Chapter 30. Inhibition of Cytochrome P-450 and Implications in Drug Development

Optimizing higher throughput methods to assess drug-drug interactions for CYP1A2, CYP2C9, CYP2C19, CYP2D6, rCYP2D6, and CYP3A4 in vitro using a single point IC(50)

Drug-drug interactions involving cytochrome P(450) (CYP) are an important factor in whether a new chemical entity will survive through to the development stage. Therefore, the identification of this potential as early as possible in vitro could save considerable future unnecessary investment. In vitro CYP interaction screening data generated for CYP2C9, CYP2D6, and CYP3A4 were initially analyzed to determine the correlation of IC(50) from 10- and 3-point determinations. A high correlation (r = 0.99) prompted the further assessment of predicting the IC(50) by a single value of percent inhibition at either 10, 3, or 1 microM. Statistical analysis of the initial proprietary compounds showed that there was a strong linear relationship between log IC(50) and percent inhibition at 3 microM, and that it was possible to predict a compound's IC(50) by the percent inhibition value obtained at 3 microM. Additional data for CYP1A2, CYP2C19, and the recombinant CYP2D6 were later obtained and used together with the initial data to demonstrate that a single statistical model could be applicable across different CYPs and different in vitro microsomal systems. Ultimately, the data for all five CYPs and the recombinant CYP2D6 were used to build a statistical model for predicting the IC(50) with a single point. The 95% prediction boundary for the region of interest was about +/- 0.37 on log(10) scale, comparable to the variability of in vitro determinations for positive control IC(50) data. The use of a single inhibitor concentration would enable determination of more IC(50) values on a 96-well plate and result in more economical use of compounds, human liver or expressed enzyme microsomes, substrates, and reagents. This approach would offer the opportunity to increase screening for CYP-mediated drug-drug interactions, which may be important given the challenges provided by the generation of orders of magnitude more new chemical entities in the field of combinatorial chemistry. In addition, the algorithmic approach we propose would obviously be applicable for other in vitro bioactivity and therapeutic target enzyme and receptor screens.

Problems associated with in vitro assessment of drug inhibition of CYP3A4 and other P-450 enzymes and its impact on drug discovery

The cytochromes P-450 recognize and metabolize a broad range of structurally diverse therapeutic agents. As a consequence, many clinically relevant drug--drug interactions (DDI) are associated with inhibition and/or induction of a specific P-450 enzymes (in particular human cytochrome P-450 3A4, CYP3A4). In addition to inhibition and induction, CYP-mediated drug metabolism may be enhanced upon coincubation with certain compounds. Moreover, some of these enzyme-based interactions appear to be substrate specific. In this presentation, several issues associated with the generation of accurate DDI information will be discussed.

Present and future in vitro approaches for drug metabolism

The 1980s through 1990s witnessed the widespread incorporation of in vitro absorption, distribution, metabolism, and excretion (ADME) approaches into drug development by drug companies. This has been exemplified by the integration of the basic science of cytochrome P450s (CYPs) into most drug metabolism departments so that information on the metabolic pathways of drugs and drug-drug interactions (DDIs) is no longer an academic exercise, but essential for regulatory submission. This has come about due to the application of a variety of new technologies and in vitro models. For example, subcellular fractions have been widely used in metabolism studies since the 1960s. The last two decades has seen the increased use of hepatocytes as the reproducibility of cell isolations improved. The 1990s saw the rejuvenation of liver slices (as new slicers were developed) and the utilization of cDNA expressed enzymes as these technologies matured. In addition, there has been considerable interest in extrapolating in vitro data to in vivo for parameters such as absorption, clearance and DDIs. The current philosophy of drug development is moving to a 'fail early--fail cheaply' paradigm. Therefore, in vitro ADME approaches are being applied to drug candidates earlier in development since they are essential for identifying compounds likely to present ADME challenges in the latter stages of drug development. These in vitro tools are also being used earlier in lead optimization biology, in parallel with approaches for optimizing target structure activity relationships, as well as identification of DDI and the involvement of metabolic pathways that demonstrate genetic polymorphisms. This would suggest that the line between discovery and development drug metabolism has blurred. In vitro approaches to ADME are increasingly being linked with high-throughput automation and analysis. Further, if we think of perhaps the fastest available way to screen for successful drugs with optimal ADME characteristics, then we arrive at predictive computational algorithms, which are only now being generated and validated in parallel with in vitro and in vivo methods. In addition, as we increase the number of ADME parameters determined early, the overall amount of data generated for both discovery and development will increase. This will present challenges for the efficient and fast interpretation of such data, as well as incorporation and communication to chemistry, biology, and clinical colleagues. This review will focus on and assess the nature of present in vitro metabolism approaches and indicate how they are likely to develop in the future.

Novel approach to predicting P450-mediated drug metabolism: development of a combined protein and pharmacophore model for CYP2D6

A combined protein and pharmacophore model for cytochrome P450 2D6 (CYP2D6) has been derived using various computational chemistry techniques. A combination of pharmacophore modeling (using 40 substrates), protein modeling, and molecular orbital calculations was necessary to derive a model which incorporated steric, electronic, and chemical stability properties. The initial pharmacophore and protein models used to construct the combined model were derived independently and showed a high level of complementarity. The combined model is in agreement with experimental results concerning the substrates used to derive the model, with site-directed mutagenesis data available for the CYP2D6 protein, and takes into account the site-directed mutagenesis results for a variety of other 2-family P450s.