Review
Chemistry, Medicinal
Angelica Ferro, Evangelia Pantazaka, Constantinos M. M. Athanassopoulos, Muriel Cuendet
Summary: Despite being an incurable disease, multiple myeloma (MM) can benefit from combined and targeted therapies, which have shown a decrease in drug resistance and an improvement in overall survival. Histone deacetylases (HDACs) play a crucial role in cancer treatment, including MM, and combining HDAC inhibitors with other regimens is of great interest. Recent advancements include the development of dual-inhibitor entities, which may reduce therapeutic doses and the risk of drug resistance.
MEDICINAL RESEARCH REVIEWS
(2023)
Article
Biochemistry & Molecular Biology
Shicong Zhu, Cheng Xing, Guangsen Zhang, Hongling Peng, Zhihua Wang
Summary: In this study, CC1007 was found to inhibit the proliferation of multiple myeloma cells and induce apoptosis and cell cycle arrest. Additionally, CC1007 decreased the expression of MEF2C and HDAC7, disrupting their interaction and promoting the overexpression of Nur77. These findings suggest that CC1007 may be a promising drug for the treatment of multiple myeloma.
BIOORGANIC CHEMISTRY
(2022)
Article
Chemistry, Medicinal
Yuqi Jiang, Jie Xu, Kairui Yue, Chao Huang, Mengting Qin, Dongyu Chi, Qixin Yu, Yue Zhu, Xiaohan Hou, Tongqiang Xu, Min Li, C. James Chou, Xiaoyang Li
Summary: The study focused on modifying HDAC inhibitors to deactivate the Michael reaction in order to improve their potency. Compound 11h showed significant improvements in both HDAC inhibitory activity and cell-based antitumor assay, demonstrating potential for clinical application and efficacy against AML.
JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Review
Oncology
Jesus G. Berdeja, Jacob P. Laubach, Joshua Richter, Steve Stricker, Andrew Spencer, Paul G. Richardson, Ajai Chari
Summary: Panobinostat, an oral histone deacetylase inhibitor, shows promising potential in treating refractory multiple myeloma by resensitizing cells and exhibiting synergy with other antimyeloma drugs. Preclinical and clinical studies have demonstrated its efficacy in this aspect.
CLINICAL LYMPHOMA MYELOMA & LEUKEMIA
(2021)
Article
Chemistry, Multidisciplinary
Hong Phuong Nguyen, Quang De Tran, Cuong Quoc Nguyen, Tran Phuong Hoa, Tran Duy Binh, Huynh Nhu Thao, Bui Thi Buu Hue, Nguyen Trong Tuan, Quang Le Dang, Nguyen Quoc Chau Thanh, Nguyen Van Ky, Minh Quan Pham, Su-Geun Yang
Summary: In this study, the antiproliferative activities of multiple HDAC inhibitors against myeloma cells were evaluated, and compound 7f was found to be the most bioactive with strong inhibitory activity against HDAC6. The synergistic interaction between 7f and the proteasome bortezomib inhibitor was also observed.
Article
Hematology
Shirong Li, Jing Fu, Jun Yang, Huihui Ma, Divaya Bhutani, Markus Y. Mapara, Christophe Marcireau, Suzanne Lentzsch
Summary: In multiple myeloma with RAS mutations, GCK was identified as a novel therapeutic target. Targeting GCK showed high effectiveness in RAS(Mut) MM cells, while GCK kinase activity was critical for regulating MM cell proliferation and survival. GCK inhibitors might represent an alternative therapy to overcome immunomodulatory drug resistance in MM.
Article
Oncology
Seiichi Okabe, Yuko Tanaka, Akihiko Gotoh
Summary: The study demonstrated that the dual inhibitor CUDC-907 targeting PI3K and HDAC could effectively suppress myeloma cells by inhibiting HDAC activity and reducing the expression of key proteins involved in cancer cell survival. Combination treatment of myeloma cells with CUDC-907 and carfilzomib showed increased cytotoxicity, indicating a potential strategy to enhance the effects of proteasome inhibitors against MM cells.
EXPERIMENTAL HEMATOLOGY & ONCOLOGY
(2021)
Article
Chemistry, Medicinal
An-Min Zhou, Meng-Meng Wang, Yan Su, Zheng-Hong Yu, Hong-Ke Liu, Zhi Su
Summary: In this study, a new agent Ir-VPA was developed, which exhibited switching between apoptosis and autophagy in cervical cancer cells due to the inhibition of HDAC6 at different levels. Ir-VPA showed the best anticancer activity to HeLa cells, inducing severe DNA damages and cell cycle arrest at G2/M phase. The anticancer mechanism of Ir-VPA was dependent on the inhibitory performance of HDAC6, with caspase-dependent apoptosis at low concentration and autophagy with autophagy flux blockage at high concentration.
Review
Pharmacology & Pharmacy
Meran Keshawa Ediriweera
Summary: Histone acetylation is a crucial epigenetic event and continues to be an area of great interest in biochemical research. The balance between histone acetyltransferases (HATs) and histone deacetylases (HDACs) is disrupted in various human cancers. Histone deacetylase inhibitors (HDACi) have shown promising results in restoring dysregulated histone acetylation profiles and are considered as potential anti-cancer therapeutics. Recent studies have identified odd-chain fatty acids as novel HDACi, further expanding the understanding of fatty acids in cancer therapy.
DRUG DISCOVERY TODAY
(2023)
Article
Chemistry, Medicinal
Nan Sun, Kexin Yang, Wenzhong Yan, Mingyue Yao, Chengying Xie, Jianjun Cheng, Chengcheng Yu, Wenwen Duan, Xiaoke Gu, Dong Guo, Hualiang Jiang
Summary: Compound 19h, a novel HDAC inhibitor, showed potent and selective inhibition of HDAC1 and exhibited good antiproliferative activity in vitro. It significantly inhibited the growth of human tumor xenografts and murine tumor in animal models. When combined with the mPD-1 antibody, 19h increased the ratio of splenic CD4+ T effector cells and promoted complete tumor regression in 5/6 animals in the MC38 model. These findings suggest that selective class I HDAC inhibitors have direct tumor growth inhibition and indirect immune cell-mediated antitumor effects, and synergize with immune checkpoint inhibitors.
JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Review
Oncology
Robert Jenke, Nina Ressing, Finn K. Hansen, Achim Aigner, Thomas Buch
Summary: Epigenetic changes can drive cancer malignancy, while histone deacetylase inhibitors (HDACis) hold promise as anticancer drugs due to their ability to target multiple pathways relevant to the disease.
Article
Virology
Shun Iida, Sohtaro Mine, Keiji Ueda, Tadaki Suzuki, Hideki Hasegawa, Harutaka Katano
Summary: The histone deacetylase inhibitor SBHA was found to efficiently induce KSHV reactivation and apoptosis in PEL cells, suggesting its potential as a tool for KSHV reactivation induction and a novel therapeutic strategy against PEL.
JOURNAL OF VIROLOGY
(2021)
Review
Pharmacology & Pharmacy
Ekta Shirbhate, Ravichandran Veerasamy, Sai H. S. Boddu, Amit K. Tiwari, Harish Rajak
Summary: One significant obstacle in cancer treatment is the decrease in drug efficacy and occurrence of adverse effects. Oncolytic viruses (OVs) have gained interest as a potential method to treat cancer due to their specificity for cancerous tissue and reduced likelihood of adverse effects. Clinical trials have shown that OVs have an acceptable safety profile and are effective in treating certain types of cancer, despite their limited availability. However, further advancements are needed to enhance tumor permeation and improve virus delivery in order to make oncolytic virotherapy more effective.
DRUG DISCOVERY TODAY
(2022)
Article
Chemistry, Medicinal
Clemens Zwergel, Elisabetta Di Bello, Rossella Fioravanti, Mariarosaria Conte, Angela Nebbioso, Roberta Mazzone, Gerald Brosch, Ciro Mercurio, Mario Varasi, Lucia Altucci, Sergio Valente, Antonello Mai
Summary: A series of HDAC inhibitors were synthesized, among which the nicotinic hydroxamate 11d showed the highest inhibitory activity and selectivity, while the nicotinic anilide 12d exhibited the best inhibitory effect on HDAC3. These compounds showed significant anti-proliferative activity in leukemia cells and other cancer cell lines.
Article
Biochemistry & Molecular Biology
Xin Yan, Deyun Chen, Yao Wang, Yelei Guo, Chuan Tong, Jianshu Wei, Yajing Zhang, Zhiqiang Wu, Weidong Han
Summary: Loss of NOXA, a BCL2 family protein, was identified as a key regulator of resistance to CAR T-cell therapy. Low NOXA expression was associated with worse survival in patients with B-cell lymphoma. In vitro and in vivo experiments showed that enhancing NOXA expression using HDAC inhibitors dramatically increased sensitivity of cancer cells to CAR T-cell clearance.
SIGNAL TRANSDUCTION AND TARGETED THERAPY
(2022)
Article
Pharmacology & Pharmacy
Lingjie Bao, Zhe Wang, Zhenxing Wu, Hao Luo, Jiahui Yu, Yu Kang, Dongsheng Cao, Tingjun Hou
Summary: In this study, a model called AMGU was developed to predict the inhibitory activities of small molecules against various kinases. The AMGU model outperformed other models on both internal and external test sets, demonstrating its enhanced generalizability. Additionally, a method called edges masking was devised to explain the predictive mechanisms, and a web server called KIP was developed for predicting the polypharmacology effects of small molecules on the kinome.
ACTA PHARMACEUTICA SINICA B
(2023)
Article
Biochemical Research Methods
Lei Wang, Shao-Hua Shi, Hui Li, Xiang-Xiang Zeng, Su-You Liu, Zhao-Qian Liu, Ya-Feng Deng, Ai-Ping Lu, Ting-Jun Hou, Dong-Sheng Cao
Summary: Machine learning-based scoring functions (MLSFs) have gained popularity due to their potential superior screening performance compared to classical scoring functions. However, little is known about the information of negative data used in constructing MLSFs, and existing databases often contain biased putative inactive molecules. In this study, we propose an easy-to-use method called AMLSF that combines active learning and MLSF to improve the quality of inactive sets and reduce false positive rate. Our results demonstrate that AMLSF outperforms the control models in terms of identifying active molecules and reducing false positives.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Kai-Yue Ji, Chong Liu, Zhao-Qian Liu, Ya-Feng Deng, Ting-Jun Hou, Dong-Sheng Cao
Summary: Identification of potential targets for known bioactive compounds and novel synthetic analogs is crucial. In silico target fishing has emerged as an alternative strategy due to the challenges of wet-lab experiments and the rapid growth of bioactivity data. This study evaluated nine popular ligand-based target fishing methods and found that SwissTargetPrediction produced the most reliable predictions with a larger target pool. Additionally, the high-recall similarity ensemble approach showed promise in identifying real targets for more compounds. The study also proposed a novel ensemble method based on consensus voting for improved prediction performance.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Dong Wang, Zhenxing Wu, Chao Shen, Lingjie Bao, Hao Luo, Zhe Wang, Hucheng Yao, De-Xin Kong, Cheng Luo, Tingjun Hou
Summary: This study explores the utility and effectiveness of evidential uncertainty in compound screening. The results demonstrate that evidential uncertainties help reduce false positives and improve experimental validation rates. The study highlights the importance of understanding the uncertainty in model predictions.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Lingling Wang, Lei Xu, Zhe Wang, Tingjun Hou, Haiping Hao, Huiyong Sun
Summary: Understanding the mechanism of drug selectivity is crucial for designing drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) is challenging due to their conserved binding pockets. By analyzing a representative CDK12 inhibitor from both thermal dynamics and kinetics perspectives, we propose to use kinetic residue energy analysis (KREA) and community network analysis (CNA) to reveal the cooperation between individual residues and protein motifs in the drug-target recognition process. The general mechanism of drug selectivity in CDKs is determined by the difference in structural cooperation between the ligand and protein motifs, resulting in different energetic contributions from key residues. These proposed mechanisms may be prevalent in drug selectivity and could aid in the design of strategies to overcome or attenuate selectivity issues.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Shukai Gu, Chao Shen, Jiahui Yu, Hong Zhao, Huanxiang Liu, Liwei Liu, Rong Sheng, Lei Xu, Zhe Wang, Tingjun Hou, Yu Kang
Summary: This study evaluated the impact of structural dynamic information on binding affinity prediction and found that the optimized molecular dynamics protocol improved the predictive performance for the TAF1-BD2 target with high structural flexibility, but not for the less flexible JAK1 and DDR1 targets.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Chemistry, Medicinal
Teng-Zhi Long, Shao-Hua Shi, Shao Liu, Ai-Ping Lu, Zhao-Qian Liu, Min Li, Ting-Jun Hou, Dong-Sheng Cao
Summary: This study constructed a high-quality dataset and established a series of classification models using machine learning algorithms to predict hematotoxicity. The best model based on Attentive FP showed excellent performance on both the validation and test sets. Additionally, the study utilized SHAP and atom heatmap methods to identify important features and structural fragments related to hematotoxicity, and employed MMPA and representative substructure derivation technique to further investigate the transformation principles and distinctive structural features of hematotoxic chemicals.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Jintu Zhang, Haotian Zhang, Zhixin Qin, Yu Kang, Xin Hong, Tingjun Hou
Summary: Direct trajectory calculations are gaining popularity in computational chemistry, but their high computational cost limits their application in mechanistic explorations. Machine learning-based potential energy surface (ML-PES) offers a powerful strategy to overcome this limitation while maintaining accuracy. This study demonstrates that localized sampling of configuration space combined with quasiclassical trajectory (QCT) calculations can efficiently obtain locally accurate ML-PESs. The approach is proven with two model reactions, showing the ML-PESs' ability to reproduce static and dynamic features.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Yuwei Yang, Chang-Yu Hsieh, Yu Kang, Tingjun Hou, Huanxiang Liu, Xiaojun Yao
Summary: A deep generation model, known as a novel drug design and discovery tool, has advantages in producing compounds with novel backbones and has been successfully used in drug discovery. However, generating molecules with desired properties, especially high activity, remains a challenge. To address this, a conditional molecular generation model (COMG) was proposed, which considered the docking score and 3D pharmacophore matching during molecular generation. The model was based on the conditional variational autoencoder architecture, constrained by the pharmacophore matching score. The evaluation of COMG showed that it improves the structural diversity of generated molecules and effectively increases the proportion of target-related drug-active molecules, indicating its usefulness as a drug design tool.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Ziyi Yang, Shaohua Shi, Li Fu, Aiping Lu, Tingjun Hou, Dongsheng Cao
Summary: Matched molecular pair analysis (MMPA) is a tool that extracts medicinal chemistry knowledge from experimental data by identifying relationships between changes in activities or properties and specific structural changes. It has been recently applied in multi-objective optimization and de novo drug design. This Perspective discusses the concept, techniques, and case studies of MMPA, providing an overview of its current development and highlighting successes and opportunities for further advances.
JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Chemistry, Multidisciplinary
Rongfan Tang, Zhe Wang, Sutong Xiang, Lingling Wang, Yang Yu, Qinghua Wang, Qirui Deng, Tingjun Hou, Huiyong Sun
Summary: Proteolysis-targeting chimeras (PROTACs) selectively degrade target proteins and are an attractive technology in drug discovery. In this study, the kinetic mechanism of PROTAC MZ1 targeting the bromodomain (BD) of BET protein and von Hippel-Lindau E3 ligase (VHL) was characterized and analyzed using simulations and free energy calculations. The results showed that MZ1 prefers to bind with E3 ligase in the formation of the target-PROTAC-E3 ligase ternary complex. The binding characteristics revealed in this study may accelerate the rational design of PROTACs with higher degradation efficiency.
Article
Biochemical Research Methods
Xujun Zhang, Chao Shen, Tianyue Wang, Yafeng Deng, Yu Kang, Dan Li, Tingjun Hou, Peichen Pan
Summary: Cracking the code of protein-ligand interaction is crucial for drug design and discovery. The ML-based PLI capturer (ML-PLIC) is a web platform that automatically characterizes PLI and generates machine learning-based scoring functions to identify potential binders. It outperforms traditional docking tools and performs competitively with deep learning-based methods. ML-PLIC integrates physical and biological knowledge to design a structure-based virtual screening pipeline.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Chemistry, Medicinal
Haiyi Chen, Yue Guo, Shengqing Ye, Jintu Zhang, Haotian Zhang, Na Liu, Rui Zhou, Tingjun Hou, Hongguang Xia, Yu Kang, Mojie Duan
Summary: Fatty acids are vital for energy sources and have diverse physiological functions. This study used molecular dynamics simulations to investigate the mechanism of binding and dissociation between heart fatty acid binding proteins and stearic acid. The results reveal two primary pathways for the interaction, improving the understanding of the transport mechanism and therapeutic development.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Multidisciplinary
Chao Shen, Xujun Zhang, Chang-Yu Hsieh, Yafeng Deng, Dong Wang, Lei Xu, Jian Wu, Dan Li, Yu Kang, Tingjun Hou, Peichen Pan
Summary: Applying machine learning algorithms to protein-ligand scoring functions has gained attention due to its high predictive accuracy and affordable computational cost. However, most machine learning-based scoring functions are limited to specific tasks, making it challenging to develop a scoring function with balanced performance across critical tasks. In this study, we propose a novel parameterization strategy that introduces an adjustable binding affinity term into the training of a mixture density network. The resulting scoring framework achieves superior docking and screening power, as well as remarkable improvements in scoring and ranking performance. Our study highlights the potential utility of this innovative parameterization strategy and scoring framework in future structure-based drug design.
Article
Chemistry, Medicinal
Sutong Xiang, Zhe Wang, Rongfan Tang, Lingling Wang, Qinghua Wang, Yang Yu, Qirui Deng, Tingjun Hou, Haiping Hao, Huiyong Sun
Summary: This study investigates the binding and dissociation process of nuclear receptor drugs and reveals the main dissociation pathways of FXR ligands using computational methods. Furthermore, key residues involved in the dissociation pathways are identified through kinetic residue energy analysis. The results suggest that RAMD simulations are suitable for large-scale exploration of binding pathways in ligands with obscure binding tunnels to the target.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
