A Quantitative Systems Pharmacology Software Platform for Biologics: reduce costs and accelerate development of innovative biotherapeutics

生物制剂定量系统药理学软件平台：降低成本并加速创新生物治疗药物的开发

• 批准号: 9266447
• 负责人: Joshua Apgar
• 金额: $ 101.66万
• 依托单位: APPLIED BIOMATH, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9266447
• 关键词: Address; Adoption; Affect; Algorithms; Annual Reports; Area; Artificial Intelligence; Benchmarking; Biological; Biological Assay; Biological Products; Biological Response Modifier Therapy; Biophysics; Biotechnology; Businesses; Capital; Cardiovascular system; Client; Clinic; Clinical Data; Clinical Trials; Cloud Computing; Computer software; Computers; Computing Methodologies; Consult; Contracts; Coupling; Data; Development; Disease; Doctor of Philosophy; Dose; Drug Costs; Drug Industry; Drug Kinetics; Drug Prescriptions; Drug Utilization; Drug effect disorder; Employee; Engineering; Ensure; Environment; Failure; Functional disorder; Funding; Goals; Grant; Guidelines; Heart; High Performance Computing; Human; Immunology; Industry; Industry Standard; Institutes; Investments; Knowledge; Lead; Licensing; Life Cycle Stages; Massachusetts; Mathematics; Medical; Metabolism; Methods; Modeling; Molecular; Patients; Penetration; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Substance; Pharmacology; Population; Price; Process; Property; Quality of life; Research; Resources; Running; Sampling; Scheme; Scientist; Services; Site; Standardization; Sum; System; Systems Biology; TNF gene; TNFRSF5 gene; Techniques; Technology; Testing; Therapeutic; Time; TimeLine; Translations; United States National Institutes of Health; Validation; absorption; application programming interface; base; cluster computing; computer science; cost; drug candidate; drug development; drug discovery; experience; inhibitor/antagonist; innovation; mathematical algorithm; novel therapeutics; oncology; open source; patient safety; pharmacokinetic model; pre-clinical; preclinical development; prevent; programs; public health relevance; research and development; skills; small molecule; software development; success; support tools; tool; usability; working group; Address; Adoption; Affect; Algorithms; Annual Reports; Area; Artificial Intelligence; Benchmarking; Biological; Biological Assay; Biological Products; Biological Response Modifier Therapy; Biophysics; Biotechnology; Businesses; Capital; Cardiovascular system; Client; Clinic; Clinical Data; Clinical Trials; Cloud Computing; Computer software; Computers; Computing Methodologies; Consult; Contracts; Coupling; Data; Development; Disease; Doctor of Philosophy; Dose; Drug Costs; Drug Industry; Drug Kinetics; Drug Prescriptions; Drug Utilization; Drug effect disorder; Employee; Engineering; Ensure; Environment; Failure; Functional disorder; Funding; Goals; Grant; Guidelines; Heart; High Performance Computing; Human; Immunology; Industry; Industry Standard; Institutes; Investments; Knowledge; Lead; Licensing; Life Cycle Stages; Massachusetts; Mathematics; Medical; Metabolism; Methods; Modeling; Molecular; Patients; Penetration; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Substance; Pharmacology; Population; Price; Process; Property; Quality of life; Research; Resources; Running; Sampling; Scheme; Scientist; Services; Site; Standardization; Sum; System; Systems Biology; TNF gene; TNFRSF5 gene; Techniques; Technology; Testing; Therapeutic; Time; TimeLine; Translations; United States National Institutes of Health; Validation; absorption; application programming interface; base; cluster computing; computer science; cost; drug candidate; drug development; drug discovery; experience; inhibitor/antagonist; innovation; mathematical algorithm; novel therapeutics; oncology; open source; patient safety; pharmacokinetic model; pre-clinical; preclinical development; prevent; programs; public health relevance; research and development; skills; small molecule; software development; success; support tools; tool; usability; working group

 DESCRIPTION (provided by applicant): Drug development is a very lengthy and expensive undertaking. Failure rate for novel drugs exceeds 95%. Therefore, successful drugs must cover the costs of these failures. As such, prescription drug prices have escalated at an alarming rate and show no signs of stopping. The need for successful drugs to cover failures also means that pharmaceutical companies primarily devote resources to pursuing drug candidates that have a large enough population to allow the company to earn a return on its investment. Thus, diseases that affect only a small portion of the populace are not investigated nearly as much as, say, oncology, cardiovascular or immunology. Current practice usually involves taking modeling techniques developed for small molecule research and trying to adapt them to biologics. However, this approach, more often than not, does not provide the scientist with predictions around feasibility and optimal drug properties, resulting in wasted effort pursuing leads that have no chance of making it through clinical trials, or to be reimbursed by payors. Applied BioMath has developed tools that address high value questions in the middle of the drug development pipeline. By coupling quantitative systems pharmacology techniques with high performance computing and sophisticated mathematical algorithms, we have proven an ability to predict optimal drug properties years before entering the clinic. For the past two years we have been offering our services to pharma and biotechs alike, to rave reviews. We have also been approached with inquiries to license our software. This project will fund the development of our proprietary algorithms and toolsets into a stable, standardized software platform, that can be automatically validated for GLP, for by biologics to develop their internal systems pharmacology models. At its heart, our toolsets are built on Kronecker Bio, an open source biophysical computational engine co­developed by one of our Founders while pursuing his PhD in Biological Engineering with the Computer Science and Artificial Intelligence Lab from the Massachusetts Institute of Technology. This robust platform is currently in use, in its raw form, in the pharmaceutical industry but is limited in its adoption due to its lack of usability, quality contro and GLP validation. This project will focus on the application and presentation layer, allowing the underlying computational functionality to be easily accessed, utilized and understood, so capital requirements are less than a typical software development project. Achieving our goal of building this software platform is only the first step. What follows is a concerted push into the biologics segment, which we are currently seeding through our services offering and gaining a reputation as a firm that delivers high value on time. We have completed our second round of fundraising, raising a total of $1.8m between both rounds. This grant, plus the additional fundraising, will ensure that we are able to roll out our tools and assist drug companies in delivering best­in­class biologics, that meet unmet medical need, on an accelerated timeline to provide patients with a better quality of life. Better, faster, cheaper drugs... truly a win­win­in.

 描述（适用提供）：药物开发是一项非常长时间昂贵的工作。新型药物的衰竭率超过95％。因此，成功的药物必须支付这些失败的成本。因此，处方药价格以惊人的速度上升，没有停止的迹象。成功的药物需要弥补失败的需求也意味着，制药公司主要将资源投入到追求人口足够大的候选药物以允许该公司获得其投资回报率。这是仅比肿瘤学，心血管或免疫学的疾病仅受到一小部分的疾病的研究。当前的实践通常涉及采用用于小分子研究开发的建模技术，并试图使其适应生物制剂。但是，这种方法经常并不能为科学家提供有关可行性和最佳药物特性的预测，从而浪费了努力，追求有潜在客户 没有机会通过临床试验或付款人偿还。 Applied Biomath开发了在药物开发管道中间解决高价值问题的工具。通过将定量系统药理学技术与高性能计算和复杂的数学算法耦合，我们证明了在进入诊所之前预测最佳药物特性的能力。在过去的两年中，我们一直为Pharma和Biotechs提供服务，以获取好评。我们还接受了查询以许可我们的软件。该项目将资助我们专有算法和工具集的开发成一个稳定的标准化软件平台，该平台可以自动为GLP验证，以便生物制剂开发其内部系统药理学模型。从本质上讲，我们的工具集建立在Kronecker Bio上，Kronecker Bio是一种开源的生物物理计算引擎，由我们的一位创始人开发，同时在马萨诸塞州技术学院通过计算机科学和人工智能实验室攻读生物工程博士学位。该强大的平台目前正在制药行业中使用，以其原始形式使用，但由于缺乏可用性，质量的侵害和GLP验证，其采用量受到限制。该项目将重点放在应用和演示层上，从而使基本的计算功能可以轻松访问，利用和理解，因此资本需求不如典型的软件开发项目。建立此软件平台的目标只是第一步。接下来是一致投入生物制剂领域的一致，我们目前正在通过服务播种，并以一家享有高价值的公司的声誉。我们已经完成了第二轮筹款活动，在两轮比赛之间总共筹集了180万美元。这笔赠款以及额外的筹款活动将确保我们能够推出工具并协助制药公司在加速时间表上提供最佳的incclass生物制剂，这些生物制剂满足未满足的医疗需求，从而为患者提供更好的生活质量。更好，更快，廉价的药物……确实是温宁。