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

 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（由申请人提供）：毒品开发是一项非常昂贵的矿石，成功的毒品必须涵盖失败的成本，处方药价格已经升级了对毒品的需求也意味着，thar剂量的主要是批准帽子的人口，以赚取足够的人口，以赢得IS投资的一小部分。说，肿瘤学，心血管或免疫学。追求 没有机会通过付款者进行临床试验，而是通过耦合定量的数学算法来进行付款与我们的软件一起，我们的软件也将获得验证。建立在我们的创始人开发的开源生物物理引擎上，同时通过Scree NCE和人工智能实验室攻读生物工程博士学位，但由于缺乏可用性，该项目的采用是有限的。 d演示层，可以轻松访问，利用和理解的基本计算，因此资本需求少于我们目前通过我们的服务播种的第一步。作为当时提供高价值的公司，我们的产品筹集了，我们在两轮比赛之间总共筹集了180万美元。在加速的时间表上提供满足医疗需求的BestInclass生物制剂的制药公司，为患者提供更好，更好，更快，更便宜的... Trury A winwinin。

项目成果

Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions.

• DOI: 10.1080/19420862.2023.2192251
• 发表时间: 2023-01
• 期刊: MABS
• 影响因子: 5.3
• 作者: Grant, Joshuaine;Hua, Fei;Apgar, Joshua F.;Burke, John M.;Marcantonio, Diana H.
• 通讯作者: Marcantonio, Diana H.