ARPA-H Project Awardees

PROTECT – Pro/Prebiotic Regulation for Optimized Treatment and Eradication of Clinical Threats

PROTECT is a platform technology that combines a precision blend of native microbiome-enhancing probiotics and prebiotics, working synergistically as a synbiotic to prevent infection and restore healthy microbiota. Probiotics consist of beneficial bacteria while prebiotics consist of nutrients that selectively enable growth of these beneficial bacteria. PROTECT is predicated on the concept of niche exclusion, allowing beneficial bacteria to outcompete the pathogens (such as Staphylococcus aureus and Pseudomonas aeruginosa (PA)) and preventing them from taking hold, spreading, or developing antibiotic resistance. The proposal is focused on systematically defining the appropriate bacteria and growth conditions for rational development of a synbiotic treatment to prevent PA infection in individuals with cystic fibrosis and other lung infections. Findings from this work could create a generalizable platform for producing protective agents against chronic and acute infections. The project intends to generate an integrated resource (the “Airway Systematic Microbial Atlas,” or ASMA). ASMA will store and present all data and analyses and link to the reagents developed (including a strain bank), providing the broader scientific and medical communities with unique data and material resources to extend analysis for novel applications. 

RADIANT: Real-time Analysis and Discovery in Integrated And Networked Technologies

The proposed Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT) toolkit seeks to develop an extensible, federated framework for rapid exchange of multimodal clinical and research data on behalf of accelerated discovery and patient impact. The development activities pair software-enabled national infrastructure for data integration and interoperability with leading-edge portals and cloud platform deployments. Together, the RADIANT toolkit seeks to revolutionize current practices by enabling a new research care framework for real-time discovery and translational impact with children diagnosed with brain tumors as a driver use case. Coordination and implementation of initial RADIANT deployments will leverage a network of more than 35 partnered health care systems and participating patient families within the Children’s Brain Tumor Network (CBTN) and the Pediatric Neuro-Oncology Consortium (PNOC). 

AVOID-OME: Structurally enabling the “avoid-ome” to accelerate drug discovery

Small molecule therapies represent a significant portion of FDA-approved drugs, yet their development is often hindered by the challenge of balancing on-target activity with desirable pharmacokinetic (PK) properties. Pharmacokinetic properties, which encompass absorption, distribution, metabolism, and excretion (ADME), determine a drug's fate within the body and are crucial for its efficacy and safety. Currently, the optimization of these properties is often addressed late in the drug discovery process, leading to costly late-stage failures. This project seeks to overcome this limitation by proactively characterizing the chemical space accessible to ADMET-associated proteins ("anti-targets"). By applying recent advances in experimental and computational techniques, a comprehensive open library of experimental and structural datasets will be generated. This precompetitive resource will provide valuable insights into the binding properties of "anti-targets" and empower researchers to develop predictive AI models for pharmacokinetic optimization. This shift towards a more proactive and data-driven approach promises to significantly expedite drug discovery by streamlining lead optimization, mitigating late-stage attrition, and ultimately accelerating the delivery of new therapies to patients. 

TARGET: Transforming Antibiotic R&D with Generative AI to stop Emerging Threats

The Transforming Antibiotic R&D with Generative AI to stop Emerging Threats (TARGET) project will use AI to speed the discovery and development of new classes of antibiotics. Bacterial infections are a leading cause of death worldwide, and there is an urgent need to develop new antibiotics as the prevalence of antibiotic-resistant bacteria grows.  Conventional efforts to identify and develop new antibiotics require extensive manual screening and testing of molecular compounds, with the majority failing to successfully show antibiotic activity. This laborious process impedes our ability to discover new antibiotics at the speed needed to address the urgent threat of antimicrobial resistance. TARGET aims to close this gap by using deep learning to identify biomolecules with antibiotic and pharmaceutical potential and using generative AI to broaden the pool of candidate molecules. Together, these approaches will speed the development of new classes of previously unknown antibiotics. 

Network of Oncology Learning Health Systems for Cancer Surveillance and Precision Health Equity

This project aims to create Oncology Learning Health Systems among health systems of all sizes and at any location. The overall scope of the University of North Carolina (UNC)-based center is to bring together structured and unstructured electronic health record (EHR)-derived data, insurance claims, and geographic data to rapidly identify cases and improve care quality and equity via access to cutting edge clinical advances for cancer. The Cancer Identification and Precision Oncology Center (CIPOC) will facilitate an Oncology Health Learning System, through rapid, real-time identification and accurate characterization of newly diagnosed cancer cases for surveillance, patient recruitment, optimization of patient care, and largescale population research, providing equitable access to precision medicine. 

CAIDF: Creating AI-enabled All-Health Team Data Fabric

The objective of the project is to create scalable solutions to unlock previously underutilized nursing, physical therapy, occupational therapy and speech and language pathology (RN/PT/OT/SLP) data with routinely collected medical data and integrate it with AI technology. This project focus on RN/PT/OT/SLP data, which is updated more frequently and regularly than data from physician providers in both inpatient and outpatient settings. Specifically, this project will target two common, costly patient populations from across the lifespan whose care needs require multiple disciplines: 1) adult patient falls with trauma/injury requiring hospital admission, and 2) the transition from the neonatal intensive care unit (NICU) to home with preterm or medically complex neonates. The team will use human-curated AI to enable access to the data and innovate on the summarization of the complex episodes of care. The project performance sites include one urban hospital serving a diverse metropolitan population and two hospital systems in smaller cities, each with a large rural catchment area, which will provide racial and ethnic diversity, as well as rural representation in our enclave data. The multidisciplinary LLMs will be released to the world at the end of this project to drive future innovation. 

SQUEEZES: Sharing Data for Rapid Collaboration on Encrypted Data

The SQUEEZES framework accelerates the sharing and collaboration of highly sensitive patient data, enabled by emerging technologies for Privacy Enhanced Computations (PECs) and Privacy Preserving Record Linkage (PPRL). This framework utilizes PECs to expedite collaborative data access and enables researchers to build higher quality models from richer aggregated data linked and joined with PPRL from multiple sources for centralized data analysis and management. SQUEEZES is particularly vital when it is difficult or impossible for data owners to share their data due to privacy concerns, allowing them to retain control over how their data is used. It is a standards-compliant framework designed to: (1) reduce the timeline of sharing data from months to hours while lowering financial and administrative barriers for data sharing, (2) automate the sharing and linking of privacy-protected data used in analytics workflows, reducing the workload of data alignment and schema matching, (3) support existing cancer research data workflows, (4) allow data owners to control the use of their data even after it is shared, protecting patients and data owners, including historically underserved communities, (5) create an incentivization framework for data owners to maintain control over the results of computations on their data, and (6) be scalable, extensible, and easily deployable on legacy and emerging standards-compliant systems. 

No Kidney Left Behind

34 Lives seeks to rehabilitate approximately 50% of the otherwise discarded donor kidneys back into the donor pool by using Normothermic Machine Perfusion (NMP) technology to enable real-time recovery of organ viability and function. The NMP and other novel work around rehabilitating kidneys that are discarded, diseased, or ischemically injured will be done at a core preservation hub operating as CLIA- certified clinical laboratory in association with designated Organ Procurement Organizations (OPOs). 

VECTORS

The VECTORS project aims to create a rapid, safe, scalable, and low-cost plant-based manufacturing platform to drastically reduce production time and cost of viral vector-based gene therapies. Cirsium Biosciences leverages plant-based manufacturing (upstream and downstream) to reduce both production costs and time. Cirsium’s goal is to establish and validate a highly scalable, reproducible, cost effective, rapid plant-based viral vector production platform. Plants have been used to produce biologics for decades and are a proven production host for biologics that have successfully entered clinical trials and been approved by regulators. Cirsium’s process already achieves up to 80% reduction in manufacturing lead times for AAV gene therapies with room for substantial improvements and optimizations. If successful, the plant-based production of viral vectors used for gene therapies will help address the increasing demand for these vectors and the looming shortages for some vector types. 

ALICE: Assessing LLM Integrity for Clinical Engagement

The goal of the project led by the University of Southern California is to develop an automated and efficient approach to medical chatbot evaluation with evaluation components designed to automatically generate questions that reflect a patient-community’s needs and scoring components that reflect common challenges (e.g., hallucinations, omissions, miscalibrated linguistic certainty) as well as pragmatic needs specific to the community. To support this development, USC will engage with stakeholders to understand their information needs and how they are addressed. Immediate use cases include pediatric infectious disease and cystic fibrosis. 

ALISS: Autonomy at a Less Invasive Scale in Surgery

This project aims to create a full set of modular tools for autonomous robotic surgery, as well as the perceptual capabilities and logical framework to integrate them into full procedures. The team has divided surgical procedures into seven core tasks: retraction, resection, hemostasis, proprioception, debridement, palpation, and suturing. The team then creates a logical framework that defines procedures in terms of these tasks and allows the robot to progress from one task to another. As the robot carries out a procedure, it not only assesses completeness of the subtask, but also its own uncertainty, allowing it to request human surgeon intervention when uncertainty is high. Development is centered around the Virtuoso, a surgical robot with a novel concentric tube form factor and narrow dimensions that allows it to bend and reach difficult anatomical locations that neither current endoscopes nor human surgeons can easily access. Although the tool attachment and the perceptual systems are specific to the Virtuoso, the task algorithms and logical framework will be robot agnostic. The technology developed in ALISS will endow the Virtuoso robot with autonomous surgical capabilities and advance the state of the field by developing generally applicable logical frameworks and algorithms. 

Improving the patient experience: LLMs for results interpretation and discharge paperwork

The goal of the project led by Stanford University is to develop a fully automated and scalable approach for optimizing large language model medical chatbots as well as clinician-grade evaluation of the results, for patient-facing use cases. Immediate use cases include the evaluation of a chatbot to help patients understand imaging results and a chatbot to aid patients in understanding instructions after hospital discharge.  

MEDIC: Monitoring, Evaluation and Diagnosing Intelligent Chatbots

The goal of the project led by Raytheon BBN Technologies is to develop an overall process for the evaluation of patient-facing medical-domain chatbots, covering a wide range of metrics and grounded in medical practice by the elicitation of desires and concerns from medical staff, caregivers, and patients. This project will use state-of-the-art large language model capabilities piloted across multiple US Government-funded projects to augment and enrich an initial starter-set of data to cover the problem space and use a collection of Machine Evaluators to produce the metrics, with a minimum of human supervision. The final deliverable will consist of a pipeline of tools for prompt enrichment and a similar system for chatbot evaluation. Immediate use cases include prenatal care in the first and second trimester and mental health in young adults.

V-CARES: Human-centered Design of an Ethical Evaluation Strategy for Chatbot Hallucinations in Health care

The goal of the project led by Vanderbilt University Medical Center is to create the Vanderbilt Chatbot Accuracy and Reliability Evaluation System (V-CARES) to effectively and efficiently detect hallucinations, omissions, and misaligned values from large language model (LLM) responses in the healthcare domain. The project aims to improve the quality of LLM-based Chatbot systems so they can serve as trustworthy, transparent, and accurate sources of information and guidance on health-related topics for the general public, patients, and their caregivers. In addition to reducing hallucinations and omissions, the project aims to create a generalizable evaluation process and technology to improve the quality of the output responses and ensure they are consistent with users’ values and expectations. Immediate use cases include two distinct and prevalent mental health disorders: Major depression and generalized anxiety disorder.  

MiRIT: Micro-Radiolabeling for Imaging and Therapy

This project aims to develop personalized radiopharmaceuticals for PET imaging-based diagnosis and targeted treatment for multiple cancers. This will be achieved through the computer-aided design of custom ligands targeted to cancer cell membrane proteins and through the development of two table-top devices. The first device will be capable of producing very small doses of radiopharmaceuticals on-site at the patient clinic through a single push of a button. The second device will permit parallelized labeling under varying reaction conditions and using varying ligands/radioisotopes, enabling fast-track development of diagnostic and treatment radiolabeled compounds. The resulting targeted imaging and treatment will enable personalized diagnostic PET imaging and radiotherapy, the latter of which could easily be extended to resource-limited clinics and hospitals via a low-dose micro-radiolabeling system developed through the project. 

PAIL: PhotoAcoustic Imaging technology for diagnostic Lung assessment

There is a critical need to develop minimally invasive in vivo imaging technologies capable of providing microscopic assessment and identification of malignancy at early stages of lung cancer, as early detection and treatment is essential to optimize patient survival. The Northeastern team will develop a photoacoustic imaging (PAI) system, with a miniature (1.5 mm diameter), flexible, and disposable probe that is fully compatible with the requirements of bronchoscopy. The PAI system is expected to achieve an unprecedented axial and lateral resolution of ~27 μm and ~100 μm at cm-scale depths. The proposed system has the potential to become the first ever commercial low-cost endobronchial PAI (EB-PAI) platform and will enable 3D microscopic visualization of lung nodules, reducing the risk, burden, and cost associated with biopsy. Though lung nodule assessment will be the proof-of-concept application for this project, the technology itself can be easily adapted to impact many other areas of clinical practice (e.g., cardiology, nerve detection). 

NEBULA: NExt-generation Biomanufacturing ULtra-scalable Approach

NEBULA aims to create a ‘cells made in a box’ small-footprint, low human resource required cell expansion platform for GMP manufacturing of autologous cell-based therapies. The project aims to unlock personalized, affordable regenerative medicine treatments for the diverse U.S. population via an autonomous biomanufacturing system scaling the production of personalized induced pluripotent stem cells (iPSCs). If successful, the project can lay the foundation for broader innovation and access to treatment for diseases like Parkinson’s, heart failure, spinal cord injury, and age-related macular degeneration, among other conditions, affecting nearly 30 million Americans. NEBULA aims to facilitate the research and development of personalized, affordable treatments, significantly reducing the national health burden of these conditions. 

DAGCAP: Democratized, AI-Guided Chart Abstraction Platform

Chart curation is a major bottleneck for oncology clinical practice and research. Existing automated approaches are insufficient to address this bottleneck as important patient data are stored as natural language rather than structured data. Current tools for automated processing of natural language remain inaccurate, require substantial human oversight, and lack oncology-specific knowledge. DAGCAP is a modular, interoperable, comprehensive end-to-end workflow aimed at decreasing chart curation effort by 90% while maintaining human-level accuracy. Users will be able to precisely define data variables using a guided, interactive process, and these variables will be mapped to commonly used data models as well as those defined or imported by users. The proposed research spans 1) Tool Features - building out the software components that will enable drastic improvement in chart curation capabilities; 2) Data Types & Semantics - introduce and train DAGCAP to understand and map between a variety of data types, data models, and enable data analysis and use of downstream tools; and 3) Use Cases - representative of real-world problems that we will use to develop, test, and improve DAGCAP. 

IndiPHARM: Individual Metabolome and Exposome Assessment for Pharmaceutical Optimization

The project will deliver a platform to optimize drug regimens for patients, especially those that have metabolic conditions or disorders. The platform will be designed to be used at the point of care to assist in decision making regarding the efficacy and precision medicine principles of existing drugs. It will consist of two main toolkits: predictive modeling software and a fully referenced high resolution mass-spectrometry (HRMS) workflow. The predictive software will leverage patient data from blood samples to model their xenobiotic metabolism, considering factors such as genomic background. The HRMS workflow, with its quick turnaround time and cost-effectiveness, will provide accurate measurements of patients' blood samples for the metabolism of therapeutics and pharmacokinetics/pharmacodynamics (PK/PD) modeling. The workflow and modeling toolkits will also have the capability to conduct parallel and integrated analyses at population scale, to help clinicians and product developers decipher the wide range of metabolism that affects the delivery of effective therapeutics. This comprehensive approach will enhance our understanding of how drugs act in different patient populations, as well as how they affect individuals on a more personalized level, in line with precision medicine approach.  

SUPPLI: Strategic Utilization of Pharmaceutical Product Location and Identification

The SUPPLI project addresses supply shortages for the key chemicals required to manufacture pharmaceuticals by creating a tool guided by artificial intelligence (AI) that (1) calculates the risk of supply side shortages and prioritizes efforts based on demand, (2) reverse engineers the relevant molecules to derive the starting ingredients, and (3) predicts appropriate manufacturing facilities, conditions, and protocols for manufacturing the chemical components in US based facilities. There is no current published framework for quantifying the interdependence of the manufacturing process from regulatory starting materials (RSMs) to final product. SUPPLI aims to de-risk the supply chain with a focus on the domestication of RSM manufacturing with a targeted goal of creating a product that will optimize pharmaceutical lifecycles, improve process traceability, and provide sustainable process designs that provide more efficient access to pharmaceuticals with fewer supply chain disruptions. It will also aid in the optimization of costs of manufacturing, which may decrease the downstream cost burden on patients and allow more accessible therapeutics to a great number of Americans. 

Safe and Explainable AI-enabled Decision Making for Personalized Treatment

The project is focused on the design and implementation of AI-based clinical decision support systems for personalized treatment and management recommendations. The proposed research spans three areas. First, it addresses AI foundations—problems in trustworthy medical AI, such as integrating medical domain knowledge in learning models effectively, making recommendations of AI algorithms explainable to clinicians, and establishing worst-case safety guarantees. The second focus is on AI systems—infrastructure to facilitate development of explainable models suitable for integration into clinician workflows. Thirdly, the project looks at AI use cases—representative clinical challenges that span inpatient and outpatient use cases, including prediction of in-hospital cardiac arrest, timely diagnosis and prediction of the need for intervention for sepsis, and prediction of response to neoadjuvant or adjuvant chemotherapy for breast cancer patients. To execute this agenda, the team brings together clinicians and researchers with expertise spanning AI, biostatistics, data science, and machine learning. 

MASCOT: Manufacturing Agile and SCalable Organoid Tumor models

One roadblock when developing cancer treatments is the lack of preclinical models that faithfully recreate the tumor microenvironment (TME). Innovative 3D tumor models can replicate the TME, leading to better diagnosis of the disease conditions, surgical planning or drug selection for the patient. The UIUC team will use advanced manufacturing strategies—Industry 4.0—to develop a revolutionary platform that will consistently and reproducibly produce three-dimensional (3D) tumor models of any cancer type, at scale. The platform will exert closed-loop control over the production of tumor organoids while generating a digital thread that documents the status of the tumor models at every stage. The effort includes significant automation for processing, development of high-speed and chemically detailed non-destructive imaging to characterize the models, and the creation of an AI-based Model Predictive Control to dictate growth conditions at each stage of the tumor model development. The prototype manufacturing technology will produce consistent and verified tumor models at a rate of ≥10 per day. This agile manufacturing technology will provide a turnkey solution for validated 3D models for any solid tumor, including cancers from individual patients, cancers prevalent in underserved and minority communities, or rare cancers unaddressed by current technologies. 

DAIRS: Disease-Agnostic Immune therapies using RNA Structure

The project will develop novel and tunable RNA-based therapeutic modalities that target retinoic acid-inducible gene I (RIG-I) in order to activate the innate immune system and stimulate the production of interferons (IFNs). The project will achieve this objective through several steps, including refining the lead molecule dsRNA-1, which selectively activates RIG-I. The project will also leverage this platform to discover additional tunable agonists and antagonists to this key immune system modulator.  

Existing IFN treatments are predominantly protein-based, which necessitates the systemic administration of recombinant IFNs. This approach has several drawbacks, such as an increased potential for side effects and a lack of modulation. On the other hand, the new approach the project team is exploring selectively activates RIG-I, inducing type I/III IFNs. As a result, superior efficacy and safety profiles have been demonstrated when these molecules were administered intravenously, compared to IFN biologics or agents that stimulate the IFN pathway. In addition, the development of RNA therapeutics needs to overcome challenges that are inherent to RNAs, such as rapid degradation, non-specific immunogenicity, and delivery across various cell membranes. The project includes de-risking strategies for these challenges.  

AB-MRI: Affordable Breast MRI

Breast cancer is the second most common cancer affecting women, with over 200,000 new cases and 40,000 deaths in the United States every year. Early detection improves outcomes, reducing mortality rates by 15-40% with current mammography technology. Although mammography is the standard of care for screening, it does not have particularly high sensitivity. MRI is approximately twice as sensitive, but MRI is an expensive technology and not commonly available outside large radiology centers. 

The project aims to develop a low-cost MRI device for breast cancer screening. A small magnet generates the magnetic field, and an innovative field cycling concept bypasses the constraint of a high, homogeneous magnetic field, enabling the smaller, cheaper magnet. The small footprint system, including the gradients, coils, and console, will all be designed specifically for breast cancer screening. The intent is to create a device that costs 10x less than the current standard and can be used in community settings. 

SARRTS: Supervised Autonomous Robotic Renal Tumor Surgery

Surgery is essential to cancer treatment, but skilled surgeons are unevenly geographically distributed. Autonomous robotic surgery has the potential to increase access to skilled surgery, improve consistency of some types of procedures compared to unaided human surgeons, and unlock new types of procedures that human surgeons cannot do due to difficulties in anatomy and visibility. SARRTS aims to demonstrate a supervised autonomous kidney resection, propelling us towards a future state where a general surgeon could supervise a resection robot in a rural hospital, and patients would no longer have to travel to major oncology centers for the best outcomes. Using a CT scan of the area registered to the 3D point cloud generated by the robot’s RGB-Depth camera, the robot plans and executes the incision and resection. While the robot generates the surgical plan, the surgeon approves the surgical plan and can stop, adjust, and replan the surgery at any time. The surgery will be tested and demonstrated in realistic kidney phantoms created for the project. The intent is to demonstrate the feasibility of a supervised autonomous tumor resection and to develop enabling technologies that facilitate the advancement and generalization of this autonomous tumor resection system and other autonomous robotic surgery capabilities. 

CT-NEURO: Cell Therapies for Neuroinflammation and Neurodegeneration

The overarching goal of the CT-NEURO program is to develop strategies for targeting therapeutic immune cells to the central nervous system using biological logic gates. Three specific objectives include 1) development of an immune cell-based, disease-agnostic platform that targets therapeutic payload to the brain, 2) demonstrating that this platform can be expanded to generate engineered cells to selectively target other organs and tissue types, and 3) employing this platform to deliver therapeutic payloads to treat diverse neurological conditions such as brain tumors, neuroinflammatory diseases, demyelination, and neurodegeneration. 

MATRIX: ML/AI-Aided Therapeutic Repurposing In eXtended uses

Millions of individuals worldwide suffer from diseases for which there are no available treatments. While the Food & Drug Administration (FDA) has approved roughly 3,000 drugs to address a corresponding number of diseases, there remain an additional 9,000 diseases without a single approved therapy. Given that numerous diseases share common underlying mechanisms of action, and individual drugs can target multiple mechanisms, the existing pool of 3,000 FDA-approved drugs holds the promise of addressing the 9,000 diseases that currently lack therapeutic options. 

MATRIX (Machine Learning/Artificial Intelligence-Enabled Therapeutic Repurposing in eXtended uses) aims to develop computational methodologies for identifying the FDA-approved drugs most likely to treat diseases with inadequate treatment options, and to identify and validate top candidates for drug repurposing using these methodologies. 

This program aims to develop the first comprehensive scoring system that queries the world’s biomedical knowledge of “all drugs vs all diseases” to predict the efficacy for every drug to treat every human disease. The resulting information on the pharmaco-phenome will be made available open-source, allowing researchers to view the probability of efficacy across the entire landscape of FDA-approved drugs and human diseases.  

PIC-OCT: Enabling Technologies for Photonic Chips-based Optical Coherence Tomography

As the US population ages, and with the increasing prevalence of obesity and related chronic health problems that affect the eye, debilitating eye disease poses a substantial medical and cost concern. With early diagnosis and appropriate management, > 90% of severe vision loss may be prevented. Imaging modalities that excel at screening, early diagnosis, staging, and tracking treatment response, and can do so safely, quickly, and inexpensively are highly valued by ophthalmologists. Optical Coherence Tomography (OCT)-based technologies have revolutionized eye disease diagnosis, along with demonstrating clinical potential across a myriad of other areas – including cardiology, urology, dentistry, and more. However, high cost and complex assembly of current systems limit their widespread adoption and hamper their broad implementation. This project will develop next-generation OCT systems based on photonic integrated circuits (PICs) and custom-designed electronic integrated circuits (ICs). By leveraging the latest advances in the nanofabrication of photonic and electronic ICs, acquisition speeds 50 times faster than the current standard will be achieved alongside immense decreases in the OCT system footprint (i.e., readily employable at walk-in clinics) and unprecedented reductions in manufacturing cost that will facilitate community-wide accessibility. Pediatric patients will especially benefit from the shorter scan times. Altogether, by enhancing patient treatment and adherence to repetitive testing, PIC-OCT will substantially reduce vision loss and its medical and societal costs. 

REO: REvolutionizing the Oral route: delivery of electroceuticals and mRNA therapeutics for transforming health

Metabolic diseases are on the rise, with roughly 40% of Americans being obese and 10% diabetic. Treating these chronic diseases currently requires daily injections, surgery, or expensive drugs. Recent innovations, such as continuous glucose monitoring and insulin pumps, have greatly lowered the burden on patients but can still be painful to use and can limit activity.  

The MIT team aims to revolutionize these treatments by developing two orally delivered pill-sized devices. The first device will sense its location in the gastrointestinal tract and then inject mRNA into the tract lining that provides long term treatment for diabetes or obesity. The second device will temporarily reside in the GI tract, electrically stimulating it to release hormones associated with hunger and satiety. The devices will be remotely controlled and wirelessly powered for enhanced efficacy and safety.  

Although the proof-of-concept effort focuses on metabolic diseases, the designs could be applied to deliver therapies for many clinical conditions. Critically, this innovative delivery of therapies could provide treatment access to socioeconomically disadvantaged classes, who are most affected by metabolic diseases. The self-administration of capsule-sized devices could also reduce healthcare worker involvement, the need for hospitalizations, and healthcare costs associated with the need to store, stabilize, and medications. 

DARTS: Defeating Antibiotic Resistance through Transformative Solutions

Bacterial infections remain a leading cause of death worldwide and will likely become an even greater health care challenge. The number of antibiotic-resistant pathogens grows daily while the discovery of new antibiotics lags dangerously. When a patient arrives at a hospital with a bloodstream infection, every minute matters but choosing the correct antibiotic is also crucial to success. Current methods of bacterial identification and antibiotic susceptibility are not up to the challenge. Testing can take hours, if not days, resulting in longer hospital stays, major complications, and higher mortality rates. Defeating Antibiotic Resistance through Transformative Solutions (DARTS) aims to address these challenges by advancing an ultra-high throughput imaging and culturing platform that can continuously track and test billions of bacteria one by one. If successful, the system will serve as a rapid platform for the discovery and development of new antibiotics. It will also be adapted for patient use as a microbial diagnostic that can rapidly identify the pathogen and the appropriate antibiotic to prescribe, enhancing the stewardship of antibiotics that remain effective. Such a rapid microbial diagnostic would enhance health outcomes, not just for the tested patient, but for everyone, as the diagnostic would greatly reduce the misuse of the antibiotics that remain effective.

CDTR: Stem Cell-Derived Thymus Rejuvenation

Thymmune Therapeutics’ Stem Cell-Derived Thymus Rejuvenation (CDTR) project aims to restore immune and endocrine function in patients lacking a functional thymus by using engineered stem cell-derived treatment. The thymus is an organ responsible for supporting normal immune cell development. The project is divided into two phases. The goal of the first phase is to use a combination of chemical and genetic factors to make best-in-class human induced pluripotent stem thymic epithelial cells (iPS-TECs) with capacity for supporting T lymphocytes (white blood cells) development in vivo. In the second phase, Thymmune plans to develop protocols for transplantation and long-term engraftment of iPS-TEC in animal models to achieve effective immune function, demonstrating a path towards using iPS-TEC to ultimately treat patients lacking functional thymus. Overall, Thymmune’s disease-agnostic approach to combat thymus dysfunction by bolstering immune responses against pathogens, cancer, and vaccines presents a potentially revolutionary means to reboot immunity. Thymmune has the potential to both rescue patients lacking a functional thymus from morbidity and mortality and addresses a crucial unmet need to rejuvenate immunity in the aging population.

CODA: Mapping the Cancer and Organ Degradome Atlas to Unlock Synthetic Biomarkers for Multi-Cancer Early Detection

For most tumor types, there are currently no effective diagnostic tests for detecting most cancers at the earliest stages, when tumors are still localized and most responsive to treatment. Ongoing efforts that focus on native tumor-shed biomarkers face significant challenges, as these markers are often found in vanishingly small quantities in blood or other fluids. The CODA (Cancer and Organ Degradome Atlas) platform uses cutting-edge synthetic biology and cell engineering technologies to catalog cellular profiles unique to diseased cancer cells and leverages them to build bioengineered sensors that can be deployed inside the body to hunt for malignant cells. These biosensors use unique metabolic changes in tumor cells to drive the release of synthetic biomarkers that can reach high enough levels in biofluids to enable earlier cancer detection. This technology has the potential to produce a highly precise, accurate, and cost-effective test for multi-cancer early detection (MCED) that can identify common cancers earlier, when treatment can be most effective, and streamline clinical intervention when tumors are still small.

HEART: Health Enabling Advancements through Regenerative Tissue Printing

Over 3 million patients in the United States need tissue transplants, with more than 100,000 patients on the national transplant waiting list. Unfortunately, many of these people die while waiting for a donated organ. The Health Enabling Advancements through Regenerative Tissue Printing (HEART) project proposes to advance multiple technologies, including the optimization of purity and scalability of human cells, improved 3D printing technology and speed, advances in computational modeling, and novel approaches to organ maturation and implantation. The end result is the 3D printing of a human heart in one hour. This ambitious project has the potential to revolutionize the fields of human tissue and organ printing through large advances across multiple technologies. HEART could create a world where a doctor could 3D print an organ for their patient instead of waiting for a donor, effectively ending waitlists for transplants. This advance would improve the lifespan and quality of life for many Americans and provide broader patient access across all communities.

SPIKEs: Programmable Scalable Therapeutics for Immune-directed Cancer-killing

Cancer immunotherapy, which harnesses the body’s own immune system to attack tumor cells, holds great potential. However, this therapy is currently hampered by very high costs, long and involved preparation processes, and frequent inefficacy against solid tumors. The University of Missouri’s Synthetic Programmable bacteria for Immune-directed Killing in tumor Environments (SPIKEs) project aims to develop a new class of living cancer immunotherapy that that can effectively address these limitations. The SPIKEs platform utilizes genetically programmable bacteria designed to sense tumor-associated metabolites as an exquisitely precise homing mechanism and then deliver therapeutic payloads that activate immune-directed killing of solid tumor cells without the need for the long and costly processes currently used. Bacterial therapeutics carrying programmable genetic circuitry that allows safe tissue targeting, on-demand activation and clearance, and multiple therapeutic functions – including immune cell recruitment, activation, and targeting – represent an innovative, scalable, cost-effective, and accessible treatment modality for cancer.

THOR: Targeted Hybrid Oncotherapeutic Regulation

In the United States alone, over 19,000 women are expected to be diagnosed with ovarian cancer in 2024. An effective treatment could save over 8,000 American lives each year, yet like many solid tumors, ovarian cancer does not respond effectively to current immunotherapies. A revolutionary approach in the fight against ovarian cancer has emerged thanks to the convergence of several technological and medical innovations. This new effort, called Targeted Hybrid Oncotherapeutic Regulation (THOR), will create a compact device designed to trigger the immune system against tumors. The device will be implanted in proximity of the tumor and will house specialized cells responsible for producing and delivering therapeutic molecules. These molecules will activate the immune system both locally around the tumor site and throughout the body. Additionally, the device will incorporate advanced sensors to detect and monitor biomarkers of cancer. The integration of these two components in a single device will enable precise delivery of therapeutic doses tailored to each patient’s needs.

CUREIT: Curing the Uncurable via RNA-Encoded Immunogene Tuning

More than 25 million Americans currently live with autoimmune disease, and almost two million are projected to be diagnosed with cancer in 2023. Immune dysregulation is an underlying component of not only cancer and autoimmune diseases, but also infectious diseases, transplant rejection, and other common medical conditions. Current methods of immune modulation used to treat and mitigate these conditions are often expensive or not completely effective. Curing the Uncurable via RNA-Encoded Immunogene Tuning (CUREIT) aims to address immune dysregulation by directly programming immune cell function. Advances in gene-encoded technology will be leveraged to develop a platform capability able to both enhance protective immune responses as well as modulate insufficient or ineffective immune profiles. CUREIT seeks to develop a disease-agnostic toolbox of methods and technologies, including the in vivo delivery of mRNA-based drugs, cell targeting lipid nanoparticles, and ex vivo modulation of immune cells. This technology has the potential to make significant advancements towards managing or eliminating many diseases and conditions affecting all ages and demographics, including diseases that are currently untreatable.