Growth differentiation factor 15 (GDF15) levels are associated with malnutrition in acutely admitted older adults.

BACKGROUND AND AIMS: The aging process is often accompanied by high risk of malnutrition and elevated levels of growth differentiation factor 15 (GDF15). GDF15 is an increasingly recognized biomarker for regulation of metabolism, but few studies have investigated the connection between GDF15 and malnutrition in older age and how it relates to other features of aging such as decreased appetite and physical function. Therefore, we investigated the associations between GDF15 levels and nutritional status, appetite, and physical function in acutely admitted older adults. METHODS: Plasma GDF15 levels were measured using immunoassays in 302 older adults (≥65 years) admitted to the emergency department (ED). Nutritional status was evaluated with the Mini Nutritional Assessment Short-Form (MNA®-SF), appetite was evaluated with the Simplified Nutritional Appetite Questionnaire (SNAQ), and physical function was evaluated with handgrip strength (HGS), 30-s chair stand test (30s-RSS), and gait speed (GS). Associations between GDF15 and each outcome was determined by logistic regression adjusted for age, sex, and C-reactive protein (CRP). RESULTS: Each doubling in plasma GDF15 level was associated with an adjusted odds ratio (OR) (95% confidence interval) of 1.59 (1.10-2.29, P = 0.01) for risk of malnutrition compared to normal nutrition and 1.19 (0.85-1.69, P = 0.3)) for malnutrition compared to risk of malnutrition. Each doubling in GDF15 was associated with an adjusted OR of 1.63 (1.21-2.23)) for having poor appetite, 1.46 (1.07-1.99) for having low HGS, 1.74 (1.23-2.51) for having low 30s-RSS, and 1.99 (1.39-2.94) for having low GS. CONCLUSION: Among older adults admitted to the ED, higher GDF15 levels were significantly associated with malnutrition, poor appetite, and low physical function independent of age, sex, and CRP.

Population pharmacokinetic-pharmacodynamic model of subcutaneous bupivacaine in a novel extended-release microparticle formulation.

The objective of this study was to develop a population pharmacokinetic-pharmacodynamic model of subcutaneously administered bupivacaine in a novel extended-release microparticle formulation for postoperative pain management. Bupivacaine was administered subcutaneously in the lower leg to 28 healthy male subjects in doses from 150 to 600 mg in a phase 1 randomized, placebo-controlled, double-blind, dose-ascending study with two different microparticle formulations, LIQ865A and LIQ865B. Warmth detection threshold was used as a surrogate pharmacodynamic endpoint. Population pharmacokinetic-pharmacodynamic models were fitted to plasma concentration-effect-time data using non-linear mixed-effects modelling. The pharmacokinetics were best described by a two-compartment model with biphasic absorption as two parallel absorption processes: a fast, zero-order process and a slower, first-order process with two transit compartments. The slow absorption process was found to be dose-dependent and rate-limiting for elimination at higher doses. Apparent bupivacaine clearance and the transit rate constant describing the slow absorption process both appeared to decrease with increasing doses following a power function with a shared covariate effect. The pharmacokinetic-pharmacodynamic relationship between plasma concentrations and effect was best described by a linear function. This model gives new insight into the pharmacokinetics and pharmacodynamics of microparticle formulations of bupivacaine and the biphasic absorption seen for several local anaesthetics.

Dose safety and pharmacodynamics of subcutaneous bupivacaine in a novel extended-release microparticle formulation: A phase 1, dose-ascending study in male volunteers.

A novel microparticle-based extended-release local anaesthetic containing a bupivacaine/poly-lactic-co-glycolic acid (PLGA; LIQ865A) or plain bupivacaine (LIQ865B) was examined in a first-in-human trial. The objectives were to examine the dose safety/tolerability and pharmacodynamics. Randomized subcutaneous injections of LIQ865A (n = 16) or LIQ865B (n = 12) and diluent, contralaterally, were administered in a dose-ascending manner (150- to 600-mg bupivacaine). Subjects were admitted 24 h post-injection and followed for 30 days post-injection. The risk ratios (RRs; 95% CI) of erythematous reactions for LIQ865A versus diluent was 9.00 (1.81-52.23; P = 0.006) and for LIQ865B versus diluent 2.50 (0.69-9.94; P = 0.37). The RR for the development of hematomas (LIQ865A versus diluent) were 3.25 (1.52-8.16; P = 0.004) and 4.00 (0.72-24.89; P = 0.32) (LIQ865B versus diluent). Subcutaneous indurations persisting for 4-13 weeks were seen in 6/16 subjects receiving LIQ865A. One subject receiving LIQ865A (600-mg bupivacaine) developed intermittent central nervous system (CNS) symptoms of local anaesthetic systemic toxicity (85 min to 51 h post-injection) coinciding with plasma peak bupivacaine concentrations (490-533 ng/ml). Both LIQ865 formulations demonstrated dose-dependent hypoesthesia and hypoalgesia. The duration of analgesia ranged between 37 and 86 h. The overall number of local adverse events, however, prohibits clinical application without further pharmacological modifications.

Evaluation of Multi-Frequency Bioelectrical Impedance Analysis against Dual-Energy X-ray Absorptiometry for Estimation of Low Muscle Mass in Older Hospitalized Patients.

The accuracy of multi-frequency (MF) bioelectrical impedance analysis (BIA) to estimate low muscle mass in older hospitalized patients remains unclear. This study aimed to describe the ability of MF-BIA to identify low muscle mass as proposed by The Global Leadership Initiative on Malnutrition (GLIM) and The European Working Group on Sarcopenia in Older People (EWGSOP-2) and examine the association between muscle mass, dehydration, malnutrition, and poor appetite in older hospitalized patients. In this prospective exploratory cohort study, low muscle mass was estimated with MF-BIA against dual-energy X-ray absorptiometry (DXA) in 42 older hospitalized adults (≥65 years). The primary variable for muscle mass was appendicular skeletal muscle mass (ASM), and secondary variables were appendicular skeletal muscle mass index (ASMI) and fat-free mass index (FFMI). Cut-off values for low muscle mass were based on recommendations by GLIM and EWGSOP-2. MF-BIA was evaluated against DXA on the ability to estimate absolute values of muscle mass by mean bias, limits of agreement (LOA), and accuracy (5% and 10% levels). Agreement between MF-BIA and DXA to identify low muscle mass was evaluated with sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The association between muscle mass, dehydration, malnutrition, and poor appetite was visually examined with boxplots. MF-BIA overestimated absolute values of ASM with a mean bias of 0.63 kg (CI: -0.20:1.46, LOA: -4.61:5.87). Agreement between MF-BIA and DXA measures of ASM showed a sensitivity of 86%, specificity of 94%, PPV of 75% and NPV of 97%. Boxplots indicate that ASM is lower in patients with malnutrition. This was not observed in patients with poor appetite. We observed a tendency toward higher ASM in patients with dehydration. Estimation of absolute ASM values with MF-BIA should be interpreted with caution, but MF-BIA might identify low muscle mass in older hospitalized patients.

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real-World Data: A Case Study on Valproic Acid.

We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real-world data. We included individuals aged 65 years or older with epilepsy who redeemed their first prescription of valproate after the diagnosis of epilepsy and had at least one valproate plasma concentration measured. A long short-term memory neural network (LSTM) was developed using the training data set to fit the LSTM and the test data set to validate the model. Predictions from the LSTM were compared with those obtained from the population predictions from a pharmacometric model by Birnbaum et al. which had the best predictive performance for population predictions of valproate concentrations in Danish databases. We used the cutoff of ± 20 mg/L of prediction error to define good predictions. A total of 1,252 individuals were included in the study. The LSTM fitted using the training data set had poor predictive performance in the test data set, but better than that of the pharmacometric model. The proportion of individuals with at least one predicted concentration within ± 20 mg/L of observed concentration was largest in case of the LSTM (64.4%, 95% confidence interval (CI): 58.4-70.2%) compared with the pharmacometric model by Birnbaum et al. (49.8%, 95% CI: 47.0-52.6%). LSTM shows better predictive performance to predict valproate plasma concentrations compared with a traditional pharmacometric model in the investigated setting with real-world data in older patients with epilepsy where information on exact timepoints for both dosing and plasma concentration measurement are missing.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate.

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia.

The International Prostate Symptom Score (IPSS), the quality of life (QoL) score, and the benign prostatic hyperplasia impact index (BII) are three different scales commonly used to assess the severity of lower urinary tract symptoms associated with benign prostatic hyperplasia (BPH-LUTS). Based on a phase II clinical trial including 403 patients with moderate to severe BPH-LUTS, the objectives of this study were to (i) develop traditional pharmacometric and bounded integer (BI) models for the IPSS, QoL score, and BII endpoints, respectively; (ii) compare the power and type I error in detecting drug effects of BI modeling with traditional methods through simulation; and (iii) obtain quantitative translation between scores on the three abovementioned scales using a BI modeling framework. All developed models described the data adequately. Pharmacometric modeling using a continuous variable (CV) approach was overall found to be the most robust in terms of type I error and power to detect a drug effect. In most cases, BI modeling showed similar performance to the CV approach, yet severely inflated type I error was generally observed when inter-individual variability (IIV) was incorporated in the BI variance function (g()). BI modeling without IIV in g() showed greater type I error control compared to the ordered categorical approach. Lastly, a multiple-scale BI model was developed and estimated the relationship between scores on the three BPH-LUTS scales with overall low uncertainty. The current study yields greater understanding of the operating characteristics of the novel BI modeling approach and highlights areas potentially requiring further improvement.

High-dose naloxone: Effects by late administration on pain and hyperalgesia following a human heat injury model. A randomized, double-blind, placebo-controlled, crossover trial with an enriched enrollment design.

Severe chronic postsurgical pain has a prevalence of 4-10% in the surgical population. The underlying nociceptive mechanisms have not been well characterized. Following the late resolution phase of an inflammatory injury, high-dose µ-opioid-receptor inverse agonists reinstate hypersensitivity to nociceptive stimuli. This unmasking of latent pain sensitization has been a consistent finding in rodents while only observed in a limited number of human volunteers. Latent sensitization could be a potential triggering venue in chronic postsurgical pain. The objective of the present trial was in detail to examine the association between injury-induced secondary hyperalgesia and naloxone-induced unmasking of latent sensitization. Healthy volunteers (n = 80) received a cutaneous heat injury (47°C, 420 s, 12.5 cm2). Baseline secondary hyperalgesia areas were assessed 1 h post-injury. Utilizing an enriched enrollment design, subjects with a magnitude of secondary hyperalgesia areas in the upper quartile ('high-sensitizers' [n = 20]) and the lower quartile ('low-sensitizers' [n = 20]) were selected for further study. In four consecutive experimental sessions (Sessions 1 to 4), the subjects at two sessions (Sessions 1 and 3) received a cutaneous heat injury followed 168 h later (Sessions 2 and 4) by a three-step target-controlled intravenous infusion of naloxone (3.25 mg/kg), or normal saline. Assessments of secondary hyperalgesia areas were made immediately before and stepwise during the infusions. Simple univariate statistics revealed no significant differences in secondary hyperalgesia areas between naloxone and placebo treatments (P = 0.215), or between 'high-sensitizers' and 'low-sensitizers' (P = 0.757). In a mixed-effects model, secondary hyperalgesia areas were significantly larger following naloxone as compared to placebo for 'high-sensitizers' (P < 0.001), but not 'low-sensitizers' (P = 0.651). Although we could not unequivocally demonstrate naloxone-induced reinstatement of heat injury-induced hyperalgesia, further studies in clinical postsurgical pain models are warranted.

Item Response Theory Modeling of the International Prostate Symptom Score in Patients with Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.

Item response theory (IRT) was used to characterize the time course of lower urinary tract symptoms due to benign prostatic hyperplasia (BPH-LUTS) measured by item-level International Prostate Symptom Scores (IPSS). The Fisher information content of IPSS items was determined and the power to detect a drug effect using the IRT approach was examined. Data from 403 patients with moderate-to-severe BPH-LUTS in a placebo-controlled phase II trial studying the effect of degarelix over 6 months were used for modeling. Three pharmacometric models were developed: a model for total IPSS, a unidimensional IRT model, and a bidimensional IRT model, the latter separating voiding and storage items. The population-level time course of BPH-LUTS in all models was described by initial improvement followed by worsening. In the unidimensional IRT model, the combined information content of IPSS voiding items represented 72% of the total information content, indicating that the voiding subscore may be more sensitive to changes in BPH-LUTS compared with the storage subscore. The pharmacometric models showed considerably higher power to detect a drug effect compared with a cross-sectional and while-on-treatment analysis of covariance, respectively. Compared with the sample size required to detect a drug effect at 80% power with the total IPSS model, a reduction of 5.9% and 11.7% was obtained with the unidimensional and bidimensional IPSS IRT model, respectively. Pharmacometric IRT analysis of the IPSS within BPH-LUTS may increase the precision and efficiency of treatment effect assessment, albeit to a more limited extent compared with applications in other therapeutic areas.

Integrated Item Response Theory Modeling of Multiple Patient-Reported Outcomes Assessing Lower Urinary Tract Symptoms Associated with Benign Prostatic Hyperplasia.

In clinical trials within lower urinary tract symptoms due to benign prostatic hyperplasia (BPH-LUTS), the International Prostate Symptom Score (IPSS) is commonly the primary efficacy outcome while the Quality of Life (QoL) score and the BPH Impact Index (BII) are common secondary efficacy markers. The current study aimed to characterize BPH-LUTS progression using responses to the IPSS, the QoL, and the BII in an integrated item response theory (IRT) framework and assess the Fisher information of each scale. The power of this approach to detect a drug effect was compared with an IRT approach considering only IPSS responses. A unidimensional and a bidimensional pharmacometric IRT model, based on item-level IPSS responses in a clinical trial with 403 patients, were extended by incorporating patients' QoL and summary BII scores over the 6-month trial period. In the developed unidimensional integrated model, the QoL score was found to be the most informative, representing 17% of the total Fisher information, while the combined information content of the seven IPSS items represented 70.6%. In the bidimensional model, "storage" and both storage and "voiding" disability drove QoL and summary BII responses, respectively. Sample size reduction of 16% to detect a drug effect at 80% power was obtained with the unidimensional integrated IRT model compared with its counterpart IPSS IRT model. This study shows that utilizing the information content across the IPSS, QoL, and BII scales in an integrated IRT framework results in a modest but meaningful increase in power to detect a drug effect.

Pharmacodynamic modelling reveals synergistic interaction between docetaxel and SCO-101 in a docetaxel-resistant triple negative breast cancer cell line.

One of the primary barriers in treating cancer patients is the development of resistance to the available treatments. This is the case for treatment of triple negative breast cancer (TNBC) with docetaxel, which is part of the neoadjuvant treatment for TNBC. The novel compound SCO-101 is under investigation for its potential treatment effect in several types of drug resistant cancer. The aim of this study was to establish a pharmacodynamic model that captures the effect of docetaxel, SCO-101, and the combination on cell survival in docetaxel resistant MDA-MB-231 TNBC cells. Several combination models were compared and a recently published combination model, the general pharmacodynamic interaction model (GPDI), provided the best fit. The model allowed for description and quantification of the interaction between docetaxel and SCO-101 with respects to both maximal effect and potency. Based on this model, SCO-101 has a synergistic effect with docetaxel. This synergy is not present in the maximal effect, but the combination of SCO-101 and docetaxel showed an approximately 60% increase in potency compared to docetaxel alone. Furthermore, the predicted model surface for the combination provided key information regarding promising dose ratios and dose levels for further studies of the combination. Lastly, the study presents a use case for the GPDI model, which provides a way to quantify and interpret drug-drug interactions.

Impact of dose-escalation schemes and drug discontinuation on weight loss outcomes with liraglutide 3.0 mg: A model-based approach.

AIMS: To investigate the impact on weight loss of the treatment changes in overweight or obese people that may be needed in case of gastrointestinal (GI) tolerability issues during escalation of the glucagon-like peptide-1 analogue liraglutide. MATERIALS AND METHODS: The individual longitudinal body weight data from the main trial periods of three phase II/III trials in overweight or obese patients (56-week treatment with once-daily liraglutide 1.2, 1.8, 2.4 or 3.0 mg or placebo, n = 4952) were analysed using a non-linear mixed-effect modelling approach. Individual pharmacokinetic profiles were derived based on published pharmacokinetic models. Baseline body weight, baseline glycated haemoglobin (HbA1c), age, gender, diabetes status (no diabetes, prediabetes or type 2 diabetes), race and trial region were investigated as covariates. As a form of external validation, the model was used to predict the weight regain after treatment cessation at week 56 (data not included in model development). RESULTS: A pharmacokinetic/pharmacodynamic model provided an adequate description of the weight loss trajectories for all studied doses. Gender and diabetes status were identified as the most influential covariates, and an underlying seasonal weight fluctuation was identified. Slower than that recommended, one-week dose-escalation algorithms led up to 2 weeks slower initial weight loss but similar long-term weight loss trajectories. CONCLUSIONS: The relationship between liraglutide systemic exposure and weight loss was successfully established in overweight or obese people. The model could predict the time course of weight regain after treatment cessation and suggests that GI tolerability can be mitigated by slower escalation with only minor impact on the weight loss trajectory.

Demonstrating Contribution of Components of Fixed-Dose Drug Combinations Through Longitudinal Exposure-Response Analysis.

Exposure-response (ER) modeling for fixed-dose combinations (FDC) has previously been found to have an inflated false positive rate (FP), i.e., observing a significant effect of FDC components when no true effect exists. Longitudinal exposure-response (LER) analysis utilizes the time course of the data and is valid for several clinical endpoints for FDCs. The aim of the study was to investigate if LER is applicable for the validation of FDCs by demonstrating the contribution of each component to the overall effect without inflation of FP rates. FP and FN rates associated with ER and LER analysis were investigated using stochastic simulation and estimation. Four hundred thirty-two scenarios with varying numbers of patients, duration, sampling frequency, dose distribution, design, and drug activity were analyzed using a range of linear, log-linear, and non-linear models to asses FP and FN rates. Lastly, the impact of the clinical trial parameters was investigated. LER analyses provided well-controlled FP rates of the expected 5% or less; however, in low information clinical trials consisting of 30 patients, 4 samples, and 20 days, LER analyses lead to inflated FN rates. Parameter investigation showed that when the clinical trial includes sufficient patients, duration, samples, and an appropriate trial design, the FN rates are in general below the expected 5% for LER analysis. Based on the results, LER analysis can be used for the validation of FDCs and fixed ratio drug combinations. The method constitutes a new avenue for providing evidence that demonstrates the contribution of each component to the overall clinical effect.

Midazolam Pharmacokinetics in Obese and Non-obese Children and Adolescents.

BACKGROUND: Midazolam is a first-line drug for the treatment of status epilepticus, both by buccal and intravenous administration. In children and adolescents with obesity, midazolam pharmacokinetics may be altered, and the current dosing guidelines may therefore be insufficient. OBJECTIVE: The objective of this study was to investigate the pharmacokinetics of midazolam, after intravenous administration, in obese and non-obese adolescents aged 11-18 years. METHODS: All trial participants received a 1-µg midazolam microdose as an intravenous bolus. 13 blood samples were collected per participant at pre-specified timepoints. Plasma concentration-time data were fitted to pharmacokinetic models using non-linear mixed-effects modeling. Covariates such as weight, age, and body mass index standard deviation score were tested to explain the inter-individual variability associated with the pharmacokinetic parameters. RESULTS: Sixty-seven adolescents were included in the analysis. The pharmacokinetics of midazolam was best described with a two-compartment model. The rate of distribution was faster, and the peripheral volume of distribution was larger in adolescents with a high body mass index standard deviation score compared with adolescents with a lower standard deviation score. Simulations revealed that long-term infusions based on total body weight could lead to high plasma concentrations in adolescents with obesity. Furthermore, simulated plasma concentrations after a fixed buccal dose indicated that adolescents with obesity may be at risk of sub-therapeutic midazolam plasma concentrations. CONCLUSIONS: The body mass index standard deviation score was shown to have a significant influence on the peripheral volume of distribution and the inter-compartmental clearance of midazolam. The current dosing guidelines for status epilepticus, where the midazolam dose is adjusted to total body weight or age, may lead to supra- and sub-therapeutic plasma concentrations, respectively, in adolescents with obesity. TRIAL REGISTRATION: EudraCT: 2014-004554-34.

Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models.

Combinations of pharmacological treatments are increasingly being investigated for potentially higher clinical benefit, especially when the combined drugs are expected to act via synergistic interactions. The clinical development of combination treatments is particularly challenging, particularly during the dose-selection phase, where a vast range of possible combination doses exists. The purpose of this work was to evaluate the added value of using optimal design for guiding the dose allocation in drug combination dose-finding studies as compared with a typical drug-combination trial. Optimizations were performed using local [D(s)-optimality] and global [ED(s)-optimality] optimal designs to maximize the precision of model parameters in a number of potential exposure-response (E-R) surfaces. A compound criterion [D(s)/V-optimality] was used to optimize the precision of model predictions in specific parts of the E-R surfaces. Optimal designs provided unbiased estimates and significantly improved the accuracy of results relative to the typical design. It was possible to improve the efficiency and overall parameter precision up to 7832% and 96.6% respectively. When the compound criterion was used, the probability to accurately identify the optimal dose-combination increased from 71% for the typical design up to 91%. These results indicate that optimal design methodology in tandem with E-R analyses is a beneficial tool that can be used for appropriate dose allocation in dose-finding studies for drug combinations.

Dose Finding in the Clinical Development of 60 US Food and Drug Administration-Approved Drugs Compared With Learning vs. Confirming Recommendations.

This review characterizes clinical development that supported the label dose in 60 drug indications recently approved by the US Food and Drug Administration. With Lewis B. Sheiner's Learning vs. Confirming clinical drug development paradigm as a reference point, the clinical development paths, the design of dose-ranging trials, and the dose-exposure-response characterization were examined using US Food and Drug Administration approval packages. It was found that 89% of clinical development programs included several doses in the first-in-patient trial, 43% proceeded directly to confirmatory trials after the first-in-patient trial, and 52% included multiple doses in confirmatory development. A low number of doses and narrow dose ranges were generally included in dose-ranging trials, with only 20% including at least four doses over an at least 10-fold dose range. In a third of approval packages, no dose-response or exposure-response evaluation was identified, and model-based dose-exposure-response characterization was rarely alluded to, as only 2 of 60 approval packages mentioned the use of a model-based approach. The findings suggest that confirmatory development may often be guided more toward learning than confirming, and furthermore that dose exposure response is robustly assessed in only a minority of clinical drug development programs, indicating that there may be room left for optimizing the benefit/risk profile of confirmatory/marketed dose(s). Significant deviation from Learning vs. Confirming may exist in clinical development practice on several levels, and the reasons for why this may be the case are discussed in light of contemporary literature.

Body of evidence and approaches applied in the clinical development programme of fixed-dose combinations in the European Union from 2010 to 2016.

AIMS: To provide insights into the clinical development pathway for fixed-dose combinations (FDCs), to consider strategies, and to elucidate the path to approval by assessing the body of evidence, as summarized in the European Public Assessment Reports. METHODS: The main resource was the European Public Assessment Reports for 36 FDCs, which included 239 clinical trials with 157 514 patients. The analyses focused on how prior knowledge of the active substances or combination, use of pharmacokinetic-pharmacodynamic modelling, and clinical trial design choice impact the size and strategy of the clinical development programme. RESULTS: FDC products primarily comprised 2 previously approved components (21/36, 71%) and had only 1 approved combination (21/36, 71%). Utilizing previously approved active substances resulted in fewer clinical trials, arms and patients, but FDC doses studied in the clinical development programme. Furthermore, dose-finding trials were performed for less than half of FDCs consisting of 2 previously approved active substances. The standard approach to demonstrate contribution of active substances was through a factorial or single combination study. Finally, the use of pharmacokinetic modelling showed a significant decrease in the number of FDC doses studied. CONCLUSIONS: The field of FDCs seems to be on the rise, utilizing new molecular entities, prior knowledge and re-profiling drugs. However, a way to move FDC development forward might be through new regulatory and scientific paradigms, in which it is encouraged to utilize model-based approaches to develop FDCs with multiple dose levels and dose ratios for exposure-based treatment that will enable personalization.

High-dose naloxone, an experimental tool uncovering latent sensitisation: pharmacokinetics in humans.

BACKGROUND: Naloxone, an opioid receptor antagonist, is used as a pharmacological tool to detect tonic endogenous activation of opioid receptors in experimental pain models. We describe a pharmacokinetic model linking naloxone pharmacokinetics to its main metabolite after high-dose naloxone infusion. METHODS: Eight healthy volunteers received a three-stage stepwise high-dose i.v. naloxone infusion (total dose 3.25 mg kg-1). Naloxone and naloxone-3-glucuronide (N3G) plasma concentrations were sampled from infusion onset to 334 min after infusion discontinuation. Pharmacokinetic analysis was performed using non-linear mixed effect models (NONMEM). The predictive performances of Dowling's and Yassen's models were evaluated, and target-controlled infusion simulations were performed. RESULTS: Three- and two-compartment disposition models with linear elimination kinetics described the naloxone and N3G concentration time-courses, respectively. Two covariate models were developed: simple (weight proportional) and complex (with the shallow peripheral volume of distribution linearly increasing with body weight). The median prediction error (MDPE) and wobble for Dowling's model were -32.5% and 33.4%, respectively. For Yassen's model, the MDPE and wobble were 1.2% and 19.9%, respectively. CONCLUSIONS: A parent-metabolite pharmacokinetic model was developed for naloxone and N3G after high-dose naloxone infusion. No saturable pharmacokinetics were observed. Whereas Dowling's model was inaccurate and over-predicted naloxone concentrations, Yassen's model accurately predicted naloxone pharmacokinetics. The newly developed covariate models may be used for high-dose TCI-naloxone for experimental and clinical practice. CLINICAL TRIALS REGISTRATION: NCT01992146.