WIP1

SAN FRANCISCO--(BUSINESS WIRE)-- Atomwise announced today results from the AIMS (Artificial Intelligence Molecular Screen) initiative that establish the AtomNet AI Platform as a viable alternative to high-throughput screening (HTS) and verify its ability to consistently discover structurally novel chemical matter. The landmark study applied AtomNet to 318 targets through collaborations with over 250 academic labs across 30 countries, representing the largest and most comprehensive virtual HTS campaign reported to date. In a paper published in Nature Scientific Reports, AtomNet successfully identified structurally novel hits for 235 of 318 targets evaluated, representing a significant improvement in hit success rate over traditional HTS. AtomNet demonstrated consistently high hit rates across all target classes, which covered a wide breadth of protein classes relevant across major therapeutic areas, confirming the broad applicability of the AI platform. AtomNet also showed a remarkable ability to discover novel chemical matter, averaging over seven structurally distinct bioactive compounds per target. Developed by Atomwise, AtomNet was the first deep neural network designed to predict the bioactivity of small molecules in structure-based drug discovery. Conventional HTS cannot evaluate over 99% of commercially available molecules, which increasingly come from synthesis-on-demand libraries. In contrast, AtomNet’s virtual HTS approach searches an expanding chemical library of more than 15 quadrillion synthesizable compounds to accurately find hits that deviate from any known binders, enabling rapid discovery from vast, unexplored chemical space. “We need new technologies for new targets. As the pharma industry increasingly moves to tackle harder and harder disease biology, we need new molecules that can address these challenges,” said Mike Varney, former Executive Vice President and Head of Genentech Research and Early Development, and Atomwise Board Member. “For a generation, the pharma industry has largely relied on a single source – HTS – to identify new bioactive compounds. Any new approach needs to demonstrate applicability and robustness across the entire proteome. It is exciting to see Atomwise demonstrate their successes on such a broad range of disease targets.” “The ability of AtomNet to consistently identify novel bioactive chemical compounds solves a fundamental challenge and inefficiency with current small molecule drug discovery,” said Gavin Hirst, Chief Scientific Officer of Atomwise. “Medicinal chemists always want new scaffolds to work on. A new scaffold lets you make progress and potentially unlocks different selectivity, pharmacokinetics, or pharmacodynamics. Access to new scaffolds increases the probability that the developed molecule will meaningfully differentiate in the clinic.” For most projects, the AtomNet training data contained no active molecules for the target protein or any close homologs. As a result, discovered hits often represent first-in-class binders for newly discovered or difficult-to-target proteins. Examples from the AIMS initiative include the first reducer for Miro1, a new target in Parkinson’s disease, the first inhibitors for OTUD7A and OTUD7B, challenging deubiquitinase targets for solid and hematological tumors, and small molecule inhibitors for CTLA-4, a well-established oncology target. “Generally the predictive power of virtual screening platforms has been extremely limited,” said Prof. Gregory Bowman of the University of Pennsylvania, who used AtomNet to uncover a cryptic site for phosphatase PPM1D, a therapeutic target in oncology. “Results of the AIMS study show that AtomNet has a high success rate in finding hits for traditionally challenging biology, like allosteric or protein-protein interactions. This noteworthy ability opens the door for many targets that have been previously deemed intractable.” “AtomNet enabled us to discover highly selective inhibitors for two emerging targets with potential applicability across a range of cancers, but lacking known non-covalent binders,” said Pengda Liu, Ph.D., assistant professor in the Department of Biochemistry and Biophysics at UNC-Chapel Hill. “Despite the novelty and challenge of these targets, the compounds AtomNet predicted are potent to the point where we are currently moving forward with medicinal chemistry efforts. It is clear that AI approaches like AtomNet can fundamentally change the way we think about what is possible, and I have no doubt that it will play a critical role in future drug development.” “We designed AtomNet to be able to extrapolate molecular learnings across targets and actively predict activity for new and challenging targets,” said Abraham Heifets, CEO, Atomwise. “Historically, one of the major challenges for AI-driven discovery has been a need for pre-existing, on-target ligand data to accurately predict activity, narrowing AI’s applicability. We’ve shown for the first time that AtomNet does not require any target-specific training data and can be applied to virtually any target, even those that have been considered undruggable.” “Comprehensively testing the capabilities of AtomNet across such a wide range of targets clearly demonstrates the unprecedented accuracy of the platform to identify novel drug-like scaffolds,” says Izhar Wallach, who led the AIMS initiative and is Chief Technology Officer of Atomwise. “We are hugely thankful for the hundreds of talented researchers across the world who made this collaboration possible.” Atomwise plans to IND application this year for its lead candidate, a novel allosteric TYK2 inhibitor with first-in-class and best-in-class potential, which was discovered using AtomNet. The company’s vision is to invent a better way to discover and develop new medicines to help patients. Our belief is structurally novel chemical matter increases the likelihood of developing first-in-class and best-in-class medicines that have the potential to transform patient care. About Atomwise Atomwise is a TechBio company leveraging AI/ML to revolutionize small molecule drug discovery. The Atomwise team invented the use of deep learning for structure-based drug design, a core technology of Atomwise’s best-in-class AI discovery and optimization engine, which is differentiated by its ability to find and optimize novel chemical matter. Atomwise has extensively verified its discovery engine, having demonstrated the ability to find compounds with therapeutic potential hundreds of times across a wide variety of protein types and multiple “hard to drug” targets. Atomwise is advancing a proprietary pipeline of small-molecule drug candidates. Our vision is to invent a better way to discover and develop new medicines to help patients. Learn more at atomwise.com or follow @AtomwiseInc.

引言克隆性造血( CH )是指造血干细胞及其子代在体细胞突变驱动下的克隆性增殖。准确的CH风险评估对疾病预防和临床决策至关重要。CH的大小已被证明与较高的疾病风险相关，然而，影响CH大小的因素尚不清楚。此外，CH在长寿个体中的特征并没有得到很好的记录。2024年2月15日，海南医学院任瑞宝、蔡望伟及上海交通大学陈竺共同通讯在PNAS 上在线发表题为“The impact of age and number of mutations on the size of clonal hematopoiesis”的研究论文，该研究报告了长寿(年龄≥90岁)和普通(年龄60 ~ 89岁)老年群体中CH的深入分析。该研究利用靶向深度测序，发现CH的发生发展与年龄和衰老生物标志物的表达密切相关。长寿老人组CH发生率显著增高，TET2和ASXL1基因突变频率显著增高，提示一定程度的CH可能有利于延长寿命。有趣的是，CH的大小与年龄( 60~110岁)无显著相关性，也与衰老生物标志物的表达无显著相关性。相反，大的CH大小与每个个体的突变数量之间有很强的相关性。这些发现为CH提供了一个风险评估的生物标志物，同时也表明CH的演变除了受年龄的影响外，还受到其他因素的影响。造血干细胞( hematopoietic stem cells，HSCs )在衰老过程中逐渐丧失增殖潜能。同时，在自我更新过程中，HSCs不断积累DNA突变。HSCs的某些突变可引起增殖增加和分化异常。由体细胞驱动突变驱动的造血干细胞及其子代的克隆性扩增被称为克隆性造血( CH )。随着年龄的增长，CH越来越常见，被称为年龄相关性克隆性造血( age-related clone hematopoietic，ARCH )。血液中存在恶性肿瘤相关基因的体细胞突变，而无血液系统恶性肿瘤、发育不良或血细胞减少的证据，且变异等位基因频率( variant allele frequency，VAF )≥2 %，称为不确定潜能的克隆性造血( clone hematopoietic of uncertain potential，CHIP )。既往研究表明，CHIP的患病率随着年龄增加而稳步增加。常见的CHIP突变影响多个基因，如DNMT3A、TET2、PPM1D、TP53、STAT3、IDH1、IDH2和ASXL1，其中大多数已知与血液系统恶性肿瘤相关。这些突变基因可归类为表观遗传调控因子、剪接因子、DNA损伤应答因子和造血转录因子。除年龄外，CHIP的发生发展还受到炎症、癌症相关治疗、吸烟、遗传因素等因素的影响。CHIP与血液系统恶性肿瘤如急性髓系白血病( acute myeloid leukemia，AML )、未明确意义的克隆性血细胞减少症、骨髓增生异常综合征和非血液系统疾病如心血管疾病、实体肿瘤和自身免疫性疾病的风险增加有关，以及总死亡率增加。CHIP还可以在体内激发炎症反应，并对这种炎症环境表现出强大的耐受性，增强CH的适应性，并增加脓毒症、严重冠状病毒感染( COVID-19 )和其他感染的风险。Setd2在自身免疫性疾病中表达减少（Credit: PNAS）并非所有的CH都会演变为血液系统恶性肿瘤或其他疾病。因此，CH的风险评估在疾病预防和临床实践中至关重要。研究表明，CH的进化受到突变类型和非突变相关因素的共同影响。研究还发现，CH的大小与疾病进展和预后相关，较大的克隆与较高的疾病风险相关。既往研究发现，高VAF (≥10 % )的个体发生肺癌、痛风、主要不良心脏事件( major adverse cardiac events，MACE )的风险显著增加。CH的大小也被纳入预测髓系恶性肿瘤的CH风险评分中。然而，影响CH大小的因素及其潜在的分子机制。然而，影响CH大小的因素及其潜在的分子机制尚不清楚。体细胞突变驱动的克隆扩增不仅发生在造血系统，而且在全身正常组织中也普遍发生。虽然某些克隆性扩张可以演变成癌症和/或促进其他疾病的发展，但某些克隆性扩张也可能在某些状态下通过对抗压力和老化条件而发挥有益的稳态作用。随着年龄的增长，HSCs的造血能力逐渐下降，这种变化在70岁以后更加明显。虽然CH在各年龄段人群中均有检出，但在长寿人群( 90岁及以上)中尚未进行系统研究。基于此，该研究比较了长寿(年龄≥90岁)和普通(年龄60 ~ 89岁)老年组的CH，发现长寿老年组的CH患病率显著升高，TET2和ASXL1突变显著增加。出乎意料的是，在60到110岁的范围内，CH的大小与年龄没有显著的相关性，也与衰老标志物的表达没有显著的相关性。相反，CH的大小与每个个体的突变数量之间存在显著的相关性。这些发现为CH的风险评估提供了生物标志物，并表明CH的演变除受年龄影响外，还受其他因素的影响。原文链接https://doi-org.libproxy1.nus.edu.sg/10.1073/pnas.2319364121责编|探索君排版|探索君文章来源|“iNature”End往期精选围观一文读透细胞死亡(Cell Death) | 24年Cell重磅综述（长文收藏版）热文Nature | 破除传统：为何我们需要重新思考肿瘤的命名方式热文Nature | 2024年值得关注的七项技术热文Nature | 自身免疫性疾病能被治愈吗？科学家们终于看到了希望热文CRISPR技术进化史 | 24年Cell综述

弥漫内生型脑桥胶质瘤（Diffuse Intrinsic Pontine Gliomas，DIPG）是一种儿童脑肿瘤，具有高度侵袭性、难以手术治疗、死亡率高的特点，目前尚无有效的治疗方法。研究表明，约80%的DIPG患者存在组蛋白H3K27M突变，导致全基因组H3K27me3的降低和转录异常，该突变被认为是DIPG发病的主要推动因素。尽管目前新型治疗药物主要集中在调节组蛋白甲基化和乙酰化平衡状态，以修复由H3K27M突变引起的级联效应，但由于临床转化效果不佳，DIPG仍然需要新的治疗方案。除了H3K27M突变外，90%的H3.3K27M型DIPG存在p53信号通路异常，包括TP53自身突变（占比65%~69%）和PPM1D突变（占比20%~28%）。PPM1D突变可降低p53的稳定性，使其失活，从而促进DIPG的肿瘤进展。这表明重新激活p53的抑癌功能可能是DIPG的潜在治疗靶点。2024年2月15日，清华大学生命学院郗乔然课题组与首都医科大学附属北京天坛医院张力伟课题组合作，在《癌症研究》（Cancer Research）杂志上发表题为“Fimepinostat通过抑制NF­κB和PI3K/AKT信号增强吉西他滨对H3K27M突变型弥漫内生型脑桥胶质瘤的治疗效果”（Fimepinostat impairs NF­κB and PI3K/AKT signaling and enhances gemcitabine efficacy in H3.3K27M-diffuse intrinsic pontine glioma）的研究论文，系统性地阐述了吉西他滨在H3.3K27M突变型DIPG中的作用机制，并提出了吉西他滨与Fimepinostat联合用药的治疗策略。该研究通过对多株DIPG原代细胞系，利用药物筛选确认吉西他滨作为潜在治疗药物。研究验证了吉西他滨能有效抑制DIPG在体外和小鼠体内的肿瘤形成，并揭示了吉西他滨引起DNA损伤、激活p53以及增加p53对凋亡相关基因的调控可及性，从而诱导细胞凋亡。此外，研究还发现吉西他滨在H3K27M存在的条件下诱导DIPG的细胞凋亡。然而，吉西他滨在引起DNA损伤的同时，也激活内源性逆转录病毒元件（Endogenous retrovirus，ERV），并激活cGAS-STING信号及下游RELB介导的非经典NF-κB信号通路，从而促使肿瘤存活。再次进行药物筛选后，研究发现Fimepinostat与吉西他滨表现出卓越的药物协同性，且Fimepinostat能够抑制吉西他滨激活的非经典NF-κB信号通路，同时阻断PI3K/AKT信号通路，产生持续的DNA损伤效应，表现出在体外和体内实验中的卓越协同抗肿瘤作用。图：使用吉西他滨重新激活p53的肿瘤抑制功能联合Fimepinostat抑制非经典NF-κB信号和PI3K/AKT信号，可能是治疗H3.3K27M突变的DIPG患者的潜在治疗方案清华大学生命学院郗乔然副教授、首都医科大学附属北京天坛医院张力伟教授为论文共同通讯作者。清华大学2018级已毕业博士生王丹为本文的第一作者。清华大学生命学院郗乔然课题组2016级已毕业博士生闫坤、2020级博士生余红杏、2019级本科生李浩成、2019级博士生周威及2019级硕士生郭书凝参与了本文部分实验工作。上海交通大学唐玉杰教授为本研究提供了部分DIPG细胞系。清华大学生命科学学院刘念副教授及其课题组的博士后洪雅强为本课题提供了转座子分析，清华大学生命科学学院吴畏教授为本课题提供了表观遗传类小分子化合物库。首都医科大学附属北京天坛医院王一、徐骋和泮长存提供了研究需要的临床样本和信息。本研究得到了国家自然科学基金、科技部国家重点研发计划、北京市自然科学基金和清华-北大生命科学联合中心的大力支持。论文链接：https://doi-org.libproxy1.nus.edu.sg/10.1158/0008-5472.CAN-23-0394原文链接：https://life-tsinghua-edu-cn.libproxy1.nus.edu.sg/info/1131/5582.htm本文仅用于学术分享，转载请注明出处。若有侵权，请联系微信：bioonSir 删除或修改！