Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice

Association between olanzapine plasma concentrations and treatment response: A systematic review, meta-analysis and individual participant data meta-analysis

AIMS

By meta-analysing pooled studies and available individual participant data, we aim to provide new insight on olanzapine therapeutic drug monitoring in schizophrenia.

METHOD

We conducted a computerized search of bibliographic databases (Pubmed, Cochrane library, Web of Science and PsycINFO) to identify studies that assessed the relationship between olanzapine plasma concentration and the change in patients' clinical scores. We investigated this relationship with olanzapine plasma level 12h00 post-intake using a random-effects model.

RESULTS

7 studies were included in the pooled data analysis (781 patients). We found no difference in oral dose between responders and non-responders but a significantly higher concentration of 4.50 µg/L in responders (p < 0.01). Olanzapine concentration above the thresholds identified in each study was associated with response (odd ratio = 3.50, p = 0.0007). We identified that non-responder patients showed greater inter-individual variability than responders. In the individual data analysis (159 patients), we found no relationship between dose and clinical response but an association between plasma level and response in the shape of a parabolic curve. The Receiver Operating Characteristic curve found a threshold of 22.07 µg/L to identify responders (96% sensitivity, 86% specificity) and a threshold of 56.47 µg/L to identify a decreased probability of response.

CONCLUSION

In contrast to oral dose, our work confirmed that plasma olanzapine levels are associated with clinical response and should therefore be used to optimise treatment. We determined a treatment response threshold of 22.07 µg/L and suggest that a concentration above the therapeutic window may result in a decreased response.

Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV

Pharmacokinetic (PK) studies in children are usually small and have ethical constraints due to the medical complexities of drawing blood in this special population. Often, population PK models for the drug(s) of interest are available in adults, and these models can be extended to incorporate the expected deviations seen in children. As a consequence, there is increasing interest in the use of optimal design methodology to design PK sampling schemes in children that maximize information using a small sample size and limited number of sampling times per dosing period. As a case study, we use the novel tuberculosis drug delamanid, and show how applications of optimal design methodology can result in highly efficient and model-robust designs in children for estimating PK parameters using a limited number of sampling measurements. Using developed population PK models based on available data from adults living with and without HIV, and limited data on children without HIV, competing designs for children living with HIV were derived and assessed based on robustness to model uncertainty.

Bayesian optimization of tacrolimus exposure in stable kidney transplant patients

STUDY OBJECTIVE

The objective was to compare tacrolimus AUC0-12 determined by Non-Compartmental Analysis (NCA) using intensive sampling to Maximum a Posteriori-Bayesian (MAP-Bayesian) estimates from robust (n = 9 samples/subject) and sparse (n = 2 samples/subject) sampling in 67 stable KTRs and a validation group of similar patients.

DESIGN

This open-label, prospective, single center 12-h PK study included nine serial samples collected in KTRs to determine steady-state NCA tacrolimus AUC0-12 .

SETTING

This study was conducted at a single site within a large, urban hospital in the western New York area.

PATIENTS

This study described tacrolimus pharmacokinetics in stable kidney transplant recipients on maintenance tacrolimus therapy.

INTERVENTION

Robust and sparse AUC0-12 estimates by a MAP-Bayesian approach were obtained using the Advanced Dosing Solutions (AdDS) and ADAPT5 freeware. Limited sampling strategies were evaluated using the original population PK model (n = 67), which was also assessed using a validation group (n = 15). AUC0-12 agreement was tested by paired t-tests with intraclass correlation coefficient (ICC) and Bland Altman analysis.

MEASUREMENTS AND MAIN RESULTS

A total of 35 Black and 32 White stable KTRs (estimated glomerular filtration rate [eGFR] = 55.2 ± 15.7 mL/min/1.73m2 ) received the tacrolimus dose of 3.4 ± 1.7 mg/study with troughs of 6.8 ± 1.8 ng/mL. The NCA-AUC0-12 was 123.8 ± 33.6 μg·h/L compared to MAP-Bayesian estimates for Robust-AUC0-12 of 124.7 ± 33.3 μg·h/L and optimal 2-specimen Sparse-AUC0-12 of 119.7 ± 32.7 μg·h/L for the training group. Comparison of Robust-AUC0-12 to NCA-AUC0-12 had an ICC of 0.96 (p = 0.99) while comparison of Robust-AUC0-12 to Sparse-AUC0-12 using Pre-dose trough [C(t0h )] and 1 h [C(t1h )] resulted in an ICC of 0.93 (p = 0.014). In the validation group, 5 Black and 10 White KTRs (eGFR = 56.4 ± 16.8 mL/min/1.73m2 ) received a mean tacrolimus dose of 1.9 ± 1.2 mg/study with a trough of 6.0 ± 1.7 ng/mL. The validation group's NCA-AUC0-12 (88.4 ± 33.1 μg·h/L) was comparable to Robust-AUC0-12 (85.1 ± 33.8 μg·h/L, ICC = 0.93; p = 0.12) and Sparse-AUC0-12 determined from C(t0h ) and C(t4h ) (86.7 ± 33.9 μg·h/L, ICC = 0.91; p = 0.61).

CONCLUSION

MAP-Bayesian estimation for patient-specific AUC0-12 using sparse, two-specimen sampling is comparable to NCA and may enhance tacrolimus TDM in stable KTRs.

Population pharmacokinetic model and limited sampling strategy for clozapine using plasma and dried blood spot samples

BACKGROUND

To improve efficacy, therapeutic drug monitoring is often used in clozapine therapy. Trough level monitoring is regular, but trough levels provide limited information about the pharmacokinetics of clozapine and exposure in time. The area under the concentration time curve (AUC) is generally valued as better marker of drug exposure in time but calculating AUC needs multiple sampling. An alternative approach is a limited sampling scheme in combination with a population pharmacokinetic model meant for Bayesian forecasting. Furthermore, multiple venepunctions can be a burden for the patient, whereas collecting samples by means of dried blood spot (DBS) sampling can facilitate AUC-monitoring, making it more patient friendly.

OBJECTIVE

Development of a population pharmacokinetic model and limited sampling strategy for estimating AUC_0-12h (a twice-daily dosage regimen) and AUC_0-24h (a once-daily dosage regimen) of clozapine, using a combination of results from venepunctions and DBS sampling.

METHOD

From 15 schizophrenia patients, plasma and DBS samples were obtained before administration and 2, 4, 6, and 8 h after clozapine intake. MwPharm^® pharmacokinetic software was used to parameterize a population pharmacokinetic model and calculate limited sampling schemes.

RESULTS

A three-point sampling strategy with samples at 2, 6, and 8 h after clozapine intake gave the best estimation of the clozapine AUC_0-12h and at 4, 10, and 11 h for the AUC_0-24h. For clinical practice, however, a two-point sampling strategy with sampling points at 2 and 6 h was sufficient to estimate AUC_0-12h and at 4 and 11 h for AUC_0-24h.

CONCLUSION

A pharmacokinetic model with a two-time point limited sampling strategy meant for Bayesian forecasting using DBS sampling gives a better prediction of the clozapine exposure in time, expressed as AUC, compared to trough level monitoring. This limited sampling strategy might therefore provide a more accurate prediction of effectiveness and occurrence of side effects compared to trough level monitoring. The use of DBS samples also makes the collection of clozapine samples easier and wider applicable.

Understanding variability in the pharmacokinetics of atypical antipsychotics - focus on clozapine, olanzapine and aripiprazole population models

Antipsychotic medicines are widely used for the management of psychotic symptoms regardless of the underlying diagnosis. Most atypical antipsychotics undergo extensive metabolism prior to excretion. Various factors may influence their pharmacokinetics, particularly elimination, leading to highly variable drug concentrations between individual patients following the same dosing regimen. Population pharmacokinetic approach, based on nonlinear mixed effects modeling, is a useful tool to identify covariates explaining pharmacokinetic variability, as well as to characterize and distinguish unexplained residual and between-subject (interindividual) variability. In addition, this approach allows the use of both sparsely and intensively sampled data. In this paper, we reviewed the pharmacokinetic characteristics of clozapine, olanzapine and aripiprazole, focusing on a population modeling approach. In particular, models based on a nonlinear mixed effects approach performed by NONMEM^® software in order to identify and quantify sources of pharmacokinetic variability are presented. Population models were identified through systematic searches of PubMed and sixteen studies were selected. Some of the factors identified that significantly contribute to variability in elimination among clozapine, olanzapine, and aripiprazole are demographic characteristics, body weight, genetic polymorphism, smoking and in some cases drug interactions. Scientific research based on pharmacometric modeling is useful to further characterize sources of variability and their combined effect.

The effects of fasting on drug metabolism

Introduction: There is considerable variability in the rates and extent of drug metabolism between patients due to physiological, genetic, pharmacologic, environmental and nutritional factors such as fasting. This variability in drug metabolism may result in treatment failure or, conversely, in increased side effects or toxicity. Preclinical studies have shown that fasting alters drug metabolism by modulating the activity of drug metabolizing enzymes involved. However, until recently little was known about the effects of fasting on drug metabolism in humans.Areas covered: This review describes the effects of fasting on drug metabolism based on both preclinical studies and studies performed in humans.Expert opinion: A better understanding of the effects of fasting may improve the efficacy and safety of pharmacotherapy for individual patients. Fasting contributes to variability in human drug metabolism by differentially affecting drug metabolizing enzymes. Although the effects of fasting on drug metabolism appear to be small (between 10-20%), fasting may be relevant for drugs with a small therapeutic range and/or in combination with other factors that contribute to variability in drug metabolism such as physiological, genetic or pharmacological factors. Therefore, additional research on this topic is warranted.

The Path to Perfect Pediatric Posology - Drug Development in Pediatrics

Reluctance to enroll pediatric subjects in clinical trials has left gaps in information about dosing, safety, and efficacy of medications. Pharmacotherapeutic information for pediatric patients may be available for only a small range of ages and may be deficient, as children respond differently as they grow and mature from prematurity to adolescence. Current regulations, however, require early planning for the participation of children in drug development, as pediatric plans must be submitted at the end of phase 1 (European Union) or the end of phase 2 (United States). These plans are extensive, outlining planned studies, subjects to be enrolled, dose and dosage form justification, planned observations, and statistical analysis as well as planned modeling, simulation, and extrapolation analyses. The extent to which efficacy information in adults can be extrapolated to children depends on how similar the disease is in adults and each of the 5 pediatric age groups. Extrapolation may not be possible for conditions that do not occur in adults, requiring a complete development plan in adults, or extrapolation may be complete because of similar pathology and response to treatment. Pharmacokinetic and safety information cannot be extrapolated and must be collected in children of all ages, unless a waiver is granted. Physiologically based pharmacokinetic modeling, optimal design, population pharmacokinetics, and scavenged samples are all examples of new methodologies being used to study pediatric therapeutics. Clinicaltrials.gov and EU Clinical Trials registry are good sources of results of pediatric trials, although sponsors are also working toward prompt publication of study results in peer-reviewed journals.

Severe quetiapine voluntary overdose successfully treated with a new hemoperfusion sorbent

Quetiapine overdose, although rare, is mainly linked with tachycardia, QTc-interval prolongation, somnolence, coma, hyperglycemia, and eventually hepatotoxicity and myocarditis. Extracorporeal techniques for quetiapine removal might be helpful, but only a few cases are reported in the literature. We here describe the case of a 27-year-old healthy woman, admitted to our Intensive Care Unit after voluntary quetiapine intake and successfully treated with CytoSorb hemoperfusion in combination with continuous renal replacement therapy (CRRT), in order to accelerate quetiapine elimination. This is the first published experience about the potential application of hemoadsorption therapies, as CytoSorb sorbent, in large overdoses of quetiapine and this approach might be feasible to rapidly remove the substance from blood, stabilizing the patient condition.

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning

Background: We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders.

Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Decision Tree and Random Forest machine learning algorithms in R.

Results: Using quantitative gene expression values reported from patient blood samples, the RBPMS2 and LILRA5 genes classify male lithium responders with an area under the receiver operator characteristic curve (AUROC) of 0.92 and the ABRACL, FHL3, and NBPF14  genes classify female lithium responders AUROC of 1. A Decision Tree rule for establishing male versus female samples, using gene expression values were found to be: if RPS4Y1 ≥ 9.643, patient is a male and if RPS4Y1 < 9.643, patient is female with a probability=100%.

Conclusions: We developed a pre-treatment gender- and gene-expression-based predictive model selective for classifying male lithium responders with a sensitivity of 96% using 2-genes and female lithium responders with sensitivity=92% using 3-genes.

A novel approach for estimating ingested dose associated with paracetamol overdose

AIM

In cases of paracetamol (acetaminophen, APAP) overdose, an accurate estimate of tissue-specific paracetamol pharmacokinetics (PK) and ingested dose can offer health care providers important information for the individualized treatment and follow-up of affected patients. Here a novel methodology is presented to make such estimates using a standard serum paracetamol measurement and a computational framework.

METHODS

The core component of the computational framework was a physiologically-based pharmacokinetic (PBPK) model developed and evaluated using an extensive set of human PK data. Bayesian inference was used for parameter and dose estimation, allowing the incorporation of inter-study variability, and facilitating the calculation of uncertainty in model outputs.

RESULTS

Simulations of paracetamol time course concentrations in the blood were in close agreement with experimental data under a wide range of dosing conditions. Also, predictions of administered dose showed good agreement with a large collection of clinical and emergency setting PK data over a broad dose range. In addition to dose estimation, the platform was applied for the determination of optimal blood sampling times for dose reconstruction and quantitation of the potential role of paracetamol conjugate measurement on dose estimation.

CONCLUSIONS

Current therapies for paracetamol overdose rely on a generic methodology involving the use of a clinical nomogram. By using the computational framework developed in this study, serum sample data, and the individual patient's anthropometric and physiological information, personalized serum and liver pharmacokinetic profiles and dose estimate could be generated to help inform an individualized overdose treatment and follow-up plan.