Human Food Safety Implications of Variation in Food Animal Drug Metabolism

Citation: Lin, Z., Vahl, C. I., & Riviere, J. E. (2016). Human Food Safety Implications of Variation in Food Animal Drug Metabolism. Scientific Reports, 6. doi:10.1038/srep27907Violative drug residues in animal-derived foods are a global food safety concern. The use of a fixed main metabolite to parent drug (M/D) ratio determined in healthy animals to establish drug tolerances and withdrawal times in diseased animals results in frequent residue violations in food-producing animals. We created a general physiologically based pharmacokinetic model for representative drugs (ceftiofur, enrofloxacin, flunixin, and sulfamethazine) in cattle and swine based on extensive published literature. Simulation results showed that the M/D ratio was not a fixed value, but a time-dependent range. Disease changed M/D ratios substantially and extended withdrawal times; these effects exhibited drug-and species-specificity. These results challenge the interpretation of violative residues based on the use of the M/D ratio to establish tolerances for metabolized drugs

Serum Vitamin D Levels and Polycystic Ovary syndrome: A Systematic Review and Meta-Analysis

Citation: He, C. L., Lin, Z., Robb, S. W., & Ezeamama, A. E. (2015). Serum Vitamin D Levels and Polycystic Ovary syndrome: A Systematic Review and Meta-Analysis. Nutrients, 7(6), 4555-4577. doi:10.3390/nu7064555Vitamin D deficiency (VDD) is common in women with and without polycystic ovary syndrome (PCOS) and may be associated with metabolic and endocrine disorders in PCOS. The aim of this meta-analysis is to assess the associations of serum vitamin D levels with metabolic and endocrine dysregulations in women with PCOS, and to determine effects of vitamin D supplementation on metabolic and hormonal functions in PCOS patients. The literature search was undertaken through five databases until 16 January 2015 for both observational and experimental studies concerning relationships between vitamin D and PCOS. A total of 366 citations were identified, of which 30 were selected (n = 3182). We found that lower serum vitamin D levels were related to metabolic and hormonal disorders in women with PCOS. Specifically, PCOS patients with VDD were more likely to have dysglycemia (e.g., increased levels of fasting glucose and homeostatic model assessment-insulin resistance index (HOMA-IR)) compared to those without VDD. This meta-analysis found no evidence that vitamin D supplementation reduced or mitigated metabolic and hormonal dysregulations in PCOS. VDD may be a comorbid manifestation of PCOS or a minor pathway in PCOS associated metabolic and hormonal dysregulation. Future prospective observational studies and randomized controlled trials with repeated VDD assessment and better characterization of PCOS disease severity at enrollment are needed to clarify whether VDD is a co-determinant of hormonal and metabolic dysregulations in PCOS, represents a consequence of hormonal and metabolic dysregulations in PCOS or both

Short-term oral atrazine exposure alters the plasma metabolome of male C57BL/6 mice and disrupts α -linolenate, tryptophan, tyrosine and other major metabolic pathways

Overexposure to the commonly used herbicide atrazine (ATR) affects several organ systems, including the brain. Previously, we demonstrated that short-term oral ATR exposure causes behavioral deficits and dopaminergic and serotonergic dysfunction in the brains of mice. Using adult male C57BL/6 mice, the present study aimed to investigate effects of a 10-day oral ATR exposure (0, 5, 25, 125, or 250 mg/kg) on the mouse plasma metabolome and to determine metabolic pathways affected by ATR that may be reflective of ATR’s effects on the brain and useful to identify peripheral biomarkers of neurotoxicity. Four h after the last dosing on day 10, plasma was collected and analyzed with high-performance, dual chromatography-Fourier-transform mass spectrometry that was followed by biostatistical and bioinformatic analyses. ATR exposure (≥5 mg/kg) significantly altered plasma metabolite profile and resulted in a dose-dependent increase in the number of metabolites with ion intensities significantly different from the control group. Pathway analyses revealed that ATR exposure strongly correlated with and disrupted multiple metabolic pathways. Tyrosine, tryptophan, linoleic acid and α-linolenic acid metabolic pathways were among the affected pathways, with α-linolenic acid metabolism being affected to the greatest extent. Observed effects of ATR on plasma tyrosine and tryptophan metabolism may be reflective of the previously reported perturbations of brain dopamine and serotonin homeostasis, respectively. ATR-caused alterations in the plasma profile of α-linolenic acid metabolism are a potential novel and sensitive plasma biomarker of ATR effect and plasma metabolomics could be used to better assess the risks, including to the brain, associated with ATR overexposure

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

Nupr1/Chop signal axis is involved in mitochondrion-related endothelial cell apoptosis induced by methamphetamine

Citation: Cai, D., Huang, E., Luo, B., Yang, Y., Zhang, F., Liu, C., . . . Wang, H. (2016). Nupr1/Chop signal axis is involved in mitochondrion-related endothelial cell apoptosis induced by methamphetamine. Cell Death & Disease, 7, 14. http://doi.org/10.1038/cddis.2016.67Methamphetamine (METH) abuse has been a serious global public health problem for decades. Previous studies have shown that METH causes detrimental effects on the nervous and cardiovascular systems. METH-induced cardiovascular toxicity has been, in part, attributed to its destructive effect on vascular endothelial cells. However, the underlying mechanism of METH-caused endothelium disruption has not been investigated systematically. In this study, we identified a novel pathway involved in endothelial cell apoptosis induced by METH. We demonstrated that exposure to METH caused mitochondrial apoptosis in human umbilical vein endothelial cells and rat cardiac microvascular endothelial cells in vitro as well as in rat cardiac endothelial cells in vivo. We found that METH mediated endothelial cell apoptosis through Nupr1-Chop/P53-PUMA/Beclin1 signaling pathway. Specifically, METH exposure increased the expression of Nupr1, Chop, P53 and PUMA. Elevated p53 expression raised up PUMA expression, which initiated mitochondrial apoptosis by downregulating antiapoptotic Bcl-2, followed by upregulation of proapoptotic Bax, resulting in translocation of cytochrome c (cyto c), an apoptogenic factor, from the mitochondria to cytoplasm and activation of caspase-dependent pathways. Interestingly, increased Beclin1, upregulated by Chop, formed a ternary complex with Bcl-2, thereby decreasing the dissociative Bcl-2. As a result, the ratio of dissociative Bcl-2 to Bax was also significantly decreased, which led to translocation of cyto c and initiated more drastic apoptosis. These findings were supported by data showing METH-induced apoptosis was significantly inhibited by silencing Nupr1, Chop or P53, or by PUMA or Beclin1 knockdown. Based on the present data, a novel mechanistic model of METH-induced endothelial cell toxicity is proposed. Collectively, these results highlight that the Nupr1-Chop/P53-PUMA/Beclin1 pathway is essential for mitochondrion-related METH-induced endothelial cell apoptosis and may be a potential therapeutic target for METH-caused cardiovascular toxicity. Future studies using knockout animal models are warranted to substantiate the present findings

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

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

Citation: Lin, Z. M., Cuneo, M., Rowe, J. D., Li, M. J., Tell, L. A., Allison, S., . . . Gehring, R. (2016). Estimation of tulathromycin depletion in plasma and milk after subcutaneous injection in lactating goats using a nonlinear mixed-effects pharmacokinetic modeling approach. Bmc Veterinary Research, 12, 10. https://doi.org/10.1186/s12917-016-0884-4Background: 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

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

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

Machine Learning and Artificial Intelligence in Nanomedicine

Nanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge. Factors such as particle size, surface chemistry, and payload interactions must be optimized, and preclinical results often fail to predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure-function relationships, AI-driven models can rationalize nanoparticle formulation, predict biodistribution, and guide optimal design. Techniques like high-throughput DNA barcoding and automated liquid handling facilitate robust, large-scale data collection, feeding into computational pipelines that expedite discovery while reducing reliance on resource-intensive trial-and-error experiments. AI-based platforms also enable improved modeling of protein corona formation, which profoundly affects nanoparticle immunogenicity and cellular uptake. Despite these advances, challenges persist in data standardization, model generalizability, and establishing a clear regulatory framework since no dedicated U.S. Food and Drug Administration (FDA) guidance addresses the intersection of AI and nanomedicine. Overcoming these limitations requires harmonized data sharing, rigorous in vivo validation, and clear ethical and regulatory guidelines. This review summarizes the rapidly evolving landscape of AI in nanomedicine, highlighting key successes in design and preclinical prediction, as well as persistent obstacles to full-scale clinical integration. By illuminating these dynamics, we aim to chart a more efficient path forward in developing next-generation nanomedicine