Dr. Alicia Paini joins esqLABS

List of peer reviewed Publications

1. Clerbaux et al., (accepted) COVID-19 through Adverse Outcome Pathways: building networks to better understand the disease – Report of the 3rd CIAO AOP Design Workshop.: Report of the 3rd CIAO AOP Workshop. ATLEX.
2. Loizou, G., McNally, K., Paini A., and Hogg A., (accepted) Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation. Frontiers in Pharmacology.
3. Bednarczyk, E., Lu, Y., Paini, A., BATISTA-LEITE, S., van Grunsven, L., Worth, A., Lekszyckil, T., Whelan, M., (accepted) Extension of the virtual cell based assay from a 2D to a 3D cell culture model. ATLA-ALTERNATIVES TO LABORATORY ANIMALS.
4. Thompson, C.V., Firman, J., Goldsmith, M., Grulke, C., Tan, Y., Paini, A., Penson, P.E., Sayre, R., Webb, S. and Madden, J., (2021) A systematic review of published physiologically-based kinetic models and an assessment of their chemical space coverage, ATLA-ALTERNATIVES TO LABORATORY ANIMALS, ISSN 0261-1929, 49 (5), p. 197-208.
5. Tan et al., (2021). Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment Regulatory Toxicology and Pharmacologythis link is disabled, 127, 105070
6. Pistollato et al., (2021) Combining in vitro assays and mathematical modelling to study developmental neurotoxicity induced by chemical mixtures. Reproductive Toxicologythis link is disabled, 105, pp. 101–119
7. Bury D., et Al., (2021) New framework for a non-animal approach adequately assures the safety of cosmetic ingredients – A case study on caffeine Regulatory Toxicology and Pharmacology Volume 123, July 2021, 104931
8. Pistollato et al., (2021) Current EU regulatory requirements for the assessment of chemicals, cosmetic products and their ingredients and new approach methodologies: challenges to face and strategies to bridge possible gaps Archives of Toxicology, 95(6), pp. 1867–1897
9. Paini A., et al., (2021) Gaining acceptance in Next Generation PBK modelling approaches for regulatory assessments – an OECD international effort Computational Toxicologythis, 18, 100163
10. Proenca S, et al., (2021) Effective exposure of chemicals in in vitro cell systems: a review of chemical distribution models Toxicology in Vitro, 73, 105133
11. Paini A., et al (2021) Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach Computational Toxicology, 18, 100159
12. Jeddi MZ, et al (2021) Towards a systematic use of effect biomarkers in population and occupational biomonitoring Environment International 146, Jan 2021, 106257. https://doi.org/10.1016/j.envint.2020.106257
13. Madden JC, et al (2020) A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications (https://doi.org/10.1177/0261192920965977)
14. Pletz J, et al (2020) Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment. Environment International. 143, October 2020, 105978
15. Punt A, et al (2020) New approach methodologies (NAMs) for human-relevant biokinetics predictions: Meeting the paradigm shift in toxicology towards an animal-free chemical risk assessment. ALTEX- Alternatives to animal experimentation. doi:10.14573/altex.2003242
16. Tan YM, et al (2020) PBPK model reporting template for chemical risk assessment applications. Regulatory Toxicology and Pharmacology. 115, August 2020, 104691
17. Ball M, et al (2020) Key Read Across Framework Components and Biology Based Improvements. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 853, May 2020, 503172
18. Madden JC, Tan YM, Blaauboer B, Paini A (2020) Development and Application of Physiologically-Based Kinetic (PBK) Models – Computational Toxicology – Editorial
19. Madden JC, (…) Paini A (2019) In silico resources to assist in the development and evaluation of physiologically-based kinetic models. Computational Toxicology. 11, 33-49
20. Proenca S, Paini A, et al (2019) Insights into in vitro biokinetics using Virtual Cell Based Assay simulations. ALTEX-Alternatives to animal experimentation. 36 (3), 447-461
21. Clerbaux LA, Paini A, et al (2019) Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives. Environment international. 126, 659-671
22. Paini A, et al (2019) Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. Computational Toxicology. 9, 61-72
23. Bopp SK, et al (2019) Regulatory assessment and risk management of chemical mixtures: challenges and ways forward. Critical reviews in toxicology. 49-2-174-189
24. Desalegn A, […] Paini A (2019) Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures – A review. Computational Toxicology. 10, 158-168
25. Clerbaux LA, […] Paini A (2018) Capturing the applicability of in vitro-in silico membrane transporter data in chemical risk assessment and biomedical research. Science of the Total Environment. 645, 97-108
26. Clippinger et al (2018) Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity. Toxicology in Vitro. 52, 131-145
27. Laroche C, et al (2018) Finding synergies for 3Rs – Toxicokinetics and read-across: Report from an EPAA partners’ Forum. Regulatory Toxicology and Pharmacology. 99, 5-21
28. Tan YM, et al (2018) Aggregate exposure pathways in support of risk assessment. Current Opinion in Toxicology. 9, 8-13
29. Bell S, et al (2018) In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicology in Vitro. 47, 213-227
30. Altenpohl A, et al (2018) CEN Standard documentation of exposure models: MERLIN-Expo case study (Handbook of Environmental Chemistry) DOI: 10.1007/978-3-319-59502-3_3
31. Villeneuve D, et al (2018) Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways. Toxicological Sciences. 163(2), 346-352
32. Terron A, et al (2018) An adverse outcome pathway for parkinsonian motor deficits associated with mitochondrial complex I inhibition. Archives of Toxicology. 92(5), 41–82
33. Paini A, et al (2017) Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications. Regulatory Toxicology and Pharmacology. 90, 104-115
34. Bessems JGM, Paini A, et al (2017) The margin of internal exposure (MOIE) concept for dermal risk assessment based on oral toxicity data – A case study with caffeine. Toxicology. 392, 119-129
35. Worth A, […] Paini A (2017) Virtual Cell Based Assay simulations of intra-mitochondrial concentrations in hepatocytes and cardiomyocytes. Toxicology in Vitro. 45 (pt2), 222-232
36. Graepel R, et al (2017) The virtual cell based assay: Current status and future perspectives. Toxicology in vitro. 45(Pt 2), 258-267
37. Paini A, et al (2017) Practical use of the Virtual Cell Based Assay: Simulation of repeated exposure experiments in liver cell lines. Toxicology in vitro. 45(Pt 2), 233-240
38. Sala Benito JV, Paini A, et al (2017) Automated workflows for modeling chemical fate, kinetics, and toxicity. Toxicology in vitro. 45(Pt 2), 249-257
39. Paini A, et al (2017) From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling. Toxicology in Vitro. 45(Pt2), 241-248
40. Zaldivar Comenges JM, […] Paini A (2017) Theoretical and mathematical foundation of the Virtual Cell Based Assay – A review. Toxicology in vitro. 45(Pt2), 209-221
41. Bois F, et al (2017) Mulitscale modelling approaches for assessing cosmetic ingredients safety. Toxicology. 392, 130-139
42. Souter E, et al (2017) Improving substance information in USEtox® , part 2: Data for estimating fate and ecosystem exposure factors. Environmental Toxicology and Chemistry. 36(12)
43. Souter E, et al (2017) Improving substance information in USEtox® , part 1: Discussion on data and approaches for estimating freshwater ecotoxicity effect factors Environmental Toxicology and Chemistry. 36(12)
44. Berggren E, et al (2017) Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods. Computational Toxicology. 4, 31-44
45. Wittwehr C, et al (2017) How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology. Toxicology Sciences. 155(2): 326-336.
46. Ciffroy P, et al (2016) Development of a standard documentation protocol for communicating exposure models. Science of the Total Environment. 568, 557-565
47. Punt A, Paini A. et al (2016) Evaluation of Interindividual Human Variation in Bioactivation and DNA Adduct Formation of Estragole in Liver Predicted by Physiologically Based Kinetic/Dynamic and Monte Carlo Modeling. Chemical Research in Toxicology. 29(4), 659–668
48. Bal-Price A, et al (2015) Putative adverse outcome pathways relevant to neurotoxicity Critical Reviews in Toxicology. 45(1), 83-91.
49. Gajewska M, Paini A, et al (2015) In vitro-to-in vivo correlation of the skin penetration, liver clearance and hepatotoxicity of caffeine. Food and Chemical Toxicology. 75, 39-49
50. Martati M, et al (2014) Malabaricone C-containing mace extract inhibits safrole bioactivation and DNA adduct formation both in vitro and in vivo. Food and Chemical Toxicology, 66, 373-384.
51. Alhusainy W, Paini A et al (2013) In vivo validation and physiologically based modelling of the inhibition of SULT-mediated estragole DNA adduct formation in the liver of male Sprague-Dawley rats by the basil flavonoid nevadensin. Molecular Nutrition and Food Research. 57(11), 1969-1978.
52. Al-Subeihi AAA, Paini A, et al (2013) Inhibition of methyleugenol bioactivation by the herb-based constituent nevadensin and prediction of possible in vivo consequences using physiologically based kinetic modeling. Food and Chemical Toxicology. 59, 564-571.
53. Paini A, et al (2012) In vivo validation of DNA adducts formation by estragole in rats predicted by physiologically based biodynamic modeling. Mutagenesis. 27(6), 653-663.
54. Alhusainy W, et al (2012) Matrix modulation of the bioactivation of estragole by different alkenylbenzene containing herbs and spices and physiologically-based biokinetic modeling (PBBK) of possible in vivo effects. Toxicological Sciences. 129(1), 174-187.
55. Paini A, et al (2011). Quantitative comparison between in vivo DNA adduct formation from exposure to selected DNA-reactive carcinogens, natural background levels of DNA adduct formation and tumor incidence in rodent bioassays. Mutagenesis. 26(5), 605-618.
56. Alhusainy W, Paini A, et al (2010). Identification of nevadensin as an important herb-based constituent inhibiting estragole bioactivation and physiology-based biokinetic modeling of its possible in vivo effect. Toxicology and Applied Pharmacology. 245(2), 179-190.
57. Paini A, et al (2010). A physiologically based biodynamic (PBBD) model for estragole DNA binding in rat liver on in vitro kinetic data and estragole DNA adduct formation in primary rat hepatocytes. Toxicology and Applied Pharmacology. 245(1), 57-66.
58. Punt A, Paini A, et al (2009). Use of physiologically based biokinetic (PBBK) modeling to study estragole bioactivation and detoxification in humans as compared with male rats. Toxicological Sciences. 110(2), 255-269.