The British Journal of Clinical Pharmacology: The first 50 years.

The British Journal of Clinical Pharmacology celebrates its 50th anniversary of publication in 2023. Here four previous Editors-in-Chief and the current Editor reflect on the Journal's history and the changes that have occurred during that time.

When Regional Anesthesia Met Pharmacokinetics.

Pharmacokinetics of Local Anaesthetic Agents. By Tucker GT, Mather LE. Br J Anaesth 1975; 47(suppl 1):213-24 Information derived from measurements of blood concentrations of local anaesthetics can be extended by the application of pharmacokinetic analysis. A better understanding of quantitative aspects of the disposition and absorption of these drugs should assist the anaesthetist in deciding the optimal agent and dosage for regional block techniques.

A series acceleration algorithm for the gamma-Pareto (type I) convolution and related functions of interest for pharmacokinetics.

The gamma-Pareto type I convolution (GPC type I) distribution, which has a power function tail, was recently shown to describe the disposition kinetics of metformin in dogs precisely and better than sums of exponentials. However, this had very long run times and lost precision for its functional values at long times following intravenous injection. An accelerated algorithm and its computer code is now presented comprising two separate routines for short and long times and which, when applied to the dog data, completes in approximately 3 min per case. The new algorithm is a more practical research tool. Potential pharmacokinetic applications are discussed.

Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation.

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.

Personalized Drug Dosage - Closing the Loop.

A brief account is given of various approaches to the individualization of drug dosage, including the use of pharmacodynamic markers, therapeutic monitoring of plasma drug concentrations, genotyping, computer-guided dosage using 'dashboards', and automatic closed-loop control of pharmacological action. The potential for linking the real patient to his or her 'virtual twin' through the application of physiologically-based pharmacokinetic modeling is also discussed.

In Vitro Characterization of the Human Liver Microsomal Kinetics and Reaction Phenotyping of Olanzapine Metabolism.

Olanzapine (OLZ) is an atypical antipsychotic used in the treatment of schizophrenia and related psychoses. The metabolism of OLZ is complex and incompletely characterized. This study aimed to elucidate the enzymes and pathways involved in the metabolism of OLZ and to determine the kinetics of OLZ oxidation and glucuronidation by human liver microsomes, recombinant cytochrome P450 (rP450) enzymes, and recombinant UDP-glucuronosyltransferase (rUGT) enzymes. An ultra-performance liquid chromatography-mass spectrometry method was developed and validated to quantify OLZ, its four oxidative metabolites (N-desmethyl-OLZ, 2-hydroxymethyl-OLZ, 7-hydroxy-OLZ, and OLZ-N-oxide), and two N-glucuronides (OLZ-10-N-glucuronide and OLZ-4'-N-glucuronide). Consistent with previous reports, UGT1A4, CYP1A2, and flavin-containing monooxygenase 3 play major roles in catalyzing the formation of OLZ-10-N-glucuronide, 7-hydroxy-OLZ, and OLZ-N-oxide, respectively. In addition, a previously uncharacterized major contribution of CYP2C8 to OLZ-N-demethylation was demonstrated. The kinetics of OLZ metabolite formation (Km and Vmax) by human liver microsomes, rP450 enzymes, and rUGT enzymes were characterized in the presence of bovine serum albumin [2% (w/v)]. Consistent with the known effect of bovine serum albumin on CYP1A2, CYP2C8, and UGT1A4 activities, Km values reported here are lower than previously reported values for OLZ metabolic pathways. In addition to CYP1A2-mediated OLZ-N-demethylation, these results suggest that other P450 enzymes, particularly CYP2C8, contribute significantly to oxidative OLZ metabolism through catalysis of OLZ-N-demethylation.

Does age affect gastric emptying time? A model-based meta-analysis of data from premature neonates through to adults.

PURPOSE: Gastric emptying (GE) is often reported to be slower and more irregular in premature neonates than in older children and adults. The aim of this study was to investigate the impact of age and other covariates on the rate of GE. METHODS: The effect of age on the mean gastric residence times (MGRT) of liquid and solid food was assessed by analysing 49 published studies of 1457 individuals, aged from 28 weeks gestation to adults. The data were modelled using the nonlinear mixed-effects approach within NONMEM version 7.2 (ICON, Dublin, Ireland), with evaluation of postnatal age, gestational age and meal type as covariates. A double Weibull function was selected as a suitable model since it could account for the typical biphasic nature of GE. RESULTS: Age was not a significant covariate for GE but meal type was. Aqueous solutions were associated with the fastest emptying time (mean simulated gastric residence time of 45 min) and solid food was associated with the slowest (98 min). CONCLUSIONS: These findings challenge the assertion that GE is different in neonates, as compared with older children and adults due to age, and they reinforce the significance of food type in modulating GE.

Pharmacokinetic-pharmacodynamic modeling of mood and withdrawal symptoms in relation to plasma concentrations of methadone in patients undergoing methadone maintenance treatment.

The aims of the present study were to characterize the relationship between plasma racemic methadone and its enantiomers' concentrations with respect to their pharmacodynamic effects and to investigate the influence of potential covariates on the pharmacodynamic parameters in patients on methadone maintenance treatment (MMT). Eighty-eight regular subjects at the Sheffield Care Trust Substance Misuse Services were studied. Samples of blood and urine were collected before the daily dose of methadone. Blood samples were taken up to 5 hours after dose. Total plasma concentrations of (RS)-methadone and total and unbound plasma concentrations of both enantiomers were measured by liquid chromatography-mass spectrometry. The Total Mood Disturbance Score (TMDS), the Objective Opioid Withdrawal Scale (OOWS), and the Subjective Opioid Withdrawal Scale (SOWS) were used as measures of mood and withdrawal. Population pharmacokinetic/pharmacodynamic analysis and subsequent multiple regression analysis were used to determine the factors influencing the pharmacodynamic effects of methadone. Significant decreases (P ≤ 0.04) were observed in the scores for the TMDS, SOWS, and OOWS for 5 hours after methadone dosage. The TMDS had returned to baseline by 10 hours after dose (P = 0.98), at which time the SOWS remained significantly below baseline (P = 0.001). Multiple regression analysis revealed that 33% of the overall variation in unbound (R)-methadone EC50 was explained by 3 variables, namely CYP3A activity (9%), age (16%), and sex (8%). Age also accounted for 8% and 9% of the variation in total (rac)- and (R)-methadone EC50. The present study has confirmed that the duration of mood change in the present study was shorter than the effect of methadone in stabilizing withdrawal symptoms. Thus, it is likely that a once-daily dose of methadone, albeit effective for preventing withdrawal, may not be sufficient to improve mood in some patients. Finally, it was established that CYP3A activity, years of dependent use, sex, and age are major determinants of methadone EC50 with respect to TMDS.

Research priorities in pharmacokinetics.

An opinion is expressed on the past, present and future roles of pharmacokinetic-pharmacodynamic research in the context of UK clinical pharmacology. On the basis of its current constitution, it seems unlikely that this area of research will be driven from within academic clinical pharmacology in the UK. Therefore, in order to bring its expertise and experience to bear effectively on the evolving emphasis on translational medicine and modelling and simulation, this community would need to reach out beyond its current preoccupations to increase interactions with the next generation of pharmacokineticists and pharmacometricians.

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.

Simulation and prediction of in vivo drug metabolism in human populations from in vitro data.

The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.

Contribution of the activities of CYP3A, CYP2D6, CYP1A2 and other potential covariates to the disposition of methadone in patients undergoing methadone maintenance treatment.

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.

Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug-drug interactions: using in vitro data as an aid to assess study power.

The clinical assessment of metabolic drug-drug interactions (mDDI) may involve population-based pharmacokinetic (POPPK) assessment as part of Phase 3 clinical trials. The elements of such POPPK study design have not been linked to prior information from in vitro experiments. Using in vitro-in vivo extrapolation techniques, implemented within Simcyp algorithms, the influence of POPPK study design (sample size, concentration-time data points, proportion of the population receiving a concomitant medication (COMED)) was studied in relation to the inhibitory potency of the each COMED. Steady-state concentrations of a candidate compound (compound X; mainly metabolized by cytochrome P450 enzymes, CYP3A4 and CYP2D6) in the presence and absence of COMEDs (including ketoconazole, fluconazole, quinidine and paroxetine as inhibitors) were analysed using non-linear mixed effect modelling (NONMEM). The NONMEM operator was blind to the nature of the COMEDs and the inhibitory effects on model parameters were classified as either statistically (p>0.01 for a change in objective function) or kinetically (COMED effect>2 fold) significant. Using a population study size of 2000, no false-positive cases were identified and, except in one case, no false-negative interaction was observed when >2.5% of patients had received an interacting COMED. The findings increase the confidence in the ability of the mixed effects modelling approach to identify 'true' interactions. However, they also emphasize the importance of study design and the potential value of using pre-clinical information from in vitro studies. Recent US Food and Drug Administration guidance on mDDI has put more emphasis on the use of in vitro systems for detecting and anticipating such effects. Combining these data with the framework of non-linear mixed effect modelling seems a natural progression in the field of assessing mDDI.

Cytochrome p450 turnover: regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions.

In vivo enzyme levels are governed by the rates of de novo enzyme synthesis and degradation. A current lack of consensus on values of the in vivo turnover half-lives of human cytochrome P450 (CYP) enzymes places a significant limitation on the accurate prediction of changes in drug concentration-time profiles associated with interactions involving enzyme induction and mechanism (time)-based inhibition (MBI). In the case of MBI, the full extent of inhibition is also sensitive to values of enzyme turnover half-life. We review current understanding of CYP regulation, discuss the pros and cons of various in vitro and in vivo approaches used to estimate the turnover of specific CYPs and, by simulation, consider the impact of variability in estimates of CYP turnover on the prediction of enzyme induction and MBI in vivo. In the absence of consensus on values for the in vivo turnover half-lives of key CYPs, a sensitivity analysis of predictions of the pharmacokinetic effects of enzyme induction and MBI to these values should be an integral part of the modelling exercise, and the selective use of values should be avoided.

The consequences of 3,4-methylenedioxymethamphetamine induced CYP2D6 inhibition in humans.

3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) is a widely abused substituted amphetamine. MDMA is predominantly O-demethylenated in humans by cytochrome P450 isoforms 2D6 and 1A2 (CYP2D6 and CYP CYP1A2, respectively). MDMA is also a mechanism-based inhibitor of CYP2D6. A controlled clinical trial was conducted in 15 healthy male subjects whereby a probe drug, dextromethorphan (DEX), was administered after an oral dose of 1.5 mg/kg MDMA. The pharmacokinetics of DEX and its metabolites were used to evaluate changes in CYP2D6 activity. The urinary metabolic ratio of DEX and dextrorphan was used to calculate a recovery half-life of CYP2D6. After MDMA, DEX Cmax and area under the curve increased approximately 10-fold with corresponding decreases in dextrorphan pharmacokinetic parameters. The metabolic ratio increased almost 100-fold from 0.0061 +/- 0.0056 to 0.4322 +/- 0.2848 after MDMA administration, with 67% of the subjects having a value greater than the antimode of 0.3 for assigning the poor metabolizer phenotype. CYP2D6 activity recovered after 10 days with a recovery half-life of 46.6 hours. In addition to the possible long-term serotonergic effects of MDMA, users must be warned of the consequences of such an inhibition.