Outcome of proximal esophageal cancer after definitive combined chemo-radiation: a Swiss multicenter retrospective study.

To report oncological outcomes and toxicity rates, of definitive platin-based chemoradiadiationtherapy (CRT) in the management of proximal esophageal cancer. We retrospectively reviewed the medical records of patients with cT1-4 cN0-3 cM0 cervical esophageal cancer (CEC) (defined as tumors located below the inferior border of the cricoid cartilage, down to 22 cm from the incisors) treated between 2004 and 2013 with platin-based definitive CRT in four Swiss institutions. Acute and chronic toxicities were retrospectively scored using the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0 (CTCAE-NCI v.4.0). Primary endpoint was loco-regional control (LRC). We also evaluated overall survival (OS) and disease-free survival (DFS) rates. The influence of patient- and treatment related features have been calculated using the Log-rank test and multivariate Cox proportional hazards model. We enrolled a total of 55 patients. Median time interval from diagnosis to CRT was 78 days (6-178 days). Median radiation dose was 56Gy (28-72Gy). Induction chemotherapy (ICHT) was delivered in 58% of patients. With a median follow up of 34 months (6-110months), actuarial 3-year LRC, DFS and OS were 52% (95% CI: 37-67%), 35% (95% CI: 22-50%) and 52% (95% CI: 37-67%), respectively. Acute toxicities (dysphagia, pain, skin-toxicity) ranged from grade 0 - 4 without significant dose-dependent differences. On univariable analyses, the only significant prognostic factor for LRC was the time interval > 78 days from diagnosis to CRT. On multivariable analysis, total radiation dose >56Gy (p <0.006) and ICHT (p < 0.004) were statistically significant positive predictive factors influencing DFS and OS. Definitive CRT is a reliable therapeutic option for proximal esophageal cancer, with acceptable treatment related toxicities. Higher doses and ICHT may improve OS and DFS and. These findings need to be confirmed in further prospective studies

Construction of a computable cell proliferation network focused on non-diseased lung cells

<p>Abstract</p> <p>Background</p> <p>Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work.</p> <p>Results</p> <p>To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data.</p> <p>Conclusions</p> <p>To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.</p

Biology-inspired microphysiological systems to advance patient benefit and animal welfare in drug development

The first microfluidic microphysiological systems (MPS) entered the academic scene more than 15 years ago and were considered an enabling technology to human (patho)biology in vitro and, therefore, provide alternative approaches to laboratory animals in pharmaceutical drug development and academic research. Nowadays, the field generates more than a thousand scientific publications per year. Despite the MPS hype in academia and by platform providers, which says this technology is about to reshape the entire in vitro culture landscape in basic and applied research, MPS approaches have neither been widely adopted by the pharmaceutical industry yet nor reached regulated drug authorization processes at all. Here, 46 leading experts from all stakeholders - academia, MPS supplier industry, pharmaceutical and consumer products industries, and leading regulatory agencies - worldwide have analyzed existing challenges and hurdles along the MPS-based assay life cycle in a second workshop of this kind in June 2019. They identified that the level of qualification of MPS-based assays for a given context of use and a communication gap between stakeholders are the major challenges for industrial adoption by end-users. Finally, a regulatory acceptance dilemma exists against that background. This t4 report elaborates on these findings in detail and summarizes solutions how to overcome the roadblocks. It provides recommendations and a roadmap towards regulatory accepted MPS-based models and assays for patients' benefit and further laboratory animal reduction in drug development. Finally, experts highlighted the potential of MPS-based human disease models to feedback into laboratory animal replacement in basic life science research.Toxicolog

Bridging inhaled aerosol dosimetry to physiologically based pharmacokinetic modeling for toxicological assessment: nicotine delivery systems and beyond

One of the challenges for toxicological assessment of inhaled aerosols is to accurately predict their deposited and absorbed dose. Transport, evolution, and deposition of liquid aerosols are driven by complex processes dominated by convection-diffusion that depend on various factors related to physics and chemistry. These factors include the physicochemical properties of the pure substance of interest and associated mixtures, the physical and chemical properties of the aerosols generated, the interplay between different factors during transportation and deposition, and the subject-specific inhalation topography. Several inhalation-based physiologically based pharmacokinetic (PBPK) models have been developed, but the applicability of these models for aerosols has yet to be verified. Nicotine is among several substances that are often delivered via the pulmonary route, with varied kinetics depending upon the route of exposure. This was used as an opportunity to review and discuss the current knowledge and state-of-the-art tools combining aerosol dosimetry predictions with PBPK modeling efforts. A validated tool could then be used to perform for toxicological assessment of other inhaled therapeutic substances. The Science Panel from the Alliance of Risk Assessment have convened at the “Beyond Science and Decisions: From Problem Formulation to Dose-Response Assessment” workshop to evaluate modeling approaches and address derivation of exposure-internal dose estimations for inhaled aerosols containing nicotine or other substances. The discussion involved PBPK model evaluation criteria, challenges, and choices that arise in such a model design, development, and application as a computational tool for use in human toxicological assessments

Systems Toxicology: From Basic Research to Risk Assessment

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.ISSN:0893-228XISSN:1520-501

Multicomponent aerosol particle deposition in a realistic cast of the human upper respiratory tract

Inhalation of aerosols generated by electronic cigarettes leads to deposition of multiple chemical compounds in the human airways. In this work, an experimental method to determine regional deposition of multicomponent aerosols in an in vitro segmented, realistic human lung geometry was developed and applied to two aerosols, i.e. a monodisperse glycerol aerosol and a multicomponent aerosol. The method comprised the following steps: (1) lung cast model preparation, (2) aerosol generation and exposure, (3) extraction of deposited mass, (4) chemical quantification and (5) data processing. The method showed good agreement with literature data for the deposition efficiency when using a monodisperse glycerol aerosol, with a mass median aerodynamic diameter (MMAD) of 2.3 μm and a constant flow rate of 15 L/min. The highest deposition surface density rate was observed in the bifurcation segments, indicating inertial impaction deposition. The experimental method was also applied to the deposition of a nebulized multicomponent aerosol with a MMAD of 0.50 μm and a constant flow rate of 15 L/min. The deposited amounts of glycerol, propylene glycol and nicotine were quantified. The three analyzed compounds showed similar deposition patterns and fractions as for the monodisperse glycerol aerosol, indicating that the compounds most likely deposited as parts of the same droplets. The developed method can be used to determine regional deposition for multicomponent aerosols, provided that the compounds are of low volatility. The generated data can be used to validate aerosol deposition simulations and to gain insight in deposition of electronic cigarette aerosols in human airways

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.

MOTIVATION: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. RESULTS: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. AVAILABILITY: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/