Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

2024-04-21·The Journal of chemical physics

Enhancing torsional sampling using fully adaptive simulated tempering

Article

作者: Essex, Jonathan W ; Ciancetta, Antonella ; Suruzhon, Miroslav ; Bodnarchuk, Michael S ; Wall, Ian D ; Abdel-Maksoud, Khaled

Enhanced sampling algorithms are indispensable when working with highly disconnected multimodal distributions. An important application of these is the conformational exploration of particular internal degrees of freedom of molecular systems. However, despite the existence of many commonly used enhanced sampling algorithms to explore these internal motions, they often rely on system-dependent parameters, which negatively impact efficiency and reproducibility. Here, we present fully adaptive simulated tempering (FAST), a variation of the irreversible simulated tempering algorithm, which continuously optimizes the number, parameters, and weights of intermediate distributions to achieve maximally fast traversal over a space defined by the change in a predefined thermodynamic control variable such as temperature or an alchemical smoothing parameter. This work builds on a number of previously published methods, such as sequential Monte Carlo, and introduces a novel parameter optimization procedure that can, in principle, be used in any expanded ensemble algorithms. This method is validated by being applied on a number of different molecular systems with high torsional kinetic barriers. We also consider two different soft-core potentials during the interpolation procedure and compare their performance. We conclude that FAST is a highly efficient algorithm, which improves simulation reproducibility and can be successfully used in a variety of settings with the same initial hyperparameters.

2024-01-01·SLAS discovery : advancing life sciences R & D

Key aspects of modern GPCR drug discovery

Review

作者: Jagger, Liz ; Unitt, John F ; Addis, Phil ; Pearce, Martin ; Rosethorne, Elizabeth M ; Campbell, Adrian ; Young, Barbara ; Milne, Gavin ; Swift, Denise ; Baron, Frank ; Bali, Utsav ; Satchell, Rupert ; Harborne, Steven

G-protein-coupled receptors (GPCRs) are the largest and most versatile cell surface receptor family with a broad repertoire of ligands and functions. We've learned an enormous amount about discovering drugs of this receptor class since the first GPCR was cloned and expressed in 1986, such that it's now well-recognized that GPCRs are the most successful target class for approved drugs. Here we take the reader through a GPCR drug discovery journey from target to the clinic, highlighting the key learnings, best practices, challenges, trends and insights on discovering drugs that ultimately modulate GPCR function therapeutically in patients. The future of GPCR drug discovery is inspiring, with more desirable drug mechanisms and new technologies enabling the delivery of better and more successful drugs.

项与 Sygnature Discovery Ltd. 相关的新闻（医药）

2024-03-07

·药智网

站在中国AI药研“十年大关”门口，我们问自己

2014-2017年，我国第一批探索AI辅助药物研发的企业相继成立，行业相关技术探索随之展开。2018-2020年，前期技术积累初见成效，靶点选择、苗头化合物识别、先导化合物优化、候选药物选择等耗费大量时间、依赖团队经验的药物发现工作，在AI辅助下获得了效率的极大提升，行业内外“欣喜若狂”。2021年，国内AI赋能药物研发赛道热度暴涨。该年完成投融资事件113起，金额总计7408万美元，在疫情冲击下，依旧实现了翻倍的增长。此后，赛道开始变得拥挤，企业同质化、门槛低级化、技术噱头化问题开始逐一暴露。AI降本增效的能力在药物发现阶段已经得到充分展示，但始终没能有AI发现药物上市，也少有AI制药企业能够将合作研发管线推进到超过1期临床试验阶段。创业环境的噱头情绪开始趋于冷静，投资人的投资热情也逐渐褪去。如果把2014年看作我国AI赋能药物研发的发展元年，如今我们站在“十年大关”的门口，看一看这条赛道是冷清了，还是冷静了。图1：2014-2023年AI赋能药物研发领域投融资事件数及涉及金额数据来源：公开资料，药智咨询整理01这盆冷水“泼”地是谁？2023年6月，英矽智能递表港交所；投后估值约8.95亿美元。图2：英矽智能收入明细图片来源：英矽智能招股说明书招股书显示，英矽智能2021年、2022年（药物研发服务、软件解决方案）营收分别为471万美元、3015万美元（约2亿元）（同比增长540%）。年内亏损分别为1.3亿美元、2.22亿美元（约15亿元），主要是因为研发开支达0.38亿美元、0.78亿美元（约5.6亿元）。图3：英矽智能综合损益及其他全面收益表图片来源：英矽智能招股说明书而在2023年12月28日，港交所在日常更新中显示“英矽智能申请版本”已经失效。2024年1月26日，在英矽智能2023年度晚宴上，创始人兼首席执行官Alex Zhavoronkov博士回顾了公司在2023年的成绩：提名5个项目的临床前候选化合物（PCC）；推进4项I期临床研究和1项II期临床研究；两次达成AI药物对外授权的行业里程碑；实现从软件授权、研发合作、自研管线到项目授权的多元化商业模式的闭环。结合其在第一版招股书过期后没有立即递交第二版的表现，2023年拿下的多个高价大单或许会让其招股书中公司亏损情况得到改善，为后续冲刺港股IPO扫清一些阻碍。此次“插曲”看似是“一盆冷水”泼向国内首家奔赴IPO的AI制药企业，其背后折射出AI制药行业乃至整个生物医药行业的一些深层次现象，如商业化困境问题、高估值项目退出难问题，值得深入思考。如何商业化？背后是对公司类型、技术产品、行业动向、市场周期等因素进行综合分析后做出的战略决策，没有参考答案，更没有统一标准。过去三年，伴随投资热度的降低，赛道内企业逐渐开始面临资金问题。前期融资充裕，但没能实现商业转化的企业发愁；将管线推进到临床试验，但没钱继续推进临床试验的企业，也发愁。如果投资机构的资金一时无法“雪中送炭”，国内AI医药企业要如何“自救”？现金流当然是“命脉”。02我们需要的是“药物发现”，还是“新药上市”？AI制药的投融资热度火了这么些年，投资人到底是在投什么？“AI还能在那些领域得到广泛的应用？制药领域好像还没有，如果药物研发过程中有哪个步骤可以用AI解决，那肯定会带来效率的提升。”可能他们一开始投的是AI的应用，而不是药。的确，AI在药物发现阶段的“降本增效”能力毋庸置疑，但是早期研发的成本只占到整个药物研发流程的3%[1]。按照“十年十亿美金”的创新药研发成本记算，一个AI发现药物的价值最高约3000万美元，绝大部分的研发成本还是聚焦在了临床试验上。图4：成功发现和开发单个新分子实体的研发成本模型图片来源：How to improve R&D productivity:the pharmaceutical industry's grand challenge.从美国FDA发布的数据可以验证，在递交IND/DNA的AI新药管线中，的确大部分AI技术集中应用在了临床研究阶段。2016-2018年期间的IND/NDA申请，均为在临床试验阶段应用AI技术的AI药物管线。尽管接下来几年管线数量开始增多，临床研究的应用比例始终保持高位，2021年AI新药管线的陡增也可完全归结于AI技术在临床研究阶段的广泛应用。图5：FDA收到AI新药的IND/NDA申请中AI技术的具体应用场景图片来源：Mike Pacanowski为何FDA披露的相关数据与我国当下AI新药的研发氛围竟有如此大的差距？其实我们也可以反问一下自己，当今市场上是否有那么多的药物发现需求？临床需要的是“药物发现”，还是“新药上市”？其实FDA与EMA对AI/ML都有着明确的定义，目前的共识是：AI/ML只是一种能够帮助医药工业发展的一个辅助性工具。既然是聚焦产业的工具，我们对应用场景的探索是否可以大胆延申。成立于2017年的UNLEARN.AI选择摒弃现状，重新思考新药研发中的临床试验，成为全球首家提出将“数字孪生(Digital Twin)”应用到临床试验对照组的公司。该公司创建了随机试验法TwinRCTsTM和数字孪生生成器TwinRCTsTM，通过建立数字孪生模型，让数字孪生试验替代部分医学中的反复试验，减少试验样本，帮助生物制药公司开展信息量更大、效率更高、更合乎道德的临床试验。放眼海外市场，不止UNLEARN.AI，以苹果为首的海外科技型大厂已陆续入局，前后也出现了AI优化临床试验方案、患者筛选、试验数据管理、数字孪生合成对照组等业务的相关创新型企业：Mondel、Deep 6 AI、Notable Health、Quris、Trials.ai、Phastar等。这类创新AI临床科技型企业受到海外资本市场的广泛亲赖，绝大部分均能在A轮就斩获数千万美元融资。借助于AI技术，行业目前已经可以实现精准临床前模型选择和患者分层，有助于在进入临床试验前，从体外检测系统和动物研究的数据结果中，计算出在人体试验中的可行性的剂量梯度、给药频率和安全范围，减小候选药物在临床前和临床试验之间的差异。以及包括在同一种疾病的患者差异下，如何通过AI技术精准筛选出适合该疗法的受试患者，并合理制定临床终点，设计出科学合理的临床方案，这也同样需要逆转化科学意识驱动下的医学与生物学基础。尽管国内现在还不常见专注于AI技术应用于临床试验的企业，但也有英矽智能、埃格林医药、零氪科技、云深智药、焕一生物、普瑞基准、白星花科技等部分业务涉及临床试验设计优化的创新企业。这批企业正在率先开展国内AI技术在临床试验场景下的应用研究。得益于企业的美国临床办公室，埃格林医药从2020年开始尝试将AI应用于其首个创新药EG-301的临床开发，同时进行扩充AI团队并将其与医药、法规团队融合，大约用了两年时间推动EG-301获得FDA批准，且直接进入了Ⅱ期临床试验。推进第二个创新药EG-101时，公司已经比较熟练掌握了AI临床开发技术，完成整个流程就只用了半年时间。2023年6月5日，国内老牌CRO上市企业博济医药科技股份有限公司（股票代码：300404，以下简称“博济医药”）与埃格林医药签订战略合作协议。埃格林利用自有的AI平台赋能CRO的临床设计，并不断训练模型达到更高的精准度；博济利用自身雄厚临床试验资源来服务国内外客户，并借助AI赋能提升在CRO行业中的竞争力，双方形成优势互补，共同推进中国创新药物的高效临床研发和商业化。03下个十年，AI会出现在医药研发的哪里？AI赋能药物研发行业发展至今，初创企业应当意识到“跑出差异化”在当下的重要性。延续将AI技术应用于整个新药研发流程的思路，企业可以选择针对单一研发环节进行深耕，也可以考虑服务于药物研发全流程。阿斯利康自创的“5R”原则将其研发成功率从4%提升至19%，领跑全球顶尖药企研发水平。2023年8月，现任阿斯利康全球研发首席数字健康官Cristina Duran博士正式提出将正确的数字解决方案（Right digital）加入“5R”，形成阿斯利康全新的“6R”研发原则[2]。（5R原则：指正确的靶点Right Target、正确的组织Right Tissue、正确的安全性Right Safety、正确的患者Right Patient、正确商业潜力Right Commercial）图6：在临床试验中实施数字医疗技术：6R框架图片来源：Implementation of digital health technology in clinical trials:the 6R framework.同年10月，阿斯利康宣布其与自动化临床关键流程开发商Saama达成了一项多年合作协议。Saama在超过3亿个数据点上训练了90+个生命科学模型，其数据中心和管理系统能将数据发现的时间缩短35%，结合AI和高级分析来自动化关键临床开发流程，阿斯利康将通过本次合作简化和促进自身的临床数据管理和医学审查流程，为加速临床开发设定新的行业标准。2023年11月，BMS宣布与美国癌症治疗方案提供商Tempus开展长期战略合作。本次合作聚焦癌症领域，两家公司将在特定癌症疾病领域使用多模态数据集、计算方法和患者衍生的疾病模型，确定新的靶点，以更高的可信度进行验证，重新定义基因组数据在临床环境中的使用。同期，药智网与英国AIA Insights公司共同创建的天然产物AI研发平台——NPAI Engine正式上线。该平台能够以高准确率完成中药天然产物有效成分的筛选、保健食品配方推荐、作用机理预测、ADMET预测、海量文献挖掘与可视化图展示，进一步促进了中药、保健食品、化妆品和功能性食品行业的研发进程，解决了诸如“同名同方”、“联合作用”等中医药研发常见问题，在“助力科研人员进行高质量的文章发表”方面也有突出的表现。烟台麦基洗德生物工程有限公司、重庆医科大学药学院、四川省中医药科学院、Sygnature Discovery、MSBChem Consulting、Professor Yun-chiCheng团队等已经成为该平台的重要客户。2024年2月6日，德睿智药（MindRank）宣布，完成与默沙东中国知识图谱合作项目关键阶段性交付。该项目以德睿智药的生物医药知识图谱PharmKG™平台为基础，覆盖疾病、药物、靶点、临床试验、文献、专利等信息，结合自研的Medical Insight™AI大模型技术，筛选、匹配、审核、确认了上亿数量级的知识图谱实体及关系。通过其中整合出大量可靠且及时的研发信息，为企业在医药研发全流程的决策提供了科学而高效的工具支持。药智网、上海交通大学和天鹭科技携手打造的AI成药性（ADMET）预测平台于2024年3月4日正式上线。该平台关联化合物结构，以及吸收、分部、代谢、排泄和毒性等上百个成药性参数，基于图神经网络架构和多项分子描述符，为苗头化合物早期筛选、先导化合物评估优化、安全性评价、不良反应预测等关键研发步骤提供关键有力的数据支持。目前，该平台构建的160+预测参数模型已经通过全球已批准药物的真实数据验证，全面对标ADMET Predctor，竞争全球领先的AI预测ADMET商业化软件。以上，可以看到，已有许多企业在正在逐渐打开AI技术在新药研发产业中的应用思路，不再追求泛泛而谈，“单点击破”正在成为AI技术在新药研发中的新话题。在我们的日常咨询工作中，也开始收到行业内许多大型传统药企对创新药产业、对数字化转型、对智能化应用的调研需求，医药行业对科学技术赋能的积极性正在快速生长。总而言之，AI技术在医药研发中的应用应该是工具化的、多样化的、产业化的。结合我国建设数字中国、推进健康中国建设等系列战略部署，中国医药产业数智化转型已经到了至关重要的时刻。参考文献：[1]Paul,S.,Mytelka,D.,Dunwiddie,C.et al.How to improve R&D productivity:the pharmaceutical industry's grand challenge.Nat Rev Drug Discov 9,203–214(2010).https://doi.org/10.1038/nrd3078[2]Durán,C.O.,Bonam,M.,Björk,E.et al.Implementation of digital health technology in clinical trials:the 6R framework.Nat Med 29,2693–2697(2023).https://doi.org/10.1038/s41591-023-02489-z声明：本内容仅用作医药行业信息传播，为作者独立观点，不代表药智网立场。如需转载，请务必注明文章作者和来源。对本文有异议或投诉，请联系maxuelian@yaozh.com。责任编辑 | 蓝色阅读原文，是受欢迎的文章哦

IPO临床1期

2023-11-08

·FierceBiotech

Sygnature Discovery pumps £3.75M into data management system

An investment into Azenta Life Sciences is part of Sygnature Discovery’s goal to become a market leader in the drug discovery market for hit-finding capabilities. Sygnature Discovery, a U.K.-based preclinical contractor, is pumping 3.75 million pounds sterling ($4.6 million) into Azenta Life Sciences’ compound management storage system that will allow it to store customer libraries. Azenta’s service, along with high-throughput screening (HTS) capacity, will allow automated management and screening of compounds from various sources at one location, according to a Nov. 2 press release. With it, Sygnature will be able to store more than 2 million compounds under optimal conditions for stability, and compound samples will be tracked automatically from the storage well to the final HTS assay data point. The investment is part of Sygnature’s goal to become a market leader in the drug discovery market for hit-finding capabilities. In August, Sygnature bought Canadian CRO NuChem Sciences for an undisclosed price, giving the company integrated and standalone discovery services that span medicinal, synthetic, scale-up, process and computational chemistry as well as drug metabolism and pharmacokinetics, in vitro biology and in vivo pharmacology.

并购

2023-11-08

·FierceBiotech

Everest Clinical acquires August Research for undisclosed price

In a move to expand its footprint in Europe, Canadian CRO Everest Clinical acquired Bulgaria-based August Research. Everest Clinical, a portfolio company of Arlington Capital Partners, picked up Bulgaria-based August Research, giving the Canadian CRO a presence in Europe. August Research has experience and infrastructure in 14 countries in Western and Eastern Europe. Through the deal, Everest will be able to provide clinical trial services (CTS) offerings and other operational capabilities to its clients, including pharma, biotech and medical device companies, in Europe, the company said in a Nov. 7 press release. Financial details of the deal weren’t disclosed. Dana Leff Niedzielska and Lukasz Niedzielski, August’s founders, as well as managing directors in the company’s European offices, will remain with the combined companies, Everest said. “The acquisition of August is highly strategic for Everest as it further expands our global service offering and establishes a beachhead in one of the largest clinical trial markets in the world,” Malcolm Little, a partner at Arlington Capital, said in the release. “With the addition of several European offices to complement Everest’s existing presence in North America and Asia, Everest further engrains itself as a truly global CRO.” Everest’s deal bucks the recent trend of Canadian CROs being the target of acquisitions versus the pursuer. In recent months, Canadian CROs Cirion Biopharma, Nucro-Technics and NuChem Sciences were snapped up in acquisition deals.