Exploring the potential and challenges of developing physiologically-based toxicokinetic models to support human health risk assessment of microplastic and nanoplastic particles.

Microplastics (MPs) and nanoplastics (NPs) pollution has emerged as a significant and widespread environmental issue. Humans are inevitably exposed to MPs and NPs via ingestion, inhalation, and dermal contacts from various sources. However, mechanistic knowledge of their distribution, interaction, and potency in the body is still lacking. To address this knowledge gap, we have undertaken the task of elucidating the toxicokinetic (TK) behaviors of MPs and NPs, aiming to provide mechanistic information for constructing a conceptual physiologically based toxicokinetic (PBTK) model to support in silico modeling approaches. Our effort involved a thorough examination of the existing literature and data collation on the presence of MPs in the human body and in vitro/ex vivo/in vivo biodistribution across various cells and tissues. By comprehending the absorption, distribution, metabolism, and excretion mechanisms of MPs and NPs in relation to their physicochemical attributes, we established a foundational understanding of the link between external exposure and internal tissue dosimetry. We observed that particle size and surface chemistry have been thoroughly explored in previous experimental studies. However, certain attributes, such as polymer type, shape, and biofilm/biocorona, warrant attention and further examination. We discussed the fundamental disparities in TK properties of MPs/NPs from those of engineered nanoparticles. We proposed a preliminary PBTK framework with several possible modeling approaches and discussed existing challenges for further investigation. Overall, this article provides a comprehensive compilation of existing TK data of MPs/NPs, a critical overview of TK processes and mechanisms, and proposes potential PBTK modeling approaches, particularly regarding their applicability to the human system, and outlines future perspectives for developing PBTK models and their integration into human health risk assessment of MPs and NPs.

Impact of protein coronas on nanoparticle interactions with tissues and targeted delivery.

A major challenge in advancing nanoparticle (NP)-based delivery systems stems from the intricate interactions between NPs and biological systems. These interactions are largely determined by the formation of the NP-protein corona (PC), in which proteins spontaneously adsorb to the surface of NPs. The PC endows the NPs with a new biological identity, capable of altering the interactions of NPs with targeting organs and subsequent biological fate. This review discusses the mechanisms behind PC-mediated effects on tissue distribution of NPs, aiming to provide insights into the role of PC and its potential applications in NP-based drug delivery.

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.

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.

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.

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.

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.

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.

Pharmacokinetics, Milk Residues, and Toxicological Evaluation of a Single High Dose of Meloxicam Administered at 30 mg/kg per os to Lactating Dairy Cattle.

Adverse effects associated with overdose of NSAIDs are rarely reported in cattle, and the risk level is unknown. If high doses of NSAIDs can be safely administered to cattle, this may provide a longer duration of analgesia than using current doses where repeated administration is not practical. Meloxicam was administered to 5 mid-lactation Holstein dairy cows orally at 30 mg/kg, which is 30 times higher than the recommended 1 mg/kg oral dose. Plasma and milk meloxicam concentrations were determined using high-pressure liquid chromatography with mass spectroscopy (HPLC-MS). Pharmacokinetic analysis was performed by using noncompartmental analysis. The geometric mean maximum plasma concentration (Cmax) was 91.06 µg/mL at 19.71 h (Tmax), and the terminal elimination half-life (T1/2) was 13.79 h. The geometric mean maximum milk concentration was 33.43 µg/mL at 23.74 h, with a terminal elimination half-life of 12.23 h. A thorough investigation into the potential adverse effects of a meloxicam overdose was performed, with no significant abnormalities reported. The cows were humanely euthanized at 10 d after the treatment, and no gross or histologic lesions were identified. As expected, significantly higher plasma and milk concentrations were attained after the administration of 30 mg/kg meloxicam with similar half-lives to previously published reports. However, no identifiable adverse effects were observed with a drug dose 30 times greater than the industry uses within 10 days of treatment. More research is needed to determine the tissue withdrawal period, safety, and efficacy of meloxicam after a dose of this magnitude in dairy cattle.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling.

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

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.

A web-based interactive physiologically based pharmacokinetic (iPBPK) model for meloxicam in broiler chickens and laying hens.

Meloxicam is a non-steroidal anti-inflammatory drug (NSAID) commonly used in food-producing animals, including chickens in an extralabel manner. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model for meloxicam in broiler chickens and laying hens to facilitate withdrawal interval (WDI) estimations. The model structure for broiler chickens contained six compartments including plasma, muscle, liver, kidney, fat and rest of body, while an additional compartment of ovary was included for laying hens. The model adequately simulated available pharmacokinetic data of meloxicam in plasma of broiler chickens as well as tissue and egg data of laying hens. The model was converted to a web-based interface and used to predict WDIs following extralabel administrations. The results showed that the estimated WDIs were 50, 44, 11, 3, 3, 22 and 4 days for liver, kidney, muscle, fat, ovary, yolk and white, respectively in laying hens after 14 repeated oral administrations of meloxicam (1 mg/kg) at 24-h intervals. This model provides a useful and flexible tool for risk assessment and management of residues for meat and eggs from chickens treated with meloxicam and will serve as a basis for extrapolation to other NSAID drugs and other poultry species to aid animal-derived food safety assessment.

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.

Machine Learning and Artificial Intelligence in Toxicological Sciences.

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.

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.

Residue depletion profiles and withdrawal interval estimations of meloxicam in eggs and ovarian follicles following intravenous (Meloxicam solution for injection) and oral (Meloxidyl®) administration in domestic chickens (Gallus domesticus).

Meloxicam is a non-steroidal anti-inflammatory drug (NSAID) commonly prescribed in an extralabel manner for treating chickens in urbanized settings. The objectives of this study were to determine meloxicam depletion profiles in eggs and ovarian follicles and to estimate associated withdrawal intervals (WDI) in laying hens following a single intravenous or repeated oral administration. The observed peak concentration of meloxicam in ovarian follicles were consistently higher than in egg yolk and egg white samples. Terminal half-lives were 31-h, 113-h and 12-h in ovarian follicles, egg yolk and egg white samples, respectively, for repeated oral administrations at 1 mg/kg for 20 doses at 12-h intervals. The terminal half-life following a single intravenous administration at 1 mg/kg was 50-h for ovarian follicles. Meloxicam WDI estimations using ovarian follicle and egg yolk concentration data following 20 doses at 12-h intervals were 36 and 12 days, respectively. Meloxicam WDI estimation using egg yolk concentration data following 8 doses at 24-h intervals was 12 days. These results improve our understanding on the residue depletion of meloxicam from chickens' reproductive tracts and egg products and provide WDIs to help ensure food safety for humans consuming eggs from treated laying hens.

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 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.