Novel 6-amino-1,3,5-triazine derivatives as potent BTK inhibitors: structure-activity relationship (SAR) analysis and preliminary mechanism investigation.

Bruton's tyrosine kinase (BTK) is a promising drug target for the treatment of B-cell related malignancies. Irreversible inhibition of BTK by a covalent inhibitor has been proved to be a clinically effective therapy. However, most irreversible BTK inhibitors also inhibit other kinases including JAK3 and EGFR, leading to some adverse events. Herein, we reported the structure-based design and optimization of a series of irreversible BTK inhibitors bearing the 6-amino-1,3,5-triazine scaffold. Most of the synthesized compounds demonstrated considerable BTK inhibition and improved anti-proliferative activity against Raji and Ramos cells. Among them, compound C11 exhibited potent BTK inhibition (BTK IC50 = 17.0 nM) and a desirable selectivity profile especially over EGFR. Moreover, C11 effectively blocked activation of BTK and downstream signaling, arrested the cell cycle in G0/G1 phase and induced apoptosis in Raji cells. Its irreversible binding mode was further investigated by both molecular modeling and a washout experiment. Collectively, C11 is a novel selective irreversible BTK inhibitor worthy of further in-depth research.

X-ray crystallography study and optimization of novel benzothiophene analogs as potent selective estrogen receptor covalent antagonists (SERCAs) with improved potency and safety profiles.

Endocrine therapy (ET) is a well-validated strategy for estrogen receptor α positive (ERα + ) breast cancer therapy. Despite the clinical success of current standard of care (SoC), endocrine-resistance inevitably emerges and remains a significant medical challenge. Herein, we describe the structural optimization and evaluation of a new series of selective estrogen receptor covalent antagonists (SERCAs) based on benzothiophene scaffold. Among them, compounds 15b and 39d were identified as two highly potent covalent antagonists, which exhibits superior antiproliferation activity than positive controls against MCF-7 cells and shows high selectivity over ERα negative (ERα-) cells. More importantly, their mode of covalent engagement at Cys530 residue was accurately illustrated by a cocrystal structure of 15b-bound ERαY537S (PDB ID: 7WNV) and intact mass spectrometry, respectively. Further in vivo studies demonstrated potent antitumor activity in MCF-7 xenograft mouse model and an improved safety profile. Collectively, these compounds could be promising candidates for future development of the next generation SERCAs for endocrine-resistant ERα + breast cancer.

Structural optimization of tetrahydroisoquinoline-hydroxamate hybrids as potent dual ERα degraders and HDAC inhibitors.

Both estrogen receptor α (ERα) and histone deacetylases (HDACs) are valid therapeutic targets for anticancer drug development. Combination therapies using diverse ERα antagonists or degraders and HDAC inhibitors have been proven effective in endocrine-resistant ER + breast cancers based on the crosstalk between ERα and HDAC pathway. In this study, we reported the optimization of a series of methoxyphenyl- or pyridinyl- substituted tetrahydroisoquinoline-hydroxamates, which were optimized from 31, a dual ERα degrader/HDAC inhibitor previously reported by our group. Most of the synthesized compounds displayed potent ERα degradation efficacy and antiproliferative activity. Among them, A04 demonstrated the best anti-proliferation activity (MCF-7 IC50 = 1.96 µM) and HDAC6 inhibitory activity (HDAC6 IC50 = 25.96 nM), which is slightly more potent than the lead compound 31 (MCF-7 IC50 = 4.38 µM, HDAC6 IC50 = 63.03 nM). In addition, compound A04 exerted ERα-independent HDAC6-inhibiting effect without agonistic activity in endometrial cells. These results demonstrated that A04 is a novel and promising dual ERα degrader/HDAC inhibitor worthy of further development.

Dual-target inhibitors based on ERα: Novel therapeutic approaches for endocrine resistant breast cancer.

Estrogen receptor alpha (ERα), a nuclear transcription factor, is a well-validated therapeutic target for more than 70% of all breast cancers (BCs). Antagonizing ERα either by selective estrogen receptor modulators (SERMs) or selective estrogen receptor degraders (SERDs) forms the foundation of endocrine therapy and has achieved great success in the treatment of ERα positive (ERα+) BCs. Unfortunately, despite initial effectiveness, endocrine resistance eventually emerges in up to 30% of ERα+ BC patients and remains a significant medical challenge. Several mechanisms implicated in endocrine resistance have been extensively studied, including aberrantly activated growth factor receptors and downstream signaling pathways. Hence, the crosstalk between ERα and another oncogenic signaling has led to surge of interest to develop combination therapies and dual-target single agents. This review briefly introduces the synergisms between ERα and another anticancer target and summarizes the recent advances of ERα-based dual-targeting inhibitors from a medicinal chemistry perspective. Accordingly, their rational design strategies, structure-activity relationships (SARs) and biological activities are also dissected to provide some perspectives on future directions for ERα-based dual target drug discovery in BC therapy.

Discovery and Proof of Concept of Potent Dual Polθ/PARP Inhibitors for Efficient Treatment of Homologous Recombination-Deficient Tumors.

DNA polymerase theta (Polθ) has recently emerged as a new attractive synthetic lethal target involved in DNA damage repair. Inactivating Polθ alone or in combination with PARP inhibitors has demonstrated substantial therapeutic potential against tumors with homologous recombination (HR) defects such as alternation of BRCA genes. Herein, we report the design and proof of concept of a highly potent dual Polθ/PARP inhibitor 25d, which exhibited low nanomolar inhibitory activities against both Polθ and PARP1. Compared to combination treatment, 25d demonstrated superior antitumor efficacy in both MDA-MB-436 cells and xenografts by inducing more DNA damage and apoptosis. Importantly, 25d retained sensitivity in PARP inhibitor-resistant MDA-MB-436 cells with 53BP1 defect. Altogether, these findings illustrate the potential advantages of 25d, a first-in-class dual Polθ/PARP inhibitor, over monotherapy in treating HR-deficient tumors, including those with acquired PARP inhibitor resistance.

Model-Free Adaptive Control for Unknown MIMO Nonaffine Nonlinear Discrete-Time Systems With Experimental Validation.

In this article, a model-free adaptive control (MFAC) algorithm based on full form dynamic linearization (FFDL) data model is presented for a class of unknown multi-input multi-output (MIMO) nonaffine nonlinear discrete-time learning systems. A virtual equivalent data model in the input-output sense to the considered plant is established first by using the FFDL technology. Then, using the obtained data model, a data-driven MFAC algorithm is designed merely using the inputs and outputs data of the closed-loop learning system. The theoretical analysis of the monotonic convergence of the tracking error dynamics, the bounded-input bounded-output (BIBO) stability, and the internal stability of the closed-loop learning system is rigorously proved by the contraction mapping principle. The effectiveness of the proposed control algorithm is verified by a simulation and a quad-rotor aircraft experimental system.

Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault.

A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.