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Abstract Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community athttps://osf.io/dzx7b/.
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Non‐Invasive Touch‐Based Lithium Monitoring Using an Organohydrogel‐Based Sensing Interface
Abstract Lithium is a drug widely employed for the treatment of bipolar disorder owing to its high efficacy in mood management and suicide prevention. However, this efficacy is often undermined by misdosing and nonadherence, and diligent drug monitoring is required during treatment. Standard lithium monitoring involves invasive blood collections and laboratory analysis with low time granularity. Recent advances in sensor technology have enabled the development of personalized drug‐monitoring devices that analyze biomarker information noninvasively. Herein, based on the fact that the analyte partition onto the fingertip with a high flux, a touch‐based noninvasive monitoring modality for managing lithium pharmacotherapy is devised. The system is built based on a thin organohydrogel‐mounted lithium ion‐selective electrode (TOH‐ISE). The TOH coating provides a stable environment for sensing. Through the utilization of a water/glycerol bi‐solvent matrix, the gel exhibits dehydration‐resist properties, rendering a controlled micro‐environment for ISE conditioning, and subsequently minimizing signal drift. To illustrate the clinical application of the solution, the system is tested on a subject prescribed lithium. The system successfully detected the increase in circulating drug levels following medication intake. Collectively, the results indicate the devised solution is capable to facilitate lithium adherence monitoring and has broader potential for optimizing lithium pharmacotherapy.
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- Award ID(s):
- 1722972
- PAR ID:
- 10418958
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- Volume:
- 8
- Issue:
- 14
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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