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Aim: The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M & S). Methods: A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment. Results: The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively. Conclusions: Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential. Refereed/Peer-reviewed

Aims Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. Methods PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. Results Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). Conclusions The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

Aims The aim of the present study was to evaluate the disposition of metoprolol after oral administration of an immediate and controlled-release formulation before and after Roux-en-Y gastric bypass (RYGB) surgery in the same individuals and to validate a physiologically based pharmacokinetic (PBPK) model for predicting oral bioavailability following RYGB. Methods A single-dose pharmacokinetic study of metoprolol tartrate 200 mg immediate release and controlled release was performed in 14 volunteers before and 6–8 months after RYGB. The observed data were compared with predicted results from the PBPK modelling and simulation of metoprolol tartrate immediate and controlled-release formulation before and after RYGB. Results After administration of metoprolol immediate and controlled release, no statistically significant difference in the observed area under the curve (AUC0–24 h) was shown, although a tendency towards an increased oral exposure could be observed as the AUC0–24 h was 32.4% [95% confidence interval (CI) 1.36, 63.5] and 55.9% (95% CI 5.73, 106) higher following RYGB for the immediate and controlled-release formulation, respectively. This could be explained by surgery-related weight loss and a reduced presystemic biotransformation in the proximal gastrointestinal tract. The PBPK values predicted by modelling and simulation were similar to the observed data, confirming its validity. Conclusions The disposition of metoprolol from an immediate-release and a controlled-release formulation was not significantly altered after RYGB; there was a tendency to an increase, which was also predicted by PBPK modelling and simulation.

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

Aim Conducting PK studies in pregnant women is challenging. Therefore, we asked if a physiologically-based pharmacokinetic (PBPK) model could be used to predict the disposition in pregnant women of drugs cleared by multiple CYP enzymes. Methods We expanded and verified our previously published pregnancy PBPK model by incorporating hepatic CYP2B6 induction (based on in vitro data), CYP2C9 induction (based on phenytoin PK) and CYP2C19 suppression (based on proguanil PK), into the model. This model accounted for gestational age-dependent changes in maternal physiology and hepatic CYP3A, CYP1A2 and CYP2D6 activity. For verification, the pregnancy-related changes in the disposition of methadone (cleared by CYP2B6, 3A and 2C19) and glyburide (cleared by CYP3A, 2C9 and 2C19) were predicted. Results Predicted mean post-partum to second trimester (PP : T2) ratios of methadone AUC, Cmax and Cmin were 1.9, 1.7 and 2.0, vs. observed values 2.0, 2.0 and 2.6, respectively. Predicted mean post-partum to third trimester (PP : T3) ratios of methadone AUC, Cmax and Cmin were 2.1, 2.0 and 2.4, vs. observed values 1.7, 1.7 and 1.8, respectively. Predicted PP : T3 ratios of glyburide AUC, Cmax and Cmin were 2.6, 2.2 and 7.0 vs. observed values 2.1, 2.2 and 3.2, respectively. Conclusions Our PBPK model integrating prior physiological knowledge, in vitro and in vivo data, allowed successful prediction of methadone and glyburide disposition during pregnancy. We propose this expanded PBPK model can be used to evaluate different dosing scenarios, during pregnancy, of drugs cleared by single or multiple CYP enzymes.

UNLABELLED: WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT ??Changes to oral drug bioavailability have been observed post bariatric surgery. However, the magnitude and the direction of changes have not been assessed systematically to provide insights into the parameters governing the observed trends. Understanding these can help with dose adjustments. WHAT THIS STUDY ADDS ??Analysis of drug characteristics based on a biopharmaceutical classification system is not adequate to explain observed trends in altered oral drug bioavailability following bariatric surgery, although the findings suggest solubility to play an important role. AIMS: To identify the most commonly prescribed drugs in a bariatric surgery population and to assess existing evidence regarding trends in oral drug bioavailability post bariatric surgery. METHODS: A retrospective audit was undertaken to document commonly prescribed drugs amongst patients undergoing bariatric surgery in an NHS hospital in the UK and to assess practice for drug administration following bariatric surgery. The available literature was examined for trends relating to drug permeability and solubility with regards to the Biopharmaceutics Classification System (BCS) and main route of elimination. RESULTS: No significant difference in the 'post/pre surgery oral drug exposure ratio' (ppR) was apparent between BCS class I to IV drugs, with regards to dose number (Do) or main route of elimination. Drugs classified as 'solubility limited' displayed an overall reduction as compared with 'freely soluble' compounds, as well as an unaltered and increased ppR. CONCLUSION: Clinical studies establishing guidelines for commonly prescribed drugs, and the monitoring of drugs exhibiting a narrow therapeutic window or without a readily assessed clinical endpoint, are warranted. Using mechanistically based pharmacokinetic modelling for simulating the multivariate nature of changes in drug exposure may serve as a useful tool in the further understanding of postoperative trends in oral drug exposure and in developing practical clinical guidance.

Aims Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies.

Apart from cancer chronic (auto)immune-mediated diseases are a major threat for patients and a challenge for physicians. These conditions include classic autoimmune diseases like systemic lupus erythematosus, systemic sclerosis and dermatomyositis and also immune-mediated inflammatory diseases such as rheumatoid arthritis and psoriasis. Traditional therapies for these conditions include unspecific immunosuppressants including steroids and cyclophosphamide, more specific compounds such as ciclosporin or other drugs which are thought to act as immunomodulators (fumarates and intravenous immunoglobulins). With increasing knowledge about the underlying pathomechanisms of the diseases, targeted biologic therapies mainly consisting of anti-cytokine or anti-cytokine receptor agents have been developed. The latter have led to a substantial improvement of the induction of long term remission but drug costs are high and are not affordable in all countries. In China an extract of the herb Tripterygium wilfordii Hook F. (TwHF) is frequently used to treat autoimmune and/or inflammatory diseases due to its favourable cost–benefit ratio. Triptolide has turned out to be the active substance of TwHF extracts and has been shown to exert potent anti-inflammatory and immunosuppressive effects in vitro and in vivo. There is increasing evidence for an immunomodulatory and partly immunosuppressive mechanism of action of triptolide. Thus, compounds such as triptolide or triptolide derivatives may have the potential to be developed as a new class of drugs for these diseases. In this review we summarize the published knowledge regarding clinical use, pharmacokinetics and the possible mode of action of triptolide in the treatment of inflammatory diseases with a particular focus on psoriasis.

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.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Several cytochromes P450 (CYPs) have been implicated in the metabolism of methadone, but there is no consensus on their relative contributions to overall disposition and hence variability in response. WHAT THIS STUDY ADDS • Variability in CYP3A4 activity has statistically significant but nonetheless modest influence on the oral clearance of methadone and its enantiomers. • However, CYPs 1A2 and 2D6 appear to have no impact at all. AIMS To investigate the influence of different cytochrome P450 (CYP) activities and other potential covariates on the disposition of methadone in patients on methadone maintenance therapy (MMT). METHODS Eighty-eight patients (58 male; 21–55 years; 84 White) on MMT were studied. CYP2D6 activity [3 h plasma metabolic ratio of dextromethorphan (DEX) to dextrorphan (DOR)] was determined in 44 patients (29 male; 24–55 years), CYP1A2 activity (salivary caffeine elimination half-life) in 44 patients (21 male; 24–55 years) and CYP3A activity (oral clearance of midazolam) in 49 patients (33 male; 23–55 years). Data on all three CYPs were obtained from 32 subjects. Total plasma concentrations of (RS)-methadone and total and unbound plasma concentrations of both enantiomers were measured by LC/MS. Population pharmacokinetics and subsequent multiple regression analysis were used to calculate methadone oral clearance and to identify its covariates. RESULTS Between 61 and 68% of the overall variation in total plasma trough concentrations of (RS)-, (R)- and (S)-methadone was explained by methadone dose, duration of addiction before starting MMT, CYP3A activity and illicit morphine use. CYP3A activity explained 22, 16, 15 and 23% of the variation in unbound (R)-, unbound (S)-, total (RS)- and total (S)-methadone clearances, respectively. Neither CYP2D6 nor CYP1A2 activity was related to methadone disposition. CONCLUSIONS CYP3A activity has a modest influence on methadone disposition. Inhibitors and inducers of this enzyme should be monitored in patients taking methadone.

Aims To determine levels of microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of human liver and their interindividual variability. Methods Triplicate liver samples were used to determine values of MPPGL (n = 20) and HPGL (n = 7) after accounting for the fractional loss of microsomal protein or hepatocytes during processing. Repeated measurements from each liver sample allowed the estimation of true interindividual variability in MPPGL and HPGL using ANOVA. Results The value of MPPGL ranged from 26 to 54 mg g−1 (meangeo= 33 mg g−1). The value of HPGL ranged from 65 to 185 × 106 cells g−1 (meangeo= 107 × 106 cells g−1). Conclusions There is significant interindividual variability in MPPGL, which has implications for the accurate extrapolation of in vitro data on drug metabolism to predict in vivo metabolic clearance.

Aims To define the relative antitussive effect of dextromethorphan (DEX) and its primary metabolite dextrorphan (DOR) after administration of DEX. Methods Data were analysed from a double-blind, randomized cross-over study in which 22 subjects received the following oral treatments: (i) placebo; (ii) 30 mg DEX hydro-bromide; (iii) 60 mg DEX hydro-bromide; and (iv) 30 mg DEX hydro-bromide preceded at 1 h by quinidine HCl (50 mg). Cough was elicited using citric acid challenge. Pharmacokinetic data from all non-placebo arms of the study were fitted simultaneously. The parameters were then used as covariates in a link PK–PD model of cough suppression using data from all treatment arms. Results The best-fit PK model assumed two- and one-compartment PK models for DEX and DOR, respectively, and competitive inhibition of DEX metabolism by quinidine. The intrinsic clearance of DEX estimated from the model ranged from 59 to 1536 l h−1, which overlapped with that extrapolated from in vitro data (12–261 l h−1) and showed similar variation (26- vs. 21-fold, respectively). The inhibitory effect of quinidine ([I]/Ki) was 19 (95% confidence interval of mean: 18–20) with an estimated average Ki of 0.017 µM. Although DEX and DOR were both active, the potency of the antitussive effect of DOR was 38% that of DEX. A sustained antitussive effect was related to slow removal of DEX/DOR from the effect site (ke0 = 0.07 h−1). Conclusions Physiologically based PK modelling with perturbation of metabolism using an inhibitor allowed evaluation of the antitussive potency of DOR without the need for separate administration of DOR.

Aims To assess CYP2D6 activity and genotype in a group of patients undergoing methadone maintenance treatment (MMT). Methods Blood samples from 34 MMT patients were genotyped by a polymerase chain reaction-based method, and results were compared with CYP2D6 phenotype (n = 28), as measured by the molar metabolic ratio (MR) of dextromethorphan (DEX)/dextrorphan (DOR) in plasma. Results Whereas 9% of patients (3/34) were poor metabolizers (PM) by genotype, 57% (16/28) were PM by phenotype (P

Aims To investigate the pharmacokinetics (PK) and pharmacodynamics (PD) of betaine in the treatment of classical homocystinuria due to cystathionine β-synthase (CβS) deficiency with a view to optimizing the dosage regimen. Methods Betaine was given as a single oral dose of 100 mg kg−1 to six patients (age range 6–17 years) who normally received betaine but whose treatment had been suspended for 1 week prior to the study. Plasma betaine and total homocysteine concentrations were measured by high performance liquid chromatography (h.p.l.c.) at frequent intervals over 24 h. The best-fit PK model was determined using the PK-PD program Win-Nonlin and the concentration-time-effect data analysed by an indirect PD model. Using the PK and PD parameters, simulations were carried out with the aim of optimizing betaine dosage. Results Betaine PK was described by both mono- and bi-exponential disposition functions with first order absorption and a lag time. The correlation coefficient between betaine oral clearance and body weight was 0.6. Mean betaine clearance was higher in males than in females (P=0.03). PK-PD simulation indicated minimal benefit from exceeding a twice-daily dosing schedule and a 150 mg kg−1 day−1 dosage for betaine. Conclusions PK-PD modelling allows recommendations for optimal dosage of betaine in the treatment of homocystinuria, that have the potential for improved patient compliance and both therapeutic and pharmacoeconomic benefit.

Aims It is not clear whether the analgesic effect following dihydrocodeine (DHC) administration is due to either DHC itself or its metabolite, dihydromorphine (DHM). We examined the relative contribution of DHC and DHM to analgesia following DHC administration in a group of healthy volunteers using a PK-PD link modelling approach. Methods A single oral dose of DHC (90 mg) was administered to 10 healthy volunteers in a randomised, double-blind, placebo-controlled study. A computerized cold pressor test (CPT) was used to measure analgesia. On each study day, the volunteers performed the CPT before study medication and at 1.25, 2.75, 4.25 and 5.75 h postdose. Blood samples were taken at 0.25 h (predose) and then at half hourly intervals for 5.75 h postdose. PK-PD link modelling was used to describe the relationships between DHC, DHM and analgesic effect. Results Mean pain AUCs following DHC administration were significantly different to those following placebo administration (P = 0.001). Mean pain AUC changes were 91 score. s−1 for DHC and −17 score. s−1 for placebo (95% CI = ± 36.5 for both treatments). The assumption of a simple linear relationship between DHC concentration and effect provided a significantly better fit than the model containing DHM as the active moiety (AIC = 4.431 vs 4.668, respectively). The more complex models did not improve the likelihood of model fits significantly. Conclusions The findings suggest that the analgesic effect following DHC ingestion is mainly attributed to the parent drug rather than its DHM metabolite. It can thus be inferred that polymorphic differences in DHC metabolism to DHM have little or no effect on the analgesic affect.

Aims Journal impact factors (IMFs) are used increasingly by institutions as performance indicators of the quality of ‘individual research output’. Although the need for discretion when using the numbers has been emphasized, there has been little formal analysis of the issues. We therefore investigated citation profiles for three clinical pharmacology journals to assess the validity of using IMF as a measure of ‘individual research’. Methods We compared the pattern of individual citations for random samples of 120 papers published in Clin Pharmacol Ther (CPT), Br J Clin Pharmacol (BCP) and Eur J Clin Pharmacol (EJCP) in 1981, 1991, 1995 and 1996. Using an analogy between citation-time profiles of papers and concentration-time profiles of drugs, it was possible to define ‘lag-time’, Cmax, tmax, t½ and AUC(t), and to investigate ‘bioequivalence’. Results Citation distributions for individual publications were widely variable and skewed (skewness = 1.47, 2.16 and 1.37 for CPT, BCP and EJCP, respectively). The 90% CI values for the IMF of a publication in each journal (i.e. 90% CI for an observation as opposed to 90% CI for the mean) were 0.24–16.94, 0.08–10.3 and 0.09–5.68. Conclusions IMF does not represent the impact of an individual paper. Furthermore, if the comparison of journals is treated as a bioequivalence issue, the citation data should be log transformed prior to calculating IMF such that they represent the likelihood of citation for the median article. After such transformation, absolute differences between the IMF of clinical pharmacology journals become much smaller.

Aims MPTP, a neurotoxin which induces parkinsonism is partially metabolized by the enzyme CYP1A2. Smoking appears to protect against Parkinson's disease (PD) and cigarette smoke induces CYP1A2 activity. Thus, we investigated the hypothesis that idiopathic PD is associated with lower CYP1A2 activity using caffeine as a probe compound. Methods CYP1A2 activity was assessed using saliva paraxanthine (PX) to caffeine (CA) ratios. Caffeine half-life was also estimated from salivary concentrations of caffeine at 2 and 5 h post dose. 117 treated and 40 untreated patients with PD and 105 healthy control subjects were studied. Results PX/CA ratios were 0.57, 0.93 and 0.77 in treated patients, untreated patients and healthy control subjects, respectively, with no significant differences between study groups (95% CI: treated patients vs controls −0.24, 0.57; untreated patients vs controls −0.75, 0.35). However, patients with PD (treated or untreated) had caffeine half-lives shorter than that in controls (treated patients: 262 min, untreated patients: 244 min, controls: 345 min; 95% CI: controls vs treated patients 23, 143 (P = 0.003); controls vs untreated patients 19, 184 (P = 0.011)). Amongst the patients with PD, caffeine half-life was also inversely related to the age of onset of disease (P = 0.012); gender and concomitant drugs did not influence this significantly. Conclusions Based on PX/CA ratio, there was no evidence of decreased CYP1A2 activity in patients compared with control subjects. The observed decrease in the elimination half-life of caffeine in PD may be caused by increased CYP2E1 activity, an enzyme that also contributes to the metabolism of caffeine. The latter warrants further investigation.

Aims To test the hypothesis that inhibition of cytochrome P450 2D6 (CYP2D6) by quinidine increases the antitussive effect of dextromethorphan (DEX) in an induced cough model. Methods Twenty-two healthy extensive metaboliser phenotypes for CYP2D6 were studied according to a double-blind, randomised cross-over design after administration of: (1) Placebo antitussive preceded at 1 h by placebo inhibitor; (2) 30 mg oral DEX preceded at 1 h by placebo inhibitor (DEX30); (3) 60 mg oral DEX preceded at 1 h by placebo inhibitor (DEX60); (4) 30 mg oral DEX preceded at 1 h by 50 mg oral quinidine sulphate (QDEX30). Cough frequency following inhalation of 10% citric acid was measured at baseline and at intervals up to 12 h. Plasma concentrations of DEX and its metabolites were measured up to 96 h by h.p.l.c. Results Inhibition of CYP2D6 by quinidine caused a significant increase in the mean ratio of DEX to dextrorphan (DEX:DOR) plasma AUC(96) (0.04 vs 1.81, P

Conducting PK studies in pregnant women is challenging. Therefore, we asked if a physiologically-based pharmacokinetic (PBPK) model could be used to predict the disposition in pregnant women of drugs cleared by multiple CYP enzymes.We expanded and verified our previously published pregnancy PBPK model by incorporating hepatic CYP2B6 induction (based on in vitro data), CYP2C9 induction (based on phenytoin PK) and CYP2C19 suppression (based on proguanil PK), into the model. This model accounted for gestational age-dependent changes in maternal physiology and hepatic CYP3A, CYP1A2 and CYP2D6 activity. For verification, the pregnancy-related changes in the disposition of methadone (cleared by CYP2B6, 3A and 2C19) and glyburide (cleared by CYP3A, 2C9 and 2C19) were predicted.Predicted mean post-partum to second trimester (PP : T2 ) ratios of methadone AUC, Cmax and Cmin were 1.9, 1.7 and 2.0, vs. observed values 2.0, 2.0 and 2.6, respectively. Predicted mean post-partum to third trimester (PP : T3 ) ratios of methadone AUC, Cmax and Cmin were 2.1, 2.0 and 2.4, vs. observed values 1.7, 1.7 and 1.8, respectively. Predicted PP : T3 ratios of glyburide AUC, Cmax and Cmin were 2.6, 2.2 and 7.0 vs. observed values 2.1, 2.2 and 3.2, respectively.Our PBPK model integrating prior physiological knowledge, in vitro and in vivo data, allowed successful prediction of methadone and glyburide disposition during pregnancy. We propose this expanded PBPK model can be used to evaluate different dosing scenarios, during pregnancy, of drugs cleared by single or multiple CYP enzymes.

1. Red blood cells from four children with lymphoblastic leukemia were age fractionated on Percoll density gradients into 'young', 'middle-aged' and 'old' cells. 2. The rates of accumulation of the mercaptopurine (MP) metabolites thioguanine nucleotides (TGNs) and methylmercaptopurine nucleotides (MeMPs) were measured in the cell fractions from the start of MP continuing chemotherapy. 3. TGNs and MeMP metabolites were present in all the red cell fractions after 3 days oral MP. There was no significant difference between the metabolite concentrations measured in either young, middle-aged or old cells (Mann-Whitney P = 1.0 to 0.12). 4. These observations suggest that MP metabolites do not enter red cells at the stem cell level at the start of therapy. 5. With respect to the monitoring of therapy, these results suggest that the concentration of TGNs after 7 to 10 days MP could be used to predict eventual steady-state concentrations using a simple model.

Changes to oral drug bioavailability have been observed post bariatric surgery. However, the magnitude and the direction of changes have not been assessed systematically to provide insights into the parameters governing the observed trends. Understanding these can help with dose adjustments.Analysis of drug characteristics based on a biopharmaceutical classification system is not adequate to explain observed trends in altered oral drug bioavailability following bariatric surgery, although the findings suggest solubility to play an important role.To identify the most commonly prescribed drugs in a bariatric surgery population and to assess existing evidence regarding trends in oral drug bioavailability post bariatric surgery.A retrospective audit was undertaken to document commonly prescribed drugs amongst patients undergoing bariatric surgery in an NHS hospital in the UK and to assess practice for drug administration following bariatric surgery. The available literature was examined for trends relating to drug permeability and solubility with regards to the Biopharmaceutics Classification System (BCS) and main route of elimination.No significant difference in the 'post/pre surgery oral drug exposure ratio' (ppR) was apparent between BCS class I to IV drugs, with regards to dose number (Do) or main route of elimination. Drugs classified as 'solubility limited' displayed an overall reduction as compared with 'freely soluble' compounds, as well as an unaltered and increased ppR.Clinical studies establishing guidelines for commonly prescribed drugs, and the monitoring of drugs exhibiting a narrow therapeutic window or without a readily assessed clinical endpoint, are warranted. Using mechanistically based pharmacokinetic modelling for simulating the multivariate nature of changes in drug exposure may serve as a useful tool in the further understanding of postoperative trends in oral drug exposure and in developing practical clinical guidance.

Apart from cancer chronic (auto)immune-mediated diseases are a major threat for patients and a challenge for physicians. These conditions include classic autoimmune diseases like systemic lupus erythematosus, systemic sclerosis and dermatomyositis and also immune-mediated inflammatory diseases such as rheumatoid arthritis and psoriasis. Traditional therapies for these conditions include unspecific immunosuppressants including steroids and cyclophosphamide, more specific compounds such as ciclosporin or other drugs which are thought to act as immunomodulators (fumarates and intravenous immunoglobulins). With increasing knowledge about the underlying pathomechanisms of the diseases, targeted biologic therapies mainly consisting of anti-cytokine or anti-cytokine receptor agents have been developed. The latter have led to a substantial improvement of the induction of long term remission but drug costs are high and are not affordable in all countries. In China an extract of the herb Tripterygium wilfordii Hook F. (TwHF) is frequently used to treat autoimmune and/or inflammatory diseases due to its favourable cost-benefit ratio. Triptolide has turned out to be the active substance of TwHF extracts and has been shown to exert potent anti-inflammatory and immunosuppressive effects in vitro and in vivo. There is increasing evidence for an immunomodulatory and partly immunosuppressive mechanism of action of triptolide. Thus, compounds such as triptolide or triptolide derivatives may have the potential to be developed as a new class of drugs for these diseases. In this review we summarize the published knowledge regarding clinical use, pharmacokinetics and the possible mode of action of triptolide in the treatment of inflammatory diseases with a particular focus on psoriasis.

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.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.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.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.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%).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.

To determine levels of microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of human liver and their interindividual variability.Triplicate liver samples were used to determine values of MPPGL (n = 20) and HPGL (n = 7) after accounting for the fractional loss of microsomal protein or hepatocytes during processing. Repeated measurements from each liver sample allowed the estimation of true interindividual variability in MPPGL and HPGL using ANOVA.The value of MPPGL ranged from 26 to 54 mg g(-1) (mean(geo)= 33 mg g(-1)). The value of HPGL ranged from 65 to 185 x 10(6) cells g(-1) (mean(geo)= 10(7) x 10(6) cells g(-1)).There is significant interindividual variability in MPPGL, which has implications for the accurate extrapolation of in vitro data on drug metabolism to predict in vivo metabolic clearance.

To assess CYP2D6 activity and genotype in a group of patients undergoing methadone maintenance treatment (MMT).Blood samples from 34 MMT patients were genotyped by a polymerase chain reaction-based method, and results were compared with CYP2D6 phenotype (n = 28), as measured by the molar metabolic ratio (MR) of dextromethorphan (DEX)/dextrorphan (DOR) in plasma.Whereas 9% of patients (3/34) were poor metabolizers (PM) by genotype, 57% (16/28) were PM by phenotype (P0.005). Eight patients, who were genotypically extensive metabolizers (EM), were assigned as PM by their phenotype. The number of CYP2D6*4 alleles and sex were significant determinants of CYP2D6 activity in MMT patients, whereas other covariates (methadone dose, age, weight) did not contribute to variation in CYP2D6 activity.There was a discordance between genotype and in vivo CYP2D6 activity in MMT patients. This finding is consistent with inhibition of CYP2D6 activity by methadone and may have implications for the safety and efficacy of other CYP2D6 substrates taken by MMT patients.

To investigate the pharmacokinetics (PK) and pharmacodynamics (PD) of betaine in the treatment of classical homocystinuria due to cystathionine beta-synthase (CbetaS) deficiency with a view to optimizing the dosage regimen.Betaine was given as a single oral dose of 100 mg kg(-1) to six patients (age range 6-17 years) who normally received betaine but whose treatment had been suspended for 1 week prior to the study. Plasma betaine and total homocysteine concentrations were measured by high performance liquid chromatography (h.p.l.c.) at frequent intervals over 24 h. The best-fit PK model was determined using the PK-PD program Win-Nonlin and the concentration-time-effect data analysed by an indirect PD model. Using the PK and PD parameters, simulations were carried out with the aim of optimizing betaine dosage.Betaine PK was described by both mono- and bi-exponential disposition functions with first order absorption and a lag time. The correlation coefficient between betaine oral clearance and body weight was 0.6. Mean betaine clearance was higher in males than in females (P=0.03). PK-PD simulation indicated minimal benefit from exceeding a twice-daily dosing schedule and a 150 mg kg(-1) day(-1) dosage for betaine.PK-PD modelling allows recommendations for optimal dosage of betaine in the treatment of homocystinuria, that have the potential for improved patient compliance and both therapeutic and pharmacoeconomic benefit.