Monocrotaline-induced liver toxicity in rat predicted by a combined in vitro physiologically based kinetic modeling approach

Abstract

The aim of the present study was to use an in vitro-in silico approach to predict the in vivo acute liver toxicity of monocrotaline and to characterize the influence of its metabolism on its relative toxic potency compared to lasiocarpine and riddelliine. In the absence of data on acute liver toxicity of monocrotaline upon oral exposure, the predicted dose-response curve for acute liver toxicity in rats and the resulting benchmark dose lower and upper confidence limits for 10% effect (BMDL 10 and BMDU 10) were compared to data obtained in studies with intraperitoneal or subcutaneous dosing regimens. This indicated the predicted BMDL 10 value to be in line with the no-observed-adverse-effect levels (NOAELs) derived from availabe in vivo studies. The predicted BMDL 10-BMDU 10 of 1.1-4.9 mg/kg bw/day also matched the oral dose range of 1-3 mg PA/kg bw/ day at which adverse effects in human are reported. A comparison to the oral toxicity of the related pyrrolizidine alkaloids (PAs) lasiocarpine and riddelliine revealed that, although in the rat hepatocytes monocrotaline was less toxic than lasiocarpine and riddelliine, due to its relatively inefficient clearance, its in vivo acute liver toxicity was predicted to be comparable. It is concluded that the combined in vitro-PBK modeling approach can provide insight in monocrotaline-induced acute liver toxicity in rats, thereby filling existing gaps in the database on PA toxicity. Furthermore, the results reveal that the kinetic and metabolic properties of PAs can vary substantially and should be taken into account when considering differences in relative potency between different PAs. Keywords Monocrotaline • Acute toxicity • Liver • PBK modeling • Reverse dosimetry Abbreviations AIC Akaike information criterion ALT Alanine aminotransferase BMD 10 Benchmark dose resulting in a 10% effect above background level BMDL 10 Lower confidence limit of the BMD 10 BMDU 10 Upper confidence limit of the BMD 10 EFSA European Food Safety Authority GI Gastrointestinal GSH Glutathione HPC Hepatic parencymal cells HVOD Hepatic veno-occlusive disease Ka Absorption rate for uptake from the GI tract compartment into the liver PAs Pyrrolizidine alkaloids PBK Physiologically based kinetic POD Point of departure NOAEL No observed adverse effect level LOAEL Lowest observed adverse effect level pRPF Provisional relative potency factors 3Rs Replacement, reduction and refinement Electronic supplementary material The online version of this article (

Novel Insights into Pyrrolizidine Alkaloid Toxicity and Implications for Risk Assessment: Occurrence, Genotoxicity, Toxicokinetics, Risk Assessment–A Workshop Report

“…An observation that can be taken into account in PBK modeling by including the kinetic parameters Vmax and Km for the respective clearance in the model code. In a QIVIVE study on the monocrotaline-induced liver toxicity in rats predicted by a PBK modeling-facilitated reverse dosimetry, the role of the OCT1 transporter was taken into account, providing a proof of principle on how to deal with differences in PA absorption [43].…”

Liquorice Extract and 18β-Glycyrrhetinic Acid Protect Against Experimental Pyrrolizidine Alkaloid-Induced Hepatotoxicity in Rats Through Inhibiting Cytochrome P450-Mediated Metabolic Activation

“…In general, most toxic PA types including retrorsine, riddelliine, senecionine, senkirkine, and lasiocarine are mainly metabolized by CYP3A4, which account for 30–50% of the hepatic CYP isoforms ( Teschke et al, 2021 ). While PAs such as monocrotaline are mainly metabolized by the CYP2A6 ( Suparmi et al, 2020 ). In the present study, because CYP3A4 is the predominant CYP isoform accounting for the RTS intoxication, we, therefore, focus on the study of the interaction between GA and CYP3A4 in our animal and cell models.…”

The Role of Kinetics as Key Determinant in Toxicity of Pyrrolizidine Alkaloids and Their N-Oxides

“…Subsequent benchmark dose (BMD) modeling can be applied on the predicted in vivo dose-response data, enabling definition of a point of departure (PoD) for risk assessment, such as a BMDL x (the lower confidence limit of the benchmark dose causing an x% effect above background level) and BMDUx (the upper confidence limit of the benchmark dose causing an x% effect above background level). PBK modelling has recently been applied to describe the kinetics of three PAs, including riddelliine, lasiocarpine and monocrotaline [20,[26][27][28][29]. ▶ Fig.…”

“…For the parent PAs, in vitro studies using either microsomal incubations or rat hepatocyte sandwich cultures have revealed substantial variation in metabolic clearance between the examined PAs [20][21][22]. Based on kinetic data from in vitro liver microsomal incubations, clearance of lasiocarpine was shown to be about 10-fold more effective than that of riddelliine and 43-fold more effective than that of monocrotaline (▶ Table 2) [22]. These results were in line with data from Lester et al, (2019) [21], who studied the clearance of PAs in rat hepatocyte sandwich cultures and reported the in vitro clearance for these three PAs to also decrease in the order: lasiocarpine > riddelliine > monocrotaline [21].…”

“…For the parent PAs, in vitro studies using either microsomal incubations or rat hepatocyte sandwich cultures have revealed substantial variation in metabolic clearance between the examined PAs 20 , 21 , 22 . Based on kinetic data from in vitro liver microsomal incubations, clearance of lasiocarpine was shown to be about 10-fold more effective than that of riddelliine and 43-fold more effective than that of monocrotaline ( Table 2 ) 22 .…”