Trends in Phase II trials for cancer therapies

Azam, F.; Vázquez, Alexei

doi:10.1101/2020.12.08.20245886

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…We did not compare the response rates between single-drug and multiple-drug studies because the majority of trials in our sample were single-drug studies. Previous studies have shown that using a combination of a few agents increases the likelihood of benefiting in a trial [ 17 , 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Risk and Benefit for Targeted Therapy Agents in Pediatric Phase II Trials in Oncology: A Systematic Review with a Meta-Analysis

et al. 2021

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Background For research with human participants to be ethical, risk must be in a favorable balance with potential benefits. Little is known about the risk/benefit ratio for pediatric cancer phase II trials testing targeted therapies. Objective Our aim was to conduct a systematic review of preliminary efficacy and safety profiles of phase II targeted therapy clinical trials in pediatric oncology. Methods Our protocol was prospectively registered in PROSPERO (CRD42020146491). We searched EMBASE and Pub-Med for phase II pediatric cancer trials testing targeted agents. We included solid and hematological malignancy studies published between 1 January, 2015 and 27 February, 2020. We measured risk using drug-related grade 3 or higher adverse events, and benefit by response rates. When possible, data were meta-analyzed. All statistical tests were two-sided. Results We identified 34 clinical trials (1202 patients) that met our eligibility criteria. The pooled overall response rate was 24.4% (95% confidence interval [CI] 14.5-34.2) and was lower in solid tumors, 6.4% (95% CI 3.2-9.6), compared with hematological malignancies, 55.1% (95% CI 35.9-74.3); p < 0.001. The overall fatal drug-related (grade 5) adverse event rate was 1.6% (95% CI 0.6-2.5), and the average drug-related grade 3/4 adverse event rate per person was 0.66 (95% CI 0.55-0.78). Conclusions We provide an estimate for the risks and benefits of participation in pediatric phase II cancer trials. These data may be used as an empirical basis for informed communication about benefits and burdens in pediatric oncology research.

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Section: Discussionmentioning

confidence: 99%

Risk and Benefit for Targeted Therapy Agents in Pediatric Phase II Trials in Oncology: A Systematic Review with a Meta-Analysis

et al. 2021

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“…The combination provides a potential treatment for combating SARS-CoV-2, which can hardly be treated by any single drug. However, drug combinations can also be harmful without precise medicine [10,11]. Therefore, it is pretty important to accurately find synergistic drug pairs for a cell type in case we want to take advantage of drug combination therapy.…”

Section: Introductionmentioning

confidence: 99%

Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning

Hu¹,

Yu²,

Guo³

et al. 2023

Preprint

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Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves the state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.

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“…Lapatinib or Rapamycin alone has little therapeutic effect, but their combined treatment has been reported to significantly increase the apoptosis rate of triple-negative breast cancer cells (Liu et al, 2011). However, some drug combinations may cause antagonistic effect and even aggravate the disease (Azam and Vazquez, 2021). Therefore, it is crucial to accurately discover synergistic drug combinations to specific diseases.…”

Section: Introductionmentioning

confidence: 99%

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

Wang

Zhang

Shen

et al. 2021

Preprint

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Motivation: Drug combination therapy becomes promising method in the treatment of cancer. However, the number of possible drug combinations toward cancer cell lines is too large, and it is challenging to screen synergistic drug combinations through wet-lab experiments. Therefore, the computational screening has become an important way to prioritize drug combinations. Graph attention network has recently shown strong performance in screening of compound-protein interactions, but it has not been applied to the screening of drug combinations. Results: In this paper, we proposed a deep learning model (DeepDDS) based on graph neural networks and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cell line. The graph representation of drug molecule structure and gene expression profiles is taken as input to predict the synergistic effects of drug combinations. We compare DeepDDS with traditional machine learning methods (random forest, support vector machine) and other deep learning methods (DeepSynergy, DTF) on the same data set. Our experimental results show that DeepDDS achieved best performance by the AUC value 0.93. Also, on an independent test set released by AstraZeneca, DeepDDS is superior to other comparative methods by 12.2% higher than the suboptimal method. We believe that DeepDDS is a effective tool that can prioritize synergistic drug combinations.

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Trends in Phase II trials for cancer therapies

Cited by 3 publications

References 57 publications

Risk and Benefit for Targeted Therapy Agents in Pediatric Phase II Trials in Oncology: A Systematic Review with a Meta-Analysis

Risk and Benefit for Targeted Therapy Agents in Pediatric Phase II Trials in Oncology: A Systematic Review with a Meta-Analysis

Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

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