Development of a miniaturized single-port automated insulin delivery system utilizing a glucose sensing catheter, ultra-concentrated insulin, and an optimized control algorithm
利用葡萄糖传感导管、超浓缩胰岛素和优化控制算法开发小型化单端口自动胰岛素输送系统
基本信息
- 批准号:10452613
- 负责人:
- 金额:$ 55.24万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-20 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:AdoptionAlgorithmsAmbulatory Care FacilitiesBlood GlucoseBlood Glucose Self-MonitoringBolus InfusionCalibrationCannulasCathetersClinicClinical ResearchCommunicationComplexCoupledDataDevelopmentDevicesDiabetes MellitusDoseElectronicsFamily suidaeGenerationsGlucoseGlucose ClampGlycosylated hemoglobin AHealthHousingHumanHyperglycemiaInfusion proceduresInsulinInsulin Infusion SystemsInsulin-Dependent Diabetes MellitusLegal patentLibrariesLiquid substanceManufacturer NameMeasurementMeasuresMechanicsMediator of activation proteinMedical Care CostsMethodologyMethodsMiniature SwineMorphologic artifactsOutcomeOutcome MeasureOutpatientsOxidation-ReductionParticipantPartner in relationshipPatientsPerformancePersonsPhasePopulationPumpReadingSalineSamplingSecureSecuritySeriesSiteSmall Business Innovation Research GrantSubcutaneous InjectionsSystemTestingTimeUnited StatesWorkbaseblood glucose regulationcommercializationcostdesigndiabetes managementdigitalefficacy evaluationglucose monitorglucose sensorglycemic controlhuman studyimprovedinteroperabilityminiaturizeoperationpredictive modelingprimary outcomeprospectiverecruitresearch clinical testingsecondary outcomesensorsignal processingsoftware developmentsubcutaneoussuccesstoolusabilitywireless communication
项目摘要
ABSTRACT
Significance: There are over 5 million people with insulin-treated diabetes in the United States who represent
a disproportionately large share of the $237B in direct medical costs attributable to diabetes. The use of
continuous glucose monitoring (CGM) has been shown to reduce HbA1c levels, a proven predictor of health
outcomes within this population, with the greatest improvement achieved when CGM is coupled with
continuous subcutaneous insulin infusion (CSII). The recent convergence of CGM and insulin pumps has
enabled the first generation of automated insulin delivery (AID) systems, promising even better glycemic
control for insulin-treated diabetes. However, current AID systems are complex, cumbersome, and expensive
for the patient because they require multiple devices to be worn on the body: a glucose sensor, an insulin
pump, and an insulin delivery catheter. We have developed a glucose sensing catheter that reduces the
number of subcutaneous components from two to one, significantly reducing the size and complexity of these
systems. The PDT interoperable sensing cannula assembly that we are proposing to commercialize in this
phase 2 SBIR will allow any insulin patch pump manufacturer to rapidly integrate CGM directly on the insulin
delivery cannula, thereby enabling people with T1D who are patch pump users to effortlessly utilize CGM
through a single subcutaneous injection site. Importantly, this platform will also improve AID system reliability
and security by replacing the wireless communication from CGM to pump controller with a direct wired
connection. Resulting reductions in system size, complexity, and cost will increase adoption rates for pump
user and people using AID, helping improve compliance, lower HbA1c levels, and improve health outcomes
among people with type 1 diabetes. Preliminary Data: PDT has recently demonstrated that delivering insulin
at the site of glucose sensing is possible using a patented redox mediator-based sensing cannula. However,
we have also shown that there is a dilution artifact that occurs immediately after a dose of insulin is delivered
through the cannula. We have shown that this artifact is independent of whether insulin or saline is delivered.
In Phase 1 of this SBIR, we demonstrated in a swine study that this artifact is related to the size of the bolus.
We further demonstrated that the artifact can be significantly reduced by using higher concentration insulin and
ultimately eliminated by using sophisticated predictive signal processing methods. Specific Aims: In Phase 2
of this project, we will use the products of Phase 1 to take the next logical steps in integration of our sensing
cannula into a dual function patch pump platform. In Specific Aim 1, we will further characterize and evaluate
the accuracy of the PDT sensing cannula in a human study. In Specific Aim 2, we will work with a commercial
pump partner (EOFlow) to develop and evaluate an interoperable sensing cannula assembly (ISCA) that is
designed for rapid integration into a patch pump. The ISCA will include the required electronics, mechanical
components, and a software development kit that will enable rapid integration into commercial patch pumps.
Working with our academic partners at OHSU, we will transfer the artifact elimination predictive signal
processing algorithm and port this algorithm to the ISCA for use in real-time operation. In Specific Aim 3, we
will integrate the sensor assembly into our commercial partner’s patch pump and validate the performance and
accuracy of the design in a swine study. At the conclusion of Phase 2, we will have a dual-function glucose-
sensing patch pump validated in a swine study and poised to enter clinical study. In Phase 2B, we will conduct
those studies, and work with our academic collaborators and commercialization partners to incorporate a
model predictive controller into the patch pump to yield an all-in-one automated insulin delivery solution.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Thomas Ludwig Seidl其他文献
Thomas Ludwig Seidl的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Thomas Ludwig Seidl', 18)}}的其他基金
Development of a miniaturized single-port automated insulin delivery system utilizing a glucose sensing catheter, ultra-concentrated insulin, and an optimized control algorithm
利用葡萄糖传感导管、超浓缩胰岛素和优化控制算法开发小型化单端口自动胰岛素输送系统
- 批准号:
10296620 - 财政年份:2019
- 资助金额:
$ 55.24万 - 项目类别:
相似海外基金
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 55.24万 - 项目类别:
Research Grant














{{item.name}}会员




