Transforming drug discovery through AI,
genomics and iPSCs
Greenstone Biosciences stands at the forefront of biotechnological innovation, combining the power of Artificial Intelligence (AI) and human induced Pluripotent Stem Cells (hiPSC) to revolutionize the drug discovery process.
Our mission is to accelerate the development of effective therapies for patients by harnessing our cutting-edge AI-hiPSC platform. This unique integration facilitates a more efficient, precise, and transformative approach to identifying potential therapeutic compounds, ultimately expediting the journey from laboratory research to patient care.
Greenstone Biosciences stands at the forefront of biotechnological innovation, combining the power of Artificial Intelligence (AI) and human induced Pluripotent Stem Cells (hiPSC) to revolutionize the drug discovery process. Our mission is to accelerate the development of effective therapies for patients by harnessing our cutting-edge AI-hiPSC platform. This unique integration facilitates a more efficient, precise, and transformative approach to identifying potential therapeutic compounds, ultimately expediting the journey from laboratory research to patient care.
Sign up now & get regular updates! News publications and news about drug discovery are waiting for you!
Fill out the form below!
We Are Greenstone
Our mission is transforming drug discovery and accelerating the development of effective therapies for patients.
The Largest hiPSC Biobank
Greenstone, situated at Stanford Research Park within the Alexandria Center for Life Sciences, is a multifaceted platform advancing drug discovery. Beyond computational biology, we house a comprehensive iPSC biobank, accelerating the development of effective therapies for patients.
follow our latest news
Greenstone scientists and collaborators regularly publish in peer-reviewed publications to inform the scientific community of discoveries from Greenstone labs and our collaborators.
This study was published in the Journal Cell on Oct 12, 2024, co-authored by several Greenstone Biosciences employees in collaboration with Stanford University, showcases the discovery of artesunate as a promising therapeutic candidate for cardiac fibrosis. The research leveraged advanced human iPSC-derived models, 3D-engineered heart tissues, and animal models to identify MD2/TLR4 signaling as a critical pathway in fibrosis treatment, marking a significant step toward addressing an unmet need in cardiac care.
- PMID: 39413786
- DOI: 10.1016/j.cell.2024.09.034
Abstract
Cardiac fibrosis impairs cardiac function, but no effective clinical therapies exist. To address this unmet need, we employed a high-throughput screening for antifibrotic compounds using human induced pluripotent stem cell (iPSC)-derived cardiac fibroblasts (CFs). Counter-screening of the initial candidates using iPSC-derived cardiomyocytes and iPSC-derived endothelial cells excluded hits with cardiotoxicity. This screening process identified artesunate as the lead compound. Following profibrotic stimuli, artesunate inhibited proliferation, migration, and contraction in human primary CFs, reduced collagen deposition, and improved contractile function in 3D-engineered heart tissues. Artesunate also attenuated cardiac fibrosis and improved cardiac function in heart failure mouse models. Mechanistically, artesunate targeted myeloid differentiation factor 2 (MD2) and inhibited MD2/Toll-like receptor 4 (TLR4) signaling pathway, alleviating fibrotic gene expression in CFs. Our study leverages multiscale drug screening that integrates a human iPSC platform, tissue engineering, animal models, in silico simulations, and multiomics to identify MD2 as a therapeutic target for cardiac fibrosis.
Keywords: artesunate; cardiac fibrosis; cardiovascular; drug screening; induced pluripotent stem cells.
Excited to share our latest commentary in Nature Reviews Drug Discovery! We delve into the challenges and advancements in using new approach methods for predicting drug effects from model systems. We focus on human-derived iPSCs and the integration of AI tools, and the field’s aim to enhance the accuracy and reliability of preclinical drug testing.
Tackling the challenges of new approach methods for predicting drug effects from model systems
Paul D.Pang, Syed Mukhtar Ahmed, Masataka Nishiga, Norman L Stockbridge, Joseph C Wu