Search

Violative chemical residues in edible tissues from food-producing animals are of global public health concern. Great efforts have been made to develop physiologically based pharmacokinetic (PBPK) models for estimating withdrawal intervals (WDIs) for extralabel prescribed drugs in food animals. Existing models are insufficient to address the food safety concern as these models are either limited to 1 specific drug or difficult to be used by non-modelers. This study aimed to develop a user-friendly generic PBPK platform that can predict tissue residues and estimate WDIs for multiple drugs including flunixin, florfenicol, and penicillin G in cattle and swine. Mechanism-based in silico methods were used to predict tissue/plasma partition coefficients and the models were calibrated and evaluated with pharmacokinetic data from Food Animal Residue Avoidance Databank (FARAD). Results showed that model predictions were, in general, within a 2-fold factor of experimental data for all 3 drugs in both species. Following extralabel administration and respective U.S. FDA-approved tolerances, predicted WDIs for both cattle and swine were close to or slightly longer than FDA-approved label withdrawal times (eg, predicted 8, 28, and 7 days vs labeled 4, 28, and 4 days for flunixin, florfenicol, and penicillin G in cattle, respectively). The final model was converted to a web-based interactive generic PBPK platform. This PBPK platform serves as a user-friendly quantitative tool for real-time predictions of WDIs for flunixin, florfenicol, and penicillin G following FDA-approved label or extralabel use in both cattle and swine, and provides a basis for extrapolating to other drugs and species.

Zhoumeng Lin,1– 4,* Wei-Chun Chou,1– 4,* Yi-Hsien Cheng,3,4 Chunla He,5 Nancy A Monteiro-Riviere,6,7 Jim E Riviere7,8 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 2Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, USA; 3Institute of Computational Comparative Medicine, Kansas State University, Manhattan, KS, USA; 4Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 5Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 6Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, KS, USA; 7Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC, USA; 8 1Data Consortium, Kansas State University, Olathe, KS, USA*These authors contributed equally to this workCorrespondence: Zhoumeng Lin, Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 1225 Center Dr., Gainesville, FL, 32610, USA, Tel +1 352-273-6160, Email linzhoumeng@ufl.eduBackground: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machi

Zhoumeng Lin,1– 4,* Wei-Chun Chou,1– 4,* Yi-Hsien Cheng,3,4 Chunla He,5 Nancy A Monteiro-Riviere,6,7 Jim E Riviere7,8 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 2Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, USA; 3Institute of Computational Comparative Medicine, Kansas State University, Manhattan, KS, USA; 4Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 5Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA; 6Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, KS, USA; 7Center for Chemical Toxicology Research and Pharmacokinetics, North Carolina State University, Raleigh, NC, USA; 8 1Data Consortium, Kansas State University, Olathe, KS, USA*These authors contributed equally to this workCorrespondence: Zhoumeng Lin, Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 1225 Center Dr., Gainesville, FL, 32610, USA, Tel +1 352-273-6160, Email linzhoumeng@ufl.eduBackground: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learn

Violative chemical residues in edible tissues from food-producing animals are of global public health concern. Great efforts have been made to develop physiologically based pharmacokinetic (PBPK) models for estimating withdrawal intervals (WDIs) for extralabel prescribed drugs in food animals. Existing models are insufficient to address the food safety concern as these models are either limited to 1 specific drug or difficult to be used by non-modelers. This study aimed to develop a user-friendly generic PBPK platform that can predict tissue residues and estimate WDIs for multiple drugs including flunixin, florfenicol, and penicillin G in cattle and swine. Mechanism-based in silico methods were used to predict tissue/plasma partition coefficients and the models were calibrated and evaluated with pharmacokinetic data from Food Animal Residue Avoidance Databank (FARAD). Results showed that model predictions were, in general, within a 2-fold factor of experimental data for all 3 drugs in both species. Following extralabel administration and respective U.S. FDA-approved tolerances, predicted WDIs for both cattle and swine were close to or slightly longer than FDA-approved label withdrawal times (eg, predicted 8, 28, and 7 days vs labeled 4, 28, and 4 days for flunixin, florfenicol, and penicillin G in cattle, respectively). The final model was converted to a web-based interactive generic PBPK platform. This PBPK platform serves as a user-friendly quantitative tool for real-time predictions of WDIs for flunixin, florfenicol, and penicillin G following FDA-approved label or extralabel use in both cattle and swine, and provides a basis for extrapolating to other drugs and species.

Yi-Hsien Cheng,1 Shu-Han You,2 Yi-Jun Lin,3 Szu-Chieh Chen,4,5 Wei-Yu Chen,6 Wei-Chun Chou,2 Nan-Hung Hsieh,7 Chung-Min Liao3 1Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 2National Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan, 3Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 4Department of Public Health, 5Department of Family and Community Medicine, Chung Shan Medical University Hospital, Taichung, 6Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan; 7Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA Background: The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection. Materials and methods: A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose–response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach. Results: We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop se

Wei-Yu Chen,1 Yi-Hsien Cheng,2 Nan-Hung Hsieh,3 Bo-Chun Wu,2 Wei-Chun Chou,4 Chia-Chi Ho,4 Jen-Kun Chen,5 Chung-Min Liao,2,* Pinpin Lin4,* 1Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, 2Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 3Institute of Labor, Occupational Safety and Health, Ministry of Labor, New Taipei City, 4National Institute of Environmental Health Sciences, 5Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Taiwan *These authors contributed equally to this work Abstract: Zinc oxide nanoparticles (ZnO NPs) have been widely used in consumer products, therapeutic agents, and drug delivery systems. However, the fate and behavior of ZnO NPs in living organisms are not well described. The purpose of this study was to develop a physiologically based pharmacokinetic model to describe the dynamic interactions of 65ZnO NPs in mice. We estimated key physicochemical parameters of partition coefficients and excretion or elimination rates, based on our previously published data quantifying the biodistributions of 10 nm and 71 nm 65ZnO NPs and zinc nitrate (65Zn(NO3)2) in various mice tissues. The time-dependent partition coefficients and excretion or elimination rates were used to construct our physiologically based pharmacokinetic model. In general, tissue partition coefficients of 65ZnO NPs were greater than those of 65Zn(NO3)2, particularly the lung partition coefficient of 10 nm 65ZnO NPs. Sensitivity analysis revealed that 71 nm 65ZnO NPs and 65Zn(NO3)2 were sensitive to excretion and elimination rates in the liver and gastrointestinal tract. Although the partition coefficient of the brain was relative low, it increased time-dependently for 65ZnO NPs and 65Zn(NO3)2. The simulation of 65Zn(NO3)2 was well fitted with the experimental data. However, replacing partition coeff

Wei-Yu Chen,1 Yi-Hsien Cheng,2 Nan-Hung Hsieh,3 Bo-Chun Wu,2 Wei-Chun Chou,4 Chia-Chi Ho,4 Jen-Kun Chen,5 Chung-Min Liao,2,* Pinpin Lin4,* 1Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, 2Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 3Institute of Labor, Occupational Safety and Health, Ministry of Labor, New Taipei City, 4National Institute of Environmental Health Sciences, 5Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Taiwan *These authors contributed equally to this work Abstract: Zinc oxide nanoparticles (ZnO NPs) have been widely used in consumer products, therapeutic agents, and drug delivery systems. However, the fate and behavior of ZnO NPs in living organisms are not well described. The purpose of this study was to develop a physiologically based pharmacokinetic model to describe the dynamic interactions of 65ZnO NPs in mice. We estimated key physicochemical parameters of partition coefficients and excretion or elimination rates, based on our previously published data quantifying the biodistributions of 10 nm and 71 nm 65ZnO NPs and zinc nitrate (65Zn(NO3)2) in various mice tissues. The time-dependent partition coefficients and excretion or elimination rates were used to construct our physiologically based pharmacokinetic model. In general, tissue partition coefficients of 65ZnO NPs were greater than those of 65Zn(NO3)2, particularly the lung partition coefficient of 10 nm 65ZnO NPs. Sensitivity analysis revealed that 71 nm 65ZnO NPs and 65Zn(NO3)2 were sensitive to excretion and elimination rates in the liver and gastrointestinal tract. Although the partition coefficient of the brain was relative low, it increased time-dependently for 65ZnO NPs and 65Zn(NO3)2. The simulation of 65Zn(NO3)2 was well fitted with the experimental data. However, replacing partition coeff