Many of CCADD members have attended the Spring Conference of The Korean Society of Medical Informatics (KOSMI), held at Samsung Medical Comprehensive Cancer Center on June 14-15, 2018. Reflecting the recent drastic increase in the demand and importance of data science in health sciences, the theme of the conference was 'Evolving Data for Better Health'. More than 600 researchers, practitioners, and vendors in health informatics gathered together to share their knowledge, experience, results of research, and expertise through a series of keynote speeches, symposia, oral/poster presentations, and exhibitions.

Four abstracts by CCADD were accepted for poster presentation. Yoomin Jeon proposed a new approach to transform clinical trials eligibility criteria (EC, free-text) into structured data using the OMOP-CDM(Feasibility of Using the OMOP Common Data Model to Assess Clinical Trial Feasibility). She found that >75% of the EC could be mapped by the proposed approach, and the most important data group was condition.

Serin Lee, a student intern, gave her poster presentation entitled 'Dimensionality Reduction Model for Common Eligibility Criteria to effectively use Electronic Medical Record: A Pilot Study.' She proposed a dimensionality reduction model for common eligibility features (CEFs) based on ontology and semantic features of EC to reasonably compose the concept sets.

Dr. Soohyun Kim proposed a new type of command data model for clinical trials (Clinical Trial Common Data Model, CT-CDM) to support decision-making processes in clinical trials, entitled 'CDISC-based Common Data Model for Developing an Integrated Decision-Support System in Clinical Trials'. This command data model fully complies with the standards of Clinical Data Interchange Standards Consortium(CDISC) as a global, platform-independent data standard guaranteeing system interoperability. 

Lastly, but not the least, Dr. Jeong-An Gim shared his research findings: Application of Machine Learning Technology to Classify Healthy Subjects into Different Pharmacokinetic Exposure Groups to Tacrolimus. Using the decision-tree approach employed in R, Dr. Gim aimed to identify SNPs that are associated with the exposure to tacrolimus. The rs776746 in the CYP3A5 gene was the most influential mutation.

More detailed research findings can be downloaded below.

Much to everyone's delighted surprise, Serin Lee won an external solid-state drive of 1TB at the raffle draw! Since Serin will graduate in August, the prize was certainly a nice present. 

Prof. Howard Lee has been recently awarded a government grant from the Korea Clinical Trials Global Initiative or KCGI to develop a clinical trial resource integration system, and a grant kick-off meeting was held on March 15, 2018. The total research funding was $560,000, which is equivalent to 500 million Korean Won. The title of Prof. Lee's proposed research plan was the AI-based Clinical Trial Resource Information System or ACTRiS, which is pronounced 'Act Ris'. All of the CCADD members have contributed a lot to planning and completing the research plan for ACTRiS, and they will also actively participate in the grant one way or another. In ACTRiS, Prof. Jang Hee Hong, Choongnam University College of Medicine and Choongnam University Hospital, will be leading a subset project to develop an integrated web-based system for review activities by the Institutional Review Board, particularly for multisite clinical trials. Additionally, Prof. Jin Wook Choi, Seoul National University Hospital, and Prof. Hyun Joong Kong, Choongnam University Hospital, have joined to lead the IT development projects. Furthermore, codings and programming works will be outsourced to Mebixon, a leading life science solution development company in Korea.    

What makes ACTRiS unique is its active employment of state-of-the-art user experience technologies, which will be led by Prof. Joongseek Lee, along with his UX lab at the Graduate School of Convergence Science and Technology, Seoul National University. Not only that, several aspects and system components of ACTRiS will be fortified by applying AI technologies. For example, the accuracy and completeness of the contents in ACTRiS will be automatically checked and corrected by AI. 

"It means a lot to CCADD that we finally secured this start-up grant for the smart clinical trials initiative in Korea, which is expected to officially start next year", Prof. Lee said. "This signifies the vision that CCADD set out was right. Smart clinical trial is the future." 

Hyun A Lee and Dr. Kyeong-Ryoon Lee were invited to speak at a seminar on February 6, 2018, held in a laboratory at the College of Pharmacy, Kyungpook National University (PI: Prof. Kwang-Hee Shin). Hyun A and Dr. Lee shared their real world drug development experience in physiologically-based pharmacokinetic (PBPK) modeling and simulation using the Simcyp Simulator (Certara USA, Inc.). They both had considerable hands-on experience in PBPK modeling and simulation since they worked as researchers at Daewoong Pharmaceuticals, Korea.  

PBPK modeling and simulation can play a significant role in drug development decision-making. Particularly, Cetara's Simcyp Simulator, used by most of the top 50 global pharmaceutical companies along with other key regulatory agencies such as FDA, EMA, and Japan's PMDA, is the most sophisticated solution and has become the standard in the pharmaceutical industry. The Simcyp Simulator can assists determination of a maximum recommended safe starting dose (MRSD) in the first-in-human clinical study, design of optimal clinical trials, evaluation of  new drug formulations, and prediction of drug-drug interactions (DDIs) and pharmacokinetic outcomes in clinical populations. 

For more details about Hyun A and Dr. Lee's presentations, please contact them by clicking one of the following links: Hyun A Lee or Dr. Kyeong-Ryoon Lee. 

The Radiological Medicine Webzine interviewed Dr. Howard Lee, who shared his perspective on the new trend in clinical trials, reflected by the active employment of innovative technologies to survive stricter regulations and scientific requirements. The full interview can be found here and the school website has also highlighted it. 

Dr. Yuchae Jung wrote an article in BRIC View on Nov 20 2017 about AI techonology for the improvement of clinical trial process. In this article, Dr. Jung introduced several examples of global pharmaceutical companies that have successfully applied AI technology to drug development and clinical studies. She also added that AI technology can be used in various fields of drug development such as simulation of drug-drug interaction, drug screening, patient recruitment, and data collection from biosensors. Dr. Jung concluded the article by emphasizing the potential of AI technology in streamlining drug development processes. Dr. Jung's article can be downloaded here.

Dr. Yuchae Jung introduced the concept of P-MATCH or Precision Machine Learning AssisTed Clinical Trial Eligibility Assessment using Hospital Records at the 5^th Joint Conference on Medical Device between Seoul National University Colleges of Medicine and Engineering and Seoul National University Hospital on November 3, 2017. The title of her talk was 'Application of Artificial Intelligence Technology for Efficient Clinical Trials'. Dr. Jung underlined why artificial intelligence, mainly deep machine learning, can and should be adopted to bring more efficiency to clinical trials, particularly for precise patient enrollment, which allows for patients who are likely to respond to a treatment under testing to have a bigger chance of enrollment. This will also make clinical trials more productive and error-free, Dr. Jung added, because all of the eligibility assessment procedures are algorithm-based using the electronic medical record (EMR). 

Dr. Jung's talk was well received, and there were a couple of questions as to what could make P-MATCH different from other approaches such as the IBM Watson for Oncology. Dr. Jung replied that the biggest difference is P-MATCH's natural language processing capability of more than one language (i.e., Korean and English), which often are used in EMR in a haphazard and inconsistent manner. 

For anyone who wants to learn more about Dr. Jung's presentation, please contact her. 

How can we increase the efficiency, accuracy, and speed of patient enrollment into clinical trials? Clinical trial investigators have long been suffering from the low yield, inaccuracy, and delay of patient enrollment. It was mainly because the current practice is labor-intensive and has to be done manually in most cases. For example, based on the eligibility criteria of a clinical trial, a clinical trial investigator or one of his/her staff (e.g., clinical research coordinator) has to go over the entire medical record of a potential patient by eyeballing many scattered fields, which is very much time-consuming while inefficient and inaccurate. A typical clinical trial protocol has an average number of 50 for the eligibility criteria, which can become easily many more up to ~100 in late phase clinical trials. Dr. Lee discussed this issue and how artificial intelligence-guideded deep learning could address it at a recent Bio-Nanomedicine Salon (August 31, 2017) co-sponsored by the Korean Society for Nanomedicine and the Seoul National University Bio-Max Institute.