2018 NCATS ASPIRE Design Challenges Winners

Through the ASPIRE Design Challenges, NCATS is using the ASPIRE platform to support the Helping to End Addiction Long-termSM Initiative, or NIH HEAL InitiativeSM, a transagency effort focused on improving prevention and treatment strategies for opioid misuse and addiction and enhancing pain management. The goal of the 2018 NCATS ASPIRE Design Challenges is to generate innovative and catalytic approaches toward solving the opioid crisis through the development of next-generation addiction-free analgesics with new chemistries, data-mining and analytical tools and technologies, as well as biological assays that will revolutionize discovery, development and pre-clinical testing of new and safer treatments for pain, opioid use disorder (OUD) and overdose.


NCATS ASPIRE Challenge 1: Integrated Chemistry Database for Translational Innovation in Pain, Opioid Use Disorder and Overdose

Collaborative Drug Discovery (CDD), Inc.

Integrated Preclinical, Clinical, and Post-Clinical Database

Dr. Barry Bunin
Dr. Krishna Dole
Dr. Jacob Bloom
Dr. Samantha Jeschonek
Dr. Lawrence Callahan
Dr. Christopher Lipinski

This proposal will provide datasets linking target and off-target information about drugs with their clinical outcomes, including adverse effects and abuse rates. The resulting database will be unique in its integrated content, standardization and metadata about experimental and study design.

ToxTrack Inc.

Integrated Chemistry Database

Dr. Thomas Luechtefeld
Daniel Marsh

This proposal describes an Integrated Chemistry Database (ICD), which utilizes novel technologies to further translational sciences and research. Data from many sources can be easily connected, versioned and visualized.

Indiana University School of Informatics, Computing and Engineering

CIPHER Knowledgebase for Pain, Opioid Abuse and Overdose

Dr. David J. Wild
Dr. Gaurav Chopra

The project team will develop an integrated chemistry database that encodes structure, properties, simulation and experimental data for compounds useful for translational innovation in pain, opioid abuse disorder and overdose. The solution extends beyond typical existing databases to provide integration with machine learning methods.

Stanford University

Real-Time Text Mining to Empower Opioid Researchers

Dr. Russ Altman
Dr. Jake Lever

This proposal will provide a living and learning database that, through advanced text mining approaches, would reflect the current knowledge available in the literature in a structured and high-quality manner. Instead of prescribing the knowledge required for addiction experts, the project team would provide them the tools to ask questions of the literature and extract meaningful data about current therapeutics.


NCATS ASPIRE Challenge 2: Electronic Synthetic Chemistry Portal for Translational Innovation in Pain, Opioid Use Disorder and Overdose

University of Michigan

A Simple Electronic Notebook for the Future of Chemistry

Dr. Tim Cernak

By modelling the electronic Synthetic Chemistry Portal (eSCP) on a traditional simple and inviting actual paper notebook, and providing a free and open source software, the project team aspires to engage a majority of chemistry users. Labs around the world will be encouraged to publish apps along with their manuscripts, to aid in the reproducibility of their science. Features of the electronic notebook will be the ability to import reaction templates, and the ability to export reaction data in a machine-readable format.

Institute of Organic Chemistry and Institute of Toxicology and Genetics, KIT, Germany

An eSCP as infrastructure of distributed eLNs

Dr. Nicole Jung
Dr. Stefan Brase
Dr. Pierre Tremouilhac
Felix Bach
Dr. Ravindra Peravali

The electronic Synthetic Chemistry Portal (eSCP) is an information system for synthetic chemists but also for medicinal chemists and biologists to store, collect, share, search and analyze data. The portal will support innovation in Pain, Opioid Use Disorder and Overdose with respect to implemented tools, algorithms and connected information but the described concept may be used for other specific projects or a generic approach.


NCATS ASPIRE Challenge 3: Predictive Algorithms for Translational Innovation in Pain, Opioid Use Disorder and Overdose

University of Pittsburgh

Deep and Generative Structure-Based Models for Drug Discovery

Dr. David Koes
Dr. Matthew Ragoza
Jocelyn Sunseri
Paul Francoeur
Jonathan King

The project team proposes to make open source tools and resources available to researchers that enable the rapid identification and optimization of small molecule therapeutics for structure-enabled molecular targets for pain and opioid abuse.

Georgia Institute of Technology

Predictive Algorithms for Pain, Opioid Abuse Disorder and Overdose

Dr. Jeffrey Skolnick
Dr. Mu Gao
Dr. Hongyi Zhao

To accelerate the discovery of ideal small molecules, the project team will design novel predictive machine learning (ML) modules. The goal is to recognize, in a diverse collection of ligands, the essential structural and functional properties for the desired mode of action.

Allchemy

Predictive Algorithms for Translational Innovation in Pain, Opioid Use Disorder and Overdose

Dr. Bartosz Grzybowski​
Dr. Sara Szymkuc
Dr. Wiktor Beker
Dr. Rafal Roszak

Allchemy proposes developing a software platform that will facilitate discovery of new, selective, and non-addictive analgesics. Overall, the proposed technology is unique in that it considers synthesizability of the virtual candidates with accurate AI evaluation of their biological activity.

Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo

Optimum Analgesic Discovery by Multiscale Interactomic Profiling

Dr. Ram Samudrala
Dr. Gaurav Chopra

The project team developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shot-gun repurposing: to screen every drug against every disease. Here, the project team will use the multiscale interactomic profiling approach implemented in CANDO to analyze the compounds in the library, as well as virtually design new molecules that will lead to the discovery of optimal nonaddictive analgesics.

ToxTrack Inc.

Deep and Active Learning for Analgesics

Dr. Thomas Luechtefeld
Daniel Marsh

This proposal describes Deep and Active Learning for Analgesics (DALA)—a platform to accelerate research of analgesics and opioid-related addition and treatments. DALA is able combine large datasets to target multiple endpoints within the same neural network, and its ability to predict for new endpoints will increase with each new endpoint.


NCATS ASPIRE Challenge 4: Biological Assays for Translational Innovation in Pain, Opioid Use Disorder and Overdose

AnaBios

Ex Vivo Human Model of Pain for Translational Drug Discovery

Dr. Andre Ghetti
Dr. Anh-Tuan Ton
Dr. Tim Indersmitten
Yannick Miron, M.Sc.

The project team proposes a novel preclinical discovery strategy, which explores the physiology and pharmacology of ex vivo human sensory neurons isolated from dorsal root ganglia of organ donors. The new strategy of testing novel non-addictive analgesics in sensory neurons of patients with chronic pain will result in a clear translational advantage over canonical cell lines and animal models.

Columbia University

Patient-on-a-Chip Exosome Bioassay for Opioid Abuse Disorders

Dr. Gordana Vunjak-Novakovic
Dr. Jorienne de Nooij
Dr. Kacey Ronaldson-Bouchard
Naveed Tavakol

The project team will establish a “patient on a chip” platform consisting of sensory neuron, liver and heart tissues, connected by vascular perfusion. The “patient on a chip” will be validated against clinical data, to develop a reproducible testing system with strong impact on the NIH Helping to End Addiction Long-termSM Initiative, or the NIH HEAL InitiativeSM.

Montana Molecular

Physiologically Relevant, Phenotypic Assay for Pain

Dr. Thomas Hughes
Anne Marie Quinn, M.P.H.
Dr. Frances Lefcort

To create a phenotypic assay in which physiologically relevant, human nociceptors in culture are stimulated with known pain stimuli that produce fluorescence changes in genetically encoded fluorescent biosensors for second messengers, the project team envisions development of a new viral vector that replicates in iPSC cells such that it is maintained, episomally, in dividing cells and avoids silencing.

Symmetric Computing

Spectral Assay for Screening MOR Allosteric Modulators

Richard Anderson
Dr. Kimberly Stieglitz

The project team proposes to develop a novel high throughput assay that will be useful for the rapid verification of the binding of small molecules to an allosteric site on the mu-opioid receptor protein as well as other proteins targeted for drug discovery.

CiBots

Biological Assay for Next Generation Analgesics (BANGA)

Dr. Babak Esmaeli-Azad
Dr. James Zapf
Dr. Evan Snyder
Dr. Mark H. Ellismann
Dr. Shaochen Chen

To produce “ex-vivo” neuronal tissues that replicate multicellular interactions seen in authentic human tissues, the project team will create a potentially game-changing process by knitting together a number of extant cutting-edge technologies. The Biological Assay for Next Generation Analgesics (BANGA) approach is a dramatic shift from the commonly used recombinant lines or animal cells and could revolutionize the discovery of analgesics.


NCATS ASPIRE Challenge 5: Integrated Solution for Translational Innovation in Pain, Opioid Use Disorder and Overdose

Arizona State University

Integrated Discovery Chemputer Toward Addiction Free Opiates

Dr. Sara Walker
Dr. Leroy Cronin
Dr. Hessam Mehr
Dr. Laia Nadal
Mathew Craven
Dr. Phil Kitson
Dr. Gerado Aragon-Camarasa

The project team will develop an integrated solution that leverages our world leading Chemputer invention for the design, encoding, discovery and synthesis of new candidate molecular entities that will be explored as addiction-free-opiates (AFOs). The outcomes will be an unprecedented set of new compound libraries, based upon either the known opioid structures, biochemical properties, or physical properties as well as assessing bioavailability and pharmokinetics.


Contact

Dobrila D. Rudnicki, Ph.D.