Dimitra Eleftheriadou (1,2); Wilhelm Huisinga (3); Christoph Hethey (1)
(1) Junior Research Group Toxicokinetic Modelling, Dept. Exposure, German Federal Institute for Risk Assessment (BfR); (2) Graduate Research Training Program PharMetrX, Berlin/Potsdam; (3) Institute of Mathematics, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam/Golm, Germany
Objectives: As an element, aluminium (Al) does not undergo metabolism in biological systems, but rather distributes into a plethora of distinct chemical states. This process is known as speciation. Among the variety of organic ligands present in biological systems, citrate (Cit) is the most relevant ligand for aluminium [1]. Detailed representation of the speciation of aluminium with citrate on the molecular level is lacking in existing aluminium biokinetic models [2]. However, this information is essential for understanding the involvement of aluminium in biological processes. For example, it is known that aluminium ions (Al3+) adsorb onto calcium hydroxyapatite crystals [3]. Accordingly, knowledge of the speciation kinetics of Al3+ is crucial for the prediction of its bone accumulation in children under conditions of life-long exposure. Due to the lack of detailed in vivo data on the speciation of aluminium, this knowledge needs to be translated from in vitro experimental data. The objective is to identify the physiologically relevant species and the underlying chemical reactions in the aquatic Al-Cit system. A further objective is to estimate the rate constants of these chemical reactions and thereby enabling the prediction of the kinetics of the Al-Cit system.
Methods: Based on existing literature, we constructed a chemical reaction network that captures the physiologically relevant speciation processes in the aquatic Al-Cit system. In order to describe the kinetics of the reaction network, we used data where species fractions were measured at various pH values and time points [4,5]. We linearly interpolated the original, pH-dependent data to obtain time-dependent data of species fractions. The reactions in the reaction network were modelled as ordinary differential equations (ODE). The corresponding kinetic parameters were estimated via the maximisation of the likelihood function by using the Nelder Mead optimisation algorithm.
Results: The resulting reaction network describes the formation of a number of different Al-Cit species, including the following: Al3+, AlHCit+, AlCit, Al(H-1Cit)– and Al3OH(H-1Cit)34-. The hydroxo-species Al(OH)4- and the polynuclear species Al3(OH)4(H-1Cit)37- were excluded from the reaction network, since they predominate in non-physiological pH ranges (higher than 9). Review of relevant literature revealed that the species identified in Al-Cit speciation studies are highly dependent on the experimental conditions (temperature, initial pH, initial Al:Cit molar concentration ratios). The reaction network was based on measurements, where the initial conditions correspond to an equimolar Al:Cit concentration ratio or citrate excess. Equimolar Al:Cit ratios are expected in the plasma after acute Al intoxication, while pronounced citrate excess is assumed to resemble typical chronic exposure scenarios [5,6].
Conclusions: Overall, our analysis shows that a system of non-linear ODEs is well-suited to describe the speciation kinetics of the Al-Cit system. The proposed chemical reaction network and corresponding ODEs pave the way for enhancing existing aluminium biokinetic models with a new level of detail on the molecular scale. Ultimately, this will lead to a deeper understanding of aluminium toxicodynamics, since it will allow to explicitly account for the impairment of biological processes by individual aluminium species.
References:
[1] Priest N. D. (2004), The biological behaviour and bioavailability of aluminium in man, with special reference to studies employing aluminium-26 as a tracer: Review and study update. Journal of environmental monitoring: JEM. 6. 375-403.
[2] Weisser K., Stübler S., Matheis W., Huisinga W. (2017), Towards toxicokinetic modelling of aluminium exposure from adjuvants in medicinal products. Regul Toxicol Pharmacol 88:310–321
[3] Christoffersen M.R. & Christoffersen J. (1985), The effect of aluminum on the rate of dissolution of calcium hydroxyapatite—A contribution to the understanding of aluminum-induced bone diseases. Calcified Tissue International. 37: 673
[4] Ohman L.O. (1988), Equilibrium and Structural Studies of Silicon(IV) and Aluminum(III) in Aqueous Solution. 17. Stable and Metastable Complexes in the System H+-Al3+-Citric Acid+. Inorg. Chem. 1988, 27, 2565-2570
[5] Lakatos A. , Bányai I. , Decock P. and Kiss T. (2001), Time‐Dependent Solution Speciation of the AlIII−Citrate System: Potentiometric and NMR Studies. Eur. J. Inorg. Chem., 2001: 461-469
[6] Harris W.R. (1992), Equilibrium model for speciation of aluminum in serum. Clin Chem. 1992 Sep;38(9):1809-18
Reference: PAGE 28 (2019) Abstr 9110 [www.page-meeting.org/?abstract=9110]
Poster: Methodology - Other topics