Pub Date : 2023-01-09DOI: 10.3389/fddsv.2022.1083198
Carlos D. Flores-León, Luis Fernando Colorado-Pablo, Miguel Á. Santos-Contreras, R. Aguayo‐Ortiz
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.
{"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}
Pub Date : 2023-01-05DOI: 10.3389/fddsv.2022.1082065
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
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.
{"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}
Pub Date : 2023-01-05DOI: 10.3389/fddsv.2022.1126955
J. Medina‐Franco
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
进入21世纪的第三个十年，人工智能（AI）继续在药物发现方面取得重大进展（Jiménez-Luna等人，2021；Jayatunga等人，2022）。当在炒作之外合理使用时，人工智能为进一步推进基础和应用研究提供了明确的承诺（Medina Franco et al.，2021）。与此同时，人工智能面临着不同层面的挑战。本研究主题汇集了来自世界各地行业、学术界、非营利组织和政府机构的专家，公开讨论药物发现的计算机方法和人工智能领域的新见解、最新进展、最新发现和当前挑战。从行业的角度来看，DiNuzzo介绍了人工智能如何使生物网络的建模和模拟加速药物发现。他强调，人工智能的预测能力与建模和模拟的机械知识的适当结合有望为制药行业做出重大贡献。DiNuzzo还得出结论，人工智能将在分析生物网络方面发挥关键作用，该网络将在改进药物靶点识别和验证、鉴定潜在的相关副作用、识别生物标志物的疗效和毒性、帮助产生假设、优化实验设计、，以及测试疾病理解和疾病生物标志物的鉴定。McDermott等人讨论了一个基于人工智能的平台，该平台有助于发现超恶性癌症非典型畸胎瘤样横纹肌样肿瘤（ATRT）的DNA损伤剂。具体而言，作者展示了使用美国国家癌症研究所（NCI）的CellMiner交叉数据库和Lantern Pharma专有的人工智能和机器学习（ML）RADR®平台来揭示酰基富烯衍生物药物LP-100（Irofulven）和LP-184的生物学见解和潜在新靶点适应症的力量。Lantern的AI和ML RADR®平台用于开发一个模型，通过计算测试LP-184是否对ATRT患者有效。RADR®表明ATRT对LP-184敏感，随后在体外和体内进行了验证。Namba Nzanguim等人综述了人工智能如何在资源匮乏的环境中帮助推进抗病毒药物的发现。作者分享了他们对AI/ML工具在发现新型抗病毒药物方面的好处、局限性和陷阱的看法。Namba Nzanguim等人还介绍了当前和新的数据共享模型，包括保护知识产权的AI/ML。作者得出结论，AI/ML为开发抗病毒药物提供了一种具有成本效益的解决方案，但AI/ML工具依赖于改进对病毒检测数据的访问和更好的数据集成协议。Schmitz等人开放访问
{"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}
Pub Date : 2023-01-01Epub Date: 2023-02-08DOI: 10.3389/fddsv.2023.1127736
Alex Pu, Gautam Ramani, Yi-Ju Chen, James A Perry, Charles C Hong
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 PIM1 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 LINC01491 gene. These results provide genetic evidence supporting the role of PIM1 inhibitors as a potential therapeutic option for PAH.
{"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}
Pub Date : 2022-12-23DOI: 10.3389/fddsv.2022.1085701
L. Shapira, Shaul Lerner, Guila Assayag, A. Vardi, D. Haham, Gideon Bar, Vicky Fidelsky Kozokaro, Maayan Elias Robicsek, Immanuel Lerner, Amit Michaeli
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.
{"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}
Pub Date : 2022-12-19DOI: 10.3389/fddsv.2022.1074797
L. Bleicher, Ton van Daelen, J. Honeycutt, Moises Hassan, J. Chandrasekhar, W. Shirley, V. Tsui, U. Schmitz
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.
{"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}
Pub Date : 2022-12-15DOI: 10.3389/fddsv.2022.1022971
S. Cassady, Danielle Soldin, G. Ramani
Pulmonary arterial hypertension (PAH), defined as a mean pulmonary artery pressure exceeding 20 mmHg with a pulmonary vascular resistance of 3 or more Wood units, is an incurable and progressive condition. The cornerstone of PAH treatment is pulmonary vasodilators, which act on the pulmonary vasculature to reduce pulmonary pressures and pulmonary vascular resistance and prevent progression to right heart failure. The number of available pulmonary vasodilator therapies has grown markedly in the last 10 years, alongside a rapidly expanding body of literature establishing strategies for their use. Up-front combination therapy, typically with two pulmonary vasodilator medications, has become the standard of care based on landmark trials showing superior outcomes over single therapies alone. Complex risk stratification matrices have begun to see widespread use as tools with which to guide changes in PAH therapies for individual patients. Strategies for using the pulmonary vasodilators in common use continue to be evaluated in trials exploring concepts such as up-front triple combination therapy and substitution of vasodilators for patients not meeting therapeutic goals. Alongside established pulmonary vasodilator therapies for PAH, there is a broad spectrum of experimental therapies that are being studied for the disease. These include both more conventional medications that act on pathways targeted by existing vasodilator therapies as well as non-vasodilator treatments with novel methods of action, that may act both to vasodilate and to address the detrimental changes of pulmonary arterial and right ventricular remodeling. Many of these emerging medications are the focus of active phase 2 and 3 trials. Finally, there has been significant interest in therapeutic pathways that are well established in left heart failure, with the hope of adapting strategies that may be efficacious in PAH and right heart failure as well. These include explorations of pathways treated by goal-directed medical therapy as well as device therapies such as pacing, resynchronization therapy, and cardiac monitoring devices. Many of these options show promise and may represent a complementary approach to treatment of PAH, allowing for multimodal therapy alongside pulmonary vasodilators to improve patient outcomes.
{"title":"Novel and emerging therapies in pulmonary arterial hypertension","authors":"S. Cassady, Danielle Soldin, G. Ramani","doi":"10.3389/fddsv.2022.1022971","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1022971","url":null,"abstract":"Pulmonary arterial hypertension (PAH), defined as a mean pulmonary artery pressure exceeding 20 mmHg with a pulmonary vascular resistance of 3 or more Wood units, is an incurable and progressive condition. The cornerstone of PAH treatment is pulmonary vasodilators, which act on the pulmonary vasculature to reduce pulmonary pressures and pulmonary vascular resistance and prevent progression to right heart failure. The number of available pulmonary vasodilator therapies has grown markedly in the last 10 years, alongside a rapidly expanding body of literature establishing strategies for their use. Up-front combination therapy, typically with two pulmonary vasodilator medications, has become the standard of care based on landmark trials showing superior outcomes over single therapies alone. Complex risk stratification matrices have begun to see widespread use as tools with which to guide changes in PAH therapies for individual patients. Strategies for using the pulmonary vasodilators in common use continue to be evaluated in trials exploring concepts such as up-front triple combination therapy and substitution of vasodilators for patients not meeting therapeutic goals. Alongside established pulmonary vasodilator therapies for PAH, there is a broad spectrum of experimental therapies that are being studied for the disease. These include both more conventional medications that act on pathways targeted by existing vasodilator therapies as well as non-vasodilator treatments with novel methods of action, that may act both to vasodilate and to address the detrimental changes of pulmonary arterial and right ventricular remodeling. Many of these emerging medications are the focus of active phase 2 and 3 trials. Finally, there has been significant interest in therapeutic pathways that are well established in left heart failure, with the hope of adapting strategies that may be efficacious in PAH and right heart failure as well. These include explorations of pathways treated by goal-directed medical therapy as well as device therapies such as pacing, resynchronization therapy, and cardiac monitoring devices. Many of these options show promise and may represent a complementary approach to treatment of PAH, allowing for multimodal therapy alongside pulmonary vasodilators to improve patient outcomes.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46595177","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}
Pub Date : 2022-12-12DOI: 10.3389/fddsv.2022.1032587
G. Sartori, Aline Albuquerque, Andrielly Santos-Costa, Luca Milério Andrade, D. Almeida, E. Gaieta, J. Sampaio, V. Albuquerque, João Hermínio Martins da Silva
Flavonoids are a class of natural products widely available in medicinal and dietary plants. Their pharmacological use has shown the potential to reduce the risk of different types of cancer, among other prevalent diseases. Their molecular scaffold inhibits the PD-1/PD-L1 axis, an important pathway related to the adaptive immune resistance of cancer cells already targeted for developing new cancer immunotherapy. However, despite the availability of kinetic and thermodynamic experimental data on the flavonoid–PD-1/PD-L1 interaction, there is still a lack of reliable information about their binding mode at the atomic level. Thus, we aimed to computationally predict the binding mode of flavonoid molecules with PD-1 and/or PD-L1 proteins using unbiased computational methodologies such as blind docking and supervised molecular dynamics simulation. The molecular interactions and dynamics of these predicted poses of protein-flavonoid complexes were further analyzed through multiple molecular dynamics simulations. This information, corroborated with the IC50 and KD values from available literature, was used to perform molecular matched-pair analysis to comprehensively describe the main interactions governing the inhibition of the complex PD-1/PD-L1 by the flavonoid scaffold. By analyzing the effect of substitutions in such a scaffold, we observed a clear correspondence with literature binding assays. Thus, we propose, for dimeric PD-L1, that the 7-O-glucoside forces the molecule displacement in the dimer interface. Furthermore, the 3-OH plays an essential role in stabilizing the buried binding mode by water-bridged hydrogen bonds with Asp122 and Gln66 in both extremities of the pocket. In PD-1, we suggest that flavonoids could bind through the BC loop by inducing a flip of Phe56 after a conformational change of the Asn58 glycosylation. Hence, our results introduced unprecedented information on flavonoid interaction and dynamics when complexed with PD-1 checkpoint pathway proteins and can pave the road for developing new flavonoid derivatives with selective anticancer activity.
黄酮类化合物是一类广泛存在于药用植物和膳食植物中的天然产物。它们的药理用途显示出，在其他流行疾病中，有可能降低不同类型癌症的风险。它们的分子支架抑制PD-1/PD-L1轴，PD-1/PD-L1轴是与癌细胞适应性免疫抵抗相关的重要途径，已成为开发新的癌症免疫疗法的靶点。然而，尽管类黄酮- pd -1/PD-L1相互作用的动力学和热力学实验数据是可用的，但在原子水平上它们的结合模式仍然缺乏可靠的信息。因此，我们旨在通过盲对接和监督分子动力学模拟等无偏计算方法，计算预测类黄酮分子与PD-1和/或PD-L1蛋白的结合模式。通过多个分子动力学模拟，进一步分析了蛋白质-类黄酮复合物的分子相互作用和动力学。这一信息与现有文献中的IC50和KD值相证实，并用于进行分子配对分析，以全面描述类黄酮支架抑制复合物PD-1/PD-L1的主要相互作用。通过分析这种支架中取代的影响，我们观察到与文献结合分析的明确对应。因此，我们提出，对于二聚体PD-L1, 7- o -葡萄糖苷迫使二聚体界面中的分子位移。此外，3-OH在稳定与Asp122和Gln66在口袋两端的水桥氢键的埋藏结合模式中起着重要作用。在PD-1中，我们认为黄酮类化合物可以通过BC环结合，在Asn58糖基化构象改变后诱导Phe56翻转。因此，我们的研究结果提供了有关类黄酮与PD-1检查点通路蛋白络合时的相互作用和动力学的前所未有的信息，并为开发具有选择性抗癌活性的新型类黄酮衍生物铺平了道路。
{"title":"In silico mapping of the dynamic interactions and structure-activity relationship of flavonoid compounds against the immune checkpoint programmed-cell death 1 pathway","authors":"G. Sartori, Aline Albuquerque, Andrielly Santos-Costa, Luca Milério Andrade, D. Almeida, E. Gaieta, J. Sampaio, V. Albuquerque, João Hermínio Martins da Silva","doi":"10.3389/fddsv.2022.1032587","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1032587","url":null,"abstract":"Flavonoids are a class of natural products widely available in medicinal and dietary plants. Their pharmacological use has shown the potential to reduce the risk of different types of cancer, among other prevalent diseases. Their molecular scaffold inhibits the PD-1/PD-L1 axis, an important pathway related to the adaptive immune resistance of cancer cells already targeted for developing new cancer immunotherapy. However, despite the availability of kinetic and thermodynamic experimental data on the flavonoid–PD-1/PD-L1 interaction, there is still a lack of reliable information about their binding mode at the atomic level. Thus, we aimed to computationally predict the binding mode of flavonoid molecules with PD-1 and/or PD-L1 proteins using unbiased computational methodologies such as blind docking and supervised molecular dynamics simulation. The molecular interactions and dynamics of these predicted poses of protein-flavonoid complexes were further analyzed through multiple molecular dynamics simulations. This information, corroborated with the IC50 and KD values from available literature, was used to perform molecular matched-pair analysis to comprehensively describe the main interactions governing the inhibition of the complex PD-1/PD-L1 by the flavonoid scaffold. By analyzing the effect of substitutions in such a scaffold, we observed a clear correspondence with literature binding assays. Thus, we propose, for dimeric PD-L1, that the 7-O-glucoside forces the molecule displacement in the dimer interface. Furthermore, the 3-OH plays an essential role in stabilizing the buried binding mode by water-bridged hydrogen bonds with Asp122 and Gln66 in both extremities of the pocket. In PD-1, we suggest that flavonoids could bind through the BC loop by inducing a flip of Phe56 after a conformational change of the Asn58 glycosylation. Hence, our results introduced unprecedented information on flavonoid interaction and dynamics when complexed with PD-1 checkpoint pathway proteins and can pave the road for developing new flavonoid derivatives with selective anticancer activity.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45787273","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}
Inflammatory bowel disease (IBD) is a term used for a variety of conditions involving persistent inflammation of the digestive system. Ulcerative colitis (UC) and Crohn’s disease (CD) are examples of IBD. There were some treatments like Amino salicylates, glucocorticoids, immunosuppressants, antibiotics, and surgery which have been used for treating IBD. However, the short and long-term disabling adverse effects, like nausea, pancreatitis, elevated liver enzymes, allergic reactions, and other life-threatening complications remain a significant clinical problem. On the other hand, herbal medicine, believed to be safer, cheaper, and easily available, has gained popularity for treating IBD. Nowadays, Ginger, the Rizhome of Z. officinale from the Zingiberaceae family, one of the most commonly used fresh spices and herbs, has been proposed as a potential option for IBD treatment. According to upper issues, IBD treatment has become one of the society’s concerns. So, this review aims to summarize the data on the yin and yang of ginger use in IBD treatment.
{"title":"Zingiber officinale (Ginger) as a treatment for inflammatory bowel disease: A review of current literature","authors":"Fatemeh Sadeghi Poor Ranjbar, F. Mohammadyari, Atharzahra Omidvar, Farhad Nikzad, Nooria Doozandeh Nargesi, Majid Varmazyar, Soroush Dehghankar, Fatemeh Vosoughian, Sepehr Olangian-Tehrani, Sepehr Nanbakhsh, Tina Mansourian, N. Deravi, Zohreh Tutunchian, Mehrnaz Salahi, Mohadeseh Poudineh, Hani Ghayyem","doi":"10.3389/fddsv.2022.1043617","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1043617","url":null,"abstract":"Inflammatory bowel disease (IBD) is a term used for a variety of conditions involving persistent inflammation of the digestive system. Ulcerative colitis (UC) and Crohn’s disease (CD) are examples of IBD. There were some treatments like Amino salicylates, glucocorticoids, immunosuppressants, antibiotics, and surgery which have been used for treating IBD. However, the short and long-term disabling adverse effects, like nausea, pancreatitis, elevated liver enzymes, allergic reactions, and other life-threatening complications remain a significant clinical problem. On the other hand, herbal medicine, believed to be safer, cheaper, and easily available, has gained popularity for treating IBD. Nowadays, Ginger, the Rizhome of Z. officinale from the Zingiberaceae family, one of the most commonly used fresh spices and herbs, has been proposed as a potential option for IBD treatment. According to upper issues, IBD treatment has become one of the society’s concerns. So, this review aims to summarize the data on the yin and yang of ginger use in IBD treatment.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44737458","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}
Pub Date : 2022-12-07DOI: 10.3389/fddsv.2022.1065231
Cherish Prashar, N. Thakur, Soumyananda Chakraborti, Syed Shah Areeb Hussain, K. Vashisht, K. Pandey
Malaria poses several challenges to the global research community on both diagnostic and therapeutic fronts. Most prominent of them are deletion of target genes (pfhrp2/3) used in rapid diagnostic tests (RDTs) and the emergence of resistance against frontline antimalarials by the evolving parasite. Exploration of novel therapeutics for malaria in view of limited vaccine options is a promising resort for malaria control and elimination. The scope of marine-derived chemotherapeutics is exciting, with a significant number of FDA-approved drugs or therapeutic leads under clinical trials for other diseases. This review article discusses the significant antimalarial potential of marine-derived natural products extracted from diverse biota including sponges, bacteria, sea hare and algae etc. Bioassay-guided fractionation of raw extracts from marine organisms for lead identification and further structural characterization of purified compounds compose a sustainable marine-derived drug discovery pipeline; which can be particularly diverted towards the exploration of antimalarials. It is to be noted that the Indian peninsula is largely unexplored, particularly for antimalarials screening; which has a huge marine biodiversity owing to the three distinct water bodies- Bay of Bengal, Indian Ocean and Arabian sea. This review also envisions a collaborative initiative to explore the potential of marine natural products in an economically feasible manner.
{"title":"The landscape of nature-derived antimalarials-potential of marine natural products in countering the evolving Plasmodium","authors":"Cherish Prashar, N. Thakur, Soumyananda Chakraborti, Syed Shah Areeb Hussain, K. Vashisht, K. Pandey","doi":"10.3389/fddsv.2022.1065231","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1065231","url":null,"abstract":"Malaria poses several challenges to the global research community on both diagnostic and therapeutic fronts. Most prominent of them are deletion of target genes (pfhrp2/3) used in rapid diagnostic tests (RDTs) and the emergence of resistance against frontline antimalarials by the evolving parasite. Exploration of novel therapeutics for malaria in view of limited vaccine options is a promising resort for malaria control and elimination. The scope of marine-derived chemotherapeutics is exciting, with a significant number of FDA-approved drugs or therapeutic leads under clinical trials for other diseases. This review article discusses the significant antimalarial potential of marine-derived natural products extracted from diverse biota including sponges, bacteria, sea hare and algae etc. Bioassay-guided fractionation of raw extracts from marine organisms for lead identification and further structural characterization of purified compounds compose a sustainable marine-derived drug discovery pipeline; which can be particularly diverted towards the exploration of antimalarials. It is to be noted that the Indian peninsula is largely unexplored, particularly for antimalarials screening; which has a huge marine biodiversity owing to the three distinct water bodies- Bay of Bengal, Indian Ocean and Arabian sea. This review also envisions a collaborative initiative to explore the potential of marine natural products in an economically feasible manner.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48810955","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}
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