Integrated in vitro analysis for the in vivo prediction of cytochrome P450-mediated drug-drug interactions

Overview of preclinical and clinical studies investigating pharmacokinetics and drug-drug interactions of padsevonil

BACKGROUND

Padsevonil is an antiseizure medication candidate intended to benefit patients with drug-resistant epilepsy. Our investigations aimed at characterizing pharmacokinetics and drug-drug interaction (DDI) profile of padsevonil.

RESEARCH DESIGN AND METHODS

An overview of preclinical and clinical pharmacology studies conducted during padsevonil development is provided.

RESULTS

In preclinical studies, cytochrome (CYP) 3A4 was identified as the main P450 isoform involved in padsevonil metabolism, with potential minor contribution from CYP2C19. Padsevonil was shown to be a time-dependent CYP2C19-inhibitor, weak CYP3A4-inducer, weak inhibitor of P-gp/OCT1/MATE2-K, and potent OCT2-inhibitor. Initial clinical pharmacology studies in healthy participants showed that padsevonil had (i) good absorption, (ii) clearance mediated mainly by metabolism, and (iii) time-dependent kinetics. A study in genotyped participants confirmed the role of CYP2C19 in clearance and time-dependent kinetics; the major contribution of CYP3A4 was confirmed in DDI studies with CYP3A4-inducers (carbamazepine, oxcarbazepine) and -inhibitor (erythromycin). Padsevonil did not affect pharmacokinetics of valproate/lamotrigine/levetiracetam/oxcarbazepine or oral contraceptives. In a cocktail clinical study, padsevonil showed moderate CYP2C19 inhibition (omeprazole) and weak CYP3A4 induction (oral midazolam). No specific effects on CYP1A2 (caffeine), CYP2C9 (S-warfarin), and CYP2D6 (dextromethorphan) were observed.

CONCLUSIONS

The studies presented helped in understanding padsevonil disposition and risks of DDIs, which would inform dosing and prescribing.

CLINICAL TRIAL REGISTRATION

https://www.clinicaltrials.gov identifiers are NCT04131517, NCT03480243, NCT03695094, NCT04075409.

Kinetic Modeling of Steady-State Situations in Cytochrome P450 Enzyme Reactions

In the course of investigations of the kinetics of individual reactions of cytochrome P450 (P450) enzymes, a number of points about the complexity of P450 enzyme kinetics have become apparent. Several of these are of particular relevance to work with P450 enzymes in the course of drug development and lead optimization, particularly with regard to estimating in vitro kinetic parameters and dealing with enzyme inhibitors. Modern simulation modeling has been applied to situations involving issues of preincubation time with moderate strength and strong inhibitors, inhibition by tightly bound ligands that have been identified in P450 enzymes, extensive substrate depletion, P450 reactions with a rate-limiting step after product formation, and the consumption of an inhibitor during a reaction by either a P450 enzyme being monitored or another one in a mixture. The results all follow from first principles, and simulations reveal the extent of their significance in various settings. The order of addition of substrate and inhibitors can change the apparent outcome (inhibition constant, K _i), and the effect of the order is more pronounced with a stronger inhibitor. Substrate depletion alters parameters (Michaelis constant, K _m) and can generate apparently sigmoidal plots. A rate-limiting step after product formation lowers the apparent K _m and distorts K _i Consumption of an inhibitor during a reaction affects K _i and differs depending on which enzyme is involved. The results are relevant with P450 enzymes and other enzymes as well. SIGNIFICANCE STATEMENT: Kinetic simulations have been used to address several potential problems in enzyme kinetic analysis. Although the simulations done here are general for enzyme reactions, several problems addressed here are particularly relevant to cytochrome P450 reactions encountered in drug development work.

Guanfu base A, an antiarrhythmic alkaloid of Aconitum coreanum, Is a CYP2D6 inhibitor of human, monkey, and dog isoforms

Guanfu base A (GFA) is a novel heterocyclic antiarrhythmic drug isolated from Aconitum coreanum (Lèvl.) rapaics and is currently in a phase IV clinical trial in China. However, no study has investigated the influence of GFA on cytochrome P450 (P450) drug metabolism. We characterized the potency and specificity of GFA CYP2D inhibition based on dextromethorphan O-demethylation, a CYP2D6 probe substrate of activity in human, mouse, rat, dog, and monkey liver microsomes. In addition, (+)-bufuralol 1'-hydroxylation was used as a CYP2D6 probe for the recombinant form (rCYP2D6), 2D1 (rCYP2D1), and 2D2 (rCYP2D2) activities. Results show that GFA is a potent noncompetitive inhibitor of CYP2D6, with inhibition constant Ki = 1.20 ± 0.33 μM in human liver microsomes (HLMs) and Ki = 0.37 ± 0.16 μM for the human recombinant form (rCYP2D6). GFA is also a potent competitive inhibitor of CYP2D in monkey (Ki = 0.38 ± 0.12 μM) and dog (Ki = 2.4 ± 1.3 μM) microsomes. However, GFA has no inhibitory activity on mouse or rat CYP2Ds. GFA did not exhibit any inhibition activity on human recombinant CYP1A2, 2A6, 2C8, 2C19, 3A4, or 3A5, but showed slight inhibition of 2B6 and 2E1. Preincubation of HLMs and rCYP2D6 resulted in the inactivation of the enzyme, which was attenuated by GFA or quinidine. Beagle dogs treated intravenously with dextromethorphan (2 mg/ml) after pretreatment with GFA injection showed reduced CYP2D metabolic activity, with the Cmax of dextrorphan being one-third that of the saline-treated group and area under the plasma concentration-time curve half that of the saline-treated group. This study suggests that GFA is a specific CYP2D6 inhibitor that might play a role in CYP2D6 medicated drug-drug interaction.

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Evaluation of various static in vitro-in vivo extrapolation models for risk assessment of the CYP3A inhibition potential of an investigational drug

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

A screening study of drug-drug interactions in cerivastatin users: an adverse effect of clopidogrel

An analysis of a case-control study of rhabdomyolysis was conducted to screen for previously unrecognized cytochrome P450 enzyme (CYP) 2C8 inhibitors that may cause other clinically important drug-drug interactions. Medication use in cases of rhabdomyolysis using cerivastatin (n = 72) was compared with that in controls using atorvastatin (n = 287) for the period 1998-2001. The use of clopidogrel was strongly associated with rhabdomyolysis (odds ratio (OR) 29.6; 95% confidence interval (CI), 6.1-143). In a replication effort that used the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS), it was found that clopidogrel was used more commonly in patients with rhabdomyolysis receiving cerivastatin (17%) than in those receiving atorvastatin (0%, OR infinity; 95% CI = 5.2-infinity). Several medications were tested in vitro for their potential to cause drug-drug interactions. Clopidogrel, rosiglitazone, and montelukast were the most potent inhibitors of cerivastatin metabolism. Clopidogrel and its metabolites also inhibited cerivastatin metabolism in human hepatocytes. These epidemiological and in vitro findings suggest that clopidogrel may cause clinically important, dose-dependent drug-drug interactions with other medications metabolized by CYP2C8.

Prediction of CYP2D6 drug interactions from in vitro data: evidence for substrate-dependent inhibition

Predicting the magnitude of potential drug-drug interactions is important for underwriting patient safety in the clinical setting. Substrate-dependent inhibition of cytochrome P450 enzymes may confound extrapolation of in vitro results to the in vivo situation. However, the potential for substrate-dependent inhibition with CYP2D6 has not been well characterized. The inhibition profiles of 20 known inhibitors of CYP2D6 were characterized in vitro against four clinically relevant CYP2D6 substrates (desipramine, dextromethorphan, metoprolol, and thioridazine) and bufuralol. Dextromethorphan exhibited the highest sensitivity to in vitro inhibition, whereas metoprolol was the least sensitive. In addition, when metoprolol was the substrate, inhibitors with structurally constrained amino moieties (clozapine, debrisoquine, harmine, quinidine, and yohimbine) exhibited at least a 5-fold decrease in inhibition potency when results were compared with those for dextromethorphan. Atypical inhibition kinetics were observed for these and other inhibitor-substrate pairings. In silico docking studies suggested that interactions with Glu216 and an adjacent hydrophobic binding pocket may influence substrate sensitivity and inhibition potency for CYP2D6. The in vivo sensitivities of the clinically relevant CYP2D6 substrates desipramine, dextromethorphan, and metoprolol were determined on the basis of literature drug-drug interaction (DDI) outcomes. Similar to the in vitro results, dextromethorphan exhibited the highest sensitivity to CYP2D6 inhibition in vivo. Finally, the magnitude of in vivo CYP2D6 DDIs caused by quinidine was predicted using desipramine, dextromethorphan, and metoprolol. Comparisons of the predictions with literature results indicated that the marked decrease in inhibition potency observed for the metoprolol-quinidine interaction in vitro translated to the in vivo situation.

Understanding CYP2D6 interactions

Owing to the polymorphic nature of CYP2D6, clinically significant issues can arise when drugs rely on that enzyme either for clearance, or metabolism to an active metabolite. Available screening methods to determine if the compound is likely to cause drug-drug interactions, or is likely to be a victim of inhibition of CYP2D6 by other compounds will be described. Computational models and examples will be given on strategies to design out the CYP2D6 liabilities for both heme-binding compounds and non-heme-binding compounds.

Predicting drug-drug interactions: an FDA perspective

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity's drug-drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.

Evaluation of Escherichia coli membrane preparations of canine CYP1A1, 2B11, 2C21, 2C41, 2D15, 3A12, and 3A26 with coexpressed canine cytochrome P450 reductase

The preparation of bacterial membranes ("Bactosomes") containing expressed canine (beagle) hepatic cytochromes P450 (P450s) is described. cDNAs from seven canine P450s were subcloned into inducible expression plasmids and, for the first time, cotransformed and expressed with a canine P450 oxidoreductase in Escherichia coli to produce active, full-length, native sequence P450s. Enzyme expression levels, although variable, were generally sufficient to enable short incubation times and to limit the total protein present in enzyme incubations. Steady-state kinetics of CYP1A1, 2C21, and 2D15 Bactosomes demonstrated similarities with dog liver microsomes or Baculosomes. However, 3A12 lacked substrate inhibition in the formation of 1'-OH midazolam, and 2B11 displayed non-Michaelis-Menten kinetics, suggesting possible differences in protein interaction effects. In monitoring the metabolites of common P450 substrates, phenacetin deethylation, temazepam demethylation, and bufuralol 1'-hydroxylation were shown to be relatively selective reactions catalyzed by CYP1A1, 2B11, and 2D15, respectively. 1'-OH midazolam was formed in higher quantities by CYP2B11 and 2C21 than by 3A12, raising questions about the use of midazolam as a CYP3A12 probe in vivo. In summary, a panel of recombinant P450s was produced to make up for the lack of commercially available canine P450 isoforms. The Bactosomes are expected to facilitate reaction phenotyping and metabolic drug-drug interaction assessment in canine drug development and to enable the study of interspecies differences in P450-mediated drug metabolism.

Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods

The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.