Three teams from Taipei Medical University win the Ministry of Science and Technology’s 2021 Future Tech Award
Source: Taipei Medical University
Published on 2022-01-18
Organ-on-a-chip: Array Platform for 3D Cell Culture and Drug Testing/ Screening
- Kang-Yun Lee (李岡遠), College of Medicine, Taipei Medical University
- Cheng-Hsien Liu(劉承賢), National Tsing Hua University
- Weilun Sun (孫偉倫), Taipei Medical University
The “biomimetic Lung-on-chip” is a miniature bioreactor equipped with a circulation system, three-dimensional tissue structure, and micro-channels for medications. It can trace till nano scale differences of cancer drugs. On top of this, it can test the effects of combination treatments. The chip will greatly shorten the time for drug screening because of this delicate structure. The most important, it can do the therapeutic try for the patient. This personalized lung-on-chip will definitely reduce the discomfort from the treatments, provide information on diagnosis and prognosis too. Ultimately it will reduce the mortality.
For the past ten years, cancer is the leading cause of death in Taiwan. Particularly, lung cancer is one of the most lethal types of cancer. Although cancer treatments have shown good response, once it recurs choosing a suitable treatment is a reality. This biomimetic lung-on-chip provides a solution to this problem. Using semi-conductor manufacturing processes and electro-mechanical engineering, the autologous cells will be arranged into tissue structure. Nowadays, different types of organs have been successfully re-constructed on the organ-on-chip. For example, the heart, the brain, the liver, the kidney, and the small intestine. Organ-on-chip will replace the traditional cell culture studies and reduce the necessity of animal studies in the near future because it is an emulating environment closer to human.
Preclinical development of a novel anticancer agent and precision medicine strategy
- Shiow-Lin Pan (潘秀玲), College of Medical Science and Technology, Taipei Medical University
- Wei-Chun HuangFu (皇甫維君), College of Medical Science and Technology, Taipei Medical University
- Chia-Ron Yang (楊家榮), Department of Pharmacy, National Taiwan University
- Min-Wu Chao (趙敏吾) , Department of Pharmacy, National Taiwan University
- Han-Li Huang(黃瀚立), New Drug Research Center, Taipei Medical University
- Huang-Ju Tu (杜皇儒), College of Medical Science and Technology, Taipei Medical University
This project is subsidized by the Academia Sinica’s National Biotechnology Research Park Translational Program for Next-Generation Therapeutics. In the third quarter of 2022, the team expects to apply for investigational new drug (IND) in both the US and Taiwan. This MPT0G211 technology has a higher selectivity and sensitivity than existing competitors (Ricolinostat, ACY1215; and Citarionostat, ACY241; which were developed by Celgene). Regardless of application in solid tumors (such as brain cancer, breast cancer, colorectal cancer, pancreatic cancer, prostate cancer) or in hematological tumors (such as leukemia, lymphoma, and multiple myeloma), MPT0G211 showed significant anti-cancer effects with no toxicity when used alone or in combined therapy in animal models. Currently, this technology is under patent review in 8 countries while the patents in Taiwan and Australia have already been granted, which will facilitate technology promotion and cooperation in many countries. In addition to helping MPT0G211 successfully complete the application for IND, technology transfer and cooperation opportunities for this technology will be actively sought for its exclusive derivative start-up company.
The research team has been selected to participate in many international accelerator training conducted in collaboration by Berkeley SkyDeck, SmartLabs, NBRP, and AstraZeneca. In 2019, the team won 2nd place in the RESI Innovation Challenge, and in 2020, the team further won distinction in the 17th National Innovation Award. This year, the team was awarded the “2021 Taiwan-Berkeley Health Innovation Accelerator Program”, supported by the National Development Council to receive three months of training at the School of Public Health, University of California, Berkeley in the US.
Multi-Module Clinical Decision Support System-Shared Decision Making for lung cancer
- Cheng-Yu Chen (陳震宇), Vice President of Taipei Medical University
- Min-huei Hsu (許明暉), Director of Office of Data Science, Taipei Medical University
- Tzu-hao Chang (張資昊), Director of Office of Information Technology, Taipei Medical University
- Shih-hsin Hsiao (蕭世欣), Department of Pulmonary Medicine, Taipei Medical University Hospital
- Chi-long Chen (陳志榮), Department of Pathology, Taipei Medical University Hospital
- Le Nguyen Quoc Khanh (黎阮國慶), Program in Artificial Intelligence, Taipei Medical University
- aetherAI Co.
- Findings Technology
Although targeted therapy drugs and immunotherapies have been advancing in recent years, 15% of the 5-year survival rate is still low. Early clinical diagnosis of lung cancer requires precise imaging analysis, and multiple factors, such as oncogene mutations, are essential indicators of treatment and drug selection. To improve treatment efficiency and achieve precise clinical path and treatment vision in lung cancer, clinical big data from TMU was used in conjunction with major medical technology companies to develop a multi-module clinical decision assistance system for lung cancer.
This technology uses innovative artificial intelligence lung cancer modules to assist with the interpretation of clinical CT and digital pathological images. It integrates clinical data and genetic data to establish a clinical decision support system and shared decision-making system (CDSS-SDM). Based on the clinical course of the disease, the system provides physicians and patients with diagnosis, medication, and prognosis assessment to achieve shared decision-making between the physician and the patient. The AI platform for big data precision medicine in lung cancer is based on deep learning to improve decision-making in the real clinical paths. It can be applied to other types of cancer in the future, and also helps the AI precision medicine industry flourish.