Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4

Consideration of Nevirapine Analogs To Reduce Metabolically Linked Hepatotoxicity: A Cautionary Tale of the Deuteration Approach

Idiosyncratic drug reactions (IDRs) in their most deleterious form can lead to serious medical complications and potentially fatal events. Nevirapine (NVP), still widely used in developing countries for combinatorial antiretroviral and prophylactic therapies against HIV infection, represents a prototypical example of IDRs causing severe skin rashes and hepatotoxicity. Complex metabolic pathways accompanied by production of multiple reactive metabolites often complicate our understanding of IDR's origin. While assessment of NVP analogs has helped characterize the pathways involved in IDRs for NVP, which are largely driven by metabolism at the 12-methyl position, it has yet to be investigated if some of these analogs could be valuable replacement drugs with reduced reactive metabolite properties and drug-drug interaction (DDI) risks. Here, we evaluated a set of eight NVP analogs, including the deuterated 12-d3-NVP and two NVP metabolites, for their efficacy and inhibitory potencies against HIV reverse transcriptase (HIV-RT). A subset of three analogs, demonstrating >85% inhibition for HIV-RT, was further assessed for their hepatic CYP induction-driven DDI risks. This led to a closer investigation of the inactivation properties of 12-d3-NVP for hepatic CYP3A4 and a comparison of its propensity in generating reactive metabolite species. The metabolic shift triggered with 12-d3-NVP, increasing formation of the 2-hydroxy and glutathione metabolites, emphasized the importance of the dynamic balance between induction and metabolism-dependent inactivation of CYP3A4 and its impact on clearance of NVP during treatment. Unfortunately, the strategy of incorporating deuterium to reduce NVP metabolism and production of the electrophile species elicited opposite results, illustrating the great challenges involved in tackling IDRs through deuteration.

Identification of Human Alanine-Glyoxylate Aminotransferase Ligands as Pharmacological Chaperones for Variants Associated with Primary Hyperoxaluria Type 1

Primary hyperoxaluria type I (PH1) is a rare kidney disease due to the deficit of alanine:glyoxylate aminotransferase (AGT), a pyridoxal-5′-phosphate-dependent enzyme responsible for liver glyoxylate detoxification, which in turn prevents oxalate formation and precipitation as kidney stones. Many PH1-associated missense mutations cause AGT misfolding. Therefore, the use of pharmacological chaperones (PCs), small molecules that promote correct folding, represents a useful therapeutic option. To identify ligands acting as PCs for AGT, we first performed a small screening of commercially available compounds. We tested each molecule by a dual approach aimed at defining the inhibition potency on purified proteins and the chaperone activity in cells expressing a misfolded variant associated with PH1. We then performed a chemical optimization campaign and tested the resulting synthetic molecules using the same approach. Overall, the results allowed us to identify a promising hit compound for AGT and draw conclusions about the requirements for optimal PC activity.

Inhibition of CYP3A7 DHEA-S Oxidation by Lopinavir and Ritonavir: An Alternative Mechanism for Adrenal Impairment in HIV Antiretroviral-Treated Neonates

Prophylactic antiretroviral therapy (ART) in HIV infected pregnant mothers and their newborns can dramatically reduce mother-to-child viral transmission and seroconversion in the neonate. The ritonavir-boosted lopinavir regimen, known as Kaletra, has been associated with premature birth and transient adrenal insufficiency in newborns, accompanied by increases in plasma dehydroepiandrosterone 3-sulfate (DHEA-S). In the fetus and neonates, cytochrome P450 CYP3A7 is responsible for the metabolism of DHEA-S into 16α-hydroxy DHEA-S, which plays a critical role in growth and development. In order to determine if CYP3A7 inhibition could lead to the adverse outcomes associated with Kaletra therapy, we conducted in vitro metabolic studies to determine the extent and mechanism of CYP3A7 inhibition by both ritonavir and lopinavir and the relative intrinsic clearance of lopinavir with and without ritonavir in both neonatal and adult human liver microsomes (HLMs). We identified ritonavir as a potent inhibitor of CYP3A7 oxidation of DHEA-S (IC₅₀ = 0.0514 μM), while lopinavir is a much weaker inhibitor (IC₅₀ = 5.88 μM). Furthermore, ritonavir is a time-dependent inhibitor of CYP3A7 with a K_I of 0.392 μM and a k_inact of 0.119 min^–1, illustrating the potential for CYP3A mediated drug–drug interactions with Kaletra. The clearance rate of lopinavir in neonatal HLMs was much slower and comparable to the rate observed in adult HLMs in the presence of ritonavir, suggesting that the addition of ritonavir in the cocktail therapy may not be necessary to maintain effective concentrations of lopinavir in neonates. Our results suggest that several of the observed adverse outcomes of Kaletra therapy may be due to the direct inhibition of CYP3A7 by ritonavir and that the necessity for the inclusion of this drug in the therapy may be obviated by the lower rate of lopinavir clearance in the neonatal liver. These results may lead to a reconsideration of the use of ritonavir in neonatal antiretroviral therapy.

Cycloserine enantiomers are reversible inhibitors of human alanine:glyoxylate aminotransferase: implications for Primary Hyperoxaluria type 1

Peroxisomal alanine:glyoxylate aminotransferase (AGT) is responsible for glyoxylate detoxification in human liver and utilizes pyridoxal 5'-phosphate (PLP) as coenzyme. The deficit of AGT leads to Primary Hyperoxaluria Type I (PH1), a rare disease characterized by calcium oxalate stones deposition in the urinary tract as a consequence of glyoxylate accumulation. Most missense mutations cause AGT misfolding, as in the case of the G41R, which induces aggregation and proteolytic degradation. We have investigated the interaction of wild-type AGT and the pathogenic G41R variant with d-cycloserine (DCS, commercialized as Seromycin), a natural product used as a second-line treatment of multidrug-resistant tuberculosis, and its synthetic enantiomer l-cycloserine (LCS). In contrast with evidences previously reported on other PLP-enzymes, both ligands are AGT reversible inhibitors showing inhibition constants in the micromolar range. While LCS undergoes half-transamination generating a ketimine intermediate and behaves as a classical competitive inhibitor, DCS displays a time-dependent binding mainly generating an oxime intermediate. Using a mammalian cellular model, we found that DCS, but not LCS, is able to promote the correct folding of the G41R variant, as revealed by its increased specific activity and expression as a soluble protein. This effect also translates into an increased glyoxylate detoxification ability of cells expressing the variant upon treatment with DCS. Overall, our findings establish that DCS could play a role as pharmacological chaperone, thus suggesting a new line of intervention against PH1 based on a drug repositioning approach. To a widest extent, this strategy could be applied to other disease-causing mutations leading to AGT misfolding.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Numerical Analysis of Time-Dependent Inhibition by MDMA

Methylenedioxymethamphetamine (MDMA) is a known drug of abuse and schedule 1 narcotic under the Controlled Substances Act. Previous pharmacokinetic work on MDMA used classic linearization techniques to conclude irreversible mechanism-based inhibition of CYP2D6. The current work challenges this outcome by assessing the possibility of two alternative reversible kinetic inhibition mechanisms known as the quasi-irreversible (QI) model and equilibrium model (EM). In addition, progress curve experiments were used to investigate the residual metabolism of MDMA by liver microsomes and CYP2D6 baculosomes over incubation periods up to 30 minutes. These experiments revealed activity in a terminal linear phase at the fractional rates with respect to initial turnover of 0.0354 ± 0.0089 in human liver microsomes and 0.0114 ± 0.0025 in baculosomes. Numerical model fits to percentage of remaining activity (PRA) data were consistent with progress curve modeling results, wherein an irreversible inhibition pathway was found unnecessary for good fit scoring. Both QI and EM kinetic mechanisms fit the PRA data well, although in CYP2D6 baculosomes the inclusion of an irreversible inactivation pathway did not allow for convergence to a reasonable fit. The kinetic complexity accessible to numerical modeling has been used to determine that MDMA is not an irreversible inactivator of CYP2D6, and instead follows what can be generally referred to as slowly reversible inhibition. SIGNIFICANCE STATEMENT: The work herein describes the usage of computational models to delineate between irreversible and slowly reversible time-dependent inhibition. Such models are used in the paper to analyze MDMA and classify it as a reversible time-dependent inhibitor.

The nested enzyme-within-enterocyte (NEWE) turnover model for predicting dynamic drug and disease effects on the gut wall

Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro-in vivo extrapolation of metabolic drug clearance. Many of the concepts in PBPK can have consequential impact on more mechanistic systems pharmacology models. In the gut wall, turnover of enzymes and enterocytes are typically lumped into one rate constant that describes the time dependent enzyme activity. This assumption may influence predictability of any sustained and dynamic effects such as mechanism-based inhibition (MBI), particularly when considering translation from healthy to gut disease. A novel multi-level systems PBPK model was developed. This model comprised a 'nested enzyme-within enterocyte' (NEWE) turnover model to describe levels of drug-metabolising enzymes. The ability of the model to predict gut metabolism following MBI and gut disease was investigated and compared to the conventional modelling approach. For MBI, the default NEWE model performed comparably to the conventional model. However, when drug-specific spatial crypt-villous absorption was considered, up to approximately 50% lower impact of MBI was simulated for substrates highly metabolised by cytochrome P450 (CYP) 3A4, interacting with potent inhibitors. Further, the model showed potential in predicting the disease effect of gastrointestinal mucositis and untreated coeliac disease when compared to indirect clinical pharmacokinetic parameters. Considering the added complexity of the NEWE model, it does not provide an attractive solution for improving upon MBI predictions in healthy individuals. However, nesting turnover may enable extrapolation to gut disease-drug interactions. The principle detailed herein may be useful for modelling drug interactions with cellular targets where turnover is significant enough to affect this process.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

A numerical method for analysis of in vitro time-dependent inhibition data. Part 2. Application to experimental data

Time-dependent inhibition (TDI) of cytochrome P450 enzymes is an important cause of drug-drug interactions. The standard approach to characterize the kinetics of TDI is to determine the rate of enzyme loss, kobs, at various inhibitor concentrations, [I], and replot the kobs versus [I] to obtain the key kinetic parameters, KI and kinact. In our companion manuscript (Part 1; Nagar et al., 2014) in this issue of Drug Metabolism and Disposition, we used simulated datasets to develop and test a new numerical method to analyze in vitro TDI data. Here, we have applied this numerical method to five TDI datasets. Experimental datasets include the inactivation of CYP2B6, CYP2C8, and CYP3A4. None of the datasets exhibited Michaelis-Menten-only kinetics, and the numerical method allowed use of more complex models to fit each dataset. Quasi-irreversible as well as partial inhibition kinetics were observed and parameterized. Three datasets required the use of a multiple-inhibitor binding model. The mechanistic and clinical implications provided by these analyses are discussed. Together with the results in Part 1, we have developed and applied a new numerical method for analysis of in vitro TDI data. This method appears to be generally applicable to model in vitro TDI data with atypical and complex kinetic schemes.

A numerical method for analysis of in vitro time-dependent inhibition data. Part 1. Theoretical considerations

Inhibition of cytochromes P450 by time-dependent inhibitors (TDI) is a major cause of clinical drug-drug interactions. It is often difficult to predict in vivo drug interactions based on in vitro TDI data. In part 1 of these manuscripts, we describe a numerical method that can directly estimate TDI parameters for a number of kinetic schemes. Datasets were simulated for Michaelis-Menten (MM) and several atypical kinetic schemes. Ordinary differential equations were solved directly to parameterize kinetic constants. For MM kinetics, much better estimates of KI can be obtained with the numerical method, and even IC50 shift data can provide meaningful estimates of TDI kinetic parameters. The standard replot method can be modified to fit non-MM data, but normal experimental error precludes this approach. Non-MM kinetic schemes can be easily incorporated into the numerical method, and the numerical method consistently predicts the correct model at errors of 10% or less. Quasi-irreversible inactivation and partial inactivation can be modeled easily with the numerical method. The utility of the numerical method for the analyses of experimental TDI data is provided in our companion manuscript in this issue of Drug Metabolism and Disposition (Korzekwa et al., 2014b).

Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems

Recent guidance on drug-drug interaction (DDI) testing recommends evaluation of circulating metabolites. However, there is little consensus on how to quantitatively predict and/or assess the risk of in vivo DDIs by multiple time-dependent inhibitors (TDIs) including metabolites from in vitro data. Fluoxetine was chosen as the model drug to evaluate the role of TDI metabolites in DDI prediction because it is a TDI of both CYP3A4 and CYP2C19 with a circulating N-dealkylated inhibitory metabolite, norfluoxetine. In pooled human liver microsomes, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, (S)-norfluoxetine was the most potent inhibitor with time-dependent inhibition affinity constant (KI) of 7 μM, and apparent maximum time-dependent inhibition rate (k(inact,app)) of 0.059 min(-1). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (K(I) = 8 μM, and k(inact,app) = 0.011 min(-1)). Based on in-vitro-to-in-vivo predictions, (S)-norfluoxetine plays the most important role in in vivo CYP2C19 DDIs, whereas (R)-norfluoxetine is most important in CYP3A4 DDIs. Comparison of two multiple TDI prediction models demonstrated significant differences between them in in-vitro-to-in-vitro predictions but not in in-vitro-to-in-vivo predictions. Inclusion of all four inhibitors predicted an in vivo decrease in CYP2C19 (95%) and CYP3A4 (60-62%) activity. The results of this study suggest that adequate worst-case risk assessment for in vivo DDIs by multiple TDI systems can be achieved by incorporating time-dependent inhibition by both parent and metabolite via simple addition of the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (λ/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.