Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-278
Weidong Xie, Xiaoyan Cheng, Zhengfang Ding, R. Deng, D. Gu
Drug discovery is resource intensive, and involves typical timelines of 10-20 years and costs that range from US$0.5 billion to US$2.6 billion. Artificial intelligence (AI) has recently started to gear-up its application in various sectors of the society and the pharmaceutical industry as a frontrunner beneficiary.Artificial intelligence can accelerate drug discovery and reduce costs by facilitating the rapid screening and identification of compounds. We have developed DM-AI drug discovery platform, including convolutional neural networks, decision treealgorithm, reinforcement learning, generative adversarial networks, big data, and knowledge graphs, along with structure and ligand-based high-throughput virtual screening , for new drug discovery and development. DM-AI optimizes biological activity,toxicity,physicochemical property. We used DM-AI to discover potent inhibitors of SHP2, PIM1, DNA-PK, kinases target implicated in solid tumor and other diseases.We started to train a biological activity prediction model on a database of the given target kinase inhibitors (positive set) and non-kinase targets molecules (negative set), and then predicted the activity of existing million data sets, obtained an initial output of thousands structures. We then evaluated these structures using a pharmacophore reward model on the basis of virtual chemical spaces of kinase inhibitors in complex with target protein. To narrow our focus to a smaller set of molecules for analysis, compounds with higher score were filtered to remove patents and applications molecules, also remove molecules bearing structural alerts and reactive groups.By day 7 after target selection, We had selected dozens structures with structural diversity for experimental validation. and by day 28, they were tested for in vitro inhibitory activity in an enzymatic kinase assay, active compounds accounted for up to 65% in some target models. This illustrates the utility of our DM-AI drug discovery platform for the successful, rapid discovery of drug candidates. Citation Format: Weidong Xie, Xing Cheng, Zhengfang Ding, Riqiang Deng, Dawei Gu. Artificial intelligence accelerate drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 278.
{"title":"Abstract 278: Artificial intelligence accelerate drug discovery","authors":"Weidong Xie, Xiaoyan Cheng, Zhengfang Ding, R. Deng, D. Gu","doi":"10.1158/1538-7445.AM2021-278","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-278","url":null,"abstract":"Drug discovery is resource intensive, and involves typical timelines of 10-20 years and costs that range from US$0.5 billion to US$2.6 billion. Artificial intelligence (AI) has recently started to gear-up its application in various sectors of the society and the pharmaceutical industry as a frontrunner beneficiary.Artificial intelligence can accelerate drug discovery and reduce costs by facilitating the rapid screening and identification of compounds. We have developed DM-AI drug discovery platform, including convolutional neural networks, decision treealgorithm, reinforcement learning, generative adversarial networks, big data, and knowledge graphs, along with structure and ligand-based high-throughput virtual screening , for new drug discovery and development. DM-AI optimizes biological activity,toxicity,physicochemical property. We used DM-AI to discover potent inhibitors of SHP2, PIM1, DNA-PK, kinases target implicated in solid tumor and other diseases.We started to train a biological activity prediction model on a database of the given target kinase inhibitors (positive set) and non-kinase targets molecules (negative set), and then predicted the activity of existing million data sets, obtained an initial output of thousands structures. We then evaluated these structures using a pharmacophore reward model on the basis of virtual chemical spaces of kinase inhibitors in complex with target protein. To narrow our focus to a smaller set of molecules for analysis, compounds with higher score were filtered to remove patents and applications molecules, also remove molecules bearing structural alerts and reactive groups.By day 7 after target selection, We had selected dozens structures with structural diversity for experimental validation. and by day 28, they were tested for in vitro inhibitory activity in an enzymatic kinase assay, active compounds accounted for up to 65% in some target models. This illustrates the utility of our DM-AI drug discovery platform for the successful, rapid discovery of drug candidates. Citation Format: Weidong Xie, Xing Cheng, Zhengfang Ding, Riqiang Deng, Dawei Gu. Artificial intelligence accelerate drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 278.","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85813141","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-305
G. Adams, Kai Ma, A. Venkatesan, F. Chen, Feixuan Wu, Melik Z. Turker, Thomas C. Gardinier, Pei-Ming Chen, Vaibhav Patel, E. Bayever, Paul Rudick, Geno Germano
CDCs are novel ultra-small (6-7 nm) nanoparticle drug conjugates that have been demonstrated to be capable of faster tumor targeting and deeper tumor penetration than antibody drug conjugates in animal models. CDCs are capable of targeting tumors in the brain and pancreas that are difficult to access, while exhibiting limited exposure to normal tissues due their efficient renal elimination. CDCs are composed of a silica core, in which Cy5, a far red dye is covalently encapsulated. The silica core is covalently coated with a layer of polyethylene glycol which is then functionalized with targeting moieties and payloads. ELU001 is a CDC functionalized with ~20 molecules of the topoisomerase-1 inhibitor exatecan linked via a proteolytic cleavable linker as a payload and ~15 folic acids to provide targeting to FRα overexpressing cancers. ELU001 is rapidly internalized into FRα expressing cells and is trafficked to the lysosome where the payload is released from the CDC. ELU001 exhibits potency in the low single digit nanomolar to sub-nanomolar range against cancer cells that express 3+ (KB, IGROV-1) and 2+ (SK-OV-3, HCC827 and OVCAR-3) levels of FRα after a 6-hr exposure in a 7-day viability study. In contrast, an anti-FRα ADC based ADC mirvetuximab soravtansine exhibits lower potency (>100 nM IC50) against SK-OV-3 and HCC827 cells and 40 nM IC50 against OVCAR-3 cells. ELU001 exhibits potent efficacy against established s.c. KB human cervical tumor xenografts in immunodeficient mice with significantly better efficacy and safety than free exatecan payload. It is also effective in treating established SK-OV-3 tumors with lower (2+) FRα expression, a setting where the ADC is again less effective. IND-enabling nonclinical studies are currently underway to prepare for initiation of a first-in-human phase 1 clinical trial in subjects with FRα overexpressing cancers in the second half of 2021. Citation Format: Gregory Paul Adams, Kai Ma, Aranapakam Venkatesan, Feng Chen, Fei Wu, Melik Turker, Thomas Gardinier, Peiming Chen, Vaibhav Patel, Eliel Bayever, Paul Rudick, Geno Germano. ELU001, a targeted C9Dot drug conjugate (CDC) for the treatment of folate receptor alpha (FRα) overexpressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 305.
cdc是一种新型的超小(6-7纳米)纳米药物偶联物，在动物模型中已被证明能够比抗体药物偶联物更快地靶向肿瘤和更深地穿透肿瘤。cdc能够靶向难以进入的脑和胰腺肿瘤，同时由于其有效的肾脏消除作用，对正常组织的暴露有限。cdc由硅芯组成，其中Cy5(一种远红色染料)被共价包裹。二氧化硅核心共价涂覆一层聚乙二醇，然后用靶向部分和有效载荷功能化。ELU001是一种CDC功能化药物，含有约20个拓扑异构酶-1抑制剂exatecan分子，通过蛋白水解可切割连接物作为有效载荷，以及约15种叶酸，用于靶向FRα过表达的癌症。ELU001被迅速内化到表达FRα的细胞中，并被运输到溶酶体，在那里有效载荷从CDC释放。在一项为期7天的生存研究中，ELU001对表达3+ (KB, IGROV-1)和2+ (SK-OV-3, HCC827和OVCAR-3)水平的FRα的癌细胞在暴露6小时后显示出低个位数纳摩尔至亚纳摩尔范围内的效力。相比之下，基于抗fr α ADC的ADC mirvetuximab soravtansine对SK-OV-3和HCC827细胞的IC50值>100 nM，对OVCAR-3细胞的IC50值为40 nM。ELU001在免疫缺陷小鼠中对已建立的s.c. KB人子宫颈肿瘤异种移植物具有强大的疗效，其疗效和安全性明显优于游离艾替替康。它也有效地治疗具有较低(2+)FRα表达的已建立的SK-OV-3肿瘤，这也是ADC效果较差的情况。支持ind的非临床研究目前正在进行中，以准备在2021年下半年启动针对FRα过表达癌症患者的首次人体i期临床试验。引用格式:Gregory Paul Adams, Kai Ma, Aranapakam Venkatesan, Feng Chen, Fei Wu, Melik Turker, Thomas Gardinier, Peiming Chen, Vaibhav Patel, Eliel Bayever, Paul Rudick, Geno Germano。ELU001是一种靶向C9Dot药物偶联物(CDC)，用于治疗叶酸受体α (FRα)过表达的癌症[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):305。
{"title":"Abstract 305: ELU001, a targeted C'Dot drug conjugate (CDC) for the treatment of folate receptor alpha (FRα) overexpressing cancers","authors":"G. Adams, Kai Ma, A. Venkatesan, F. Chen, Feixuan Wu, Melik Z. Turker, Thomas C. Gardinier, Pei-Ming Chen, Vaibhav Patel, E. Bayever, Paul Rudick, Geno Germano","doi":"10.1158/1538-7445.AM2021-305","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-305","url":null,"abstract":"CDCs are novel ultra-small (6-7 nm) nanoparticle drug conjugates that have been demonstrated to be capable of faster tumor targeting and deeper tumor penetration than antibody drug conjugates in animal models. CDCs are capable of targeting tumors in the brain and pancreas that are difficult to access, while exhibiting limited exposure to normal tissues due their efficient renal elimination. CDCs are composed of a silica core, in which Cy5, a far red dye is covalently encapsulated. The silica core is covalently coated with a layer of polyethylene glycol which is then functionalized with targeting moieties and payloads. ELU001 is a CDC functionalized with ~20 molecules of the topoisomerase-1 inhibitor exatecan linked via a proteolytic cleavable linker as a payload and ~15 folic acids to provide targeting to FRα overexpressing cancers. ELU001 is rapidly internalized into FRα expressing cells and is trafficked to the lysosome where the payload is released from the CDC. ELU001 exhibits potency in the low single digit nanomolar to sub-nanomolar range against cancer cells that express 3+ (KB, IGROV-1) and 2+ (SK-OV-3, HCC827 and OVCAR-3) levels of FRα after a 6-hr exposure in a 7-day viability study. In contrast, an anti-FRα ADC based ADC mirvetuximab soravtansine exhibits lower potency (>100 nM IC50) against SK-OV-3 and HCC827 cells and 40 nM IC50 against OVCAR-3 cells. ELU001 exhibits potent efficacy against established s.c. KB human cervical tumor xenografts in immunodeficient mice with significantly better efficacy and safety than free exatecan payload. It is also effective in treating established SK-OV-3 tumors with lower (2+) FRα expression, a setting where the ADC is again less effective. IND-enabling nonclinical studies are currently underway to prepare for initiation of a first-in-human phase 1 clinical trial in subjects with FRα overexpressing cancers in the second half of 2021. Citation Format: Gregory Paul Adams, Kai Ma, Aranapakam Venkatesan, Feng Chen, Fei Wu, Melik Turker, Thomas Gardinier, Peiming Chen, Vaibhav Patel, Eliel Bayever, Paul Rudick, Geno Germano. ELU001, a targeted C9Dot drug conjugate (CDC) for the treatment of folate receptor alpha (FRα) overexpressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 305.","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"571 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77786844","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-324
Micah J. Maxwell, A. Arnold, Heather Sweeney, Ljun Chen, T. Lih, M. Schnaubelt, C. Eberhart, Jeffrey A. Rubens, Hui Zhang, D. Clark, E. Raabe
{"title":"Abstract 324: Unbiased proteomic and phosphoproteomic analysis identifies response signatures and novel susceptibilities after combined MEK and mTOR inhibition in BRAFV600Emutant glioma","authors":"Micah J. Maxwell, A. Arnold, Heather Sweeney, Ljun Chen, T. Lih, M. Schnaubelt, C. Eberhart, Jeffrey A. Rubens, Hui Zhang, D. Clark, E. Raabe","doi":"10.1158/1538-7445.AM2021-324","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-324","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79849264","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-267
S. Nagaraju, Alexis van Venrooy, P. Hensley, K. Bree, C. Ayala-Orozco, N. Brooks, D. Izhaky, J. Tour, A. Kamat
{"title":"Abstract 267: Light-activated molecular nanomachines kill bladder cancer cells","authors":"S. Nagaraju, Alexis van Venrooy, P. Hensley, K. Bree, C. Ayala-Orozco, N. Brooks, D. Izhaky, J. Tour, A. Kamat","doi":"10.1158/1538-7445.AM2021-267","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-267","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79946325","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-17
Liwei Cao, Chen Huang, D. Zhou, O. Bathe, Daniel W. Chan, R. Hruban, Lisha Ding, Bing Zhang, Hui Zhang
{"title":"Abstract 17: Proteogenomic characterization of pancreatic ductal adenocarcinoma","authors":"Liwei Cao, Chen Huang, D. Zhou, O. Bathe, Daniel W. Chan, R. Hruban, Lisha Ding, Bing Zhang, Hui Zhang","doi":"10.1158/1538-7445.AM2021-17","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-17","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77891862","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-19
Qimin Quan, J. Ritchey, J. Wilkinson, John Geanacopoulos, Alaina Kaiser, J. Boyce
{"title":"Abstract 19: Proteome-wide biomarker discovery using digital MosaicNeedles","authors":"Qimin Quan, J. Ritchey, J. Wilkinson, John Geanacopoulos, Alaina Kaiser, J. Boyce","doi":"10.1158/1538-7445.AM2021-19","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-19","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"12369 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79470443","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-283
Lindsay Bourdeau, Carlos Cruz, Taylor Williams
One of the rarest and most aggressive pediatric cancers to date is the malignant rhabdoid tumor (MRT), maintaining a survival rate of 16%. Considered a renal cancer, approximately 20-25 new cases are diagnosed in the USA each year, with the average age of diagnosis being about 11 months old. Conventional strategies for treating MRT are limited due to several factors including off-target associated toxicities, patient population, age, metastasis to brain tissue, and diminished survival rates. CLENs (cell membrane lipid-extracted nanoliposomes), a novel drug delivery system, was previously developed and evaluated for selective delivery of cytotoxic drug agents to breast cancer cells and compared to more conventional liposomes. The purpose of this investigation was to optimize and characterize G401-CLENs for selective targeting and delivery of model payloads to a cellular model of rhabdoid tumors. The MRT cell line (G-401 [G401] (ATCC® CRL-1441™) was cultured in McCoy9s 5A Medium (ATCC® 30-2007™), supplemented by 10% FBS. The G401 cellular lipid materials (otherwise known as lipid extracts (LE)) were derived from G401 cells. Follow up studies include the incorporation of G401-LE in liposomes to form G401-CLENs. For development, special consideration was given to distinct determinants of targeting (i.e., particle size and zeta potential) and cellular uptake by G401-CLENs. Other analyses include a comparison of delivery of model and functional siRNA (BRD9 Silencer Select Pre-designed, siRNA ID s35295, Ambion) to G401 target cells using G401-CLENs, and conventional nano-preparations in vitro. G401-LE cell membrane components were extracted from rhabdoid G401 cells. On-going physiochemical characterization studies of G401-CLENs and functional in vitro and fluorescence microscopic analyses are currently underway. Citation Format: Lindsay Bourdeau, Carlos Cruz, Taylor Williams. Target cell-derived, G401-CLENs for selective delivery of model therapeutics to rhabdoid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 283.
恶性横纹肌样瘤(MRT)是迄今为止最罕见和最具侵袭性的儿科癌症之一，其生存率为16%。被认为是肾癌，在美国每年大约有20-25例新诊断病例，平均诊断年龄约为11个月大。由于多种因素，包括脱靶相关毒性、患者群体、年龄、脑组织转移和生存率降低，治疗MRT的传统策略受到限制。CLENs(细胞膜脂质提取纳米脂质体)是一种新型的药物递送系统，先前已被开发并评估用于选择性递送细胞毒性药物到乳腺癌细胞，并与更传统的脂质体进行比较。本研究的目的是优化和表征G401-CLENs选择性靶向并将模型有效载荷递送到横纹肌样肿瘤细胞模型。将MRT细胞系G-401 [G401] (ATCC®CRL-1441™)培养于McCoy9s 5A培养基(ATCC®30-2007™)中，添加10%胎牛血清。G401细胞脂质物质(也称为脂质提取物(LE))来源于G401细胞。后续研究包括在脂质体中掺入G401-LE形成G401-CLENs。为了开发，特别考虑了G401-CLENs的靶向性(即粒径和zeta电位)和细胞摄取的不同决定因素。其他分析包括使用G401- clens和常规纳米制剂将模型siRNA和功能性siRNA (BRD9 Silencer Select Pre-designed, siRNA ID s35295, Ambion)递送到G401靶细胞的比较。从横纹肌样G401细胞中提取G401- le细胞膜成分。目前正在进行G401-CLENs的理化特性研究以及体外功能和荧光显微镜分析。引文格式:Lindsay Bourdeau, Carlos Cruz, Taylor Williams。靶细胞衍生的G401-CLENs用于横纹肌样肿瘤模型疗法的选择性递送[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第283期。
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Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-21
N. Beaton, Yuehan Feng, R. Bruderer, Adam Hendricks, Ghaith M. Hamza, Eric Miele, Rick Davies, K. Beeler, Ilaria Piazza, P. Picotti, P. Castaldi, L. Reiter
{"title":"Abstract 21: LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes","authors":"N. Beaton, Yuehan Feng, R. Bruderer, Adam Hendricks, Ghaith M. Hamza, Eric Miele, Rick Davies, K. Beeler, Ilaria Piazza, P. Picotti, P. Castaldi, L. Reiter","doi":"10.1158/1538-7445.AM2021-21","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-21","url":null,"abstract":"","PeriodicalId":9563,"journal":{"name":"Cancer Chemistry","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73927834","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 : 2021-07-01DOI: 10.1158/1538-7445.AM2021-299
M. Pitz, Alexandra Nukovic, M. Elpers, Sarah Wilde, Angela A Alexander-Bryant
Traditional treatment methods for glioblastoma multiforme (GBM) including resection, radiation, and chemotherapy have been largely unsuccessful, with a current 5-year survival rate of 5.6%. In this project we examine the potential of nanosized self-assembling peptide hydrogels to locally deliver and convert temozolomide (TMZ), an FDA-approved pH-sensitive prodrug, for GBM treatment. The peptide hydrogel is designed to load TMZ into the hydrophobic regions of the hydrogels, and during hydrogel degradation in vivo, convert TMZ into its active form. Hydrogel characterization, drug loading and conversion, and cellular uptake and viability are examined to determine the in vitro efficacy of this delivery method. A combination of dynamic light scattering (DLS), scanning electron microscopy (SEM), and circular dichroism (CD) are used to characterize size and structure of the hydrogels. Loading and conversion of TMZ are quantified using UV-Vis spectroscopy. Fluorescent imaging and cell viability assays are used to determine uptake and anti-cancer effects of the drug-loaded hydrogels on glioblastoma cells. Our results show high uptake in drug-resistant T98G and non-resistant LN-18 glioblastoma cell lines using several of our tunable peptide formulations. CD has shown that all peptide formulations form mostly beta-sheet and random structures during self-assembly. SEM and DLS show that peptide hydrogels formed in a water solvent are more polydisperse than hydrogels in a PBS solvent. Using a pH-meter, we have shown that as the peptides in PBS degrade, there is an increase in local pH. Additionally, TMZ conversion is observed to occur more quickly in drug-loaded hydrogels than TMZ alone. Preliminary cell viability studies have shown that unassembled peptides are not cytotoxic; some of the assembled peptide hydrogels are cytotoxic while others maintain greater than 80% viability when compared to untreated cells. Future studies for the project will include cell viability assays with the most promising peptide formulations loaded with TMZ to determine efficacy of the delivery and conversion system. Finally, this project will culminate in an in vivo study to confirm the overall anti-cancer effect of the drug-loaded peptide hydrogels in a tumor model of GBM. Acknowledgements: This research was supported in part by the National Science Foundation EPSCoR Program under NSF Award # OIA-1655740, the National Institute of Health Award # P30GM131959, and National Science Foundation9s Graduate Research Fellowship Program. Citation Format: Megan Pitz, Alexandra Nukovic, Margaret Elpers, Sarah Wilde, Angela Alexander-Bryant. Self-assembling peptide hydrogel for delivery and conversion of temozolomide in glioblastoma treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 299.
多形性胶质母细胞瘤(GBM)的传统治疗方法包括切除、放疗和化疗，在很大程度上是不成功的，目前的5年生存率为5.6%。在这个项目中，我们研究了纳米级自组装肽水凝胶局部递送和转化替莫唑胺(TMZ)的潜力，替莫唑胺是一种经fda批准的ph敏感前药，用于治疗GBM。肽水凝胶被设计成将TMZ装载到水凝胶的疏水区域，并在水凝胶的体内降解过程中将TMZ转化为活性形式。考察了水凝胶表征、药物装载和转化、细胞摄取和活力，以确定这种递送方法的体外功效。采用动态光散射(DLS)、扫描电子显微镜(SEM)和圆二色性(CD)相结合的方法表征了水凝胶的大小和结构。利用紫外可见光谱对TMZ的加载和转化进行了定量分析。荧光成像和细胞活力测定用于确定载药水凝胶对胶质母细胞瘤细胞的摄取和抗癌作用。我们的研究结果显示，使用我们的几种可调肽制剂，耐药T98G和非耐药LN-18胶质母细胞瘤细胞系的摄取很高。CD表明，所有的肽制剂在自组装过程中大多形成β -片和随机结构。SEM和DLS表明，在水溶剂中形成的肽凝胶比在PBS溶剂中形成的肽凝胶更具多分散性。使用ph计，我们发现随着PBS中的肽降解，局部ph值增加。此外，在载药水凝胶中观察到TMZ的转化比单独的TMZ更快。初步的细胞活力研究表明，未组装的肽没有细胞毒性;与未经处理的细胞相比，一些组装的肽水凝胶具有细胞毒性，而另一些则保持80%以上的活力。该项目未来的研究将包括用最有希望的装载TMZ的肽制剂进行细胞活力测定，以确定传递和转化系统的功效。最后，该项目将在体内研究中达到高潮，以确认载药肽水凝胶在GBM肿瘤模型中的整体抗癌作用。致谢:本研究得到了美国国家科学基金会EPSCoR项目(NSF奖# OIA-1655740)、美国国立卫生研究院奖# P30GM131959和美国国家科学基金会研究生研究奖学金项目的部分支持。引文格式:Megan Pitz, Alexandra Nukovic, Margaret Elpers, Sarah Wilde, Angela Alexander-Bryant。替莫唑胺在胶质母细胞瘤治疗中的传递和转化的自组装肽水凝胶[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):299。
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