About
A combinatorial in vitro/in silico NAM for automated neurodevelopmental phenotyping of prenatal opioid exposure.
ConductOrganoid
ConductOrganoid is a combinatorial New Approach Methodology (NAM) that integrates two complementary modalities: in vitro human iPSC-derived neural organoid culture and in silico automated computational image analysis. The platform addresses the growing crisis of prenatal opioid exposure, where approximately 80,000 infants per year are born in the United States with Neonatal Opioid Withdrawal Syndrome (NOWS).
The ConductImage module extracts 16 morphological, intensity, and texture features from standard brightfield microscopy images and applies rigorous non-parametric statistics to detect neurodevelopmental phenotypes — entirely automated, observer-independent, and animal-free. Published studies show that opioid compounds (methadone, buprenorphine, fentanyl) cause the same types of morphological changes our pipeline is validated to detect.
Our Approach
A three-phase plan progressing from feasibility demonstration to regulatory-grade validation.
Phase 1 — Feasibility
Validate that the ConductImage pipeline reliably detects neurodevelopmental morphological differences using published organoid data (1,407 images, 64 organoids, 4 cell lines, 2 labs). Establish that validated features map to published opioid-organoid effects.
Current phase
Phase 2 — Validation
Apply the validated pipeline to opioid-exposed organoids (methadone, buprenorphine, fentanyl at 3 concentrations each). Establish dose-response morphological relationships. 3+ lab reproducibility study with FAIR-compliant NDHCC data submission. Integrate electrophysiology (ConductSignal).
Phase 3 — Translation
Working platform transfer to VQN with operating instructions. Comprehensive opioid dose-response dataset. Documentation for independent testing, validation, and regulatory qualification through the Complement-ARIE VQN framework.
The Team
The ConductOrganoid team combines academic and industry expertise across clinical medicine, addiction biology, computational engineering, and statistics.
Shuhan He, MD
PI / Clinical Translation
MGH / Harvard Medical School; CEO, ConductScience
Platform architecture, statistical framework, project leadership.
Shivani Pimparkar, MS
Co-PI / Addiction Biology & Disease Modeling
Boston University Bioinformatics; MGH Translational Research
Biological validation, opioid-organoid evidence base, Phase 2 experimental design.
Yijian Henry He, PhD
Technical Lead
PhD Economics; CTO, ConductScience
System architecture, cloud infrastructure, API development.
Louise Corscadden, PhD
Community & Governance
PhD Molecular Genetics; Director, ConductScience
Regulatory coordination, FAIR compliance, partnerships.
Bifei Hao, MS
Statistical Validation
Northwestern University Electrical Engineering
Statistical integrity, validation methodology.
Boyu Peng, MS
Platform Engineering
ConductScience
Deployment pipeline, reproducibility verification.
Pedram Safari, PhD
Mathematical Foundations
MGH Institute of Health Professions
Statistical framework, formal properties.
Santosh Adhikari, MS
ML & Interpretability
Computer vision and robotics; ConductScience
Pipeline interpretability, extension modules.
ConductScience, Inc.
Scientific equipment, software, and data services for the research community. ConductScience provides the infrastructure for developing, deploying, and distributing computational tools for biomedical research.
conductscience.comContact
For questions about the ConductOrganoid platform, the analysis pipeline, or collaboration opportunities:
Explore the Platform
See the analysis results, compare disease models, or read about our computational methods.