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

Residue depletion profiles and withdrawal intervals of florfenicol and its metabolite florfenicol amine in plasma and milk of lactating goats after repeated subcutaneous administrations

Florfenicol is a broad-spectrum and bacteriostatic antibiotic with a time-dependent killing action. It is commonly used to treat respiratory diseases in goats in an extra-label manner. This study aimed to determine the plasma pharmacokinetics and milk residue depletion profiles and calculate the milk withdrawal interval (WDI) of florfenicol and its main metabolite florfenicol amine in lactating goats. Five healthy lactating goats were administered with 40 mg/kg florfenicol by subcutaneous injection, twice, 96 h apart. Plasma and milk samples were collected up to 864 h post the first injection. Non-compartmental analysis was used to estimate the plasma pharmacokinetic parameters. Milk WDIs were calculated using the U.S. Food and Drug Administration (FDA) method and European Medicines Agency (EMA) method. A Monte Carlo simulation was performed to generate simulated data for five virtual animals to meet the data requirement of the FDA method. The calculated milk WDIs based on florfenicol, florfenicol amine, and the combined (the sum of florfenicol and florfenicol amine) were 720.28, 690.45, and 872.69 h after the last injection using the FDA method. In conclusion, this study improves our understanding on the plasma pharmacokinetics and milk residue depletion kinetics of florfenicol and florfenicol amine in lactating ruminants after subcutaneous injections.

Development and application of a physiologically-based pharmacokinetic model for ractopamine in goats

Physiologically Based Pharmacokinetic (PBPK) models can provide forecasts of the drug residues within the organism. Ractopamine (RAC) is a typical β-agonist. In this study, we developed a PBPK model for RAC in goats. The goal was to predict the distribution of the drug after multiple oral administrations. The preliminary PBPK model for RAC in goats performed well in predicting the drug's distribution in most tissues. In our sensitivity analysis, we found that the parameter of Qclu (Blood Flow Volume through Lungs) had the greatest impact on the RAC concentrations in plasma, liver, and kidney and was the most sensitive parameter. Furthermore, our study aimed to assess the withdrawal time (WT) of RAC in different tissues after RAC long-term exposure in goats. We found that the WT of RAC in the kidney was the longest, lasting for 13  days. Overall, the insights gained from this study have important implications for optimizing drug administration in goats and ensuring appropriate withdrawal times to prevent any potential risks.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against Streptococcus suis

Introduction: Streptococcus suis (S. suis) is a zoonotic pathogen threatening public health. Aditoprim (ADP), a novel veterinary medicine, exhibits an antibacterial effect against S. suis. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was used to determine the dosage regimens of ADP against S. suis and withdrawal intervals. Methods: The PBPK model of ADP injection can predict drug concentrations in plasma, liver, kidney, muscle, and fat. A semi-mechanistic pharmacodynamic (PD) model, including susceptible subpopulation and resistant subpopulation, is successfully developed by a nonlinear mixed-effect model to evaluate antibacterial effects. An integrated PBPK/PD model is conducted to predict the time-course of bacterial count change and resistance development under different ADP dosages. Results: ADP injection, administrated at 20 mg/kg with 12 intervals for 3 consecutive days, can exert an excellent antibacterial effect while avoiding resistance emergence. The withdrawal interval at the recommended dosage regimen is determined as 18 days to ensure food safety. Discussion: This study suggests that the PBPK/PD model can be applied as an effective tool for the antibacterial effect and safety evaluation of novel veterinary drugs.

Pharmacokinetics, tissue residue depletion, and withdrawal interval estimations of florfenicol in goats following repeated subcutaneous administrations

Florfenicol is a broad-spectrum antibiotic commonly used in the U.S. to treat respiratory and enteric infections in goats in an extra-label manner, which requires scientifically based withdrawal intervals (WDIs) for edible tissues. This study aimed to determine the depletion profiles for florfenicol and florfenicol amine in plasma and tissues samples and to estimate WDIs for goats following subcutaneous injection of 40 mg/kg florfenicol, twice, 96 h apart. The samples were collected up to 50 days after the second dose. Pharmacokinetic parameters were calculated using non-compartmental analysis. Three different pharmacostatistical methods with different operational tolerances were used to calculate WDIs. The plasma half-life was 101.80 h for florfenicol and 207.69 h for florfenicol amine after the second dose. Using the FDA tolerance limit method, WDIs were 202 and 101 days, while the EMA maximum residue limit method estimated 179 and 96 days for the respective tissue concentrations to fall below limits of detection (0.12 μg/g for liver and 0.05 μg/g for kidney). This study characterizes plasma pharmacokinetics and tissue depletion profiles of florfenicol and florfenicol amine in goats following subcutaneous injections and reports estimated WDIs for food safety assessment of florfenicol in goats.

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.

A physiologically based pharmacokinetic model to optimize the dosage regimen and withdrawal time of cefquinome in pigs

Cefquinome is widely used to treat respiratory tract diseases of swine. While extra-label dosages of cefquinome could improve clinical efficacy, they might lead to excessively high residues in animal-derived food. In this study, a physiologically based pharmacokinetic (PBPK) model was calibrated based on the published data and a microdialysis experiment to assess the dosage efficiency and food safety. For the microdialysis experiment, in vitro/in vivo relative recovery and concentration-time curves of cefquinome in the lung interstitium were investigated. This PBPK model is available to predict the drug concentrations in the muscle, kidney, liver, plasma, and lung interstitial fluid. Concentration-time curves of 1000 virtual animals in different tissues were simulated by applying sensitivity and Monte Carlo analyses. By integrating pharmacokinetic/pharmacodynamic target parameters, cefquinome delivered at 3-5 mg/kg twice daily is advised for the effective control of respiratory tract infections of nursery pig, which the bodyweight is around 25 kg. Based on the predicted cefquinome concentrations in edible tissues, the withdrawal interval is 2 and 3 days for label and the extra-label doses, respectively. This study provides a useful tool to optimize the dosage regimen of cefquinome against respiratory tract infections and predicts the concentration of cefquinome residues in edible tissues. This information would be helpful to improve the food safety and guide rational drug usage.

Development of a physiologically based pharmacokinetic model to predict irinotecan disposition during inflammation

Irinotecan, a first-line chemotherapy for gastrointestinal (GI) cancers has been causing fatal toxicities like bloody diarrhea and steatohepatitis for years. Irinotecan goes through multiple-step drug metabolism after injection and one of its intermediates 7-ethyl-10-hydroxy-camptothecin (SN-38) is responsible for irinotecan side effect. However, it is unclear what is the disposition kinetics of SN-38 in the organs subjected to toxicity. No studies ever quantified the effect of each enzyme or transporter on SN-38 distribution. In current study, we established a new physiologically based pharmacokinetic (PBPK) model to predict the disposition kinetics of irinotecan. The PBPK model was calibrated with in-house mouse pharmacokinetic data and evaluated with external datasets from the literature. We separated the contribution of each parameters in irinotecan pharmacokinetics by calculating the normalized sensitivity coefficient (NSC). The model gave robust prediction of SN-38 distribution in GI tract, the site of injury. We identified that bile excretion and UDP-glucuronosyltransferases (UGT) played more important roles than fecal excretion and renal clearance in SN-38 pharmacokinetics. Our NSC showed that the impact of enzyme and transporter on irinotecan and SN-38 pharmacokinetics evolved when time continued. Additionally, we mapped out the effect of inflammation on irinotecan metabolic pathways with PBPK modelling. We discovered that inflammation significantly increased the blood and liver exposure of irinotecan and SN-38 in the mice receiving bacterial endotoxin. Inflammation suppressed UGT, microbial metabolism but increased fecal excretion. The present PBPK model can serve as an efficacious and versatile tool to quantitively assess the risk of irinotecan toxicity.

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.