Chang Shu-Kai, Liu Danlu, Mitchem Jonathan, Papageorgiou Christos, Kaifi Jussuf, Shyu Chi-Ren
Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA.
Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211, USA.
J Biomed Inform. 2023 Mar;139:104321. doi: 10.1016/j.jbi.2023.104321. Epub 2023 Feb 16.
Clinical trials are essential to the process of new drug development. As clinical trials involve significant investments of time and money, it is crucial for trial designers to carefully investigate trial settings prior to designing a trial. Utilizing trial documents from ClinicalTrials.gov, we aim to understand the common characteristics of successful and unsuccessful cancer drug trials to provide insights about what to learn and what to avoid. In this research, we first computationally classified cancer drug trials into successful and unsuccessful cases and then utilized natural language processing to extract eligibility criteria information from the trial documents. To provide explainable and potentially modifiable recommendations for new trial design, contrast mining was applied to discoverhighly contrasted patterns with a significant difference in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups. Our method identified contrast patterns consisting of combinations of drug categories, eligibility criteria, study organization, and study design for nine major cancers. In addition to a literature review for the qualitative validation of mined contrast patterns, we found that contrast-pattern-based classifiers using the top 200 contrast patterns as feature representations can achieve approximately 80% F score for eight out of ten cancer types in our experiments. In summary, aligning with the modernization efforts of ClinicalTrials.gov, our study demonstrates that understanding the contrast characteristics of successful and unsuccessful cancer trials may provide insights into the decision-making process for trial investigators and therefore facilitate improved cancer drug trial design.
临床试验对于新药研发过程至关重要。由于临床试验涉及大量的时间和资金投入，对于试验设计者而言，在设计试验之前仔细研究试验环境至关重要。利用来自ClinicalTrials.gov的试验文档，我们旨在了解成功和失败的癌症药物试验的共同特征，以便为应该吸取的经验教训和应该避免的事项提供见解。在本研究中，我们首先通过计算将癌症药物试验分为成功和失败案例，然后利用自然语言处理从试验文档中提取纳入标准信息。为了为新的试验设计提供可解释且可能可修改的建议，我们应用对比挖掘来发现成功（完成并进入下一阶段）和失败（暂停、撤回或终止）组之间在患病率上有显著差异的高度对比模式。我们的方法识别出了由药物类别、纳入标准、研究组织和针对九种主要癌症的研究设计组合而成的对比模式。除了对挖掘出的对比模式进行定性验证的文献综述外，我们发现，在我们的实验中，使用前200个对比模式作为特征表示的基于对比模式的分类器，对于十种癌症类型中的八种能够达到约80%的F分数。总之，与ClinicalTrials.gov的现代化努力相一致，我们的研究表明，了解成功和失败的癌症试验的对比特征可能为试验研究者的决策过程提供见解，从而有助于改进癌症药物试验设计。
