Understanding common key indicators of successful and unsuccessful cancer drug trials using a contrast mining framework on ClinicalTrials.gov.

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in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups.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% F1 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.

Comparison of xMAP and ELISA assays for detecting cerebrospinal fluid biomarkers of Alzheimer's disease.

The best-studied biomarkers of Alzheimer's disease (AD) are the pathologically-linked cerebrospinal fluid (CSF) proteins amyloid-ß 42 (Aß(1-42)), total tau (t-tau), and tau phosphorylated on amino acid 181 (p-tau(181)). Many laboratories measure these proteins using enzyme-linked immunosorbent assay (ELISA). Multiplex xMAP Luminex is a semi-automated assay platform with reduced intra-sample variance, which could facilitate its use in CLIA-approved clinical laboratories. CSF concentrations of these three biomarkers reported using xMAP technology differ from those measured by the most commonly used ELISA, confounding attempts to compare results. To develop a model for converting between xMAP and ELISA levels of the three biomarkers, we analyzed CSF samples from 140 subjects (59 AD, 30 controls, 34 with mild cognitive impairment, and 17 with Parkinson's disease, including 1 with dementia). Log-transformation of ELISA and xMAP levels made the variance constant in all three biomarkers and improved the linear regression: t-tau concentrations were highly correlated (r = 0.94); p-tau(181) concentrations by ELISA can be better predicted using both the t-tau and p-tau(181) xMAP values (r = 0.96) as compared to p-tau(181) concentrations alone (r = 0.82); correlation of Aß(1-42) concentrations was relatively weaker but still high (r = 0.77). Among all six protein/assay combinations, xMAP Aß(1-42) had the best accuracy for diagnostic classification (88%) between AD and control subjects. In conclusion, our study demonstrates that multiplex xMAP is an appropriate assay platform providing results that can be correlated with research-based ELISA values, facilitating the incorporation of this diagnostic biomarker into routine clinical practice.