Changing the World, One Image at a Time
Qualitative Imaging | Quantitative AI Analytics | Predictive Simulation
Solutions for Drug Development
Microstructures are at the core of modern drug and delivery systems. At DigiM, we apply high-resolution 3D imaging, AI analytics, and predicitve release simulation to accelerate formulation design, drug development, process optimization, and regulartory science.Visit website
Solutions for Materials Manufacturing and Design
From batteries to rubber, from filters to potato chips, microstructures dominate how everyday materials work. At DigiM we work with material scientists hand in hand to improve industrial and consumer material development through non-destructive imaging and AI techniques.Visit website
Rock samples are difficult to characterize due to uncertainties in subsurface conditions and complex fluid, mineral, and organic matter interactions. At DigiM, we aid petrophysical assessment on reservoir quality and transport potential through systematic and validated digital rock physics software tools.Learn More
Press Release and News
- Most recently at 2021 CRS Meeting, we are proud to present our work using high-resolution 3D imaging, AI analytics, and image-based simulations. In collaboration with our clients, we demonstrated how image analysis can assess the impact of microstructures on the performance of long-acting implants and injectables. Learn more here.
- At AAPS-NERDG 2021 Annual Meeting, DigiM gave a rapid fire presentation titled: “Digital drug formulation and design: critical insights from microstructure imaging”. Read about our work here.
- In collaboration with FDA, DigiM participated in several presentations at IFPAC 2021 Annual Meeting to evaluate container closure system effects on lyophilized products. Click here to learn more.
- In the AIChE 2021 Annual Meeting, DigiM talked about how digital images, when quantified and analyzed with AI machine learning algorithms, can help understand the process, performance, and formulation design of drugs. Learn about our work here.
- DigiM gave several talks and posters covering our AI image analytics and simulation to quantify and characterize drug products of all kinds at the AAPS PharmSci 360 event. Read more about our work here.
- At the CRS 2020 Annual meeting, DigiM presented our latest drug release simulations for PLGA microspheres, long-acting implants and more. Click here to learn more.
- DigiM Awarded FDA Contract to Study Microstructure Bioequivalence of Polymer-based Long-acting Drugs (in collaboration with Dr. Diane Burgess’ Laboratory at UConn). Read the press release here. Medical News press release available here.
- DigiM’s Quantitative Image Characterization Featured in FDA Workshop. Watch the presentation and read the press release here. AZO Materials press release available here.
- American Association of Pharmaceutical Scientists (AAPS) 2021 PharmSci 360
Philadelphia, PA – October 17th to October 20th
- American Institute of Chemical Engineers (AIChE) 2021 Annual Meeting
Boston, MA – November 7th to November 11th
- Society of Core Analysts (SCA) Annual Symposium 2021
Virtual – September 13th to September 16th
Recent Presentations and Publications
- S. Zhang, J. Lomeo. Cloud-based image management solutions for digital transformation of drug product development, Microscopy & Microanalysis 27 (S1) (2021) 296-297. DOI.
- S. Horava, J. Lomeo, M. Shen, R. Ayyar, K. Eddy, S. Zhang, M. Swinney. Evaluating API Release Performance from Controlled Release Polymer Matrices with Microstructure Imaging and Image-based Prediction Models, Talk presented at CRS 2021 Virtual Annual Meeting, 2021 Jul 25-29; Virtual. Eli Lilly talk featuring DigiM’s analysis.
- I. Terzic, M. Staal, A. Doornbos, I. Kok, K. Staal, J. Visscher, A. Funhoff, T. Nguyen, W. Vos, P. V. Midwoud, R. Steendam. Next generation bioresorbable contraceptive implants, Talk presented at CRS 2021 Virtual Annual Meeting, 2021 Jul 25-29; Virtual. Innocore talk featuring DigiM’s analysis.
- M. Shen, Y. Wang, S. Zhang, B. Qin, Q. Bao, D. Burgess. Characterization of the Stability of PLGA Microspheres Using Image-based Key Performance Attributes and Release Prediction, Poster presented at CRS 2021 Virtual Annual Meeting, 2021 Jul 25-29; Virtual.
- K. Nagapudi, A. Zhu, D. Chang, J. Lomeo, K. Rajagopal, R. Hannoush, S. Zhang. Microstructure, quality, and release performance characterization of long-acting polymer implant formulations with X-ray microscopy and quantitative AI analytics, Journal of Pharmaceutical Sciences. (2021). DOI. With Genentech.
- Xi, H., Zhu, A., Klinzing, G.R., Zhou, L., Zhang, S., Gmitter, A.J., Ploeger, K., Sundararajan, P., Mahjour, M., Xi, W. (2020). Characterization of Spray Dried Particles Through Microstructural Imaging. Journal of Pharmaceutical Sciences.August 2020. With Merck. More details here.
- Wu, D., Zhou, L., Zhang, S. (2020). Characterization of Controlled Release Microspheres Using FIB-SEM and Image-Based Release Prediction. AAPS PharmSciTech. July 2020. With Bausch Health.
- Zhang, S., Nagapudi, K. (2020). Micro-imaging Based Characterization and Release Prediction of a Long-Acting PLGA Implant. Poster presented at the Long-Acting Injectables and Implantables meeting in La Jolla, CA. With Genentech.
- Zhang, S., Goldman, J., Chen, X., Rowe, J., Lin, S., Zhou, L. (2020). Non-Invasive, Quantitative Characterization of Lyophilized Drug Product Using Three-Dimensional X-Ray Microscopy Analytics. Drug Development & Delivery.
- Howard, J. (2019). Machine-Learning Methods in Image Processing and Analysis of Porous Materials. DigiM whitepaper.
- Zhou, L., Stroud, P., Zhu, A., Hinds, J., Blakely, K., Zhang, S. (2019). Characterization of Spray-Dried Aggregates for Tablet Formulation with Microstructure 3D Imaging Analytics. Poster presented at 2019 AAPS PharmSci 360 in San Antonio, Texas. With Eli Lilly.
- Zhu, L., Yin, D., Qin, Y., Konda, S., Zhang, S., Zhu, A., . . . Lin, H. (2019). Sorption‐Enhanced Mixed Matrix Membranes with Facilitated Hydrogen Transport for Hydrogen Purification and CO2 Capture. Advanced Functional Materials.
- Howard, J., Lin, S., & Zhang, S. (2019). Uncertainty Quantification in Image Segmentation for Image-Based Rock Physics in a Shaly Sandstone. Petrophysics,60(2), 240-254.