Chemical concentrations in cell culture compartments (C5) – free concentrations

Abstract

In biological systems (cell culture media, cells, body fluids), drugs/toxicants are usually not freely dissolved but partially bound to biomolecules; only a fraction of the chemical is free/unbound (fu). To predict pharmacological effects and toxicity, it is important that the fu of the drug is known. As the differences between free and nominal concentrations are determined by test system parameters (e.g., the protein and lipid content, and the type of surface material), comparison of nominal concentrations between two different new approach methods (NAM) may lead to faulty conclusions. The same problem exists when in vitro concentrations are compared to those in human subjects. Therefore, the respective fu of a chemical in a test system needs to be determined for in vitro-to-in vivo extrapolations (IVIVE). Besides direct measurements, prediction models can help to obtain fu. Here we describe a simplified approach to approximate fu and provide background information on the underlying assumptions. Comparative predictions and measurements of fu of various drugs are shown to exemplify the approach. Basic input data, like protein and lipid concentrations, are also provided. Beyond such test systems data, the only required chemical-specific inputs are the lipophilicity of the candidate drug and its ionization state, as determined by the dissociation constants of its acidic or basic groups. This overview is intended to be used by any lab scientist without specific toxicokinetics training to obtain an estimate of fu in a given cell culture medium.

Toward Realistic Dosimetry In Vitro: Determining Effective Concentrations of Test Substances in Cell Culture and Their Prediction by an In Silico Mass Balance Model

“…29,37,63 COL also appeared to accumulate more than expected based on its log P ow , which may be due to it binding to tubulin, where it blocks the polymerization of microtubules and suppresses the cell division and proliferation. 21,69 Prediction Capacity of the Mass Balance Model. The mass balance model predicted the total C Medium within 30% of the measured values for all but one of the test chemicals.…”

“…These include adsorption of test chemicals, e.g., binding to vessels of culture flasks and/or serum proteins and lipids, ,, evaporation, or spontaneous and enzymatic degradation of the test chemical. Other phenomena govern the uptake of chemicals into cells, including their ionization state and affinity to cellular targets such as binding to receptors and cell membranes, as well as accumulation into lysosomes. ,− The extent of these processes depends on the test system (e.g., the constituents of the culture medium, material of the vessels, coatings), as well as incubation conditions such as gas atmosphere and temperature, ,, the metabolic competence of the cells, and the physicochemical properties of the test chemical. ,− …”

A framework for chemical safety assessment incorporating new approach methodologies within REACH

“…In most cases, they do not give direct information on pathological outcomes, and the NAMs data are difficult to relate to external doses and to various exposure situations. Two fundamental strategies have been developed which work together to bridge the gap between the types of data provided by NAMs and the type of data used for regulation: the adverse outcome pathway (AOP) concept and in vitro to in vivo extrapolation (IVIVE) (Terron et al 2018 ; Punt et al 2020 ; Bos et al 2020 ; Kisitu et al 2020 ).…”

Identifying, naming and documenting of test and tool compound stocks

“…A typical example is valproic acid (iso-octanoic acid, CID: 3121, CASN: 99-66-1). In a physiological buffer, it would be mostly present in its ionized form as valproate (CID: 3549980) (Kisitu et al, 2020). In the presence of sodium ions (always present in large quantities in any physiological buffer or body fluid), it may also be called sodium valproate (CID: 16760703, CASN: 1069-66-5).…”