Download PDF
CC BY 4.0 · Planta Med
DOI: 10.1055/a-2523-3987
Original Papers

Characterization of Lipophilicity and Blood Partitioning of Pyrrolizidine Alkaloids and Their N-Oxides In Vitro and In Silico for Toxicokinetic Modeling

Anja Lehmann  ‡
1   German Federal Institute for Risk Assessment (BfR), Berlin, Germany
2   Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Germany
,
Manuel Haas‡
1   German Federal Institute for Risk Assessment (BfR), Berlin, Germany
,
Julian Taenzer
1   German Federal Institute for Risk Assessment (BfR), Berlin, Germany
,
Gerd Hamscher
3   Institute of Food Chemistry and Food Biotechnology, Justus Liebig University, Giessen, Germany
,
Charlotte Kloft
2   Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Germany
,
Anja These‡
1   German Federal Institute for Risk Assessment (BfR), Berlin, Germany
,
Christoph Hethey‡
1   German Federal Institute for Risk Assessment (BfR), Berlin, Germany
› Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Abstract

Lipophilicity and blood partitioning are important determinants for predicting toxicokinetics using physiologically-based toxicokinetic modeling. In this study, the logarithm of the n-octanol : water partition coefficient and the blood-to-plasma concentration ratio were, for the first time, experimentally determined for the pyrrolizidine alkaloids intermedine, lasiocarpine, monocrotaline, retrorsine, and their N-oxides. Validated in vitro assays for determination of the n-octanol : water partition coefficient (miniaturized shake-flask method) and the blood-to-plasma conentration ratio (LC-MS/MS-based depletion assay) were compared to an ensemble of in silico models. The experimentally determined octanol : water partition coefficient indicates a higher affinity of pyrrolizidine alkaloids and their N-oxides to the aqueous compared to the organic phase. Depending on the method, in silico determined n-octanol : water partition coefficients overpredicted the experimental values by ≥ 1 log unit for three out of four pyrrolizidine alkaloids (SPARC), four out of six (CLOGP), five out of eight (KowWIN), and three out of eight (S+logP) pyrrolizidine alkaloids and their N-oxides. The blood-to-plasma concentration ratio obtained in vitro suggested a low binding affinity of pyrrolizidine alkaloids and their N-oxides towards red blood cells. For all eight pyrrolizidine alkaloids and their N-oxides, in silico predicted blood-to-plasma ratios deviated from experimental values by less than 50%. In conclusion, for physiologically-based toxicokinetic modeling of pyrrolizidine alkaloids and their N-oxides, the experimental octanol : water partition coefficient should be preferred, while the blood-to-plasma concentration ratio predicted by the acid/base classification model is a suitable surrogate for experimental data.

Keywords

logP - octanol : water partition coefficient - blood-to-plasma ratio - drug distribution - pharmacokinetics - PBTK modeling

Supporting Information

    Linear regression of logP values (Fig. 1 aS), and Rb values (Fig 1 bS) of reference substances experimentally determined in this work versus literature, the effect of the concentration on the Rb value of reference substances (Fig. 2S), experimentally determined and predicted logP values of reference substances (Fig. 3S), representative ddMS2 data of reference substances (Figs. 4S–10S), predicted pKa values and predominant charge state at experimental pH (logP determination) of PAs, PANOs and reference substances (Table 1S), logP values and Rb values of reference substances (Table 2S), MS/MS transitions and parameters (Table 3S), and mass spectrometric peak areas and calculated recovery of reference substances for logP determination (Table 4S) are available as Supporting Information.

  • Supporting Information


Publication History

Received: 29 July 2024

Accepted after revision: 05 January 2025

Accepted Manuscript online:
23 January 2025

Article published online:
21 February 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Dusemund B, Nowak N, Sommerfeld C, Lindtner O, Schäfer B, Lampen A. Risk assessment of pyrrolizidine alkaloids in food of plant and animal origin. Food Chem Toxicol 2018; 115: 63-72
    CrossrefPubMedSearch in Google Scholar
  • 2 Mengbi Y, Ma J, Ruan J, Ye Y, Fu P, Lin G. Intestinal and hepatic biotransformation of pyrrolizidine alkaloid N-oxides to toxic pyrrolizidine alkaloids. Arch Toxicol 2019; 93: 2197-2209
    CrossrefPubMedSearch in Google Scholar
  • 3 Widjaja F, Alhejji Y, Rietjens IMCM. The role of kinetics as key determinant in toxicity of pyrrolizidine alkaloids and their N-oxides. Planta Med 2022; 88: 130-143
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Chen L, Peijnenburg A, de Haan L, Rietjens IMCM. Prediction of in vivo genotoxicity of lasiocarpine and riddelliine in rat liver using a combined in vitro-physiologically based kinetic modelling-facilitated reverse dosimetry approach. Arch Toxicol 2019; 93: 2385-2395
    CrossrefPubMedSearch in Google Scholar
  • 5 Lehmann A, Geburek I, Hessel-Pras S, Hengstler JG, Albrecht W, Mielke H, Müller-Graf C, Yang X, Kloft C, Hethey C. PBTK modeling of the pyrrolizidine alkaloid retrorsine to predict liver toxicity in mouse and rat. Arch Toxicol 2023; 97: 1319-1333
    CrossrefPubMedSearch in Google Scholar
  • 6 Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: Predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci 2005; 94: 1259-1276
    CrossrefPubMedSearch in Google Scholar
  • 7 Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 2006; 95: 1238-1257
    CrossrefPubMedSearch in Google Scholar
  • 8 Schmitt W. General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro 2008; 22: 457-467
    CrossrefPubMedSearch in Google Scholar
  • 9 Schmitt W. Corrigendum to: “General approach for the calculation of tissue to plasma partition coefficients” [Toxicol In Vitro 2008; 22:457–467]. Toxicol In Vitro 2008; 22: 1666-1666
    CrossrefPubMedSearch in Google Scholar
  • 10 Kah M, Brown CD. LogD: Lipophilicity for ionisable compounds. Chemosphere 2008; 72: 1401-1408
    CrossrefPubMedSearch in Google Scholar
  • 11 Aliagas I, Gobbi A, Lee ML, Sellers BD. Comparison of logP and logD correction models trained with public and proprietary data sets. J Comput Aided Mol Des 2022; 36: 253-262
    CrossrefPubMedSearch in Google Scholar
  • 12 Soares JX, Santos Á, Fernandes C, Pinto MMM. Liquid chromatography on the different methods for the determination of lipophilicity: An Essential analytical tool in medicinal chemistry. Chemosensors 2022; 10: 340
    CrossrefPubMedSearch in Google Scholar
  • 13 OECD. Test No. 107: Partition Coefficient (n-octanol/water): Shake Flask Method. Paris, France: OECD Publishing; 1995
    CrossrefSearch in Google Scholar
  • 14 Bharate SS, Kumar V, Vishwakarma RA. Determining Partition Coefficient (Log P), Distribution Coefficient (Log D) and Ionization Constant (pKa) in Early Drug Discovery. Comb Chem High Throughput Screen 2016; 19: 461-469
    CrossrefPubMedSearch in Google Scholar
  • 15 OECD. Test No. 117: Partition Coefficient (n-octanol/water), HPLC Method. Paris, France: OECD Publishing; 2022
    Search in Google Scholar
  • 16 Mannhold R, Poda GI, Ostermann C, Tetko IV. Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96000 compounds. J Pharm Sci 2009; 98: 861-893
    CrossrefPubMedSearch in Google Scholar
  • 17 Testa B, Crivori P, Reist M, Carrupt PA. The influence of lipophilicity on the pharmacokinetic behavior of drugs: Concepts and examples. Perspect Drug Discov Des 2000; 19: 179-211
    CrossrefPubMedSearch in Google Scholar
  • 18 Paixão P, Gouveia LF, Morais JAG. Prediction of drug distribution within blood. Eur J Pharm Sci 2009; 36: 544-554
    CrossrefPubMedSearch in Google Scholar
  • 19 Yu S, Li S, Yang H, Lee F, Wu JT, Qian MG. A novel liquid chromatography/tandem mass spectrometry based depletion method for measuring red blood cell partitioning of pharmaceutical compounds in drug discovery. Rapid Commun Mass Spectrom 2005; 19: 250-254
    CrossrefPubMedSearch in Google Scholar
  • 20 Liu XR, Wu KC, Huang Y, Sun JB, Ke XY, Wang JC, Lu WL, Zhang X, Zhang Q. In vitro and in vivo studies on plasma-to-blood ratio of paclitaxel in human, rabbit and rat blood fractions. Biol Pharm Bull 2008; 31: 1215-1220
    CrossrefPubMedSearch in Google Scholar
  • 21 Uchimura T, Kato M, Saito T, Kinoshita H. Prediction of human blood-to-plasma drug concentration ratio. Biopharm Drug Dispos 2010; 31: 286-297
    CrossrefPubMedSearch in Google Scholar
  • 22 Mamada H, Iwamoto K, Nomura Y, Uesawa Y. Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans. Mol Divers 2021; 25: 1261-1270
    CrossrefPubMedSearch in Google Scholar
  • 23 Geburek I, Preiss-Weigert A, Lahrssen-Wiederholt M, Schrenk D, These A. In vitro metabolism of pyrrolizidine alkaloids – Metabolic degradation and GSH conjugate formation of different structure types. Food Chem Toxicol 2020; 135: 110868
    CrossrefPubMedSearch in Google Scholar
  • 24 Caron G, Carrupt PA, Testa B, Ermondi G, Gasco A. Insight into the lipophilicity of the aromatic N-oxide moiety. Pharm Res 1996; 13: 1186-1190
    CrossrefPubMedSearch in Google Scholar
  • 25 Snoeck E, Jacqmin P, Van Peer A, Danhof M, Ver Donck K, Van Belle H, Woestenborghs R, Crabbé R, Van Gool R, Dupont A, Heykants J. The implications of non-linear red blood cell partitioning for the pharmacokinetics and pharmacodynamics of the nucleoside transport inhibitor draflazine. Br J Clin Pharmacol 1996; 42: 605-613
    PubMedSearch in Google Scholar
  • 26 Dash RP, Veeravalli V, Thomas JA, Rosenfeld C, Mehta N, Srinivas N. Whole blood or plasma: What is the ideal matrix for pharmacokinetic-driven drug candidate selection?. Future Med Chem 2021; 13: 157-171
    CrossrefPubMedSearch in Google Scholar
  • 27 Colson RO. Unexpected results of some simple exercises in equilibrium melting based on experimentally determined partition coefficients. Int Geol Rev 1998; 40: 936-943
    CrossrefPubMedSearch in Google Scholar
  • 28 US EPA. Estimation Programs Interface Suite™ for Microsoft® Windows, 2019. Accessed 2024-07-15 at https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface
    PubMed
  • 29 Merz KH, Schrenk D. Interim relative potency factors for the toxicological risk assessment of pyrrolizidine alkaloids in food and herbal medicines. Toxicol Lett 2016; 263: 44-57
    CrossrefPubMedSearch in Google Scholar
  • 30 ARChem LLC. SPARC Automated Reasoning in Chemistry. Accessed 2025-07-15 at http://www.archemcalc.com/sparc.html
    PubMed
  • 31 SimulationsPlus. ADMET Predictor 12. ADMET Property Estimation and Model Building, 2024. Accessed 2024-10-29 at https://www.simulations-plus.com/software/admetpredictor/
    PubMed
  • 32 Watanabe R, Esaki T, Kawashima H, Natsume-Kitatani Y, Nagao C, Ohashi R, Mizuguchi K. Predicting fraction unbound in human plasma from chemical structure: Improved accuracy in the low value ranges. Mol Pharm 2018; 15: 5302-5311
    CrossrefPubMedSearch in Google Scholar
  • 33 EU Reference Laboratories. Guidance document on analytical quality control and method validation procedures for pesticide residues and analysis in food and feed. SANTE/11813/2017. 2017. Accessed October 26, 2023 at: https://www.eurl-pesticides.eu/userfiles/file/EurlALL/SANTE_11813_2017-fin.pdf
    PubMed