Schaut William, Shrivastav Akash, Ramakrishnan Srikanth, Bowden Robert
Cell Collection, CAR-T Advanced Therapeutics Supply Chain, Janssen Pharmaceutical, Inc., Horsham, PA, United States.
Intelligent Automation and Analytics, Research and Development Business Technology, Janssen Pharmaceutical, Inc., Raritan, NJ, United States.
Front Med (Lausanne). 2023 Mar 6;10:1072767. doi: 10.3389/fmed.2023.1072767. eCollection 2023.
Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.
Combining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.
Compared to manual searching, which required 3-4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.
Compared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.
手动跟进与细胞和基因治疗相关的指令、指南、法律和条例等法规是一项劳动密集型工作。我们使用机器学习（ML）算法创建了一个增强智能系统，以优化对全球法规的系统筛选，提高效率，减少总体人力投入和法规遗漏。
在搜索过程中结合布尔逻辑和人工智能（即增强智能），使用ML算法识别并推荐相关的细胞和基因治疗法规。推荐的法规会被发送到一个着陆页，以便主题专家（SME）对单词/短语进行进一步标注，从而在功能词方面提高系统相关性。从法规库中持续学习不断提高系统的可靠性和性能。系统的自动训练和再训练能力使系统准确性能够持续优化和提升。每天自动在全球数据库中搜索适用法规为更新法规库提供了持续的机会。
与需要3 - 4名主题专家审查约115条法规的手动搜索相比，当前系统在持续学习的情况下，只需相当于1个全时工作量的人力，每天就能处理约9000条法规。目前，系统性能的总体准确率为86%，推荐召回率为87%，拒绝召回率为84%。有意采用保守的搜索策略，以便主题专家能够评估推荐度较低的法规，防止遗漏任何适用法规。
与手动搜索相比，我们定制的自动搜索系统极大地改善了细胞和基因治疗法规的管理，具有高效、经济且准确的特点。
