Strategy For Discovery Research Articles

Total Synthesis of Antiausterity Agent Callistrilone O Reveals Promising Antitumor Activity in a Melanoma Homograft Mouse Model.

The antiausterity strategy in anticancer drug discovery has attracted much attention as a way to exterminate cancer cells under nutrient deprived conditions which are commonly found in solid tumors. These tumors under low nutrient stress are known to be malignant and often resist conventional drug therapy. As a potential drug candidate, we focused on the meroterpenoid natural product callistrilone O which has demonstrated extremely potent antiausterity properties toward PANC-1 pancreatic carcinoma in vitro. Here, we report for the first time the total synthesis of callistrilone O in seven steps from phloroglucinol. A Friedel-Crafts-type Michael addition and an oxidative [3 + 2] cycloaddition with Fetizon's reagent were used to construct the molecular skeleton. The preferential cytotoxicity of callistrilone O was also evaluated with multiple starvation-resistant cancer cell lines under low nutrient conditions. Furthermore, callistrilone O was found to strongly suppress B16 melanoma tumor growth without critical toxicity in vivo. Overall, this study presents a novel anticancer agent candidate from natural products with a concise synthetic route which can be readily applied to the synthesis of derivatives.

Applications of innovative synthetic strategies in anticancer drug discovery: The driving force of new chemical reactions.

The discovery of novel anticancer agents remains a critical goal in medicinal chemistry, with innovative synthetic methodologies playing a pivotal role in advancing this field. Recent breakthroughs in CH activation reactions, cyclization reactions, multicomponent reactions, cross-coupling reactions, and photo- and electro-catalytic reactions have enabled the efficient synthesis of new molecular scaffolds exhibiting potent biological activities, including anticancer properties. These methodologies have facilitated the functionalization of natural products, the modification of bioactive molecules, and the generation of entirely new compounds, many of which demonstrate strong antitumor activity. This review summarizes the latest synthetic strategies employed over the past five years for discovering anticancer agents, focusing on their influence on drug design. Additionally, the role of new chemical reactions in expanding chemical space and overcoming challenges, such as drug resistance and selectivity, is highlighted, further emphasizing the importance of discovering novel reactions as a key trend in future drug development.

Seizing the opportunity to therapeutically address neuronal hyperexcitability in Alzheimer's disease.

Seizures in people with Alzheimer's disease are increasingly recognized to worsen disease burden and accelerate functional decline. Harnessing established antiseizure medicine discovery strategies in rodents with Alzheimer's disease associated risk genes represents a novel way to uncover disease modifying treatments that may benefit these Alzheimer's disease patients. This commentary discusses the recent evaluation by Dejakaisaya and colleagues to assess the antiseizure and disease-modifying potential of the repurposed cephalosporin antibiotic, ceftriaxone, in the Tg2576 mouse model. The use of established epilepsy models in Alzheimer's disease research carries the potential to advance novel disease-modifying treatments.

Repurposing of Metabolic Drugs Metformin and Simvastatin as an Emerging Class of Cancer Therapeutics.

Metabolic alterations are commonly associated with various cancers and are recognized as contributing factors to cancer progression, invasion, and metastasis. Drug repurposing, a strategy in drug discovery, utilizes existing knowledge to recommend established drugs for new indications based on clinical data or biological evidence. This approach is considered a less risky alternative to traditional drug development. Metformin, a biguanide, is a product of Galega officinalis (French lilac) primarily prescribed for managing type 2 diabetes, is recognized for its ability to reduce hepatic glucose production and enhance insulin sensitivity, particularly in peripheral tissues such as muscle. It also improves glucose uptake and utilization while decreasing intestinal glucose absorption. Statins, first isolated from the fungus Penicillium citrinum is another class of medication mainly used to lower cholesterol levels in individuals at risk for cardiovascular diseases, work by inhibiting the enzyme 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, which is essential for cholesterol biosynthesis in the liver. Metformin is frequently used in conjunction with statins to investigate their potential synergistic effects. Combination of metformin and simvastatin has gathered much attention in cancer research because of its potential advantages for cancer prevention and treatment. In this review, we analyze the effects of metformin and simvastatin, both individually and in combination, on key cancer hallmarks, and how this combination affects the expression of biomolecules and associated signaling pathways. We also summarize preclinical research, including clinical trials, on the efficacy, safety, and potential applications of repurposing metformin and simvastatin for cancer therapy.

Targeting cryptic allosteric sites of G protein-coupled receptors as a novel strategy for biased drug discovery.

G protein-coupled receptors (GPCRs) represent the largest family of membrane receptors and are highly effective targets for therapeutic drugs. GPCRs couple different downstream effectors, including G proteins (such as Gi/o, Gs, G12, and Gq) and β-arrestins (such as β-arrestin 1 and β-arrestin 2) to mediate diverse cellular and physiological responses. Biased signaling allows for the specific activation of certain pathways from the full range of receptors' signaling capabilities. Targeting more variable allosteric sites, which are spatially different from the highly conserved orthosteric sites, represents a novel approach in biased GPCR drug discovery, leading to innovative strategies for targeting GPCRs. Notably, the emergence of cryptic allosteric sites on GPCRs has expanded the repertoire of available drug targets and improved receptor subtype selectivity. Here, we conduct a summary of recent progress in the structural determination of cryptic allosteric sites on GPCRs and elucidate the biased signaling mechanisms induced by allosteric modulators. Additionally, we discuss means to identify cryptic allosteric sites and design biased allosteric modulators based on cryptic allosteric sites through structure-based drug design, which is an advanced pharmacotherapeutic approach for treating GPCR-associated diseases.

Overview of the epigenetic/cytotoxic dual-target inhibitors for cancer therapy.

Epigenetic dysregulation plays a pivotal role in the initiation and progression of various cancers, influencing critical processes such as tumor growth, invasion, migration, survival, apoptosis, and angiogenesis. Consequently, targeting epigenetic pathways has emerged as a promising strategy for anticancer drug discovery in recent years. However, the clinical efficacy of epigenetic inhibitors, such as HDAC inhibitors, has been limited, often accompanied by resistance. To overcome these challenges, innovative therapeutic approaches are required, including the combination of epigenetic inhibitors with cytotoxic agents or the design of dual-acting inhibitors that target both epigenetic and cytotoxic pathways. In this review, we provide a comprehensive overview of the structures, biological functions and inhibitors of epigenetic regulators (such as HDAC, LSD1, PARP, and BET) and cytotoxic targets (including tubulin and topoisomerase). Furthermore, we discuss recent advancement of combination therapies and dual-target inhibitors that target both epigenetic and cytotoxic pathways, with a particular focus on recent advances, including rational drug design, pharmacodynamics, pharmacokinetics, and clinical applications.

A metabologenomics strategy for rapid discovery of polyketides derived from modular polyketide synthases.

Bioinformatics-guided metabolomics is a powerful means for the discovery of novel natural products. However, the application of such metabologenomics approaches on microbial polyketides, a prominent class of natural products with diverse bioactivities, remains largely hindered due to our limited understanding on the mass spectrometry behaviors of these metabolites. Here, we present a metabologenomics approach for the targeted discovery of polyketides biosynthesized by modular type I polyketide synthases. We developed the NegMDF workflow, which uses mass defect filtering (MDF) supported by bioinformatic structural prediction, to connect the biosynthetic gene clusters to corresponding metabolite ions obtained under negative ionization mode. The efficiency of the NegMDF workflow is illustrated by rapid characterization of 22 polyketides synthesized by three gene clusters from a well-characterized strain Streptomyces cattleya NRRL 8057, including cattleyatetronates, new members of polyketides containing a rare tetronate moiety. Our results showcase the effectiveness of the MDF-based metabologenomics workflow for analyzing microbial natural products, and will accelerate the genome mining of microbial polyketides.

Efficient synthesis of dihydronaphthalenes via cerium-catalyzed annulation of 1-alkoxy substituted 1H-isochromenes with cinnamic acids.

Dihydronaphthalenes play a crucial role in bioactive natural products and new drug discovery, and efficient and economic strategies to build them are needed. Herein, we disclose a highly efficient method to prepare dihydronaphthalenes via a cerium-catalyzed cycloaddition of 1H-isochromenes with cinnamic acids. This newly developed method not only features a broad and low-cost substrate scope and mild conditions but also exhibits very high functional group tolerance, including hydroxyl, borate ester and ester group substituents. In addition, the practicality of this general strategy is demonstrated by gram-scale reactions and derivatizations, and all of the reactions proceed smoothly to afford the desired compounds in excellent yields.

The Impact of the Guided Discovery Strategy on Developing Creative Thinking among Sports Students

The Impact of the Guided Discovery Strategy on Developing Creative Thinking among Sports Students

Uncovering the Mechanism of Action of Antiprotozoal Agents: A Survey on Photoaffinity Labeling Strategy

Plasmodium, Leishmania, and Trypanosoma parasites are responsible for infectious diseases threatening millions of people worldwide. Despite more recent efforts devoted to the search for new antiprotozoal agents, efficacy, safety, and resistance issues still hinder the development of suited therapeutic options. The lack of robustly validated targets and the complexity of parasite’s diseases have made phenotypic screening a preferential drug discovery strategy for the identification of new chemical entities. However, via this approach, no information on biological target(s) and mechanisms of action of compounds are provided. Among the target deconvolution strategies useful to fill this gap, photoaffinity labeling (PAL) has emerged as one of most suited to enable investigation in a complex cellular environment. More recently, PAL has been exploited to unravel the molecular basis of bioactive compounds’ function in live parasites, allowing elucidation of the mechanism of action of both approved drugs and new chemical entities. Besides highlighting new potential drug targets, PAL can provide valuable information on efficacy and liabilities of small molecules at the molecular level, which could be exploited to greatly facilitate the rational optimization of compounds in terms of potency and safety. In this review, we will report the most recent studies that have leveraged PAL to disclose the biological targets and mechanism of action of phenotypically active compounds targeting kinetoplastid diseases (i.e., human African trypanosomiasis, leishmaniasis, and Chagas disease) and malaria. Moreover, we will comment on potential perspectives that this innovative approach can provide in aiding the discovery and development of new antiprotozoal drugs.

Using UX Testing to Optimize Discoverability of Non-traditional Resources

Objective – The accessibility of non-traditional resources presents ongoing challenges for users and librarians. This study investigates methods for optimizing metadata and the placement of search results to enhance the discoverability of these resources within library systems. Researchers conducted A/B testing to compare two features of Ex Libris Primo: the Resource Recommender and Discovery Import Profiles. The objective was to enhance user access to a broader range of informational assets beyond conventional collections. This study posed the research question: Is inclusion in the results list (Discovery Import Profiles) or are visually appealing advertisement-style cards above results (Resource Recommender) a more effective method for discovery of non-traditional library resources? Methods – Researchers identified four key resource types for testing: librarians, frequently asked questions (FAQs), databases, and research guides. An A/B test was conducted with each resource presented in the Discovery Import Profiles and Resource Recommender formats. Following the A/B test, a combined C test was conducted to validate findings. Results – The ad-style cards achieved higher engagement rates, particularly for databases and FAQs, while research guides performed better when embedded directly in search results. This study highlights the strengths and limitations of each method. Databases and FAQs benefited from the visual prominence of the ad-style cards, while research guides were more discoverable within search results. However, minimal engagement with librarians as a resource type across both methods suggests the need for improved tagging and metadata strategies. Conclusion – Findings underscore the importance of institution-specific research and localized assessments to ensure effective implementation of discovery strategies. This study provides a useful method for libraries aiming to enhance the discoverability of their non-traditional resources, ultimately improving user access and satisfaction.

A Catch-Release Strategy for the Genomics-Driven Discovery of Antiproliferative Furan-Functionalized Peptides.

Furan-functionalized peptides are of significant pharmacological interest due to their pronounced bioactivities and unique potential for orthogonal bioconjugation and derivatization. However, naturally occurring peptides with furyl side chains are exceedingly rare. This study presents a streamlined method to predict and assess the microbial production of peptides incorporating 3-furylalanine (Fua) moieties. The approach integrates genome mining and the reversible, chemoselective tagging of furyl residues, utilizing their unique Diels-Alder reactivity, for mass spectrometry-guided identification of candidate compounds. By employing the rhizonin Fua synthase as a bioinformatic handle and through heterologous reconstitution of Fua biosynthesis, we identified previously unknown Fua biosynthetic pathways in diverse bacterial phyla, including actinomycetes, cyanobacteria, actinobacteria, and g-proteobacteria, suggesting that Fua-containing peptides are remarkably widely distributed. Metabolic profiling by reversible tagging facilitated the detection of native Fua-containing metabolites. The successful adaptation of this method for solid support enabled the direct enrichment of furyl-substituted peptides from complex mixtures. This multi-pronged approach enabled the discovery and characterization of two novel families of Fua cyclopeptides (rubriamides and typhamides) with potent antiproliferative effects against human tumor cells and nematodes. The innovative catch-and-release strategy, in conjunction with genome mining, represents a valuable tool for the discovery of new furan-substituted natural products.

Nrf2-Independent Anti-Inflammatory Effects of Dimethyl Fumarate: Challenges and Prospects in Developing Electrophilic Nrf2 Activators for Neurodegenerative Diseases.

The NF-E2-related factor 2 (Nrf2)-antioxidant response element (ARE) pathway is a potential therapeutic target for central nervous system diseases. This review emphasizes the role of oxidative stress and neuroinflammation in neurodegenerative diseases, highlighting the therapeutic potential of Nrf2 activators such as dimethyl fumarate (DMF). DMF, initially administered for treating psoriasis, has demonstrated efficacy in multiple sclerosis and is metabolized to monomethyl fumarate, which may exert significant therapeutic effects. DMF activates the Nrf2-ARE pathway, and recent studies have indicated that its anti-inflammatory effects occur through Nrf2-independent mechanisms. Electrophilic Nrf2 activators, such as DMF, covalently bind to cysteine residues in proteins and modulate their function. We discuss the implications of cysteine residue modifications by DMF, which may cause both therapeutic benefits and potential off-target effects. Furthermore, we propose a chemical proteomics-based drug discovery approach to achieve desired therapeutic effects by selectively covalently modifying cysteines in target proteins. These findings advocate for a broader understanding of the Nrf2-independent mechanisms of electrophilic Nrf2 activators, thereby improving drug discovery strategies that target neurodegenerative diseases while minimizing toxicity.

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles.

Recent advances in the treatment of non-small cell lung cancer with MET inhibitors

Recently, research into the oncogenic driver genes associated with non-small cell lung cancer (NSCLC) has advanced significantly, leading to the development and clinical application of an increasing number of approved therapeutic agents. Among these, small molecule inhibitors that target mesenchymal-epithelial transition (MET) have demonstrated successful application in clinical settings. Currently, three categories of small molecule MET inhibitors, characterized by distinct binding patterns to the MET kinase region, have been developed: types Ia/Ib, II, and III. This review thoroughly examines MET’s structure and its crucial role in NSCLC initiation and progression, explores discovery strategies for MET inhibitors, and discusses advancements in understanding resistance mechanisms. These insights are anticipated to enhance the development of a new generation of MET inhibitors characterized by high efficiency, selectivity, and low toxicity, thereby offering additional therapeutic alternatives for patients diagnosed with NSCLC.

Abstract IA013: Discovery of the dual A2A/A2B receptor antagonist MK-1088 for the treatment of solid tumors

Abstract In the tumor microenvironment (TME), adenosine levels have been shown to be elevated relative to normal tissues. This increase in adenosine levels renders an immunosuppressive effect via direct effects on T cells via agonism of the A2A receptor and indirect effects via agonism of both A2A and A2B receptors on myeloid cells. With these observations as a backdrop, we sought to develop a dual A2A/A2B receptor antagonist with properties that would enable maintenance of high levels of target engagement of the A2A and A2B receptors even at trough concentration. Drawing on our organization’s significant prior experience developing A2A receptor antagonists for the potential treatment of Parkinson’s disease, we developed MK-1088, a highly potent A2A/A2B dual receptor antagonist that was purposefully designed to possess excellent selectivity over the related A1 and A3 receptors. This presentation will detail the discovery and development strategy that was employed to identify molecules that met the profile exemplified by this molecule. A single ascending dose study of MK-1088 in healthy human volunteers demonstrated our ability to achieve, at trough concentration, >99% target engagement (TE) at the A2A receptor and >90% TE at the A2B receptor. Details of the pharmacokinetics, safety, and tolerability from this study will be highlighted. Citation Format: Duane E. DeMong, Sheila Ranganath, Jared Cumming, Matthew Larsen, Yonglian Zhang, Christopher Plummer, Amjad Ali, Anthony Palmieri, Evan Barry, Pierre Daublain, Pranav Gupta, Manash Chatterjee, Jeremy Presland, Sebastian Schneider, Paul Ciaccio, Daniel Tatosian, Aaron Sather, Ben Turnbull, Steven Silverman, Harry Chobanian, Harini Krishnamurthy, Richard Wnek, Stephen Crowley, Alita Miller, Mark Ayers, Marlene Hinton, Jill Chrencik, Sylvie Rottey, Jennifer O'Neil. Discovery of the dual A2A/A2B receptor antagonist MK-1088 for the treatment of solid tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA013.

DVGEDR: a drug repositioning method based on dual-view fusion and graph enhancement mechanism in heterogeneous networks

Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which also will introduces noise into the data and affect the performance of the model. In this paper, first, a novel drug repositioning method, namely DVGEDR, is proposed, which generates two subgraphs of the target drug–disease pair to fuse biological information and integrate drug–disease associations from two distinct perspectives: drug–disease heterogeneous network and similarity networks. Next, a Multiple Attention Graph encoder (MAGencoder) module is designed to learn subgraph features and explore relationships between entities, which also improve the interpretability of the model. Finally, a graph enhancement mechanism is devised to improve the perception of critical information of model, enabling the model to flexibly process different graph structures. Performance comparisons with baseline models on three public datasets validate the state-of-the-art performance of DVGEDR in the field of drug repositioning. In case study, DVGEDR identifies 10 new candidate drugs for breast cancer and COVID-19, demonstrating not only superior performance in experimental settings but also potential therapeutic advantages in clinical environments. Furthermore, we select two sets of instances and further analyzed the attention distribution of the different nodes in the subgraph to explain the decision process of the model.

Synthesis, Biological Evaluation and in Silico Studies of Novel Urea/Thiourea Derivatives of Lenalidomide.

Designing new compounds from existing chemotherapeutic drugs to enhance inhibitory effects on tumor cells while overcoming multidrug resistance is one of the important strategies for new drug discovery in medicinal chemistry. A new series of urea and thiourea derivatives based on Lenalidomide as potential anticancer agents have been designed and synthesized. In vitro anticancer activity assay against Caki cancer cells and HUVEC endothelial cells revealed that 1-(4-methylphenyl)-3-[2-(2,6-dioxopiperidin-3-yl)-1-oxoisoindolin-4-yl]urea (11) exhibited the highest anticancer activity and selectivity in the series with IC50 values of 9.88 and 179.03 µM, respectively. Among the compounds, 11 showed significant HDAC1 inhibiton of 68.02 ± 2.44% at 10 µM concentration. TGF-β, Bax, Bcl-2 protein levels and scratch assay were analyzed in Caki cells. As a result, compound 11 induced apoptosis in Caki cells. In this study, it has been demonstrated that compound 11 can be a lead compound for further detailed investigation in renal cancer treatment. Through molecular docking studies, it was determined that the most active compound, 11, forms stable interactions with key residues in the enzyme's active site, particularly engaging in hydrogen bonds with GLY149 and coordinating with the zinc ion in the HDAC1 active site. These interactions are crucial for the observed inhibitory activity. Molecular dynamics simulation revealed the binding event of the most active compound with class I histone deacetylase and the stability of the complex in a biological environment.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma.

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

Application of large-scale gene expression profiling in modern research of traditional Chinese medicine

Large-scale gene expression profiling generates or integrates massive data of gene expression under drug induction and employs artificial intelligence algorithms for pattern matching and association analysis. This approach facilitates the identification of complex relationships and functional networks between drugs, genes, and diseases, thereby significantly advancing drug research. Traditional Chinese medicine(TCM), with its characteristic multi-component, multi-target, and multi-pathway mechanisms, poses challenges to conventional methodologies in the comprehensive elucidation of its biological effects. The drug discovery strategy that combines large-scale gene expression profiling with artificial intelligence offers distinct advantages since it does not need the prior knowledge of specific drug targets or mechanisms. This article comprehensively reviews the innovative applications of large-scale gene expression profiling in TCM research as well as the recent advancements in the development of these technologies, the optimization of pattern matching algorithms, and the construction of related databases. In summary, the integration of large-scale gene expression profiling with artificial intelligence provides a powerful hypothesis-generating tool for the modern application and theoretical innovation of TCM.