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Complement-ARIE Challenge Prize Winner Summaries

The National Institutes of Health has announced the winners of the Common Fund Complement Animal Research In Experimentation (Complement-ARIE) crowdsourcing competition for innovative ideas on New Approach Methodologies, or NAMs. The Complement-ARIE Challenge prize competition offered $1,000,000 in total prize money to diverse teams with ideas for new ways of using NAMs to conduct basic research, uncover disease mechanisms, and translate knowledge into products and practice. 

The NIH Common Fund Complement-ARIE program hosted this challenge as part of the strategic planning process to refine the program concept. This program will develop, standardize, and validate the use of new approaches that will more accurately model human biology and complement, or in some cases, replace traditional research models. This challenge provided NIH with information about where innovation can be incorporated into NAMs and what types of new NAMs may benefit from further investment. Concepts from the winning entries of the Complement-ARIE Challenge Prize will be incorporated into the ongoing planning process for the Complement-ARIE program. 

View the winning solution summaries and project team members below.

4D Tissue Fabrication of Human Tissue Models #4dtissuefabrication

Team members: Zev Gartner, University of California San Francisco (team captain); Ophir Klein, University of California San Francisco, Cedars-Sinai; Faranak Fattahi, University of California San Francisco. #4dtissuefabricationteam

Models of the human gut will revolutionize our ability to test drugs and study disease but existing models make inherent compromises with respect to physiological relevance, complex functions, and reproducibility that limit their ultimate applications. We propose a new approach to direct the formation of gastrointestinal tissue that will include multiple integrated organ systems capable of involuntary gut muscle contractions and digestive functions. To build this model we use a process called 4D tissue fabrication. We use a next generation bioprinter to specify the initial 3D coordinates of dense slurries of stem cells in support baths optimized for their self-organization—a process mimicking their natural development. The same bioprinter then actively guides self-organization into more complex and functional tissues over time (i.e. the 4th dimension). We have demonstrated proof of principle of this approach to build complex and perfusable models of intestinal tissue and vasculature. Here, we propose to expand this approach to make models with orders of magnitude more structural complexity and in the most physiologically relevant context. The platform is generalizable to a variety of other tissues.


A Sensor-Enhanced Isogenic Model of Alzheimer's #asensorenhancedmodel

Team members: Riccardo Barrile, University of Cincinnati (team captain), Ryan White, University of Cincinnati; Wayne Poon, NeuCyte. #asensorenhancedmodelteam

Brain diseases like tumors and age-related conditions are one of the main causes of disability and a common cause of death worldwide. The challenge is that we lack good ways to study and treat these diseases. Current methods, often involving testing on animals, don't always give results that apply to humans because the human brain has unique characteristics. Our project offers a new approach using special cells called stem cells to assemble miniaturized models of the brain (minibrains) including a protective barrier of the brain called the blood-brain barrier. This barrier normally shields the brain from harmful substances in the blood. But in people with brain diseases, this barrier often doesn't work properly, making the disease worse in ways we don't completely understand. Think of our model as a personalized "Avatar" of a patient's brain, capturing important aspects of their condition to enable personalized treatments. We're focusing on Alzheimer's disease and using cells from patients. These cells are grown in a platform with multiple sensors that monitor brain cell activity in real-time, helping us gather important data in a consistent way. Our goal is to create a model that closely imitates human conditions, giving us better insights and more accurate predictions on how patients could respond to treatments. This could be a game-changer in understanding and treating brain diseases.


Agent-based Models for Adoptive Cell Therapy #agentbasedmodels

Team members: Ken Chen (team captain), University of Texas MD Anderson Cancer Center; Yujia Wang, University of Texas MD Anderson Cancer Center; Stefano Casarin, Houston Methodist Research Institute; May Dahar, University of Texas MD Anderson Cancer Center; Vakul Mohanty, Univ. of Texas MD Anderson Cancer Center. #agentbasedmodelsteam

Adoptive cellular therapies (ACT) treat cancer using immune cells harvested from patients or healthy donors/sources. Despite their tremendous potential, predicting benefits is difficult due to multifaceted complexity at the molecular, cellular and systems levels. Numerous experiments using cells, animals and patients are required to study the complexity, before an effective treatment can be developed. Similar to other complex domains, computer models such as agent-based models (ABM) can provide cost-efficient, complementary, and meaningful ways to simulate the tumor environment and predict treatment outcomes. Leveraging our strengths in ACT development and trials, single-cell omics technologies, and computational modeling, we plan to incorporate molecular profiles of tumor cells and immune cells into ABMACT, an agent-based model for adoptive cell therapies to study treatment responses of B cell lymphoma by Chimeric Antigen Receptor Natural Killer (CAR-NK) cells, a type of engineered immune cells under various conditions. With ABMACT, we aim to improve adoptive cellular therapy product design and treatment planning for personalized cancer immunotherapies.


Cancer Immunotherapy Clinical Trials on a Chip #cancerimmunoclinicaltrials

Team members: Weiqiang Chen, New York University (team captain); Chao Ma, New York University; Lunan Liu, New York University #cancerimmunoclinicaltrialsteam

CAR T-cell immunotherapy that uses and enhances patients’ own T-cells to fight cancer has emerged as an innovative method for treating various types of cancers. Despite its remarkable success in treating blood cancers, CAR T-cell therapy is less effective in solid tumors such as pancreatic cancer, which is largely attributed to the tumor heterogeneity and its “immune cold” tumor microenvironment. Lack of reliable clinical method to rapidly and accurately assess the potency of patient-derived CAR T-cell products before administration thus predict patient response poses a major clinical challenge. To develop improved and personalized immunotherapies, we will invent a novel patient-derived “pancreatic cancer organoids-on-a-chip” microphysiological system to reconstruct patient-specific tumor microenvironment on a chip. We will use this system as an integrated precision medicine tool for an accurate evaluation of CAR T-cell therapy efficiency in individual patients. Such a hybrid “in silico-in vitro” modeling system integrates in vitro organoids, organ-on-a-chip technologies, in silico modeling and in vivo clinical data to better emulate the in vivo patient pathology and heterogeneity for CAR T-cell therapy screening. Our solution can be implemented into clinical practice and provide a new paradigm for “clinical trials on a chip” that leads to the development of personalized CAR T-cell immunotherapy strategies for cancer patients. 


Click Chemistry Magic for Drug Screening #clickchemistrymagic

Team members: X. Lucas Lu, University of Delaware (team captain); Michael Axe, University of Delaware; Zugui Zhang, University of Delaware; Joseph Fox, University of Delaware. #clickchemistrymagicteam

Our New Approach Methodology (NAM) is based on click chemistry and bioorthogonal reactions, powerful techniques that received the Nobel Prize in Chemistry in 2022. This method measures how quickly cells and tissues can create new proteins, making it highly useful for medical research and drug discovery.
When compared to traditional methods, our NAM is more reliable, accurate, flexible, easier to use, cost-effective, and safer. It can significantly enhance our understanding of human health and diseases, reducing the reliance on animal and human testing. This method's global applicability ensures that research findings are trustworthy and repeatable. 

We are using this versatile method to test over 3,000 FDA-approved drugs on human knee cartilage samples, evaluating their potential for treating osteoarthritis. We'll assess the effectiveness of these drugs under both normal and inflammatory conditions. We are employing machine learning and artificial intelligence (AI) algorithms to analyze the data. 

Beyond the realm of drug discovery, our NAM has the potential to revolutionize cancer treatment by enabling the creation of personalized therapies from tumor biopsies. It is also highly effective for fabricating artificial tissues in laboratories. Overall, our NAM could significantly transform medical research, making it more inclusive, effective, and globally impactful.

DevTox - Developmental Toxicity Predictor #devtox

Team members: Alexander Tropsha, Predictive LLC. (team captain); Eugene Muratov, University of North Carolina at Chapel Hill (UNC), Predictive, LLC.; Kevin Causey, Predictive LLC.; Greg Sokolsky, Predictive LLC.; Ricardo Tieghi, UNC. #devtoxteam

The proposed research addresses a critical gap in understanding the safety of medications for pregnant women, where 80% use prescription drugs, yet drug safety is grossly understudied. Clinical trials rarely include pregnant women, leaving gaps in knowledge about potential fetal toxicity. The project's aims include creating a knowledge base on developmental toxicants, or agents that cause toxicity, building molecular models, constructing a knowledge graph, and developing a web portal for real-time toxicity predictions. The significance lies in the unmet need for testing toxicity in pregnant populations, using artificial intelligence (AI)-driven technologies to enhance drug safety during pregnancy. Predictive LLC proposes innovative approaches to identify developmental toxicant associations, emphasizing the creation of a novel developmental toxicity knowledge graph. The team's prior research demonstrates rigor, including developing approaches like Chemotext and ROBOKOP. The research introduces novel computational methodologies, leveraging AI to accelerate drug discovery for pregnant populations. The cost-effectiveness of computational approaches is emphasized, highlighting potential cost and time savings compared to traditional drug discovery. The unique capabilities include establishing a comprehensive knowledge graph and machine-learning models to infer and test novel associations, addressing a critical need in pharmaceutical research.


E-validation – Unleashing AI for Validation #evalidation

Team members: Thomas Hartung, Johns Hopkins University (JHU) Center for Alternatives to Animal Testing (CAAT) (team captain); Tom Luechtefeld, JHU CAAT; Alexandra Maertens, JHU CAAT. #evalidationteam

New testing methods to replace animal studies often fail because validating them takes too long and costs too much money. Our idea uses artificial intelligence (AI) to plan validation studies in a smarter way. The AI software suggests the best and most representative reference chemicals to test. It runs computer simulations to figure out exactly how much data is needed to get reliable results. The AI also checks databases of past studies so new studies don't repeat work already done. Additionally, it provides training for researchers on how to correctly use and interpret the new testing methods. By making the validation process faster, cheaper, and more rigorous, our AI platform opens the door for more innovative non-animal testing approaches to be put into practice. This will accelerate the testing of medicines and chemicals for safety, unlocking the potential of human-focused research methods to improve human health.


Exploring Omics for Liver Toxicity Assessment #omicsforlivertox

Team members: Shantanu Singh, Broad Institute of MIT and Harvard (team captain); Constance Mitchell, Health and Environmental Sciences Institute (HESI); Christine Crute, HESI; David Rouquie, Bayer; Andreas Bender, University of Cambridge; Anne Carpenter, Broad Institute of MIT and Harvard; Srijit Seal, Broad Institute of MIT and Harvard. #omicsforlivertoxteam

It is currently very difficult to predict whether a potential new medicine or agricultural chemical is safe even after animal testing. Often toxicity is only discovered later - during clinical trial testing in people or broader use. This causes tremendous harm and these failures drive up the costs of new, useful chemicals. As part of a Consortium, “Omics for Assessing Signatures for Integrated Safety” (OASIS), we are pioneering a new approach to predicting a chemical’s liver toxicity, via a first-of-its-kind strategy. OASIS aims to capture a broad spectrum of information from liver cells treated with compounds using assays that simultaneously measure more than a thousand cell responses, including Cell Painting, mRNA levels, and protein levels to predict compound liver toxicity. We will identify which of these responses when combined using machine learning, can best predict outcomes in the whole organism. We bring together scientists from 14 pharmaceutical and agrochemical companies and 6 technology companies. If successful, the Cell Painting assay, coupled with molecular-omic data, will reduce animal testing and allow earlier, more accurate, and less expensive liver safety testing of candidate compounds across two industries. As well, our datasets will be made public to serve as a foundation for future advancements.


Facing the Future of Microphysiological Modeling #facingthefutureofmicrophys

Team members: Brian Johnson, Michigan State University (MSU) (team captain); Sudin Bhattacharya, MSU Dept of Biomedical Engineering (BME) and PHM & TOX; Jacob Reynolds, MSU Dept BME. #facingthefutureofmicrophysteam

The human body is made up of cells that communicate with each other to develop and function properly. If this signaling is disrupted during development, birth defects such as orofacial clefts can occur. Since the process of cell to cell signaling is complex and not well understood the cause of most structural birth defects remains unknown. To address this challenge, we have created a microphysiological model that mimics the interactions between cells during orofacial development. Our model is designed to study normal and abnormal development, such as clefting. It incorporates important tissue structure and function and is manufactured within a microplate using computer numerical control (CNC) micromachining, making it easy to adopt by other labs. By adding human-derived cranial neural crest cells and familial risk variants for clefting, we can capture population variability and create a model predisposed to clefting. We will perform single cell sequencing of the mesenchyme, neural crest, and epithelium to gain insights into cell to cell interactions. This will help us understand the causes of birth defects and develop ways to prevent them in the future. This work aims to increase understanding of the cell to cell communication that drives development so birth defects can be better understood and prevented. 


Future Fertility: AI-driven Human Mini-Testis Model #futurefertility

Team members: Lei Yin (team captain), Reprotox Biotech LLC. #futurefertilityteam

There's growing concern about the impact of chemicals in our environment and drugs on male reproductive health. These chemicals or drugs can potentially harm sperm production and fertility. However, testing for these effects is currently expensive, time-consuming, and often relies on animal models which may not perfectly reflect human reactions. This research proposes a pioneering approach to tackle this challenge. The goal is to develop a new method for testing chemicals that directly uses human cells, grown in the lab. This method aims to create tiny models of human testes, using stem cells and other supporting cells involved in sperm production. These "Mini-Testis" will then be exposed to different chemicals to observe how they affect the cells and reproductive processes. This novel model will provide faster and more affordable testing as compared to animal testing. Also, this human mini-testis model using human cells will provide more accurate predictions of how chemicals might affect people, as it directly reflects human biology. Studying these "human Mini-Testis" could also lead to a deeper understanding of how chemicals cause reproductive harm. This knowledge could pave the way for developing new treatments for male infertility and other reproductive health issues. 

Overall, this research has the potential to transform how we test for male reproductive toxicity, leading to more efficient, accurate, and ethical testing methods.

Interpreting Brains and Artificial Networks #interpretingbrains

Team members: Carlos Ramon Ponce, Harvard Medical School (team captain); Alireza A. Dehaqani, Harvard Medical School; Antonio Montanaro, Harvard Medical School; Giordano Ramos-Traslosheros, Harvard Medical School; Olivia Rose, Washington University in St. Louis, Visiting Student of Harvard Medical School; Binxu Wang, Harvard University. #interpretingbrainsteam

Deep learning models have achieved extraordinary accuracy in image recognition, reaching and sometimes exceeding human levels. However, their internal analyses are largely unknown, making it difficult to know if these systems could malfunction and if so, how to fix them. This project aims to demystify these models, using principles of visual neuroscience. Our library, ATHENA-N (Analyzing The Hidden ENcoding in Artificial Neural Networks), will methodically analyze deep neural networks, illuminating their learned representational structure and information processing methods. 

ATHENA-N includes several modules to investigate how these networks represent and process visual information. The goal is to establish connections between artificial and biological neural encoding, enhancing our understanding of both fields. 

ATHENA-N will set the path to new biological discoveries, identifying new potential functional neurons present in visual cortex, and helping us define how “brain-like” some artificial intelligence (AI) models are. This will be instrumental in developing AI systems that are transparent, trustworthy, and reliable for real-world applications. Moreover, this project is poised to generate numerous research questions and hypotheses, fostering cross-disciplinary collaborations. 

Machine Learning Assisted HTP Tissue Array #mlassistedhtptissuearray

Team members: Wei Tan, University of Colorado at Boulder (team captain); Chuangqi Wang, University of Colorado at Denver. #mlassistedhtptissuearray

Cardiovascular diseases are the leading cause of morbidity and mortality in the United States and around the world. A major form of the diseases is coronary heart disease or atherosclerosis. There are constant needs for a deeper understanding of the pathways that lead to this disease and how to better prevent and treat it with new therapies. Our multi-disciplinary team aims to leverage the power of artificial intelligence, multi-functional biomatrix engineering, and high-throughput screening tool to create human-based disease model. If successful, such a model may facilitate mechanistic and therapeutic investigations of vascular diseases, specifically atherosclerosis. Additionally, the integrated platform model would allow one to predict and validate the impacts of diseased tissue properties on the efficacy and potency of cardiovascular treatments. The platform technology also has the potential to extend well beyond the optimization of cardiovascular treatments, expediting the therapeutic discovery for other types of diseases. The innovations of the proposed model include its capacity of offering (a) realistic, interactive parametric explorations for a multitude of inputs (e.g., various treatments, biophysical and biochemical properties of diseased tissues) and outputs (e.g., disease stage-characteristic behaviors such as inflammation, proliferation, function loss), and (b) highly efficient and economic approach towards translation and commercialization of new therapies.


NAMKG: LLM Powered Registry To Foster NAM Adoption #namkg

Team members: Thomas Luechtefeld (team captain), Insilica LLC.; Zakariyya Mughal, Insilica LLC.; Thomas Hartung, Johns Hopkins University. #namkgteam

NAMKG stands for New Approach Methodology - Knowledge Graph. It's like a big online library where researchers can find and share information about new scientific tests used in labs. These tests are called New Approach Methodologies (NAMs). Researchers can put details about their NAMs in NAMKG, making it easier for others to find and understand them. 

NAMKG uses artificial intelligence (AI) to keep this information up-to-date. It's helpful for scientists working on new drugs and for people making policies about science and health. NAMKG brings together researchers, industry experts, and regulatory people to make sure scientific methods are good and reliable. This helps science work better and makes it easier for everyone to understand and use scientific discoveries. 

NAMKG also allows technology developers to more easily access information about new scientific methods. This means that software developers can make their applications better by pulling in this public data. NAMKG even allows software developers to add their own software tools for the system. Once a tool is added to the system, NAMKG allows it's AI agents to leverage those tools in conversations with users, so you can ask a question like "Is my chemical hazardous?" and NAMKG will run all the automated tests it can to answer your question. 

Ultimately, NAMKG is a resource that integrates information about new scientific methods, and makes it easier for people to access, learn about, and leverage those methods for their own work.


Neuro-Immuno-Cutaneous Human Skin Equivalent #neuroimmunocutaneous

Team members: David Kaplan, Tufts University (team captain); Liam Harrington Power, Tufts University; Olivia Foster, Tufts University; Michael Lovett, Tufts University. #neuroimmunocutaneousteam

Historically, animals have been used to test new cosmetics, drugs, and other chemicals to understand their effects and evaluate safety concerns, but animals are not always good options to mimic human body function, physiology, and behavior. Many countries have banned the use of animals for these purposes, requiring other options for safety and efficacy testing. Lab developed models of three-dimensional tissue systems with human cells have been shown to be good models of human tissues and can be used to predict how chemicals and pharmaceuticals may interact with the body. Tissue models of human skin exist, but they are simplified by focusing only on the top layers of skin and do not consider deeper layers that contain relevant features for testing, such as immune system and nerve components, which are critical to assess toxicity and irritation from chemicals. More complex models that include the full thickness of human skin and its various features such as immune cells, blood vessels, and nerves would provide more accurate comparisons as testing platforms relative to the human body than current, more simplified skin models are able to provide. We are proposing a new model system that includes all three layers of human skin and many relevant features, including immune cells, blood vessels, and nerves, to generate a more accurate system to test new compounds for safety and efficacy outcomes with human relevance. 


Organ-on-a-Chip for Studying Osteoarthritis Pain #oocforosteoarthritis

Team members: Hang Lin, University of Pittsburgh (team captain); Meagan Makarczyk, University of Pittsburgh; Johannes Plate, University of Pittsburgh; Daniel Kaplan, University of Pittsburgh; Claudette M. St. Croix, University of Pittsburgh; Michael Gold, University of Pittsburgh #oocforosteoarthritisteam

Osteoarthritis (OA) is a painful and debilitating disease that is also highly prevalent. For example, 27 million US citizens suffer from osteoarthritis, including ~25% of those >50 years of age. Pain is one of the primary reasons that osteoarthritis patients seek medical attention, yet there are no consistently effective treatments for osteoarthritis pain that are not associated with potentially fatal side effects. 

Limited progress has been made in the study of current models of OA pain, such as animals. The team recently pioneered the development of an in vitro microphysiological tissue chip, which contains four critical elements of the knee joint (cartilage, bone, synovium, and fat pad) engineered from human cells (patent granted). Moreover, human sensory neurons (the cells that generate pain activities) were included to form pain-enabled knee joint chips (Neu-miniJoint, patent pending). 

Here, we aim to further increase the complexity and clinical relevance of the Neu-miniJoint by including immune cells (the cells that provide molecules to induce pain) in the system. OA-like conditions will be induced through mechanical injury. The pain-associated activities in the neurons will be examined by established methods in the team. 

Successful generation of this new model containing neural and immune cells (ImNeu-miniJoint) will not only identify factors responsible for OA pain but also enable the development of truly personalized osteoarthritis pain medications.

Organoid Intelligence (OI) - Learning in a Dish #learninginadish

Team members: Lena Smirnova, Johns Hopkins University (team captain); Thomas Hartung, Johns Hopkins University; David Gracias, Johns Hopkins University; Brian Caffo,  Johns Hopkins University; Itzy Erin Morales Pantoja, Johns Hopkins University; Dowlette Alam El Din, Johns Hopkins University; Lomax Boyd, Johns Hopkins University. #learninginadishteam

The human brain is amazingly complex, but studying it is challenging. Experiments often use animals, especially monkeys and apes. But advanced cell models using human induced pluripotent stem cells now allow us to grow brain organoids (miniature 3D cultures of brain cells) and study their functions in the lab dish.

We propose combining organoids with artificial intelligence (AI) for "organoid intelligence" to create a learning-in-a-dish model. We will give input signals and track output to see how brain organoids react and change behavior over time, like simple learning. This will help us understand how the brain works without using animals.

Our brain organoids will start simple but add more brain cell types/regions and connections over time. We will give electrical and chemical signals and track responses with electronics to study memory and information processing. AI will analyze the complex data so we can understand and improve the brain organoids.

This research will let scientists study real human brain cells in action! We hope it advances brain science by showing how brains compute. We have to carefully address ethical concerns, but organoid intelligence has huge potential for discovery about cognition without distressing animals like monkeys. Exciting innovations could lead to better brain-like computers and therapies too!

Organoid Modeling of Rheumatoid Arthritis Joints #organoidofrheumarthritisjoints

Team members: Deepak Rao, Brigham and Women's Hospital (team captain); Kathryne Marks, Brigham and Women's Hospital; Kevin Wei, Brigham and Women's Hospital. #organoidofrheumarthritisjointsteam

Autoimmune diseases affect over 20 million Americans and cause disease in almost every organ system. Current drugs to treat these diseases provide blunt tools that are broadly immunosuppressive, affecting both pathologic and protective immune responses. There is a critical need for new, more selective therapies and improved understanding of how to use therapies. New methodologies leveraging human cells and tissues are needed to improve our ability to interrogate new therapies and select the right drug for individuals.  Rheumatoid arthritis (RA), a common, severe autoimmune disease that targets joints, provides an ideal context in which to develop new methodologies since multiple therapies are approved to treat RA, yet there are no robust biomarkers to predict which drug a patient will respond to. We propose to use synovial organoids, built using cells from RA patients, to model the pathologic autoimmune response in joints. We propose that organoid systems will recapitulate relevant cell interactions and capture the diversity in tissue injury patterns observed in RA patients. We expect that in vitro organoids of RA joint tissue will enable rapid, more faithful interrogation of effects of new therapeutics on the inflammatory environment in the target tissue and will define the cellular pathways most affected by individual therapies. This organoid methodology can be readily extended to dissect inflammatory pathways in other autoimmune and inflammatory diseases. 


Population Diversity in Responses to Vaccination #popdiversityvaccines

Team members: Rebecca Pompano, The Rector and Visitors of the University of Virginia (team captain); Chance John Luckey, The Rector and Visitors of the University of Virginia; Jennifer Munson, Fralin Biomedical Research Institute at Virginia Tech; Evangelia Bellas, Temple University; Aarthi Narayanan, George Mason University. #popdiversityvaccinesteam

Vaccination and immunotherapies are critical tools for public health but remain challenging to develop, often requiring decades to reach clinical use. Progress is especially slow for brain or immune diseases, as these tissues are largely inaccessible for study in humans and animal models are insufficient to replicate their complexity or the impacts of human population diversity. Therefore, we envision a groundbreaking, in vitro model to simulate the interactions between the human brain and immune system, while beginning to account for diversity of sex, age, race, and changes in metabolic state due to leanness or obesity. Our model starts with cells originally from human donors that are cultured to form tiny replicas of the brain, lymph node, and adipose (fat) tissue. The three replicas then integrated into a single housing, smaller than a credit card, and connected via two intersecting loops of continuously recirculating fluid that represent the intersection between the brain and the rest of the body.  We will add more cell types and functions to this system until it replicates specific aspects of infection with viruses that infect the human brain, as well as the ability of clinical vaccine candidates to protect (or not) against those viruses. Success will yield a platform for studying and treating neuro-immune infections including influenza, SARS-CoV-2, and potential biothreats, along with neurodegenerative conditions like Alzheimer’s, multiple sclerosis, and brain cancer.


Pregnancy on Chip to Improve Reproductive Health #pregnancyonchip

Team members: Ramkumar Menon, University of Texas Medical Branch (UTMB) (team captain); Arum Han, Texas A&M University; Lauren Richardson, UTMB; Ananth Kumar Kammala, UTMB; Moumita Chakraborty, UTMB. #pregnancyonchipteam

Human pregnancy and giving birth are fascinating but perplexing phenomena to understand, as two independent biological and physiological systems (fetus and the mother) co-exist and must be simultaneously considered. Pregnancy associated complications like premature birth, miscarriages, and stillbirths are challenging to study, and medical interventions are difficult as researchers do not have good models to predict drug efficacy under these circumstances. There is a lack of knowledge of whether therapeutics are beneficial to the fetus (or harmful to in-utero fetal growth) and regulatory agencies (FDA) do not approve drug use during pregnancy. To better understand pregnancy biology, the birthing process (normal and abnormal), and to test drugs during pregnancy, we will create the entire human Pregnancy-On-a-Chip (POC) (a miniaturized device on a plastic slide) using cells derived from both the mother and the fetus after term delivery of a healthy live baby. POC recreates human pregnancy in the lab with all fetomaternal uterine components. This device will allow us to study cellular mechanisms that maintain normal pregnancy, labor, and delivery and recreate situations in which adverse pregnancy occurs. This understanding and model will enable us to test drugs that can mitigate pregnancy risk. Preclinical drug trial data using POC will accelerate clinical trials during pregnancy and save maternal and fetal lives. Further, POC will reduce the use of unreliable animal models as they don’t mimic human pregnancy.


Pulse of the Future: AI-human MEHC #aihumanmehc

Team members: Huaxiao 'Adam' Yang, University of North Texas; Gautham Mohanraj, University of North Texas; Marcel El-Mokahal, University of North Texas. #aihumanmehcteam

Multi-scaled engineered heart constructs (MEHCs) are revolutionary multiscale tissue cultures derived from human stem cells. At a microscopic level, these models can imitate intricate heart functions and structures, enabling groundbreaking studies of human heart physiology and diseases to complement animal models. With immense potential, the exploration of MEHCs promises to transform disease modeling and drug discovery.

At the center of this research is the novel software: Organalysis. Armed with formidable processing and analytical capabilities, this technology empowers researchers at the frontline of innovation by enriching MEHCs images and gleaning quantitative data about the function of the MEHCs. As such, Organalysis can transform biomedical research, opening the door to AI and ML-based techniques.

After developing this software, we then aimed to create a predictive model that detects cardiovascular disease patterns using data from MEHCs. Building off of the established framework for effective multiscale tissue modeling, this approach elevates in vitro research and enables customized predictive modeling tailored to patients. 

Overall, the integration of these technologies could catalyze pioneering discoveries that accelerate the clinical workflow from diagnosis to optimized, personalized treatment. 

This page last reviewed on May 8, 2024