74 research outputs found
Clinical Development of Novel Drug-Radiotherapy Combinations.
Radiotherapy is a fundamental component of treatment for the majority of patients with cancer. In recent decades, technological advances have enabled patients to receive more targeted doses of radiation to the tumor, with sparing of adjacent normal tissues. There had been hope that the era of precision medicine would enhance the combination of radiotherapy with targeted anticancer drugs; however, this ambition remains to be realized. In view of this lack of progress, the FDA-AACR-ASTRO Clinical Development of Drug-Radiotherapy Combinations Workshop was held in February 2018 to bring together stakeholders and opinion leaders from academia, clinical radiation oncology, industry, patient advocacy groups, and the FDA to discuss challenges to introducing new drug-radiotherapy combinations to the clinic. This Perspectives in Regulatory Science and Policy article summarizes the themes and action points that were discussed. Intelligent trial design is required to increase the number of studies that efficiently meet their primary outcomes; endpoints to be considered include local control, organ preservation, and patient-reported outcomes. Novel approaches including immune-oncology or DNA-repair inhibitor agents combined with radiotherapy should be prioritized. In this article, we focus on how the regulatory challenges associated with defining a new drug-radiotherapy combination can be overcome to improve clinical outcomes for patients with cancer
Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing
In recent decades, the development of new drugs has become increasingly
expensive and inefficient, and the molecular mechanisms of most pharmaceuticals
remain poorly understood. In response, computational systems and network
medicine tools have emerged to identify potential drug repurposing candidates.
However, these tools often require complex installation and lack intuitive
visual network mining capabilities. To tackle these challenges, we introduce
Drugst.One, a platform that assists specialized computational medicine tools in
becoming user-friendly, web-based utilities for drug repurposing. With just
three lines of code, Drugst.One turns any systems biology software into an
interactive web tool for modeling and analyzing complex protein-drug-disease
networks. Demonstrating its broad adaptability, Drugst.One has been
successfully integrated with 21 computational systems medicine tools. Available
at https://drugst.one, Drugst.One has significant potential for streamlining
the drug discovery process, allowing researchers to focus on essential aspects
of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table
Minimum Technical Data Elements for Liquid Biopsy Data Submitted to Public Databases
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154656/1/cpt1747.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154656/2/cpt1747-sup-0001-FigS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154656/3/cpt1747_am.pd
Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis
"Background Pathological complete response has been proposed as a surrogate endpoint for prediction of long-term clinical benefit, such as disease-free survival, event-free survival (EFS), and overall survival (OS). We had four key objectives: to establish the association between pathological complete response and EFS and OS, to establish the definition of pathological complete response that correlates best with long-term outcome, to identify the breast cancer subtypes in which pathological complete response is best correlated with long-term outcome, and to assess whether an increase in frequency of pathological complete response between treatment groups predicts improved EFS and OS. Methods We searched PubMed, Embase, and Medline for clinical trials of neoadjuvant treatment of breast cancer. To be eligible, studies had to meet three inclusion criteria: include at least 200 patients with primary breast cancer treated with preoperative chemotherapy followed by surgery; have available data for pathological complete response, EFS, and OS; and have a median follow-up of at least 3 years. We compared the three most commonly used definitions of pathological complete response—ypT0 ypN0, ypT0/is ypN0, and ypT0/is—for their association with EFS and OS in a responder analysis. We assessed the association between pathological complete response and EFS and OS in various subgroups. Finally, we did a trial-level analysis to assess whether pathological complete response could be used as a surrogate endpoint for EFS or OS. Findings We obtained data from 12 identified international trials and 11 955 patients were included in our responder analysis. Eradication of tumour from both breast and lymph nodes (ypT0 ypN0 or ypT0/is ypN0) was better associated with improved EFS (ypT0 ypN0: hazard ratio [HR] 0·44, 95% CI 0·39–0·51; ypT0/is ypN0: 0·48, 0·43–0·54) and OS (0·36, 0·30–0·44; 0·36, 0·31–0·42) than was tumour eradication from the breast alone (ypT0/is; EFS: HR 0·60, 95% CI 0·55–0·66; OS 0·51, 0·45–0·58). We used the ypT0/is ypN0 definition for all subsequent analyses. The association between pathological complete response and long-term outcomes was strongest in patients with triple-negative breast cancer (EFS: HR 0·24, 95% CI 0·18–0·33; OS: 0·16, 0·11–0·25) and in those with HER2-positive, hormone-receptor-negative tumours who received trastuzumab (EFS: 0·15, 0·09–0·27; OS: 0·08, 0·03, 0·22). In the trial-level analysis, we recorded little association between increases in frequency of pathological complete response and EFS (R2=0·03, 95% CI 0·00–0·25) and OS (R2=0·24, 0·00–0·70). Interpretation Patients who attain pathological complete response defined as ypT0 ypN0 or ypT0/is ypN0 have improved survival. The prognostic value is greatest in aggressive tumour subtypes. Our pooled analysis could not validate pathological complete response as a surrogate endpoint for improved EFS and OS. Funding US Food and Drug Administration.
The National Early Warning Score and its subcomponents recorded within ±24 hours of emergency medical admission are poor predictors of hospital-acquired acute kidney injury
YesBackground: Hospital-acquired Acute Kidney Injury (H-AKI) is a common cause of avoidable morbidity and mortality.
Aim: To determine if the patients’ vital signs data as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital.
Methods: Analyses of emergency admissions to York hospital over 24-months with NEWS data. We report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3). Likewise for maximum NEWS (models B0,B1,B2,B3).
Results: 4.05% (1361/33608) of emergency admissions had H-AKI. Models using the index NEWS had the lower AUCs (0.59 to 0.68) than models using the maximum NEWS AUCs (0.75 to 0.77). The maximum NEWS model (B3) was more sensitivity than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5.
Conclusions: The index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but seems unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload albeit with improved sensitivity.The Health Foundatio
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Application of the Lung Immune Prognostic Index From Research to Clinical Practice-Reply
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Prognostic Value of the Lung Immune Prognostic Index for Patients Treated for Metastatic Non–Small Cell Lung Cancer
This exploratory pooled analysis of 11 randomized clinical trials examines whether the lung immune prognostic index is associated with long-term outcomes of treatment with immune checkpoint inhibitors and targeted therapy for patients with metastatic non–small cell lung cancer
Meta-analysis exploring the effect of oncogenic driver mutations on outcome of metastatic non-small cell lung cancer (mNSCLC) patients (pts) treated with immune checkpoint inhibitors (ICI) or docetaxel (doc).
Characterization of outcomes in patients with metastatic non-small cell lung cancer treated with programmed cell death protein 1 inhibitors past RECIST version 1.1–defined disease progression in clinical trials
Based on anecdotal cases of clinically important decreases in tumor size following initial evidence of disease progression when treating patients with anti-PD-1 therapies, investigators have conducted clinical trials in patients with metastatic non-small lung cancer (mNSCLC) receiving anti–PD-1 therapy allowing for treatment past RECIST-defined disease progression (TPP). We describe the findings of a pooled analysis of three clinical trials submitted to the US Food and Drug administration (FDA) where treatment of patients with mNSCLC permitted TPP in terms of reduction in the sum of target lesions following initial RECIST-defined progression. We identified patients who received TPP and the characteristics and post-TPP change in tumor burden. All patients had advanced or mNSCLC and had previously received a platinum-based doublet regimen. In total, 535 patients were treated with anti–PD-1 therapy in three clinical trials of which 121 patients (23%) received TPP. Among all 535 patients treated with anti–PD-1 therapy, the partial response (PR) rate (≥30% reduction in the size of target lesions compared to baseline) following TPP was 1.9% (10 of 535) or 8.3% (10 of 121) in the TPP subgroup. Patients who responded to TPP were more likely to have responded to the initial course of anti–PD-1 therapy, prior to progression. The subgroup of patients who received TPP appeared to have similar baseline characteristics and response to initial treatment compared to the overall population. This suggests that a treatment strategy that includes TPP may not benefit the overall population. The risks of TPP should be weighed against the low likelihood of a PR and the potential for changing to a different therapy with a higher likelihood of benefit
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