Drug Discovery Today最新文献

Eukaryotic elongation factor 2 kinase (eEF2K) is a member of the α-kinase family that is activated by calcium/calmodulin. Of note, eEF2K is crucial for regulating translation and is often highly overexpressed in malignant cells. Therefore in this review, we summarize the molecular structure of eEF2K and its oncogenic roles in cancer. Moreover, we further discuss the inhibition of eEF2K with small-molecule inhibitors and other new emerging therapeutic strategies in cancer therapy. Taken together, these inspiring findings provide new insights into a promising strategy for inhibiting eEF2K to greatly improve future cancer therapy.

The identification and analysis of network motifs has been widely used in the functional analysis of signaling components, disease discovery and other fields. The positive feedback loop (PFL) is a simple but important network motif. The formation of a PFL is regulated by mechanical cues such as substrate stiffness, fiber stretching and cell compression in the cell microenvironment. Here, we propose a new term, ‘mechanical PFL’, and analyze the mechanisms of mechanical PFLs at molecular, subcellular and cellular scales. More and more therapies are being targeted against mechanosignaling pathways at the experimental and preclinical stages, and exploring mechanical PFLs as potential mechanomedicine targets could be a new direction for disease treatment.

Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology, and uncover novel targets for precision medicine. The mission of the Target 2035 initiative is to discover chemical tools for all human proteins by the year 2035. Here, we draw a landscape of the current chemical coverage of human biological pathways. Although available chemical tools target only 3% of the human proteome, they already cover 53% of human biological pathways and represent a versatile toolkit to dissect a vast portion of human biology. Pathways targeted by existing drugs may be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.

Identification of high-quality hit chemical matter is of vital importance to the success of drug discovery campaigns. However, this goal is becoming ever harder to achieve as the targets entering the portfolios of pharmaceutical and biotechnology companies are increasingly trending towards novel and traditionally challenging to drug. This demand has fuelled the development and adoption of numerous new screening approaches, whereby the contemporary hit identification toolbox comprises a growing number of orthogonal and complementary technologies including high-throughput screening, fragment-based ligand design, affinity screening (affinity-selection mass spectrometry, differential scanning fluorimetry, DNA-encoded library screening), as well as increasingly sophisticated computational predictive approaches. Herein we describe how an integrated strategy for hit discovery, whereby multiple hit identification techniques are tactically applied, selected in the context of target suitability and resource priority, represents an optimal and often essential approach to maximise the likelihood of identifying quality starting points from which to develop the next generation of medicines.

Currently, various antimitotic inhibitors applied in tumor therapy. However, these inhibitors exhibit targeted toxicity to some extent. As a motor protein, kinesin family member 18A (KIF18A) is crucial to spindle formation and is associated with tumors exhibiting ploidy-specific characteristics such as chromosomal aneuploidy, whole-genome doubling (WGD), and chromosomal instability (CIN). Differing from traditional antimitotic targets, KIF18A exhibits tumor-specific selectivity. The functional loss or attenuation of KIF18A results in vulnerability of tumor cells with ploidy-specific characteristics, with lesser effects on diploid cells. Research on inhibitors targeting KIF18A with ploidy-specific lethality holds significant importance. This review provides a brief overview of the regulatory mechanisms of the ploidy-specific lethality target KIF18A and the research advancements in its inhibitors, aiming to facilitate the development of KIF18A inhibitors.

Orthosteric and allosteric modulators, which constitute the majority of current drugs, bind to the orthosteric and allosteric sites of target proteins, respectively. However, the clinical efficacy of these agents is frequently compromised by poor selectivity or reduced potency. Dualsteric modulators feature two linked pharmacophores that bind to orthosteric and allosteric sites of the target proteins simultaneously, thereby offering a promising avenue to achieve both potency and specificity. In this review, we summarize recent structures available for dualsteric modulators in complex with their target proteins, elucidating detailed drug–target interactions and dualsteric action patterns. Moreover, we provide a design and optimization strategy for dualsteric modulators based on structure-based drug design approaches.

Glioblastoma multiforme (GBM) is a highly severe primary brain tumor. Despite extensive research, effective treatments remain elusive. Long noncoding RNAs (lncRNAs) play a significant role in both cancer and normal biology. They influence alternative splicing (AS), which is crucial in cancer. Advances in lncRNA-specific microarrays and next-generation sequencing have enhanced understanding of AS. Abnormal AS contributes to cancer invasion, metastasis, apoptosis, therapeutic resistance, and tumor development, including glioma. lncRNA-mediated AS affects several cellular signaling pathways, promoting or suppressing cancer malignancy. This review discusses the lncRNAs regulating AS in glioblastoma and their mechanisms.

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients’ clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent years have witnessed an explosion of powerful machine learning (ML) and natural language processing (NLP) models that can streamline the patient accrual process. In this PRISMA-based scoping review, we comprehensively evaluate existing literature on the application of ML/NLP models for parsing clinical trial eligibility criteria. The review covers 9160 papers published between 2000 and 2024, with 88 publications subjected to data charting along 17 dimensions. Our review indicates insufficient use of state-of-the-art artificial intelligence (AI) models in the analysis of clinical protocols.

Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.