Humanising mice for CYP modulation and DDI studies


In a study of 300,000 patients in Scotland it was estimated that the average number of drugs taken by an individual rose from 3.3 to 4.4 between the years 1995 and 2010. Of that figure 16% of patients over 65 were taking 10 or more drugs. Considering the aged population is set to double over the next 35 years (by 2050 one in four people in the UK will be 65+) polypharmacy represents a major issue for clinicians (1) and pharmaceutical research alike.

The CYP 450 superfamily of enzymes mediate the turnover and represent the major clearance route of xenobiotics, accounting for the phase 1 metabolism of 80 % of all known drugs. Whilst investigational drugs may be substrates for these enzymes they can also potentially act as inhibitors or inducers, producing a range of undesired PK effects including reduced exposure, enhanced production of reactive metabolite, altered processing of endogenous substrates and modification of the pharmacokinetics of concomitant medications (Drug- drug interactions (DDI)).

In light of increases in polypharmacy, especially in Oncology where anticancer drugs are administered close to maximum tolerated levels, DDIs present a significant safety hazard to patients requiring a thorough investigation of the metabolic/clearance modulating potential of new drugs.

A number of in vitro assays can now readily identify CYP450 inhibitors and active metabolites but identification of CYP450 inducers requires the use of more complex model systems. Immortalised hepatocyte cell lines tend to exhibit a significant reduction in CYP expression level and whilst cryo-preserved hepatocytes are the most accessible in vitro system for analysing CYP induction, they lack the host factors involved in complex DDIs and non-hepatic clearance. Animal models offer the most complex systems for characterising investigational drug PK however results must regarded with caution and only interpreted via established correlations.

Humans have 57 CYP genes compared to 102 in mice and of the 23 genes in the human CYP1-3 families (the CYPs most responsible for the metabolism of xenobiotics) there are only 5 orthologous genes in mice(2) (CYP1A1/2/B2, CYP2E1/S1) offering limited utility in accurate prediction of metabolism and CYP450 modulation by investigational drugs in humans. This difference is compounded by the CYP transcriptional activators (e.g. Pregnane X Receptor (PXR) and Constitutive Androstane Receptor (CAR), both activators of CYP3A4) exhibiting differing substrate affinities between the species.

Recently Sheer el al (3) have described the generation of a humanised mouse in which the endogenous Cyp3a, Cyp2d and Cyp2c gene-clusters, in addition to the PXR and CAR genes, were replaced by the human CYP3A4, CYP2D6, CYP2C9, PXR and CAR genes respectively (whilst not necessarily structurally orthologous, the three mice CYP clusters are responsible for the majority of xenobiotic metabolism in mice). In humans CYP3A4 accounts for 45% of CYP activity, with all three enzymes (including CYP2D6 and CYP2C9) collectively responsible for ≈75% of all phase 1 metabolism. The level of promiscuity exhibited by CYP3A4 is matched by the transcriptional activators PXR and CAR that, upon binding a broad spectrum of ligands, translocate to the nucleus and bind to response elements in the CYP3A4 promoter (as a heterodimer with the Retinoid X Receptor (RXR)) and numerous other phase 1, 2 and 3 metabolising and transport protein genes. To ensure a response to xenobiotic stimuli similar to that observed in humans the mice PXR and CAR genes were swapped with their human counterparts as whilst the human and mouse genes/proteins share moderate homology (PXR: 83% DNA/77% protein. CAR: 80% DNA/73% protein), affinity for various ligands differ significantly (4). Mouse and human RXR however are highly homologous (89%DNA/97% protein) negating the need to replace the endogenous gene.

Treatment of PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mice with Rifampicin (RIF; a potent activator of PXR) demonstrated a significant up-regulation of CYP3A4 whilst, as expected, not significantly altering the basal levels of expression of CYP2C9 (under transcriptional control of the liver specific albumin promoter )or CYP2D6 (not responsive to CAR or PXR).

Marcus 11-01-2015 Picture 1

Figure 1. A comparison of CYP3A4, 2D6 and 2C9 expression in microsomes derived from human and RIF/vehicle (Corn oil) treated PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mouse livers. Donor 1 and 2 represent human individuals with low and high activity respectively for both CYP3A4 and CYP2D6.

The kinetics of CYP3A4 in microsomes from RIF/vehicle treated PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mice was verified by midazolam (MDZ; CYP3A4 specific substrate) metabolism and a series of CYP3A4 specific inhibitors: Ketoconazole (KTZ; mixed competitive/non-competitive), Erythromycin (Mechanism based inhibitor) and CYP3cide (time dependant inhibitor) with controls provided by wild type mice and human derived microsomes. A comparison to Cyp2c and Cpy3c KO mice also verified that MDZ was metabolised solely by CYP3A4 in PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mice. A 58 fold increase in Vmax between RIF and vehicle treated PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 confirmed western blot and LC/MS-MS measured increases in protein expression of CYP3A4

Similarly, activity of CYP2D6 and 2C9 was verified with substrates Bufuralol and diclofenac and inhibitors Quinidine and Sulphenozole respectively.

A single In Vivo study was performed in which PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mice were treated i.p. with either vehicle or RIF for three days before p.o. administration of MDZ or KTZ followed 30 min later by MDZ (figure 2). RIF treatment resulted in an 8-fold decrease in MDZ exposure over vehicle treated mice. KTZ treatment resulted in a 1.9-fold increase of the AUC over RIF only treated mice indicative of the inhibition of hepatic/intestinal CYP3A4.

Marcus 11-01-2015 Picture 2

Figure 2. A) Whole blood concentrations of MDZ vs time in vehicle, RIF and RIF + KTZ treated PXR/ CAR/CYP3A4/ CYP2D6/CYP2C9 mice. B) MDZ AUC values for graph A.

In generating a humanised mouse Scheer et al have demonstrated the utility of this model for the prediction of drug PK in humans enabling the investigation of the relationship of efficacy and exposure, CYP induction or inhibition and subsequently the effects of various drug combinations possibly resulting in DDIs. Whilst not a comprehensive assay of drug/human CYP interaction this multiply humanised mouse may be the starting point for the generation of an array of transgenic animals that might go further to model a more complete system to include (inducible, or not) human transporters and phase 2 enzymes. Further, by introducing tissue specific promoters relative contributions of hepatic and extra-hepatic clearance routes could potentially be delineated.

Viable Cyp3a and Cyp2d KO mice have previously (5) been generated with only minor phenotypic variances from w/t demonstrating a high level of redundancy of the CYP metabolic system. Considering the promiscuity of human CYPs and their corresponding inducers perhaps these models can be further humanised without jeopardising the processing of important endogenous substrates thereby enhancing the predictive power of the model.

The attrition rate of drugs in clinical development due to PK related issues fell 4-fold between the years of 1991 and 2000 as the industry better understood both the predictive (or lack of) and correlative value of in vitro and in vivo DMPK assays. Monitoring complex DDIs in a broader population falls within the remit of Phase 4 studies so perhaps the development of an array of animal models that are directly predictive (or in the least demonstrate much stronger correlation to humans than we have at present) would facilitate the earlier triage of candidates with clear DDI issues, further reducing the risk of late stage failure.

Blog written by Marcus Hanley

1 – Guthrie B et al (2012) Primary Health Care Research & Development, vol 13, supp S1: 45 2E.2.

2 – Nelson DR et al (2004) Pharmacogenetics, 14:1-18

3 – Scheer N et al (2015) Drug Metab Dispos 43:1679–1690

4 – Xie W et al. (2000) Nature 406:435–439.

5 – Scheer N et al (2012) Mol Pharmacol 82:1022–102.

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