Drug Discovery Strategies Research Articles

Discovery of a potent, orally available furopyridine derivative as a novel selective bromodomain and extra-terminal domain (BET)-first bromodomain (BD1) inhibitor

We explored novel immunosuppressive agents with immune tolerance using a phenotypic drug discovery strategy, focusing on costimulatory molecules in T cells, and obtained triazolothienodiazepine derivatives. Their mechanism of action is to inhibit the bromodomain and extra-terminal domain (BET) family, as we have previously reported. Selective inhibition of the first bromodomain (BD1) of the BET family is expected to exert antitumor and immunosuppressive effects, similar to BET inhibitors.This study identified furopyridine derivatives 7 and 8 with high BD1 inhibitory activity and high selectivity over BD2. Compound 7 was found to be orally bioavailable and exhibited anti-inflammatory activity in a lipopolysaccharide-induced model.

Natural Products Revolutionizing and Innovative Drug Discovery and Development Strategies: Healthcare Challenges and Future Perspectives

Natural Products Revolutionizing and Innovative Drug Discovery and Development Strategies: Healthcare Challenges and Future Perspectives

Remapping the Chemical Space and the Pharmacological Space of Drugs: What Can We Expect from the Road Ahead?

This work examines the current landscape of drug discovery and development, with a particular focus on the chemical and pharmacological spaces. It emphasizes the importance of understanding these spaces to anticipate future trends in drug discovery. The use of cheminformatics and data analysis enabled in silico exploration of these spaces, allowing a perspective of drugs, approved drugs after 2020, and clinical candidates, which were extracted from the newly released ChEMBL34 (March 2024). This perspective on chemical and pharmacological spaces enables the identification of trends and areas to be occupied, thereby creating opportunities for more effective and targeted drug discovery and development strategies in the future.

A small-molecule degron with a phenylpropionic acid scaffold

Targeted protein degradation (TPD), employing proteolysis-targeting chimeras (PROTACs) composed of ligands for both a target protein and ubiquitin ligase (E3) to redirect the ubiquitin–proteasome system (UPS) to the target protein, has emerged as a promising strategy in drug discovery. However, despite the vast number of E3 ligases, the repertoire of E3 ligands utilized in PROTACs remains limited. Here, we report the discovery of a small-molecule degron with a phenylpropionic acid skeleton, derived from a known ligand of S-phase kinase-interacting protein 2 (Skp2), an E3 ligase. We used this degron to design PROTACs inducing proteasomal degradation of HaloTag-fused proteins, and identified key structural relationships. Surprisingly, our mechanistic studies excluded the involvement of Skp2, suggesting that this degron recruits other protein(s) within the UPS.

Applications of Surface Plasmon Resonance (SPR) to the Study of Diverse Protein-Ligand Interactions.

Functional characterization of enzymes/proteins requires determination of the binding affinity of small molecules or other biomolecules with the target proteins. Several available techniques, such as proteomics and drug discovery strategies, require a precise and high-throughput assay for rapid and reliable screening of potential candidates for further testing. Surface plasmon resonance (SPR), a well-established label-free technique, directly measures biomolecular affinities. SPR assays require immobilization of one interacting component (ligand) on a conductive metal (mostly gold or silver) and a continuous flow of solution containing potential binding partner (analyte) across the surface. The SPR phenomenon occurs when polarized light excites the electrons at the interface of the metal and the dielectric medium to generate electromagnetic waves that propagate parallel to the surface. Changes in the refractive index due to interaction between the ligand and analyte are measured by detecting the reflected light, providing real-time data on kinetics and specificity. A prominent use of SPR is identifying compounds in crude plant extracts that bind to specific molecules. Procedures that utilize SPR are becoming increasingly applicable outside the laboratory setting, and SPR imaging and localized SPR (LSPR) are cheaper and more portable alternative for in situ detection of plant or mammalian pathogens and drug discovery studies. LSPR, in particular, has the advantage of direct attachment to test tissues in live-plant studies. Here, we describe three protocols utilizing SPR-based assays for precise analysis of protein-ligand interactions. © 2024 Wiley Periodicals LLC. Basic Protocol 1: SPR comparison of binding affinities of viral reverse transcriptase polymorphisms Basic Protocol 2: SPR screening of crude plant extract for protein-binding agents Basic Protocol 3: Localized SPR-based antigen detection using antibody-conjugated gold nanoparticles.

Application of 3Rs in Caenorhabditis elegans Research for the Identification of Health-Promoting Natural Products.

The average age of the population is increasing worldwide, which has a profound impact on our society. This leads to an increasing demand for medicines and requires the development of new strategies to promote health during the additional years. In the search for resources and therapeutics for improved health during an extended life span, attention has to be paid to environmental exposure and ecosystem burdens that inevitably emerge with the extended consumption of medicines and drug development, even in the preclinical stage. The hereby introduced sustainable strategy for drug discovery is built on 3Rs, "R: obustness, R: eliability, and saving R: esources", inspired by both the 3Rs used in animal experiments and environmental protection, and centers on the usefulness and the variety of the small model organism Caenorhabditis elegans for detecting health-promoting natural products. A workflow encompassing a multilevel screening approach is presented to maximize the amount of information on health-promoting samples, while considering the 3Rs. A detailed, methodology- and praxis-oriented compilation and discussion of proposed C. elegans health span assays and more disease-specific assays are presented to offer guidance for scientists intending to work with C. elegans, thus facilitating the initial steps towards the integration of C. elegans assays in their laboratories.

PROteolysis‐Targeting Chimeras (PROTACs) in leukemia: overview and future perspectives

AbstractLeukemia is a heterogeneous group of life‐threatening malignant disorders of the hematopoietic system. Immunotherapy, radiotherapy, stem cell transplantation, targeted therapy, and chemotherapy are among the approved leukemia treatments. Unfortunately, therapeutic resistance, side effects, relapses, and long‐term sequelae occur in a significant proportion of patients and severely compromise the treatment efficacy. The development of novel approaches to improve outcomes is therefore an unmet need. Recently, novel leukemia drug discovery strategies, including targeted protein degradation, have shown potential to advance the field of personalized medicine for leukemia patients. Specifically, PROteolysis‐TArgeting Chimeras (PROTACs) are revolutionary compounds that allow the selective degradation of a protein by the ubiquitin–proteasome system. Developed against a wide range of cancer targets, they show promising potential in overcoming many of the drawbacks associated with conventional therapies. Following the exponential growth of antileukemic PROTACs, this article reviews PROTAC‐mediated degradation of leukemia‐associated targets. Chemical structures, in vitro and in vivo activities, pharmacokinetics, pharmacodynamics, and clinical trials of PROTACs are critically discussed. Furthermore, advantages, challenges, and future perspectives of PROTACs in leukemia are covered, in order to understand the potential that these novel compounds may have as future drugs for leukemia treatment.

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

Computational Strategies in Drug Discovery: Leveraging Subtractive Genomic Analysis for Target Identification.

The utilization of In-silico subtractive genomic analysis has emerged as an important and essential method in modern drug discovery and development since it significantly improves the process of identifying and validating potential targets for discovering novel therapeutic compounds to treat severe infections caused by Antimicrobial-resistant (AMR) - pathogenic species. This review provides a complete overview of the methodology, advantages, disadvantages, and prospects, associated with subtractive genomic research in the context of drug discovery and development. The initial phase of analysis encompasses the retrieval of data, which serves as a foundation for the subsequent data mining process in Phase 1. After data mining, Phase 2 utilizes subtractive channels for the target's non-homology and essentiality analysis. Phase 3 of the study aims to provide a comprehensive understanding of prospective targets by their qualitative characterization. Further, Phase 4 of the study emphasizes on conducting structure-based analyses, which involves the determination, refinement, evaluation, and validation of three-dimensional structures of the target proteins, along with their active site prediction and selection of the novel therapeutic compounds against that active site on the obtained targets through virtual screening and docking studies by utilizing various databases and servers. The therapeutic compounds obtained can be then validated by in vitro and in vivo testing, thereby establishing a connection between the computational predictions and real applications.

Development of Peptide Displacement Assays to Screen for Antagonists of DDB1 Interactions.

The DNA damage binding protein 1 (DDB1) is an essential component of protein complexes involved in DNA damage repair and the ubiquitin-proteasome system (UPS) for protein degradation. As an adaptor protein specific to Cullin-RING E3 ligases, DDB1 binds different receptors that poise protein substrates for ubiquitination and subsequent degradation by the 26S proteasome. Examples of DDB1-binding protein receptors are Cereblon (CRBN) and the WD-repeat containing DDB1- and CUL4-associated factors (DCAFs). Cognate substrates of CRBN and DCAFs are involved in cancer-related cellular processes or are mimicked by viruses to reprogram E3 ligases for the ubiquitination of antiviral host factors. Thus, disrupting interactions of DDB1 with receptor proteins might be an effective strategy for anticancer and antiviral drug discovery. Here, we developed fluorescence polarization (FP)-based peptide displacement assays that utilize full-length DDB1 and fluorescein isothiocyanate (FITC)-labeled peptide probes derived from the specific binding motifs of DDB1 interactors. A general FP-based assay condition applicable to diverse peptide probes was determined and optimized. Mutagenesis and biophysical analyses were then employed to identify the most suitable peptide probe. The FITC-DCAF15 L49A peptide binds DDB1 with a dissociation constant of 68 nM and can be displaced competitively by unlabeled peptides at sub-μM to low nM concentrations. These peptide displacement assays can be used to screen small molecule libraries to identify novel modulators that could specifically antagonize DDB1 interactions toward development of antiviral and cancer therapeutics.

FragGrow: A Web Server for Structure-Based Drug Design by Fragment Growing within Constraints.

Fragment growing is an important ligand design strategy in drug discovery. In this study, we present FragGrow, a web server that facilitates structure-based drug design by fragment growing. FragGrow offers two working modes: one for growing molecules through the direct replacement of hydrogen atoms or substructures and the other for growing via virtual synthesis. FragGrow works by searching for suitable fragments that meet a set of constraints from an indexed 3D fragment database and using them to create new compounds in 3D space. The users can set a range of constraints when searching for their desired fragment, including the fragment's ability to interact with specific protein sites; its size, topology, and physicochemical properties; and the presence of particular heteroatoms and functional groups within the fragment. We hope that FragGrow will serve as a useful tool for medicinal chemists in ligand design. The FragGrow server is freely available to researchers and can be accessed at https://fraggrow.xundrug.cn.

Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study.

Visceral Leishmaniasis (VL), the second neglected tropical disease caused by various Leishmania species, presents a significant public health challenge due to limited treatment options and the absence of vaccines. The agent responsible for visceral leishmaniasis, also referred to as "black fever" in India, is Leishmania donovani. This study focuses on L. donovani Minichromosome maintenance 10 (LdMcm10), a crucial protein in the DNA replication machinery, as a potential therapeutic target in Leishmania therapy using in silico and in vitro approaches. We employed bioinformatics tools, molecular docking, and molecular dynamics simulations to predict potential inhibitors against the target protein. The research revealed that the target protein lacks homologues in the host, emphasizing its potential as a drug target. Ligands from the DrugBank database were screened against LdMcm10 using PyRx software. The top three compounds, namely suramin, vapreotide, and pasireotide, exhibiting the best docking scores, underwent further investigation through molecular dynamic simulation and in vitro analysis. The observed structural dynamics suggested that LdMcm10-ligand complexes maintained consistent binding throughout the 300ns simulation period, with minimal variations in their backbone. These findings suggest that these three compounds hold promise as potential lead compounds for developing new drugs against leishmaniasis. In vitro experiments also demonstrated a dose-dependent reduction in L. donovani viability for suramin, vapreotide, and pasireotide, with computed IC50 values providing quantitative metrics of their anti-leishmanial efficacy. The research offers a comprehensive understanding of LdMcm10 as a drug target and provides a foundation for further investigations and clinical exploration, ultimately advancing drug discovery strategies for leishmaniasis treatment.

Accelerating PROTACs Discovery Through a Direct-to-Biology Platform Enabled by Modular Photoclick Chemistry.

Proteolysis targeting chimeras (PROTACs) have emerged as a promising strategy for drug discovery and exploring protein functions, offering a revolutionary therapeutic modality. Currently, the predominant approach to PROTACs discovery mainly relies on an empirical design-synthesis-evaluation process involving numerous cycles of labor-intensive synthesis-purification and bioassay data collection. Therefore, the development of innovative methods to expedite PROTAC synthesis and exploration of chemical space remains highly desired. Here, a direct-to-biology strategy is reported to streamline the synthesis of PROTAC libraries on plates, enabling the seamless transfer of reaction products to cell-based bioassays without the need for additional purification. By integrating amide coupling and light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) photoclick chemistry into a plate-based synthetic process, this strategy produces PROTAC libraries with high efficiency and structural diversity. Moreover, by employing this platform for PROTACs screening, wesmoothly found potent PROTACseffectively inhibit triple-negative breast cancer (TNBC) cell growth and induce rapid, selective targeted degradation of cyclin-dependent kinase 9 (CDK9). The study introduces a versatile platform for assembling PROTACs on plates, followed by direct biological evaluation. This approach provides a promising opportunity for high-throughput synthesis of PROTAC libraries, thereby enhancing the efficiency of exploring chemical space and accelerating the discovery of PROTACs.

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500–700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Straightforward access to C2-formyl indoles using an oxidative combination of N-chlorosuccinimide and dimethyl sulfoxide

C2-formyl indoles are important building blocks for the organic synthesis of alkaloids and small molecules with broad biological activities. Generally, its preparation involves both direct formylation of indoles at the activated C2-position with as unstable carbonyl sources and oxidative cleavage of tedious prepared tetrahydro-β-carbolines (THβCs) in the harsh conditions. In continuation of our research on the oxidative functionalization of indoles, in this letter, we report that straightforward effectively oxidation of easily available 2-methylindoles bearing C3-heteroatom functional group proceeds regioselectively C2-formyl indoles in good and excellent yields employing an oxidative combination of NCS/DMSO. This methodology presents a reasonably broad substrate scope and excellent functional group tolerance, thus enabling the preparation of highly versatile building blocks susceptible to further functionalization. An additional feature of this approach includes high efficiency, ease of operation, facile purification, and short reaction time, making it a promising strategy for organic synthesis and drug discovery.

Design, Synthesis, Formulation, and Bioevaluation of Trisubstituted Triazines as Highly Selective mTOR Inhibitors for the Treatment of Human Breast Cancer.

The aberrant activation of the PI3K/mTOR signaling pathway is implicated in various human cancers. Thus, the development of inhibitors targeting mTOR has attracted considerable attention. In this study, we used a structure-based drug design strategy to discover a highly potent and kinase-selective mTOR inhibitor 24 (PT-88), which demonstrated an mTOR inhibitory IC50 value of 1.2 nM without obvious inhibition against another 195 kinases from the kinase profiling screening. PT-88 displayed selective inhibition against MCF-7 cells (IC50: 0.74 μM) with high biosafety against normal cells, in which autophagy induced by mTOR inhibition was implicated. After successful encapsulation in a lipodisc formulation, PT-88 demonstrated favorable pharmacokinetic and biosafety profiles and exerted a large antitumor effect in an MCF-7 subcutaneous bearing nude mice model. Our study shows the discovery of a highly selective mTOR inhibitor using a structure-based drug discovery strategy and provides a promising antitumor candidate for future study and development.

The Prediction of LptA and LptC Protein-Protein Interactions and Virtual Screening for Potential Inhibitors.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.

1,2,3-Triazole Hybrids Containing Isatins and Phenolic Moieties: Regioselective Synthesis and Molecular Docking Studies.

The synthesis of hybrid molecules is one of the current strategies of drug discovery for the development of new lead compounds. The 1,2,3-triazole moiety represents an important building block in Medicinal Chemistry, extensively present in recent years. In this paper, we presented the design and the synthesis of new 1,2,3-triazole hybrids, containing both an isatine and a phenolic core. Firstly, the non-commercial azide and the alkyne synthons were prepared by different isatines and phenolic acids, respectively. Then, the highly regioselective synthesis of 1,4-disubstituted triazoles was obtained in excellent yields by a click chemistry approach, catalyzed by Cu(I). Finally, a molecular docking study was performed on the hybrid library, finding four different therapeutic targets. Among them, the most promising results were obtained on 5-lipoxygenase, an enzyme involved in the inflammatory processes.

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery

BackgroundColorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.MethodsThis study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds.ResultsThese two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model.ConclusionsThis study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.