This $900,804 Project Grant award from the National Institute of Environmental Health Sciences (NIEHS) under the Environmental Health (CFDA 93.113) program supports the development and validation of an integrated microphysiological screening platform (MPS) with "virtual human" models to assess chemical toxicity and variability in human responses. The key objectives are to create an MPS platform that can elucidate inter-donor variabilities in mitochondrial toxicity and...
This $500,000 Project Grant from the National Science Foundation's Technology, Innovation, and Partnerships program aims to develop a novel human stem cell-based microfluidic developmental toxicology platform. The University of Michigan will receive funding from September 2022 to August 2024 to conduct technological development of a repeatable, controllable, high-throughput microfluidic system for generating three-dimensional multicellular organoid models for toxicity testing. The platform seeks...
This $150,000 Project Grant awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) aims to develop a personalized, home-use system for noninvasive monitoring of drug levels. The key objectives are to: (1) create a personalized drug titration pharmacokinetic model; (2) develop electrochemistry assays for measuring anti-seizure drug levels in saliva;...
This Project Grant award from the National Institute of Environmental Health Sciences (NIEHS) under the Environmental Health (CFDA 93.113) federal grant program provides $295,734 to Litron Laboratories LTD, a for-profit biotechnology company, to develop a comprehensive hen's egg model for genotoxicity testing. The project aims to create a non-animal, metabolically-active in vitro assay that can effectively evaluate genotoxic risk in humans, addressing the pressing need for alternatives to...
This $249,943 Project Grant award from the National Institutes of Health's National Institute of Environmental Health Sciences (NIEHS) under the Environmental Health (CFDA 93.113) program supports the research and development of an adverse outcome pathway-focused mechanistic inference tool for 'omics data using semantic knowledge graphs. The key products and services to be delivered through this one-year award include: Improving statistical models to better quantify adverse outcome pathway...
This Project Grant award from the National Institutes of Health (NIH) under the Research Infrastructure Programs (CFDA 93.351) supports the development and optimization of a shell-free quail xenograft assay to streamline preclinical oncology drug development. The award of $270,428 to TEO Therapeutics Incorporated will be used to: 1) Refine and test a hardware product line for the quail xenograft assay, optimizing throughput and using environmentally-friendly materials; and 2) Develop and test...
This $768,122 Project Grant from the National Institute of General Medical Sciences will support the development of statistical software to predict adverse medical events in preterm infants using multi-sensor streaming data. The awardee, William D Shannon Consulting LLC doing business as Biorankings, will develop an algorithm to transform sensor data from neonatal intensive care units into graphical representations of associations. Decision rules from statistical process control will then...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $275,956 to the University of Massachusetts Lowell to develop an engineered cyber-physical system that combines advanced biological models with state-of-the-art artificial intelligence (AI) methods. The goal is to enable accurate, real-time prediction of patient responses to anti-cancer therapies, advancing the precision medicine paradigm...
This federal Project Grant award from the National Institute of Environmental Health Sciences (NIEHS) under the Medical Library Assistance (CFDA 93.879) program provides $367,876 to develop robust Bayesian adaptive designs and methods to address practical challenges in real-world multi-arm, multi-dose, multi-stage platform clinical trials. The goal is to create user-friendly software tools that can help optimize sequential monitoring, establish proof-of-concept, manage incompatibility issues,...
This Project Grant award from the National Institute on Deafness and Other Communication Disorders (NIDCD), under the Federal Grant Program "Research Related to Deafness and Communication Disorders" (CFDA 93.173), aims to develop and validate a machine learning platform to predict the ototoxic (hearing loss-inducing) potential of pharmaceutical drugs. The award, totaling $275,737, will enable the small business Rewire Neuroscience LLC to: 1) optimize and apply their machine learning...
DEVELOPMENT OF A WEB-BASED PLATFORM IMPLEMENTING NOVEL PREDICTOR OF TOXICITY FOR MEDICAL DEVICES (PREDTOX/MD) - 1 MEDICAL DEVICES CONTAIN CHEMICALS THAT CAN LEACH AND CAUSE ADVERSE EFFECTS. INTERNATIONAL STANDARDS (ISO 2 10993) REQUIRE THE EVALUATION OF SUCH CHEMICALS FOR SPECIFIC TOXICITY ENDPOINTS, INCLUDING SKIN SENSITIZATION, 3 IRRITATION, AND CYTOTOXICITY. SHORT-TERMS ASSAYS COMMONLY USED FOR THIS TASK ARE TIME-CONSUMING, EXPENSIVE, AND 4 REQUIRE THE SACRIFICE OF MANY ANIMALS. EMERGING FDA DIRECTIVES CALL TO RESTRICT AND, EVENTUALLY, ELIMINATE ANIMAL 5 TESTING OF MEDICAL AND COSMETIC PRODUCTS AND DEVELOP ALTERNATIVE METHODS INCLUDING COMPUTATIONAL TOOLS. TO 6 ADDRESS THIS UNMET NEED, IN PHASE I OF THIS PROJECT WE HAVE CREATED THE LARGEST CAREFULLY CURATED AND PUBLICLY 7 AVAILABLE GUINEA PIG MAXIMIZATION TEST (GPMT) DATASET AND DEVELOPED FIRST-IN-CLASS MACHINE LEARNING MODELS 8 THAT PREDICT THE GPMT OUTCOME. WE IMPLEMENTED OUR MODELS WITHIN THE FULLY OPERATIONAL PREDICTOR OF SKIN 9 SENSITIZATION FOR MEDICAL DEVICES (PRESS/MD) WEB PORTAL. IN PHASE II, WE WILL CREATE NEW MODELS AND SOFTWARE 10 MODULES FOR RELIABLE ASSESSMENT OF CHEMICALS FOUND IN MEDICAL DEVICES FOR SENSITIZATION, IRRITATION, AND 11 CYTOTOXICITY PER ISO 10993 GUIDANCE. THESE MODULES WILL BE BOTH AVAILABLE FOR LICENSING AS STANDALONE TOOLS OR 12 WEB APPLICATIONS AS WELL AS INTEGRATED INTO NOVEL PREDICTOR OF TOXICITY FOR MEDICAL DEVICES (PREDTOX/MD) WEB 13 PORTAL. THE PROPOSED R & D STUDIES ARE STRUCTURED AROUND THE FOLLOWING SPECIFIC AIMS: SPECIFIC AIM 1: DEVELOP 14 A HIGHLY CURATED, COMPREHENSIVE PREDTOX/MD DATABASE. WE WILL COLLECT, THOROUGHLY CURATE, AND INTEGRATE 15 PUBLIC DATA FOR ALL HUMAN, IN VIVO, AND IN VITRO REGULATORY ASSAYS FOR SKIN SENSITIZATION, IRRITATION/CORROSION, AND 16 CYTOTOXICITY. WE WILL EXTEND OUR DATABASE TO INCLUDE ALL AVAILABLE DATA ON CHEMICAL MIXTURES AND DEVELOP SPECIAL 17 CURATION WORKFLOWS TO HANDLE MIXTURES OF ANY COMPOSITION. SPECIFIC AIM 2: DEVELOP VALIDATED COMPUTATIONAL 18 MODELS TO PREDICT SENSITIZATION, IRRITATION, AND CYTOTOXICITY FOR CHEMICALS LEACHING FROM MEDICAL DEVICES. 19 WE WILL EMPLOY OUR WIDELY ACCEPTED PREDICTIVE QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING 20 WORKFLOW FULLY COMPLIANT WITH OECD MODEL VALIDATION PRINCIPLES. CONSENSUS ENSEMBLE MODELS WILL BE DEVELOPED 21 WITH SEVERAL DESCRIPTOR TYPES AND MACHINE LEARNING ALGORITHMS, INCLUDING DEEP AND ACTIVE LEARNING AND A 22 BAYESIAN MODEL INTEGRATING MULTIPLE INDIVIDUAL ASSAY MODELS TO PREDICT THE OVERALL CHEMICAL SAFETY. SPECIFIC AIM 23 3: DEVELOP SOFTWARE MODULES FOR ASSESSING MEDICAL DEVICE TOXICITY AND INCORPORATE THESE MODULES INTO 24 PREDTOX/MD PORTAL. MODELS AND WORKFLOWS DEVELOPED IN AIM 2 WILL BE PROGRAMMED AS AUTONOMOUS SOFTWARE 25 MODULES THAT WILL BE INTEGRATED INTO PREDTOX/MD PLATFORM AND AVAILABLE FOR INDIVIDUAL LICENSING TO ENABLE RAPID 26 MULTI-POINT TOXICITY ASSESSMENT FOR EXTRACTABLES AND LEACHABLES FOUND IN MEDICAL DEVICES. SUCCESSFUL 27 COMPLETION OF PHASE II STUDIES WILL RESULT IN THE NOVEL COMPUTATIONAL TOOLKIT AND WEB-BASED RESOURCE TO 28 EVALUATE TOXICITY OF MEDICAL DEVICES AS REQUIRED BY ISO 10993 GUIDANCE.