AMP-activated protein kinase (AMPK) activators have garnered significant attention for their potential to prevent the progression of metabolic dysfunction-associated steatotic liver disease (MASLD) into liver fibrosis and to fundamentally improve liver function. The broad spectrum of pathways regulated by AMPK activators makes them promising alternatives to conventional liver replacement therapies and the limited pharmacological treatments currently available. In this study, we aim to illustrate the newly detailed multiple mechanisms of MASLD progression based on the multiple-hit hypothesis. This model posits that impaired lipid metabolism, combined with insulin resistance and metabolic imbalance, initiates inflammatory cascades, gut dysbiosis, and the accumulation of toxic metabolites, ultimately promoting fibrosis and accelerating MASLD progression to irreversible hepatocellular carcinoma (HCC). AMPK plays a multifaceted protective role against these pathological conditions by regulating several key downstream signaling pathways. It regulates biological effectors critical to metabolic and inflammatory responses, such as SIRT1, Nrf2, mTOR, and TGF-β, through complex and interrelated mechanisms. Due to these intricate connections, AMPK's role is pivotal in managing metabolic and inflammatory disorders. In this review, we demonstrate the specific roles of AMPK and its related pathways. Several agents directly activate AMPK by binding as agonists, while some others indirectly activate AMPK by modulating upstream molecules, including adiponectin, LKB1, and the AMP: ATP ratio. As AMPK activators can target each stage of MASLD progression, the development of AMPK activators offers immense potential to expand therapeutic strategies for liver diseases such as MASH, MASLD, and liver fibrosis.
Abstract Hypoxia-inducible factor-1α (HIF-1α) mediates tumor cell adaptation to hypoxic conditions and is a potentially important anticancer therapeutic target. We previously developed a method for synthesizing a benzofuran-based natural product, (R)-(-)-moracin-O, and obtained a novel potent analog, MO-460 that suppresses the accumulation of HIF-1α in Hep3B cells. However, the molecular target and underlying mechanism of action of MO-460 remained unclear. In the current study, we identified heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) as a molecular target of MO-460. MO-460 inhibits the initiation of HIF-1α translation by binding to the C-terminal glycine-rich domain of hnRNPA2B1 and inhibiting its subsequent binding to the 3’-untranslated region of HIF-1α mRNA. Moreover, MO-460 suppresses HIF-1α protein synthesis under hypoxic conditions and induces the accumulation of stress granules. The data provided here suggest that hnRNPA2B1 serves as a crucial molecular target in hypoxia-induced tumor survival and thus offer an avenue for the development of novel anticancer therapies.
Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO$_3$. Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.
Molecular glue degraders, such as lenalidomide and pomalidomide, bind to cereblon (CRBN) E3 ligase and subsequently recruit neosubstrate proteins, Ikaros (IKZF1) and Aiolos (IKZF3), for the ubiquitination-proteasomal degradation process. In this study, we explored structure-activity relationship analysis for novel GSPT1 degraders utilizing a benzotriazinone scaffold previously discovered as a novel CRBN binder. In particular, we focused on the position of the ureido group on the benzotriazinone scaffold, substituent effect on the phenylureido group, and methyl substitution on the benzylic position of benzotriazinone. As a result, we identified 34f (TD-522), which exhibits strong anti-proliferative effects in both KG-1 (EC50 = 0.5 nM) and TMD-8 (EC50 = 5.2 nM) cell lines. Compound 34f effectively induced GSPT1 degradation with a DC50 of 0.269 nM and Dmax of >95 % at 10 nM concentration in KG-1 cells. An in vivo xenograft study showed that compound 34f effectively suppressed TMD8-driven tumor growth, suggesting a potential role in the development of novel GSPT1 degraders.
Increased investments and development of new technologies in drug discovery have barely improved the outcome of medicinal entities in the drug discovery market from a long time. Minimal success rates of drug approvals, poor safety profiles, and long development processes are some of many hurdles encountered in the drug discovery field. Therefore, drug repurposing can provide an alternative approach to meet the demands of the new, potent and safe anti-cancer agents in terms of both economic cost and time efficiency. The common molecular pathways of different diseases and secondary indications of most of the approved drugs, and advances in genomics, informatics and biology, as well as the availability of approved or safe drug libraries can certainly provide an improved and efficient way of screening safer drugs for new indications. Promising results of drug repurposing in different therapeutic areas have encouraged the scientific community to discover new drugs for different diseases using this methodology. Herein, we provide a general overview of structurally and functionally diverse approved drugs that have been repurposed as anti-cancer drugs.
Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO[Formula: see text]. Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.
We developed a hypoxia-inducible factor-1 (HIF-1) inhibitor, IDF-11774, as a clinical candidate for cancer therapy. To understand the mechanism of action of IDF-11774, we attempted to isolate target proteins of IDF-11774 using bioconjugated probes. Multifunctional chemical probes containing sites for click conjugation and photoaffinity labeling were designed and synthesized. After fluorescence and photoaffinity labeling of proteins, two-dimensional electrophoresis (2DE) was performed to isolate specific molecular targets of IDF-11774. Heat shock protein (HSP) 70 was identified as a target protein of IDF-11774. We revealed that IDF-11774 inhibited HSP70 chaperone activity by binding to its allosteric pocket, rather than the ATP-binding site in its nucleotide-binding domain (NBD). Moreover, IDF-11774 reduced the oxygen consumption rate (OCR) and ATP production, thereby increasing intracellular oxygen tension. This result suggests that the inhibition of HSP70 chaperone activity by IDF-11774 suppresses HIF-1α refolding and stimulates HIF-1α degradation. Taken together, these findings indicate that IDF-11774-derived chemical probes successfully identified IDF-11774's target molecule, HSP70, and elucidated the mode of action of IDF-11774 in inhibiting HSP70 chaperone activity and stimulating HIF-1α degradation in cancer cells.
Abstract Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO 3 . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.
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