Overview of model-building strategies in population PK/PD analyses: 2002-2004 literature survey

[Translated article] Safe administration of noradrenaline by the peripheral route: A systematic review

PURPOSE

To review and analyse the available literature on peripheral administration of noradrenaline (NA) with the aim of providing recommendations to ensure correct use and patient safety.

METHODS

Systematic review on the databases PubMed, ISI Web of Science, SCOPUS, and Science Direct, using the following search terms: ("Noradrenaline" [Mesh]) AND ("Norepinephrine" [Mesh]) AND ("Vasopressors" [Mesh]) AND ("Peripheral infusions" [Mesh]) OR ("Extravasations" [Mesh]). A total of 1040 articles were identified. Animal studies and studies written in languages other than English were excluded. Finally, 83 articles were included.

RESULTS

NA can be administered peripherally. The risk of extravasation should be taken into account, with phentolamine being the first pharmacological line of treatment. It has also been related to the appearance of thrombophlebitis, cellulitis, tissue necrosis, limb ischaemia, and gangrene, although its incidence seems to be low. The use of peripheral NA in children seems to be carried out without obvious complications. The use of standard concentrations is suggested to reduce the risk of errors. It is recommended to use 0.9% saline as the default diluent for peripheral NA.

CONCLUSIONS

Peripheral infusions of NA could be a safe and beneficial option in early resuscitation provided that a number of guidelines are followed that reduce the likelihood of complications associated with this route.

Safe administration of noradrenaline by the peripheral route: A systematic review

PURPOSE

To review and analyze the available literature on peripheral administration of noradrenaline (NA) with the aim of providing recommendations to ensure correct use and patient safety.

METHODS

Systematic review on the databases PubMed, ISI Web of Science, SCOPUS and Science Direct, using the following search terms: ("Noradrenaline" [Mesh]) AND ("Norepinephrine" [Mesh]) AND ("Vasopressors" [Mesh]) AND ("Peripheral infusions" [Mesh]) OR ("Extravasations" [Mesh]). A total of 1,040 articles were identified. Animal studies and studies written in languages other than English were excluded. Finally, 83 articles were included.

RESULTS

NA can be administered peripherally. The risk of extravasation should be taken into account, with phentolamine being the first pharmacological line of treatment. It has also been related to the appearance of thrombophlebitis, cellulitis, tissue necrosis, limb ischemia and gangrene, although its incidence seems to be low. The use of peripheral NA in children seems to be carried out without obvious complications. The use of standard concentrations is suggested to reduce the risk of errors. It is recommended to use 0.9% saline as the default diluent for peripheral NA.

CONCLUSIONS

Peripheral infusions of NA could be a safe and beneficial option in early resuscitation provided that a number of guidelines are followed that reduce the likelihood of complications associated with this route.

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Do Chinese HIV-infected adult patients with altered renal function need to adjust tenofovir disoproxil fumarate dosage? A population pharmacokinetics analysis

Tenofovir disoproxil fumarate (TDF), a prodrug of tenofovir (TFV), is an effective drug in treating patients infected with human immunodeficiency virus (HIV). Previous population pharmacokinetics (PPK) studies have showed the large variabilities in PK of TFV. Furthermore, limited information was known in Chinese populations. Therefore, the aim of this study was to characterize PPK of TDF in Chinese and identify factors that may affect its PK. TFV concentrations (n = 552) from 30 healthy subjects and 162 HIV-infected Chinese adult patients were pooled for PPK analysis by a nonlinear mixed-effects method. The PK of TFV was adequately described as a two-compartment model with first order absorption and elimination. The typical apparent clearance (CL/F) of TFV in 70-kg adults was 137 L/h, higher than that reported in Caucasians and Blacks (45.8-93 L/h). Estimated glomerular filtration rate was identified to be a significant factor influencing CL/F. Monte Carlo simulation showed that the exposure of standard dosing regimen of TDF 300 mg every 24 h in Chinese people with mild renal impairment (60 to 90 ml/min/1.73 m2) was close to that in individuals with normal renal function (90 mL/min). Dose adjustment is not required for patients with mild renal impairment. Our study might offer new clues for optimal dosing strategies in Chinese patients with HIV-infected.

Modeling an evaluation of the efficacy of the novel neuroanalgesic drug mirogabalin for diabetic peripheral neuropathic pain and postherpetic neuralgia therapy

Diabetic peripheral neuropathic pain (DPNP) and postherpetic neuralgia (PHN) are challenging and often intractable complex medical conditions, with a substantial impact on the quality of life. Mirogabalin, a novel voltage-gated Ca2+ channel α2δ ligand, was approved for the indication of DPNP and PHN. However, the time course of effects has not yet been clarified.We aimed to establish pharmacodynamic and placebo effect models of mirogabalin and pregabalin in DPNP and PHN, and to quantitatively compare the efficacy characteristics (maximum efficacy, onset time, and other pharmacodynamic parameters) and safety of mirogabalin and pregabalin. Public databases were comprehensively searched for randomized placebo-controlled clinical trials. A model-based meta-analysis (MBMA) was developed to describe the time course of drug efficacy and placebo effects. Adverse events were compared using a fixed-effects meta-analysis. Sixteen studies including 5,147 participants were eligible for this study. The placebo effect was relatively high and gradually increased with time, and it required at least eight weeks to reach a plateau. The pharmacodynamic model revealed that the maximum pure efficacy for mirogabalin and pregabalin was approximately -7.85 % and -8.86 %, respectively; the efficacy of mirogabalin to relieve DPNP and PHN was not superior to that of pregabalin, and both drugs had similar safety. While the rate constant of the onset rate of pregabalin was approximately thrice as high as that of mirogabalin. In addition, the baseline level of pain was an important factor affecting pregabalin efficacy. These findings are helpful in evaluating the clinical extension value of mirogabalin. They suggest that the high placebo effect and the baseline level of pain should be considered when grouping patients in future research and development of voltage-gated Ca2+ channel neuroanalgesic.

Confirmation of the utility of the CA-125 elimination rate (KELIM) as an indicator of the chemosensitivity in advanced-stage ovarian cancer in a "real-life setting"

OBJECTIVE

The modeled CA-125 ELIMination rate constant K (KELIM) has been validated as a marker of response to chemotherapy in >12,000 patients with advanced epithelial ovarian carcinoma (EOC) treated in first-line setting enrolled in >12 clinical trials. Patient KELIM is calculable online https://www.biomarker-kinetics.org/presentation. The objective was to investigate the prognostic value of KELIM in a large real-life national cancer registry with non-selected patients.

METHODS

We investigated 4,025 EOC patients from the Netherlands Cancer Registry treated with neoadjuvant chemotherapy (NACT) ± followed by interval debulking surgery (IDS). Patient KELIM values were calculated in patients with ≥ 3 CA-125 measurements during NACT. KELIM was standardized with a pre-specified cut-off and scored as unfavorable/favorable (<1.0/≥1.0). KELIM's prognostic value regarding radiological response, completeness of IDS, progression-free survival (PFS), and overall survival (OS) was assessed using univariate/multivariate analyses.

RESULTS

The data from 1,582 patients treated with heterogeneous chemotherapy regimens and sequences were assessable. KELIM was prognostic for radiological response and the likelihood of complete IDS after NACT (odds ratio=2.59; 95% confidence interval [CI]=2.04-3.29). Moreover, KELIM was independently associated with PFS (hazard ratio [HR]=0.76; 95% CI=0.66-0.87), and OS (HR=0.79; 95% CI=0.69-0.91). Combining KELIM with the completeness of the IDS resulted in 3 prognostic groups (satisfactory, intermediate, and poor) with significant OS differences, namely a good, intermediate, and poor survival respectively.

CONCLUSION

The value of KELIM, as a pragmatic indicator of response to chemotherapy, was maintained in a large real-life population-based cohort, highlighting its applicability in routine conditions.

Bevacizumab, olaparib, and durvalumab in patients with relapsed ovarian cancer: a phase II clinical trial from the GINECO group

Most patients with advanced ovarian cancer (AOC) ultimately relapse after platinum-based chemotherapy. Combining bevacizumab, olaparib, and durvalumab likely drives synergistic activity. This open-label phase 2 study (NCT04015739) aimed to assess activity and safety of this triple combination in female patients with relapsed high-grade AOC following prior platinum-based therapy. Patients were treated with olaparib (300 mg orally, twice daily), the bevacizumab biosimilar FKB238 (15 mg/kg intravenously, once-every-3-weeks), and durvalumab (1.12 g intravenously, once-every-3-weeks) in nine French centers. The primary endpoint was the non-progression rate at 3 months for platinum-resistant relapse or 6 months for platinum-sensitive relapse per RECIST 1.1 and irRECIST. Secondary endpoints were CA-125 decline with CA-125 ELIMination rate constant K (KELIM-B) per CA-125 longitudinal kinetics over 100 days, progression free survival and overall survival, tumor response, and safety. Non-progression rates were 69.8% (90%CI 55.9%-80.0%) at 3 months for platinum-resistant relapse patients (N = 41), meeting the prespecified endpoint, and 43.8% (90%CI 29.0%-57.4%) at 6 months for platinum-sensitive relapse (N = 33), not meeting the prespecified endpoint. Median progression-free survival was 4.1 months (95%CI 3.5-5.9) and 4.9 months (95%CI 2.9-7.0) respectively. Favorable KELIM-B was associated with better survival. No toxic deaths or major safety signals were observed. Here we show that further investigation of this triple combination may be considered in AOC patients with platinum-resistant relapse.

Population Pharmacokinetic/Pharmacodynamic Models for P2Y12 Inhibitors: A Systematic Review and Clinical Appraisal Using Exposure Simulation

BACKGROUND AND OBJECTIVE

Recent research indicates a correlation between plasma concentration of P2Y12 inhibitors and clinical events, particularly bleeding, which significantly impeded their clinical therapeutic performance. It is therefore vital to delve into the factors that might affect the plasma concentration. The study aims to summarize population pharmacokinetics/pharmacodynamics (PopPKPD) models for commonly prescribed P2Y12 inhibitors (clopidogrel, prasugrel, and ticagrelor) and assess bleeding risk in specific individual groups.

METHODS

The PopPKPD models of P2Y12 inhibitors were collected and summarized based on predetermined inclusion and exclusion criteria. The collected models were replicated in simulations, which were used to assess factors affecting plasma concentrations of P2Y12 inhibitors. Simulation results for special populations were compared to therapeutic window based on reported exposure-effect relationships (PK/PD-related bleeding and thrombotic clinical outcomes) to predict bleeding risk in special populations with different dosing regimens and cumulative covariates.

RESULT

Finally, 12 studies were included for PK simulation, 7 of which that also included PD data were subjected to further analysis, with the majority being based on Phase I or II trials. Simulations showed that several covariates such as female gender, weight, elderly can significantly impact on exposure, with special populations reaching up to 179% of the general population. However, after dose adjustment, blood concentrations for special populations can reach approximately ±20% of general population exposure. Therefore, lowering the maintenance dose of ticagrelor from 90 to 60 mg bid was first recommended to reduce bleeding risk without significantly increasing ischemic risk, particularly in elderly, small-weight Asian females.

CONCLUSION

Lowering the maintenance dose of ticagrelor from 90 to 60 mg bid effectively reduces bleeding risk without increasing thrombotic infarction risk in elderly, small-weight Asian females.

Prognostic Value of the Modeled Prostate-Specific Antigen KELIM Confirmation in Metastatic Castration-Resistant Prostate Cancer Treated With Taxanes in FIRSTANA

PURPOSE

In a previous exploratory study, modeled early longitudinal prostate-specific antigen (PSA) kinetics observed within the 100-first treatment days with androgen deprivation therapy with or without docetaxel was associated with progression-free survival (PFS) and overall survival (OS) in patients with prostate cancer with rising PSA levels after primary local therapy. This prognostic value had to be confirmed in different settings. The objectives were to assess PSA kinetics modeling in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with chemotherapy in FIRSTANA trial and to investigate modeled PSA kinetic parameters prognostic/predictive value.

MATERIALS AND METHODS

FIRSTANA phase III trial (ClinicalTrials.gov identifier: NCT01308567) assessed whether cabazitaxel is superior to docetaxel in terms of PFS/OS in patients with chemotherapy-naïve mCRPC. PSA longitudinal kinetics was assessed using the previous kinetic-pharmacodynamics model. Patient modeled ELIMination rate constant K (PSA.KELIM) was used to categorize favorable/unfavorable PSA declines (standardized PSA.KELIM < or ≥ 1.0 days-1) and further correlated with PFS/OS.

RESULTS

In total, 1,050 of 1,168 enrolled patients were assessable for PSA.KELIM estimation. The median PSA.KELIM was 0.02 days-1. In univariate analyses, PSA.KELIM exhibited a significant prognostic value regarding survival: unfavorable versus favorable PSA.KELIM; median PFS, 3.6 months (95% CI, 3.0 to 4.2) versus 4.7 months (95% CI, 3.9 to 5.2), P = .002; median OS, 17.4 months (95% CI, 14.8 to 19.3) versus 28.4 months (95% CI, 26.7 to 31.6), P < .001. In multivariate analyses, PSA.KELIM was significant for PFS (hazard ratio [HR], 0.79 [95% CI, 0.67 to 0.93], P = .005) and OS (HR, 0.51 [95% CI, 0.44 to 0.60], P < .001), together with baseline radiological tumor progression and PSA doubling time. PSA.KELIM predictive value was not significant across treatment arms.

CONCLUSION

This external validation study confirmed previous results about modeled PSA longitudinal kinetics prognostic value regarding PFS/OS in patients with mCRPC treated with taxanes. PSA.KELIM could be used to identify a subpopulation with poor prognosis, who may benefit from treatment intensification.

Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: application of a double gamma absorption model with SAEM algorithm

INTRODUCTION

Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients.

MATERIAL AND METHODS

A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set.

RESULTS

Mean patient age and dose was 13 years old (5-18) and 18.1 mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30 min, 2 h, and 3 h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%).

CONCLUSION

The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review

BACKGROUND AND OBJECTIVES

Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research.

METHODS

The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach.

RESULTS

Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h^-1 (median of 14.9 L·h^-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1.

CONCLUSION

The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.

Benefit From Fractionated Dose-Dense Chemotherapy in Patients With Poor Prognostic Ovarian Cancer: ICON-8 Trial

PURPOSE

An international meta-analysis identified a group of patients with advanced epithelial ovarian cancer (EOC) with a very poor survival because of two unfavorable features: (1) a poor chemosensitivity defined by an unfavorable modeled CA-125 ELIMination rate constant K (KELIM) score <1.0 with the online calculator CA-125-Biomarker Kinetics, and (2) an incomplete debulking surgery. We assumed that patients belonging to this poor prognostic group would benefit from a fractionated densified chemotherapy regimen.

METHODS

The data set of ICON-8 phase III trial (ClinicalTrials.gov identifier: NCT01654146), where patients with EOC were treated with the standard three-weekly, or the weekly dose-dense, carboplatin-paclitaxel regimens and debulking primary surgery (immediate primary surgery [IPS] or delayed primary [or interval] surgery [DPS]), was investigated. The association between treatment arm efficacy, standardized KELIM (scored as favorable ≥1.0, or unfavorable <1.0), and surgery completeness was assessed by univariate/multivariate analyses in IPS and DPS cohorts.

RESULTS

Of 1,566 enrolled patients, KELIM was calculated with the online model in 1,334 with ≥3 CA-125 available values (85%). As previously reported, both KELIM and surgery completeness were complementary prognostic covariates, and could be combined into three prognostic groups with large OS differences: (1) good if favorable KELIM and complete surgery; (2) intermediate if either unfavorable KELIM or incomplete surgery; and (3) poor if unfavorable KELIM and incomplete surgery. Weekly dose-dense chemotherapy was associated with PFS/OS improvement in the poor prognostic group in both the IPS cohort (PFS: hazard ratio [HR], 0.50; 95% CI, 0.31 to 0.79; OS: HR, 0.58; 95% CI, 0.35 to 0.95) and the DPS cohort (PFS: HR, 0.53; 95% CI, 0.37 to 0.76; OS: HR, 0.57; 95% CI, 0.39 to 0.82).

CONCLUSION

Fractionated dose-dense chemotherapy might be beneficial for patients belonging to the poor prognostic group characterized by lower tumor chemosensitivity assessed with the online calculator CA-125-Biomarker Kinetics and incomplete debulking surgery. Further investigation in the future SALVOVAR trial is warranted.

Mathematical modeling of the early modeled CA-125 longitudinal kinetics (KELIM-PARP) as a pragmatic indicator of rucaparib efficacy in patients with recurrent ovarian carcinoma in ARIEL2 & STUDY 10

BACKGROUND

PARP inhibitors (PARPi) have revolutionized the management of advanced ovarian carcinoma, and were investigated as forefront treatment in recurrent disease. The objective was to explore if mathematical modeling of the early longitudinal CA-125 kinetics could be used as a pragmatic indicator of the subsequent rucaparib efficacy, like it is for platinum-based chemotherapy.

METHODS

The datasets of ARIEL2 and Study 10 involving recurrent HGOC patients treated with rucaparib were retrospectively investigated. The same strategy as those successfully developed for platinum chemotherapy, based on CA-125 ELIMination rate constant K (KELIM™), was implemented. Individual values of rucaparib-adjusted KELIM (KELIM-PARP) were estimated based on the longitudinal CA-125 kinetics during the first 100 treatment days, and then scored as favorable (KELIM-PARP ≥1.0) or unfavorable (KELIM-PARP <1.0). The prognostic value of KELIM-PARP regarding treatment efficacy (radiological response, and progression-free survival (PFS)) was assessed using univariable/multivariable analyses, with respect to platinum-sensitivity and homologous recombination deficiency (HRD) status.

FINDINGS

The data from 476 patients were assessed. The CA-125 longitudinal kinetics during the first 100-treatment days could be accurately assessed using the KELIM-PARP model. In patients with platinum-sensitive diseases, BRCA mutational status KELIM-PARP score and were associated with subsequent complete/partial radiological responses (KELIM-PARP: odds-ratio = 2.81, 95% CI 1.86-4.52), and PFS (KELIM-PARP: hazard-ratio = 0.67, 95% CI 0.50-0.91). The patients with BRCA-wild type cancer and favorable KELIM-PARP experienced long PFS with rucaparib regardless of HRD. In platinum-resistant disease patients, KELIM-PARP was associated with subsequent radiological response (odds-ratio = 2.80, 95% CI 1.82-4.72).

INTERPRETATION

This proof-of-concept study confirms the early CA-125 longitudinal kinetics during rucaparib in recurrent HGOC patients are assessable by mathematical modeling, to generate individual a KELIM-PARP score associated with the subsequent efficacy. This pragmatic strategy might be useful for selecting the patients for PARPi-based combination regimens, when identifying efficacy biomarker is challenging. Further assessment of this hypothesis is warranted.

FUNDING

The present study was supported by Clovis Oncology with a grant to academic research association.

Identification of Patients With Ovarian Cancer Experiencing the Highest Benefit From Bevacizumab in the First-Line Setting on the Basis of Their Tumor-Intrinsic Chemosensitivity (KELIM): The GOG-0218 Validation Study

PURPOSE

In patients with high-grade ovarian cancer, predictors of bevacizumab efficacy in first-line setting are needed. In the ICON-7 trial, a poor tumor intrinsic chemosensitivity (defined by unfavorable modeled cancer antigen-125 [CA-125] ELIMination rate constant K [KELIM] score) was a predictive biomarker. Only the patients with high-risk disease (suboptimally resected stage III, or stage IV) exhibiting unfavorable KELIM score < 1.0 had overall survival (OS) benefit from bevacizumab (median: 29.7 v 20.6 months; hazard ratio [HR], 0.78). An external validation study in the GOG-0218 trial was performed.

METHODS

In GOG-0218, 1,873 patients were treated with carboplatin-paclitaxel ± concurrent-maintenance bevacizumab/placebo. Patient KELIM values were calculated with CA-125 kinetics during the first 100 chemotherapy days by the Lyon University team. The association between KELIM score (favorable ≥ 1.0, or unfavorable < 1.0) and bevacizumab benefit for progression-free survival (PFS)/OS was independently assessed by NGR-GOG using univariate/multivariate analyses.

RESULTS

KELIM was assessable in 1,662 patients with ≥ 3 CA-125 available values. An unfavorable KELIM score was associated with bevacizumab benefit compared with placebo (PFS: HR, 0.70; 95% CI, 0.59 to 0.82; OS: HR, 0.87; 95% CI, 0.73 to 1.03), whereas a favorable KELIM was not (PFS: HR, 0.96; 95% CI, 0.79 to 1.17; OS: HR, 1.11; 95% CI, 0.89 to 1.39). The highest benefit was observed in patients with a high-risk disease exhibiting unfavorable KELIM, for PFS (median: 9.1 v 5.6 months; HR, 0.64; 95% CI, 0.53 to 0.78), and for OS (median: 35.1 v 29.1 months; HR, 0.79; 95% CI, 0.65 to 0.97).

CONCLUSION

This GOG-0218 trial investigation validates ICON-7 findings about the association between poor tumor chemosensitivity and benefit from concurrent-maintenance bevacizumab, suggesting that bevacizumab may mainly be effective in patients with poorly chemosensitive disease. Bevacizumab may be prioritized in patients with a high-risk and poorly chemosensitive disease to improve their PFS/OS (patient KELIM score calculator available on the Biomarker Kinetics website).

Population Pharmacokinetic Models for Direct Oral Anticoagulants: A Systematic Review and Clinical Appraisal Using Exposure Simulation

Available data have shown an association between direct oral anticoagulant (DOAC) plasma concentration and clinical, particularly bleeding, events. Factors that may influence DOAC plasma concentration are therefore the focus of particular attention. Population pharmacokinetic (PopPK) analyses can help in identifying such factors while providing predictive models. The main aim of the present study was to identify all the PopPK models to date for the four most frequently used DOACs (dabigatran, apixaban, rivaroxaban, and edoxaban). The secondary aim was to use these models to simulate different DOAC plasma concentration–time profiles in relevant clinical scenarios. The results of our model‐based simulations confirm the clinical relevance of the known major factors influencing DOAC exposure and support the current approved dose adaptation, at least for atrial fibrillation. They also highlight how the accumulation of covariates, not currently considered for dose adaptation due to their seemingly minor influence on DOAC exposure, lead to supratherapeutic blood concentrations and could thus enhance the risk of major bleeding. The present results therefore question DOAC dose adaptation in the presence of these covariates, such as drug–drug interaction or genotypes, alongside the known existing covariates. As the overall effect of accumulation of several covariates could be difficult to apprehend for the clinicians, PopPK modeling could represent an interesting approach for informed precision dosing and to improve personalized prescription of DOACs.

Assessment of the predictive capability of modelling and simulation to determine bioequivalence of inhaled drugs: A systematic review

OBJECTIVES

There are a multitude of different modelling techniques that have been used for inhaled drugs. The main objective of this review was to conduct an exhaustive survey of published mathematical models in the area of asthma and chronic obstructive pulmonary disease (COPD) for inhalation drugs. Additionally, this review will attempt to assess the applicability of these models to assess bioequivalence (BE) of orally inhaled products (OIPs).

EVIDENCE ACQUISITION

PubMed, Science Direct, Web of Science, and Scopus databases were searched from 1996 to 2020, to find studies that described mathematical models used for inhaled drugs in asthma/COPD.

RESULTS

50 articles were finally included in this systematic review. This research identified 22 articles on in silico aerosol deposition models, 20 articles related to population pharmacokinetics and 8 articles on physiologically based pharmacokinetic modelling (PBPK) modelling for inhaled drugs in asthma/COPD. Among all the aerosol deposition models, computational fluid dynamics (CFD) simulations are more likely to predict regional aerosol deposition pattern in human respiratory tracts. Across the population PK articles, body weight, gender, age and smoking status were the most common covariates that were found to be significant. Further, limited published PBPK models reported approximately 29 parameters relevant for absorption and distribution of inhaled drugs. The strengths and weaknesses of each modelling technique has also been reviewed.

CONCLUSION

Overall, while there are different modelling techniques that have been used for inhaled drugs in asthma and COPD, there is very limited application of these models for assessment of bioequivalence of OIPs. This review also provides a ready reference of various parameters that have been considered in various models which will aid in evaluation if one model or hybrid in silico models need to be considered when assessing bioequivalence of OIPs.

Quantitative evaluation of drug efficacy in the treatment of myasthenia gravis

OBJECTIVES

This study aimed to quantitatively evaluate placebo effect and drug efficacy characteristics and identify associated factors that affect quantitative myasthenia gravis (MG) score (QMGs) and MG activities of daily living score (MG-ADLs) in patients with MG.

METHODS

Randomized placebo-controlled clinical trials were comprehensively searched in public databases (PubMed, EMBASE, and Cochrane Library databases).A model-based meta-analysis was developed to describe time-course about drug efficacy and placebo effect.

RESULTS

Twelve articles including 13 trials (673 participants) that were eligible for this study evaluated four immunosuppressants (tacrolimus, cyclosporine, prednisone, and mycophenolate mofetil) and five targeted therapy drugs (eculizumab, belimumab, zilucoplan, efgartigimod, and iscalimab). The pharmacodynamic model showed that eculizumab had the highest efficacy in reducing QMGs scores (3.66 points), and efgartigimod had the highest efficacy in reducing MG-ADLs scores (1.97 points). The placebo effect of QMGs and MG-ADLs increased apparently with time and reached 52% and 90% of their maximum effect in 12 weeks, respectively. In addition, this study found that the activities of daily living ability increased with the increase of the proportion of patients undergoing thymectomy.

CONCLUSION

This study analyzed the efficacy characteristics of nine drugs. The present findings provide necessary quantitative information for drug development of MG.

Step-by-step comparison of ordinary differential equation and agent-based approaches to pharmacokinetic-pharmacodynamic models

Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.

Evaluation of covariate effects using variance-based global sensitivity analysis in pharmacometrics

In pharmacometrics, understanding a covariate effect on an interested outcome is essential for assessing the importance of the covariate. Variance-based global sensitivity analysis (GSA) can simultaneously quantify contribution of each covariate effect to the variability for the interested outcome considering with random effects. The aim of this study was to apply GSA to pharmacometric models to assess covariate effects. Simulations were conducted with pharmacokinetic models to characterize the GSA for assessment of covariate effects and with an example of quantitative systems pharmacology (QSP) models to apply the GSA to a complex model. In the simulations, covariate and random variables were generated to simulate the outcomes using the models. Ratios of variance explained by each factor (each covariate and random effect) over the overall variance of the outcome were used as sensitivity indices. The sensitivity indices were consistent with the effect size of covariate. The sensitivity indices identified the importance of creatinine clearance on the pharmacokinetic exposure for a renally-excreted drug. These sensitivity indices could be applied to plasma concentrations over time (repeated measurable outcomes over time) as interested outcomes. Using the GSA, each contribution of all of the covariate effects could be efficiently identified even in the complex QSP model. Variance-based GSA can provide insight when considering the importance of covariate effects by simultaneously and quantitatively assessing all covariate and random effects on interested outcomes in pharmacometrics.

Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses

The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate‐to‐parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness‐of‐fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness‐of‐fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age‐dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness‐of‐fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof‐of‐concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well‐founded covariate models.

A systematic review of population pharmacokinetic analyses of polyclonal immunoglobulin G therapy

BACKGROUND

Population pharmacokinetics (popPK) using the nonlinear mixed-effect (NLME) modeling approach is an essential tool for guiding dose individualization. Several popPK analyses using the NLME have been conducted to characterize the pharmacokinetics of immunoglobulin G (IgG).

OBJECTIVE

To summarize the current information on popPK of polyclonal IgG therapy.

METHOD

A systematic search was conducted in the PubMed and Web of Science databases from inception to December 2020. Additional relevant studies were also included by reviewing the reference list of the reviewed articles. All popPK studies that employed the NLME modeling approach were included and data were synthesized descriptively.

RESULTS

This review included seven studies. Most of the popPK models were developed in patients with primary immunodeficiency (PID). IgG pharmacokinetics was described as a two-compartment model in five studies, while it was described as a one-compartment model in two other studies. Among all tested covariates, weight was consistently identified as a significant predictor for clearance (CL) of IgG. Whereas, weight and disease type were found to be significant predictors for the volume of distribution in central compartment (Vc). In a typical 70 kg adult, the median estimated values of Vc and CL were 4.04 L and 0.144 L/day, respectively. The between subject variability of Vc was considered large. Only two studies evaluated their models using external data.

CONCLUSIONS

Seven popPK studies of IgG were found and discussed, with only weight being a significant covariate across all studies. Future studies linking pharmacokinetics with pharmacodynamics in PID and other patient populations are required.

Population Pharmacokinetics of Levetiracetam: a Systematic Review

BACKGROUND

The use of levetiracetam (LEV) has been increasing given its favorable pharmacokinetic profile. Numerous population pharmacokinetic studies for LEV have been conducted. However, there are some discrepancies regarding factors affecting its pharmacokinetic variability. Therefore, this systematic review aimed to summarize significant predictors for LEV pharmacokinetics as well as the need for dosage adjustments.

METHODS

We performed a systematic search for population pharmacokinetic studies of LEV conducted using a nonlinear-mixed effect approach from PubMed, Scopus, CINAHL Complete, and Science Direct databases from their inception to March 2020. Information on study design, model methodologies, significant covariate-parameter relationships, and model evaluation was extracted. The quality of the reported studies was also assessed.

RESULTS

A total of 16 studies were included in this review. Only two studies were conducted with a two-compartment model, while the rest were performed with a one-compartment structure. Bodyweight and creatinine clearance were the two most frequently identified covariates on LEV clearance (CLLEV). Additionally, postmenstrual age (PMA) or postnatal age (PNA) were significant predictors for CLLEV in neonates. Only three studies externally validated the models. Two studies conducted pharmacodynamic models for LEV with relatively small sample size.

CONCLUSION

Significant predictors for LEV pharmacokinetics are highlighted in this review. For future research, a population pharmacokinetic-pharmacodynamic model using a larger sample size should be conducted. From a clinical perspective, the published models should be externally evaluated before clinical implementation.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis

AIMS AND BACKGROUND

Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters.

METHOD

A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020.

RESULTS

Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104 L/h/kg and 0.37 to 1.21 L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach.

CONCLUSION

Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Scores of Health-Related Quality of Life Questionnaire Worsen Consistently in Patients of COPD: Estimating Disease Progression over 30 Years by SReFT with Individual Data Collected in SUMMIT Trial

The aim of this study was to elucidate the lifelong disease progression of chronic obstructive pulmonary disease (COPD) with biomarker changes and identify their influencing factors, by utilizing a new analysis method, Statistical Restoration of Fragmented Time-course (SReFT). Individual patient data (n = 1025) participating in the Study to Understand Mortality and MorbidITy (SUMMIT, NCT01313676), which was collected within the observational period of 4 years, were analyzed. The SReFT analysis suggested that scores of St. George’s Respiratory Questionnaire and COPD assessment test, representative scores of the health-related quality of life (HRQOL) questionnaire, increased consistently for 30 years of disease progression, which was not detected by conventional analysis with a linear mixed effect model. It was estimated by the SReFT analysis that normalized forced expiratory volume in one second for age, sex, and body size (%FEV1) reduced for the initial 10 years from the onset of the disease but thereafter remained constant. The analysis of HRQOL scores and lung functions suggested that smoking cessation slowed COPD progression by approximately half and that exacerbation accelerated it considerably. In conclusion, this retrospective study utilizing SReFT elucidated the progression of COPD over 30 years and associated quantitative changes in the HRQOL scores and lung functions.

Comparison of covariate selection methods with correlated covariates: prior information versus data information, or a mixture of both?

The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: [Formula: see text], Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates ([Formula: see text]), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is < 80%, PACS when > 80% but < 100%, and SCM when the expected power is 100%.

A systematic review of population pharmacokinetic analyses of digoxin in the paediatric population

This is a PROSPERO registered systematic review (CRD42018105207), conducted to summarize the available knowledge regarding the population pharmacokinetics of digoxin in paediatrics and to identify the sources of variability in its disposition. PubMed, ISI Web of Science, SCOPUS and Science Direct databases were searched from inception to January 2019. All paediatric population pharmacokinetic studies of digoxin that utilized the nonlinear mixed-effect modelling approach were incorporated in this review, and data were synthesized descriptively. After application of the inclusion-exclusion criteria 8 studies were included. Most studies described digoxin pharmacokinetics as a 1-compartment model with only 1 study describing its pharmacokinetics as 2-compartments. Age was an important predictor of clearance in studies involving neonates or infants, other predictors of clearance were weight, height, serum creatinine, coadministration of spironolactone and presence of congestive heart failure. Congestive heart failure was also associated with an increased volume of distribution in 1 study. The estimated value of apparent clearance in a typical individual standardized by mean weight ranged between 0.24 and 0.56 L/h/kg, the interindividual variability in clearance ranged between 7.0 and 35.1%. Half of the studies evaluated the performance of their developed models via external evaluation. In conclusion, substantial predictors of digoxin pharmacokinetics in the paediatric population in addition to model characteristics and evaluation techniques are presented. For clinicians, clearance could be predicted using age especially in neonates or infants, weight, height, serum creatinine, coadministration of medications and disease status. For future researchers, designing pharmacokinetic studies that allow 2-compartment modelling and linking pharmacokinetics with pharmacodynamics is recommended.

CA-125 ELIMination Rate Constant K (KELIM) Is a Marker of Chemosensitivity in Patients with Ovarian Cancer: Results from the Phase II CHIVA Trial

PURPOSE

In patients with ovarian cancer receiving neoadjuvant chemotherapy, the first-line treatment success will depend on both the tumor-primary chemosensitivity and the completeness of interval debulking surgery (IDS). The modeled CA-125 ELIMination rate constant K (KELIM), calculated with the CA-125 longitudinal kinetics during the first 100 chemotherapy days, is a validated early marker of tumor chemosensitivity. The objective was to investigate the role of the chemosensitivity relative to the success of first-line medical-surgical treatment.

EXPERIMENTAL DESIGN

The CA-125 concentrations were prospectively measured in the randomized phase II trial CHIVA (NCT01583322, carboplatin-paclitaxel regimen ± nintedanib, and IDS, n = 188 patients). The KELIM predictive value regarding the tumor response rate, likelihood of complete IDS, risk of subsequent platinum-resistant relapse (PtRR), progression-free survival (PFS), and overall survival (OS) was assessed using univariate and multivariate tests.

RESULTS

The data from 134 patients were analyzed. KELIM was an independent and major predictor of subsequent PtRR risk, and of survivals. The final logistic regression model, including KELIM [OR = 0.13; 95% confidence interval (CI), 0.03-0.49] and complete IDS (no vs. yes, OR = 0.30; 95% CI, 0.11-0.76) highlights the preponderant role of chemosensitivity on the success of the first-line treatment. In patients with highly chemosensitive diseases, the patient prognosis was driven more by the chemotherapy-induced antitumor effects than by the surgery.

CONCLUSIONS

The tumor-primary chemosensitivity, assessed by the modeled CA-125 KELIM calculated during neoadjuvant chemotherapy (http://www.biomarker-kinetics.org/CA-125-neo), may be a major parameter to consider for decision-making regarding IDS attempt, and selecting patients for treatments meant to reverse the primary chemoresistance.See related commentary by May and Oza, p. 4432.

Modeling Tumor Evolutionary Dynamics to Predict Clinical Outcomes for Patients with Metastatic Colorectal Cancer: A Retrospective Analysis

Over 50% of colorectal cancer patients develop resistance after a transient response to therapy. Understanding tumor resistance from an evolutionary perspective leads to better predictions of treatment outcomes. The objectives of this study were to develop a computational framework to analyze tumor longitudinal measurements and recapitulate the individual evolutionary dynamics in metastatic colorectal cancer (mCRC) patients. A stochastic modeling framework was developed to depict the whole spectrum of tumor evolution prior to diagnosis and during and after therapy. The evolutionary model was optimized using a nonlinear mixed effect (NLME) method based on the longitudinal measurements of liver metastatic lesions from 599 mCRC patients. The deterministic limits in the NLME model were applied to optimize the stochastic model for each patient. Cox proportional hazards models coupled with the least absolute shrinkage and selection operator (LASSO) algorithm were applied to predict patients' progression-free survival (PFS) and overall survival (OS). The stochastic evolutionary model well described the longitudinal profiles of tumor sizes. The evolutionary parameters optimized for each patient indicated substantial interpatient variability. The number of resistant subclones at diagnosis was found to be a significant predictor to survival, and the hazard ratios with 95% CI were 1.09 (0.79-1.49) and 1.54 (1.01-2.34) for patients with three or more resistant subclones. Coupled with several patient characteristics, evolutionary parameters strongly predict patients' PFS and OS. A stochastic computational framework was successfully developed to recapitulate individual patient evolutionary dynamics, which could predict clinical survival outcomes in mCRC patients. SIGNIFICANCE: A data analysis framework depicts the individual evolutionary dynamics of mCRC patients and can be generalized to project patient survival outcomes.

Reproducible pharmacokinetics

Reproducibility is a highly desired feature of scientific investigation in general, and it has special connotations for research in pharmacokinetics, a vibrant field with over 500,000 publications to-date. It is important to be able to differentiate between genuine heterogeneity in pharmacokinetic parameters from heterogeneity that is due to errors and biases. This overview discusses efforts and opportunities to diminish the latter type of undesirable heterogeneity. Several reporting and research guidance documents and standards have been proposed for pharmacokinetic studies, but their adoption is still rather limited. Quality problems in the methods used and model evaluations have been examined in some empirical studies of the literature. Standardization of statistical and laboratory tools and procedures can be improved in the field. Only a small fraction of pharmacokinetic studies become pre-registered and only 9995 such studies have been registered in ClinicalTrials.gov as of August 2018. It is likely that most pharmacokinetic studies remain unpublished. Publication bias affecting the results and inferences has been documented in case studies, but its exact extent is unknown for the field at-large. The use of meta-analyses in the field is still limited. Availability of raw data, detailed protocols, software and codes is hopefully improving with multiple ongoing initiatives. Several research practices can contribute to greater transparency and reproducibility for pharmacokinetic investigations.

How to optimise drug study design: pharmacokinetics and pharmacodynamics studies introduced to paediatricians

OBJECTIVES

In children, there is often lack of sufficient information concerning the pharmacokinetics (PK) and pharmacodynamics (PD) of a study drug to support dose selection and effective evaluation of efficacy in a randomised clinical trial (RCT). Therefore, one should consider the relevance of relatively small PKPD studies, which can provide the appropriate data to optimise the design of an RCT.

METHODS

Based on the experience of experts collaborating in the EU-funded Global Research in Paediatrics consortium, we aimed to inform clinician-scientists working with children on the design of investigator-initiated PKPD studies.

KEY FINDINGS

The importance of the identification of an optimal dose for the paediatric population is explained, followed by the differences and similarities of dose-ranging and efficacy studies. The input of clinical pharmacologists with modelling expertise is essential for an efficient dose-finding study.

CONCLUSIONS

The emergence of new laboratory techniques and statistical tools allows for the collection and analysis of sparse and unbalanced data, enabling the implementation of (observational) PKPD studies in the paediatric clinic. Understanding of the principles and methods discussed in this study is essential to improve the quality of paediatric PKPD investigations, and to prevent the conduct of paediatric RCTs that fail because of inadequate dosing.

Use of Therapeutic Drug Monitoring, Electronic Health Record Data, and Pharmacokinetic Modeling to Determine the Therapeutic Index of Phenytoin and Lamotrigine

BACKGROUND

Defining a drug's therapeutic index (TI) is important for patient safety and regulating the development of generic drugs. For many drugs, the TI is unknown. A systematic approach was developed to characterize the TI of a drug using therapeutic drug monitoring and electronic health record (EHR) data with pharmacokinetic (PK) modeling. This approach was first tested on phenytoin, which has a known TI, and then applied to lamotrigine, which lacks a defined TI.

METHODS

Retrospective EHR data from patients in a tertiary hospital were used to develop phenytoin and lamotrigine population PK models and to identify adverse events (anemia, thrombocytopenia, and leukopenia) and efficacy outcomes (seizure-free). Phenytoin and lamotrigine concentrations were simulated for each day with an adverse event or seizure. Relationships between simulated concentrations and adverse events and efficacy outcomes were used to calculate the TI for phenytoin and lamotrigine.

RESULTS

For phenytoin, 93 patients with 270 total and 174 free concentrations were identified. A de novo 1-compartment PK model with Michaelis-Menten kinetics described the data well. Simulated average total and free concentrations of 10-15 and 1.0-1.5 mcg/mL were associated with both adverse events and efficacy in 50% of patients, resulting in a TI of 0.7-1.5. For lamotrigine, 45 patients with 53 concentrations were identified. A published 1-compartment model was adapted to characterize the PK data. No relationships between simulated lamotrigine concentrations and safety or efficacy endpoints were seen; therefore, the TI could not be calculated.

CONCLUSIONS

This approach correctly determined the TI of phenytoin but was unable to determine the TI of lamotrigine due to a limited sample size. The use of therapeutic drug monitoring and EHR data to aid in narrow TI drug classification is promising, but it requires an adequate sample size and accurate characterization of concentration-response relationships.

Population pharmacokinetic model of lithium and drug compliance assessment

Population pharmacokinetic analysis of lithium during therapeutic drug monitoring and drug compliance assessment was performed in 54 patients and 246 plasma concentrations levels were included in this study. Patients received several treatment cycles (1-9) and one plasma concentration measurement for each patient was obtained always before starting next cycle (pre-dose) at steady state. Data were analysed using the population approach with NONMEM version 7.2. Lithium measurements were described using a two-compartment model (CL/F=0.41Lh^-1, V₁/F=15.3L, Q/F=0.61Lh^-1, and V₂/F = 15.8L) and the most significant covariate on lithium CL was found to be creatinine clearance (reference model). Lithium compliance was analysed using inter-occasion variability or Markovian features (previous lithium measurement as ordered categorical covariate) on bioavailability parameter. Markov-type model predicted the lithium compliance in the next cycle with higher success rate (79.8%) compared to IOV model (65.2%) and reference model (43.2%). This model becomes an efficient tool, not only being able to adequately describe the observed outcome, but also to predict the individual drug compliance in the next cycle. Therefore, Bipolar disorder patients can be classified regarding their probability to become extensive or poor compliers in the next cycle and then, individual probabilities lower than 0.5 highlight the need of intensive monitoring, as well as other pharmaceutical care measurements that might be applied to enhance drug compliance for a better and safer lithium treatment.

Validation of the Predictive Value of Modeled Human Chorionic Gonadotrophin Residual Production in Low-Risk Gestational Trophoblastic Neoplasia Patients Treated in NRG Oncology/Gynecologic Oncology Group-174 Phase III Trial

OBJECTIVES

In low-risk gestational trophoblastic neoplasia, chemotherapy effect is monitored and adjusted with serum human chorionic gonadotrophin (hCG) levels. Mathematical modeling of hCG kinetics may allow prediction of methotrexate (MTX) resistance, with production parameter "hCGres." This approach was evaluated using the GOG-174 (NRG Oncology/Gynecologic Oncology Group-174) trial database, in which weekly MTX (arm 1) was compared with dactinomycin (arm 2).

METHODS

Database (210 patients, including 78 with resistance) was split into 2 sets. A 126-patient training set was initially used to estimate model parameters. Patient hCG kinetics from days 7 to 45 were fit to: [hCG(time)] = hCG7 * exp(-k * time) + hCGres, where hCGres is residual hCG tumor production, hCG7 is the initial hCG level, and k is the elimination rate constant. Receiver operating characteristic (ROC) analyses defined putative hCGRes predictor of resistance. An 84-patient test set was used to assess prediction validity.

RESULTS

The hCGres was predictive of outcome in both arms, with no impact of treatment arm on unexplained variability of kinetic parameter estimates. The best hCGres cutoffs to discriminate resistant versus sensitive patients were 7.7 and 74.0 IU/L in arms 1 and 2, respectively. By combining them, 2 predictive groups were defined (ROC area under the curve, 0.82; sensitivity, 93.8%; specificity, 70.5%). The predictive value of hCGres-based groups regarding resistance was reproducible in test set (ROC area under the curve, 0.81; sensitivity, 88.9%; specificity, 73.1%). Both hCGres and treatment arm were associated with resistance by logistic regression analysis.

CONCLUSIONS

The early predictive value of the modeled kinetic parameter hCGres regarding resistance seems promising in the GOG-174 study. This is the second positive evaluation of this approach. Prospective validation is warranted.

Reporting Guidelines for Clinical Pharmacokinetic Studies: The ClinPK Statement

BACKGROUND AND OBJECTIVE

Transparent reporting of all research is essential for assessing the validity of any study. Reporting guidelines are available and endorsed for many types of research but are lacking for clinical pharmacokinetic studies. Such tools promote the consistent reporting of a minimal set of information for end users, and facilitate knowledge translation of research. The objective of this study was to create a guideline to assist in the transparent and complete reporting of clinical pharmacokinetic studies.

METHODS

Preliminary content to be considered was identified from a systematic search of the literature and regulatory documents. Stakeholders were identified to participate in a modified Delphi exercise and a virtual meeting to generate consensus for items considered essential in the reporting of clinical pharmacokinetic studies. The proposed checklist was pilot tested on 100 recently published clinical pharmacokinetic studies. Overall and itemized compliance with the proposed guidance was determined for each study.

RESULTS

Sixty-eight stakeholders from nine countries consented to participate. Four rounds of a modified Delphi survey and a series of small virtual meetings were required to generate consensus for a 24-item checklist considered to be essential to the reporting of clinical pharmacokinetic studies. When applied to the 100 most recently published clinical pharmacokinetic studies, 45 were determined to be compliant with at least 80 % of the checklist items. Explanatory text was prepared using examples of compliant reporting from these and other relevant studies.

CONCLUSIONS

The reader's ability to judge the validity of pharmacokinetic research can be greatly compromised by the incomplete reporting of study information. Using consensus methods, we have developed a tool to guide transparent and accurate reporting of clinical pharmacokinetic studies. Endorsement and implementation of these guidelines by researchers, clinicians and journals would promote more consistent reporting of these studies and allow for better assessment of utility for clinical applications.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

AIMS

The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes.

METHODS

Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10-15 kg and 30-40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups.

RESULTS

Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population.

CONCLUSIONS

In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population.

Dosage assessment of valnemulin in pigs based on population pharmacokinetic and Monte Carlo simulation

To estimate the valnemulin pharmacokinetic profile in a swine population and to assess a dosage regimen for increasing the likelihood of optimization. This study was, respectively, performed in 22 sows culled by p.o. administration and in 80 growing-finishing pigs by i.v. administration at a single dose of 10 mg/kg to develop a population pharmacokinetic model and Monte Carlo simulation. The relationships among the plasma concentration, dose, and time of valnemulin in pigs were illustrated as C(i,v) = X(0 )(8.4191 × 10(-4) × e(-0.2371t) + 1.2788 × 10(-5) × e(-0.0069t)) after i.v. and C(p.o) = X(0) (-8.4964 × 10(-4) × e(-0.5840t) + 8.4195 × e(-0.2371t) + 7.6869 × 10(-6) × e(-0.0069t)) after p.o. Monte Carlo simulation showed that T(>MIC) was more than 24 h when a single daily dosage at 13.5 mg/kg BW in pigs was administrated by p.o., and MIC was 0.031 mg/L. It was concluded that the current dosage regimen at 10-12 mg/kg BW led to valnemulin underexposure if the MIC was more than 0.031 mg/L and could increase the risk of treatment failure and/or drug resistance.

Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein

Inflammation underlies many diseases and is an undesired effect of several therapy modalities. Biomathematical modeling can help unravel the complex inflammatory processes and the mechanisms triggering their emergence. We developed a model for induction of C-reactive protein (CRP), a clinically reliable marker of inflammation, by interleukin (IL)-11, an approved cytokine for treatment of chemotherapy-induced thrombocytopenia. Due to paucity of information on the mechanisms underlying inflammation-induced CRP dynamics, our model was developed by systematically evaluating several models for their ability to retrieve variable CRP profiles observed in IL-11-treated breast cancer patients. The preliminary semi-mechanistic models were designed by non-linear mixed-effects modeling, and were evaluated by various performance criteria, which test goodness-of-fit, parsimony and uniqueness. The best-performing model, a robust population model with minimal inter-individual variability, uncovers new aspects of inflammation dynamics. It shows that CRP clearance is a nonlinear self-controlled process, indicating an adaptive anti-inflammatory reaction in humans. The model also reveals a dual IL-11 effect on CRP elevation, whereby the drug has not only a potent immediate influence on CRP incline, but also a long-term influence inducing elevated CRP levels for several months. Consistent with this, model simulations suggest that periodic IL-11 therapy may result in prolonged low-grade (chronic) inflammation post treatment. Future application of the model can therefore help design improved IL-11 regimens with minimized long-term CRP toxicity. Our study illuminates the dynamics of inflammation and its control, and provides a prototype for progressive modeling of complex biological processes in the medical realm and beyond.

Understanding the linked kinetics of benzo(a)pyrene and 3-hydroxybenzo(a)pyrene biomarker of exposure using physiologically-based pharmacokinetic modelling in rats

3-hydroxybenzo(a)pyrene (3-OHBaP) in urine has been proposed as a biomarker of occupational exposure to polycyclic aromatic hydrocarbons. However, to reconstruct exposure doses in workers from biomarker measurements, a thorough knowledge of the kinetics of the benzo(a)pyrene (BaP) and 3-OHBaP given different routes of exposure is needed. A rat physiologically-based pharmacokinetic model of BaP and 3-OHBaP was built. Organs (tissues) represented as compartments were based on in vivo experimental data in rats. Tissue: blood partition coefficients, permeability coefficients, metabolism rates, excretion parameters, and absorption fractions and rates for different routes-of-entry were obtained directly from published in vivo time courses of BaP and 3-OHBaP in blood, various tissues and excreta of rats. The latter parameter values were best-fitted by least square procedures and Monte Carlo simulations. Sensitivity analyses were then carried out to ensure the stability of the model and the key parameters driving the overall modeled kinetics. This modeling pointed out critical determinants of the kinetics: (1) hepatic metabolism of BaP and 3-OHBaP elimination rate as the most sensitive parameters; (2) the strong partition of BaP in lungs compared to other tissues, followed by adipose tissues and liver; (3) the strong partition of 3-OHBaP in kidneys; (4) diffusion-limited tissue transfers of BaP in lungs and 3-OHBaP in lungs, adipose tissues and kidneys; (5) significant entero-hepatic recycling of 3-OHBaP. Very good fits to various sets of experimental data in rats from four different routes-of-entry (intravenous, oral, dermal and inhalation) were obtained with the model.

Early prediction of treatment resistance in low-risk gestational trophoblastic neoplasia using population kinetic modelling of hCG measurements

Background:

In low-risk gestational trophoblastic neoplasia (GTN) patients, a predictive marker for early identification of methotrexate (MTX) resistance would be useful. We previously demonstrated that kinetic modelling of human chorionic gonadotrophin (hCG) measurements could provide such a marker. Here we validate this approach in a large independent patient cohort.

Methods:

Serum hCG measurements of 800 low-risk GTN patients treated with MTX were analysed. The cohort was divided into Model and Test data sets. hCG kinetics were described from initial treatment day to day 50 using: ‘(hCG(time))=hCG0*exp(–k*time)+hCGres', where hCGres is the modelled residual production, hCG0 is the baseline hCG level, and k is the rate constant. HCGres-predictive value was investigated against previously reported predictors of MTX resistance.

Results:

Declining hCG measurements were well fitted by the model. The best discriminator of MTX resistance in the Model data set was hCGres, categorised by an optimal cut-off value of >20.44 IU l⁻¹: receiver-operating characteristic (ROC) area under the curve (AUC)=0.87; Se=0.91; Sp=0.83. The predictive value of hCGres was reproducible using the Test data set: ROC AUC=0.87; Se=0.88; Sp=0.86. Multivariate analyses revealed hCGres as a better predictor of MTX resistance (HR=1.01, P<0.0001) and MTX failure-free survival (HR=13.25, P<0.0001) than other reported predictive factors.

Conclusion:

hCGres, a modelled kinetic parameter calculated after fully dosed three MTX cycles, has a reproducible value for identifying patients with MTX resistance.