Efficacy of Daily IV Administration of Dornase Alfa Up to 14 Days Post Subarachnoid Hemorrhage on Functional Independence At 6 Months: a PROBE Multicenter Open-label Randomized Controlled Trial

Subarachnoid hemorrhage due to aneurysm rupture (SAH) results in high mortality, while survivors frequently suffer reduced quality of life and even loss of autonomy, particularly in the active population. A significant proportion of this morbidity and mortality is linked to the occurrence of delayed cerebral ischemia (DCI), defined as a new focal neurological deficit or reduced level of consciousness unrelated to the treatment of the aneurysm or a concomitant condition.
DCI mainly occurs between days 4 and 14 after SAH, with an estimated incidence of 30%, and is significantly associated with an unfavorable functional prognosis at 3 months. Currently, the only treatment for post-SAH DCI is to prevent or reverse the onset of vasospasm, with limited efficacy, for example through nimodipine administration or hemodynamic optimization. However, according to existing data, vasospasm is not the only cause of DCI, as it may occur elsewhere than in the arterial territory affected by vasospasm, or even in the absence of any vasospasm at all. Recent reviews of the literature highlight the role of microvascular thrombo-inflammation in the pathophysiology of DCI.
This phenomenon begins as soon as SAH occurs, with the appearance of multiple microvascular obstructions responsible for ischemia of downstream territories and loss of distal autoregulatory capacity. Among the effectors of thrombo-inflammation, the NETose phenomenon (production of NETs - Neutrophil Extracellular Traps or extracellular DNA network) has recently been associated with the onset of DCI. Indeed, the concentration of NETs increases in the cerebrospinal fluid (CSF) and blood of SAH patients, and correlates with the severity of the hemorrhage. Furthermore, intravenous or intraperitoneal administration of DNAse in an animal model of SAH has been shown to reduce NET concentration and improve functional prognosis by acting directly on cerebral perfusion through the reduction of micro-thrombosis.
In humans, recombinant DNAse (dornase alfa, Pulmozyme®) has marketing authorization for inhaled administration in cystic fibrosis. The toxicology report accompanying the marketing authorization demonstrates the absence of serious side effects following administration of high IV doses of Pulmozyme® in monkeys and rats. Other studies evaluating IV administration of bovine DNAse at high doses report no complications.
In 1999, a study was published evaluating intravenous (IV) Pulmozyme® in lupus patients, reporting no serious adverse events (SAEs) among the 14 patients receiving the treatment. We are currently conducting a clinical trial of the same molecule in IV administration in patients treated with mechanical thrombectomy and IV thrombolysis for ischemic stroke (NCT04785066).
This study is the first randomized clinical trial to target NETs as effectors of the thrombo-inflammation responsible for post-HSA DCI.

开始日期2025-02-01

申办/合作机构

Fondation Ophtalmologique Adolphe de Rothschild

NCT05880524 / Not yet recruiting临床2期IIT

Reduction of SystemiC Inflammation After Ischemic Stroke by Intravenous DNase Administration (ReSCInD)

The goal of this (monocentric, randomised, placebo-controlled single-blinded; phase 2) clinical trial is to test the hypothesis that DNase 1 administration leads to a reduction in systemic immune response measured in patients after acute ischaemic stroke compared to control treatment.
Participants will receive intravenous DNase 1 (500 µg/kg) or placebo (NaCl 0.9%) twice within 24±6 hours after symptom onset (last seen well). Blood samples will be taken at baseline, day 1 and 3. Personal visits will occur on baseline, day 1, 3 and discharge date. A telephone interview will be conducted on day 30±3.

开始日期2024-12-01

申办/合作机构

Ludwig-Maximilians-Universität München

[+2]

NCT04409925 / Completed临床1期IIT

Phase I Pilot Study Investigating the Safety and Feasibility of Inhaled rhDNase1 and Its Impact on Neutrophil Extracellular Traps (NETs) in Non-Ventilated COVID-19 Infected Patients

This is a pilot study to investigate the safety and feasibility of rhDNase1 and its impact on neutrophil extracellular traps (NETs) in COVID-19 infected patients.

开始日期2020-12-25

申办/合作机构

Centre universitaire de santé McGill

[+3]

100 项与阿法链道酶相关的临床结果

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100 项与阿法链道酶相关的转化医学

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100 项与阿法链道酶相关的专利（医药）

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1,832

项与阿法链道酶相关的文献（医药）

2025-03-01·CELL AND TISSUE BANKING

Advances in preparation of acellular human dermis for tissue banking and transplantation

Article

作者: Barrera, Valentina ; Randles, Michael ; Stern, Irit ; Rooney, Paul

Abstract:

Non-healing wounds cost the National Health Service over £5.6 billion annually in wound management. Skin allografts are used to treat non-healing wounds, ulcers and burns, offering the best protection against infection. In order to allow host cells to repopulate and to avoid immunogenicity, cell components are removed through decellularisation. Decellularisation of human dermis has so far been performed in NHS Blood and Transplant using a combination of two enzymes (RNase T1 and the recombinant human DNase Pulmozyme)®. This study aims at validating a new method to remove DNA from donated dermis via the use of a single enzyme, Benzonase, known for its effectiveness of DNA digestion. Skin samples were decellularised by removing the epidermis, lysing of dermal cells, removal of cellular fragments by a detergent wash and removal of nucleic acids by a nuclease incubation with either Benzonase or Pulmozyme + RNase T1. DNA quantification with PicoGreen, as well as histology on wax-embedded biopsies, stained with DAPI and haemotoxylin and eosin, were performed. In vitro toxicity test on human osteosarcoma immortalised cells and skin fibroblasts, and biomechanical (tensile) testing, were also performed. The effectiveness of DNA digestion with the new methodology was comparable to previous procedure. Mean DNA removal percentage following decellularisation with Pulmozyme + RNase was 99.9% (3.83 ng/mg). Mean DNA removal percentage with Benzonase was 99.8% (9.97 ng/mg). Histology staining showed complete decellularisation following either method. Benzonase was proven to be non-toxic to both cell lines used, and a one-way Anova test showed no significant difference in neither stress nor strain between acellular dermal matrix decellularised with either Benzonase or Pulmozyme + RNase T1. Benzonase was able to effectively decellularise dermis after prior removal of epidermis. It performed just as well as the combination of Pulmozyme + RNase T1, but represents significant advantages in terms of cost effectiveness, procurement and storage; Benzonase has been successfully used in the decellularisation of other tissues, thus would be better for Tissue Banking use. Switching to this combined DNase/RNase can have far-reaching consequences in the production of acellular human dermal matrix by NHSBT and in the treatment of patients requiring it.

2024-12-01·Cellular and molecular neurobiology

Neutrophil Extracellular Traps Regulate Surgical Brain Injury by Activating the cGAS-STING Pathway

Article

作者: Xu, Lixia ; Wu, Qiaoli ; Cui, Yang ; Shi, Qi ; Li, Bingbing ; Wang, Zhengang ; Tong, Xiaoguang ; Yan, Hua ; Fan, Weijia

Abstract:

Surgical brain injury (SBI), induced by neurosurgical procedures or instruments, has not attracted adequate attention. The pathophysiological process of SBI remains sparse compared to that of other central nervous system diseases thus far. Therefore, novel and effective therapies for SBI are urgently needed. In this study, we found that neutrophil extracellular traps (NETs) were present in the circulation and brain tissues of rats after SBI, which promoted neuroinflammation, cerebral edema, neuronal cell death, and aggravated neurological dysfunction. Inhibition of NETs formation by peptidylarginine deiminase (PAD) inhibitor or disruption of NETs with deoxyribonuclease I (DNase I) attenuated SBI-induced damages and improved the recovery of neurological function. We show that SBI triggered the activation of cyclic guanosine monophosphate–adenosine monophosphate synthase stimulator of interferon genes (cGAS-STING), and that inhibition of the cGAS-STING pathway could be beneficial. It is worth noting that DNase I markedly suppressed the activation of cGAS-STING, which was reversed by the cGAS product cyclic guanosine monophosphate–adenosine monophosphate (cGMP-AMP, cGAMP). Furthermore, the neuroprotective effect of DNase I in SBI was also abolished by cGAMP. NETs may participate in the pathophysiological regulation of SBI by acting through the cGAS-STING pathway. We also found that high-dose vitamin C administration could effectively inhibit the formation of NETs post-SBI. Thus, targeting NETs may provide a novel therapeutic strategy for SBI treatment, and high-dose vitamin C intervention may be a promising translational therapy with an excellent safety profile and low cost.Graphical AbstractThe schematic diagram shows the formation of NETs activated cGAS-STING pathway after SBI, leading to increased microglia activation, accompanied with elevation of inflammatory factors, which in turn aggravated brain injury.

2024-12-01·PHYTOMEDICINE

Qingxin Jieyu Granule alleviates myocardial infarction through inhibiting neutrophil extracellular traps via activating ANXA1/FPR2 axis

Article

作者: Kong, Xiangying ; Bai, Ruina ; Su, Xiaohui ; Liu, An ; Zhang, Xinzhuo ; Wang, Jingbo ; Huang, Helan ; Gao, Zhuye ; Huang, Fengyu ; Qi, Mingzhu ; Li, Zhuohang ; Chen, Peiping ; Quan, Jianye ; Chen, Chang

BACKGROUND:

Myocardial infarction (MI), representing the most severe manifestation of coronary artery disease (CAD), stands as a primary concern in the prevention and management of cardiovascular diseases. Clinical evidence demonstrates that Qingxin Jieyu Granule (QXJYG) is efficacious in treatment of MI patients. However, the mechanisms underlying its therapeutic effects remain to be elucidated.

PURPOSE:

This study aimed to evaluate the effects of QXJYG on MI and investigate its underlying mechanisms.

MATERIALS AND METHODS:

The MI model in rats was developed through ligating the left anterior descending (LAD) artery. The effect of QXJYG on cardiac function impairment in MI rats was assessed by echocardiography, while the improvement of cardiomyocyte morphology and myocardial fibrosis after treatment with QXJYG was evaluated through hematoxylin-eosin (H&E) staining and Masson staining. The chemical constituents of QXJYG in blood were identified by using the UPLC-Q-TOF/MS technique. Furthermore, the molecular mechanism underlying the QXJYG therapeutic effect in MI was postulated based on the disease gene-drug target network analysis. Other technical methods such as ELISA, immunohistochemical staining, Western Blot analysis and application of pharmacological inhibitors were employed to verify the effectiveness of QXJYG in treating MI and explore its potential molecular targets.

RESULTS:

The cardiac function in experimental rats post-MI was significantly impaired, as evidenced by an enlarged infarction area, disordered arrangement of cardiomyocytes, and aggravated myocardial fibrosis. QXJYG treatment significantly enhanced the cardiac function and reduced the pathological damage of the cardiac tissue in MI rats. Through the network pharmacology analysis, we identified that FPR2 might be a potential target of QXJYG in its cardiac protection role. QXJYG markedly downregulated the level of neutrophil extracellular traps (NETs) in MI rats, specifically manifested as a significant reduction in the Histone-DNA level and expression of myeloperoxidase (MPO) and citrullinated histone H3 (CitH3) proteins. Furthermore, QXJYG upregulated the levels of ANXA1 and FPR2 proteins in MI rats. The level of FPR2 was markedly reduced in MI rats upon administration of WRW4, a specific inhibitor of FPR2, which was associated with exacerbated MI injury and an elevated level of NETs. When WRW4 was co-administered with QXJYG, the cardioprotective effects of QXJYG on MI were significantly diminished. However, the addition of DNase I did not result in significant changes of the outcomes in MI rats after QXJYG intervention.

CONCLUSION:

QXJYG treatment alleviates cardiac tissue injury in MI rats by inhibiting NETs through activating the ANXA1/FPR2 axis. The findings extend our understanding of the therapeutic effectiveness of QXJYG and offer a scientific foundation for the clinical utilization of QXJYG.

139

项与阿法链道酶相关的新闻（医药）

2024-12-16

·access wire

Xenetic Biosciences, Inc. Extends Research Agreement with the University of Virginia for the Advancement of its DNase-Based Oncology Platform

Company continuing to advance DNase-based oncology program towards clinical proof-of-concept studies in multiple indications FRAMINGHAM, MA / ACCESSWIRE / December 16, 2024 / Xenetic Biosciences, Inc. (NASDAQ:XBIO) ("Xenetic" or the "Company"), a biopharmaceutical company focused on advancing innovative immuno-oncology technologies addressing hard to treat cancers, today announced it has extended its previously announced Research Funding Agreement (the "Agreement") with the University of Virginia ("UVA") to advance the development of its systemic DNase program through 2025. Xenetic's DNase-based oncology platform is designed to target neutrophil extracellular traps (NETs), which are weblike structures composed of extracellular chromatin coated with histones and other proteins. In cancer, NETs are expelled by activated neutrophils into the tumor microenvironment (TME) and blood, thereby promoting cancer spread, local and systemic immunosuppression, as well as cancer-associated thrombosis. Reduction of NETs burden via application of Xenetic's proprietary recombinant human DNase I has been shown to improve efficacy of immunotherapy, adoptive cell therapy and chemotherapy in preclinical animal models."Allan Tsung, MD, and the team at UVA continue to be valued partners and we are pleased with the progress under the Agreement in evaluating the potential addition of DNase to available treatment options in areas of significant unmet need. The preclinical and translational data that UVA has accumulated under the initial Scope of Work to the Agreement further strengthens the scientific rationale for investigating combinations of DNase I with immunotherapies and chemotherapies. In the next phase of this partnership, UVA will continue to investigate combinations of DNase I with immunotherapies in models of primary and metastatic colorectal cancer. As a surgical oncologist and scientist, Dr. Tsung is internationally recognized for leading substantial research on the role of NETs in tumor growth, metastasis, and resistance to existing cancer therapies and we look forward to leveraging that expertise as we advance our technology toward the clinic," commented Reid P. Bissonnette, Ph.D., Executive Consultant for Translational Research and Development of Xenetic.Under the terms of the Agreement, in addition to advancing Xenetic's existing intellectual property, Xenetic has an option to acquire an exclusive license to any new intellectual property arising from the DNase research program. Allan Tsung, MD, a member of the Company's Scientific Advisory Board and Chair of the Department of Surgery at the UVA School of Medicine, will continue to oversee the research conducted under the Agreement. Xenetic is working toward its planned first-in-human study to evaluate DNase combined with immune checkpoint inhibitors or chemotherapy.About Xenetic BiosciencesXenetic Biosciences, Inc. is a biopharmaceutical company focused on advancing innovative immune-oncology technologies addressing hard to treat cancers. The Company's DNase I platform is designed to improve outcomes of existing treatments, including immunotherapies, by targeting neutrophil extracellular traps (NETs), which are involved in cancer progression. Xenetic is currently focused on advancing its systemic DNase program into the clinic as an adjunctive therapy for pancreatic carcinoma, colorectal cancer and locally advanced or metastatic solid tumors.For more information, please visit the Company's website at www.xeneticbio.com and connect on X, LinkedIn, and Facebook.Forward-Looking StatementsThis press release contains forward-looking statements that we intend to be subject to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. All statements contained in this press release other than statements of historical facts may constitute forward-looking statements within the meaning of the federal securities laws. These statements can be identified by words such as "expects," "plans," "projects," "will," "may," "anticipates," "believes," "should," "intends," "estimates," "remain," "focus", "confidence in", "potential", "making", and other words of similar meaning, including, but not limited to: all statements regarding the collaboration agreement with UVA, including regarding the next phase of the partnership with UVA where UVA will continue to investigate combinations of DNase I with immunotherapies in models of primary and metastatic colorectal cancer and our plans to continue to leverage the knowledge and expertise of the team at UVA to potentially expand and broaden the utility of our proprietary platform technology; and expectations regarding our DNase-based oncology platform, including statements regarding: advancing DNase-based oncology program towards clinical proof-of-concept studies in multiple indications; focusing on advancing innovative immuno-oncology technologies addressing hard to treat oncology indications; advancing the development of the Company's program on the combination of systemic DNase and CAR T-cell therapies; the DNase platform improving outcomes of existing treatments, including immunotherapies, by targeting neutrophil extracellular traps (NETs), which are involved in cancer progression; and our focus on advancing our systemic DNase program into the clinic as an adjunctive therapy for pancreatic carcinoma, colorectal cancer and locally advanced or metastatic solid tumors. All forward-looking statements contained herein are based on current expectations and are subject to a number of risks and uncertainties. Many factors could cause our actual activities, performance, achievements, or results to differ materially from the activities and results anticipated in forward-looking statements. Important factors that could cause actual activities, performance, achievements, or results to differ materially from such plans, estimates or expectations include, among others, (1) failure to achieve the expected outcomes and realize the anticipated benefits from our collaboration agreements, including the agreement with UVA; (2) unexpected costs, charges or expenses resulting from our manufacturing and collaboration agreements, including the agreement with UVA; (3) unexpected costs, charges or expenses resulting from the licensing of the DNase platform; (4) uncertainty of the expected financial performance of the Company following the licensing of the DNase platform; (5) failure to realize the anticipated potential of the DNase or PolyXen technologies; (6) the ability of the Company to obtain funding and implement its business strategy; and (7) other risk factors as detailed from time to time in the Company's reports filed with the SEC, including its annual report on Form 10-K, periodic quarterly reports on Form 10-Q, current reports on Form 8-K and other documents filed with the SEC. The foregoing list of important factors is not exclusive. In addition, forward-looking statements may also be adversely affected by general market factors, general economic and business conditions, including potential adverse effects of public health issues, such as the COVID-19 outbreak, and geopolitical events, such as the conflicts in Ukraine and in the Middle East, on economic activity, competitive product development, product availability, federal and state regulations and legislation, the regulatory process for new product candidates and indications, manufacturing issues that may arise, patent positions, litigation and shareholder activism, among other factors. The forward-looking statements contained in this press release speak only as of the date the statements were made, and the Company does not undertake any obligation to update forward-looking statements, except as required by law.Contact:JTC Team, LLCJenene Thomas(908) 824-0775[email protected]SOURCE: Xenetic Biosciences, Inc.

免疫疗法引进/卖出细胞疗法

2024-12-05

·生物世界

Science大地震！天才少年一周三篇Science，打破百年规则

年终福利大放送 2024年10月9日，瑞典皇家科学院决定将2024年诺贝尔化学奖的一半授予David Baker，以表彰他在“计算蛋白质设计”方面的贡献，并将另一半授予 Demis Hassabis 和 John M. Jumper，以表彰他们在“蛋白质结构预测”方面的贡献。 2024 年 5 月 8 日，谷歌 DeepMind 与 Isomorphic Labs 联合在《自然》期刊上发布蛋白质领域最新人工智能模型 AlphaFold 3！这一模型能够准确预测蛋白质、DNA、RNA 以及配体等生命分子的结构及其相互作用方式。这是继AlphaFold 2 之后的又一重大突破。在预测类药物相互作用方面，AlphaFold 3 实现了前所未有的准确度，包括蛋白质与配体的结合以及抗体与其靶蛋白的结合。在 PoseBusters 的基准测试中，AlphaFold 3 的准确率比现有最佳传统方法高出 50%，而且无需任何结构信息输入，成为首个超越传统物理预测工具的人工智能系统。这种预测抗体与蛋白质结合的能力，对于理解人类免疫反应的各个方面以及新抗体的设计至关重要。四大序列模型大比拼 1.ProteinMPNN是基于信息传递神经网络（MPNN）开发的，能够通过给定的蛋质骨架结构找到正确折叠的氨基酸序列。它的功能是通过蛋白质骨架特征如Cα-Cα原子间距离、二面角等信息，生成预测的氨基酸序列。与传统的Rosetta（Rosetta的原理为基于物理的方法将序列设计视为能量优化问题，在给定的输入结构中寻找具有最低能量的氨基酸身份和构象的组合）相比，ProteinMPNN在序列恢复、计算效率和适用范围上具有显著优势。该方法广泛应用于单体、环低聚物、蛋白质纳米颗粒和蛋白质-蛋白质界面的设计 2.ABACUS-R是一个深度学习模型，在给定主链的情况下可以设计序列。方法使用一个具有编码器和解码器的网络模型，使用多任务方法训练。环境的特征使用氨基酸类型，但是没有显式描述氨基酸的侧链构象（Rotamer-Free）。通过X-ray的实验验证，ABACUS-R超过了目前最优秀的序列从头设计算法，可以作为非常有效的蛋白质设计工具 3.CarbonDesign 是蛋白质序列设计版的 AlphaFold，它从用于蛋白质结构预测的 AlphaFold 模型中汲取灵感，并专门针对蛋白质序列设计进行了算法改进。该工具能够准确且稳健的设计蛋白质序列，可以被广泛应用于不同蛋白质设计场景，并且可以预测蛋白质突变的功能影响。 4.CARBonAra模型。该模型基于几何变换器架构,可以同时处理蛋白质骨架和周围的任何类型分子,包括其他蛋白质、核酸、配体、离子等,从而实现更加精确和灵活的蛋白质序列设计国内主要从事AI蛋白质设计的高校北京大学、国际机器学习研究中心、清华大学中国科学院计算技术研究所、中国人民大学、上海交通大学复旦大学、上海科技大学、中国科学院上海药物研究所中国科学院分子细胞科学卓越创新中心浙江大学、西湖大学、浙江工业大学、深圳先进技术研究院、清华大学深圳国际研究生院南方科技大学、中国科学技术大学、中国科学院微生物研究所厦门大学、山东大学、中国科学院天津工业生物技术研究所 2 2 发CNS必须要学的五大课程 0 01.AI蛋白质设计 02.AIDD人工智能药物发现与设计 03.CADD计算机辅助药物设计 04.深度学习基因组学 05.机器学习代谢组学 2 2 01 AI蛋白质设计 *涉及使用代码/计算工具的操作一、蛋白质相关的深度学习简介 1.基础概念 1.1.机器学习简介：从手写数字识别到大语言模型 1.2.蛋白质结构预测与设计回顾 1.3.Linux简介 1.4.代码环境：VS code和Jupyter notebook* 1.5.Python关键概念介绍* 2.常用的分析/可视化蛋白质及相关分子的方法 2.1.常用数据库与同源序列搜索和MSA构建 2.2.使用pymol和Mol*可视化蛋白质结构* 2.3.使用biopython与biotite分析生物序列与结构数据* 2.4.使用fpocket与point-site分析蛋白质结构口袋* 3.深度学习蛋白质设计与传统蛋白质设计之间的差异 3.1.深度学习的本质 3.2.传统方法：全原子能量函数Rosetta与统计势 3.3.深度学习：几何深度学习 3.4.深度学习与传统的物理方法的互补性 3.5.深度学习蛋白质设计的优越性 4.蛋白质语言模型 4.1.语言模型：从RNN到Transformers 4.2.理解蛋白质语言 4.3.生成式蛋白质语言模型 4.4.结构模型与语言模型的比较分析 5.基于深度学习的蛋白质功能与性质预测 5.1.蛋白质功能分类预测* 5.2.信号肽、跨膜区、亚细胞定位预测 5.3.蛋白质同源结构搜索 5.4.酶活性位点预测二、深度学习与蛋白质结构预测 1.传统蛋白质（复合物）结构预测 1.1.使用modeller、swiss-model进行同源建模 1.2.基于分子动力学的从头建模 1.3.分子对接加入非蛋白质部分，AutoDock Vina实际操作* 2.现代深度学习用于蛋白质结构预测 2.1.RaptorX：从计算机视觉到蛋白质结构 2.2.AlphaFold2 2.3.AlphaFold3：生成式结构预测 2.4.ESMFold：语言模型与结构预测的融合 3.AlphaFold2 原理回顾 3.1.从共进化到结构 3.2.注意力机制 3.3.EvoFormer 3.4.Structural Module 4.AlphaFold3 介绍 4.1.扩散模型 4.2.训练数据 4.3.AlphaFold3 的成绩与不足 5.AlphaFold2/3 实际操作与结果分析 5.1.AlphaFold2实操* 5.2.AlphaFold2分析* 5.3.AlphaFold server使用* 5.4.本地版的AlphaFold3* 5.5.AlphaFold3分析* 6.ESMFold 6.1.从语言模型到结构预测 6.2.什么时候使用ESMFold，什么时候使用AlphaFold 6.3.ESMFold使用* 三、固定主链蛋白质序列设计 1.传统的蛋白质序列设计 1.1.基于全原子力场* 1.2.基于统计势 2.融入结构知识的语言模型设计蛋白质序列 2.1.ESM-IF原理介绍 2.2.ESM-IF的应用* 3.基于CNN的序列设计 3.1.CNN原理简介 3.2.DenseCPD设计方法 3.3.有侧链构象的设计方法 4.基于GNN设计序列 4.1.ProteinMPNN 的成功经验分析 4.2.ProteinMPNN 的广泛应用 4.3.ProteinMPNN 实际操作* 5.其他的序列设计模型 5.1.ABACUS-R 简介与实际操作* 5.2.CarbonDesign 从结构预测来到序列设计去* 5.3.CARBonAra 环境感知的序列设计* 6.固定主链序列设计在功能蛋白设计中的应用 6.1.新骨架蛋白质表达量优化（Science文章复现）* 6.2.抗体亲和力优化（Science文章复现）* 6.3.结合进化信息的酶性质全方位优化（JACS文章复现）* 四、深度学习蛋白质结构设计 1.传统思路回顾 1.1.结构域拼接 1.2.SCUBA：无侧链的蛋白质力场 2.基于蛋白质表面几何深度学习的binder设计 2.1.masif原理简介 2.2.masif用于识别蛋白表面的PPI热点 2.3.masif设计binder 3.基于扩散模型的蛋白质骨架设计模型 3.1.FrameDiff：基于IPA的主链生成* 3.2.Chroma：等变图神经网络结构设计 3.3.RFDiffusion：基于RosettaFold 3.4.RFDiffusion-All-Atom：基于RosettaFold-All-Atom 4.序列-结构共设计 4.1.trDesign 4.2.AlphaFold Hallucination 4.3.Rfjoint 4.4.Protein Generator 5.结合蛋白从头设计Nature Communication文章流程* 5.1.功能表位的选取 5.2.带限制条件的骨架生成 5.3.迭代优化 6.荧光素酶结构从头设计 6.1.Theozyme理论解释 6.2.骨架生成策略 6.3.活性位点设计与活性进化五、面向功能的蛋白质序列设计 1.语言的深度学习建模方法 1.1.Transformer 1.2.BERT: Bidirectional Encoder Representations from Transformers 1.3.GPT: Generative Pre-trained Transformers 2.蛋白质语言模型的代表：ESM 2.1.模型框架 2.2.ESM系列工作：ESM-1/2，MSA Transformer，ESM3 2.3.ESM模型实际操作* 3.基于蛋白质语言模型的功能蛋白设计 3.1.预训练+微调的范式 3.2.条件式生成模型：Progen与ZymCTRL 3.3.Progen案例分析 3.4.上手微调ZymCTRL* 4.非自回归的序列生成模型 4.1.ProteinGAN：生成序列 4.2.DeepEvo：生成耐热酶 4.3.Prot-VAE 4.4.P450Diffusion：基于扩散模型设计功能P450* 5.功能蛋白生成后的评估指标 5.1.天然序列相似性评估* 5.2.多样性评估* 5.3.结构合理性评估* 六、基于深度学习的蛋白质挖掘与改造应用 1.酶学性质预测 1.1.DLKcat与GotEnzyme数据库介绍 1.2.UniKP：利用预训练模型挖掘、改造Kcat* 1.3.CLEAN：基于对比学习的EC号预测挖掘稀有脱卤酶* 2.蛋白质热稳定性改造 2.1.MutCompute介绍 2.2.利用MutCompute改造PETase（Nature）* 2.3.ThermoMPNN介绍与使用* 2.4.Pythia介绍与使用* 3.机器学习辅助定向进化/蛋白质工程 3.1.零样本突变效应预测原理 3.2.零样本改造基因编辑酶* 3.3.Low-N策略用于蛋白质工程 3.4.预训练模型的Evo-tuning* 3.5.ECNet介绍 3.6.蛋白质相互作用中的突变效应预测 4.针对自己的实验数据，训练自己的神经网络* 4.1.神经网络训练框架 4.2.数据收集、整理 4.3.特征提取方式 4.4.预训练模型的选取 4.5.模型训练、测试 4.6.新突变的预测 5.深度学习辅助的新酶挖掘* 5.1.基因编辑脱氨酶挖掘（Cell工作复现） 5.2.耐热塑料水解酶挖掘（Nature Communications 5.3.使用FoldSeek进行基于结构的挖掘可以上下滚动查看 02 AIDD人工智能药物设计与发现第一天 1.AIDD概述及药物综合数据库介绍 2.人工智能辅助药物设计AIDD概述 3.安装环境 (1)anaconda (2)vscode (3)pycharm (4)虚拟环境 4.第三方库基本使用方法 (1)numpy (2)pandas (3)matplotlib (4)requests 5.多种药物综合数据库的获取方式 (1)KEGG（requests爬虫） (2)Chebi（libChEBIpy） (3)PubChem（pubchempy / requests） (4)ChEMBL（chembl_webresource_client） (5)BiGG（curl） (6)PDB（pypdb）第二天 ML-based AIDD 1.机器学习 (1)机器学习种类： ①监督学习 ②无监督学习 ③强化学习 (2)典型机器学习方法 ①决策树 ②支持向量机 ③朴素贝叶斯 ④神经网络 ⑤卷积神经网络 (3)模型的评估与验证 (4)分类评估：准确率、精确率、召回率、F1分数、ROC曲线、AUC计算 (5)回归评估：平均绝对误差、均方差、R2分数、可释方差分数 (6)交叉验证 2.sklearn工具包基本使用 3.rdkit工具包的基本使用 4.化合物编码方式和化合物相似性理论知识 5.项目实战1：基于ADME和Ro5的分子筛选 6.项目实战2：基于化合物相似性的配体筛选 7.项目实战3：基于化合物相似性的分子聚类 8.项目实战4: 基于机器学习的生物活性预测 9.项目实战5：基于机器学习的分子毒性预测第三天 GNN-based AIDD 1.图神经网络 (1)框架介绍: PyG，DGL，TorchDrug (2)图神经网络消息传递机制 (3)图神经网络数据集设计 (4)图神经网络节点预测、图预测任务和边预测任务实战 2.论文精讲：DeepTox: Toxicity Prediction using Deep Learning 3.项目实战1：基于图神经网络的分子毒性预测 (1)SMILES分子数据集构建PyG图数据集 (2)基于GNN进行分子毒性预测 4.项目实战2：基于图神经网络的蛋白质-配体相互作用预测 (1)蛋白质分子图形化，构建PyG图数据集 (2)基于GIN进行网络搭建及相互作用预测第四天 NLP-based AIDD 1.自然语言处理 (1)Encoder-Decoder模型 (2)循环神经网络 RNN (3)Seq2seq (4)Attention (5)Transformer 2.项目实战1：基于自然语言的分子毒性预测 (1)SMILES分子数据集词向量表示方法 (2)基于NLP模型进行分子毒性预测 3.项目实战2：基于Transformer的有机化学反应产量预测（Prediction of chemical reaction yields using deep learning） 4.论文精读及代码讲解：《Mapping the space of chemical reactions using attention-based neural networks》第五天分子生成与药物设计 1.蛋白质数据库介绍与相关数据爬取 (1)PDB数据库 (2)UniProt数据库 2.蛋白质相关药物设计项目实战项目实战 (1)：基于RDKit的生化反应与蛋白质基本处理项目实战 (2)：基于序列的蛋白质属性预测项目实战 (3)：基于结构的蛋白质属性预测项目实战 (4)：基于NGLView可视化的蛋白质-配体相互作用项目实战 (5)：基于机器学习的蛋白质-配体相互作用亲和力预测项目实战 (6)：基于蛋白质三维结构使用原子卷积网络的进行蛋白质-配体相互作用预测可以上下滚动查看 03 CADD计算机辅助药物设计(即报即学) 第一天上午背景与理论知识以及工具准备 1.PDB数据库的介绍和使用 1.1数据库简介 1.2靶点蛋白的结构查询与选取 1.3靶点蛋白的结构序列下载 1.4靶点蛋白的下载与预处理 1.5批量下载蛋白晶体结构 2.Pymol的介绍与使用 2.1软件基本操作及基本知识介绍 2.2蛋白质-配体相互作用图解 2.3蛋白-配体小分子表面图、静电势表示 2.4蛋白-配体结构叠加与比对 2.5绘制相互作用力 3.notepad的介绍和使用 3.1优势及主要功能介绍 3.2界面和基本操作介绍 3.3插件安装使用下午一般的蛋白 -配体分子对接讲解 1.对接的相关理论介绍 1.1分子对接的概念及基本原理 1.2分子对接的基本方法 1.3分子对接的常用软件 1.4分子对接的一般流程 2.常规的蛋白-配体对接 2.1收集受体与配体分子 2.2复合体预构象的处理 2.3准备受体、配体分子 2.4蛋白-配体对接 2.5对接结果的分析以新冠病毒蛋白主蛋白酶靶点及相关抑制剂为例第二天虚拟筛选 1.小分子数据库的介绍与下载 2.相关程序的介绍 2.1 openbabel的介绍和使用 2.2 chemdraw的介绍与使用 3.虚拟筛选的前处理 4.虚拟筛选的流程及实战演示案例：筛选新冠病毒主蛋白酶抑制剂 5.结果分析与作图 6.药物ADME预测 6.1ADME概念介绍 6.2预测相关网站及软件介绍 6.3预测结果的分析第三天拓展对接的使用方法 1.蛋白-蛋白对接 1.1蛋白-蛋白对接的应用场景 1.2相关程序的介绍 1.3目标蛋白的收集以及预处理 1.4使用算例进行运算 1.5关键残基的预设 1.6结果的获取与文件类型 1.7结果的分析以目前火热的靶点 PD-1/PD-L1等为例。 2.涉及金属酶蛋白的对接 2.1金属酶蛋白-配体的背景介绍 2.2蛋白与配体分子的收集与预处理 2.3金属离子的处理 2.4金属辅酶蛋白-配体的对接 2.5结果分析以人类法尼基转移酶及其抑制剂为例 3.蛋白-多糖分子对接 4.1蛋白-多糖相互作用 4.2对接处理的要点 4.3蛋白-多糖分子对接的流程 4.4蛋白-多糖分子对接 4.5相关结果分析以α-糖苷转移酶和多糖分子对接为例 5.核酸-小分子对接 5.1核酸-小分子的应用现状 5.2相关的程序介绍 5.3核酸-小分子的结合种类 5.4核酸-小分子对接 5.5相关结果的分析以人端粒 g -四链和配体分子对接为例。操作流程介绍及实战演示第四天拓展对接的使用方法 1.柔性对接 1.1柔性对接的使用场景介绍 1.2柔性对接的优势 1.3蛋白-配体的柔性对接重点：柔性残基的设置方法 1.4相关结果的分析以周期蛋白依赖性激酶 2（CDK2）与配体1CK为例 2.共价对接 2.1两种共价对接方法的介绍 2.1.1柔性侧链法 2.1.2两点吸引子法 2.2蛋白和配体的收集以及预处理 2.3共价药物分子与靶蛋白的共价对接 2.4结果的对比以目前火热的新冠共价药物为例。 3.蛋白-水合对接 3.1水合作用在蛋白-配体相互作用中的意义及方法介绍 3.2蛋白和配体的收集以及预处理 3.3对接相关参数的准备重点：水分子的加入和处理 3.4蛋白-水分子-配体对接 3.5结果分析以乙酰胆碱结合蛋白 (AChBP)与尼古丁复合物为例第五天分子动力学模拟（linux与gromacs使用安装） 1. linux系统的介绍和简单使用 1.1 linux常用命令行 1.2 linux上的常用程序安装 1.3体验：如何在linux上进行虚拟筛选 2.分子动力学的理论介绍 2.1分子动力学模拟的原理 2.2分子动力学模拟的方法及相关程序 2.3相关力场的介绍 3.gromacs使用及介绍重点：主要命令及参数的介绍 4.origin介绍及使用第六天溶剂化分子动力学模拟的执行 1.一般的溶剂化蛋白的处理流程 2.蛋白晶体的准备 3.结构的能量最小化 4.对体系的预平衡 5.无限制的分子动力学模拟 6.分子动力学结果展示与解读以水中的溶菌酶为例第七天蛋白-配体分子动力学模拟的执行 1.蛋白-配体在分子动力学模拟的处理流程 2.蛋白晶体的准备 3.蛋白-配体模拟初始构象的准备 4.配体分子力场拓扑文件的准备 4.1高斯的简要介绍 4.2 ambertool的简要介绍 4.3生成小分子的力场参数文件 5.对复合物体系温度和压力分别限制的预平衡 6.无限制的分子动力学模拟 7.分子动力学结果展示与解读 8.轨迹后处理及分析以新冠病毒蛋白主蛋白酶靶点及相关抑制剂为例可以上下滚动查看 04 深度学习基因组学第一天理论部分深度学习算法介绍 1.有监督学习的神经网络算法 1.1全连接深度神经网络DNN在基因组学中的应用举例 1.2卷积神经网络CNN在基因组学中的应用举例 1.3循环神经网络RNN在基因组学中的应用举例 1.4图卷积神经网络GCN在基因组学中的应用举例 2.无监督的神经网络算法 2.1自动编码器AE在基因组学中的应用举例 2.2生成对抗网络GAN在基因组学中的应用举例实操内容 1.Linux操作系统 1.1常用的Linux命令 1.2 Vim编辑器 1.3基因组数据文件管理,修改文件权限 1.4查看探索基因组区域 2.Python语言基础 2.1.Python包安装和环境搭建 2.2.常见的数据结构和数据类型第二天理论部分基因组学基础 1.基因组数据库 2.表观基因组 3.转录基因组 4.蛋白质组 5.功能基因组实操内容基因组常用深度学习框架 1.安装并介绍深度学习工具包tensorflow, keras，pytorch 2.在工具包中识别深度学习模型要素 2.1.数据表示 2.2.张量运算 2.3.神经网络中的“层” 2.4.由层构成的模型 2.5.损失函数与优化器 2.6.数据集分割 2.7.过拟合与欠拟合 3.基因组数据处理 3.1安装并使用keras_dna处理各种基因序列数据如BED、GFF、GTF、BIGWIG、BEDGRAPH、WIG等 3.2使用keras_dna设计深度学习模型 3.3使用keras_dna分割训练集、测试集 3.4使用keras_dna选取特定染色体的基因序列等 4．深度神经网络DNN在识别基序特征中应用 4.1实现单层单过滤器DNN识别基序 4.2实现多层单过滤器DNN识别基序 4.3实现多层多过滤器DNN识别基序第三天理论部分卷积神经网络CNN在基因调控预测中的应用 1.Chip-Seq中识别基序特征G4，如DeepG4 2.Chip-Seq中预测DNA甲基化，DeepSEA 3.Chip-Seq中预测转录调控因子结合，DeepSEA 4.DNase-seq中预测染色体亲和性，Basset 5.DNase-seq中预测基因表达eQTL，Enformer 实操内容复现卷积神经网络CNN识别基序特征DeepG4、非编码基因突变DeepSEA，预测染色体亲和性Basset，基因表达eQTL 1.复现DeepG4从Chip-Seq中识别G4特征 2.安装selene_sdk，复现DeepSEA从Chip-Seq中预测DNA甲基化，非编码基因突变 3.复现Basset，从Chip-Seq中预测染色体亲和性 4.复现Enformer，从Chip-Seq中预测基因表达eQTL 第四天理论部分深度学习在识别拷贝数变异DeepCNV、调控因子DeepFactor上的应用 1.SNP微阵列中预测拷贝数变异CNV，DeepCNV 2.RNA-Seq中预测premiRNA，dnnMiRPre 3.从蛋白序列中预测调控因子蛋白质，DeepFactor 实操内容 1.复现DeepCNV利用SNP微阵列联合图像分析识别拷贝数变异 2.复现循环神经网络RNN工具dnnMiRPre，从RNA-Seq中预测premiRNA 3.复现DeepFactor，从蛋白序列中识别转录调控因子蛋白质第五天理论部分深度学习在识别及疾病表型及生物标志物上的应用 1.从基因表达数据中识别乳腺癌分型的深度学习工具DeepType 2.从高维多组学数据中识别疾病表型，XOmiVAE 3.基因序列及蛋白质相互作用网络中识别关键基因的深度学习工具DeepHE 实操内容 1.复现DeepType，从METABRIC乳腺癌数据中区分乳腺癌亚型 2.复现XOmiVAE，从TCGA多维数据库中识别乳腺癌亚型 3.复现DeepHE利用基因序列及蛋白质相互作用网络识别关键基因第六天理论部分深度学习在RNA测序数据中准确鉴别RNA编辑与DNA突变的应用 1.通过深度学习模型DeepDDR高效准确地从单个RNA测序数据中识别RNA编辑和DNA突变 2.介绍DEMINING框架的创新设计和应用实操内容 1.数据筛选：筛选标准去除转录组数据中的测序和比对错误 2.提取高可信度的RNA编辑位点和相同数量的DNA突变位点，分别用于训练、验证和测试模型 3. 使用多个层次的卷积和池化操作，提取突变位点周围的序列和读段比对特征，增强模型对突变的识别能力 4.识别突变位点，并捕捉到更大范围内突变的上下文信息，以准确区分RNA编辑和DNA突变 5.对识别出的突变进行功能分析，评估其对宿主基因表达的影响，为潜在的疾病机制提供新的见解 6.利用迁移学习将DeepDDR模型应用于非灵长类RNA测序样本，展示其在不同物种中的适用性和有效性可以上下滚动查看 05 机器学习代谢组学第一天上午： A1 代谢物及代谢组学的发展与应用（1）代谢与生理过程；（2）代谢与疾病；（3）非靶向与靶向代谢组学；（4）空间代谢组学与质谱成像（MSI）；（5）代谢组学与药物和生物标志物；（6）代谢流与机制研究。 A2 代谢通路及代谢数据库（1）几种经典代谢通路简介；（2）三大常见代谢物库：HMDB、METLIN 和 KEGG; （3）代谢组学原始数据库：Metabolomics Workbench 和 Metabolights. A3 参考资料推荐第一天下午： A4 代谢组学实验流程简介 A5 色谱、质谱硬件与原理解析（1）色谱分析原理与构造；（2）色谱仪和色谱柱的选择；（3）色谱的流动相：梯度洗脱法；（4）离子源、质量分析器与质量检测器解析；（5）质谱分析原理及动画演示；（6）色谱质谱联用技术（LC-MS）；第二天上午： B1 代谢物样本处理与抽提（1）各种组织、血液和体液等样本的提取流程与注意事项；（2）代谢物抽提流程与注意事项；（3）样本及代谢物的运输与保存问题； B2 LC-MS 数据质控与搜库（1） LC-MS 实验过程中 QC 和 Blank 样本的设置方法；（2） LC-MS 上机过程的数据质控监测和分析；（3）代谢组学上游分析原理——基于 Compound Discoverer 与 Xcms 软件；（4） Xcms 软件数据转换、提峰、峰对齐与搜库；第二天下午： B3 R 语言基础（1） R 和 Rstudio 的安装；（2） Rstudio 的界面配置；（3） R 中的基础运算和统计计算；（4） R 中的包：包，函数与参数的使用；（5） R 语言语法，数据类型与数据结构；（6） R 基础画图； B4 R 语言画图利器——ggplot2 包（1） ggplot2 简介（2） ggplot2 的画图哲学；（3） ggplot2 的配色系统；（4） ggplot2 数据挖掘与作图实战；第三天上午：机器学习 C1 有监督式机器学习在代谢组学数据处理中的应用（1）人工智能、机器学习、深度学习的关系；（2）回归算法：从线性回归、Logistic 回归与 Cox 回归讲起；（3） PLS-DA 算法：PCA 降维后没有差异的数据还有救吗？（4） VIP score 的意义及选择；（5）分类算法：决策树，随机森林和贝叶斯网络模型； C2 一组代谢组学数据的分类算法实现的 R 演练 (1) 数据解读； (2) 演练与操作；第三天下午： C3 无监督式机器学习在代谢组学数据处理中的应用（1）大数据处理中的降维；（2） PCA 分析作图；（3）三种常见的聚类分析：K-means、层次分析与 SOM （4）热图和 hcluster 图的 R 语言实现； C4 一组代谢组学数据的降维与聚类分析的 R 演练 (1) 数据解析； (2) 演练与操作；第四天上午： D1 在线代谢组分析网页 Metaboanalyst 操作（1）用 R 将数据清洗成网页需要的格式；（2）独立组、配对组和多组的数据格式问题；（3） Metaboanalyst 中的上游分析（原始数据峰提取、峰对齐与搜库）（4） Metaboanalyst 的 pipeline 以及参数设置和注意事项；（5） Metaboanalyst 的结果查看和导出；（6） Metaboanalyst 的数据编辑；（7）全流程演练与操作。第四天下午： D2 代谢组学数据清洗与 R 语言进阶（1）代谢组学中的 t、fold-change 和响应值；（2）数据清洗流程；（3） R 语言 tidyverse；（4）数据预处理：数据过滤与数据标准化（样本的 Normalization 和代谢物的 Scaling）；（5）代谢组学数据清洗演练；第五天上午： E1 文献数据分析部分复现（1 篇）（1）文献深度解读；（2）实操：从原始数据下载到图片复现；（3）学员实操。第五天下午： E2 机器学习与代谢组学顶刊解读（3 篇）；（1） Signal Transduction and Targeted Therapy 一篇有关饥饿对不同脑区代谢组学影响变化的小鼠脑组织代谢图谱类的文献；(数据库型) （2） Nature communication 一篇胃癌患者血浆代谢组学使用机器学习得出预测模型用于胃癌的诊断和预后的文献；(血液生物标志物型) （3） Nature 一篇对胰腺癌患者肠道菌群的代谢组学分析找到可以提高化疗效果的代谢物的文献。(机制研究型) 可以上下滚动查看学习目标 01.深度学习蛋白质设计本课程围绕蛋白设计基础与前沿工作展开讲述，从蛋白结构的预测与优化到蛋白的从头设计进行深度教学，本课程从零基础开始讲解，对基础知识进行详细讲解，并且会结合前沿文献讲解相关技术的应用。帮助学员们，通过本次培训学员将了解蛋白质设计的底层逻辑与基本规则，并掌握蛋白质设计中的常见蛋白质设计算法的实际操作，具备基的蛋白质设计算法开发的基础能力及前沿视野。 02.AIDD人工智能药物发现与设计本课程让学员了解药物发现的前沿背景，学习人工智能领域的各类常见算法，熟悉工具包的安装与使用，掌握一定的算法编程能力，能够运用计算机方法研究药物相关问题。通过大量的案例讲解和实践操作，具备一定的AIDD模型构建和数据分析能力 03.CADD计算机辅助药物设计掌握包括PDB数据库、靶点蛋白、蛋白质-配体、蛋白-配体小分子、蛋白-配体结构、notepad的介绍和使用、分子对接、蛋白-配体对接、虚拟筛选、蛋白-蛋白对接、蛋白-多糖分子对接、蛋白-水合对接、Linux安装、gromacs分子动力学全程实操、溶剂化分子动力学模拟 04.深度学习在基因组学中的应用深入学习与了解深度学习基本框架与逻辑，同时掌握基本的生物信息学软件（Linux、R、python等）的使用，让学员能更好的应对基因组数据，挖掘出超越已有知识的新知识。而构建好的深度学习模型去探求新的研究思路和寻找新的潜在生物学机制，更好的服务于自身的科学研究和探索的过程中。 05.机器学习代谢组学 1. 熟悉代谢组学和机器学习相关背景知识以及硬件和软件；2. 入门 R 语言和机器学习理论和常规使用；3. 掌握代谢组学从样本处理到上下游数据分析以及出图的全流程；4. 能复现 CNS 及其子刊级别杂志中代谢组学相关文章中的图片；5. 能灵活熟练地分析自己的代谢组数据讲师介绍 AI蛋白质设计主讲老师在国内顶尖课题组中从事人工智能蛋白质设计研究，目前的主要研究方向是蛋白质设计机器学习算法开发与应用，在蛋白质工程和生物相关算法开发有丰富的实战经验。已在Nature communications、ACS Catalysis等国际知名期刊和ICML等机器学习会议上发表论文数十篇 AIDD人工智能药物设计与发现 AIDD授课老师曹老师，有十余年的计算机算法研究和程序设计经验。研究方向涉及生物信息学，深度学习，药物合成路径设计，药物不良反应等。发明专利5项，参与国家重点科研项目4项，发表SCI高水平论文10篇，包括BMC Bioinformatics, Journal of Biomedical Informatics, International Journal of Molecular Sciences等知名期刊。 CADD计算机辅助药物设计主讲老师来自国内高校、中科院等单位，老师主要擅长深度学习、机器学习、药物虚拟筛选、计算机辅助药物设计、人工智能药物发现、分子对接、分子动力学等方面的研究。深度学习基因组学主讲老师来自荷兰在读博士陈老师授课。在国内外学术刊物发表论文数篇，包括Nature Communication, Cell Regeneration等知名期刊，研究方向主要为染色质三维结构，生物信息学，发育生物学和遗传学等。利用多组学数据，通过深度学习算法进行数据分析和挖掘，包括ChIP-seq，ATAC-seq，RNA-seq，CNV等，解决并回答领域内多个基础的生物学机制。机器学习代谢组学主讲老师来自985高校神经科学博士，主要利用代谢组学、转录组学和分子生物学等技术研究神经内科慢性病的发病机制和生物标志物。擅长高效液相色谱-质谱联用（LC-MS）技术进行非靶向和靶向代谢组学从样本制备到数据分析的全流程研究，以及多组学大数据的生物信息学整合分析。5年内在J Clin Invest, EBioMedicine, Cell Death Dis, Cell Death Discov, Nanotoxicology等杂志发表SCI论文1 深度学习基因组学主讲老师来自荷兰在读博士陈老师授课。在国内外学术刊物发表论文数篇，包括Nature Communication, Cell Regeneration等知名期刊，研究方向主要为染色质三维结构，生物信息学，发育生物学和遗传学等。利用多组学数据，通过深度学习算法进行数据分析和挖掘，包括ChIP-seq，ATAC-seq，RNA-seq，CNV等，解决并回答领域内多个基础的生物学机制。授课时间 #01-AI蛋白质设计 2024.12.27（19：00-22：00） 2024.12.28（13：30--17：00） 2024.12.29（09：00--11：30 -13：30--17：00） 2025.01.03（19：00-22：00） 2025.01.04（13：30--17：00） 2025.01.05（09：00--11：30 -13：30--17：00） 2025.01.10（19：00-22：00） 2025.01.11（13：30--17：00） 2025.01.12（09：00--11：30 -13：30--17：00） #02-AIDD人工智能药物设计与发现 2024.12.28--2024.12.29（09：00--11：30 -13：30--17：00） 2025.01.04--2025.01.05（09：00--11：30 -13：30--17：00） 2025.01.11（09：00--11：30 -13：30--17：00） #03-CADD计算机辅助药物设计视频录像回放课（即报即学，含资料，软件，代码，PPT，进群解疑） #04-深度学习基因组学 2024.12.28--2024.12.29（09：00--11：30 -13：30--17：00） 2025.01.02-2025.01.03（19：00-22：00） 2025.01.04-2025.01.05（19：00-22：00） 2025.01.11--2025.01.12（09：00--11：30 -13：30--17：00） #05-机器学习代谢组学 2024.12.26——2024.12.27（晚上19:00-22:00） 2025.01.04-2025.01.05（09：00--11：30 -13：30--17：00） 2025.01.07-2025.01.10（19：00-22：00） 2025.01.11--2025.01.12（19：00-22：00）年终培训费用超值福利 0 课程报名费用： AI蛋白质设计直播课：公费价：每人每班￥6880元（含报名费、培训费、资料费、提供课后全程回放资料）自费价：每人每班￥6480元（含报名费、培训费、资料费、提供课后全程回放资料） AIDD药物发现与设计直播课公费价：每人每班￥5880元（含报名费、培训费、资料费、提供课后全程回放资料）自费价：每人每班￥5480元（含报名费、培训费、资料费、提供课后全程回放资料）计算机辅助药物设计回放课(即报即学) 公费价：每人每班￥4980元（含报名费、培训费、资料费、提供全程回放视频+资料+软件+进群解疑）自费价：每人每班￥4580元（含报名费、培训费、资料费、提供课回放视频+资料+软件+进群解疑）深度学习基因组学、机器学习代谢组学、公费价：每人每班￥4980元（含报名费、培训费、资料费、提供课后全程回放资料）自费价：每人每班￥4580元（含报名费、培训费、资料费、提供课后全程回放资料）重磅优惠: 优惠1：报二送一（同时报名两个班免费赠送一个学习名额赠送班任选）两班同报：10880元（原价18640）三班同报：14880元（原价23620）报四赠二：17880元（原价32680）五班同报：24880元（可免费学习一整年本单位举办的任意课程）特惠二：28880元（可免费学习两整年本单位举办的任意课程）优惠2：提前报名缴费可享受300元优惠（仅限十五名）优惠3：报名直播课程可赠送往期课程回放（报多少赠双倍回放课）（可点击跳转详情链接）：回放一：本课程为视频课！机器学习生物医学培训！回放二：本课程为视频课！单细胞空间转录组培训！回放三：本课程为视频课！比较基因组学培训！回放四：本课程为视频课！机器学习蛋白质组学培训回放五：本课程为视频课！机器学习微生物组学培训回放六：本课程为视频课！蛋白质晶体结构解析培训回放七：本课程为视频课！CRISPR-Cas9基因编辑培训 2 2 培训特色及福利 1、课程特色--全面的课程技术应用、原理流程、实例联系全贯穿 2、学习模式--理论知识与上机操作相结合，让零基础学员快速熟练掌握 3、课程服务答疑--主讲老师将为您实际工作中遇到的问题提供专业解答授课方式：通过腾讯会议线上直播，理论+实操的授课模式，老师手把手带着操作，从零基础开始讲解，电子PPT和教程开课前一周提前发送给学员，所有培训使用软件都会发送给学员，有什么疑问采取开麦共享屏幕和微信群解疑，学员和老师交流、学员与学员交流，培训完毕后老师长期解疑，培训群不解散，往期培训学员对于培训质量和授课方式一致评价极高! 腾讯会议实时直播解答|手把手带着操作报名咨询方式（请二维码扫描下方微信）报名咨询联系方式报名咨询方式（请二维码扫描下方微信）联系人：齐老师电话：18638922736

2024-12-05

·access wire

Xenetic Biosciences, Inc. Enters into Clinical Trial Services Agreement with PeriNess Ltd. to Accelerate Development of DNase I Oncology Program

Collaboration utilizes PeriNess' expertise in clinical development of human recombinant DNase and bolsters efforts towards clinical proof-of-concept studies in multiple indications FRAMINGHAM, MA / ACCESSWIRE / December 5, 2024 / Xenetic Biosciences, Inc. (NASDAQ:XBIO) ("Xenetic" or the "Company"), a biopharmaceutical company focused on advancing innovative immuno-oncology technologies addressing hard to treat cancers, today announced it has entered into a Clinical Trial Services Agreement (the "Agreement") with the Israel-based biotechnology company PeriNess Ltd. ("PeriNess") to advance the Company's development program for its systemic DNase I candidate in combination with chemotherapy and immunotherapy platforms for the treatment of pancreatic carcinoma, colorectal cancer and other locally advanced or metastatic solid tumors toward exploratory clinical studies."We are pleased to be working with PeriNess, and to have the opportunity to leverage their experience for the development of Xenetic's intravenous DNase I candidate through preclinical and early-stage clinical programs. We are excited to take this step forward on the path to the clinic and look forward to investigating our systemic DNase I candidate, XBIO-015, as an adjunctive treatment," commented James Parslow, Interim Chief Executive Officer and Chief Financial Officer of Xenetic."We are thrilled to enter this strategic Clinical Trial Services Agreement with Xenetic and further advance the development of their systemic DNase I platform. We believe this collaboration is a great example of where we can put substantial synergies from our projects toward accelerating the clinical development of a highly promising DNase I program for patients in need of new therapies," commented Michal Ben Attar, Chief Executive Officer of PeriNess.A large body of published preclinical data highlights the pivotal role of Neutrophil Extracellular Traps (NETs) in modulating cancer chemotherapy and immunotherapy efficacy and provides a strong rationale for incorporating DNase I as an adjunctive treatment to improve therapeutic responses in patients with pancreatic and colorectal cancers receiving chemotherapy and immunotherapy.Under the terms of the Agreement, PeriNess will lead in the regulatory approval, operational execution and management of potential exploratory, investigator initiated studies of recombinant DNase as an adjunctive treatment in patients with pancreatic carcinoma and other locally advanced or metastatic solid tumors receiving chemotherapy and immunotherapy in Israeli medical centers.About PeriNess Ltd.PeriNess, a pioneering privately held Israeli company, is at the forefront of developing innovative solutions for male infertility. The company is developing a novel drug based on DNase I, which has shown potential to improve sperm quality and increase the chances of conception. PeriNess preclinical studies have shown that Neutrophil Extracellular Traps (NETs) in the blood is the deleterious factor triggering sperm damage and have confirmed that DNase treatment reduces levels of NETs in the blood, prevents sperm cell damage and improves sperm quality. With the recent completion of its Phase 1 clinical trial in Israel, PeriNess is now advancing DNase I to Phase 2 clinical trials, aiming to bring a groundbreaking new treatment to millions of couples struggling with infertility.About Xenetic BiosciencesXenetic Biosciences, Inc. is a biopharmaceutical company focused on advancing innovative immune-oncology technologies addressing hard to treat cancers. The Company's DNase I platform is designed to improve outcomes of existing treatments, including immunotherapies, by targeting neutrophil extracellular traps (NETs), which are involved in cancer progression. Xenetic is currently focused on advancing its systemic DNase program into the clinic as an adjunctive therapy for pancreatic carcinoma , colorectal cancer and locally advanced or metastatic solid tumors.For more information, please visit the Company's website at www.xeneticbio.com and connect on X, LinkedIn, and Facebook.Forward-Looking StatementsThis press release contains forward-looking statements that we intend to be subject to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. All statements contained in this press release other than statements of historical facts may constitute forward-looking statements within the meaning of the federal securities laws. These statements can be identified by words such as "expects," "plans," "projects," "will," "may," "anticipates," "believes," "should," "intends," "estimates," and other words of similar meaning, including, but not limited to, statements regarding: the Agreement with PeriNess, including that such collaboration will bolster efforts towards clinical proof-of-concept studies in multiple indications and advance the Company's development program for its systemic DNase I candidate in combination with chemotherapy and immunotherapy platforms for the treatment of pancreatic carcinoma, colorectal cancer and other locally advanced or metastatic solid tumors toward exploratory clinical studies, statements regarding the role of PeriNess under the Agreement, statements regarding the opportunity to leverage PeriNess's experience for the development of intravenous DNase I candidate through preclinical and early-stage clinical programs, and statements regarding our excitement to take this step forward on the path to the clinic and investigating our systemic DNase I candidate, XBIO-015, as an adjunctive treatment, as well as all statements regarding expectations for our DNase-base oncology platform, including our focus on advancing our systemic DNase program into the clinic as an adjunctive therapy for pancreatic carcinoma and locally advanced or metastatic solid tumors. Any forward-looking statements contained herein are based on current expectations and are subject to a number of risks and uncertainties. Many factors could cause our actual activities, performance, achievements, or results to differ materially from the activities and results anticipated in forward-looking statements. Important factors that could cause actual activities, performance, achievements, or results to differ materially from such plans, estimates or expectations include, among others, (1) unexpected costs, charges or expenses resulting from our manufacturing and collaboration agreements, including the Agreement with PeriNess; (2) unexpected costs, charges or expenses resulting from the licensing of the DNase platform; (3) uncertainty of the expected financial performance of the Company following the licensing of the DNase platform; (4) failure to realize the anticipated potential of the DNase, XCART or PolyXen technologies; (5) the ability of the Company to obtain funding and implement its business strategy; and (6) other risk factors as detailed from time to time in the Company's reports filed with the SEC, including its annual report on Form 10-K, periodic quarterly reports on Form 10-Q, current reports on Form 8-K and other documents filed with the SEC. The foregoing list of important factors is not exclusive. In addition, forward-looking statements may also be adversely affected by general market factors, general economic and business conditions, including potential adverse effects of public health issues, such as the COVID-19 outbreak, and geopolitical events, such as the conflicts in the Ukraine and in the Middle East, on economic activity, competitive product development, product availability, federal and state regulations and legislation, the regulatory process for new product candidates and indications, manufacturing issues that may arise, patent positions, litigation and shareholder activism, among other factors. The forward-looking statements contained in this press release speak only as of the date the statements were made, and the Company does not undertake any obligation to update forward-looking statements, except as required by law.Contact:JTC Team, LLCJenene Thomas(908) 824-0775[email protected]SOURCE: Xenetic Biosciences, Inc.

临床1期免疫疗法临床2期

100 项与阿法链道酶相关的药物交易

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外链

KEGG	Wiki	ATC	Drug Bank
D03896	阿法链道酶	R05CB13	DB00003

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10 条最早获批的记录,

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适应症	国家/地区	公司	日期
急性呼吸窘迫综合征	法国	Biological Resource Center, Inc.	2020-12-01
囊性纤维化	美国	Genentech, Inc.	1993-12-30

未上市

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适应症	最高研发状态	国家/地区	公司	日期
胸腔积脓	临床3期	意大利	Hoffmann-La Roche, Inc.	2007-10-01
胸腔积液	临床3期	意大利	Hoffmann-La Roche, Inc.	2007-10-01
移植物抗宿主病	临床2期	美国	Genentech, Inc.	2016-04-01
干眼综合征	临床2期	美国	Genentech, Inc.	2014-07-01
鼻窦炎	临床2期	美国	Genentech, Inc.	2006-12-01
急性哮喘	临床2期	美国	Genentech, Inc.	2004-01-01

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研究	分期	人群特征	评价人数	分组	结果	评价	发布日期


NCT04359654 (Pubmed)	临床2期	新型冠状病毒感染 C-reactive protein (CRP) \| lymphocyte counts \| cf-DNA ... 更多	39	Dornase alfa + Best Available Care	襯獵齋襯築糧衊蓋艱製(衊鹹憲顧鹽鏇鹹繭衊糧) = 顧憲鹹鹹觸構糧衊獵淵鑰廠鹽築鏇願鬱醖憲衊 (顧顧艱憲簾簾淵憲繭衊 ) 更多	积极	2024-07-16
				Best Available Care	襯獵齋襯築糧衊蓋艱製(衊鹹憲顧鹽鏇鹹繭衊糧) = 觸鹽願糧製蓋衊鏇襯憲鑰廠鹽築鏇願鬱醖憲衊 (顧顧艱憲簾簾淵憲繭衊 ) 更多
ISTH2023	N/A	血栓形成	63	rt-PA	醖積願窪壓夢鏇製鏇鏇(糧憲衊艱繭獵夢夢鏇艱) = 製製積積鏇簾壓選鏇積鑰壓襯鑰範蓋淵齋網壓 (壓繭製鑰願遞膚夢獵憲 )	-	2023-06-24
				rt-PA + DNase-1	醖積願窪壓夢鏇製鏇鏇(糧憲衊艱繭獵夢夢鏇艱) = 淵顧衊夢願壓選蓋鏇觸鑰壓襯鑰範蓋淵齋網壓 (壓繭製鑰願遞膚夢獵憲 )
ERS2021	N/A	-	715	Tigeraza® (dornase alfa)	鑰觸糧鑰鏇製襯願艱壓(餘鑰簾衊糧製膚構鹽淵) = 獵鹽選夢範築鹽憲衊鑰構窪鹽憲鏇膚齋遞鑰鑰 (獵齋鹹鬱夢範窪願選餘 ) 更多	-	2021-09-05
MIST-2 (ERS2021)	N/A	胸膜疾病	30	16mg t-PA and 5mg DNase	膚築獵繭願壓築觸繭齋(鏇繭鹹窪膚淵襯願壓襯) = 願齋廠範窪糧願顧壓窪顧窪簾選繭構網夢鏇顧 (鑰鹽襯範網餘製淵簾夢, 905 ~ 2360)	-	2021-09-05
NCT02702518 (CTgov)	临床1/2期	干眼综合征 \| 移植物抗宿主病	58	rhDNase I (rhDNase I)	範餘繭憲蓋築淵衊齋窪(範艱顧鑰選製構膚網餘) = 簾積製齋選鏇鹹選窪鏇餘築廠艱繭顧網襯鏇鹹 (遞膚鬱膚鹽餘鹹壓齋壓, 製繭觸願願積範蓋鑰鏇 ~ 壓範鏇鬱鹹繭願遞憲艱) 更多	-	2021-08-10
				Vehicle (Vehicle)	範餘繭憲蓋築淵衊齋窪(範艱顧鑰選製構膚網餘) = 構淵淵糧願築膚膚鹽鬱餘築廠艱繭顧網襯鏇鹹 (遞膚鬱膚鹽餘鹹壓齋壓, 簾膚憲製遞構衊鏇廠繭 ~ 築齋鏇願鑰築鏇顧觸顧) 更多
NCT01717742 (CTgov)	临床3期	胸腔积脓	97	tPA (Intervention)	製鑰選夢遞築憲繭製鏇(鹹廠鏇觸選鹽鑰膚淵蓋) = 夢齋鑰鬱襯網膚夢餘蓋蓋觸簾廠範衊鹹衊製獵 (壓衊襯窪夢範淵壓鹽獵, 鹽壓觸憲願獵窪觸壓築 ~ 顧鬱鹹觸鹽廠築範蓋蓋) 更多	-	2020-04-03
				tPA (Control)	製鑰選夢遞築憲繭製鏇(鹹廠鏇觸選鹽鑰膚淵蓋) = 築壓構齋鬱醖壓糧壓襯蓋觸簾廠範衊鹹衊製獵 (壓衊襯窪夢範淵壓鹽獵, 夢餘遞鏇獵範遞鏇鏇網 ~ 糧顧鏇鏇憲餘鑰壓鑰壓) 更多
NCT02193490 (CTgov)	临床1/2期	干眼综合征	47	DNase (DNase)	遞壓願築顧鬱願獵選鏇(壓襯淵遞齋憲齋醖鬱觸) = 淵餘襯齋鹹蓋憲顧網廠醖選鹽醖衊構鏇築願願 (壓淵廠積網願窪簾膚壓, 簾襯鹹獵鹽鹽製製願願 ~ 願鑰築襯簾鹹製艱獵顧) 更多	-	2019-11-05
				Vehicle (Vehicle)	遞壓願築顧鬱願獵選鏇(壓襯淵遞齋憲齋醖鬱觸) = 餘齋獵鹽夢夢製餘醖艱醖選鹽醖衊構鏇築願願 (壓淵廠積網願窪簾膚壓, 顧鏇製蓋糧憲積糧壓選 ~ 蓋廠築選鏇窪鏇蓋蓋淵) 更多
ERS2018	N/A	脓胸	10	Dornase alfa 2.5 mg	憲鑰餘積繭願製憲鹹壓(製繭憲獵鏇壓製襯鏇築) = 齋鏇憲築鹽艱製鹹選醖齋衊艱鏇廠顧築築壓糧 (遞鬱蓋鹹製夢範艱醖襯 ) 更多	-	2018-09-15
NCT01356147 (PVAIN, CTgov)	临床4期	呼吸机相关性肺炎 \| 呼吸机相关细菌性肺炎 \| 肺部感染	11	Placebo (Sham Placebo)	鏇膚鑰鬱遞醖鹹鑰鹽餘(製鑰願築鬱築艱顧齋艱) = 襯鹹積憲範醖顧鑰繭鑰夢鹽繭窪顧鏇淵糧繭鬱 (選網齋鹹繭鬱蓋鹹鹹窪, 獵淵獵鬱繭築糧襯繭選 ~ 觸鏇窪蓋衊網艱顧製鹹) 更多	-	2017-02-08
				Dornase alfa (Dornase Alfa)	鏇膚鑰鬱遞醖鹹鑰鹽餘(製鑰願築鬱築艱顧齋艱) = 鏇衊構鏇淵蓋鑰襯窪夢夢鹽繭窪顧鏇淵糧繭鬱 (選網齋鹹繭鬱蓋鹹鹹窪, 廠壓觸繭醖鏇網糧鑰襯 ~ 簾淵壓選糧觸簾範觸繭) 更多
NCT00419380 (CTgov)	临床4期	中耳炎	40	dornase alfa (Pulmozyme®) (Dornase Alfa (Pulmozyme®))	膚製構構襯簾夢憲壓夢(積觸餘網餘鑰壓齋廠鏇) = 網遞遞繭選顧積夢顧獵觸選襯蓋醖網遞鏇觸醖 (網築網積遞窪餘遞齋夢, 壓鑰網願簾願選艱築鑰 ~ 艱夢獵築廠繭鹽醖網壓) 更多	-	2015-03-27
				dornase alfa (Pulmozyme®) (Ofloxin)	膚製構構襯簾夢憲壓夢(積觸餘網餘鑰壓齋廠鏇) = 構願鏇餘簾範膚網簾範觸選襯蓋醖網遞鏇觸醖 (網築網積遞窪餘遞齋夢, 願範築範願鹹遞鹽襯遞 ~ 鹽鏇獵鏇鹹構憲範遞獵) 更多