芋螺毒素的药用价值研发进展

芋螺毒素是一种海洋软体动物芋螺分泌的一类用于自卫和捕食的小肽神经性毒素。芋螺毒素具有很高的药用开发价值和潜力。近年来,具有高度特异性生物活性的芋螺毒素一直广泛应用于研制特异性诊断试剂以及开发疗效特异的新药之中,并作为分子模型用于相关新药的设计。本文对近年来芋螺毒素药用开发研究的最新进展做一综述。     

Design,synthesis, and biological evaluation of novel ubiquitin-activating enzyme inhibitors

Ubiquitin-activating enzyme (E1), which catalyzes the activation of ubiquitin in the initial step of the ubiquitination cascade, is a potential therapeutic target in multiple myeloma and breast cancer treatment. However, only a few E1 inhibitors have been reported to date. Moreover, there has been little medicinal chemistry research on the three-dimensional structure of E1. Therefore, in the present study, we attempted to identify novel E1 inhibitors using structure-based drug design. Following the rational design, synthesis, and in vitro biological evaluation of several such compounds, we identified a reversible E1 inhibitor (4b). Compound 4b increased p53 levels in MCF-7 breast cancer cells and inhibited their growth. These findings suggest that reversible E1 inhibitors are potential anticancer agents.     

Antibody-drug conjugates: recent advances in conjugation and linker chemistries

The antibody-drug conjugate (ADC), a humanized or human monoclonal antibody conjugated with highly cytotoxic small molecules (payloads) through chemical linkers, is a novel therapeutic format and has great potential to make a paradigm shift in cancer chemotherapy. Thisnewantibody-based molecular platform enables selective delivery of a potent cytotoxic payload to target cancer cells, resulting in improved efficacy, reduced systemic toxicity, and preferable pharmacokinetics (PK)/ pharmacodynamics (PD) and biodistribution compared to traditional chemotherapy. Boosted by the successes of FDA-approved Adcetris® and Kadcyla®, this drug class has been rapidly growing along with about 60 ADCs currently in clinical trials. In this article, we briefly review molecular aspects of each component (the antibody, payload, and linker) of ADCs, and then mainly discuss traditional and new technologies of the conjugation and linker chemistries for successful construction of clinically effective ADCs. Current efforts in the conjugation and linker chemistries will provide greater insights into molecular design and strategies for clinically effective ADCs from medicinal chemistry and pharmacology standpoints. The development of site-specific conjugation methodologies for constructing homogeneousADCs is an especially promising path to improving ADC design, which will open the way for novel cancer therapeutics.     

Can we rationally design promiscuous drugs?

Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.     

Determination of the binding mode and interacting amino-acids for dibasic H3 receptor antagonists

Due to its involvement in major CNS functions, the histamine H3 receptor (H3R) is the subject of intensive medicinal chemistry investigation, supported by the range of modern drug discovery tools, such as receptor modeling and ligand docking. Although the receptor models described to date share a majority of common traits, they display discrete alternatives in amino-acid conformation, rendering ligand binding modes quite different. Such variations impede structure-based drug design in the H3R field. In the present study, we used a combination of medicinal chemistry, receptor-guided and ligand-based methods to elucidate the binding mode of antagonists. The approaches converged towards a ligand orientation perpendicular to the membrane plane, bridging Glu206 of the transmembrane helix 5 to acidic amino acids of the extracellular loops. This consensus will help future structure-based drug design for H3R ligands.     

Signaling protein networks as targets of new antineoplastic drugs

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from "-omic" approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, "intelligent" drug candidates. An additional medicinal chemistry strategy (via 40-50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.     

Illuminator: increasing synergies between medicinal and computational chemists

We present Illuminator, a user-friendly web front end to computational models such as docking and 3D shape similarity calculations. Illuminator was specifically created to allow non-experts to design and submit molecules to computational chemistry programs. As such it provides a simple user interface allowing users to submit jobs starting from a 2D structure. The models provided are pre-optimized by computational chemists for each specific target. We provide an example of how Illuminator was used to prioritize the design of molecular substituents in the Anadys HCV Polymerase (NS5B) project. With 7500 submitted jobs in 1.5 years, Illuminator has allowed project teams at Anadys to accelerate the optimization of novel leads. It has also improved communication between project members and increased demand for computational drug discovery tools.     

Anti-cholinesterase hybrids as multi-target-directed ligands against Alzheimer’s disease (1998–2018)

Alzheimer’s disease (AD) is a genetically complex, progressive and irreversible neurodegenerative disorder of the brain which involves multiple associated etiological targets. The complex pathogenesis of AD gave rise to multi-target-directed ligands (MTDLs) principle to combat this dreaded disease. Within this approach, the design and synthesis of hybrids prevailed greatly because of their capability to simultaneously target the intertwined pathogenesis components of the disease. The hybrids include pharmacophoric hybridization of two or more established chemical scaffolds endowed with the desired pharmacological properties into a single moiety. In AD, the primary foundation of medication therapy and drug design strategies includes the inhibition of cholinesterase (ChE) enzymes. Hence the development of ChE inhibition based hybrids is the central choice of AD medicinal chemistry research. To illustrate the progress of ChE inhibition based hybrids and novel targets, we reviewed the medicinal chemistry and pharmacological properties of the multi-target molecules published since 1998-December 2018. We hope that this article will allow the readers to easily follow the evolution of this prominent medicinal chemistry approach to develop a more efficient inhibitor.     

Systematic analysis to identify novel disease indications and plausible potential chemical leads of glutamate ionotropic receptor NMDA type subunit 1, GRIN1

Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein-protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta-analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge-based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia.     

New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening

Using ligand and receptor based virtual screening approaches we have identified potential virtual screening hits targeting type II dehydroquinase from Mycobacterium tuberculosis, an effective and validated anti-mycobacterial target. Initially, we applied a virtual screening workflow based on a combination of 2D structural fingerprints, 3D pharmacophore and molecular docking to identify compounds that rigidly match specific aspects of ligand bioactive conformation. Subsequently, the resulting compounds were ranked and prioritized using receptor interaction fingerprint based scoring and quantitative structure activity relationship model developed using already known actives. The virtual screening hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance binding affinity. Finally, identified hits may be useful to a medicinal chemist or combinatorial chemist to pick up the new molecular starting points for medicinal chemistry optimization for the design of novel type II dehydroquinase inhibitors.     

Molecular imaging with copper-64

Molecular imaging is expected to change the face of drug discovery and development. The ability to link imaging to biology for guiding therapy should improve the rate at which novel imaging technologies, probes, contrast agents, drugs and drug delivery systems can be transferred into clinical practice. Nuclear medicine imaging, in particular, positron emission tomography (PET) allows the detection and monitoring of a variety of biological and pathophysiological processes, at tracer quantities of the radiolabelled target agents, and at doses free from pharmacological effects. In the field of drug discovery and development, the use of radiotracers for radiolabelling target agents has now become one of the essential tools in identifying, screening and development of new target agents. In this regard, (64)Cu (t(1/2)=12.7 h) has been identified as an emerging PET isotope. Its half-life is sufficiently long for radiolabelling a range of target agents and its ease of production and adaptable chemistry make it an excellent radioisotope for use in molecular imaging. This review describes recent advances, in the routes of (64)Cu production, design and application of bi-functional ligands for use in radiolabelling with (64/67)Cu(2+), and their significance and anticipated impact on the field of molecular imaging and drug development.     

Structure-based drug design with geometric deep learning

Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented.     

α-Sulfonamidophosphonates as new anti-mycobacterial chemotypes: Design,development of synthetic methodology,and biological evaluation

Tuberculosis (TB) is the leading cause of death worldwide due to bacterial infection. The scarcity of effective drugs to treat the disease and the compounded problems due to the development of resistance to the available therapeutics and TB-HIV synergism drive medicinal chemists to search for new anti-Mtb chemotypes. Towards this endeavor, the α-sulfonamidophosphonate moiety has been identified as new anti-Mtb chemotype through the scaffold hopping as the design strategy, development of an effective synthetic methodology using green chemistry tools, and evaluation of anti-TB activity of the synthesized compounds against Mtb (Mycobacterium tuberculosis) H37Rv. Out of the sixteen compounds, five have been found to have MIC values of 1.56 μg/mL and one 3.125 μg/mL. The five most active compounds are non-cytotoxic to RAW 264.7 (mouse leukemic monocyte macrophage) cell lines. The compounds are found to possess acceptable values of the various parameters for drug likeness in accordance with the Lipinski rule with the topological surface area (tPSA) of >70 that suggest eligibility of these new molecular entities for further consideration as potential drug candidates.     

Crystal structures of DPP-IV (CD26) from rat kidney exhibit flexible accommodation of peptidase-selective inhibitors

Dipeptidyl peptidase IV (DPP-IV) belongs to a family of serine peptidases, and due to its indirect regulatory role in plasma glucose modulation, DPP-IV has become an attractive pharmaceutical target for diabetes therapy. DPP-IV inactivates the glucagon-like peptide (GLP-1) and several other naturally produced bioactive peptides that contain preferentially a proline or alanine residue in the second amino acid sequence position by cleaving the N-terminal dipeptide. To elucidate the details of the active site for structure-based drug design, we crystallized a natural source preparation of DPP-IV isolated from rat kidney and determined its three-dimensional structure using X-ray diffraction techniques. With a high degree of similarity to structures of human DPP-IV, the active site architecture provides important details for the design of inhibitory compounds, and structures of inhibitor-protein complexes offer detailed insight into three-dimensional structure-activity relationships that include a conformational change of Tyr548. Such accommodation is exemplified by the response to chemical substitution on 2-cyanopyrrolidine inhibitors at the 5 position, which conveys inhibitory selectivity for DPP-IV over closely related homologues. A similar conformational change is also observed in the complex with an unrelated synthetic inhibitor containing a xanthine core that is also selective for DPP-IV. These results suggest the conformational flexibility of Tyr548 is unique among protein family members and may be utilized in drug design to achieve peptidase selectivity.     

Synthesis and biological evaluation of Doxorubicin-containing conjugate targeting PSMA

Prostate-specific membrane antigen (PSMA), also known as glutamate carboxypeptidase II (GCPII), has recently emerged as a prominent biomarker of prostate cancer (PC) and as an attractive protein trap for drug targeting. At the present time, several drugs and molecular diagnostic tools conjugated with selective PSMA ligands are actively evaluated in different preclinical and clinical trials. In the current work, we discuss design, synthesis and a preliminary biological evaluation of PSMA-specific small-molecule carrier equipped by Doxorubicin (Dox). We have introduced an unstable azo-linker between Dox and the carrier hence the designed compound does release the active substance inside cancer cells thereby providing a relatively high Dox concentration in nuclei and a relevant cytotoxic effect. In contrast, we have also synthesized a similar conjugate with a stable amide linker and it did not release the drug at all. This compound was predominantly accumulated in cytoplasm and did not cause cell death. Preliminary in vivo evaluation has showed good efficiency for the degradable conjugate against PC3-PIP(PSMA⁺)-containing xenograft mine. Thus, we have demonstrated that the conjugate can be used as a template to design novel analogues with improved targeting, anticancer activity and lower rate of potential side effects. 3D molecular docking study has also been performed to elucidate the underlying mechanism of binding and to further optimization of the linker area for improving the target affinity.     

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.     

Drug Guru: a computer software program for drug design using medicinal chemistry rules

Drug Guru (drug generation using rules) is a new web-based computer software program for medicinal chemists that applies a set of transformations, that is, rules, to an input structure. The transformations correspond to medicinal chemistry design rules-of-thumb taken from the historical lore of drug discovery programs. The output of the program is a list of target analogs that can be evaluated for possible future synthesis. A discussion of the features of the program is followed by an example of the software applied to sildenafil (Viagra) in generating ideas for target analogs for phosphodiesterase inhibition. Comparison with other computer-assisted drug design software is given.     

1,2,5-Trisubstituted 1,4-diazepine-3-one: A novel dipeptidomimetic molecular scaffold

Summary The continuing effort to transform bioactive peptides into non-peptide peptidomimetics of therapeutic potential requires a diversity of tools such as molecular scaffolds, pseudopeptide modifications, and conformation mimetics. To this end, a novel polyfunctional monoheterocyclic system, 1,2,5-trisubstituted hexahydro-3-oxo-1H-1,4-diazepine ring (DAP), was designed. The linear precursor for the DAP was generated through a reductive alkylation step including a modified side chain and an α-amino function of two amino acid derivatives. Structural analysis of model diastereomeric DAPs, employing¹H and¹³C NMR and computer simulation, revealed the conformational preferences of this system. The structural similarities to the 1,4-benzodiazepine, a common molecular scaffold for many non-peptidic peptidomimetic agents, and the pronounced dipeptidomimetic character of the DAP system offer a new powerful tool to medicinal chemists engaged in rational peptide-based drug design.