Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans.

Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits can be anticipated for the assessment of safety and toxicity profile of novel molecules.

Dopamine D2 receptor occupancy as a predictor of catalepsy in rats: a pharmacokinetic-pharmacodynamic modeling approach.

OBJECTIVES: Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans. METHODS: Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT. RESULTS: Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats. CONCLUSION: A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

Population pharmacokinetic-pharmacodynamic modeling of haloperidol in patients with schizophrenia using positive and negative syndrome rating scale.

The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that quantifies the efficacy of haloperidol, accounting for the placebo effect, the variability in exposure-response, and the dropouts. Subsequently, the developed model was utilized to characterize an effective dosing strategy for using haloperidol as a comparator drug in future antipsychotic drug trials. The time course of plasma haloperidol concentrations from 122 subjects and the Positive and Negative Syndrome Scale (PANSS) scores from 473 subjects were used in this analysis. A nonlinear mixed-effects modeling approach was utilized to describe the time course of PK and PANSS scores. Bootstrapping and simulation-based methods were used for the model evaluation. A 2-compartment model adequately described the haloperidol PK profiles. The Weibull and Emax models were able to describe the time course of the placebo and the drug effects, respectively. An exponential model was used to account for dropouts. Joint modeling of the PKPD model with dropout model indicated that the probability of patients dropping out is associated with the observed high PANSS score. The model evaluation results confirmed that the precision and accuracy of parameter estimates are acceptable. Based on the PKPD analysis, the recommended oral dose of haloperidol to achieve a 30% reduction in PANSS score from baseline is 5.6 mg/d, and the corresponding steady-state effective plasma haloperidol exposure is 2.7 ng/mL. In conclusion, the developed model describes the time course of PANSS scores adequately, and a recommendation of haloperidol dose was derived for future antipsychotic drug trials.

Pharmacokinetic-pharmacodynamic modeling of antipsychotic drugs in patients with schizophrenia Part I: the use of PANSS total score and clinical utility.

BACKGROUND: To develop a pharmacokinetic-pharmacodynamic (PK-PD) model using individual-level data of Positive and Negative Syndrome Scale (PANSS) total score to characterize the antipsychotic drug effect taking into account the placebo effect and dropout rate. In addition, a clinical utility (CU) criterion that describes the usefulness of a drug therapy was calculated using the efficacy of the drug and dropout rates. METHODS: Data from 12 clinical trials in schizophrenia patients was used to quantify the effects of the antipsychotic drugs (APs), namely, haloperidol, risperidone, olanzapine, ziprasidone and paliperidone. Compartmental PK models were used to describe the time course of plasma drug concentrations. The combination of an Emax and the Weibull model was used to describe the drug and placebo effects. The steady-state drug concentrations were assumed to be the drivers of the exposure-response relationship. An exponential model was utilized to identify the predictors of probability of dropout. Simulations were performed to check the predictability of the model, and to calculate the CU of the drugs based on PANSS scores and dropout rates. RESULTS: The maximal drug effect (E(max)) was highest for olanzapine whilst it was lowest for ziprasidone. Higher observed PANSS scores resulted in a greater likelihood of dropout. Taking into account the efficacy and the drop-out rate, all APs possessed a comparable CU at the therapeutic doses. The resulting PK-PD model parameters were used to compute the effective concentration and dose required to produce a clinically meaningful 30% drop in PANSS score from the baseline. CONCLUSIONS: The developed PK-PD model and the associated CU score allow the evaluation of the time course of the PANSS scores of the different APs and a proper comparison of their clinically relevant treatments effects.

Towards a European strategy for medicines research (2014-2020): The EUFEPS position paper on Horizon 2020.

As to the alignment of "Horizon 2020", ir is a more integrated approach to European science policy than expressed in the proposals previously drafted, and specifically considers: (i) promoting excellence in Science, (ii) establishing a sound industrial leadership and (iii) expressing an ambition to address current and future societal challenges. In this respect, the quest for a knowledge-based economy in Europe should result in proposals for industrial and employment policies that will consolidate the major European advantages in the biomedical, healthcare and pharmaceutical sectors. Horizon 2020 also provides the possibility of adopting a more flexible and simplified management route to drive European research through innovation, research and development. What should be additionally considered? Unmet medical needs, under pressure from demographic changes, await the generation of new medicines and health technologies which will evolve into a driver for a unified European policy. We believe that this should be focused on harnessing pharmaceutical knowledge for clinical use, as part of a response to accommodate patient needs and economic growth based on a robust, scientific approach. The bolder ambition for European research is to unlock key bottlenecks currently undermining European competitiveness. The historical lack of an appropriate business/innovation environment with reduced access to adequate risk finance instruments has severed the path for economic growth and industrial development. These issues are of critical importance and a solution is urgently needed to foster translation from the university to the healthcare sector through the generation and support of start-ups, spin-offs, university-industry consortia, and other platforms, which support translational research. The ultimate goal is implementation of holistic programmes: the 'bench to bedside' paradigm of medicines and other healthcare products. The European Research Council supports the basic biomedical research programmes of long term importance for development of medicines; however, fundamental research initiatives on medicines development will not be competitive in such an environment. In order to strengthen the long term outlook, we must foster innovative research within the university sector, EUFEPS proposes that a fund for such research be set up within Horizon 2020, which would be open for individual research groups and which would include Public-Public Partnerships (complementing already existing Public-Private Partnerships). How do we look for implementation? There is an established research agenda for medicines research that is globally focused, and which incorporates a cooperative model between universities and industry, facilitating integration of complex technologies. Regulatory Science will play an important role in this integration. This agenda uses tools arising from systems approaches (including discovery with systems biology and also systems pharmacology) and has the potential for providing better knowledge management, as well as technological innovation (including manufacturing). It also addresses the drive towards personalised medicines and can, with support from both public and private sectors, foster translation of knowledge to new technologies and from that, to new medicinal products and complex integrated systems. This is a part of a strategy capable of solving unmet medical needs, which would increase the quality of life of patients suffering from chronic and debilitating diseases. The instruments to allow the development of a research agenda should strengthen existing partnerships such as the IMI-JU model; allow for the creation of European-network infrastructures that can bring together existing competences with adequate European coordination, thus promoting advanced training and continuous professional development for the pharmaceutical sciences. This will be the cornerstone of a knowledge management strategy, providing education and training for healthcare professionals and scientists. A key role for EUFEPS is to help the research community to embrace these new holistic policies applied to the spectrum of pharmaceutical, medical and cognate sciences.

Pharmacokinetics of clindamycin in pregnant women in the peripartum period.

The study presented here was performed to determine the pharmacokinetics of intravenously administered clindamycin in pregnant women. Seven pregnant women treated with clindamycin were recruited. Maternal blood and arterial and venous umbilical cord blood samples were obtained. Maternal clindamycin concentrations were analyzed by nonlinear mixed-effects modeling with the NONMEM program. The data were best described by a linear three-compartment model. The clearance and the volume of distribution at steady state were 10.0 liters/h and 6.32 x 10(3) liters, respectively. Monte Carlo simulations were performed to determine the area under the concentration curve (AUC) for the free (unbound) drug (f) in maternal serum for 24 h divided by the MIC (fAUC(0-24)/MIC). At a MIC of 0.5 mg/liter, which is the EUCAST breakpoint, the attainment at the lower 95% confidence interval (CI) was 24.6 if the level of protein binding was 65%, and this value concurred well with the target value of 27. However, for higher degrees of protein binding, as has been described in the literature, the attainment was lower, down to 10.2 for a protein binding level of 85% (lower 95% CI). The concentrations in umbilical cord blood were lower than those in maternal blood. The concentration-time profiles in maternal serum indicate that the level of exposure to clindamycin may be too low in these patients. Together with the lower concentrations in umbilical cord blood, this finding suggests that the current dosing regimen may not be adequate to protect all neonates from group B streptococcal disease.

Sensitivity of the Montgomery Asberg Depression Rating Scale to response and its consequences for the assessment of efficacy.

An increasing body of evidence is available suggesting that the Hamilton Depression Rating Scale (HAMD) is not a sensitive measure of treatment effect. In this investigation, we explore the sensitivity of the individual items of the Montgomery Asberg Depression Rating Scale (MADRS) and compare the consequences of selecting a different scale as primary endpoint in the analysis of efficacy. A graphical approach is proposed for the evaluation of the sensitivity of individual items to response, using data from randomised, placebo-controlled clinical trials in which HAMD and MADRS were measured concurrently. Subsequently, we illustrate the impact of differences in the sensitivity of the primary endpoint for the detection of statistical significance in treatment effect. In contrast to the HAMD, our item-by-item analysis of the MADRS reveals that all individual items are sensitive to response, irrespective of treatment type. However, some HAMD subscales still outperform MADRS in the detection of treatment effect. The selection of these subscales as primary endpoints in clinical trials could save over 1/3 in patients compared to the full HAMD whilst keeping the same statistical power.

Relevance of absorption rate and lag time to the onset of action in migraine.

OBJECTIVE: The objective of this analysis was to simulate the performance of oral triptan formulations with varying absorption characteristics and their impact on the onset and magnitude of the antimigraine effect using a Markov model for migraine attacks. ANALYSIS: Sumatriptan pharmacokinetic data were obtained from clinical pharmacology studies in which marketed solid formulations were administered. Based on a population pharmacokinetic model, mean concentration-time profiles were generated by varying the absorption rate constant and lag time. Subsequently, the simulated profiles were evaluated in a disease model of migraine to predict the onset and duration of the effect (the pain-free, pain-relief response). RESULTS: Based on a therapeutic dose of 50 mg of sumatriptan, a maximum gain in the pain-free response of 12% was achieved with an increased absorption rate. This gain in the response was reached approximately 0.5 hours after administration. A decrease only in the lag time with respect to the currently available formulations (i.e. 0.24 hours) resulted in a maximum gain of 5% in the pain-free response, which in contrast may not be interpreted as clinically relevant. CONCLUSION: Model-based predictions suggest that increases in the absorption rate of the currently marketed oral formulation of sumatriptan result in a gain in the pain-free response that is both clinically and statistically relevant.

Sensitivity of the individual items of the Hamilton depression rating scale to response and its consequences for the assessment of efficacy.

The Hamilton depression rating scale (HAM-D(17)) has been the gold standard in depression trials since its introduction in 1960 by Max Hamilton. However, several authors have shown that the HAM-D(17) is multi-dimensional and that subscales of the HAM-D(17) outperform the total scale. In the current study, we assess the sensitivity of the individual HAM-D(17) items in differentiating responders from non-responders over the typical treatment period used in clinical efficacy trials. Based on data from randomised, placebo-controlled trials with paroxetine, a graphical analysis and a statistical analysis were performed to identify the items that are most sensitive to the rate and extent of response irrespective of treatment. From these analyses, two subscales consisting of seven items each were derived and compared to the Bech and Maier and Philip subscales using a linear mixed-effects modelling approach for repeated measures. The evaluation of two clinical trials revealed endpoint sensitivity comparable to the existing subscales. Using a bootstrap technique, we show that the subscales consistently yield higher statistical power compared to the HAM-D(17), although no subscale consistently outperforms the others. In conclusion, this study provides further evidence that not all items of the HAM-D(17) scale are equally sensitive to detect responding patients in a clinical trial. A HAM-D(7) subscale with higher sensitivity to drug effect is proposed consisting of the HAM-D(6) and the suicide item. This response-based subscale increases signal-to-noise ratio and could reduce failure rate in efficacy trials with antidepressant drugs.

Analysis of responses in migraine modelling using hidden Markov models.

Markov-type models have been used in the analysis of disease progression. Although standard errors of model parameters are usually estimated, available software often does not permit the construction of confidence intervals around predictions of the dependent or response variable. A method is presented to calculate means and confidence intervals of model-predicted responses in time governed by a non-homogeneous hidden Markov model in continuous time. The Kolmogorov equations serve as the basis for the calculations. The method is realised in S-Plus and is applied to the prediction of headache responses in clinical studies of anti-migraine treatment. Means and confidence intervals are calculated by numerically solving differential equations that are non-linear in the explanatory variable. Results indicate that uncertainty on predicted drug responses is larger than that on predicted placebo responses and that pain-free responses are less precisely predicted than pain-relief responses. This is due to the uncertainty in the drug-specific parameters which is not present in predicted placebo responses.

A model-based approach to treatment comparison in acute migraine.

AIMS: Currently, direct comparisons between 5-HT(1B/d) receptor agonists are used to assess differences and similarities in antimigraine response. Such comparisons depend on the selected sampling time and do not allow evaluation of entire response profiles. A thorough evaluation of drug properties requires that the time course of the response be taken into account. In this investigation we show the advantages of a model-based approach to compare the efficacy of two triptans (sumatriptan vs. naratriptan). METHODS: A Markov model was used to describe the course of a migraine attack over three clinically identified stages. Drug effects were modelled as concentration-dependent increases in transition rates and were parameterised as potency (EC(50)) and maximum effect (E(max)). Parameters were estimated using headache measurements from efficacy studies. Model estimates were then used to compare the pharmacodynamics of the two drugs in a time-independent manner. RESULTS: Efficacy parameters could be derived, allowing for comparison between compounds. The potency ratio (EC50(suma)/EC50(nara)) for headache relief was 3.3 (0.9, 12). The ratio of maximum effects (Emax(suma)/Emax(nara)) for this endpoint was 0.74 (0.55, 0.97). To interpret these efficacy measures and explore their value for the development of antimigraine drugs, results were evaluated against the reported in vitro potency at 5-HT(1B) and 5-HT(1D) receptors. CONCLUSIONS: Comparison of the effects of two or more drugs based on preset sampling times does not allow proper assessment of the antimigraine properties in vivo. Disease dynamics must be considered to evaluate treatment response adequately and optimise the dosing regimen in migraine.