Assessing potential drug-drug interactions between clofazimine and other frequently used agents to treat drug-resistant tuberculosis.

Clofazimine is included in drug regimens to treat rifampicin/drug-resistant tuberculosis (DR-TB), but there is little information about its interaction with other drugs in DR-TB regimens. We evaluated the pharmacokinetic interaction between clofazimine and isoniazid, linezolid, levofloxacin, and cycloserine, dosed as terizidone. Newly diagnosed adults with DR-TB at Klerksdorp/Tshepong Hospital, South Africa, were started on the then-standard treatment with clofazimine temporarily excluded for the initial 2 weeks. Pharmacokinetic sampling was done immediately before and 3 weeks after starting clofazimine, and drug concentrations were determined using validated liquid chromatography-tandem mass spectrometry assays. The data were interpreted with population pharmacokinetics in NONMEM v7.5.1 to explore the impact of clofazimine co-administration and other relevant covariates on the pharmacokinetics of isoniazid, linezolid, levofloxacin, and cycloserine. Clofazimine, isoniazid, linezolid, levofloxacin, and cycloserine data were available for 16, 27, 21, 21, and 6 participants, respectively. The median age and weight for the full cohort were 39 years and 52 kg, respectively. Clofazimine exposures were in the expected range, and its addition to the regimen did not significantly affect the pharmacokinetics of the other drugs except levofloxacin, for which it caused a 15% reduction in clearance. A posteriori power size calculations predicted that our sample sizes had 97%, 90%, and 87% power at P < 0.05 to detect a 30% change in clearance of isoniazid, linezolid, and cycloserine, respectively. Although clofazimine increased the area under the curve of levofloxacin by 19%, this is unlikely to be of great clinical significance, and the lack of interaction with other drugs tested is reassuring.

The cycle effect quantified: reduced tumour uptake in subsequent cycles of [177Lu]Lu-HA-DOTATATE during peptide receptor radionuclide therapy.

BACKGROUND: Clear evidence regarding the effect of reduced tumour accumulation in later peptide receptor radionuclide therapy (PRRT) cycles is lacking. Therefore, we aimed to quantify potential cycle effects for patients treated with [177Lu]Lu-HA-DOTATATE using a population pharmacokinetic (PK) modelling approach. METHODS: A population PK model was developed using imaging data from 48 patients who received multiple cycles of [177Lu]Lu-HA-DOTATATE. The five-compartment model included a central, kidney, spleen, tumour and lumped rest compartment. Tumour volume and continued use of long-acting somatostatin analogues (SSAs) were tested as covariates in the model. In addition, the presence of a cycle effect was evaluated by relating the uptake rate in a specific cycle as a fraction of the (tumour or organ) uptake rate in the first cycle. RESULTS: The final PK model adequately captured observed radioactivity accumulation in kidney, spleen and tumour. A higher tumour volume was identified to increase the tumour uptake rate, where a twofold increase in tumour volume resulted in a 2.3-fold higher uptake rate. Also, continued use of long-acting SSAs significantly reduced the spleen uptake rate (68.4% uptake compared to SSA withdrawal (10.5% RSE)). Lastly, a cycle effect was significantly identified, where tumour uptake rate decreased to 86.9% (5.3% RSE) in the second cycle and even further to 79.7% (5.6% RSE) and 77.6% (6.2% RSE) in the third and fourth cycle, respectively, compared to cycle one. CONCLUSIONS: Using a population PK modelling approach, the cycle effect of reduced tumour uptake in subsequent PRRT cycles was quantified. Our findings implied that downregulation of target receptors is probably not the major cause of the cycle effect, due to a plateau in the decrease of tumour uptake in the fourth cycle.

Population pharmacokinetics of tamsulosine in patients with benign prostatic hyperplasia.

PURPOSE: The study aimed to determine the typical clearance and volume of distribution values of tamsulosin in patients with benign prostatic hyperplasia (BPH), and to identify factors with a measurable impact on the drug's elimination. METHODS: This open-label, single-arm population pharmacokinetic study involved 65 adult men with BPH who had been on tamsulosin therapy for at least seven days. The steady-state serum concentrations of tamsulosin were measured using liquid chromatography-tandem quadrupole mass spectrometry. Population pharmacokinetic parameters, their variability, and influencing factors were estimated based on a two-compartment pharmacokinetic model using NONMEM software. RESULTS: The estimated tamsulosin clearance in BPH patients was 0.719 L/h, and the steady-state volume of distribution was 32 L. Neither renal nor liver function parameters had a statistically significant effect on tamsulosin clearance. However, a positive correlation was observed between hemoglobin levels and tamsulosin clearance in the BPH patient cohort. CONCLUSION: Our investigation reveals significant associations between tamsulosin pharmacokinetics and specific characteristics of patients with lower urinary tract symptoms (LUTS) due to BPH. The study highlights that tamsulosin clearance is associated with hemoglobin levels in patients with LUTS/BPH. This study underscores the importance of considering patient-specific factors when managing BPH treatment with tamsulosin, emphasizing associations rather than causative relationships.

A population pharmacokinetic model of methotrexate in Korean patients with haematologic malignancy.

AIMS: This study was conducted to develop a population pharmacokinetic (PK) model of methotrexate in Korean patients with haematologic malignancy, identify factors affecting methotrexate PK, and propose an optimal dosage regimen for the Korean population. METHODS: Data were retrospectively collected from 188 patients with acute leukaemia or non-Hodgkin's lymphoma who were admitted to Severance Hospital during the period from November 2005 to January 2016. Using demographic factors and laboratory results as potential covariates for PK parameters, model development was performed using NONMEM and optimal dosing regimens were developed using the final PK model. RESULTS: A two-compartment model incorporating body weight via allometry best described the data, yielding typical parameter values of 25.09 L for central volume of distribution ( V 1 ), 17.65 L for peripheral volume of distribution ( V 2 ), 12.89 L/h for clearance (CL) and 0.655 L/h for inter-compartmental clearance in a 50 kg patient. Covariate analyses showed that, at the weight of 50 kg, CL decreased by 0.11 L/h for each 1-year increase in age above 14 years old and decreased 0.8-fold when serum creatinine level doubled, indicating the importance of age-specific dose individualization in methotrexate treatment. Volume of distribution at steady state derived from PK parameters (= V 1 + V 2 ) was 0.85 L/kg, which was similar to those in the Western or Chinese populations. Optimal doses simulated from the final model successfully produced the PK measures close to the target chosen. CONCLUSIONS: The population PK model and optimal dosage regimens developed in this study can be used as a basis to achieve precision dosing in Korean patients with haematologic malignancy.

The development and evaluation of dose-prediction tools for allopurinol therapy (Easy-Allo tools).

AIMS: Dose escalation at the initiation of allopurinol therapy can be protracted and resource intensive. Tools to predict the allopurinol doses required to achieve target serum urate concentrations would facilitate the implementation of more efficient dose-escalation strategies. The aim of this research was to develop and externally evaluate allopurinol dosing tools, one for use when the pre-urate-lowering therapy serum urate is known (Easy-Allo1) and one for when it is not known (Easy-Allo2). METHODS: A revised population pharmacokinetic-pharmacodynamic model was developed using data from 653 people with gout. Maintenance doses to achieve the serum urate target of <0.36 mmol L-1 in >80% of individuals were simulated and evaluated against external data. The predicted and observed allopurinol doses were compared using the mean prediction error (MPE) and root mean square error (RMSE). The proportion of Easy-Allo predicted doses within 100 mg of the observed was quantified. RESULTS: Allopurinol doses were predicted by total body weight, baseline urate, ethnicity and creatinine clearance. Easy-Allo1 produced unbiased and suitably precise dose predictions (MPE 2 mg day-1 95% confidence interval [CI] -13-17, RMSE 91%, 90% within 100 mg of the observed dose). Easy-Allo2 was positively biased by about 70 mg day-1 and slightly less precise (MPE 70 mg day-1 95% CI 52-88, RMSE 131%, 71% within 100 mg of the observed dose). CONCLUSIONS: The Easy-Allo tools provide a guide to the allopurinol maintenance dose requirement to achieve the serum urate target of <0.36 mmol L-1 and will aid in the development of novel dose-escalation strategies for allopurinol therapy.

Model-based precision dosing and remedial dosing recommendations for delayed or missed doses of isoniazid in Chinese patients with tuberculosis.

AIMS: Isoniazid (INH) has been used as a first-line drug to treat tuberculosis (TB) for more than 50 years. However, large interindividual variability was found in its pharmacokinetics, and effects of nonadherence to INH treatment and corresponding remedy regime remain unclear. This study aimed to develop a population pharmacokinetic (PPK) model of INH in Chinese patients with TB to provide model-informed precision dosing and explore appropriate remedial dosing regimens for nonadherent patients. METHODS: In total, 1012 INH observations from 736 TB patients were included. A nonlinear mixed-effects modelling was used to analyse the PPK of INH. Using Monte Carlo simulations to determine optimal dosage regimens and design remedial dosing regimens. RESULTS: A 2-compartmental model, including first-order absorption and elimination with allometric scaling, was found to best describe the PK characteristics of INH. A mixture model was used to characterize dual rates of INH elimination. Estimates of apparent clearance in fast and slow eliminators were 28.0 and 11.2 L/h, respectively. The proportion of fast eliminators in the population was estimated to be 40.5%. Monte Carlo simulations determined optimal dosage regimens for slow and fast eliminators with different body weight. For remedial dosing regimens, the missed dose should be taken as soon as possible when the delay does not exceed 12 h, and an additional dose is not needed. delay for an INH dose exceeds 12 h, the patient only needs to take the next single dose normally. CONCLUSION: PPK modelling and simulation provide valid evidence on the precision dosing and remedial dosing regimen of INH.

Population pharmacokinetics of a novel PCSK9 antisense oligonucleotide.

AIMS: The aim of this study was to characterize the population pharmacokinetics of AZD8233, an antisense oligonucleotide (ASO) that targets the PCSK9 transcript to reduce hepatocyte PCSK9 protein production and plasma levels. AZD8233 utilizes generation 2.5 S-constrained ethyl motif (cET) chemistry and is conjugated to a triantennary N-acetylgalactosamine (GalNAc3) ligand for targeted hepatocyte uptake. METHODS: A non-linear mixed-effect modelling approach utilizing NONMEM software was applied to AZD8233 concentration-time data from 3416 samples in 219 participants from four phase 1-2 studies, one in healthy volunteers (NCT03593785) and three in patients with dyslipidaemia (NCT04155645, NCT04641299 and NCT04823611). RESULTS: The final model described the AZD8233 plasma concentration-time profile from four phase 1-2 studies in healthy volunteers or participants with dyslipidaemia, covering a dose range of 4 to 120 mg. The pharmacokinetics of AZD8233 were adequately described by a two-compartment model with first-order absorption. The supra-proportional increase in maximum plasma concentration (Cmax) across the observed dose range was described by non-linear Michaelis-Menten elimination (maximum elimination rate, 9.9 mg/h [12% relative standard error]; concentration yielding half-maximal elimination rate, 4.8 mg/L [18% relative standard error]). Body weight, sex, estimated glomerular filtration rate and disease status (healthy participant vs. patient with dyslipidaemia) were identified as factors affecting exposure to AZD8233. CONCLUSIONS: Covariate analysis showed body weight to be the main factor affecting exposure to AZD8233, which largely explained the higher Cmax observed in the Asian population relative to non-Asians.

Development of a population pharmacokinetic model for the novel long-acting injectable antipsychotic risperidone ISM®.

AIMS: The aims of this study were to develop a population pharmacokinetic (PK) model for risperidone ISM® and to investigate the relationships between active moiety exposure, as described by apparent clearance (CL40), and several covariates using all data from five clinical studies. METHODS: A population PK model was developed using active moiety concentrations from a study in healthy volunteers and two studies in patients with schizophrenia. Data from a comparative bioavailability study in medically stable patients and a Phase III study in patients with acute exacerbation of schizophrenia were then incorporated, using empirical Bayesian feedback and model refinement in NONMEM. Finally, covariate analysis was performed on CL40. RESULTS: The final model adequately described the pharmacokinetics of 6288 active moiety concentrations in 17 healthy volunteers and 430 patients with schizophrenia. This one-compartment disposition model had a complex absorption process, combining a small amount immediately entering the central active moiety compartment, two first-order absorption processes and a combined zero-order and first order process, with first-order elimination from the central compartment. Significant covariates on CL40 were BMI and sex. Goodness-of-fit (GOF) plots and visual predictive checks (VPC) confirmed acceptable description of the data. CONCLUSIONS: The population PK model adequately described active moiety concentrations from five clinical studies after risperidone ISM® administration. Relationships between active moiety exposure and covariates were defined in order to facilitate simulations for future studies. The model showed that risperidone ISM® rapidly achieves therapeutic plasma levels within the first hours after the first injection that are maintained sustainedly throughout the whole dosing interval following once-monthly gluteal injections of 100 mg and 75 mg.

Model-informed dose optimization of mycophenolic acid in pediatric kidney transplant patients.

PURPOSE: We aimed to develop and evaluate a population PK model of mycophenolic acid (MPA) in pediatric kidney transplant patients to aid MPA dose optimization. METHODS: Data were collected from pediatric kidney transplant recipients from a Dutch academic hospital (Radboudumc, the Netherlands). Pharmacokinetic model-building and model-validation analyses were performed using NONMEM. Subsequently, we externally evaluated the final model using data from another academic hospital. The final model was used to develop an optimized dosing regimen. RESULTS: Thirty pediatric patients were included of whom 266 measured MPA plasma concentrations, including 20 full pharmacokinetic (PK) curves and 24 limited sampling curves, were available. A two-compartment model with a transition compartment for Erlang-type absorption best described the data. The final population PK parameter estimates were Ktr (1.48 h-1; 95% CI, 1.15-1.84), CL/F (16.0 L h-1; 95% CI, 10.3-20.4), Vc/F (24.9 L; 95% CI, 93.0-6.71E25), Vp/F (1590 L; 95% CI, 651-2994), and Q/F (36.2 L h-1; 95% CI, 9.63-74.7). The performance of the PK model in the external population was adequate. An optimized initial dose scheme based on bodyweight was developed. With the licensed initial dose, 35% of patients were predicted to achieve the target AUC, compared to 42% using the optimized scheme. CONCLUSION: We have successfully developed a pharmacokinetic model for MPA in pediatric renal transplant patients. The optimized dosing regimen is expected to result in better target attainment early in treatment. It can be used in combination with model-informed follow-up dosing to further individualize the dose when PK samples become available.

Understanding hemoglobin contribution to high-dose methotrexate disposition-population pharmacokinetics in pediatric patients with hematological malignancies.

PURPOSE: The aim of the present study was to develop a population pharmacokinetic model for methotrexate (MTX) during high-dose treatment (HDMTX) in pediatric patients with acute lymphoblastic leukemia (ALL) and non-Hodgkin's lymphoma (NHL) and to describe the influence of variability factors. METHODS: The study included 50 patients of both sexes (aged 1-18 years) who received 3 or 5 g/m2 of HDMTX. A nonlinear mixed effect modeling approach was applied for data analysis. Parameter estimation was performed by first-order conditional estimation method with interaction (FOCEI), whereas stepwise covariate modeling was used to assess variability factors. RESULTS: The final model is a two-compartment model that incorporates the effect of body surface area and the influence of hemoglobin and serum creatinine on MTX clearance (CL). Population pharmacokinetic values for a typical subject were estimated at 5.75 L/h/m2 for clearance (CL), 21.3 L/m2 for volume of the central compartment (V1), 8.2 L/m2 for volume of the peripheral compartment (V2), and 0.087 L/h/m2 for intercompartmental clearance (Q). According to the final model, MTX CL decreases with increasing serum creatinine, whereas a positive effect was captured for hemoglobin. A difference of almost 32% in MTX CL was observed among patients' hemoglobin values reported in the study. CONCLUSION: The developed population pharmacokinetic model can contribute to the therapy optimization during HDMTX in pediatric patients with ALL and NHL. In addition to renal function and body weight, it describes the influence of hemoglobin on CL, allowing better understanding of its contribution to the disposition of HDMTX.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Dolutegravir pharmacokinetics in Ugandan patients with TB and HIV receiving standard- versus high-dose rifampicin.

Higher rifampicin doses may improve tuberculosis treatment outcomes. This could however exacerbate the existing drug interaction with dolutegravir. Moreover, the metabolism of dolutegravir may also be affected by polymorphism of UGT1A1, a gene that codes for uridine diphosphate glucuronosyltransferase. We used population pharmacokinetic modeling to compare the pharmacokinetics of dolutegravir when coadministered with standard- versus high-dose rifampicin in adults with tuberculosis and HIV, and investigated the effect of genetic polymorphisms. Data from the SAEFRIF trial, where participants were randomized to receive first-line tuberculosis treatment with either standard- 10 mg/kg or high-dose 35 mg/kg rifampicin alongside antiretroviral therapy, were used. The dolutegravir model was developed with 211 plasma concentrations from 44 participants. The median (interquartile range) rifampicin area under the curve (AUC) in the standard- and high-dose arms were 32.3 (28.7-36.7) and 153 (138-175) mg·h/L, respectively. A one-compartment model with first-order elimination and absorption through transit compartments best described dolutegravir pharmacokinetics. For a typical 56 kg participant, we estimated a clearance, absorption rate constant, and volume of distribution of 1.87 L/h, 1.42 h-1, and 12.4 L, respectively. Each 10 mg·h/L increase in the AUC of coadministered rifampicin from 32.3 mg·h/L led to a 2.3 (3.1-1.4) % decrease in dolutegravir bioavailability. Genetic polymorphism of UGT1A1 did not significantly affect dolutegravir pharmacokinetics. Simulations of trough dolutegravir concentrations show that the 50 mg twice-daily regimen attains both the primary and secondary therapeutic targets of 0.064 and 0.3 mg/L, respectively, regardless of the dose of coadministered rifampicin, unlike the once-daily regimen.

Population pharmacokinetics, pharmacodynamics and pharmacogenetics modelling of oxypurinol in Hmong adults with gout and/or hyperuricemia.

AIMS: The aim of this study was to quantify identifiable sources of variability, including key pharmacogenetic variants in oxypurinol pharmacokinetics and their pharmacodynamic effect on serum urate (SU). METHODS: Hmong participants (n = 34) received 100 mg allopurinol twice daily for 7 days followed by 150 mg allopurinol twice daily for 7 days. A sequential population pharmacokinetic pharmacodynamics (PKPD) analysis with non-linear mixed effects modelling was performed. Allopurinol maintenance dose to achieve target SU was simulated based on the final PKPD model. RESULTS: A one-compartment model with first-order absorption and elimination best described the oxypurinol concentration-time data. Inhibition of SU by oxypurinol was described with a direct inhibitory Emax model using steady-state oxypurinol concentrations. Fat-free body mass, estimated creatinine clearance and SLC22A12 rs505802 genotype (0.32 per T allele, 95% CI 0.13, 0.55) were found to predict differences in oxypurinol clearance. Oxypurinol concentration required to inhibit 50% of xanthine dehydrogenase activity was affected by PDZK1 rs12129861 genotype (-0.27 per A allele, 95% CI -0.38, -0.13). Most individuals with both PDZK1 rs12129861 AA and SLC22A12 rs505802 CC genotypes achieve target SU (with at least 75% success rate) with allopurinol below the maximum dose, regardless of renal function and body mass. In contrast, individuals with both PDZK1 rs12129861 GG and SLC22A12 rs505802 TT genotypes would require more than the maximum dose, thus requiring selection of alternative medications. CONCLUSIONS: The proposed allopurinol dosing guide uses individuals' fat-free mass, renal function and SLC22A12 rs505802 and PDZK1 rs12129861 genotypes to achieve target SU.

A semimechanistic pharmacokinetic/pharmacodynamic model for alanine aminotransferase-based hepatotoxicity of methotrexate in paediatric patients with acute lymphoid leukaemia.

AIMS: Methotrexate (MTX) is recognized for its potential to induce hepatotoxicity, commonly manifested by elevated alanine aminotransferase (ALT) levels. However, the quantitative relationship between the pharmacokinetics (PK) of MTX and ALT-based hepatotoxicity remains unclear. This study aimed to develop a semimechanistic PK/pharmacodynamic (PD) model to characterize the MTX-induced hepatotoxicity based on ALT in paediatric patients with acute lymphoid leukaemia. METHODS: A retrospective study was conducted on paediatric patients who received high-dose (3-5 g/m2 ) MTX treatment. MTX concentrations were assessed at 24-h intervals until the concentration dropped below 0.1 µmol/L. ALT concentrations were measured both before and after MTX administration. A population PK model was initially developed, which was later connected to a semimechanistic hepatotoxicity model. RESULTS: The PK model was developed using 354 MTX concentrations obtained from 51 patients, while the PD model was constructed using 379 ALT concentrations collected from 48 patients. The optimal PK model for MTX consisted of a 2-compartment structure, where body surface area served as a covariate for clearance and central volume of distribution. An indirect response model coupled to a liver injury signal transduction model was developed to describe the dynamics of ALT after MTX administration. The drug effect was adequately described by a linear model, exhibiting considerable interoccasion variability for each treatment session. No significant covariates were identified to have an impact on the PD parameters. CONCLUSION: A semimechanistic model was developed to describe ALT-based hepatotoxicity of MTX, and it has the potential to serve as a valuable tool for characterizing drug-induced hepatotoxicity.

Prediction of the pharmacokinetics of pemetrexed with a low test dose: A proof-of-concept study.

PURPOSE: Pemetrexed is a cytotoxic drug used for the treatment of lung cancer and mesothelioma. The use of a low test dosing of cytotoxic drugs may aid in dose individualization without causing harm. The aim of this proof-of-concept study was to assess if the pharmacokinetics (PKs) of a test dose could predict the PKs of a therapeutic pemetrexed dose. METHODS: Ten patients received both a low test dose (100 µg) and a therapeutic dose of pemetrexed after which plasma concentrations pemetrexed were measured. PK analysis was performed by means of nonlinear mixed-effects modelling. The predictive performances of test dose clearance and renal function towards a therapeutic dose were assessed. RESULTS: The PKs of a pemetrexed test dose were best described by a one-compartment model with linear elimination. A high variability in the administered dose was observed for the test dose, but not for the therapeutic dose. A statistically significant correlation between test dose clearance and therapeutic dose clearance was observed (Spearman's rho: 0.758, P = 0.02). The predictive performance of test dose clearance was worse than renal function: mean predictive error (+95% confidence interval [CI]) 53.9% (50.1-57.6%) vs 19.4% (12.4-26.4%) and normalized root-mean square error (+95% CI) 57.8% (30.5-85.1%) vs 25.7% (20.3-31.0%). CONCLUSION: We show that test dosing of pemetrexed is feasible, but there seems no added value for a low test dosing in the dose individualization of pemetrexed.

Evaluation of treosulfan cumulative exposure in paediatric patients through population pharmacokinetics and dosing simulations.

AIM: To investigate the pharmacokinetics (PK) of intravenous treosulfan in paediatric patients undergoing haematopoietic stem cell transplantation (HSCT) for a broad range of diseases and to explore the impact of different dosing regimens on treosulfan exposure (area under the concentration-time curve, AUC0→∞ ) through dosing simulations. METHODS: A prospective multicentre PK study was conducted using treosulfan concentration data (n = 423) collected from 53 children (median age 3.5, range 0.2-17.0 years) receiving three daily age-guided doses (10-14 g/m2 ). Population PK modelling was performed using NONMEM software, utilising a stepwise forward selection backward elimination method and likelihood-ratio test for screening covariates to describe PK variability. Monte Carlo simulation was used to generate patient PK data for 10 000 virtual paediatric patients and cumulative AUC0→∞ values were evaluated using age, body surface area (BSA) and model-based dosing regimens, targeting 4800 mg*h/L. RESULTS: Treosulfan concentration data were described using a one-compartment PK model with first-order elimination. Population mean (95% CI) estimates for clearance (CL) and volume of distribution (V) were 16.3 (14.9-18.1) L/h and 41.9 (38.8-45.1) L, respectively. Allometrically scaled body weight was the best covariate descriptor for CL and V, and maturational age further explained variability in CL. Dosing simulations indicated that in young patient groups (<2 years), a model-based dosing regimen more accurately achieved the target AUC0→∞ (58.3%) over the age (42.6%) and BSA-based (51.3%) regimens. CONCLUSION: Treosulfan disposition was described through allometric body weight and maturational age descriptors. Model-informed dosing is recommended for patients under 2 years. Treosulfan PK parameters and AUC0→∞ were not influenced by patient disease.

Operational characteristics of full random effects modelling ('frem') compared to stepwise covariate modelling ('scm').

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique ('scm') was compared to full random effects modelling ('frem'). We evaluated the power to identify a 'true' covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20-500) and covariate correlations (0-90% cov-corr). The PsN 'frem' routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as 'fremposthoc' and to facilitate the comparison to 'scm'. 'Fremposthoc' had a higher power to detect the true covariate with lower bias in small n studies compared to 'scm', applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For 'fremposthoc', power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step 'frem' models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final 'scm' model precision obtained using common settings. We conclude that 'fremposthoc' is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.

Pharmacokinetic comparability between two populations using nonlinear mixed effect models: a Monte Carlo study.

'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful.

Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis.

Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.

Optimizing Dosing and Fixed-Dose Combinations of Rifampicin, Isoniazid, and Pyrazinamide in Pediatric Patients With Tuberculosis: A Prospective Population Pharmacokinetic Study.

BACKGROUND: In 2010, the World Health Organization (WHO) revised dosing guidelines for treatment of childhood tuberculosis. Our aim was to investigate first-line antituberculosis drug exposures under these guidelines, explore dose optimization using the current dispersible fixed-dose combination (FDC) tablet of rifampicin/isoniazid/pyrazinamide; 75/50/150 mg, and suggest a new FDC with revised weight bands. METHODS: Children with drug-susceptible tuberculosis in Malawi and South Africa underwent pharmacokinetic sampling while receiving first-line tuberculosis drugs as single formulations according the 2010 WHO recommended doses. Nonlinear mixed-effects modeling and simulation was used to design the optimal FDC and weight-band dosing strategy for achieving the pharmacokinetic targets based on literature-derived adult AUC0-24h for rifampicin (38.7-72.9), isoniazid (11.6-26.3), and pyrazinamide (233-429 mg ∙ h/L). RESULTS: In total, 180 children (42% female; 13.9% living with human immunodeficiency virus [HIV]; median [range] age 1.9 [0.22-12] years; weight 10.7 [3.20-28.8] kg) were administered 1, 2, 3, or 4 FDC tablets (rifampicin/isoniazid/pyrazinamide 75/50/150 mg) daily for 4-8, 8-12, 12-16, and 16-25 kg weight bands, respectively. Rifampicin exposure (for weight and age) was up to 50% lower than in adults. Increasing the tablet number resulted in adequate rifampicin but relatively high isoniazid and pyrazinamide exposures. Administering 1, 2, 3, or 4 optimized FDC tablets (rifampicin/isoniazid/pyrazinamide 120/35/130 mg) to children < 6, 6-13, 13-20. and 20-25 kg, and 0.5 tablet in < 3-month-olds with immature metabolism, improved exposures to all 3 drugs. CONCLUSIONS: Current pediatric FDC doses resulted in low rifampicin exposures. Optimal dosing of all drugs cannot be achieved with the current FDCs. We propose a new FDC formulation and revised weight bands.