarget inhibition activity of this class of compounds was assessed in cellular experiments. We expect that the 2-acylaminothiophene-3-carboxamide scaffold may serve as a promising starting point for developing multitarget inhibitors in cancer treatment by targeting both kinases and microtubules

Evaluation and Application of MD-PB/SA in Structure-Based Hierarchical Virtual Screening

Molecular dynamics (MD) based molecular mechanics Poisson–Boltzmann and surface area (MM-PB/SA) calculation (MD-PB/SA) has been widely used to estimate binding free energies for receptor–ligand complexes. While numerous reports have focused on assessing accuracy and efficiency, fewer studies have paid attention to performance in lead discovery. In the present study, we report a critical evaluation of MD-PB/SA in hierarchical virtual screening (HVS) both theoretically and practically. It is shown that based on native poses, MD-PB/SA could be well applied to predict the relative binding energy for both congeneric and diverse ligands for different protein targets. However, there is a limitation for MD-PB/SA to distinguish the native pose of one ligand from the artificial pose of another when a huge difference exists between two molecules. By combining a physics-based scoring function with a knowledge-based structural filter, we improve the predictability and validate the practical use of MD-PB/SA in lead discovery by identifying novel inhibitors of p38 MAP kinase. We also expand our study to other protein targets such as HIV-1 RT and NA to assess the general validity of MD-PB/SA

Table_3_Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma.docx

PurposeTo investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma.MethodsA primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors.ResultsThe RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models.ConclusionsThis study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.</p

Table_2_Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma.docx

PurposeTo investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma.MethodsA primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors.ResultsThe RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models.ConclusionsThis study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.</p

Table_1_Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma.docx

PurposeTo investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma.MethodsA primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors.ResultsThe RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models.ConclusionsThis study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.</p

Transparent and Flexible Triboelectric Sensing Array for Touch Security Applications

Tactile sensors with large-scale array and high sensitivity is essential for human–machine interaction, smart wearable devices, and mobile networks. Here, a transparent and flexible triboelectric sensing array (TSA) with fingertip-sized pixels is demonstrated by integrating ITO electrodes, FEP film, and signal transmission circuits on an undivided palm-sized polyethylene terephthalate substrate. The sensing pixels can be triggered by the corresponding external contact to induce the electrostatic potential in the transparent electrodes without power consumption, which is individually recognized by the sensor. By testing the response of the pixels, the electrical characterization is systematically investigated. The proposed TSA exhibits excellent durability, independence, and synchronicity, which is able to realize real-time touch sensing, spatial mapping, and motion monitoring. The integrated TSA has great potential for an active tactile system, human–machine interface, wearable electronics, private communication, and advanced security identification

Rotating-Sleeve Triboelectric–Electromagnetic Hybrid Nanogenerator for High Efficiency of Harvesting Mechanical Energy

Currently, a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) have been hybridized to effectively scavenge mechanical energy. However, one critical issue of the hybrid device is the limited output power due to the mismatched output impedance between the two generators. In this work, impedance matching between the TENG and EMG is achieved facilely through commercial transformers, and we put forward a highly integrated hybrid device. The rotating-sleeve triboelectric–electromagnetic hybrid nanogenerator (RSHG) is designed by simulating the structure of a common EMG, which ensures a high efficiency in transferring ambient mechanical energy into electric power. The RSHG presents an excellent performance with a short-circuit current of 1 mA and open-circuit voltage of 48 V at a rotation speed of 250 rpm. Systematic measurements demonstrate that the hybrid nanogenerator can deliver the largest output power of 13 mW at a loading resistance of 8 kΩ. Moreover, it is demonstrated that a wind-driven RSHG can light dozens of light-emitting diodes and power an electric watch. The distinctive structure and high output performance promise the practical application of this rotating-sleeve structured hybrid nanogenerator for large-scale energy conversion

A Soluble Bis-Chelated Gold(I) Diphosphine Compound with Strong Anticancer Activity and Low Toxicity

Gold-containing compounds have shown anticancer potential, but their clinical applications have been severely limited by poor stability and high toxicity in vivo. Here, we report a novel soluble bis-chelated gold­(I)–diphosphine compound (GC20) with strong anticancer activity and low toxicity. GC20 shows strong antiproliferation potency against a broad spectrum of cancer cell lines including cisplatin-resistant cancer cells (IC₅₀ ≈ 0.5 μM) and significantly reduces tumor growth in several tumor xenografts in mouse models at doses as low as 2 mg/kg. Studies of its mechanism revealed that GC20 specifically inhibits the enzymatic activity of thioredoxin reductase by binding to selenocysteine residue, without targeting other well-known selenol and thiol groups contained in biomolecules. Remarkably, in animal studies GC20 was shown to be well tolerated even at the high dose of 8 mg/kg. Our results strongly suggest that GC20 represents a promising candidate for the development of novel anticancer drugs

In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery

To explore novel kinase hinge-binding scaffolds, we carried out structure-based virtual screening against p38α MAPK as a model system. With the assistance of developed kinase-specific structural filters, we identify a novel lead compound that selectively inhibits a panel of kinases with threonine as the gatekeeper residue, including BTK and LCK. These kinases play important roles in lymphocyte activation, which encouraged us to design novel kinase inhibitors as drug candidates for ameliorating inflammatory diseases and cancers. Therefore, we chemically modified our substituted triazole-class lead compound to improve the binding affinity and selectivity via a “minimal decoration” strategy, which resulted in potent and selective kinase inhibitors against LCK (18 nM) and BTK (8 nM). Subsequent crystallographic experiments validated our design. These rationally designed compounds exhibit potent on-target inhibition against BTK in B cells or LCK in T cells, respectively. Our work demonstrates that structure-based virtual screening can be applied to facilitate the development of novel chemical entities in crowded chemical space in the field of kinase inhibitor discovery

Lithium-Ion Batteries: Charged by Triboelectric Nanogenerators with Pulsed Output Based on the Enhanced Cycling Stability

The triboelectric nanogenerator (TENG) has been used to store its generated energy into lithium-ion batteries (LIBs); however, the influences of its pulse current and high voltage on LIB polarization and dynamic behaviors have not been investigated yet. In this paper, it is found that LIBs based on the phase transition reaction of the lithium storage mechanism [LiFePO₄ (LFP) and Li₄Ti₅O₁₂ (LTO) electrodes] are more suitable for charging by TENGs. Thus, the enhanced cycling capacity, Coulombic efficiency (nearly 100% for LTO electrode), and energy storage efficiency (85.3% for the LFP–LTO electrode) are successfully achieved. Moreover, the pulse current has a positive effect on the increase of the Li-ion extraction, reducing the charge-transfer resistance (<i>R</i>_ct) for all studied electrodes as well (LFP, LiNi_0.6Co_0.2Mn_0.2O₂, LTO, and graphite). The excellent cyclability, high Coulombic, and energy storage efficiencies demonstrated the availability of storing pulsed energy generated by TENGs. This research has provided a promising analysis to obtain an enhanced charging methodology, which provides significant guidance for the scientific research of the LIBs