Dinh Lab
Challenges in Cancer Therapy
•Heterogeneity (cells, tumors & patients)
•Mono-therapy has low efficacy
New Drug Development
•Cost: US$2.8B / drugs
•Time: > 12 years / drugs
•Success Rate: <10%
Our approach is revolutionizing oncology drug discovery, exploring phenotypic heterogeneity in single-cells, using cutting-edge science technologies, involving Artificial Intelligence (AI) and Microfluidic Technology to develop intelligent phenotypic screening platform and biopsy examination machine to rapidly identify novel combination drugs for facilitating the development of more effective personalized cancer therapies.
1. Microfluidic Technology (Single-Cell / Multicell Analysis)
- Single Cell-line / Biopsy -on-a-chip (Single-cell Model)
- Single Spheroid / Organoid-on-a-chip (Multicell Model)
2. Big data (multi-omics of patients' samples & anticancer drug library)
- Phenotypic Responses of Single-cell Model (Cancer Cell lines + Biopsy)
- Phenotypic Responses of Multicell Model (Spheroid + patient-derived iPSC / organoids)
- Correlation between the molecular target (genome/proteomics) and disease state
- FDA-approved anticancer drugs & drug library
3. Artificial Intelligence (dynamic and predictive models)
- Deep learning on Image Processing, Feature Extraction and Single-cell Recognition
- Neural Network, Machine Learning Algorithm for Data Analysis and Combination Drugs Optimization
''Artificial Intelligence and Microfluidics Platform for Precision Medicine '' Ngoc-Duy Dinh, Suen Man Kin, and To Sai Shing, US Patent Filed. No. 63/377,141 (2022)