Targeting the TGF-ß signaling pathway: an updated patent review (2021-present).

INTRODUCTION: The TGF-ß signaling pathway is a complex network that plays a crucial role in regulating essential biological functions and is implicated in the onset and progression of multiple diseases. This review highlights the recent advancements in developing inhibitors targeting the TGF-ß signaling pathway and their potential therapeutic applications in various diseases. AREA COVERED: The review discusses patents on active molecules related to the TGF-ß signaling pathway, focusing on three strategies: TGF-ß activity inhibition, blocking TGF-ß receptor binding, and disruption of the signaling pathway using small molecule inhibitors. Combination therapies and the development of fusion proteins targeting multiple pathways are also explored. The literature search was conducted using the Cortellis Drug Discovery Intelligence database, covering patents from 2021 onwards. EXPERT OPINION: The development of drugs targeting the TGF-ß signaling pathway has made significant progress in recent years. However, addressing challenges such as specificity, systemic toxicity, and patient selection is crucial for their successful clinical application. Targeting the TGF-ß signaling pathway holds promise as a promising approach for the treatment of various diseases.

Design, synthesis and biological evaluation of novel 3C-like protease inhibitors as lead compounds against SARS-CoV-2.

Background: The epidemic caused by SARS-CoV-2 swept the world in 2019. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in viral replication, and its inhibition could inhibit viral replication. Materials & methods: The virtual screen based on receptor-ligand pharmacophore models and molecular docking were conducted to obtain the novel scaffolds of the 3CLpro. The molecular dynamics simulation was also carried out. All compounds were synthesized and evaluated in biochemical assays. Results: The compound C2 could inhibit 3CLpro with a 72% inhibitory rate at 10 µM. The covalent docking showed that C2 could form a covalent bond with the Cys145 in 3CLpro. Conclusion: C2 could be a potent lead compound of 3CLpro inhibitors against SARS-CoV-2.

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).

GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47.

Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 µM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.

Targeting the LPA1 signaling pathway for fibrosis therapy: a patent review (2010-present).

INTRODUCTION: Fibrosis is a disease that damages organs and even causes death. Because of the complicated pathogenesis, the development of drugs for fibrosis is challenging. In the lysophosphatidic acid receptor type 1 (LPA1) signaling pathway, LPA1 and its downstream Rho-associated coiled-coil forming protein kinase (ROCK) are related to the process of fibrosis. Targeting LPA1 signaling pathway is a potential strategy for the treatment of fibrosis. AREA COVERED: This review describes the process of fibrosis mediated by the LPA1 signaling pathway and then summarizes LPA1 antagonist patents reported since 2010 and ROCK inhibitor patents since 2017 according to their scaffolds based on the Cortellis Drug Discovery Intelligence database. Information on LPA1 antagonists entering clinical trials is integrated. EXPERT OPINION: Over the past decade, a large number of antagonists targeting the LPA1 signaling pathway have been patented for fibrosis therapy. A limited number of compounds have entered clinical trials. Different companies and research groups have used different scaffolds when designing compounds for fibrosis therapy. Therefore, LPA1 and ROCK are competitive targets for the development of new therapies for fibrosis to provide a potential treatment method for fibrosis in the future.

Structural analysis and binding sites of inhibitors targeting the CD47/SIRPα interaction in anticancer therapy.

Cluster of differentiation 47 (CD47)/signal regulatory protein alpha (SIRPα) is a negative innate immune checkpoint signaling pathway that restrains immunosurveillance and immune clearance, and thus has aroused wide interest in cancer immunotherapy. Blockade of the CD47/SIRPα signaling pathway shows remarkable antitumor effects in clinical trials. Currently, all inhibitors targeting CD47/SIRPα in clinical trials are biomacromolecules. The poor permeability and undesirable oral bioavailability of biomacromolecules have caused researchers to develop small-molecule CD47/SIRPα pathway inhibitors. This review will summarize the recent advances in CD47/SIRPα interactions, including crystal structures, peptides and small molecule inhibitors. In particular, we have employed computer-aided drug discovery (CADD) approaches to analyze all the published crystal structures and docking results of small molecule inhibitors of CD47/SIRPα, providing insight into the key interaction information to facilitate future development of small molecule CD47/SIRPα inhibitors.

Targeting the TGF-ß signaling pathway for fibrosis therapy: a patent review (2015-2020).

INTRODUCTION: Fibrosis is a serious disease that occurs in many organs, such as kidney, liver and lung. The deterioration of these organs ultimately leads to death. Due to the complex mechanisms of fibrosis, research and development of antifibrotic drugs is difficult. One solution is to focus on core pathways, one of which is the TGF-ß signaling pathway. In virtually every type of fibrosis, TGF-ß signaling is recognized as a critical pathway. AREA COVERED: This review discusses patents on active molecules related to the TGF-ß signaling. Molecules targeting components related to the activation of TGF-ß are introduced. Several strategies preventing signal propagation from active TGF-ß to downstream targets are also introduced, including TGF-ß antibodies, TGF-ß ligand traps, and inhibitors of TGF-ß receptor kinases. Finally, molecules affecting downstream targets in both canonical and noncanonical TGF-ß signaling pathways are described. EXPERT OPINION: Since the approval of pirfenidone, targeting TGF-ß signaling has been anticipated as an effective therapy for fibrosis. The potential of this therapy has been further supported by emerging patents on the TGF-ß signaling. This pathway can be entirely inhibited, from the activation of TGF-ß to downstream signaling. Inhibiting TGF-ß signaling is expected to provide more effective treatments for fibrosis.

Convolutional Neural Network-based Virtual Screening.

Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results. Recently, neural networks, especially convolutional neural networks, have constantly been penetrating drug design and most CNN-based virtual screening methods are superior to traditional docking methods, such as Dock and AutoDock. CNNbased virtual screening is expected to improve the previous model of overreliance on computational chemical screening. Utilizing the powerful learning ability of neural networks provides us with a new method for evaluating compounds. We review the latest progress of CNN-based virtual screening and propose prospects.

NMR-based metabolomic analysis for the effects of Huiyang Shengji extract on rat diabetic skin ulcers.

ETHNOPHARMACOLOGICAL RELEVANCE: Huiyang Shengji formula (HSF) is a compound Chinese herbal medicine prescription, and has long been used for treating chronic non-healing wounds. AIM OF THE STUDY: The purpose of this study was to provide new insight into molecular mechanisms of healing effects of the HSF treatments. MATERIALS AND METHODS: We established a rat diabetic skin ulcer (DSU) model, and assessed healing effects of four HSF treatments on DSUs by calculating wound healing rates and immunohistochemical detection of the expressions of angiogenesis-related factors in the model rats (Mod) relative to normal rats (Nor), including Huiyang extract (HE), Shengji extract (SE), Huiyang Shengji extract (HSE) and HSE associated with acupuncture (Ac-HSE). We then performed NMR-based metabolomic analyses on skin tissues of the Nor, Mod, HSE-treated, Ac-HSE-treated rats to address metabolic mechanisms underlying these effects. RESULTS: These treatments up-regulated expressions of two angiogenesis-related factors VEGF and CD31, and improved efficacy of healing DSUs, in which HSE and Ac-HSE exhibited the most significant effects. Compared with Mod, HSE and Ac-HSE groups shared four characteristic metabolites (lactate, histidine, succinate and acetate) and four significantly altered metabolic pathways with Nor. Both HSE and Ac-HSE treatments could partly reverse the metabolically disordered pathological state of DSUs to the normal state. They might improve wound healing through promoting glucose metabolism, BCAAs metabolism, and enhancing antioxidant capacity and angiogenesis in DSU tissues. Ac-HSE significantly enhanced wound healing rates compared to HSE, potentially owing to significant capacities of enhancing anti-oxidation and angiogenesis and interfering three more metabolic pathways. CONCLUSIONS: This work provides a mechanistic understanding of the healing effects of the HSE and Ac-HSE treatments on DSUs, is of benefit to improvements of the HSF treatments for clinically healing chronic non-healing wounds.

Discovery and evaluation of new compounds targeting ribosomal protein S1 in antibiotic-resistant Mycobacterium Tuberculosis.

The emergence of antibiotic-resistant Mycobacterium Tuberculosis (Mtb) infections compels new treatment strategies, of which targeting trans-translation is promising. During the trans-translation process, the ribosomal protein S1 (RpsA) plays a key role, and the Ala438 mutant is related to pyrazinamide (PZA) resistance, which shows its effects after being hydrolysed to pyrazinoic acid (POA). In this study, based on the structure of the RpsA C-terminal domain (RpsA-CTD) and POA complex, new compounds were designed. After being synthesized, the compounds were tested in vitro with saturation transfer difference (STD), fluorescence quenching titration (FQT) and chemical shift perturbation (CSP) experiments. Finally, six of the 17 new compounds have high affinity for both RpsA-CTD and its Ala438 deletion mutant. The active compounds provide new choices for targeting trans-translation in Mtb, and the analysis of the structure-activity relationships will be helpful for further structural modifications based on derivatives of 2-((hypoxanthine-2-yl)thio)acetic acid and 2-((5-hydroxylflavone-7-yl)oxy)acetamide.

NMR-based metabonomic analysis of HUVEC cells during replicative senescence.

Cellular senescence is a physiological process reacting to stimuli, in which cells enter a state of irreversible growth arrest in response to adverse consequences associated with metabolic disorders. Molecular mechanisms underlying the progression of cellular senescence remain unclear. Here, we established a replicative senescence model of human umbilical vein endothelial cells (HUVEC) from passage 3 (P3) to 18 (P18), and performed biochemical characterizations and NMR-based metabolomic analyses. The cellular senescence degree advanced as the cells were sequentially passaged in vitro, and cellular metabolic profiles were gradually altered. Totally, 8, 16, 21 and 19 significant metabolites were primarily changed in the P6, P10, P14 and P18 cells compared with the P3 cells, respectively. These metabolites were mainly involved in 14 significantly altered metabolic pathways. Furthermore, we observed taurine retarded oxidative damage resulting from senescence. In the case of energy deficiency, HUVECs metabolized neutral amino acids to replenish energy, thus increased glutamine, aspartate and asparagine at the early stages of cellular senescence but decreased them at the later stages. Our results indicate that cellular replicative senescence is closely associated with promoted oxidative stress, impaired energy metabolism and blocked protein synthesis. This work may provide mechanistic understanding of the progression of cellular senescence.

Development and strategies of CDK4/6 inhibitors.

Aim: CDK4/6 have critical roles in the early stage of the cell cycle. CDK2 acts later in the cell cycle and has a considerably broader range of protein substrates, some of which are essential for normal cell proliferation. Therefore, increasing the selectivity of cyclin-dependent kinase (CDK) inhibitors is critical. Methodology: In this study, we construct a versatile, specific CDK4 pharmacophore model that not only matches well with 8119 of the reported 9349 CDK4/6 inhibitors but also differentiates from the CDK2 pharmacophore. Results & Conclusion: we demonstrate the activity and selectivity determinants of CDK4/6 selective inhibitors based on the CDK4 pharmacophore model. Finally, we propose the future optimization strategy for CDK4/6 selective inhibitors, providing a theoretical basis for further research and development of CDK4/6 selective inhibitors.

Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors.

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

The Characteristics of PD-L1 Inhibitors, from Peptides to Small Molecules.

The programmed cell death ligand protein 1 (PD-L1) is a member of the B7 protein family and consists of 290 amino acid residues. The blockade of the PD-1/PD-L1 immune checkpoint pathway is effective in tumor treatment. Results: Two pharmacophore models were generated based on peptides and small molecules. Hypo 1A consists of one hydrogen bond donor, one hydrogen bond acceptor, two hydrophobic points and one aromatic ring point. Hypo 1B consists of one hydrogen bond donor, three hydrophobic points and one positive ionizable point. Conclusions: The pharmacophore model consisting of a hydrogen bond donor, hydrophobic points and a positive ionizable point may be helpful for designing small-molecule inhibitors targeting PD-L1.

Characteristics of parathyroid hormone-1 receptor agonists and antagonists.

Aim: Parathyroid hormone-1 receptor (PTH1R) is a member of B G protein-coupled receptors. The agonistic activation of the PTH1R results in the production and secretion of osteoclast-stimulating cytokines while antagonists may be used to treat bone metastases, hypercalcemia, cachexia and hyperparathyroidism. Results: We built pharmacophore models and investigated the characteristics of PTH1R agonists and antagonists. The agonist model consists of three hydrophobic points, one hydrogen bond acceptor and one positive ionizable point. The antagonist model consists of one hydrogen bond donor and three hydrophobic points. Conclusion: The features of the two models are similar, but the hydrogen bond acceptor, which is the main difference between PTH1R agonists and antagonists, suggests it may be essential for the agonist.

Discovery of CDK4 inhibitors by convolutional neural networks.

Aim: Descriptors of molecules are important in the discovery of lead compounds. Most of these descriptors are used to represent molecular structures, although structural formulas are the most intuitive representation. Convolutional neural networks (ConvNets) are effective for managing intuitive information. Results/methodology: Convolutional neural networks (ConvNets) based on two-dimensional structural formulas were used for the preliminary screening of CDK4 inhibitors. After supervised learning of our homemade dataset, our models screened out ten approved drugs, including indocyanine green and candesartan cilexetil, with IC50 values of 2.0 and 5.2 µM, respectively. Conclusion: Depending only on intuitive information, the developed method was shown to be feasible, thus providing a new method of lead compound discovery.

AdipoRs- a potential therapeutic target for fibrotic disorders.

Introduction: Fibrotic disorders are a leading cause of morbidity and mortality; hence effective treatments are still vigorously sought. AdipoRs (AdipoR1 and Adipo2) are responsible for the antifibrotic effects of adiponectin (APN). APN exerts antifibrotic effects by binding to its receptors. APN concentration and AdipoR expression are closely associated with fibrotic disorders. Decreased AdipoR expression may reduce APN-AdipoR signaling, while the upregulation of AdipoR expression may restore the anti-fibrotic effects of APN. Loss of APN signaling exacerbates fibrosis in vivo and in vitro. Areas covered: We assess the relationship between APN and fibrotic disorders, the structure of receptors for APN and the pathways accounting for APN or its analogs blocking fibrotic disorders. This article also discusses designed APN products and their therapeutic prospects for fibrotic disorders. Expert opinion: AdipoRs have a critical role in blocking fibrosis. The development of small-molecule agonists toward this target represents a valid drug development pathway.

Lead compounds and key residues of ribosomal protein S1 in drug-resistant Mycobacterium tuberculosis.

Ribosomal protein S1 (RpsA) has been identified as a novel target of pyrazinoic acid (POA), which is the active form of pyrazinamide (PZA), in vivo. RpsA plays a crucial role in trans-translation, which is widespread in microbes. In our investigation, we first described the discovery of promising RpsA antagonists for drug-resistant mycobacterium (MtRpsAd438A) and M. smegmatis, as well as wild-type M. tuberculosis. These antagonists were discovered via structure/ligand-based virtual screening approaches. A total of 21 targeted compounds were selected by virtual screening, combined scores, affinity, similarities and rules for potential as drugs. Next, the affinities of these compounds for three targeted proteins were tested in vitro by applying various technologies, including fluorescence quenching titration (FQT), saturation transfer difference (STD), and chemical shift perturbation (CSP) assays. The results showed that seven compounds had a high affinity for the targeted proteins. Our discovery set the stage for discovering new chemical entities (NCEs) for PZA-resistant tuberculosis and providing key residues for rational drug design to target RpsA.

An overview of neural networks for drug discovery and the inputs used.

Introduction: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as inputs for artificial neural networks (ANNs) for a long time, while other ways for describing molecules are used only for storing and presenting molecules. With the development of deep learning, variants of ANNs are now able to use different kinds of inputs, which provide researchers with more choices for drug discovery. Areas covered: The authors provide a brief overview of the applications of NNs in drug discovery. Combined with the characteristics of different ways for describing molecules, corresponding methods based on NNs provide new choices for drug discovery, including de novo drug design, ligand-based drug design, and receptor-based drug design. Expert opinion: Various ways for describing molecules can be inputs of NN-based models, and these models achieve satisfactory results in metrics. Although most of the models have not been widely applied and tested in practice, they can be the basis for automatic drug discovery in the future.