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Journal articles on the topic 'Toxicodynamie'

Author: Grafiati

Published: 22 June 2023

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1

Kasteel, Emma E. J., Sandra M. Nijmeijer, Keyvin Darney, et al. "Acetylcholinesterase inhibition in electric eel and human donor blood: an in vitro approach to investigate interspecies differences and human variability in toxicodynamics." Archives of Toxicology 94, no. 12 (2020): 4055–65. http://dx.doi.org/10.1007/s00204-020-02927-8.

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Abstract In chemical risk assessment, default uncertainty factors are used to account for interspecies and interindividual differences, and differences in toxicokinetics and toxicodynamics herein. However, these default factors come with little scientific support. Therefore, our aim was to develop an in vitro method, using acetylcholinesterase (AChE) inhibition as a proof of principle, to assess both interspecies and interindividual differences in toxicodynamics. Electric eel enzyme and human blood of 20 different donors (12 men/8 women) were exposed to eight different compounds (chlorpyrifos, chlorpyrifos-oxon, phosmet, phosmet-oxon, diazinon, diazinon-oxon, pirimicarb, rivastigmine) and inhibition of AChE was measured using the Ellman method. The organophosphate parent compounds, chlorpyrifos, phosmet and diazinon, did not show inhibition of AChE. All other compounds showed concentration-dependent inhibition of AChE, with IC50s in human blood ranging from 0.2–29 µM and IC20s ranging from 0.1–18 µM, indicating that AChE is inhibited at concentrations relevant to the in vivo human situation. The oxon analogues were more potent inhibitors of electric eel AChE compared to human AChE. The opposite was true for carbamates, pointing towards interspecies differences for AChE inhibition. Human interindividual variability was low and ranged from 5–25%, depending on the concentration. This study provides a reliable in vitro method for assessing human variability in AChE toxicodynamics. The data suggest that the default uncertainty factor of ~ 3.16 may overestimate human variability for this toxicity endpoint, implying that specific toxicodynamic-related adjustment factors can support quantitative in vitro to in vivo extrapolations that link kinetic and dynamic data to improve chemical risk assessment.

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2

Boak, Lauren M., Craig R. Rayner, M. Lindsay Grayson, et al. "Clinical Population Pharmacokinetics and Toxicodynamics of Linezolid." Antimicrobial Agents and Chemotherapy 58, no. 4 (2014): 2334–43. http://dx.doi.org/10.1128/aac.01885-13.

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ABSTRACTThrombocytopenia is a common side effect of linezolid, an oxazolidinone antibiotic often used to treat multidrug-resistant Gram-positive bacterial infections. Various risk factors have been suggested, including linezolid dose and duration of therapy, baseline platelet counts, and renal dysfunction; still, the mechanisms behind this potentially treatment-limiting toxicity are largely unknown. A clinical study was conducted to investigate the relationship between linezolid pharmacokinetics and toxicodynamics and inform strategies to prevent and manage linezolid-associated toxicity. Forty-one patients received 42 separate treatment courses of linezolid (600 mg every 12 h). A new mechanism-based, population pharmacokinetic/toxicodynamic model was developed to describe the time course of plasma linezolid concentrations and platelets. A linezolid concentration of 8.06 mg/liter (101% between-patient variability) inhibited the synthesis of platelet precursor cells by 50%. Simulations predicted treatment durations of 5 and 7 days to carry a substantially lower risk than 10- to 28-day therapy for platelet nadirs of <100 ×109/liter. The risk for toxicity did not differ noticeably between 14 and 28 days of therapy and was significantly higher for patients with lower baseline platelet counts. Due to the increased risk of toxicity after longer durations of linezolid therapy and large between-patient variability, close monitoring of patients for development of toxicity is important. Dose individualization based on plasma linezolid concentration profiles and platelet counts should be considered to minimize linezolid-associated thrombocytopenia. Overall, oxazolidinone therapy over 5 to 7 days even at relatively high doses was predicted to be as safe as 10-day therapy of 600 mg linezolid every 12 h.

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3

Blanchette, Alexander D., Sarah D. Burnett, Fabian A. Grimm, Ivan Rusyn, and Weihsueh A. Chiu. "A Bayesian Method for Population-wide Cardiotoxicity Hazard and Risk Characterization Using an In Vitro Human Model." Toxicological Sciences 178, no. 2 (2020): 391–403. http://dx.doi.org/10.1093/toxsci/kfaa151.

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Abstract Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Interindividual variability in toxicodynamic sensitivity has also been demonstrated in vitro; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 54 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 nondiseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for 5 phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors. Toxicokinetic modeling was used for probabilistic in vitro-to-in vivo extrapolation to derive population-wide margins of safety for pharmaceuticals and margins of exposure for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. Toxicodynamic variability factor estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally assumed default of approximately 3. Population variability-based margins of safety for pharmaceuticals were correctly predicted to be relatively narrow, including some below 10; however, margins of exposure for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at current general population exposures even to sensitive subpopulations. Overall, this study demonstrates how a high-throughput, human population-based, in vitro-in silico model can be used to characterize toxicodynamic population variability in cardiotoxic risk.

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4

Kavlock, Robert J., and Gabriel L. Plaa. "Session summary: toxicodynamic interactive mechanisms." Toxicology 105, no. 2-3 (1995): 235–36. http://dx.doi.org/10.1016/0300-483x(95)03218-5.

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5

Ashauer, Roman, and Colin Brown. "TOXICODYNAMIC ASSUMPTIONS IN ECOTOXICOLOGICAL HAZARD MODELS." Environmental Toxicology and Chemistry preprint, no. 2008 (2007): 1. http://dx.doi.org/10.1897/07-642.

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6

Ashauer, Roman, and Colin D. Brown. "TOXICODYNAMIC ASSUMPTIONS IN ECOTOXICOLOGICAL HAZARD MODELS." Environmental Toxicology and Chemistry 27, no. 8 (2008): 1817. http://dx.doi.org/10.1897/07-642.1.

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7

Serkova, Natalie J., and Uwe Christians. "Biomarkers for Toxicodynamic Monitoring of Immunosuppressants." Therapeutic Drug Monitoring 27, no. 6 (2005): 733–37. http://dx.doi.org/10.1097/01.ftd.0000179846.30342.65.

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8

Gots, Ronald E., and Suellen W. Pirages. "Multiple Chemical Sensitivities: Psychogenic or Toxicodynamic Origins." International Journal of Toxicology 18, no. 6 (1999): 393–400. http://dx.doi.org/10.1080/109158199225107.

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The multiple chemical sensitivity (MCS) phenomenon can cause significant dysfunction and symptomatology and presents a difficult challenge for patient management. Central to the MCS debate is whether this phenomenon results from a primary emotional response to perceived chemical exposures or from pathological interactions between chemicals and biological systems. Those who believe the latter argue that toxic interactions result in physiological impairment and that subsequent emotional problems derive from such impairment. Distinguishing between psychogenic (emotional) or a toxicodynamic (chemical toxicity) origin is essential to the medical management of an MCS patient. A psychogenic basis requires treatment with appropriate behavioral therapies; in contrast, a belief in a strictly toxicodynamic etiology argues for avoidance and often precludes treatments that address the psychological responses. Current scientific evidence strongly suggests that behavioral or psychogenic explanations predominate for reported MCS symptoms. Acceptance of a purely toxic origination (i.e., pathological abnormalities result from a low level chemical exposure) defies known toxicological and medical principles; whereas psychogenic explanations are consistent with these principles. Because symptoms are the end points of many diseases with many causes, both physical and emotional, modern medicine is charged with and expected to consider both when treating MCS patients. The argument can be made that insufficient information exists about the causal nature of many diseases, and future research may provide support for a strict toxicodynamic cause. However, the practice of medicine must be based upon current knowledge, not future possibilities. Proper care of MCS patients requires identifying the existence of both psychological and organic pathological dysfunction. The rejection of a psychological aspect of the MCS phenomenon and appropriate behavioral treatments is both illogical and detrimental to MCS sufferers.

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9

Reeves, Andrew L. "Beryllium: Toxicological Research of the Last Decade." Journal of the American College of Toxicology 8, no. 7 (1989): 1307–13. http://dx.doi.org/10.3109/10915818909009122.

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10

Danilov-Danilyan, V. I., and O. M. Rozental. "Logistic Model of Population Toxicodynamics." Water Resources 49, no. 2 (2022): 231–39. http://dx.doi.org/10.1134/s0097807822020038.

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Abstract The effect of pollutant in water on a population of aquatic organisms as a function of exposure time is studied. Natural assumptions are formulated regarding the character of this process, primarily, the linear relationship between the rate of decrease in the population, on the one hand, and the population size and the number of organisms killed by intoxication, on the other hand. The formulated assumptions are used to construct a model of population toxicodynamics, which describes the kinetics of suppression of the population by a logistic function. The results of model calculations are shown to agree with the available experimental data, thus justifying the formulated assumptions regarding the character of the intoxication process. An extension of the model is proposed through the incorporation of the dependence of the result of intoxication on pollutant concentration by the well-known Haber’s formula. The developed model was used to propose an equation of regulated toxicodynamics for organization of water use without violation of the regime of natural functioning of ecosystems. The obtained specification of the notions of the mechanisms of intoxication process is necessary for the substantiation of hygienic standards on the concentration of chemicals in water, forecasts of biodiversity, and the choice of measures for supporting weak components of trophic chains in aquatic ecosystems.

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11

Frazier, J. M. "Predictive toxicodynamics: Empirical/mechanistic approaches." Toxicology in Vitro 11, no. 5 (1997): 465–72. http://dx.doi.org/10.1016/s0887-2333(97)00073-8.

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12

Maxwell, D. M., K. M. Brecht, F. C. T. Chang, I. Koplovitz, T. M. Shih, and R. E. Sweeney. "Toxicodynamic Modeling of Highly Toxic Organophosphorus Compounds." Journal of Molecular Neuroscience 30, no. 1-2 (2006): 129–32. http://dx.doi.org/10.1385/jmn:30:1:129.

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13

Batra, Vijay K. "Toxicokinetics/Toxicodynamic Correlations: Goals, Methods, and Limitations." Toxicologic Pathology 23, no. 2 (1995): 158–64. http://dx.doi.org/10.1177/019262339502300209.

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14

Gergs, André, Faten Gabsi, Armin Zenker, and Thomas G. Preuss. "Demographic Toxicokinetic–Toxicodynamic Modeling of Lethal Effects." Environmental Science & Technology 50, no. 11 (2016): 6017–24. http://dx.doi.org/10.1021/acs.est.6b01113.

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15

Duval, Jérôme F. L. "Coupled metal partitioning dynamics and toxicodynamics at biointerfaces: a theory beyond the biotic ligand model framework." Physical Chemistry Chemical Physics 18, no. 14 (2016): 9453–69. http://dx.doi.org/10.1039/c5cp07780j.

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A theory is developed for coupled toxicodynamics and interfacial metal partitioning dynamics, with integration of intertwined metal adsorption–internalisation–excretion-transport at the biointerface, cell growth and metal depletion from solution.

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16

Novo, Marta, Elma Lahive, María Díez Ortiz, David J. Spurgeon, and Peter Kille. "Toxicogenomics in a soil sentinel exposure to Zn nanoparticles and ions reveals the comparative role of toxicokinetic and toxicodynamic mechanisms." Environmental Science: Nano 7, no. 5 (2020): 1464–80. http://dx.doi.org/10.1039/c9en01124b.

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Exposures to Zn in ion or NPs form results in stimulation of the same cellular pathways (conserved toxicodynamics), whilst exposure to NPs enhances the amplitude of the response by influencing the mechanism of uptake (altered toxicokinetics).

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17

Abass, Khaled, Olavi Pelkonen, and Arja Rautio. "Corrigendum to: Chloro-s-triazenes-toxicokinetic, Toxicodynamic, Human Exposure, and Regulatory Considerations." Current Drug Metabolism 22, no. 12 (2021): 996. http://dx.doi.org/10.2174/138920022212211220114628.

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An error appeared in the graphical abstract and figure no. 1 of the article entitled “Chloro-s-triazines-toxicokinetic, toxicodynamic, human exposure, and regulatory considerations” by Khaled Abass, Olavi Pelkonen and Arja Rautio, Current Drug Metabolism 2021, 22(8), 645-656. <p> We regret the error and apologize to readers. <p> The original article can be found online at https://doi.org/10.2174/1389200222666210701164945

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18

Albert, C., R. Ashauer, H. R. Künsch, and P. Reichert. "Bayesian experimental design for a toxicokinetic–toxicodynamic model." Journal of Statistical Planning and Inference 142, no. 1 (2012): 263–75. http://dx.doi.org/10.1016/j.jspi.2011.07.014.

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19

Spyker, Daniel A., and Anil Minocha. "Toxicodynamic approach to management of the poisoned patient." Journal of Emergency Medicine 6, no. 2 (1988): 117–20. http://dx.doi.org/10.1016/0736-4679(88)90150-3.

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20

Gots, Ronald E. "Multiple Chemical Sensitivities: Distinguishing between Psychogenic and Toxicodynamic." Regulatory Toxicology and Pharmacology 24, no. 1 (1996): S8—S15. http://dx.doi.org/10.1006/rtph.1996.0071.

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21

Golovko, A. I. "Amethystic agents influencing toxicodynamics of ethanol." Biomeditsinskaya Khimiya 59, no. 6 (2013): 604–21. http://dx.doi.org/10.18097/pbmc20135906604.

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The pathogenetic mechanisms of acute alcoholic intoxications are examined and is based the expediency of the search for the amethystic agents, which influence neurotransmitter systems. Promising should be considered the agents, which modulate GABA-systems (partial reverse agonists of benzodiazepine receptors), glutamate (antagonists of metabotropic receptors mGluR2/3), opioid neuropeptides (antagonists of opioid receptors), acetylcholine (reversible inhibitors of acetylcholinesterase and M-cholinoagonists), adenosine (selective antagonists of A -receptors). The amethystic effect manifest also the substances, which modify the second messengers systems (calcium, nitrergic and cascade of arachidonic acid). The most of the means examined possesses the moderate amethystic potential, and effectiveness is manifested predominantly during the preventive application.

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22

Tenenbein, Milton. "Toxicokinetics and toxicodynamics of iron poisoning." Toxicology Letters 102-103 (December 1998): 653–56. http://dx.doi.org/10.1016/s0378-4274(98)00279-3.

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23

Tenenbein, M. "Toxicokinetics and toxicodynamics of iron poisoning." Toxicology Letters 95 (July 1998): 35. http://dx.doi.org/10.1016/s0378-4274(98)80137-9.

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24

Knudsen, Thomas B., Richard M. Spencer, Jocylin D. Pierro, and Nancy C. Baker. "Computational biology and in silico toxicodynamics." Current Opinion in Toxicology 23-24 (October 2020): 119–26. http://dx.doi.org/10.1016/j.cotox.2020.11.001.

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25

Jung, Suryun, Mingyu Kim, Suji Kim та Sooyeun Lee. "Interaction between γ-Hydroxybutyric Acid and Ethanol: A Review from Toxicokinetic and Toxicodynamic Perspectives". Metabolites 13, № 2 (2023): 180. http://dx.doi.org/10.3390/metabo13020180.

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Gamma-hydroxybutyric acid (GHB) is a potent, short-acting central nervous system depressant as well as an inhibitory neurotransmitter or neuromodulator derived from gamma-aminobutyric acid (GABA), a major inhibitory neurotransmitter. The sodium salt of GHB, sodium oxybate, has been used for the treatment of narcolepsy and cataplexy, whereas GHB was termed as a date rape drug or a club drug in the 1990s. Ethanol is the most co-ingested drug in acute GHB intoxication. In this review, the latest findings on the combined effects of GHB and ethanol are summarized from toxicokinetic and toxicodynamic perspectives. For this purpose, we mainly discussed the pharmacology and toxicology of GHB, GHB intoxication under alcohol consumption, clinical cases of the combined intoxication of GHB and ethanol, and previous studies on the toxicokinetic and toxicodynamic interactions between GHB and ethanol in humans, animals, and an in vitro model. The combined administration of GHB and ethanol enhanced sedation and cardiovascular dysfunction, probably by the additive action of GABA receptors, while toxicokinetic changes of GHB were not significant. The findings of this review will contribute to clinical and forensic interpretation related to GHB intoxication. Furthermore, this review highlights the significance of studies aiming to further understand the enhanced inhibitory effects of GHB induced by the co-ingestion of ethanol.

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26

Gergs, André, Jutta Hager, Eric Bruns, and Thomas G. Preuss. "Disentangling Mechanisms Behind Chronic Lethality through Toxicokinetic–Toxicodynamic Modeling." Environmental Toxicology and Chemistry 40, no. 6 (2021): 1706–12. http://dx.doi.org/10.1002/etc.5027.

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27

Hack, C. Eric. "Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models." Toxicology 221, no. 2-3 (2006): 241–48. http://dx.doi.org/10.1016/j.tox.2005.12.017.

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28

Baek, Jin Hyen, Ayla Yalamanoglu, Ronald P. Brown, David M. Saylor, Richard A. Malinauskas, and Paul W. Buehler. "Renal Toxicodynamic Effects of Extracellular Hemoglobin After Acute Exposure." Toxicological Sciences 166, no. 1 (2018): 180–91. http://dx.doi.org/10.1093/toxsci/kfy193.

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29

Vale, J. A. "Toxicokinetic and toxicodynamic aspects of organophosphorus (OP) insecticide poisoning." Toxicology Letters 102-103 (December 1998): 649–52. http://dx.doi.org/10.1016/s0378-4274(98)00277-x.

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30

Vale, J. A. "Toxicokinetic and toxicodynamic aspects of organophosphorus (OP) insecticide poisoning." Toxicology Letters 95 (July 1998): 35. http://dx.doi.org/10.1016/s0378-4274(98)80136-7.

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31

Alrushaid, Samaa, Casey Sayre, Jaime Yáñez, et al. "Pharmacokinetic and Toxicodynamic Characterization of a Novel Doxorubicin Derivative." Pharmaceutics 9, no. 4 (2017): 35. http://dx.doi.org/10.3390/pharmaceutics9030035.

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32

Anand, Sathanandam S., and Harihara M. Mehendale. "Liver regeneration: a critical toxicodynamic response in predictive toxicology." Environmental Toxicology and Pharmacology 18, no. 2 (2004): 149–60. http://dx.doi.org/10.1016/j.etap.2004.02.011.

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33

Fiserova-Bergerova, V., and J. Vlach. "Exposure limits for unconventional shifts: Toxicokinetic and toxicodynamic considerations." American Journal of Industrial Medicine 31, no. 6 (1997): 744–55. http://dx.doi.org/10.1002/(sici)1097-0274(199706)31:6<744::aid-ajim12>3.0.co;2-y.

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34

Heinrich-Hirsch, Barbara, Stephan Madle, Axel Oberemm, and Ursula Gundert-Remy. "The use of toxicodynamics in risk assessment." Toxicology Letters 120, no. 1-3 (2001): 131–41. http://dx.doi.org/10.1016/s0378-4274(01)00291-0.

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35

Hergenhahn, M., U. Kloz, M. Fellhauer, G. L. Tremp, and E. Hecker. "Toxicodynamics of tumour promoters of mouse skin." Journal of Cancer Research and Clinical Oncology 117, no. 5 (1991): 385–95. http://dx.doi.org/10.1007/bf01612756.

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36

He, Erkai, and Cornelis A. M. van Gestel. "Toxicokinetics and toxicodynamics of nickel inEnchytraeus crypticus." Environmental Toxicology and Chemistry 32, no. 8 (2013): 1835–41. http://dx.doi.org/10.1002/etc.2253.

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37

Silva, Bárbara, Jorge Soares, Carolina Rocha-Pereira, Přemysl Mladěnka, and Fernando Remião. "Khat, a Cultural Chewing Drug: A Toxicokinetic and Toxicodynamic Summary." Toxins 14, no. 2 (2022): 71. http://dx.doi.org/10.3390/toxins14020071.

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Khat (Catha edulis) is a recreational, chewed herbal drug that has been used as a psychostimulant for centuries in East Africa and the Arabian Peninsula, namely in Somalia, Ethiopia, and Yemen. However, the growing worldwide availability of khat has produced widespread concern. The plant comprises a large number of active substances, among which cathinone, cathine, and norephedrine are the main constituents, which can be included in the group of sympathomimetics of natural origin. In fact, these compounds are amphetamine analogues, and, as such, they have amphetamine-like nervous system stimulant effects. Chewing the leaves gives people a sensation of well-being and increases energy, alertness, and self-confidence. The chronic use of khat is, however, associated with severe cardiac, neurological, psychological, and gastrointestinal complications. The psychological dependence and withdrawal symptoms of khat are the reasons for its prolonged use. The aim of this paper is to review current knowledge on the khat plant with toxicokinetic and toxicodynamic perspectives. Namely, this review paper addresses in vitro, in vivo, and human studies. The models used, as well as the concentrations and doses with the respective biological effects, are discussed. Additionally, the main drug interactions involved with khat are described.

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38

Soontornchat, S., MH Li, PS Cooke, and LG Hansen. "Toxicokinetic and Toxicodynamic Influences on Endocrine Disruption by Polychlorinated Biphenyls." Environmental Health Perspectives 102, no. 6-7 (1994): 568–71. http://dx.doi.org/10.1289/ehp.94102568.

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39

Mégarbane, Bruno, Nicolas Deye, Vanessa Bloch, et al. "Intentional overdose with insulin: prognostic factors and toxicokinetic/toxicodynamic profiles." Critical Care 11, no. 5 (2007): R115. http://dx.doi.org/10.1186/cc6168.

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40

Christians, Uwe, Volker Schmitz, Wenzel Schöning, et al. "Toxicodynamic Therapeutic Drug Monitoring of Immunosuppressants: Promises, Reality, and Challenges." Therapeutic Drug Monitoring 30, no. 2 (2008): 151–58. http://dx.doi.org/10.1097/ftd.0b013e31816b9063.

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41

Matsui, Megumi, Shinji Kobuchi, Yukako Ito, and Toshiyuki Sakaeda. "UFT-induced myelosuppression and its pharmacokinetic-toxicodynamic model in rats." Proceedings for Annual Meeting of The Japanese Pharmacological Society WCP2018 (2018): PO2–14–35. http://dx.doi.org/10.1254/jpssuppl.wcp2018.0_po2-14-35.

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42

Blaauboer, Bas J. "Toxicodynamic modelling and the interpretation of in vitro toxicity data." Toxicology Letters 120, no. 1-3 (2001): 111–23. http://dx.doi.org/10.1016/s0378-4274(01)00289-2.

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43

Roeben, Vanessa, Susanne Oberdoerster, Kim J. Rakel, et al. "Towards a spatiotemporally explicit toxicokinetic-toxicodynamic model for earthworm toxicity." Science of The Total Environment 722 (June 2020): 137673. http://dx.doi.org/10.1016/j.scitotenv.2020.137673.

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44

Jager, Tjalling, Benoit Goussen, and André Gergs. "Using the standard DEB animal model for toxicokinetic-toxicodynamic analysis." Ecological Modelling 475 (January 2023): 110187. http://dx.doi.org/10.1016/j.ecolmodel.2022.110187.

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45

Xu, X., P. M. Dixon, Y. Zhao, and M. C. Newman. "Diagnostics to assess toxicokinetic-toxicodynamic models with interval-censored data." Environmetrics 24, no. 5 (2013): 332–41. http://dx.doi.org/10.1002/env.2216.

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46

Hultén, Bengt-Åke, Andrew Heath, Kai Knudsen, Gösta Nyberg, Jan-Erik Starmark, and Eric Mårtensson. "Severe amitriptyline overdose: Relationship between toxicokinetics and toxicodynamics." Journal of Toxicology: Clinical Toxicology 30, no. 2 (1992): 171–79. http://dx.doi.org/10.3109/15563659209038629.

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47

Lestner, Jodi M., Steven A. Roberts, Caroline B. Moore, Susan J. Howard, David W. Denning, and William W. Hope. "Toxicodynamics of Itraconazole: Implications for Therapeutic Drug Monitoring." Clinical Infectious Diseases 49, no. 6 (2009): 928–30. http://dx.doi.org/10.1086/605499.

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48

Gekle, Michael, and Stefan Silbernagl. "Renal Toxicodynamics of Ochratoxin A: A Pathophysiological Approach." Kidney and Blood Pressure Research 19, no. 5 (1996): 225–35. http://dx.doi.org/10.1159/000174080.

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49

Ringot, Diana, Abalo Chango, Yves-Jacques Schneider, and Yvan Larondelle. "Toxicokinetics and toxicodynamics of ochratoxin A, an update." Chemico-Biological Interactions 159, no. 1 (2006): 18–46. http://dx.doi.org/10.1016/j.cbi.2005.10.106.

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50

Trevisan, Andrea, Federica Chiara, Michele Mongillo, Luigi Quintieri, and Patrizia Cristofori. "Sex-related differences in renal toxicodynamics in rodents." Expert Opinion on Drug Metabolism & Toxicology 8, no. 9 (2012): 1173–88. http://dx.doi.org/10.1517/17425255.2012.698262.

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