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.

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.

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.

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.

Pharmacovigilance in patients with diabetes: A data-driven analysis identifying specific RAS antagonists with adverse pulmonary safety profiles that have implications for COVID-19 morbidity and mortality.

OBJECTIVES: The current demographic information from China reports that 10%-19% of patients hospitalized with coronavirus disease (COVID-19) were diabetic. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are considered first-line agents in patients with diabetes because of their nephroprotective effects, but administration of these drugs leads to upregulation of angiotensin-converting enzyme 2 (ACE2), which is responsible for the viral entry of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). Data are lacking to determine what pulmonary effects ACEIs or ARBs may have in patients with diabetes, which could be relevant in the management of patients infected with SARS-CoV-2. This study aims to assess the prevalence of pulmonary adverse drug effects (ADEs) in patients with diabetes who were taking ACEI or ARBs to provide guidance as to how these medications could affect outcomes in acute respiratory illnesses such as SARS-CoV-2 infection. METHODS: 1DATA, a unique data platform resulting from collaboration across veterinary and human health care, used an intelligent medicine recommender system (1DrugAssist) developed using several national and international databases to evaluate all ADEs reported to the Food and Drug Administration for patients with diabetes taking ACEIs or ARBs. RESULTS: Mining of this data elucidated the proportion of a cluster of pulmonary ADEs associated with specific medications in these classes, which may aid health care professionals in understanding how these medications could worsen or predispose patients with diabetes to infections affecting the respiratory system, specifically COVID-19. Based on this data mining process, captopril was found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs (P = 0.005) as well as ARBs (P = 0.012), though other specific drugs also had important pulmonary ADEs associated with their use. CONCLUSION: These analyses suggest that pharmacists and clinicians will need to consider the specific medication's adverse event profile, particularly captopril, on how it may affect infections and other acute disease states that alter pulmonary function, such as COVID-19.

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.

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.

Integration of Food Animal Residue Avoidance Databank (FARAD) empirical methods for drug withdrawal interval determination with a mechanistic population-based interactive physiologically based pharmacokinetic (iPBPK) modeling platform: example for flunixin meglumine administration.

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

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.

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 (Kb = 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.

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.

Influence of some plant extracts on the transdermal absorption and penetration of marker penetrants.

CONTEXT: Plant extracts are commonly used in a number of cosmetics and topical pharmaceuticals. The effects on such extracts on the subsequent dermal absorption and penetration of other cosmetic ingredients needs to be evaluated. OBJECTIVE: This study demonstrates the effect of some natural extracts routinely found in cosmetics on the dermal absorption and penetration of marker penetrants. METHODS: Aqueous ethanolic extracts of Gingko biloba, Lavendula angustifolia, Rosmarinus officinale, Mentha piperita, Matricaria recutita, Persea Americana, Avena sativa, Zingiber officinale were prepared. 14C-caffeine and 14C-salicylic acid were topically dosed with either 10% solutions of natural extracts or ethanol (control) using a flow through in vitro porcine skin diffusion system. Samples were analyzed with liquid scintillation counter. The parameters of flux, permeability, and percent dose absorbed/retained were calculated and compared. RESULTS: The dermal absorption of 14C-caffeine was significantly higher (p ≥ 0.05) with avocado, chamomile, ginger and peppermint extract as compared to the control ethanol; while dermal absorption of 14C-salicylic acid was significantly greater with ginkgo and chamomile extract as compared to ethanol. Over four fold increase in flux and permeability of caffeine with avocado extract was observed while chamomile and peppermint extracts increased the flux and permeability of caffeine over three fold. Gingko and chamomile extracts increased salicylic acid's flux and permeability by two fold. Sum of %dose skin residue + %absorption in receptor fluid for different extracts exhibited the similar trend as shown by flux and permeability. The other natural extracts tested did not produce statistically significant effects on dermal penetration parameters for both caffeine and salicylic acid (p ≥ 0.05). CONCLUSION: These results emphasize the influence of natural plant extracts on the transdermal penetration of hydrophilic (caffeine) and hydrophobic (salicylic acid) penetrants and thus warrants the consideration as to their safety in cosmetics and topical pharmaceuticals containing natural extracts.

Modulation of chemical dermal absorption by 14 natural products: a quantitative structure permeation analysis of components often found in topical preparations.

A large number of cosmetics and topical pharmaceuticals contain compounds of natural origin. There is a rising concern if these compounds can interact with the activity of other topically applied components in these formulations. The current study demonstrates modulation of dermal absorption of model components often found in topical preparations (14C caffeine and 14C salicylic acid) by a set of 14 compounds of natural origin using a flow through in vitro porcine skin diffusion system. The parameters of flux and permeability were calculated and quantitative structure permeation relationship (QSPR) analysis conducted on different molecular descriptors of modulator compounds. Terpinyl acetate was the greatest permeability/flux enhancer for caffeine followed by s-perillyl acetate and limonene 1,2-epoxide. The permeability/flux of salicylic acid was highest with hydroxycitronellal followed by limonene 1,2-epoxide and s-perillyl acetate. The optimum descriptors using stepwise regression analysis for predicting additive modulation on penetrant permeability/flux were polar surface area, log P for caffeine and Henry's Law constant, number of freely rotatable bonds, and water solubility for salicylic acid. In parallel with the experimental techniques, a novel mathematical model was developed to estimate the permeability coefficients and improve the stepwise regression analysis for assessing modulator effects. The r2 values significantly increased for multicomponent QSPR models. Notably, limonene 1,2-epoxide and s-perillyl acetate were excellent enhancers for both caffeine and salicylic acid. These results confirm that some natural products incorporated into topical formulations will enhance absorption of other components which could affect their safety and efficacy profiles.

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.

Assessment of penetrant and vehicle mixture properties on transdermal permeability using a mixed effect pharmacokinetic model of ex vivo porcine skin.

The accurate prediction of the rate and extent of transdermal absorption from topical exposure to chemical mixtures would be beneficial in risk assessment and drug delivery applications. The isolated perfused porcine skin flap (IPPSF) has been used as an ex vivo model for assessing transdermal absorption from topical exposures. A mixed effect, pharmacokinetic tissue model was used to model finite dose, transdermal, absorption data from IPPSF experiments for 12 penetrants dosed in up to 10 different vehicles. The model was able to identify permeability constant, while accounting for between and within unit variability, across the entire data set. This approach provides a platform for exploring the relationship between covariates (chemical descriptors and functions thereof) and the model parameters. Successive models were employed that reduced the overall variability in the parameter estimate by modeling the parameters as functions of the covariates. Log kp was initially modeled as a function of LogP and MW of the pure penetrant (adjusted r2  = 0.48). The addition of mixture factors to account for the different dosing vehicles further improved the relationship: to r2  = 0.56 with Connolly molecular area (CMA) and r2  = 0.78 with the further addition of total polar surface area difference (TPSAd). The pharmacokinetic model and quantitative structure property relationship (QSPR) developed for the IPPSF may be relevant to clinical transdermal formulation development. Copyright © 2016 John Wiley & Sons, Ltd.

Exploring Post-Treatment Reversion of Antimicrobial Resistance in Enteric Bacteria of Food Animals as a Resistance Mitigation Strategy.

Antimicrobial drug use in food animals is associated with an elevation in relative abundance of bacteria resistant to the drug among the animal enteric bacteria. Some of these bacteria are potential foodborne pathogens. Evidence suggests that at least in the enteric nontype-specific Escherichia coli, after treatment the resistance abundance reverts to the background pre-treatment levels, without further interventions. We hypothesize that it is possible to define the distribution of the time period after treatment within which resistance to the administered drug, and possibly other drugs in case of coselection, in fecal bacteria of the treated animals returns to the background pre-treatment levels. Furthermore, it is possible that a novel resistance mitigation strategy for microbiological food safety could be developed based on this resistance reversion phenomenon. The strategy would be conceptually similar to existing antimicrobial drug withdrawal periods, which is a well-established and accepted mitigation strategy for avoiding violative drug residues in the edible products from the treated animals. For developing resistance-relevant withdrawals, a mathematical framework can be used to join the necessary pharmacological, microbiological, and animal production components to project the distributions of the post-treatment resistance reversion periods in the production animal populations for major antimicrobial drug classes in use. The framework can also help guide design of empirical studies into the resistance-relevant withdrawal periods and development of mitigation approaches to reduce the treatment-associated elevation of resistance in animal enteric bacteria. We outline this framework, schematically and through exemplar equations, and how its components could be formulated.

Interspecies mixed-effect pharmacokinetic modeling of penicillin G in cattle and swine.

Extralabel drug use of penicillin G in food-producing animals may cause an excess of residues in tissue which will have the potential to damage human health. Of all the antibiotics, penicillin G may have the greatest potential for producing allergic responses to the consumer of food animal products. There are, however, no population pharmacokinetic studies of penicillin G for food animals. The objective of this study was to develop a population pharmacokinetic model to describe the time-concentration data profile of penicillin G across two species. Data were collected from previously published pharmacokinetic studies in which several formulations of penicillin G were administered to diverse populations of cattle and swine. Liver, kidney, and muscle residue data were also used in this study. Compartmental models with first-order absorption and elimination were fit to plasma and tissue concentrations using a nonlinear mixed-effect modeling approach. A 3-compartment model with extra tissue compartments was selected to describe the pharmacokinetics of penicillin G. Typical population parameter estimates (interindividual variability) were central volumes of distribution of 3.45 liters (12%) and 3.05 liters (8.8%) and central clearance of 105 liters/h (32%) and 16.9 liters/h (14%) for cattle and swine, respectively, with peripheral clearance of 24.8 liters/h (13%) and 9.65 liters/h (23%) for cattle and 13.7 liters/h (85%) and 0.52 liters/h (40%) for swine. Body weight and age were the covariates in the final pharmacokinetic models. This study established a robust model of penicillin for a large and diverse population of food-producing animals which could be applied to other antibiotics and species in future analyses.

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.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

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.