Frontiers in drug discovery最新文献

{"title":"Numaswitch, a biochemical platform for the efficient production of disulfide-rich pepteins","authors":"Bach-Ngan Nguyen, Florian Tieves, Florian G. Neusius, H. Götzke, L. Schmitt, C. Schwarz","doi":"10.3389/fddsv.2023.1082058","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1082058","url":null,"abstract":"The application of long-chained peptides (+30 aa) and relatively short proteins (<300 aa) has experienced an increasing interest in recent years. However, a reliable production platform is still missing since manufacturing is challenged by inherent problems such as mis-folding, aggregation, and low production yields. And neither chemical synthesis nor available recombinant approaches are effective and efficient. This in particular holds true for disulfide-rich targets where the correct isomer needs to be formed. With the technology Numaswitch, we have now developed a biochemical tool that circumvents existing limitations and serves as first production platform for pepteins, hard-to-be-produced peptides and proteins between 30 and 300 amino acids in length, including disulfide-rich candidates. Numaswitch is based on bifunctional Switchtag proteins that force the high-titer expression of pure inclusion bodies and simultaneously assist in the efficient refolding of pepteins into functional pepteins. Here, we demonstrate the successful application of the Numaswitch platform for disulfide-containing pepteins, such as an antimicrobial fusion peptide, a single-chain variable fragment (scFv), a camelid heavy chain antibody fragment (VHH) and the human epidermal growth factor.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47786351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HMM-based profiling identifies the binding to divalent cations and nucleotides as common denominators of suramin targets","authors":"Dennis A. Hauser, P. Mäser","doi":"10.3389/fddsv.2023.1112992","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1112992","url":null,"abstract":"Introduction: Suramin is one of the pharmacopeia’s most promiscuous drugs. Originally developed for African trypanosomiasis, suramin was also used for onchocerciasis and it has been proposed as an anticancer agent, antiviral drug, therapy for arthritis, autism, and antidote for snake bites. Target proteins of suramin have been described from different species. Here we identify the common motifs among these various targets, aiming to explain the promiscuous nature of suramin. Methods: We have searched for suramin target proteins in the literature and in chemical databases. Applying rigorous inclusion criteria, a list of 44 diverse proteins was assembled with experimental evidence for direct interaction with, and inhibition by, suramin. Hidden Markov model-based target profiling was performed by running the full set of Pfam protein family domains against these proteins. Results: Common denominators were identified by mapping the identified Pfam domains to molecular function gene ontology terms. This in silico pipeline identified nucleotide binding, nucleic acid binding, and binding to divalent cations as the most common denominators of the suramin targets. Discussion: Our results suggest that the extraordinary polypharmacology of suramin may be caused by its ability to inhibit the interaction of proteins with nucleotides or nucleic acids and with divalent cations (Mg2+, Ca2+, Zn2+). Suramin is well known to inhibit nucleotide receptors and nucleic acid-binding enzymes. The association with divalent cations is new and might be key towards the design of better, more selective inhibitors.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46032731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico identification of potential inhibitors of acyl carrier protein reductase and acetyl CoA carboxylase of Plasmodium falciparum in antimalarial therapy","authors":"Elliasu Y. Salifu, James Abugri, Issahaku A Rashid, F. Osei, Joseph Atia Ayariga","doi":"10.3389/fddsv.2023.1087008","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1087008","url":null,"abstract":"Malaria caused by Plasmodium falciparum, remains one of the most fatal parasitic diseases that has affected nearly a third of the world’s population. The major impediment to the treatment of malaria is the emergence of resistance of the P. falciparum parasite to current anti-malaria therapeutics such as Artemisinin (ART)-based combination therapy (ACT). This has resulted in countless efforts to develop novel therapeutics that will counter this resistance with the aim to control and eradicate the disease. The application of in silico modelling techniques has gained a lot of recognition in antimalarial research in recent times through the identification of biological components of the parasite for rational drug design. In this study we employed various in silico techniques such as the Virtual screening, molecular docking and molecular dynamic simulations to identify potential new inhibitors of biotin acetyl-coenzyme A (CoA) carboxylase and enoyl-acyl carrier reductase, two enzyme targets that play a crucial role in fatty acid synthesis in the Plasmodium parasite. Initially, nine hit compounds were identified for each of the two enzymes from the ZINCPharmer database. Subsequently, all hit compounds bind favourably to the active sites of the two enzymes as well as show excellent pharmacokinetic properties. Three 3) of the hits for the biotin acetyl-coenzyme A (CoA) carboxylase and six 6) of the enoyl-acyl carrier reductase showed good toxicity properties. The compounds were further evaluated based on the Molecular Dynamics simulation that confirmed the binding stability of the compounds to the targeted proteins. Overall, the lead compounds ZINC38980461, ZINC05378039, and ZINC15772056, were identified for acetyl-coenzyme A (CoA) carboxylase whiles ZINC94085628, ZINC93656835, ZINC94080670, ZINC1774609, ZINC94821232 and ZINC94919772 were identified as lead compounds for enoyl-acyl carrier reductase. The identified compounds can be developed as a treatment option for the malaria disease although, experimental validation is suggested for further evaluation of the work.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43816255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of cyclic peptide antigens to generate LRP8 specific antibodies","authors":"M. Argiriadi, Kangwen Deng, D. Egan, Lei Gao, F. Gizatullin, J. Harlan, Denise Karaoglu Hanzatian, W. Qiu, Ruth Villanueva, Andrew Goodearl","doi":"10.3389/fddsv.2022.1093153","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1093153","url":null,"abstract":"LRP8 is a member of the LDLR-like protein family. It is a transport receptor, which can be used in the design of antibodies specific for investigating increasing exposure to therapeutics with respect to the blood brain barrier (BBB). In this study, a LRP8 peptide immunization strategy was implemented to generate antibodies to a specific epitope of the CR1 domain of LRP8 that could enable transport function and cross-react in mice, cynomolgus monkeys and humans. Additionally, a cyclized peptide immunogen was designed to conserve the structural β-hairpin element observed in a previously solved crystal structure of a related CR domain. As a result of this structure-based antigenic design, an LRP8 specific antibody, 11H1, was selected and characterized in ligand binding assays and crystallographic structure determination. The high-resolution structure of the 11H1 Fab complexed to the cyclized CR1 peptide revealed key interactions driving epitope recognition that were confirmed using a site-directed mutagenesis approach. A critical observation was that the identified structural CR1 epitope of 11H1 did not compete with reelin’s recognition of CR1 allowing for simultaneous binding. This was predicted by an in silico ternary model and confirmed by reelin binding data. These simultaneous binding events (11H1/CR1/reelin) could therefore enable the CR1 domain of LRP8, 11H1 and reelin to be used as a “BBB transporter” ternary complex in the design of therapeutic proteins. More importantly, 11H1 showed enhanced brain penetration after systemic intravenous dosing in a mouse study, which confirmed its potential function as BBB transporter for therapeutic proteins.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41635901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of nucleoside DOT1L inhibitors’ residence times by τRAMD simulations","authors":"Carlos D. Flores-León, Luis Fernando Colorado-Pablo, Miguel Á. Santos-Contreras, R. Aguayo‐Ortiz","doi":"10.3389/fddsv.2022.1083198","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1083198","url":null,"abstract":"Human epigenetic enzyme disruptor of telomeric silencing 1-like (DOT1L) is a key drug target for treating acute myeloid leukemia. Several nucleoside and non-nucleoside DOT1L inhibitors have been developed to inhibit its histone methyltransferase activity. Non-mechanism-based nucleoside DOT1L inhibitors have shown good inhibitory activity and high on-target residence times. Previous computational studies have explored the dynamic behavior of this group of molecules on DOT1L to design compounds with enhanced binding affinities. Nevertheless, it is well known that drug-target kinetics also plays a crucial role in the discovery of new drugs. Therefore, we performed τ-Random Acceleration Molecular Dynamics (τRAMD) simulations to estimate the residence times of nucleoside DOT1L inhibitors. The high correlation between the calculated and experimental residence times suggested that the method can reliably estimate the residence time of nucleoside DOT1L inhibitors when modifications are made to those substituents that occupy the buried hydrophobic pocket of the active site, exhibit hydrophobic interactions with F245 or that form H-bonds with D161 and G163. Overall, this study will be a step toward understanding the binding kinetics of nucleoside DOT1L inhibitors for the treatment of acute myeloid leukemia.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48062743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug repurposing screening validated by experimental assays identifies two clinical drugs targeting SARS-CoV-2 main protease","authors":"Denis N Prada Gori, S. Ruatta, Martín Fló, L. Alberca, C. Bellera, Soonju Park, Jinyeong Heo, Honggun Lee, K. P. Park, O. Pritsch, D. Shum, M. Comini, A. Talevi","doi":"10.3389/fddsv.2022.1082065","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1082065","url":null,"abstract":"The COVID-19 pandemic prompted several drug repositioning initiatives with the aim to rapidly deliver pharmacological candidates able to reduce SARS-CoV-2 dissemination and mortality. A major issue shared by many of the in silico studies addressing the discovery of compounds or drugs targeting SARS-CoV-2 molecules is that they lacked experimental validation of the results. Here we present a computer-aided drug-repositioning campaign against the indispensable SARS-CoV-2 main protease (MPro or 3CLPro) that involved the development of ligand-based ensemble models and the experimental testing of a small subset of the identified hits. The search method explored random subspaces of molecular descriptors to obtain linear classifiers. The best models were then combined by selective ensemble learning to improve their predictive power. Both the individual models and the ensembles were validated by retrospective screening, and later used to screen the DrugBank, Drug Repurposing Hub and Sweetlead libraries for potential inhibitors of MPro. From the 4 in silico hits assayed, atpenin and tinostamustine inhibited MPro (IC50 1 µM and 4 μM, respectively) but not the papain-like protease of SARS-CoV-2 (drugs tested at 25 μM). Preliminary kinetic characterization suggests that tinostamustine and atpenin inhibit MPro by an irreversible and acompetitive mechanisms, respectively. Both drugs failed to inhibit the proliferation of SARS-CoV-2 in VERO cells. The virtual screening method reported here may be a powerful tool to further extent the identification of novel MPro inhibitors. Furthermore, the confirmed MPro hits may be subjected to optimization or retrospective search strategies to improve their molecular target and anti-viral potency.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41404972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Insights in silico methods and artificial intelligence for drug discovery: 2022","authors":"J. Medina‐Franco","doi":"10.3389/fddsv.2022.1126955","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1126955","url":null,"abstract":"Entering the third decade of the 21st Century, artificial intelligence (AI) continues to offer significant advances in drug discovery (Jiménez-Luna et al., 2021; Jayatunga et al., 2022). When used rationally beyond the hype, AI offers clear promise to advance further basic and applied research (Medina-Franco et al., 2021). At the same time, AI faces challenges to address at different levels. The present Research Topic brings together experts worldwide from industry, academic, not-for-profit, and governmental settings to openly discuss novel insights, recent advances, latest discoveries, and current challenges in the field of In silico Methods and Artificial Intelligence for Drug Discovery. From an industry point of view, DiNuzzo presents a perspective on how AI enables the modeling and simulation of biological networks to accelerate drug discovery. He emphasizes that the proper combination of the predictive capability of AI with the mechanistic knowledge of modeling and simulation is expected to provide a major contribution to the pharmaceutical industry. DiNuzzo also concludes that AI will be a key player in analyzing biological networks that will deliver substantial progress towards the improvement of drug target identification and validation, qualify potentially associated side-effects, identify the efficacy and toxicity of biomarkers, aid with hypothesis generation, optimal experimental design, and testing for disease understanding and identification of disease biomarkers. McDermott et al. discuss a platform based on AI that aids in the discovery of DNA damaging agents for ultra-rare cancer atypical teratoid rhabdoid tumors (ATRT). Specifically, the authors showed the power of using the public USA’s National Cancer Institute (NCI)’s CellMiner Cross Database and Lantern Pharma’s proprietary AI and machine learning (ML) RADR® platform to uncover biological insights and potentially new target indications for the acylfulvene derivative drugs LP-100 (Irofulven) and LP-184. Lantern’s AI and ML RADR® platform was used to develop a model to test, computationally, if LP-184 would be effective in ATRT patients. RADR® suggested that ATRT would be sensitive to LP-184, which was then validated in vitro and in vivo. Namba-Nzanguim et al. review how AI is helping to advance antiviral drug discovery in low-resourced settings. Authors shared their perspectives on the benefits, limitations, and pitfalls of AI/ML tools in the discovery of novel antivirals. Namba-Nzanguim et al. also present current and novel data sharing models, including intellectual property-preserving AI/ML. Authors concluded that AI/ML provides a cost-effective solution for developing antivirals, but AI/ML tools depend on improved access to viral assays data and better data integration protocols. Schmitz et al. OPEN ACCESS","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of novel genetic variants, including PIM1 and LINC01491, with ICD-10 based diagnosis of pulmonary arterial hypertension in the UK Biobank cohort.","authors":"Alex Pu, Gautam Ramani, Yi-Ju Chen, James A Perry, Charles C Hong","doi":"10.3389/fddsv.2023.1127736","DOIUrl":"10.3389/fddsv.2023.1127736","url":null,"abstract":"<p><p>Pulmonary arterial hypertension (PAH) is characterized by remodeling and narrowing of the pulmonary vasculature which results in elevations of pulmonary arterial pressures. Here, we conducted a genome-wide association study (GWAS) using the UK Biobank, analyzing the genomes of 493 individuals diagnosed with primary pulmonary hypertension, based on ICD-10 coding, compared to 24,650 age, sex, and ancestry-matched controls in a 1:50 case-control design. Genetic variants were analyzed by Plink's firth logistic regression and assessed for association with primary pulmonary hypertension. We identified three linked variants in the <i>PIM1</i> gene, which encodes a protooncogene that has been garnering interest as a potential therapeutic target for PAH, that were associated with PAH with genome wide significance, one (rs192449585) of which lies in the promoter region of the gene. We also identified 15 linked variants in the <i>LINC01491</i> gene. These results provide genetic evidence supporting the role of <i>PIM1</i> inhibitors as a potential therapeutic option for PAH.</p>","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9380802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning","authors":"L. Shapira, Shaul Lerner, Guila Assayag, A. Vardi, D. Haham, Gideon Bar, Vicky Fidelsky Kozokaro, Maayan Elias Robicsek, Immanuel Lerner, Amit Michaeli","doi":"10.3389/fddsv.2022.1085701","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1085701","url":null,"abstract":"Introduction: The COVID-19 pandemic has cast a heavy toll in human lives and global economics. COVID-19 is caused by the SARS-CoV-2 virus, which infects cells via its spike protein binding human ACE2. Methods: To discover potential inhibitory peptidomimetic macrocycles for the spike/ACE2 complex we deployed Artificial Intelligence guided virtual screening with three distinct strategies: 1) Allosteric spike inhibitors 2) Competitive ACE2 inhibitors and 3) Competitive spike inhibitors. Screening was performed by docking macrocycles to the relevant sites, clustering and synthesizing cluster representatives. Synthesized molecules were screened for inhibition using AlphaLISA and RSV particles. Results: All three strategies yielded inhibitory peptides, but only the competitive spike inhibitors showed “hit” level activity. Discussion: These results suggest that direct inhibition of the spike RBD domain is the most attractive strategy for peptidomimetic, “head-to-tail” macrocycle drug development against the ongoing pandemic.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41496741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information","authors":"L. Bleicher, Ton van Daelen, J. Honeycutt, Moises Hassan, J. Chandrasekhar, W. Shirley, V. Tsui, U. Schmitz","doi":"10.3389/fddsv.2022.1074797","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1074797","url":null,"abstract":"AI/ML methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery including lead finding and lead optimization. Typical methods utilize ML-models for structure-property prediction with simple 2D-based chemical representations of the small molecules. Further, limited data, especially pertaining to novel targets, make it difficult to build effective structure-activity ML-models. Here we describe our recent work using the BIOVIA Generative Therapeutics Design (GTD) application, which is equipped to take advantage of 3D structural models of ligand protein interaction, i.e., pharmacophoric representation of desired features. Using an SAR data set pertaining to the discovery of SYK inhibitors entospletinib and lanraplenib in addition to two unrelated clinical SYK inhibitors, we show how several common problems in lead finding and lead optimization can be effectively addressed with GTD. This includes an effort to retrospectively re-identify drug candidate molecules based on data from an intermediate stage of the project using chemical space constraints and the application of evolutionary pressure within GTD. Additionally, studies of how the GTD platform can be configured to generate molecules incorporating features from multiple unrelated molecule series show how the GTD methods apply AI/ML to drug discovery.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91186704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}