GNQ Insilico's AI-Driven Digital Twin Platform Shows Promising Results in First Virtually Simulated Clinical Drug Trial
GNQ Insilico's AI-Driven Digital Twin Platform Shows Promising Results in First Virtually Simulated Clinical Drug Trial
GNQ Insilico's ("GNQ") proprietary genomics-driven platform is leveraging Artificial Intelligence (AI) and Quantum Computing technologies to create "intelligent digital twins" of human patients that can mimic how a drug will interact with an individual patient's unique biology, down to the cellular level.
GNQ's platform has demonstrated success in synthesizing digital twins of human patients.
Additionally, GNQ was able to simulate the effects of a drug on these digital twins.
The results highlight how genomics and AI can be used by the pharmaceuticals and life sciences industries to improve the efficiency of clinical trial designs for new drug development.
GNQ Insilico(“GNQ”)专有的基因组学驱动平台正在利用人工智能(AI)和量子计算技术来创造 “智能” 数字双胞胎“能够模仿药物如何与个体患者的独特生物学相互作用的人类患者,直至细胞层面。
GNQ 的平台在合成人类患者的数字双胞胎方面取得了成功。
此外,GNQ 能够模拟药物对这些数字双胞胎的影响。
研究结果突显了制药和生命科学行业如何使用基因组学和人工智能来提高新药开发临床试验设计的效率。
Vancouver, British Columbia--(Newsfile Corp. - June 18, 2024) - Trenchant Technologies Capital (CSE: AITT) (OTC: AITTF) (FSE: 5730) "Trenchant" or "the Company"), is pleased to announce that its portfolio company GNQ Insilico ("GNQ") has demonstrated promising results in synthesizing digital twins of human patients, and simulating the effects of an infertility drug on these digital replicas using its proprietary AI-driven platform.
不列颠哥伦比亚省温哥华--(Newsfile Corp.,2024年6月18日)——Trenchant Technologies Capital(CSE:AITT)(场外交易代码:AITT)(FSE:5730)“Trenchant” 或 “公司”)欣然宣布,其投资组合公司GNQ Insilico(“GNQ”)在合成人类患者数字双胞胎和模拟不孕症药物的作用方面取得了令人鼓舞的结果使用其专有的人工智能驱动平台在这些数字副本上绘制。
Applications of Digital Twins in Drug Discovery and Development
数字双胞胎在药物发现和开发中的应用
In the healthcare industry, digital twins are an emerging technology that has the potential to advance patient care and personalized medicine. Medical digital twins are computer-based virtual models of living and non-living entities which can range from an individual human patient to organs, tissue cells, neural networks, micro-environments, or entire populations. Rather than 3D models, medical digital twins are dynamic virtual replicas of real-life entities and processes, continually interacting with and adapting to real-time data and predicting corresponding future scenarios within a complex system, using AI and quantum computer technologies.
在医疗保健行业,数字双胞胎是一项新兴技术,有可能推进患者护理和个性化医疗。医疗数字双胞胎是基于计算机的活体和非生命实体的虚拟模型,其范围从个人体患者到器官、组织细胞、神经网络、微环境或整个人群。医疗数字双胞胎不是三维模型,而是现实生活中的实体和过程的动态虚拟副本,使用人工智能和量子计算机技术,持续与实时数据交互并适应实时数据,预测复杂系统中相应的未来场景。
Medical digital twins have the potential to significantly improve the drug discovery and drug development process by improving the efficiency, efficacy and outcome of clinical trials. Currently, the average new drug experiences a 90% failure rate1 during clinical trials, while the average cost to bring a new drug to market is estimated at between $161 million - $1.8 billion (fully capitalized costs inclusive of failures)2. The average timeframe for bringing a typical new drug to market, from discovery to FDA approval, is between 10 - 15 years3.
医疗数字双胞胎有可能通过提高临床试验的效率、疗效和结果,显著改善药物发现和药物开发过程。目前,新药的平均失败率为90%1 在临床试验期间,将新药推向市场的平均成本估计在1.61亿美元至18亿美元之间(包括失败在内的全部资本化成本)2。从发现到美国食品药品管理局批准,典型新药上市的平均时间在10至15年之间3。
Significant improvements in drug discovery and development can be made possible through "in silico" drug simulations using digital twins, by mimicking how a drug will interact with an individual patient's unique biology, down to the cellular level. This could assist pharmaceutical companies in better designing and optimizing clinical trial protocols by enabling them to more accurately predict how these drug compounds will behave prior to human trials, thereby reducing costs and failure rates.
通过使用数字双胞胎进行的 “计算机化” 药物模拟,通过模仿药物如何与个体患者独特的生物学相互作用,直至细胞层面,可以实现药物发现和开发的重大改进。这可以帮助制药公司更好地设计和优化临床试验方案,使他们能够在人体试验之前更准确地预测这些药物化合物的表现,从而降低成本和失败率。
GNQ's Virtually Simulated Clinical Trial
GNQ 的虚拟模拟临床试验
GNQ Insilico simulated the pharmacokinetics and pharmacodynamics of an existing infertility treatment on thousands of digital twins, spanning diverse genetic backgrounds and health profiles, that were synthesized using its platform. GNQ's AI optimizer then analyzed the simulated outcomes to identify optimal dosing strategies tailored to each digital twin's characteristics, accounting for factors like genetics, epigenetics, and environmental exposures.
GNQ Insilico使用其平台合成的数千种数字双胞胎模拟了现有不孕症治疗的药代动力学和药效学,这些数字双胞胎涵盖了不同的遗传背景和健康状况。然后,GNQ 的人工智能优化器分析了模拟结果,以确定根据每个数字双胞胎的特性量身定制的最佳给药策略,同时考虑遗传学、表观遗传学和环境暴露等因素。
Sudhir Saxena, CTO of GNQ Insilico commented: "Human clinical trials are often hindered by variability in how patients respond to drugs. Our AI-driven digital twins platform will enable us to better optimize the trial design for precise patient subpopulations, before ever running an expensive clinical trial."
GNQ Insilico 首席技术官 Sudhir Saxena 评论道: “人体临床试验通常受到患者对药物反应的可变性的阻碍。我们的人工智能驱动的数字双胞胎平台将使我们能够在进行昂贵的临床试验之前,更好地针对精确的患者亚群优化试验设计。”
Two of GNQ's team members, in collaboration with other technologists from leading organizations, also co-authored a recently published paper on a related subject, which illustrates how quantum computing may be leveraged to optimize clinical trial design. To learn more, read the paper: 'Towards Quantum Computing for Clinical Trial Design and Optimization: A Perspective on New Opportunities and Challenges'.
GNQ的两名团队成员还与来自领先组织的其他技术专家合作,共同撰写了最近发表的一篇有关相关主题的论文,该论文说明了如何利用量子计算来优化临床试验设计。要了解更多信息,请阅读论文:'迈向用于临床试验设计和优化的量子计算:从新机遇和挑战的角度来看'.
About GNQ Insilico
关于 GNQ Insilico
GNQ Insilico is an AI-biotechnology company pioneering the development and application of next-generation artificial intelligence capabilities to accelerate therapeutic research, clinical development, and individualized patient care. For more information, visit .
GNQ Insilico是一家人工智能生物技术公司,率先开发和应用下一代人工智能能力,以加速治疗研究、临床开发和个性化患者护理。欲了解更多信息,请访问。
About Trenchant Technologies Capital
关于 Trenchant 科技资本
Trenchant Technologies Capital (CSE: AITT) is an investment issuer focused primarily on seeking investment in companies introducing novel technologies, including Artificial Intelligence and Quantum Computing, to traditional business models that are due for disruption. Trenchant's team undergoes a rigorous due diligence process to identify companies led by seasoned management teams that are strong candidates for acquisition or an initial public offering (IPO).
Trenchant Technologies Capital(CSE:AITT)是一家投资发行人,主要致力于向将人工智能和量子计算等新技术引入即将发生颠覆的传统商业模式的公司寻求投资。Trenchant的团队经过严格的尽职调查流程,以确定由经验丰富的管理团队领导的公司,这些公司是收购或首次公开募股(IPO)的有力候选人。
In May 2024, Trenchant Technologies Capital acquired a 20% ownership interest in GNQ Insilico from parent company My Next Health Inc. Further, Trenchant holds an option to acquire up to 40% of GNQ Insilico. Learn more here.
ON BEHALF OF THE BOARD TRENCHANT CAPITAL CORP.
2024年5月,Trenchant Technologies Capital从母公司My Next Health Inc.手中收购了GNQ Insilico的20%所有权。此外,Trenchant持有收购GNQ Insilico高达40%股份的期权。在此处了解更多。
代表董事会 TRENCHANT CAPITAL CORP.
Per: "Eric Boehnke"
Eric Boehnke, CEO
每个: “Eric Boehnke”
首席执行官埃里克·博恩克
For further information, please contact:
Trenchant Technologies Capital Corp.
Eric Boehnke, CEO
Phone: (604) 307-4274
欲了解更多信息,请联系:
Trenchant 科技资本公司
首席执行官埃里克·博恩克
电话:(604) 307-4274
Forward-Looking Statements
前瞻性陈述
This news release contains certain "forward-looking statements" within the meaning of such statements under applicable securities law. Forward-looking statements are frequently characterized by words such as "anticipates", "plan", "continue", "expect", "project", "intend", "believe", "anticipate", "estimate", "may", "will", "potential", "proposed", "positioned" and other similar words, or statements that certain events or conditions "may" or "will" occur. These statements, including but not limited to GNQ's ability to successful complete all necessary trials and regulatory approval processes necessary to be in a position to commercialize any of its technologies, including but not limited to its proprietary genomics-driven platform are only predictions. Various assumptions were used in drawing the conclusions or making the predictions contained in the forward-looking statements throughout this news release. Forward-looking statements are based on the opinions and estimates of management of GNQ at the date the statements are made and are subject to a variety of risks and uncertainties and other factors that could cause actual events or results to differ materially from those projected in the forward-looking statements. Trenchant Capital and GNQ are under no obligation, and expressly disclaims any intention or obligation, to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as expressly required by applicable law.
本新闻稿包含适用证券法下此类陈述所指的某些 “前瞻性陈述”。前瞻性陈述通常以 “预期”、“计划”、“继续”、“期望”、“项目”、“打算”、“相信”、“预期”、“估计”、“可能”、“将来”、“潜在”、“提议”、“定位” 等词语来表征,或某些事件或条件 “可能” 或 “将” 发生的陈述。这些声明,包括但不限于GNQ成功完成其任何技术商业化所必需的所有必要试验和监管批准程序的能力,包括但不限于其专有的基因组学驱动平台,只是预测。在本新闻稿中,在得出结论或做出前瞻性陈述中包含的预测时,使用了各种假设。前瞻性陈述基于GNQ管理层在陈述发表之日的观点和估计,受各种风险和不确定性以及其他因素的影响,这些因素可能导致实际事件或结果与前瞻性陈述中的预测存在重大差异。除非适用法律明确要求,否则Trenchant Capital和GNQ没有义务更新或修改任何前瞻性陈述,也明确表示不打算或义务更新或修改任何前瞻性陈述,无论是由于新信息、未来事件还是其他原因。
Neither the Canadian Securities Exchange nor its Market Regulator (as that term is defined in the policies of the Canadian Securities Exchange) accepts responsibility for the adequacy or accuracy of this news release.
加拿大证券交易所及其市场监管机构(该术语在加拿大证券交易所的政策中定义)均不对本新闻稿的充分性或准确性承担责任。
1 Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 12(7), 3049-3062.
2 Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., & Greyson, D. (2011). The cost of drug development: A systematic review. Health Policy, 100(1), 4-17.
3 Sertkaya, A., Birkenbach, A., Berlind, A., & Eyraud, J., Eastern Research Group, Inc. (2014). Examination of Clinical Trial Costs and Barriers for Drug Development. Assistant Secretary of Planning and Evaluation (ASPE).
1 Sun, D.、Gao, W.、Hu、H. 和 Zhou, S. (2022)。为什么 90% 的临床药物开发失败以及如何改进?中国制药学报 B, 12 (7), 3049-3062。
2 Morgan,S.,Grootendorst,P.,Lexchin,J.,Cunningham,C.,Greyson,D.(2011)。药物研发成本:系统综述。 健康政策, 100(1)、4-17。
3 A. Sertkaya、A. Birkenbach、A. Berlind 和 J. Eyraud,J.,东方研究集团有限公司(2014)。 审查临床试验成本和药物研发的障碍。规划和评估部助理部长(ASPE)。