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.

Disease Systems Analysis of Bone Mineral Density and Bone Turnover Markers in Response to Alendronate, Placebo, and Washout in Postmenopausal Women.

A previously established mechanism-based disease systems model for osteoporosis that is based on a mathematically reduced version of a model describing the interactions between osteoclast (bone removing) and osteoblast (bone forming) cells in bone remodeling has been applied to clinical data from women (n = 1,379) receiving different doses and treatment regimens of alendronate, placebo, and washout. The changes in the biomarkers, plasma bone-specific alkaline phosphatase activity (BSAP), urinary N-telopeptide (NTX), lumbar spine bone mineral density (BMD), and total hip BMD, were linked to the underlying mechanistic core of the model. The final model gave an accurate description of all four biomarkers for the different treatments. Simulations were used to visualize the dynamics of the underlying network and the natural disease progression upon alendronate treatment and discontinuation. These results complement the previous applications of this mechanism-based disease systems model to data from various treatments for osteoporosis.

Levofloxacin-Induced QTc Prolongation Depends on the Time of Drug Administration.

Understanding the factors influencing a drug's potential to prolong the QTc interval on an electrocardiogram is essential for the correct evaluation of its safety profile. To explore the effect of dosing time on drug-induced QTc prolongation, a randomized, crossover, clinical trial was conducted in which 12 healthy male subjects received levofloxacin at 02:00, 06:00, 10:00, 14:00, 18:00, and 22:00. Using a pharmacokinetic-pharmacodynamic (PK-PD) modeling approach to account for variations in PKs, heart rate, and daily variation in baseline QT, we find that the concentration-QT relationship shows a 24-hour sinusoidal rhythm. Simulations show that the extent of levofloxacin-induced QT prolongation depends on dosing time, with the largest effect at 14:00 (1.73 (95% prediction interval: 1.56-1.90) ms per mg/L) and the smallest effect at 06:00 (-0.04 (-0.19 to 0.12) ms per mg/L). These results suggest that a 24-hour variation in the concentration-QT relationship could be a potentially confounding factor in the assessment of drug-induced QTc prolongation.

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.

Understanding the Behavior of Systems Pharmacology Models Using Mathematical Analysis of Differential Equations: Prolactin Modeling as a Case Study.

In this tutorial, we introduce basic concepts in dynamical systems analysis, such as phase-planes, stability, and bifurcation theory, useful for dissecting the behavior of complex and nonlinear models. A precursor-pool model with positive feedback is used to demonstrate the power of mathematical analysis. This model is nonlinear and exhibits multiple steady states, the stability of which is analyzed. The analysis offers insight into model behavior and suggests useful parameter regions, which simulations alone could not.

Semiphysiologically based pharmacokinetic model for midazolam and CYP3A mediated metabolite 1-OH-midazolam in morbidly obese and weight loss surgery patients.

This study aimed to describe the pharmacokinetics of midazolam and its cytochrome P450 3A (CYP3A) mediated metabolite 1-OH-midazolam in morbidly obese patients receiving oral and i.v. midazolam before (n = 20) and one year after weight loss surgery (n = 18), thereby providing insight into the influence of weight loss surgery on CYP3A activity in the gut wall and liver. In a semiphysiologically based pharmacokinetic (semi-PBPK) model in which different blood flow scenarios were evaluated, intrinsic hepatic clearance of midazolam (CLint,H) was 2 (95% CI 1.40-1.64) times higher compared to morbidly obese patients before surgery (P < 0.01). Midazolam gut wall clearance (CLint,G) was slightly lower in patients after surgery (P > 0.05), with low values for both groups. The results of the semi-PBPK model suggest that, in patients after weight loss surgery, CYP3A hepatic metabolizing capacity seems to recover compared to morbidly obese patients, whereas CYP3A mediated CLint,G was low for both populations and showed large interindividual variability.

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.

Application of a Systems Pharmacology-Based Placebo Population Model to Analyze Long-Term Data of Postmenopausal Osteoporosis.

Osteoporosis is a progressive bone disease characterized by decreased bone mass resulting in increased fracture risk. The objective of this investigation was to test whether a recently developed disease systems analysis model for osteoporosis could describe disease progression in a placebo-treated population from the Early Postmenopausal Intervention Cohort (EPIC) study. First, we qualified the model using a subset from the placebo arm of the EPIC study of 222 women who had similar demographic characteristics as the 149 women from the placebo arm of the original population. Second, we applied the model to all 470 women. Bone mineral density (BMD) dynamics were changed to an indirect response model to describe lumbar spine and total hip BMD in this second population. This updated disease systems analysis placebo model describes the dynamics of all biomarkers in the corresponding datasets to a very good approximation; a good description of an individual placebo response will be valuable for evaluating treatments for osteoporosis.

Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice.

Based on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis.

BACKGROUND AND PURPOSE: Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug-induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans. EXPERIMENTAL APPROACH: We derived typical ΔQTc predictions at therapeutic exposure (ΔQTcTHER ) with 95% confidence intervals (95%CI) for 3 Kv 11.1 (hERG) channel blockers (moxifloxacin, dofetilide and sotalol) from a total of 14 CVS with variable designs in the conscious dog. Population pharmacokinetic-pharmacodynamic (PKPD) analysis of each study was followed by a meta-analysis (pooling 2-6 studies including 10-32 dogs per compound) to derive meta-predictions of typical ΔQTcTHER . Meta-predictions were used as a reference to evaluate the consistency of study predictions and to relate results to those found in the clinical literature. KEY RESULTS: The 95%CIs of study-predicted ΔQTcTHER comprised in 13 out of 14 cases the meta-prediction. Overall inter-study variability (mean deviation from meta-prediction at upper level of therapeutic exposure) was 30% (range: 1-69%). Meta-ΔQTcTHER predictions for moxifloxacin, dofetilide and sotalol overlapped with reported clinical QTc prolongation when expressed as %-prolongation from baseline. CONCLUSIONS AND IMPLICATIONS: Consistent exposure-ΔQTc predictions were obtained from single preclinical dog studies of highly variable designs by systematic PKPD analysis, which is suitable for translational purposes. The good preclinical-clinical pharmacodynamic correlations obtained suggest that such an analysis should be more routinely applied to increase the informative and predictive value of results obtained from animal experiments.

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.

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 Emax 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.

Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modelling.

BACKGROUND AND PURPOSE: Previously, a systems pharmacology model was developed characterizing drug effects on the interrelationship between mean arterial pressure (MAP), cardiac output (CO) and total peripheral resistance (TPR). The present investigation aims to (i) extend the previously developed model by parsing CO into heart rate (HR) and stroke volume (SV) and (ii) evaluate if the mechanism of action (MoA) of new compounds can be elucidated using only HR and MAP measurements. EXPERIMENTAL APPROACH: Cardiovascular effects of eight drugs with diverse MoAs (amiloride, amlodipine, atropine, enalapril, fasudil, hydrochlorothiazide, prazosin and propranolol) were characterized in spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats following single administrations of a range of doses. Rats were instrumented with ascending aortic flow probes and aortic catheters/radiotransmitters for continuous recording of MAP, HR and CO throughout the experiments. Data were analysed in conjunction with independent information on the time course of the drug concentration following a mechanism-based pharmacokinetic-pharmacodynamic modelling approach. KEY RESULTS: The extended model, which quantified changes in TPR, HR and SV with negative feedback through MAP, adequately described the cardiovascular effects of the drugs while accounting for circadian variations and handling effects. CONCLUSIONS AND IMPLICATIONS: A systems pharmacology model characterizing the interrelationship between MAP, CO, HR, SV and TPR was obtained in hypertensive and normotensive rats. This extended model can quantify dynamic changes in the CVS and elucidate the MoA for novel compounds, with one site of action, using only HR and MAP measurements. Whether the model can be applied for compounds with a more complex MoA remains to be established.

Population pharmacokinetic modelling of total and unbound cefazolin plasma concentrations as a guide for dosing in preterm and term neonates.

OBJECTIVES: Cefazolin is frequently administered for antimicrobial prophylaxis and treatment of infections. In neonates, pharmacokinetic observations are limited and dosing regimens variable. The aim of this study was to describe the pharmacokinetics of cefazolin in neonates based on total and unbound concentrations to optimize cefazolin dosing. METHODS: Thirty-six neonates [median birth body weight 2720 (range 540-4200) g, current body weight (cBW) 2755 (830-4200) g and postnatal age (PNA) 9 (1-30) days] receiving intravenous cefazolin (50 mg/kg/8 h) were included. Based on 119 total and unbound plasma concentrations, a population pharmacokinetic analysis with a covariate analysis was performed. Monte Carlo simulations were performed aiming for unbound concentrations above an MIC of 8 mg/L (>60% of the time) in all patients. RESULTS: A one-compartment pharmacokinetic model was developed in which total and unbound concentrations were linked by maximum protein binding (Bmax) of 136 mg/L and a dissociation constant (KD) for cefazolin protein binding of 46.5 mg/L. cBW was identified as covariate for volume of distribution (V), bBW and PNA for clearance and albumin plasma concentration for Bmax, explaining 50%, 58% and 41% of inter-individual variability in V, clearance and Bmax, respectively. Based on Monte Carlo simulations, a body weight- and PNA-adapted dosing regimen that resulted in similar exposure across different weight and age groups was proposed. CONCLUSIONS: A neonatal pharmacokinetic model taking into account total and unbound cefazolin concentrations with saturable plasma protein binding was identified. As cBW and PNA were the most important covariates, these may be used for individualized dosing in neonates.

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.

Establishing in vitro to clinical correlations in the evaluation of cardiovascular safety pharmacology.

Preclinical studies are vital in establishing the efficacy and safety of a new chemical entity (NCE) in humans. To deliver meaningful information, experiments have to be well defined and provide outcome that is relevant and translatable to humans. This review briefly surveys the various preclinical experiments that are frequently conducted to assess drug effects on cardiac conductivity in early drug development. We examine the different approaches used to establish correlations between non-clinical and clinical settings and discuss their value in the evaluation of cardiovascular risk.

PKPD modelling of the interrelationship between mean arterial BP, cardiac output and total peripheral resistance in conscious rats.

BACKGROUND AND PURPOSE: The homeostatic control of arterial BP is well understood with changes in BP resulting from changes in cardiac output (CO) and/or total peripheral resistance (TPR). A mechanism-based and quantitative analysis of drug effects on this interrelationship could provide a basis for the prediction of drug effects on BP. Hence, we aimed to develop a mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model in rats that could be used to characterize the effects of cardiovascular drugs with different mechanisms of action (MoA) on the interrelationship between BP, CO and TPR. EXPERIMENTAL APPROACH: The cardiovascular effects of six drugs with diverse MoA, (amlodipine, fasudil, enalapril, propranolol, hydrochlorothiazide and prazosin) were characterized in spontaneously hypertensive rats. The rats were chronically instrumented with ascending aortic flow probes and/or aortic catheters/radiotransmitters for continuous recording of CO and/or BP. Data were analysed in conjunction with independent information on the time course of drug concentration using a mechanism-based PKPD modelling approach. KEY RESULTS: By simultaneous analysis of the effects of six different compounds, the dynamics of the interrelationship between BP, CO and TPR were quantified. System-specific parameters could be distinguished from drug-specific parameters indicating that the model developed is drug-independent. CONCLUSIONS AND IMPLICATIONS: A system-specific model characterizing the interrelationship between BP, CO and TPR was obtained, which can be used to quantify and predict the cardiovascular effects of a drug and to elucidate the MoA for novel compounds. Ultimately, the proposed PKPD model could be used to predict the effects of a particular drug on BP in humans based on preclinical data.

Capturing the dynamics of systemic Renin-Angiotensin-Aldosterone System (RAAS) peptides heightens the understanding of the effect of benazepril in dogs.

In dogs, activation of the Renin-Angiotensin-Aldosterone System (RAAS) is an important feature of congestive heart failure (CHF). Long-term increases in angiotensin II (AII) and aldosterone (ALD) lead to the progression of heart failure to its end stage. Angiotensin-converting enzyme inhibitors (ACEIs) are the foremost therapeutic option in the management of CHF. Recent literature has challenged the efficacy of ACEIs, based on modest reduction in urinary aldosterone (UALD) excretion despite marked inhibition of ACE activity. This study was designed to heighten the understanding of the effect of benazepril, a potent ACEI, on the RAAS, using a low-sodium diet as an experimental model of RAAS activation. Time course profiles of RAAS peptides and related areas under the curve (AUC) were used for comparison between benazepril and placebo groups. Results indicated substantial changes in the dynamics of these biomarkers. At presumed benazeprilat steady state, significant differences in AUC of plasma renin activity (+90%), angiotensin I (+43%), and AII (-53%) were found between benazepril and placebo-treated dogs. ALD decreased by 73% in plasma but only by 5% in urine. In conclusion, despite modest reduction in UALD excretion, benazepril markedly influences RAAS dynamics in dogs.