Assessment of degradation rate constants for quantitative predictions of drug-drug interactions arising from CYP450 drug metabolising enzymes



Chan, CY
(2018) Assessment of degradation rate constants for quantitative predictions of drug-drug interactions arising from CYP450 drug metabolising enzymes. PhD thesis, University of Liverpool.

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Abstract

The first-order degradation rate constant (kdeg) of drug metabolising enzymes (DMEs) is a known source of uncertainty in the prediction of time-dependent drug-drug interactions (DDIs) in physiologically-based pharmacokinetic (PBPK) modelling. There is a large disparity or paucity of published kdeg and related half-life (t1/2) values for DMEs. Physiologically-relevant kdeg values should ideally be derived in vivo to facilitate accurate DDI predictions. However, direct measurement of DME degradation in humans is not routinely possible and indirect measurements utilising changes in levels of specific endogenous substrates have only been described for a few DMEs. This thesis aims to develop an in vitro method of measuring DME protein degradation rates to improve the prediction accuracy of time-dependent DDIs. One in vitro approach of measuring protein degradation rates involves inhibiting de novo protein synthesis, followed by tracking residual protein or activity decline over time. Pharmacological protein synthesis inhibitor agents are commonly used for this purpose but may cause cytotoxicity. Four commonly used inhibitor agents were assessed for their capacity to inhibit protein synthesis without overt cytotoxicity. However, all selected compounds were too cytotoxic for subsequent use in kdeg studies. Small-interfering ribose nucleic acid (siRNA) can be added to in vitro systems to initiate gene-specific silencing by inhibiting messenger RNA (mRNA) translation. It was hypothesised that siRNA would inhibit de novo protein synthesis with less cytotoxicity owing to its specificity. CYP3A4 is the most widely studied cytochrome P450 (CYP) enzyme in terms of DDIs because of its well-recognised role in xenobiotic metabolism. Primary human hepatocytes were treated with CYP3A4-specific siRNA to suppress mRNA translation, followed by the tracking of enzyme activity and protein loss over time to derive kdeg. CYP3A4 kdeg was calculated at 0.019 (± 0.044) and 0.020 (± 0.0003) h-1 from protein and activity loss, respectively. These values were in good agreement with existing published values. The siRNA approach was subsequently applied to determine CYP2B6 kdeg. The CYP2B6 kdeg values derived from siRNA-treated hepatocytes were 0.081 (± 0.009) h-1 from protein loss and 0.058 (± 0.010) and 0.062 (± 0.006) h-1 from activity loss, which were assessed by different methods. The CYP2B6 kdeg values derived from untreated hepatocytes were similar to values in literature. This novel approach can now be used for other less well-characterised DMEs that are associated with time-dependent DDIs. Cellular protein abundance is a balance between synthesis and degradation. Dysregulation of the lysosomal or proteasomal protein degradation mechanisms affects steady-state protein levels and impacts on overall cellular functions. It was hypothesised that single nucleotide polymorphisms (SNPs) in the CYP3A4 protein degradation machinery could affect CYP3A4 protein abundance and downstream activity. Five SNPs were investigated for associations with plasma atazanair (ATV) concentrations, which was a surrogate measure for CYP3A4 activity. No associations were found and this was likely due to the lack of clear understanding of the specific mechanisms that commits CYP proteins for degradation. Further work in this field will identify targets that may be exploited in the future for more accurate measurements of DME kdeg. The data presented in this thesis enhances the understanding of methods used to study protein degradation and this can be applied to multiple fields of cellular research. Importantly, work herein has generated a novel approach to measuring kdeg of proteins that can be applied to other less-well characterised enzymes for better prediction of time-dependent DDIs.

Item Type: Thesis (PhD)
Additional Information: Permanent email address: cchan2106@gmail.com
Divisions: Faculty of Health and Life Sciences > Faculty of Health and Life Sciences
Depositing User: Symplectic Admin
Date Deposited: 22 Aug 2018 08:43
Last Modified: 08 Aug 2021 07:10
DOI: 10.17638/03020976
Supervisors:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3020976