J. G. Coen van Hasselt

The status of pharmacometrics in pregnancy: highlights from the 3rd American conference on pharmacometrics

Physiological changes during pregnancy may alter drug pharmacokinetics. Therefore, mechanistic understanding of these changes and, ultimately, clinical studies in pregnant women are necessary to determine if and how dosing regimens should be adjusted. Because of the typically limited number of patients who can be recruited in this patient group, efficient design and analysis of these studies is of special relevance. This paper is a summary of a conference session organized at the American Conference of Pharmacometrics in April 2011, around the topic of applying pharmacometric methodology to this important problem. The discussion included both design and analysis of clinical studies during pregnancy and in silico predictions. An overview of different pharmacometric methods relevant to this subject was given. The impact of pharmacometrics was illustrated using a range of case examples of studies around pregnancy.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia.

AIMS Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia. METHODS A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework. RESULTS Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 μg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time. CONCLUSIONS The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

Pharmacokinetic–pharmacodynamic relationships of central nervous system effects of scopolamine in healthy subjects

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The cholinergic system is important for different central nervous system functions, including memory, learning and attention. Scopolamine, a centrally active muscarinic antagonist, has been used to model dementia and to demonstrate the pharmacological effects of cholinergic drugs, but for most effects the concentration-effect relationships are unknown. WHAT THIS STUDY ADDS • We determined the pharmacokinetic-pharmacodynamic relationships of scopolamine using a multidimensional central nervous system test battery in a large group of healthy volunteers. The results suggested there are various functional cholinergic systems with different pharmacological characteristics, which can be used to study the effects of drugs that directly or indirectly modify cholinergic systems. The design of such studies should take the different concentration-effect relationships into account. AIM(S) Although scopolamine is a frequently used memory impairment model, the relationships between exposure and corresponding central nervous system (CNS) effects are mostly unknown. The aim of our study was to characterize these using pharmacokinetic-pharmacodynamic (PK-PD) modelling. METHODS In two double-blind, placebo-controlled, four-way crossover studies, 0.5-mg scopolamine was administered i.v. to 90 healthy male subjects. PK and PD/safety measures were monitored pre-dose and up to 8.5 h after administration. PK-PD relationships were modelled using non-linear mixed-effect modelling. RESULTS Most PD responses following scopolamine administration in 85 subjects differed significantly from placebo. As PD measures lagged behind the plasma PK profile, PK-PD relationships were modelled using an effect compartment and arbitrarily categorized according to their equilibration half-lives (t(1/2) k(eo) ; hysteresis measure). t(1/2) k(eo) for heart rate was 17 min, saccadic eye movements and adaptive tracking 1-1.5 h, body sway, smooth pursuit, visual analogue scales alertness and psychedelic 2.5-3.5 h, pupil size, finger tapping and visual analogue scales feeling high more than 8 h. CONCLUSIONS Scopolamine affected different CNS functions in a concentration-dependent manner, which based on their distinct PK-PD characteristics seemed to reflect multiple distinct functional pathways of the cholinergic system. All PD effects showed considerable albeit variable delays compared with plasma concentrations. The t(1/2) k(eo) of the central effects was longer than of the peripheral effects on heart rate, which at least partly reflects the long CNS retention of scopolamine, but possibly also the triggering of independent secondary mechanisms. PK-PD analysis can optimize scopolamine administration regimens for future research and give insight into the physiology and pharmacology of human cholinergic systems.

Towards integrative systems pharmacology models in oncology drug development.

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine

Novel developments in biomarkers discovery are essential in modern health care, notably in treatment individualization and precision medicine. Clinical metabolomics, which aims to identify small molecule metabolites present in patient-derived samples, has attracted much attention to support discovery of novel biomarkers. However, the step from discriminatory features of disease states towards biomarkers that can truly individualize treatments is challenging. Biomarkers used for treatment individualization can either be dynamic or static prognostic biomarkers. Dynamic biomarkers are relevant for describing the clinical response, including dynamical disease progression and associated treatment response. Static (prognostic) biomarkers do not describe but rather predict a clinical response, and typically reflect aspects of the physiological state of a patient related to drug treatment response or disease progression dynamics. Pharmacokinetic-pharmacodynamic (PK-PD) modeling represents an established approach for drug treatment individualization based on drug exposure or treatment response biomarkers, as well as for the description of disease progression dynamics. Here, we discuss how novel treatment individualization biomarkers can be identified using a clinical metabolomics-based approach, and how concepts inspired from the field of PK-PD modeling can be integrated in this process in order to increase the clinical relevance of identified biomarkers and precision medicine.

Pronounced between‐subject and circadian variability in thymidylate synthase and dihydropyrimidine dehydrogenase enzyme activity in human volunteers

AIMS The enzymatic activity of dihydropyrimidine dehydrogenase (DPD) and thymidylate synthase (TS) are important for the tolerability and efficacy of the fluoropyrimidine drugs. In the present study, we explored between-subject variability (BSV) and circadian rhythmicity in DPD and TS activity in human volunteers. METHODS The BSVs in DPD activity (n = 20) in peripheral blood mononuclear cells (PBMCs) and in plasma, measured by means of the dihydrouracil (DHU) and uracil (U) plasma levels and DHU : U ratio (n = 40), and TS activity in PBMCs (n = 19), were examined. Samples were collected every 4 h throughout 1 day for assessment of circadian rhythmicity in DPD and TS activity in PBMCs (n = 12) and DHU : U plasma ratios (n = 23). In addition, the effects of genetic polymorphisms and gene expression on DPD and TS activity were explored. RESULTS Population mean (± standard deviation) DPD activity in PBMCs and DHU : U plasma ratio were 9.2 (±2.1) nmol mg(-1) h(-1) and 10.6 (±2.4), respectively. Individual TS activity in PBMCs ranged from 0.024 nmol mg(-1) h(-1) to 0.596 nmol mg(-1) h(-1) . Circadian rhythmicity was demonstrated for all phenotype markers. Between 00:30 h and 02:00 h, DPD activity in PBMCs peaked, while the DHU : U plasma ratio and TS activity in PBMCs showed trough activity. Peak-to-trough ratios for DPD and TS activity in PBMCs were 1.69 and 1.62, respectively. For the DHU : U plasma ratio, the peak-to-trough ratio was 1.43. CONCLUSIONS BSV and circadian variability in DPD and TS activity were demonstrated. Circadian rhythmicity in DPD might be tissue dependent. The results suggested an influence of circadian rhythms on phenotype-guided fluoropyrimidine dosing and supported implications for chronotherapy with high-dose fluoropyrimidine administration during the night.

Integrated semi‐physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662

AIMS Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice. METHODS One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens. RESULTS Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy. CONCLUSIONS A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Design of a Drug-Drug Interaction Study of Vincristine With Azole Antifungals in Pediatric Cancer Patients Using Clinical Trial Simulation

The aim of the current work was to perform a clinical trial simulation (CTS) analysis to optimize a drug‐drug interaction (DDI) study of vincristine in children who also received azole antifungals, taking into account challenges of conducting clinical trials in this population, and, to provide a motivating example of the application of CTS in the design of pediatric oncology clinical trials.

Renal function, body surface area, and age are associated with risk of early-onset fluoropyrimidine-associated toxicity in patients treated with capecitabine-based anticancer regimens in daily clinical care

BACKGROUND The objective of this analysis was to determine the factors associated with early onset treatment-related toxicity in patients treated with capecitabine-based anticancer regimens in daily clinical care. PATIENTS AND METHODS A total of 1463 patients previously included in a prospective cohort study and treated with standard-of-care capecitabine-based anticancer regimens (monotherapy or combined with other chemotherapy or radiotherapy) were analysed. Logistic regression models were developed to investigate associations between patient- and treatment-related factors and occurrence of early--i.e. cycle one or two--severe (grade ≥ 3) treatment-related toxicity, toxicity-related hospitalisation, and toxicity-related treatment discontinuation. Performance of models was evaluated using receiver-operating characteristic (ROC) curves and internal validity was explored using bootstrap analysis. RESULTS Among 1463 patients included, 231 patients (16%) experienced early severe toxicity, 132 patients (9%) were hospitalised for toxicity, and 146 patients (10%) discontinued treatment for toxicity; in total, 321 patients (22%) experienced any early toxicity-related adverse outcome. Predictors of early grade ≥ 3 toxicity, after adjustment for treatment regimen, were renal function (odds ratio [OR] 0.85 per 10 ml/min/1.73 m(2), p = 0.0007), body surface area (BSA) (OR 0.33 per m(2), p = 0.0053), age (OR 1.14 per decade, p = 0.0891), and elevated pre-treatment uracil concentrations (OR 2.41 per 10 ng/ml, p = 0.0046). Age was significantly associated with fatal treatment-related toxicity (OR 5.75, p = 0.0008). Area under the ROC curve (AUC) of a model to predict early grade ≥ 3 toxicity was 0.704 (95% confidence interval 0.666-0.743, optimism-corrected AUC 0.690). CONCLUSION Renal function, BSA, and age, in addition to pre-treatment uracil, are associated with clinically relevant differences in risk of early severe toxicity in patients treated with capecitabine in routine clinical care.

Semiphysiological versus empirical modelling of the population pharmacokinetics of free and total cefazolin during pregnancy.

This work describes a first population pharmacokinetic (PK) model for free and total cefazolin during pregnancy, which can be used for dose regimen optimization. Secondly, analysis of PK studies in pregnant patients is challenging due to study design limitations. We therefore developed a semiphysiological modeling approach, which leveraged gestation-induced changes in creatinine clearance (CrCL) into a population PK model. This model was then compared to the conventional empirical covariate model. First, a base two-compartmental PK model with a linear protein binding was developed. The empirical covariate model for gestational changes consisted of a linear relationship between CL and gestational age. The semiphysiological model was based on the base population PK model and a separately developed mixed-effect model for gestation-induced change in CrCL. Estimates for baseline clearance (CL) were 0.119 L/min (RSE 58%) and 0.142 L/min (RSE 44%) for the empirical and semiphysiological models, respectively. Both models described the available PK data comparably well. However, as the semiphysiological model was based on prior knowledge of gestation-induced changes in renal function, this model may have improved predictive performance. This work demonstrates how a hybrid semiphysiological population PK approach may be of relevance in order to derive more informative inferences.