MedChemComm最新文献

The elaboration of anti-breast cancer agents targeting EGFR represents a promising strategy in medicinal chemistry. Consequently, under optimized Cu(I)-catalyzed click synthesis, a new library of 1,4-disubstituted 1,2,3-triazole-based benzo[d]thiazole scaffold carrying benzamide and/or benzoate tethers 5a–t was designed, synthesized, and characterized by appropriate spectral techniques. They were also screened for their in vitro anti-cancer activity against a panel of cancer cell lines, breast (T47D), prostate (PC3), lung (A549), and colon (HCT116) human cancer, along with normal fibroblast cells. Notably, the hybrid triazoles, 5p, 5s, and 5t emerged as the most potent candidates, especially against T47D, with IC₅₀ values of 15, 26, and 28 μM, respectively. Compound 5p significantly induced apoptosis in T47D by 27.3-fold, causing total apoptosis of 19.39% compared to 0.71%, arresting cell proliferation at the G₂/M phase. Regarding EGFR as the molecular target, among the tested compounds, 5p significantly inhibited EGFR by 96.8%, with an IC₅₀ value of 65.6 nM, compared to erlotinib, having an IC₅₀ value of 84.1 nM. Compound 5p showed promising PI3K/AKT/mTOR inhibition as the EGFR-dependent signaling pathway with IC₅₀ values of 4.98 μM, 0.21 μM, and 0.49 nM, respectively, compared to their reference inhibitors. Finally, a molecular docking study highlighted the binding mode disposition and binding interactions with key amino acids as a promising EGFR inhibitor.

Biology is reliant on molecular proximity in order to generate the networks and control systems on which it is reliant. The ability to induce or reinforce proximity of macromolecules with exogenous small molecules has emerged as an exciting new potential drug mode of action. This opinion piece looks at some of the background and recent progress, with a particular focus on the alternative opportunities beyond degradation for inducing novel pharmacology.

Cancer remains a significant global health concern, with breast cancer ranking among the leading causes of cancer-related mortality in women. In pursuit of multi-targeted anticancer agents, we designed and synthesized a novel series of 7-hydroxy azacoumarin–α-cyanocinnamate hybrids and evaluated their therapeutic potential through comprehensive in vitro and in vivo studies. Structural characterization was confirmed using NMR, IR, and elemental analysis. Among the synthesized compounds, compound 7 exhibited the most potent cytotoxic activity against MCF-7 cells (IC₅₀ = 7.65 μM) and MDA-MB-231 (IC₅₀ = 9.7 ± 1.15 μM), with notable selectivity over non-tumorigenic MCF-10A cells (IC₅₀ = 52.02 μM), as compared to the reference drug doxorubicin. Mechanistic in vitro investigations revealed that compound 7 induced G₂/M phase arrest and apoptosis, accompanied by upregulation of pro-apoptotic markers (Bax, p53) and suppression of Bcl-2. Additionally, compound 7 significantly inhibited tubulin polymerization and demonstrated marked antioxidant activity in the FRAP assay (IC₅₀ = 144.71 μM), as well as selective COX-2 inhibition (IC₅₀ = 1.264 μM, SI = 5.93). In vivo evaluation using the Ehrlich ascites carcinoma (EAC) model confirmed its anticancer efficacy, with 85.92% reduction in viable EAC cells and substantial tumor volume suppression at 10 mg kg⁻¹. Notably, compound 7 mitigated EAC-induced hepatorenal toxicity by restoring liver and kidney biomarkers and reducing oxidative stress and lipid peroxidation. Furthermore, it significantly downregulated pro-inflammatory (TNF-α) and angiogenic (VEGFR-II) markers while preserving normal tissue histoarchitecture. Collectively, these findings highlight compound 7 as a promising multi-functional lead candidate with cytotoxic, antioxidant, anti-inflammatory, and anti-angiogenic activities, meriting further development in cancer therapeutics.

Plasmodium falciparum (Pf) is the most prevalent cause of malaria infections in humans. Due to the development of resistant strains, newer drugs, or drugs acting at novel targets are constantly being sought. Here, we report the design and preparation of 48 new 2,4-diaminopyrimidine derivatives targeting the Pf protein kinome. Bioinformatics methods have been used to identify the most probable target(s). Cheminformatics and molecular modelling have been used to guide the structural modifications. Our primary goal was to enhance the antimalarial activity of the series, reduce mammalian cytotoxicity, and increase aqueous solubility. The antimalarial activity of all 48 compounds has been assessed in chloroquine-resistant Pf3D7 strain and for their mammalian cytotoxicity in HepG2 cell lines. Phosphate buffer solubility, MDCK permeability, and metabolic clearance in human and rat microsomes were also assessed. Compounds 68 and 69 demonstrated good antimalarial activity (Pf IC₅₀) of 0.05 and 0.06 μM, respectively, and good selectivity over the mammalian cell line (SI >100 fold). The compounds also demonstrated much improved aqueous solubilities of 989.7 and 1573 μg mL⁻¹, respectively, along with moderate intrinsic clearance (∼3 mL min⁻¹ g⁻¹) and permeability (>60 nm s⁻¹).

Cell-penetrating peptides (CPPs) facilitate the intracellular delivery of cargo molecules. However, they can induce cytotoxicity when conjugated with certain bioactive peptides. We incorporated a branched polyethylene glycol unit to effectively minimize their nonspecific toxicity while preserving their essential biological functions, such as the inhibition of the p53/MDM2 interaction.

The emergence of multidrug-resistant pathogens, particularly Pseudomonas aeruginosa, represents a global health concern. Among its major virulence factors, elastase B (LasB), a zinc-dependent metalloprotease, plays a pivotal role in host tissue degradation, immune evasion, and biofilm formation. Targeting LasB with selective inhibitors offers a promising therapeutic strategy to mitigate bacterial virulence while minimizing selective pressure for resistance development. In this study, a series of N-benzyloxy amino acid derivatives were designed, synthesized, and evaluated for their inhibitory activity against LasB. Structure-based optimization led to the identification of compound 12 as the most potent inhibitor (K_i = 0.92 μM), exhibiting high selectivity for LasB over human matrix metalloproteinases. Cell-based assays demonstrated its ability to inhibit LasB proteolytic activity and reduce biofilm formation without affecting bacterial viability. These findings highlight the potential of LasB inhibitors as pathoblockers, providing a targeted approach to disarm bacterial virulence rather than exerting bactericidal pressure.

Despite the pharmacological relevance of the histamine H₁ receptor (H₁R), the second most therapeutically targeted G protein-coupled receptor (GPCR), an effective photoswitchable ligand to optically control this receptor remains elusive. In this work, we aimed to identify a suitable photoswitchable H₁R ligand by performing an ‘azoscan’ on the H₁R antagonist desloratadine. Taking advantage of the synthetic toolbox available for the desloratadine scaffold, aniline groups were regioselectively installed on the aromatic positions of this scaffold to enable the synthesis of azobenzene analogs targeting the orthosteric binding pocket of H₁R. Additionally, we functionalized the piperidine ring of desloratadine with azobenzene moieties. These two strategies resulted in a total of nine photoswitchable compounds, displaying efficient trans to cis isomerization (PSS_cis > 87%) and a broad range of thermal relaxation half-lives. Pharmacological evaluation revealed the 2-position (10a) to be most suitable for accommodation of a photoswitchable group, as it exhibits the most balanced profile in absolute affinity (K_itrans = 2 nM) and a 3.2-fold light-induced affinity shift. Computational docking studies provide a rationale, with the binding pose of the trans and cis isomer in the H₁R binding pocket potentially being inverted. While the development of effective photoswitchable ligands for H₁R remains challenging, this study provides promising opportunities for future optimization to achieve optical control of this GPCR.

Bicyclic peptides have emerged as promising inhibitors due to their high binding affinity and selectivity for target receptors. While peptide inhibitors are highly target-specific and exhibit strong protein-binding capabilities, their potential is often limited by challenges such as proteolytic instability and flexible secondary structures, which can reduce their efficacy and bioavailability. This study focuses on designing and synthesizing bicyclic peptides and their molecular dynamics insights using scaffolds like 1,3,5-tris(bromomethyl)benzene (TBMB) and 1,3,5-triacryloylhexahydro-1,3,5-triazine (TATA) to enhance their stability and efficacy. The inhibitory activity of these peptides was assessed by targeting the main protease (Mpro), a key enzyme in viral replication of SARS-CoV-2. Mass spectrometry confirmed the purity of these peptides, and their inhibitory activity was evaluated using fluorescence resonance energy transfer (FRET) and selected ion monitoring (SIM)-based LC-MS assays. Computational modeling and molecular dynamics (MD) simulations revealed the structural basis of peptide–Mpro interactions, highlighting improved conformational stability and binding mechanisms. Bicyclic peptides demonstrated superior inhibition compared to linear analogs, with constraints significantly improving peptide stability and binding properties. Our findings highlight the potential of bicyclic peptides as a robust platform for developing next-generation therapeutics with enhanced pharmacokinetic and pharmacodynamic profiles.

Parallel syntheses and their throughput capabilities are powerful tools for the rapid generation of molecule libraries, making them highly beneficial for accelerating hit identification in early-stage drug discovery. Utilizing chemical spaces and virtual libraries enhances time and cost efficiency, enabling the faster exploitation of chemically diverse compounds. In this study, a parallel synthesis method for rapidly generating a 5′-amino-5′-deoxy adenosine-based amide and sulfonamide library of 42 compounds is described with high yields and purity, which is economical and ecological due to the reduced requirements for extensive purification. Methyltransferases recently emerged as promising drug targets. The adenosine-derived library was screened using a fluorescence polarization (FP) assay against model enzymes human DNMT2 and METTL3/14, and SARS-CoV-2 nsp14/10, resulting in the identification of three compounds binding with nanomolar affinity to nsp14/10 and three compounds binding METTL3/14 with low micromolar affinity. To demonstrate the accessibility of a broad variety of adenosine derivatives, a focused virtual chemical space of 25 241 5′-amino-5′-deoxy adenosine amides and sulfonamides, which are accessible via the described synthetic procedure, was generated. This chemical space was further investigated for potential biological applications through virtual screening against nsp14/10 which led to the identification of four additional ligands with low micromolar affinities.

Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors—such as imatinib and osimertinib—has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative solutions to these obstacles across the drug discovery pipeline. This review explores how AI/ML methods, including deep learning, graph neural networks, and generative models, are revolutionizing the design, optimization, and repurposing of kinase inhibitors. We detail applications in target identification, virtual screening, structure–activity relationship modeling, resistance prediction, and clinical trial design. Representative case studies—such as AI-optimized BTK and EGFR inhibitors—highlight real-world impact. We also examine current limitations, including data sparsity, model interpretability, and translational gaps between in silico and experimental results. Finally, we discuss emerging directions such as federated learning, personalized kinase inhibitors, and AI-enabled combination therapies. By integrating computational innovation with medicinal chemistry, AI/ML holds immense promise to accelerate and refine the next generation of kinase-targeted therapeutics.