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.

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.

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.

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.

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.

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.

Health concerns and management of select veterinary drug residues.

The aim of this manuscript is to review the potential adverse health effects in humans if exposed to residues of selected veterinary drugs used in food-producing animals. Our other objectives are to briefly inform the reader of why many of these drugs are or were approved for use in livestock production and how drug residues can be mitigated for these drugs. The selected drugs include several antimicrobials, beta agonists, and phenylbutazone. The antimicrobials continue to be of regulatory concern not only because of their acute adverse effects but also because their use as growth promoters have been linked to antimicrobial resistance. Furthermore, nitroimidazoles and arsenicals are no longer approved for use in food animals in most jurisdictions. In recent years, the risk assessment and risk management of beta agonists, have been the focus of national and international agencies and this manuscript attempts to review the pharmacology of these drugs and regulatory challenges. Several of the drugs selected for this review can cause noncancer effects (e.g., penicillins) and others are potential carcinogens (e.g., nitroimidazoles). This review also focuses on how regulatory and independent organizations manage the risk of these veterinary drugs based on data from human health risk assessments.

Oxidative stress response in canine in vitro liver, kidney and intestinal models with seven potential dietary ingredients.

In vitro cell culture systems are a useful tool to rapidly assess the potential safety or toxicity of chemical constituents of food. Here, we investigated oxidative stress and organ-specific antioxidant responses by 7 potential dietary ingredients using canine in vitro culture of hepatocytes, proximal tubule cells (CPTC), bone marrow-derived mesenchymal stem cells (BMSC) and enterocyte-like cells (ELC). Cellular production of free radical species by denatonium benzoate (DB), epigallocatechin gallate (EPI), eucalyptol (EUC), green tea catechin extract (GTE) and sodium copper chlorophyllin (SCC), tetrahydroisohumulone (TRA) as well as xylitol (XYL) were continuously measured for reactive oxygen/nitrogen species (ROS/RNS) and superoxide (SO) for up to 24h. DB and TRA showed strong prooxidant activities in hepatocytes and to a lesser degree in ELC. DB was a weak prooxidant in BMSC. In contrast DB and TRA were antioxidants in CPTC. EPI was prooxidant in hepatocytes and BMSC but showed prooxidant and antioxidant activity in CPTC. SCC in hepatocytes (12.5mg/mL) and CPTC (0.78mg/mL) showed strong prooxidant and antioxidant activity in a concentration-dependent manner. GTE was effective antioxidant only in ELC. EUC and XYL did not induce ROS/RNS in all 4 cell types. SO production by EPI and TRA increased in hepatocytes but decreased by SCC in hepatocytes and ELC. These results suggest that organ-specific responses to oxidative stress by these potential prooxidant compounds may implicate a mechanism of their toxicities.

Oxidative stress and dermal toxicity of iron oxide nanoparticles in vitro.

A number of commercially available metal/metal oxide nanoparticles (NPs) such as superparamagnetic iron oxide (SPION) are utilized by the medical field for a wide variety of applications. These NPs may able to induce dermal toxicity via their physical nature and reactive surface properties. We hypothesize that SPION may be toxic to skin via the ability of particles to be internalized and thereby initiate oxidative stress, inducing redox-sensitive transcription factors affecting/leading to inflammation. Due to the skin's susceptibility to UV radiation, it is also of importance to address the combined effect of UVB and NPs co-exposure. To test this hypothesis, the effects of dextran-coated SPION of different sizes (15-50 nm) and manufacturers (MicroMod, Rostock-Warnemunde, Germany and KTH-Royal Institute of Technology, Stockholm, Sweden) were evaluated in two cell lines: normal human epidermal keratinocytes (HEK) and murine epidermal cells (JB6 P(+)). HEK cells exposed to 20 nm (KTH and MicroMod) had a decrease in viability, while the 15 and 50 nm particles were not cytotoxic. HEK cells were also capable of internalizing the KTH particles (15 and 20 nm) but not the MicroMod SPION (20 and 50 nm). IL-8 and IL-6 were also elevated in HEK cells following exposure to SPION. Exposure of JB6 P(+) cells to all SPIONs evaluated resulted in activation of AP-1. Exposure to SPION alone was not sufficient to induce NF-κB activation; however, co-exposure with UVB resulted in significant NF-κB induction in cells exposed to 15 and 20 nm KTH SPION and 50 nm MicroMod particles. Pre-exposure of JB6 P(+) cells to UVB followed by NPs induced a significant depletion of glutathione, release of cytokines, and cell damage as assessed by release of lactate dehydrogenase. Altogether, these data indicate that co-exposure to UVB and SPIONs was associated with induction of oxidative stress and release of inflammatory mediators. These results verify the need to thoroughly evaluate the adverse effects of UVB when evaluating dermal toxicity of engineered NPs on skin.

Surface coatings determine cytotoxicity and irritation potential of quantum dot nanoparticles in epidermal keratinocytes.

Quantum dot (QD) nanoparticles have potential applications in nanomedicine as drug delivery vectors and diagnostic agents, but the skin toxicity and irritation potential of QDs are unknown. Human epidermal keratinocytes (HEKs) were used to assess if QDs with different surface coatings would cause differential effects on HEK cytotoxicity, proinflammatory cytokine release, and cellular uptake. Commercially available QDs of two different sizes, QD 565 and QD 655, with neutral (polyethylene glycol (PEG)), cationic (PEG-amine), or anionic (carboxylic acid) coatings were utilized. Live cell imaging and transmission electron microscopy were used to determine that all QDs localized intracellularly by 24 hours, with evidence of QD localization in the nucleus. Cytotoxicity and release of the proinflammatory cytokines IL-1beta, IL-6, IL-8, IL-10, and tumor necrosis factor-alpha were assessed at 24 and 48 hours. Cytotoxicity was observed for QD 565 and QD 655 coated with carboxylic acids or PEG-amine by 48 hours, with little cytotoxicity observed for PEG-coated QDs. Only carboxylic acid-coated QDs significantly increased release of IL-1beta, IL-6, and IL-8. These data indicate that QD surface coating is a primary determinant of cytotoxicity and immunotoxicity in HEKs, which is consistent across size. However, uptake of QDs by HEKs is independent of surface coating.

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.

A novel in-vitro technique for studying percutaneous permeation with a membrane-coated fiber and gas chromatography/mass spectrometry: part I. Performances of the technique and determination of the permeation rates and partition coefficients of chemical mixtures.

PURPOSE: To develop a novel in-vitro technique for rapid assessment of percutaneous absorption of chemical mixtures. METHODS: A silastic membrane was coated on to a fiber to be used as a permeation membrane. The membrane-coated fiber was immersed in the donor phase to partition the compounds into the membrane. At a given partition time, the membrane-coated fiber was transferred into a GC injector to evaporate the partitioned compounds for quantitative and qualitative analyses. RESULTS: This technique was developed and demonstrated to study the percutaneous permeation of a complex mixture consisting of 30 compounds. Each compound permeated into the membrane was identified and quantified with GC/MS. The standard deviation was less than 10% in 12 repeated permeation experiments. The partition coefficients and permeation rates in static and stirred donor solution were obtained for each compound. The partition coefficients measured by this technique were well correlated (R2 = 0.93) with the reported octanol/water partition coefficients. CONCLUSIONS: This technique can be used to study the percutaneous permeation of chemical mixtures. No expensive radiolabeled chemicals are required. Each compound permeated into the membrane can be identified and quantified. The initial permeation rate and equilibrium time can be obtained for each compound, which could serve as characteristic parameters regarding the skin permeability of the compound.

Pyridostigmine bromide modulates topical irritant-induced cytokine release from human epidermal keratinocytes and isolated perfused porcine skin.

Gulf War personnel were given pyridostigmine bromide (PB) as a prophylactic treatment against organophosphate nerve agent exposure, and were exposed to the insecticide permethrin and the insect repellent N,N-diethyl-m-toluamide (DEET). The purpose of this study was to assess the effects of PB to modulate release of inflammatory biomarkers after topical chemical exposure to chemical mixtures containing permethrin and DEET applied in ethanol or water vehicles. Treatments were topically applied to isolated perfused porcine skin flaps (IPPSFs). Concentrations of interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-alpha) and prostaglandin E(2) (PGE(2)) were assayed in perfusate to probe for potential inflammatory effects after complex mixture application. IPPSFs (n=4/treatment) were topically dosed with mixtures of permethrin, DEET, and permethrin/DEET, in ethanol. Each treatment was repeated with perfusate spiked with 50 ng/ml of PB. Perfusate was also spiked with 30 ng/ml diisopropylfluorophosphate to simulate low level organophosphate nerve agent exposure. Timed IPPSF venous effluent samples (0.5,1,2,4, and 8 h) were assayed by ELISA for IL-8 and TNF-alpha and by EIA for PGE(2). Overall, PB infusion caused a decrease or IL-8 and PGE(2) release. Effects on TNF-alpha were vehicle dependent. To probe the potential mechanism of this PB effect, human epidermal keratinocyte HEK cell cultures were exposed to permethrin DEET permethrin/DEET, with and without PB in DMSO. IL-8 was assayed at 1, 2, 4, 8, 12 and 24 h. PB suppressed IL-8 in permethrin and ethanol treatment from 4 to 24 h confirming the IPPSF results. In conclusion, these studies suggest that systemic exposure to PB suppressed IL-8 release at multiple time points in two skin model systems. This interaction merits further study.