Altered Bioavailability and Pharmacokinetics in Crohn's Disease: Capturing Systems Parameters for PBPK to Assist with Predicting the Fate of Orally Administered Drugs

Backgrond and Objective

Crohn’s disease (CD) is a chronic inflammatory bowel disease that affects a wide age range. Hence, CD patients receive a variety of drugs over their life beyond those used for CD itself. The changes to the integrity of the intestine and its drug metabolising enzymes and transporters (DMETs) can alter the oral bioavailability of drugs. However, there are other changes in systems parameters determining the fate of drugs in CD, and understanding these is essential for dose adjustment in patients with CD.

Methods

The current analysis gathered all the available clinical data on the kinetics of drugs in CD (by March 2021), focusing on orally administered small molecule drugs. A meta-analysis of the systems parameters affecting oral drug pharmacokinetics was conducted. The systems information gathered on intestine, liver and blood proteins and other physiological parameters was incorporated into a physiologically based pharmacokinetic (PBPK) platform to create a virtual population of CD patients, with a view for guiding dose adjustment in the absence of clinical data in CD.

Results

There were no uniform trends in the reported changes in reported oral bioavailability. The nature of the drug as well as the formulation affected the direction and magnitude of variation in kinetics in CD patients relative to healthy volunteers. Even for the same drug, the reported changes in exposure varied, possibly due to a lack of distinction between the activity states of CD. The highest alteration was seen with S-verapamil and midazolam, 8.7- and 5.3-fold greater exposure, respectively, in active CD patients relative to healthy volunteers. Only one report was available on liver DMETs in CD, and indicated reduced CYP3A4 activity. In a number of reports, mRNA expression of DMETs in the ileum and colon of CD patients was measured, focussing on P-glycoprotein (p-gp) transporter and CYP3A4 enzyme, and showed contradictory results. No data were available on protein expression in duodenum and jejunum despite their dominant role in oral drug absorption.

Conclusion

There are currently inadequate dedicated clinical or quantitative proteomic studies in CD to enable predictive PBPK models with high confidence and adequate verification. The PBPK models for CD with the available systems parameters were able to capture the major physiological influencers and the gaps to be filled by future research. Quantification of DMETs in the intestine and the liver in CD is warranted, alongside well-defined clinical drug disposition studies with a number of index drugs as biomarkers of changes in DMETs in these patients, to avoid large-scale dedicated studies for every drug to determine the effects of disease on the drug’s metabolism and disposition and the consequential safety and therapeutic concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-022-01169-4.

Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib

Purpose

Fedratinib (INREBIC^®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios.

Methods

The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp^® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data.

Results

The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates.

Conclusions

The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users.

Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling

Abemaciclib, a selective inhibitor of cyclin‐dependent kinases 4 and 6, is metabolized mainly by cytochrome P450 (CYP)3A4. Clinical studies were performed to assess the impact of strong inhibitor (clarithromycin) and inducer (rifampin) on the exposure of abemaciclib and active metabolites. A physiologically based pharmacokinetic (PBPK) model incorporating the metabolites was developed to predict the effect of other strong and moderate CYP3A4 inhibitors and inducers. Clarithromycin increased the area under the plasma concentration‐time curve (AUC) of abemaciclib and potency‐adjusted unbound active species 3.4‐fold and 2.5‐fold, respectively. Rifampin decreased corresponding exposures 95% and 77%, respectively. These changes influenced the fraction metabolized via CYP3A4 in the model. An absolute bioavailability study informed the hepatic and gastric availability. In vitro data and a human radiolabel study determined the fraction and rate of formation of the active metabolites as well as absorption‐related parameters. The predicted AUC ratios of potency‐adjusted unbound active species with rifampin and clarithromycin were within 0.7‐ and 1.25‐fold of those observed. The PBPK model predicted 3.78‐ and 7.15‐fold increases in the AUC of the potency‐adjusted unbound active species with strong CYP3A4 inhibitors itraconazole and ketoconazole, respectively; and 1.62‐ and 2.37‐fold increases with the concomitant use of moderate CYP3A4 inhibitors verapamil and diltiazem, respectively. The model predicted modafinil, bosentan, and efavirenz would decrease the AUC of the potency‐adjusted unbound active species by 29%, 42%, and 52%, respectively. The current PBPK model, which considers changes in unbound potency‐adjusted active species, can be used to inform dosing recommendations when abemaciclib is coadministered with CYP3A4 perpetrators.

Isolation, analysis and in vitro assessment of CYP3A4 inhibition by methylxanthines extracted from Pu-erh and Bancha tea leaves

Methylxanthines, purine alkaloids found in plants, are found in beverages (coffee, tea, cocoa) and foods (chocolate and other cocoa-containing foods) commonly consumed worldwide. Members of this family include caffeine, theophylline and theobromine. Methylxanthines have a variety of pharmacological effects, and caffeine and theophylline are used as pharmaceuticals. Methylxanthines are metabolized in the liver predominantly by the enzyme CYP1A2. Their co-administration with CYP1A2 inhibitors may lead to pharmacokinetic interactions. Little is known about the possible drug interactions between caffeine and substrates of other CYP450 enzymes. In our study, methylxanthine fractions inhibited CYP3A4 in a concentration-dependent manner. Concomitant consumption of green tea with CYP3A4 substrates could increase the possibility of interactions, and this requires further clarification. The inhibition of CYP3A4 is not only due to the presence of catechin derivatives but methylxanthines may also contribute to this effect.

Quantitative Prediction of CYP3A4- and CYP3A5-Mediated Drug Interactions

We verified a physiologically-based pharmacokinetic (PBPK) model to predict cytochrome P450 3A4/5-mediated drug-drug interactions (DDIs). A midazolam (MDZ)-ketoconazole (KTZ) interaction study in 24 subjects selected by CYP3A5 genotype, and liquid chromatography and mass spectroscopy quantification of CYP3A4/5 abundance from independently acquired and genotyped human liver (n = 136) and small intestinal (N = 12) samples, were conducted. The observed CYP3A5 genetic effect on MDZ systemic and oral clearance was successfully replicated by a mechanistic framework incorporating the proteomics-informed CYP3A abundance and optimized small intestinal CYP3A4 abundance based on MDZ intestinal availability (F_G ) of 0.44. Furthermore, combined with a modified KTZ PBPK model, this framework recapitulated the observed geometric mean ratio of MDZ area under the curve (AUCR) following 200 or 400 mg KTZ, which was, respectively, 2.7-3.4 and 3.9-4.7-fold in intravenous administration and 11.4-13.4 and 17.0-19.7-fold in oral administration, with AUCR numerically lower (P > 0.05) in CYP3A5 expressers than nonexpressers. In conclusion, the developed mechanistic framework supports dynamic prediction of CYP3A-mediated DDIs in study planning by bridging DDIs between CYP3A5 expressers and nonexpressers.

Development of a Physiologically Based Pharmacokinetic Model for Itraconazole Pharmacokinetics and Drug-Drug Interaction Prediction

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modeling for itraconazole has been challenging due to highly variable in vitro d ata used for 'bottom-up' model building. Under-prediction of pharmacokinetics and drug-drug interactions (DDIs) following multiple doses of itraconazole has limited the use of PBPK model simulation to aid an itraconazole clinical DDI study design. The aim of this work is to develop an itraconazole PBPK model predominantly using a 'top-down' approach to enable a more accurate pharmacokinetic and DDI prediction.

METHODS

An itraconazole PBPK model describing itraconazole and hydroxyl-itraconazole (OH-ITZ) was constructed in Simcyp(®). The key parameters that govern the pharmacokinetic profile, including non-linear clearance (i.e., maximum rate of reaction [V max] and the Michaelis-Menten constant [K m]) and volume of distribution for both itraconazole and OH-ITZ, were redefined by leveraging existing in vivo data. Model verification was performed by comparing the simulated itraconazole and OH-ITZ pharmacokinetic profiles with the observed clinical data. Finally, the model was used to simulate clinical DDIs between itraconazole and midazolam.

RESULTS

The developed PBPK model well-described the pharmacokinetics of itraconazole and OH-ITZ, and particularly captured their accumulation after repeated doses of itraconazole. This was verified with the observed data from 29 clinical studies where itraconazole solution or capsule was given as a single or multiple dose. The predicted DDI between itraconazole and midazolam was within 1.25-fold of the observed data for seven of ten studies and within 1.5-fold for nine of ten studies.

CONCLUSION

The improvement of the itraconazole PBPK model increased our confidence in using PBPK model simulations to optimize clinical itraconazole DDI study design.

Effect of enzyme inhibition on perampanel pharmacokinetics: Why study design matters

OBJECTIVES

Perampanel, a selective, noncompetitive AMPA receptor antagonist, is indicated as adjunctive therapy for the treatment of partial seizures with or without secondarily generalized seizures and primary generalized tonic-clonic seizures in patients with epilepsy aged 12years and older. In vitro studies and Phase I trials indicate that perampanel is metabolized almost exclusively by CYP3A, with an elimination half-life (t_1/2) averaging approximately 105h. Understanding of pharmacokinetic (PK) interactions-enzyme inhibition or induction-and anticipating their occurrence are important for management of patients with epilepsy. Here we report PK results from a Phase I drug-drug interaction (DDI) study (Study 005) combining perampanel with the CYP3A inhibitor ketoconazole, as well as supplementary in silico predictions further exploring this interaction.

METHODS

A Phase I, randomized, open-label, two-period, two-treatment, two-way crossover study was conducted in 26 healthy adult male volunteers. Subjects were randomized to 1 of 2 treatment sequences. In one period, subjects received a single 1-mg fasting dose of perampanel (Day1); in the other period, subjects received ketoconazole 400mg once daily for 10days with a single 1-mg perampanel dose while fasting (Day3). Blood samples were drawn at multiple time points up to 288h after the perampanel dose. Pharmacokinetic parameters of perampanel were calculated by noncompartmental analysis, and safety was recorded. An integrated, physiologically based PK model built in Simcyp^® provided additional insight into this interaction. Drug-drug interaction intensity was measured by the ratio of systemic exposure (area under plasma concentration-time curve [AUC]) of perampanel in the presence or absence of concomitant ketoconazole.

RESULTS

Single oral doses of 1mg perampanel and once-daily oral doses of ketoconazole 400mg were safe and well tolerated. Maximum perampanel plasma concentration (C_max) and time to C_max showed no apparent differences when perampanel was administered alone versus with ketoconazole. Ketoconazole co-administration resulted in an approximate 20% increase in perampanel AUC (P<0.001). This increase, although statistically significant, was a<2.0-fold AUC change and alone would suggest a modest effect of ketoconazole. To further explore these results, DDI simulations were performed to query the findings and test additional study conditions. Using the actual trial conditions of Study 005, the simulations also predicted an AUC ratio increase <2-fold, providing verification of the simulation assumptions and the modest effect of ketoconazole for 10days. Simulations further suggested that an interaction effect of ketoconazole on perampanel exposure (>2-fold) of potential clinical significance could be predicted when using larger doses of ketoconazole (e.g., 200mg every 6h) coadministered for a greater time period (e.g., 30days), with AUC ratio as high as 3.36. Additionally, simulations suggested that a significant interaction with co-administration of perampanel and an inhibitor more potent than ketoconazole (such as itraconazole) could not be ruled out.

CONCLUSIONS

Selecting an appropriate study design is critical to fully characterize the PK interaction for drugs such as perampanel that have a long t_1/2. Although a negligible effect on perampanel PK was observed following co-administration of ketoconazole 400mg/day for 10days, this is likely due in part to the relatively brief co-administration period of ketoconazole and perampanel (<3 times the t_1/2 of perampanel). While short-term administration of a CYP3A inhibitor may not significantly increase perampanel exposure, such increases may be expected following chronic and larger dosing or with a more potent inhibitor.

Comparative Hepatotoxicity of Fluconazole, Ketoconazole, Itraconazole, Terbinafine, and Griseofulvin in Rats

Oral ketoconazole was recently the subject of regulatory safety warnings because of its association with increased risk of inducing hepatic injury. However, the relative hepatotoxicity of antifungal agents has not been clearly established. The aim of this study was to compare the hepatotoxicity induced by five commonly prescribed oral antifungal agents. Rats were treated with therapeutic oral doses of griseofulvin, fluconazole, itraconazole, ketoconazole, and terbinafine. After 14 days, only ketoconazole had significantly higher ALT levels (p = 0.0017) and AST levels (p = 0.0008) than the control group. After 28 days, ALT levels were highest in the rats treated with ketoconazole followed by itraconazole, fluconazole, griseofulvin, and terbinafine, respectively. The AST levels were highest in the rats treated with ketoconazole followed by itraconazole, fluconazole, terbinafine, and griseofulvin, respectively. All drugs significantly elevated ALP levels after 14 days and 28 days of treatment (p < 0.0001). The liver enzyme levels suggested that ketoconazole had the highest risk in causing liver injury followed by itraconazole, fluconazole, terbinafine, and griseofulvin. However, histopathological changes revealed that fluconazole was the most hepatotoxic, followed by ketoconazole, itraconazole, terbinafine, and griseofulvin, respectively. Given the poor correlation between liver enzymes and the extent of liver injury, it is important to confirm liver injury through histological examination.

Inhibition of cytochrome P450 enzymes by saturated and unsaturated fatty acids in human liver microsomes, characterization of enzyme kinetics in the presence of bovine serum albumin (0.1 and 1.0% w/v) and in vitro - in vivo extrapolation of hepatic clearance

The objective of the study was to determine the effect of fatty acids on CYP enzymes and the effect of BSA on intrinsic clearance of probe substrates. The inhibitory effect of thirteen fatty acids including saturated, mono-unsaturated and polyunsaturated fatty acids on CYP enzymes, kinetic parameters and intrinsic clearance values of nine CYP marker probe substrate reactions in the absence and presence of BSA (0.1 and 1.0% w/v) were characterized in human liver microsomes. The results demonstrate that most of the unsaturated fatty acids showed marked inhibition towards CYP2C8 mediated amodiaquine N-deethylation followed by inhibition of CYP2C9 and CYP2B6 mediated activities. The addition of 0.1% BSA in the incubation markedly improved the unbound intrinsic clearance values of probe substrates by reducing the K_m values with little or no effect on maximal velocity. The addition of BSA (0.1 and 1.0% w/v) did not influence the unbound intrinsic clearance of marker reactions for CYP2A6, and CYP3A4 enzymes. The addition of 0.1% w/v BSA is sufficient to determine the intrinsic clearance of marker probe reactions by metabolite formation approach. The predicted hepatic clearance values for the substrates using the well-stirred model, in the presence of BSA (0.1% BSA), are comparable to the in vivo hepatic clearance values.

Ibrutinib Dosing Strategies Based on Interaction Potential of CYP3A4 Perpetrators Using Physiologically Based Pharmacokinetic Modeling

Based on ibrutinib pharmacokinetics and potential sensitivity towards CYP3A4-mediated drug-drug interactions (DDIs), a physiologically based pharmacokinetic approach was developed to mechanistically describe DDI with various CYP3A4 perpetrators in healthy men under fasting conditions. These models were verified using clinical data for ketoconazole (strong CYP3A4 inhibitor) and used to prospectively predict and confirm the inducing effect of rifampin (strong CYP3A4 inducer); DDIs with mild (fluvoxamine, azithromycin) and moderate inhibitors (diltiazem, voriconazole, clarithromycin, itraconazole, erythromycin), and moderate (efavirenz) and strong CYP3A4 inducers (carbamazepine), were also predicted. Ketoconazole increased ibrutinib area under the curve (AUC) by 24-fold, while rifampin decreased ibrutinib AUC by 10-fold; coadministration of ibrutinib with strong inhibitors or inducers should be avoided. The ibrutinib dose should be reduced to 140 mg (quarter of maximal prescribed dose) when coadministered with moderate CYP3A4 inhibitors so that exposures remain within observed ranges at therapeutic doses. Thus, dose recommendations for CYP3A4 perpetrator use during ibrutinib treatment were developed and approved for labeling.

Evaluation of drug-drug interactions for oncology therapies: in vitro-in vivo extrapolation model-based risk assessment

AIMS

Understanding drug-drug interactions (DDI) is a critical part of the drug development process as polypharmacy has become commonplace in many therapeutic areas including the cancer patient population. The objectives of this study were to investigate cytochrome P450 (CYP)-mediated DDI profiles available for therapies used in the oncology setting and evaluate how models based on in vitro-in vivo extrapolation performed in predicting CYP-mediated DDI risk.

METHODS

A dataset of 125 oncology therapies was collated using drug label and approval history information, incorporating in vitro and clinical PK data. The predictive accuracy of the basic and net effect mechanistic static models was assessed using this oncology drug dataset, for both victim and perpetrator potential of CYP3A-mediated DDI.

RESULTS

The incidence of CYP3A-mediated interaction potential was 47%, 22% and 11% for substrates, inhibitors and inducers, respectively. The basic models for precipitants gave conservative predictions with no false negatives, whilst the mechanistic static models provided reasonable quantitative predictions (2.3-3-fold error). Further analysis revealed that incorporating DDI at the level of the intestine was in most cases over-predicting interaction magnitude due to overestimates of the rate and extent of oral absorption of the precipitant. Quantifying victim DDI potential was also demonstrated using fmCYP3A estimates from ketoconazole clinical DDI studies to predict the magnitude of interaction on co-administration with the CYP3A inducer, rifampicin (1.6-3.3 fold error).

CONCLUSIONS

This work illustrates the utility and limitations of current DDI risk assessment approaches applied to a range of contemporary anti-cancer agents, and discusses the implications for therapeutic combination strategies.

Evacetrapib: in vitro and clinical disposition, metabolism, excretion, and assessment of drug interaction potential with strong CYP3A and CYP2C8 inhibitors

Evacetrapib is an investigational cholesteryl ester transfer protein inhibitor (CETPi) for reduction of risk of major adverse cardiovascular events in patients with high-risk vascular disease. Understanding evacetrapib disposition, metabolism, and the potential for drug-drug interactions (DDI) may help guide prescribing recommendations. In vitro, evacetrapib metabolism was investigated with a panel of human recombinant cytochromes P450 (CYP). The disposition, metabolism, and excretion of evacetrapib following a single 100-mg oral dose of (14)C-evacetrapib were determined in healthy subjects, and the pharmacokinetics of evacetrapib were evaluated in the presence of strong CYP3A or CYP2C8 inhibitors. In vitro, CYP3A was responsible for about 90% of evacetrapib's CYP-associated clearance, while CYP2C8 accounted for about 10%. In the clinical disposition study, only evacetrapib and two minor metabolites circulated in plasma. Evacetrapib metabolism was extensive. A mean of 93.1% and 2.30% of the dose was excreted in feces and urine, respectively. In clinical DDI studies, the ratios of geometric least squares means for evacetrapib with/without the CYP3A inhibitor ketoconazole were 2.37 for area under the curve (AUC)(0-∞) and 1.94 for C max. There was no significant difference in evacetrapib AUC(0-τ) or C max with/without the CYP2C8 inhibitor gemfibrozil, with ratios of 0.996 and 1.02, respectively. Although in vitro results indicated that both CYP3A and CYP2C8 metabolized evacetrapib, clinical studies confirmed that evacetrapib is primarily metabolized by CYP3A. However, given the modest increase in evacetrapib exposure and robust clinical safety profile to date, there is a low likelihood of clinically relevant DDI with concomitant use of strong CYP3A or CYP2C8 inhibitors.

Exploratory effects of a strong CYP3A inhibitor (ketoconazole), a strong CYP3A inducer (rifampicin), and concomitant ethanol on piragliatin pharmacokinetics and pharmacodynamics in type 2 diabetic patients

Piragliatin is a CYP3A substrate; its inactive metabolite M4, formed through cytosolic reductase, is reversibly metabolized back to piragliatin through CYP3A. The impact of concomitant CYP3A modifiers thus cannot be predicted. Drinking alcohol under fasting conditions is associated with a recognized glucose-lowering effect, which might be synergistic with piragliatin's hypoglycemic effect. Two exploratory studies were conducted to examine these potential interactions in type 2 diabetes (T2D) patients: 16 completed an open-label, sequential 2-way crossover, 2-arm (randomized to ketoconazole and rifampicin) CYP3A study; another 18 participated in a double-blind, placebo-controlled, randomized 3-way crossover ethanol study. Administration of piragliatin (100-mg single dose) resulted in a 32% Cmax and 44% area under the curve (AUC∞ ) increase in piragliatin exposure without affecting glucose AUC0-6h following ketoconazole (400 mg QD × 5 days); 30% Cmax and 72% AUC∞ decrease in piragliatin exposure with a 13% increase in glucose AUC0-6h following rifampicin (600 mg QD × 5 days); and, unexpectedly, a 32% Cmax and 23% AUC0-6h decrease (no change in AUC∞ ) in piragliatin exposure with a 13% increase in glucose AUC0-6h following alcohol (40-g single dose). In conclusion, a strong CYP3A modifier or concomitant alcohol could lead to a change in exposure to piragliatin with a potential alteration in glucose-lowering effect.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Cytochrome P450 time-dependent inhibition and induction: advances in assays, risk analysis and modelling

INTRODUCTION

It is widely accepted that current practice of polypharmacy inevitably increases the incidence of drug-drug interactions (DDIs). Serious DDIs are a major liability for new molecular entities entering the pharmaceutical market. Various strategies are employed to avoid problematic compounds for clinical development. Progress made with reversible CYP DDIs has prompted a switch to study and model time-dependent inhibition and induction interactions.

AREAS COVERED

An overview of popular experimental practices is presented with discussion of techniques and algorithms used to analyse the clinical DDI risk. Emphasis is placed on the transition from early, simple static equations, via more complex net mechanistic, static models to dynamic approaches involving multiple perpetrators and metabolites, simultaneous inhibition and induction.

EXPERT OPINION

Inclusion of the more conservative terms for parameters required for DDI evaluation may eliminate promising chemical space, encourages poor practice and hampers innovation. Breakthroughs have originated from understanding of 'outliers' from such analyses where CYP enzyme-transporter interplay may be involved. The role of key transporters in drug disposition is firmly established as the chemistry required to address new targets deviates from traditional 'drug-like' space. Attempts to model more complex interactions for substrates of both CYP enzymes and drug transporters are still in their infancy and will benefit from dynamic modelling.