Sorafenib for advanced hepatocellular carcinoma provides better prognosis after liver transplantation than without liver transplantation

Lee, Soon Kyu; Jang, Jeong Won; Nam, Heechul; Sung, Pil Soo; Kim, Hee Yeon; Kwon, Jung Hyun; Lee, Sung Won; Song, Do Seon; Kim, Chang Wook; Song, Myeong Jun; Choi, Ho Joong; You, Young Kyoung; Bae, Si Hyun; Choi, Jong Young; Yoon, Seung Kew

doi:10.1007/s12072-020-10131-0

Cited by 15 publications

(16 citation statements)

References 30 publications

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“…This probability is computed as the fraction of the data labeled 'Pass' for the 20 most frequent drug-disease pairs. Some entries are consistent with observations in the literature, such as a high probability of 'Pass' for Sorafenib for carcinoma (Keating & Santoro, 2009, Llovet et al, 2008, Lee et al, 2021 or Risperidone for anxiety disorders (Brawman et al, 2005, Simon et al, 2006.…”

Section: Data Exploration Analysis and Preprocessingsupporting

confidence: 88%

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Athri

Murali

Muralidhar

et al. 2022

Preprint

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The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. Biological activities have never been used as a predictive feature. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the 'Pass' class. 'Pass' refers to one of the two classes (Pass/ Fail) of all clinical trials and the model performed well in predicting the 'Pass' category. An analysis of the features demonstrates that bioactivity plays an important role in predicting the outcome of a clinical trial. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. All code to map these entities is available through this study, and all data are from publicly available sources. While our model identifies low-lying inferences when biological activities are included, the code to integrate biological activity and target information provide researchers with access to deep curated and proprietary clinical trial databases the ability to get deeper insights, better statistical significance, and capabilities to better predict trial failures.

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Section: Data Exploration Analysis and Preprocessingsupporting

confidence: 88%

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Athri

Murali

Muralidhar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This probability is computed as the fraction of the data labeled "Pass" for the 20 most frequent drug-disease pairs. Some entries are consistent with observations in the literature, such as a high probability of "Pass" for Sorafenib for carcinoma (Keating & Santoro, 2009;Lee et al, 2021;Llovet et al, 2008) or Risperidone for anxiety disorders (Brawman-Mintzer et al, 2005;Simon et al, 2006).…”

Section: Data Exploration Analysis and Preprocessingsupporting

confidence: 88%

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

et al. 2022

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“…Another important issue in the treatment of HCC after LT is the risk of rejection. Sorafenib treatment for recurrent HCC after LT has been shown to be safe and effective, and demonstrated better outcomes compared to advanced HCC without LT [ 81 , 82 ]. However, there is a concern for the use of ICBs targeting PD-1/PDL-1 or CTLA-4 in LT patients with recurrent HCC.…”

Section: Liver Transplantation (Lt)mentioning

confidence: 99%

Immunological Markers, Prognostic Factors and Challenges Following Curative Treatments for Hepatocellular Carcinoma

Lee

Jang

et al. 2021

IJMS

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Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortalities worldwide. Patients with early-stage HCC are eligible for curative treatments, such as surgical resection, liver transplantation (LT) and percutaneous ablation. Although curative treatments provide excellent long-term survival, almost 70–80% of patients experience HCC recurrence after curative treatments. Tumor-related factors, including tumor size, number and differentiation, and underlying liver disease, are well-known risk factors for recurrence following curative therapies. Moreover, the tumor microenvironment (TME) also plays a key role in the recurrence of HCC. Many immunosuppressive mechanisms, such as an increase in regulatory T cells and myeloid-derived suppressor cells with a decrease in cytotoxic T cells, are implicated in HCC recurrence. These suppressive TMEs are also modulated by several factors and pathways, including mammalian target of rapamycin signaling, vascular endothelial growth factor, programmed cell death protein 1 and its ligand 1. Based on these mechanisms and the promising results of immune checkpoint blockers (ICBs) in advanced HCC, there have been several ongoing adjuvant studies using a single or combination of ICB following curative treatments in HCC. In this review, we strive to provide biologic and immunological markers, prognostic factors, and challenges associated with clinical outcomes after curative treatments, including resection, LT and ablation.

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Sorafenib for advanced hepatocellular carcinoma provides better prognosis after liver transplantation than without liver transplantation

Cited by 15 publications

References 30 publications

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Immunological Markers, Prognostic Factors and Challenges Following Curative Treatments for Hepatocellular Carcinoma

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