Karen Rowland-Yeo

A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance.

AbstractBackground and Objective: Liver cirrhosis is characterized by a decrease in functional hepatocytes, lowered circulating levels of plasma proteins and alterations in blood flow due to the development of portacaval shunts. Depending on the interplay between these parameters and the characteristics of an administered drug, varying degrees of impaired systemic clearance and first-pass metabolism are anticipated. The Simcyp Population-based ADME Simulator has already been used successfully to incorporate genetic, physiological and demographic attributes of certain subgroups within healthy populations into in vitro-in vivo extrapolation (IVIVE) of xenobiotic clearance. The objective of this study was to extend population models to predict systemic and oral drug clearance in relation to the severity of liver cirrhosis. Methods: Information on demographics, changes in hepatic blood flow, cytochrome P450 enzymes, liver size, plasma protein binding and renal function was incorporated into three separate population libraries. The latter corresponded to Child-Pugh were used to predict tscores A (mild), B (moderate) and C (severe) liver cirrhosis. These libraries, together with mechanistic IVIVE within the Simcyp Simulator, he clearance of intravenous and oral midazolam, oral caffeine, intravenous and oral theophylline, intravenous and oral metoprolol, oral nifedipine, oral quinidine, oral diclofenac, oral sildenafil, and intravenous and oral omeprazole. The simulated patients matched the clinical studies as closely as possible with regard to demographics and Child-Pugh scores. Predicted clearance values in both healthy control and liver cirrhosis populations were compared with observed values, as were the fold increases in clearance values between these populations. Results: There was good agreement (lack of statistically significant difference, two-tailed paired t-test) between observed and predicted clearance ratios, with the exception of those for two studies of intravenous omeprazole. Predicted clearance ratios were within 0.8- to 1.25-fold of observed ratios in 65% of cases (range 0.34- to 2.5-fold). Conclusion: The various drugs that were studied showed different changes in clearance in relation to disease severity, and a ‘one size fits all’ solution does not exist without considering the multiple sources of the changes. Predictions of the effects of liver cirrhosis on drug clearance are of potential value in the design of clinical studies during drug development and, clinically, in the assessment of likely dosage adjustment.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model. WHAT THIS PAPER ADDS Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude. AIMS Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model. METHODS Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated. RESULTS Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%). CONCLUSIONS High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: Expected variations in clearance

Background and Objectives: The maintenance dose of a drug is dependent on drug clearance, and thus any biochemical and physiological changes in obesity that affect parameters such as cardiac output, renal function, expression of drug-metabolizing enzymes and protein binding may result in altered clearance compared with that observed in normal-weight subjects (corrected or uncorrected for body weight). Because of the increasing worldwide incidence of obesity, there is a need for more information regarding the optimal dosing of drug therapy to be made available to prescribers. This is usually provided via clinical studies in obese people; however, such studies are not available for all drugs that might be used in obese subjects. Incorporation of the relevant physiological and biochemical changes into predictive bottom-up pharmacokinetic models in order to optimize dosage regimens may offer a logical way forward for the cases where no clinical data exist. The aims of the current report are to apply such a ‘systems approach’ to identify the likelihood of observing variations in the clearance of drugs in obesity and morbid obesity for a set of compounds for which clinical data, as well as the necessary in vitro information, are available, and to provide a framework for assessing other drugs in the future.Methods: The population-specific changes in demographic, physiological and biochemical parameters that are known to be relevant to obese and morbidly obese subjects were collated and incorporated into two separate population libraries. These libraries, together with mechanistic in vitro-in vivo extrapolations (IVIVE) within the Simcyp Population-based Simulator™, were used to predict the clearance of oral alprazolam, oral caffeine, oral chlorzoxazone, oral ciclosporin, intravenous and oral midazolam, intravenous phenytoin, oral theophylline and oral triazolam. The design of the simulated studies was matched as closely as possible with that of the clinical studies. Outcome was measured by the predicted ratio of the clearance of the drug in obese and lean subjects ± its 90% confidence interval, compared with observed values. The overall statistical measures of the performance of the model to detect differences in compound clearance between obese and lean populations were investigated by measuring sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A power calculation was carried out to investigate the impact of the sample size on the overall outcome of clinical studies.Results: The model was successful in predicting clearance in obese subjects, with the degree to which simulations could mimic the outcome of in vivo studies being greater than 60% for six of the eight drugs. A clear difference in the clearance of chlorzoxazone was correctly picked up via simulation. The overall statistical measures of the performance of the Simcyp Simulator were 100% sensitivity, 66% specificity, 60% PPV and 100% NPV. Studies designed on the basis of the ratio of the absolute values required substantial numbers of participants in order to detect a significant difference, except for phenytoin and chlorzoxazone, where the ratios of the weight-normalized clearances generally showed statistically significant differences with a smaller number of subjects.Conclusion: Extension of a mechanistic predictive pharmacokinetic model to accommodate physiological and biochemical changes associated with obesity and morbid obesity allowed prediction of changes in drug clearance on the basis of in vitro data, with reasonable accuracy across a range of compounds that are metabolized by different enzymes. Prediction of the effects of obesity on drug clearance, normalized by various body size scalars, is of potential value in the design of clinical studies during drug development and in the introduction of dosage adjustments that are likely to be needed in clinical practice.

Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug–drug interactions

Metformin is used as a probe for OCT2 mediated transport when investigating possible DDIs with new chemical entities. The aim of the current study was to investigate the ability of physiologically-based pharmacokinetic (PBPK) models to simulate the effects of OCT and MATE inhibition by cimetidine on metformin kinetics. PBPK models were developed, incorporating mechanistic kidney and liver sub-models for metformin (OCT and MATE substrate) and a mechanistic kidney sub-model for cimetidine. The models were used to simulate inhibition of the MATE1, MATE2-K, OCT1 and OCT2 mediated transport of metformin by cimetidine. Assuming competitive inhibition and using cimetidine Ki values determined in vitro, the predicted metformin AUC ratio was 1.0 compared to an observed value of 1.46. The observed AUC ratio could only be recovered with this model when the cimetidine Ki for OCT2 was decreased 1000-fold or the Kis for both OCT1 and OCT2 were decreased 500-fold. An alternative description of metformin renal transport by OCT1 and OCT2, incorporating electrochemical modulation of the rate of metformin uptake together with 8-18-fold decreases in cimetidine Kis for OCTs and MATEs, allowed recovery of the extent of the observed effect of cimetidine on metformin AUC. While the final PBPK model has limitations, it demonstrates the benefit of allowing for the complexities of passive permeability combined with active cellular uptake modulated by an electrochemical gradient and active efflux.

The impact of experimental design on assessing mechanism-based inactivation of CYP2D6 by MDMA (Ecstasy):

MDMA (3-4-methylenedioxymethamphetamine, commonly known as Ecstasy) is a potent mechanism-based inhibitor (MBI) of cytochrome P450 2D6 (CYP2D6), causing quasi-irreversible inhibition of the enzyme in vitro. An evaluation of the in vivo implications of this phenomenon depends on the accuracy of the estimates of the parameters that define the inhibition in vitro, namely kinact (the maximal inhibition rate) and KI (the inactivation constant). These values are determined in two steps, pre-incubation of the enzyme with the inhibitor (enzyme inactivation), followed by dilution and further incubation to measure residual enzyme activity with a probe substrate. The aim of this study was to assess the impact of different dilutions and probe substrate concentrations on the estimates of kinact and KI using recombinantly expressed CYP2D6. Enzyme activity was measured by the conversion of dextromethorphan (DEX) to dextrorphan (DOR). Dilution factors of 1.25, 2, 5, 10, 25 and 50 (DEX at 30µM) gave mean (±SE) values of kinact (min 1) of 0.20±0.06, 0.21±0.05, 0.31±0.06, 0.37±0.11, 0.51±0.10 and 0.58±0.08, respectively, and KI (µM) values (after correction for non-specific microsomal binding) of 2.22±1.90, 2.80±1.34, 5.78±2.07, 6.36±2.93, 3.99±1.57 and 4.86±1.37, respectively. Accordingly, high (e.g. 50 fold) and low (e.g. 1.25 fold) dilutions were associated with statistically significant differences in kinetic values (p <0.05). Varying DEX concentration (10–100µM) was not associated with significant changes in kinact and K values when a five-fold dilution was used (with the exception of a lower KI at 10µM DEX). High dilution was also shown to reduce non-specific microsomal binding of MDMA. The changes in the two kinetic parameters were dependent on the experimental procedure and shown to be unlikely to have a material influence on the maximum inhibition of CYP2D6 expected in vivo after typical recreational doses of MDMA (50–100mg), since the potency of inhibition was high. The different values of the kinetic parameters were predicted to have a marginal influence on the time for recovery of enzyme activity following re-synthesis of CYP2D6.

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Indmax) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Indmax was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Indmax = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

Towards a Best Practice Approach in PBPK Modeling: Case Example of Developing a Unified Efavirenz Model Accounting for Induction of CYPs 3A4 and 2B6.

In this study, we present efavirenz physiologically based pharmacokinetic (PBPK) model development as an example of our best practice approach that uses a stepwise approach to verify the different components of the model. First, a PBPK model for efavirenz incorporating in vitro and clinical pharmacokinetic (PK) data was developed to predict exposure following multiple dosing (600 mg q.d.). Alfentanil i.v. and p.o. drug‐drug interaction (DDI) studies were utilized to evaluate and refine the CYP3A4 induction component in the liver and gut. Next, independent DDI studies with substrates of CYP3A4 (maraviroc, atazanavir, and clarithromycin) and CYP2B6 (bupropion) verified the induction components of the model (area under the curve [AUC] ratios within 1.0–1.7‐fold of observed). Finally, the model was refined to incorporate the fractional contribution of enzymes, including CYP2B6, propagating autoinduction into the model (Racc 1.7 vs. 1.7 observed). This validated mechanistic model can now be applied in clinical pharmacology studies to prospectively assess both the victim and perpetrator DDI potential of efavirenz.

Using human recombinant UDP-glucuronosyltransferase isoforms and a relative activity factor approach to model total body clearance of laropiprant (MK-0524) in humans

Abstract 1. A major pathway of elimination of the prostaglandin D2 receptor 1 antagonist laropiprant in humans is by uridine diphosphate-glucuronosyltransferase (UGT)-mediated biotransformation. In this study, liver and kidney relative activity factors were developed for UGT1A1, 1A9 and 2B7 to allow for in vitro–in vivo extrapolation of intrinsic clearance data to whole organ clearance using recombinant human UGT isoforms applying this to laropiprant as a model substrate. 2. The total body metabolic clearance of laropiprant determined using this approach (5.0 L/hr) agreed well with the value determined in vivo following intravenous administration to healthy human volunteers (5.1 L/hr). 3. The results suggest that approximately 36%, 36% and 28% of the hepatic metabolic clearance of laropiprant was mediated by UGT1A1, 1A9 and 2B7, respectively. Likewise, 80% and 20% of the renal metabolic clearance was mediated by UGT1A9 and 2B7, respectively. Furthermore, the data suggested that the contribution of the kidney to the overall total metabolic clearance was minor relative to the liver (∼ 12%).

Evaluation of the novel in vitro systems for hepatic drug clearance and assessment of their predictive utility.

A valuable strategy for the assessment of in vitro systems is proposed which involves a tiered approach consisting of four levels valuable for both selection of probe compounds and designing experiments for evaluation. At level 1, a Preliminary Assessment is used to triage novel systems based on existing information generated using the methods employed in the development of the system. There is no special requirement for specific probes or experimental conditions. At level 2, Metabolic and Transporter Competence and Cellular Integrity are investigated with a number of specific probes which are generally accepted as appropriate. The information obtained at this level (as with level 1) is largely qualitative in nature. At level 3, Quantitative Utility is established by kinetic studies conducted with specific probes under standard conditions of linearity with respect to time and protein concentration. It is essential that the latter be adhered to if subsequent scale up of the output metrics for uptake and clearance are to have appropriate (scalable) units. Finally level 4, Predictive Utility, is the most detailed level of evaluation involving several model compounds for which in vivo correlates are available. Model compounds have been collated that cover a wide range of metabolic clearance values, and it is important that comparisons are made with existing in vitro systems in order to show the added value of a novel approach including modelling and familiarity with in vivo investigations.

Emerging areas of research in the assessment of pharmacokinetics in patients with chronic kidney disease

Chronic kidney disease (CKD) has been shown to alter the pharmacokinetics of drugs that are eliminated not only via the renal pathway but also by nonrenal clearance and transport. Dosing recommendations in subjects with CKD have historically come from small pharmacokinetic (PK) studies, which have been insulated from the broader clinical development strategy. Opportunities for prospective strategic integration of both preclinical and clinical data on drug clearance mechanisms, model‐based approaches, and clinical knowledge of therapeutic index are therefore often missed in designing and analyzing the results of PK studies in subjects with CKD, and eventually providing dosing recommendations. These considerations are valuable in designing informative PK studies in subjects with CKD, as well as for guiding kidney function‐related inclusion/exclusion criteria in the broader clinical program and ultimately defining dosing guidelines that optimize benefit‐risk balance for these special patient populations based on all available data. This paper offers points to consider for drug developers to increase adoption of a contemporary multidisciplinary approach, which includes key considerations on study design and conduct, methodologies for analysis (population PK and physiologically based PK modeling), and a roadmap to interpret the effect of kidney function on the overall benefit‐risk profile of drugs in development.