Simplified Dosing Regimens for Gentamicin in Neonatal Sepsis

Background: The effectiveness of antibiotics for the treatment of severe bacterial infections in newborns in resource-limited settings has been determined by empirical evidence. However, such an approach does not warrant optimal exposure to antibiotic agents, which are known to show different disposition characteristics in this population. Here we evaluate the rationale for a simplified regimen of gentamicin taking into account the effect of body size and organ maturation on pharmacokinetics. The analysis is supported by efficacy data from a series of clinical trials in this population.

Methods: A previously published pharmacokinetic model was used to simulate gentamicin concentration vs. time profiles in a virtual cohort of neonates. Model predictive performance was assessed by supplementary external validation procedures using therapeutic drug monitoring data collected in neonates and young infants with or without sepsis. Subsequently, clinical trial simulations were performed to characterize the exposure to intra-muscular gentamicin after a q.d. regimen. The selection of a simplified regimen was based on peak and trough drug levels during the course of treatment.

Results: In contrast to current World Health Organization guidelines, which recommend gentamicin doses between 5 and 7.5 mg/kg, our analysis shows that gentamicin can be used as a fixed dose regimen according to three weight-bands: 10 mg for patients with body weight <2.5 kg, 16 mg for patients with body weight between 2.5 and 4 kg, and 30 mg for those with body weight >4 kg.

Conclusion: The choice of the dose of an antibiotic must be supported by a strong scientific rationale, taking into account the differences in drug disposition in the target patient population. Our analysis reveals that a simplified regimen is feasible and could be used in resource-limited settings for the treatment of sepsis in neonates and young infants with sepsis aged 0–59 days.

Pharmacokinetics of plasma infusion in congenital thrombotic thrombocytopenic purpura

Essentials Congenital thrombotic thrombocytopenic purpura (TTP) is primarily treated with plasma infusion. We present a pharmacokinetic analysis of ADAMTS-13 in six patients following plasma infusion. A median half-life of 130 h was demonstrated, ranging between 82.6 and 189.5 h. Investigation of interindividual clearance of ADAMTS-13 is necessary to optimize treatment. SUMMARY: Background Congenital thrombotic thrombocytopenic purpura (TTP) is defined by persistent severe deficiency of ADAMTS-13 in the absence of anti-ADAMTS-13 inhibitory antibodies, confirmed by mutational analysis. Replacement of the missing protease prevents disease relapse, primarily using plasma infusion (PI). Objectives, patients and methods There is scant evidence regarding optimal dose and frequency of treatment, which tends to be empirically guided. We present a pharmacokinetic analysis of ADAMTS-13 in six patients with congenital TTP on established regimes following PI. Results We found a median clearance of 25.41 mL h-1 and half-life of 130 h, ranging between 82.6 and 189.5 h (3.4-7.9 days, respectively). All patients reached baseline ADAMTS-13 level within 7-10 days post-plasma. Median ADAMTS-13 activity peak post-PI was 24.05 IU dL-1 . Variation was related to elimination rate, which, in turn, was affected by weight and metabolism, but not to von Willebrand factor antigen or activity levels. Using the pharmacokinetic parameters, we simulated individualized protocols based on PI dose or frequency to target hypothetical optimal plasma levels of ADAMTS-13 of 10 and 50 IU dL-1 , respectively. Results suggest a target trough ADAMTS-13 of 10 IU dL-1 is feasible but 50 IU dL-1 would not be achievable taking into account volume required. Conclusions Further work is needed to compare treatment of congenital TTP with PI vs. recombinant ADAMTS-13. PI may provide longer duration of ADAMTS-13 effect, but is limited by plasma volume required, whereas recombinant therapy can provide a higher ADAMTS-13 peak. We propose that investigation of interindividual clearance of ADAMTS-13 is necessary to optimize treatment and provide the rationale for dose and frequency of prophylaxis.

Challenges in translational drug research in neuropathic and inflammatory pain: the prerequisites for a new paradigm

AIM

Despite an improved understanding of the molecular mechanisms of nociception, existing analgesic drugs remain limited in terms of efficacy in chronic conditions, such as neuropathic pain. Here, we explore the underlying pathophysiological mechanisms of neuropathic and inflammatory pain and discuss the prerequisites and opportunities to reduce attrition and high-failure rate in the development of analgesic drugs.

METHODS

A literature search was performed on preclinical and clinical publications aimed at the evaluation of analgesic compounds using MESH terms in PubMed. Publications were selected, which focused on (1) disease mechanisms leading to chronic/neuropathic pain and (2) druggable targets which are currently under evaluation in drug development. Attention was also given to the role of biomarkers and pharmacokinetic-pharmacodynamic modelling.

RESULTS

Multiple mechanisms act concurrently to produce pain, which is a non-specific manifestation of underlying nociceptive pathways. Whereas these manifestations can be divided into neuropathic and inflammatory pain, it is now clear that inflammatory mechanisms are a common trigger for both types of pain. This has implications for drug development, as the assessment of drug effects in experimental models of neuropathic and chronic pain is driven by overt behavioural measures. By contrast, the use of mechanistic biomarkers in inflammatory pain has provided the pharmacological basis for dose selection and evaluation of non-steroidal anti-inflammatory drugs (NSAIDs).

CONCLUSION

A different paradigm is required for the identification of relevant targets and candidate molecules whereby pain is coupled to the cause of sensorial signal processing dysfunction rather than clinical symptoms. Biomarkers which enable the characterisation of drug binding and target activity are needed for a more robust dose rationale in early clinical development. Such an approach may be facilitated by quantitative clinical pharmacology and evolving technologies in brain imaging, allowing accurate assessment of target engagement, and prediction of treatment effects before embarking on large clinical trials.

Pharmacokinetic-pharmacodynamic modelling of drug-induced QTc interval prolongation in man: prediction from in vitro human ether-à-go-go-related gene binding and functional inhibition assays and conscious dog studies

BACKGROUND AND PURPOSE

Functional measures of human ether-à-go-go-related gene (hERG; Kv 11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans.

EXPERIMENTAL APPROACH

Pharmacokinetic-pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio-labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo.

KEY RESULTS

An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin.

CONCLUSIONS AND IMPLICATIONS

The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro-in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system- from drug-specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro-arrhythmia assay initiative.

Assessment of Interspecies Differences in Drug-Induced QTc Interval Prolongation in Cynomolgus Monkeys, Dogs and Humans

Background and Purpose

The selection of the most suitable animal species and subsequent translation of the concentration-effect relationship to humans are critical steps for accurate assessment of the pro-arrhythmic risk of candidate molecules. The objective of this investigation was to assess quantitatively the differences in the QTc prolonging effects of moxifloxacin between cynomolgus monkeys, dogs and humans. The impact of interspecies differences is also illustrated for a new candidate molecule.

Experimental Approach

Pharmacokinetic data and ECG recordings from pre-clinical protocols in monkeys and dogs and from a phase I trial in healthy subjects were identified for the purpose of this analysis. A previously established Bayesian model describing the combined effect of heart rate, circadian variation and drug effect on the QT interval was used to describe the pharmacokinetic-pharmacodynamic relationships. The probability of a ≥10 ms increase in QT was derived as measure of the pro-arrhythmic effect.

Key Results

For moxifloxacin, the concentrations associated with a 50% probability of QT prolongation ≥10 ms (Cp₅₀) varied from 20.3 to 6.4 and 2.6 μM in dogs, monkeys and humans, respectively. For NCE05, these values were 0.4 μM vs 2.0 μM for monkeys and humans, respectively.

Conclusions and Implications

Our findings reveal significant interspecies differences in the QT-prolonging effect of moxifloxacin. In addition to the dissimilarity in pharmacokinetics across species, it is likely that differences in pharmacodynamics also play an important role. It appears that, regardless of the animal model used, a translation function is needed to predict concentration-effect relationships in humans.

Busulfan dosing algorithm and sampling strategy in stem cell transplantation patients

AIM

The aim of this investigation was to develop a model-based dosing algorithm for busulfan and identify an optimal sampling scheme for use in routine clinical practice.

METHODS

Clinical data from an ongoing study (n = 29) in stem cell transplantation patients were used for the purposes our analysis. A one compartment model was selected as basis for sampling optimization and subsequent evaluation of a suitable dosing algorithm. Internal and external model validation procedures were performed prior to the optimization steps using ED-optimality criteria. Using systemic exposure as parameter of interest, dosing algorithms were considered for individual patients with the scope of minimizing the deviation from target range as determined by AUC(0,6 h).

RESULTS

Busulfan exposure after oral administration was best predicted after the inclusion of adjusted ideal body weight and alanine transferase as covariates on clearance. Population parameter estimates were 3.98 h(-1), 48.8 l and 12.3 l h(-1) for the absorption rate constant, volume of distribution and oral clearance, respectively. Inter-occasion variability was used to describe the differences between test dose and treatment. Based on simulation scenarios, a dosing algorithm was identified, which ensures target exposure values are attained after a test dose. Moreover, our findings show that a sparse sampling scheme with five samples per patient is sufficient to characterize the pharmacokinetics of busulfan in individual patients.

CONCLUSION

The use of the proposed dosing algorithm in conjunction with a sparse sampling scheme may contribute to considerable improvement in the safety and efficacy profile of patients undergoing treatment for stem cell transplantation.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

Purpose

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.

Methods

COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.

Results

Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.

Conclusion

Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

Prediction of disease progression, treatment response and dropout in chronic obstructive pulmonary disease (COPD)

Purpose

Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV₁), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients.

Methods

Data from six phase II trials lasting up to 6 months were used. Disease progression (trough FEV₁ measurements) was modelled by a time–varying function, whilst the treatment effect was described by an indirect response model. A time-to-event model was used for dropout

Results

All relevant parameters were characterised with acceptable precision. Two parameters were necessary to model the dropout patterns, which was found to be partly linked to the treatment failure. Disease severity at baseline, previous use of corticosteroids, gender and height were significant covariates on disease baseline whereas disease severity and reversibility to salbutamol/salmeterol were significant covariates on E_max for salmeterol active arm.

Conclusion

Incorporation of the various interacting factors into a single model will offer the basis for patient enrichment and improved dose rationale in COPD.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-014-1490-4) contains supplementary material, which is available to authorized users.

Semi-mechanistic modelling of the analgesic effect of gabapentin in the formalin-induced rat model of experimental pain

PURPOSE

The formalin-induced rat model of nociception involves moderate continuous pain. Formalin-induced pain results in a typical repetitive flinching behaviour, which displays a biphasic pattern characterised by peaks of pain. Here we described the time course of pain response and the analgesic effect of gabapentin using a semi-mechanistic modelling approach.

METHODS

Male Sprague-Dawley rats received gabapentin (10-100 mg/kg) or placebo 1 h prior to the formalin injection, as per standard protocol. A reduction in the frequency of the second peak of flinching was used as a behavioural measure of gabapentin-mediated anti-nociception. The flinching response was modelled using a mono-exponential function to characterise the first peak and an indirect response model with a time variant synthesis rate for the second. PKPD modelling was performed using a population approach in NONMEM v.7.1.2.

RESULTS

The time course of the biphasic response was adequately described by the proposed model, which included separate expressions for each phase. Gabapentin was found to reversibly decrease, but not suppress the flinching frequency of the second response peak only. The mean IC50 estimate was 7,510 ng/ml, with relative standard error (RSE%) of 40%.

CONCLUSIONS

A compartmental, semi-mechanistic model provides the basis for further understanding of the formalin-induced flinching response and consequently to better characterisation of the properties of gabapentin, such as the potency in individual animals. Moreover, despite high exposure levels, model predictions show that gabapentin does not completely suppress behavioural response in the formalin-induced pain model.

Modeling and simulation as a tool to bridge efficacy and safety data in special populations

The registration and approval of novel medicines have traditionally been based on evidence arising from large prospective trials. Such an approach is often not possible or unsuitable to evaluate the benefit-risk balance in special populations (e.g., children, ethnic groups, rare diseases). Inferences by modeling and simulation can play a major role in evidence synthesis. A framework is proposed that promotes its acceptability and the basis for decision making during development, registration, and therapeutic use of drugs.

Optimised protocol design for the screening of analgesic compounds in neuropathic pain

We have previously shown how screening experiments for neuropathic pain can be optimised taking into account parameter and model uncertainty. Here we demonstrate how optimised protocols can be used to screen and rank candidate molecules. The concept is illustrated by pregabalin as a new chemical entity and gabapentin as a reference compound. ED-optimality was applied to a logistic regression model describing the relationship between drug exposure and response to evoked pain in the complete Freund's adjuvant (CFA) model in rats. Design variables for optimisation of the experimental protocol included dose levels and sampling times. Prior information from the reference compound was used in conjunction with relative in vitro potency as priors. Results from simulated scenarios were then combined with fitting of experimental data to estimate precision and bias of model parameters for the empirical and optimised designs. The pharmacokinetics of pregabalin was described by a two-compartment model. The expected value of EC(50) of pregabalin was 637.5 ng ml(-1). Model-based analysis of the data yielded median (range) of EC(50) values of 1,125 (898-2412) ng ml(-1) for the empirical protocol and 755 (189-756) ng ml(-1) for the optimised design. In contrast to current practice, optimal design entails different sampling schedule across dose levels. ED-optimised designs should become standard practice in the screening of candidate molecules. It ensures lower bias when estimating the drug potency, facilitating accurate ranking and selection of compounds for further development.

Application of ED-optimality to screening experiments for analgesic compounds in an experimental model of neuropathic pain

In spite of the evidence regarding high variability in the response to evoked pain, little attention has been paid to its impact on the screening of drugs for inflammatory and neuropathic pain. In this study, we explore the feasibility of introducing optimality concepts to experimental protocols, enabling estimation of parameter and model uncertainty. Pharmacokinetic (PK) and pharmacodynamic data from different experiments in rats were pooled and modelled using nonlinear mixed effects modelling. Pain data on gabapentin and placebo-treated animals were generated in the complete Freund's adjuvant model of neuropathic pain. A logistic regression model was applied to optimise sampling times and dose levels to be used in an experimental protocol. Drug potency (EC(50)) and interindividual variability (IIV) were considered the parameters of interest. Different experimental designs were tested and validated by SSE (stochastic simulation and estimation) taking into account relevant exposure ranges. The pharmacokinetics of gabapentin was described by a two-compartment PK model with first order absorption (CL = 0.159 l h(-1), V(2) = 0.118 l, V(3) = 0.253 l, Ka = 0.26 h(-1), Q = 1.22 l h(-1)). Drug potency (EC(50)) for the anti-allodynic effects was estimated to be 1400 ng ml(-1). Protocol optimisation improved bias and precision of the EC50 by 6 and 11.9. %, respectively, whilst IIV estimates showed improvement of 31.89 and 14.91 %, respectively. Our results show that variability in behavioural models of evoked pain response leads to uncertainty in drug potency estimates, with potential impact on the ranking of compounds during screening. As illustrated for gabapentin, ED-optimality concepts enable analysis of discrete data taking into account experimental constraints.

Assessing the probability of drug-induced QTc-interval prolongation during clinical drug development

Early in the course of clinical development of new non-antiarrhythmic drugs, it is important to assess the propensity of these drugs to prolong the QT/QTc-interval. The current regulatory guidelines suggest using the largest time-matched mean difference between drug and placebo (baseline-adjusted) groups over the sampling interval, thereby neglecting any potential exposure-effect relationship and nonlinearity in the underlying physiological fluctuation in QT values. Thus far, most of the attempted models for characterizing drug-induced QTc-interval prolongation have disregarded the possibility of model parameterization in terms of drug-specific and system-specific properties. Using a database consisting of three compounds with known dromotropic activity, we built a bayesian hierarchical pharmacodynamic (PD) model to describe QT interval, encompassing an individual correction factor for heart rate, an oscillatory component describing the circadian variation, and a truncated maximum-effect model to account for drug effect. The explicit description of the exposure-effect relationship, incorporating various sources of variability, offers advantages over the standard regulatory approach.

From trial and error to trial simulation III: a framework for interim analysis in efficacy trials with antidepressant drugs

Clinical trials with antidepressant drugs often fail to detect drug effect, even with drugs that are known to be efficacious. In a previous publication, we showed that a model-based approach is required to address some of the existing challenges in the design of clinical trial protocols. Here, we illustrate how the implementation of an interim analysis (IA) may help to identify studies that are headed for failure, early in the trial before completion of treatment. In contrast to traditional IA procedures, an adaptive Bayesian approach is proposed to optimize the timing of analysis and decision criteria for futility and efficacy, taking into account enrollment rate and treatment response at intermediate visits in the trial. Validation procedures involving re-enrollment of patients confirmed the performance of the method. Our findings reveal that optimization of the timing and decision criteria at the interim stage is critical for the accuracy of the conclusions about treatment efficacy or futility.

Prediction of attack frequency in migraine treatment

Usually limited information about the frequency of migraine episodes is derived from acute migraine trials. However, the design of some studies is such that they also provide relevant information about the attack frequency without the bias associated with patient expectations of treatment effect between attacks during prophylaxis trials. Using clinical data from repeated migraine attacks treated with placebo, naratriptan 2.5 mg or sumatriptan 100 mg, we show that attack and interictal periods can be described by a random probability distribution. Based on a gamma distribution, the mean interval between attacks was estimated to be 24 (17-34) days for placebo, 23 (18-29) days for naratriptan 2.5 mg and 22 (17-28) for sumatriptan 100 mg. These findings suggest that the interictal interval is not affected by abortive treatment with triptans. Interpretation of these results may be limited by the study type, yet the method represents a new tool for the evaluation of disease dynamics and treatment effect in the prophylaxis of migraine.

Population pharmacokinetic-pharmacodynamic modelling of S 15535, a 5-HT(1A) receptor agonist, using a behavioural model in rats

The pharmacokinetic-pharmacodynamic relationship of S 15535 (1-(benzodioxan-5-yl) 4-(indan-2-yl)piperazine) and its active 5-hydroxy metabolite S 32784 (1-(benzodioxan-5-yl) 4-(5-hydroxyindan-2-yl)piperazine), and buspirone as a reference, were studied in male Wistar rats using a behavioural model of anxiety by determining the reduction in the number of fear-induced ultrasonic vocalisations. S 15535 and buspirone were administered p.o. and i.v. S 32784, present in man but not in rat, was administered i.v. The pharmacokinetics and pharmacokinetic-pharmacodynamic relationships were described using non-linear mixed effects modelling. The no-drug effect was constant and all compounds were active in the model, reducing ultrasonic vocalisations immediately after administration. The sigmoid E(max) model was used to describe the pharmacokinetic-pharmacodynamic relationships, with E(max) values of a 90% decrease in baseline ultrasonic vocalisations. Corrected for plasma protein binding, all compounds showed similar potency. The study shows that ultrasonic vocalisations can be considered a suitable endpoint for the anxiolytic effect when used in conjunction with non-linear mixed effects modelling to overcome the limited sampling and effect measurements.