In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

INTRODUCTION

Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies.

AREAS COVERED

Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023.

EXPERT OPINION

Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Development and application of an interactive generic physiologically based pharmacokinetic (igPBPK) model for adult beef cattle and lactating dairy cows to estimate tissue distribution and edible tissue and milk withdrawal intervals for per- and polyfluoroalkyl substances (PFAS)

Humans can be exposed to per- and polyfluoroalkyl substances (PFAS) through dietary intake from milk and edible tissues from food animals. This study developed a physiologically based pharmacokinetic (PBPK) model to predict tissue and milk residues and estimate withdrawal intervals (WDIs) for multiple PFAS including PFOA, PFOS and PFHxS in beef cattle and lactating dairy cows. Results showed that model predictions were mostly within a two-fold factor of experimental data for plasma, tissues, and milk with an estimated coefficient of determination (R2) of >0.95. The predicted muscle WDIs for beef cattle were <1 day for PFOA, 449 days for PFOS, and 69 days for PFHxS, while the predicted milk WDIs in dairy cows were <1 day for PFOA, 1345 days for PFOS, and zero day for PFHxS following a high environmental exposure scenario (e.g., 49.3, 193, and 161 ng/kg/day for PFOA, PFOS, and PFHxS, respectively, for beef cattle for 2 years). The model was converted to a web-based interactive generic PBPK (igPBPK) platform to provide a user-friendly dashboard for predictions of tissue and milk WDIs for PFAS in cattle. This model serves as a foundation for extrapolation to other PFAS compounds to improve safety assessment of cattle-derived food products.

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines

Establishing maximum-residue limits (MRLs) for veterinary medicine helps to protect the human food supply. Guidelines for establishing MRLs are outlined by regulatory authorities that drug sponsors follow in each country. During the drug approval process, residue limits are targeted for specific animal species and matrices. Therefore, MRLs are commonly not established for other species. This study demonstrates unestablished MRLs can be reliably predicted for under-represented food commodity groups using machine learning (ML). Classification methods with imbalanced data were used to analyze MRL data from multiple countries by implementing resampling techniques in different ML classifiers. Afterward, we developed and evaluated a data-mining method for predicting unestablished MRLs. Seven different ML classifiers such as support vector classifier, multi-layer perceptron (MLP), random forest, decision tree, k-neighbors, Gaussian NB, and AdaBoost have been selected in this baseline study. Among these, the neural network MLP classifier reliably scored the highest average-weighted F1 score (accuracy >99% with markers and ≈88% without markets) in predicting unestablished MRLs. This provides the first study to apply ML algorithms in regulatory food animal medicine. By predicting and estimating MRLs, we can potentially decrease the use and cost of live animals and the overall research burden of determining new MRLs.

Pharmacokinetics and tumor delivery of nanoparticles

Nanoparticles (NPs) have been widely used in different areas, including consumer products and medicine. In terms of biomedical applications, NPs or NP-based drug formulations have been extensively investigated for cancer diagnostics and therapy in preclinical studies, but the clinical translation rate is low. Therefore, a thorough and comprehensive understanding of the pharmacokinetics of NPs, especially in drug delivery efficiency to the target therapeutic tissue tumor, is important to design more effective nanomedicines and for proper assessment of the safety and risk of NPs. This review article focuses on the pharmacokinetics of both organic and inorganic NPs and their tumor delivery efficiencies, as well as the associated mechanisms involved. We discuss the absorption, distribution, metabolism, and excretion (ADME) processes following different routes of exposure and the mechanisms involved. Many physicochemical properties and experimental factors, including particle type, size, surface charge, zeta potential, surface coating, protein binding, dose, exposure route, species, cancer type, and tumor size can affect NP pharmacokinetics and tumor delivery efficiency. NPs can be absorbed with varying degrees following different exposure routes and mainly accumulate in liver and spleen, but also distribute to other tissues such as heart, lung, kidney and tumor tissues; and subsequently get metabolized and/or excreted mainly through hepatobiliary and renal elimination. Passive and active targeting strategies are the two major mechanisms of tumor delivery, while active targeting tends to have less toxicity and higher delivery efficiency through direct interaction between ligands and receptors. We also discuss challenges and perspectives remaining in the field of pharmacokinetics and tumor delivery efficiency of NPs.

An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository

Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases.

A retrospective study of adverse drug events in anticoagulant administration with relevance to COVID-19

Initial studies in COVID-19 patients reported lower mortality rates associated with the use of the drug heparin, a widely used anticoagulant. The objective of this analysis was to determine whether there are adverse events associated with the administration of anticoagulants, and specifically how this might apply in patients known to have COVID-19. Data for this study were obtained from the Food and Drug Administration's Adverse Event Reporting System (FAERS) public database and from the NIH's clinical trials website. Proportional Reporting Ratios (PRR) with lower 95% confidence intervals (lower CI) and empirical Bayes geometric mean (EBGM) scores with lower 95% confidence limits were calculated for data from the FAERS database where the adverse events studied mimicked COVID-19 symptoms.

Toxicokinetics, dose-response, and risk assessment of nanomaterials: Methodology, challenges, and future perspectives

The rapid growth of nanomaterial applications has raised safety concerns for human health. A number of studies have been conducted to assess the toxicokinetics, toxicology, dose-response, and risk assessment of different nanomaterials using in vitro and in vivo animal and human models. However, current studies cannot meet the demand for efficient assessment of toxicokinetics, dose-response relationships, or the toxicological risk arising from the rapidly increasing number of newly synthesized nanomaterials. In this article, we review the methods for conducting toxicokinetics, hazard identification, dose-response, exposure, and risk assessment studies of nanomaterials, identify the knowledge gaps, and discuss the challenges remaining. We provide the rationale behind the appropriate design of nanomaterial plasma toxicokinetic and tissue distribution studies, including caveats on the interpretation and correlation of in vitro and in vivo toxicology studies. The potential of using physiologically based pharmacokinetic (PBPK) models to extrapolate toxicokinetic and toxicity findings from in vitro to in vivo and from animals to humans is discussed, and the knowledge gaps of PBPK modeling for nanomaterials are identified. While challenges still exist, there has been progress in the toxicokinetics, hazard identification, and risk assessment of nanomaterials in the past two decades. Recent advancements in the field are highlighted with relevant examples. We also share latest guidelines as well as our perspectives on future studies needed to characterize the toxicokinetics, toxicity, and dose-response relationship in support of nanomaterial risk assessment. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats

Background

Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4–200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs.

Results

We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure–activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration.

Conclusions

This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00489-4.

An Interactive Generic Physiologically Based Pharmacokinetic (igPBPK) Modeling Platform to Predict Drug Withdrawal Intervals in Cattle and Swine: A Case Study on Flunixin, Florfenicol and Penicillin G

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.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Background

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 learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.

Conclusion

This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

Development and Application of an interactive Physiologically Based Pharmacokinetic (iPBPK) Model to Predict Oxytetracycline Tissue Distribution and Withdrawal Intervals in Market-Age Sheep and Goats

Oxytetracycline (OTC) is a widely used antibiotic in food-producing animals. Extralabel use of OTC is common and may lead to violative residues in edible tissues. It is important to have a quantitative tool to predict scientifically-based withdrawal intervals (WDIs) after extralabel use in food animals to ensure human food safety. This study focuses on developing a physiologically based pharmacokinetic (PBPK) model for OTC in sheep and goats. The model included seven compartments: plasma, lung, liver, kidneys, muscle, fat, and rest of the body. The model was calibrated with serum and tissue (liver, muscle, kidney, and fat) concentration data following a single intramuscular (IM, 20 mg/kg) and/or intravenous (IV, 10 mg/kg) administration of a long-acting formulation in sheep and goats. The model was evaluated with independent datasets from Food Animal Residue Avoidance Databank (FARAD). Results showed that the model adequately simulated the calibration datasets with an overall estimated coefficient of determination (R2) of 0.95 and 0.92, respectively, for sheep and goat models and had acceptable accuracy for the validation datasets. Monte Carlo sampling technique was applied to predict the time needed for drug concentrations in edible tissues to fall below tolerances for the 99th percentiles of the population. The model was converted to a web-based interactive PBPK (iPBPK) interface to facilitate model applications. This iPBPK model provides a useful tool to estimate WDIs for OTC after extralabel use in small ruminants to ensure food safety and serves as a basis for extrapolation to other tetracycline drugs and other food animals.

Physiological parameter values for physiologically based pharmacokinetic models in food-producing animals. Part III: Sheep and goat

This report is the third in a series of studies that aimed to compile physiological parameters related to develop physiologically based pharmacokinetic (PBPK) models for drugs and environmental chemicals in food‐producing animals including swine and cattle (Part I), chickens and turkeys (Part II), and finally sheep and goats (the focus of this manuscript). Literature searches were conducted in multiple databases (PubMed, Google Scholar, ScienceDirect, and ProQuest), with data on relevant parameters including body weight, relative organ weight (% of body weight), cardiac output, relative organ blood flow (% of cardiac output), residual blood volume (% of organ weight), and hematocrit reviewed and statistically summarized. The mean and standard deviation of each parameter are presented in tables. Equations describing the growth curves of sheep and goats are presented in figures. When data are sufficient, parameter values are reported for different ages or production classes of sheep, including fetal sheep, lambs, and market‐age sheep (mature sheep). These data provide a reference database for developing standardized PBPK models to predict drug withdrawal intervals in sheep and goats, and also provide a basis for extrapolating PBPK models from major species such as cattle to minor species such as sheep and goats.

Physiological parameter values for physiologically based pharmacokinetic models in food-producing animals. Part II: Chicken and turkey

Physiologically based pharmacokinetic (PBPK) models are growing in popularity due to human food safety concerns and for estimating drug residue distribution and estimating withdrawal intervals for veterinary products originating from livestock species. This paper focuses on the physiological and anatomical data, including cardiac output, organ weight, and blood flow values, needed for PBPK modeling applications for avian species commonly consumed in the poultry market. Experimental and field studies from 1940 to 2019 for broiler chickens (1-70 days old, 40 g - 3.2 kg), laying hens (4-15 months old, 1.1-2.0 kg), and turkeys (1 day-14 months old, 60 g -12.7 kg) were searched systematically using PubMed, Google Scholar, ProQuest, and ScienceDirect for data collection in 2019 and 2020. Relevant data were extracted from the literature with mean and standard deviation (SD) being calculated and compiled in tables of relative organ weights (% of body weight) and relative blood flows (% of cardiac output). Trends of organ or tissue weight growth during different life stages were calculated when sufficient data were available. These compiled data sets facilitate future PBPK model development and applications, especially in estimating chemical residue concentrations in edible tissues to calculate food safety withdrawal intervals for poultry.

A study to assess the correlation between plasma, oral fluid and urine concentrations of flunixin meglumine with the tissue residue depletion profile in finishing-age swine

Background

Flunixin meglumine (FM) was investigated for the effectiveness of plasma, oral fluid, and urine concentrations to predict tissue residue depletion profiles in finishing-age swine, along with the potential for untreated pigs to acquire tissue residues following commingled housing with FM-treated pigs. Twenty pigs were housed in groups of three treated and one untreated control. Treated pigs received one 2.2 mg/kg dose of FM intramuscularly. Before treatment and at 1, 3, 6, 12, 24, 36, and 48 h (h) after treatment, plasma samples were taken. At 1, 4, 8, 12 and 16 days (d) post-treatment, necropsy and collection of plasma, urine, oral fluid, muscle, liver, kidney, and injection site samples took place. Analysis of flunixin concentrations using liquid chromatography/tandem mass spectrometry was done. A published physiologically based pharmacokinetic (PBPK) model for flunixin in cattle was extrapolated to swine to simulate the measured data.

Results

Plasma concentrations of flunixin were the highest at 1 h post-treatment, ranging from 1534 to 7040 ng/mL, and were less than limit of quantification (LOQ) of 5 ng/mL in all samples on Day 4. Flunixin was detected in the liver and kidney only on Day 1, but was not found 4–16 d post-treatment. Flunixin was either not seen or found less than LOQ in the muscle, with the exception of one sample on Day 16 at a level close to LOQ. Flunixin was found in the urine of untreated pigs after commingled housing with FM-treated pigs. The PBPK model adequately correlated plasma, oral fluid and urine concentrations of flunixin with residue depletion profiles in liver, kidney, and muscle of finishing-age pigs, especially within 24 h after dosing.

Conclusions

Results indicate untreated pigs can be exposed to flunixin by shared housing with FM-treated pigs due to environmental contamination. Plasma and urine samples may serve as less invasive and more easily accessible biological matrices to predict tissue residue statuses of flunixin in pigs at earlier time points (≤24 h) by using a PBPK model.

Physiological parameter values for physiologically based pharmacokinetic models in food-producing animals. Part I: Cattle and swine

Physiologically based pharmacokinetic (PBPK) models for chemicals in food animals are a useful tool in estimating chemical tissue residues and withdrawal intervals. Physiological parameters such as organ weights and blood flows are an important component of a PBPK model. The objective of this study was to compile PBPK‐related physiological parameter data in food animals, including cattle and swine. Comprehensive literature searches were performed in PubMed, Google Scholar, ScienceDirect, and ProQuest. Relevant literature was reviewed and tables of relevant parameters such as relative organ weights (% of body weight) and relative blood flows (% of cardiac output) were compiled for different production classes of cattle and swine. The mean and standard deviation of each parameter were calculated to characterize their variability and uncertainty and to allow investigators to conduct population PBPK analysis via Monte Carlo simulations. Regression equations using weight or age were created for parameters having sufficient data. These compiled data provide a comprehensive physiological parameter database for developing PBPK models of chemicals in cattle and swine to support animal‐derived food safety assessment. This work also provides a basis to compile data in other food animal species, including goats, sheep, chickens, and turkeys.

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.

An integrated experimental and physiologically based pharmacokinetic modeling study of penicillin G in heavy sows

Penicillin G is widely used in food-producing animals at extralabel doses and is one of the most frequently identified violative drug residues in animal-derived food products. In this study, the plasma pharmacokinetics and tissue residue depletion of penicillin G in heavy sows after repeated intramuscular administrations at label (6.5 mg/kg) and 5 × label (32.5 mg/kg) doses were determined. Plasma, urine, and environmental samples were tested as potential antemortem markers for penicillin G residues. The collected new data and other available data from the literature were used to develop a population physiologically based pharmacokinetic (PBPK) model for penicillin G in heavy sows. The results showed that antemortem testing of urine provided potential correlation with tissue residue levels. Based on the United States Department of Agriculture Food Safety and Inspection Service action limit of 25 ng/g, the model estimated a withdrawal interval of 38 days for penicillin G in heavy sows after 3 repeated intramuscular injections at 5 × label dose. This study improves our understanding of penicillin G pharmacokinetics and tissue residue depletion in heavy sows and provides a tool to predict proper withdrawal intervals after extralabel use of penicillin G in heavy sows, thereby helping safety assessment of sow-derived meat products.

Modeling gold nanoparticle biodistribution after arterial infusion into perfused tissue: effects of surface coating, size and protein corona

A detailed understanding of the factors governing nanomaterial biodistribution is needed to rationally design safe nanomedicines. This research details the pharmacokinetics of gold nanoparticle (AuNP) biodistribution after arterial infusion of 40 or 80 nm AuNP (1 μg/ml) into the isolated perfused porcine skin flap (IPPSF). AuNP had surface coatings consisting of neutral polyethylene glycol (PEG), anionic lipoic acid (LA), or cationic branched polyethylenimine (BPEI). Effect of a porcine plasma corona (PPC) on 40 nm BPEI and PEG-AuNP were assessed in the IPPSF. Au concentrations were determined by ICP/MS and arterial to venous concentration-time profiles were analyzed over 8 hr (4 hr infusion, 4 hr washout) using a two-compartment pharmacokinetic model. IPPSF viability and vascular function were assessed by change in glucose utilization, vascular resistance, or weight gain after perfusion. All AuNP demonstrated some degree of AuNP arterial extraction and skin flap retention, as well as enhanced kinetic parameters of tissue uptake; with BPEI-AuNP consistently having the greatest biodistribution even with a PPC. Toxicological effects were not detected. Transmission electron microscopy confirmed intracellular uptake of AuNP. These studies paralleled previous in vitro cell culture studies using the same AuNP in human endothelial and renal proximal tubule cells, hepatocytes, keratinocytes, showing BPEI-AuNP having the greatest uptake, although the presence of a PPC did not reduce IPPSF biodistribution as in the cell culture studies. These findings clearly indicate arterial to the venous extraction of AuNP after infusion with the magnitude of extraction being greatest with the BPEI surface coating and provide data and model structure necessary to construct the whole body physiologically based pharmacokinetic models capable of utilizing available in vitro data.

Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.

Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics

A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between-patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food-producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population-associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.

NanoEHS beyond Toxicity - Focusing on Biocorona

The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.

Surface chemistry of gold nanoparticles determines the biocorona composition impacting cellular uptake, toxicity and gene expression profiles in human endothelial cells

This study investigated the role of nanoparticle size and surface chemistry on biocorona composition and its effect on uptake, toxicity and cellular responses in human umbilical vein endothelial cells (HUVEC), employing 40 and 80 nm gold nanoparticles (AuNP) with branched polyethyleneimine (BPEI), lipoic acid (LA) and polyethylene glycol (PEG) coatings. Proteomic analysis identified 59 hard corona proteins among the various AuNP, revealing largely surface chemistry-dependent signature adsorbomes exhibiting human serum albumin (HSA) abundance. Size distribution analysis revealed the relative instability and aggregation inducing potential of bare and corona-bound BPEI-AuNP, over LA- and PEG-AuNP. Circular dichroism analysis showed surface chemistry-dependent conformational changes of proteins binding to AuNP. Time-dependent uptake of bare, plasma corona (PC) and HSA corona-bound AuNP (HSA-AuNP) showed significant reduction in uptake with PC formation. Cell viability studies demonstrated dose-dependent toxicity of BPEI-AuNP. Transcriptional profiling studies revealed 126 genes, from 13 biological pathways, to be differentially regulated by 40 nm bare and PC-bound BPEI-AuNP (PC-BPEI-AuNP). Furthermore, PC formation relieved the toxicity of cationic BPEI-AuNP by modulating expression of genes involved in DNA damage and repair, heat shock response, mitochondrial energy metabolism, oxidative stress and antioxidant response, and ER stress and unfolded protein response cascades, which were aberrantly expressed in bare BPEI-AuNP-treated cells. NP surface chemistry is shown to play the dominant role over size in determining the biocorona composition, which in turn modulates cell uptake, and biological responses, consequently defining the potential safety and efficacy of nanoformulations.

Zinc Oxide Nanoparticle-Poly I:C RNA Complexes: Implication as Therapeutics against Experimental Melanoma

There is current interest in harnessing the combined anticancer and immunological effect of nanoparticles (NPs) and RNA. Here, we evaluate the bioactivity of poly I:C (pIC) RNA, bound to anticancer zinc oxide NP (ZnO-NP) against melanoma. Direct RNA association to unfunctionalized ZnO-NP is shown by observing change in size, zeta potential, and absorption/fluorescence spectra upon complexation. RNA corona was visualized by transmission electron microscopy (TEM) for the first time. Binding constant (K_b = 1.6-2.8 g^-1 L) was determined by modified Stern-Volmer, absorption, and biological surface activity index analysis. The pIC-ZnO-NP complex increased cell death for both human (A375) and mouse (B16F10) cell lines and suppressed tumor cell growth in BALB/C-B16F10 mouse melanoma model. Ex vivo tumor analysis indicated significant molecular activity such as changes in the level of phosphoproteins JNK, Akt, and inflammation markers IL-6 and IFN-γ. High throughput proteomics analysis revealed zinc oxide and poly I:C-specific and combinational patterns that suggested possible utility as an anticancer and immunotherapeutic strategy against melanoma.

Pharmacokinetics and tissue elimination of flunixin in veal calves

OBJECTIVE To describe plasma pharmacokinetic parameters and tissue elimination of flunixin in veal calves. ANIMALS 20 unweaned Holstein calves between 3 and 6 weeks old. PROCEDURES Each calf received flunixin (2.2 mg/kg, IV, q 24 h) for 3 days. Blood samples were collected from all calves before the first dose and at predetermined times after the first and last doses. Beginning 24 hours after injection of the last dose, 4 calves were euthanized each day for 5 days. Plasma and tissue samples were analyzed by ultraperformance liquid chromatography. Pharmacokinetic parameters were calculated by compartmental and noncompartmental methods. RESULTS Mean ± SD plasma flunixin elimination half-life, residence time, and clearance were 1.32 ± 0.94 hours, 12.54 ± 10.96 hours, and 64.6 ± 40.7 mL/h/kg, respectively. Mean hepatic and muscle flunixin concentrations decreased to below FDA-established tolerance limits (0.125 and 0.025 μg/mL, respectively) for adult cattle by 3 and 2 days, respectively, after injection of the last dose of flunixin. Detectable flunixin concentrations were present in both the liver and muscle for at least 5 days after injection of the last dose. CONCLUSIONS AND CLINICAL RELEVANCE The labeled slaughter withdrawal interval for flunixin in adult cattle is 4 days. Because administration of flunixin to veal calves represents extralabel drug use, any detectable flunixin concentrations in edible tissues are considered a violation. Results indicated that a slaughter withdrawal interval of several weeks may be necessary to ensure that violative tissue residues of flunixin are not detected in veal calves treated with that drug.

Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules

Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

Estimation of tulathromycin depletion in plasma and milk after subcutaneous injection in lactating goats using a nonlinear mixed-effects pharmacokinetic modeling approach

BACKGROUND

Extra-label use of tulathromycin in lactating goats is common and may cause violative residues in milk. The objective of this study was to develop a nonlinear mixed-effects pharmacokinetic (NLME-PK) model to estimate tulathromycin depletion in plasma and milk of lactating goats. Eight lactating goats received two subcutaneous injections of 2.5 mg/kg tulathromycin 7 days apart; blood and milk samples were analyzed for concentrations of tulathromycin and the common fragment of tulathromycin (i.e., the marker residue CP-60,300), respectively, using liquid chromatography mass spectrometry. Based on these new data and related literature data, a NLME-PK compartmental model with first-order absorption and elimination was used to model plasma concentrations and cumulative excreted amount in milk. Monte Carlo simulations with 100 replicates were performed to predict the time when the upper limit of the 95% confidence interval of milk concentrations was below the tolerance.

RESULTS

All animals were healthy throughout the study with normal appetite and milk production levels, and with mild-moderate injection-site reactions that diminished by the end of the study. The measured data showed that milk concentrations of the marker residue of tulathromycin were below the limit of detection (LOD = 1.8 ng/ml) 39 days after the second injection. A 2-compartment model with milk as an excretory compartment best described tulathromycin plasma and CP-60,300 milk pharmacokinetic data. The model-predicted data correlated with the measured data very well. The NLME-PK model estimated that tulathromycin plasma concentrations were below LOD (1.2 ng/ml) 43 days after a single injection, and 62 days after the second injection with a 95% confidence. These estimated times are much longer than the current meat withdrawal time recommendation of 18 days for tulathromycin in non-lactating cattle.

CONCLUSIONS

The results suggest that twice subcutaneous injections of 2.5 mg/kg tulathromycin are a clinically safe extra-label alternative approach for treating pulmonary infections in lactating goats, but a prolonged withdrawal time of at least 39 days after the second injection should be considered to prevent violative residues in milk and any dairy goat being used for meat should have an extended meat withdrawal time.

Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction

The understanding of nano-bio interactions is deemed essential in the design, application, and safe handling of nanomaterials. Proper characterization of the intrinsic physicochemical properties, including their size, surface charge, shape, and functionalization, is needed to consider the fate or impact of nanomaterials in biological and environmental systems. The characterizations of their interactions with surrounding chemical species are often hindered by the complexity of biological or environmental systems, and the drastically different surface physicochemical properties among a large population of nanomaterials. The complexity of these interactions is also due to the diverse ligands of different chemical properties present in most biomacromolecules, and multiple conformations they can assume at different conditions to minimize their conformational free energy. Often these interactions are collectively determined by multiple physical or chemical forces, including electrostatic forces, hydrogen bonding, and hydrophobic forces, and calls for multidimensional characterization strategies, both experimentally and computationally. Through these characterizations, the understanding of the roles surface physicochemical properties of nanomaterials and their surface interactions with biomacromolecules can play in their applications in biomedical and environmental fields can be obtained. To quantitatively decipher these physicochemical surface interactions, computational methods, including physical, statistical, and pharmacokinetic models, can be used for either analyses of large amounts of experimental characterization data, or theoretical prediction of the interactions, and consequent biological behavior in the body after administration. These computational methods include molecular dynamics simulation, structure-activity relationship models such as biological surface adsorption index, and physiologically-based pharmacokinetic models. WIREs Nanomed Nanobiotechnol 2017, 9:e1440. doi: 10.1002/wnan.1440 For further resources related to this article, please visit the WIREs website.

Egg residue considerations during the treatment of backyard poultry

The purpose of this digest was to provide US veterinarians guidance on the responsible treatment of backyard poultry flocks. The treatment of backyard poultry can be a daunting task for veterinarians because only limited resources are available; however, it is likely to become an increasingly common task owing to the increasing popularity of backyard poultry throughout the United States, especially in urban and suburban areas. Although backyard poultry flock owners may consider their birds pets, the FDA considers them food-producing animals, and veterinarians should follow all regulations that pertain to food-producing animals when administering or prescribing drugs to those birds. The lack of FDA-approved drugs for use in laying hens frequently necessitates the use of drugs in an extralabel manner in backyard poultry. Unfortunately, information regarding the depletion of drug residues in eggs from hens treated with various drugs in an extralabel manner is sparse or lacking, and veterinarians need to be cognizant of this issue, especially when the eggs from treated hens are intended for human consumption.

Observations on macrolide resistance and susceptibility testing performance in field isolates collected from clinical bovine respiratory disease cases

The objectives of this study were; first, to describe gamithromycin susceptibility of Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni isolated from cattle diagnosed with bovine respiratory disease (BRD) and previously treated with either gamithromycin for control of BRD (mass medication=MM) or sham-saline injected (control=CON); second, to describe the macrolide resistance genes present in genetically typed M. haemolytica isolates; third, use whole-genome sequencing (WGS) to correlate the phenotypic resistance and genetic determinants for resistance among M. haemolytica isolates. M. haemolytica (n=276), P. multocida (n=253), and H. somni (n=78) were isolated from feedlot cattle diagnosed with BRD. Gamithromycin susceptibility was determined by broth microdilution. Whole-genome sequencing was utilized to determine the presence/absence of macrolide resistance genes and to genetically type M. haemolytica. Generalized linear mixed models were built for analysis. There was not a significant difference between MM and CON groups in regards to the likelihood of culturing a resistant isolate of M. haemolytica or P. multocida. The likelihood of culturing a resistant isolate of M. haemolytica differed significantly by state of origin in this study. A single M. haemolytica genetic subtype was associated with an over whelming majority of the observed resistance. H. somni isolation counts were low and statistical models would not converge. Phenotypic resistance was predicted with high sensitivity and specificity by WGS. Additional studies to elucidate the relationships between phenotypic expression of resistance/genetic determinants for resistance and clinical response to antimicrobials are necessary to inform judicious use of antimicrobials in the context of relieving animal disease and suffering.

NDELA and nickel modulation of triazine disposition in skin

Cutting fluids can become contaminated with metals (e.g., nickel, Ni) and nitrosamines (e.g., N-nitrosodiethanolamine, NDELA) and there is concern that these classes of contaminants can modulate dermal disposition and ultimately the toxicity of cutting fluid additives, such as irritant biocides (e.g., triazine). Biocides are added to these formulations to prevent bacterial degradation of commercial cutting fluids. The purpose of this study was to assess the dermal absorption and skin deposition of 14C-triazine when topically applied to porcine skin in an in vitro flow-through diffusion cell system as aqueous soluble oil (mineral oil, MO) or aqueous synthetic (polyethylene glycol, PEG) mixtures. 14C-Triazine mixtures were formulated with NDELA and/or Ni, or with a combination of three additional cutting fluid additives; namely, 5% linear alkylbenzene sulfonate (LAS), 5% triethanolamine (TEA) and 5% sulfurized ricinoleic acid. Neither Ni nor NDELA was absorbed during these 8-h studies. However, 14C-triazine absorption ranged from 2.72 to 3.29% dose in MO and 2.29-2.88% dose in PEG with significantly greater triazine absorption in MO than PEG when all additives and contaminates were present. The difference between these two diluents was most pronounced when NDELA and/or Ni were present in cutting fluids. These contaminants also enhanced triazine deposition on the skin surface and skin tissues especially with PEG-based mixtures. In essence, the dermal disposition of irritant biocides could be dependent on whether the worker is exposed to a soluble oil or synthetic fluid when these contaminants are present. Workers should therefore not only be concerned about dermatotoxicity of these contaminants, but also the modulated dermal disposition of cutting fluid additives when these contaminants are present in cutting fluid formulations.