Modelling human cytochromes P450 for evaluating drug metabolism: an update

Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System

PURPOSE

To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions.

METHODS

A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability.

RESULTS

Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters.

CONCLUSIONS

ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL_int) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL_int, calculated as the ratio of the in vivo V_max (μmol/h/kg bw rat) to the K_m (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R² = 0.8; leave-one-out cross-validation Q² = 0.75) for CL_int normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL_int (L PL/h/kg bw rat) from the input data on log P_ow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL_int) to minimum (using UMCI for CL_int) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL_int in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL_int on human pharmacokinetics of VOCs.

Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges

Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.

Baseline lipophilicity relationships in human cytochromes P450 associated with drug metabolism

From analyses of human P450 substrates and their physicochemical properties, it is apparent that baseline lipophilicity relationships exist for over 70 substrates of eight drug-metabolizing P450 enzymes from families CYP1, CYP2, and CYP3. Equations of the general form shown below result in all cases investigated thus far: deltaG(bind) = adeltaG(part) + b where a is the slope of the line which can be termed the hydrophobicity factor of the enzyme active site, possibly being related to the extent of hydrophobic amino acid residues lining the heme pocket; b is the intercept on the y axis and can be regarded as the sum of nonhydrophobic interactions between enzyme and substrate; deltaG(bind) is the free energy change for substrate binding to P450, based on the relationship deltaG(bind) = RTlnKm where Km is the Michaelis constant, and deltaG(part) is the free energy change for partitioning between n-octanol and water based on the relationship deltaG(part) = -RTlnP where P is the n-octanol/water partition coefficient. These findings facilitate the analysis of P450 enzyme-substrate binding interactions and provide information about the likely hydrophobic character of human P450 active site regions. This shows that there are common interactions for certain numbers of substrates in each case composed of hydrogen bonding and pi-pi stacking, the extent of which varies from one P450 enzyme to another.

Modelling human cytochromes P450 involved in drug metabolism from the CYP2C5 crystallographic template

A historical background to homology modelling of human P450s involved in drug metabolism is outlined, showing that the progress in crystallographic studies of bacterial forms of enzyme and, latterly, determination of a mammalian P450 crystal structure, has enabled the production of increasingly satisfactory models of human P450 enzymes. The methodology for the generation of P450 models by homology with crystallographic template structures is summarized, and recent results of CYP2C5-constructed models of P450s are described. These indicate that selective substrates are able to fit within the putative active sites of each enzyme, where key contacts with complementary amino acid residues are largely consistent with the results of site-directed mutagenesis experiments and metabolic studies. Consequently, the CYP2C5 crystal structure can be regarded at the current paradigm for homology modelling of the drug metabolizing P450s, especially those from the CYP2 family.

Quantitative structure--activity relationships for inducers of cytochromes P450 and nuclear receptor ligands involved in P450 regulation within the CYP1, CYP2, CYP3 and CYP4 families

The results of quantitative structure-activity relationships (QSARs) are reported for several series of cytochrome P450 inducers, including those which also act as ligands for the various nuclear receptors involved in regulation of the relevant P450 genes, namely, CYP1, CYP2, CYP3 and CYP4. In several examples presented, the QSARs are consistent with homology modelling studies of the nuclear receptor ligand-binding domains (LBDs) based on available crystal structures of the oestrogen and peroxisome proliferator-activated receptors' LBDs. Good correlations (R=0.91-0.99) are found between various structural parameters and biological activity (either in the form of P450 induction or ligand-binding affinity) for the Ah receptor (AhR), human estrogen receptor alpha (hER alpha), human glucocorticoid receptor (hGR) and the rat peroxisome proliferator-activated receptor alpha (rPPAR alpha).

Molecular modeling of human cytochrome P450-substrate interactions

The results of homology modeling of 10 human cytochrome P450 (CYP) enzymes involved in the Phase 1 metabolism of drugs and other foreign compounds are reported. The models have been constructed from the CYP102 hemoprotein domain template for which the substrate-bound crystallographic coordinates are available. Selective substrates of individual human P450s: CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP4A11 are all shown to fit within the corresponding enzymes' active sites in such a manner that is consistent with reported experimental data for both known pathways of substrate metabolism and from the results of site-directed mutagenesis, either in those particular human P450 enzymes concerned or for ones within the same subfamily. The self-consistency of these homology models indicates that they may have potential utility for the pre-screening of novel drug structures.

Structure-activity relationship for human cytochrome P450 substrates and inhibitors

Criteria governing the avidity of substrate binding to human hepatic cytochromes P450 (CYP) associated with Phase 1 metabolism of drugs are described. The results of extensive quantitative structure-activity relationship (QSAR) analyses are reported for substrates of human P450s: CYPIA2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, representing the enzymes exhibiting major involvement in the metabolism of drug substrates in Homo sapiens. In particular, it is shown that hydrogen bond properties in each class of enzyme-substrate complex are especially important factors in determining substrate binding affinity towards those human P450s which are involved in drug metabolism.