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

A simple accurate method for concentration-QTc analysis in preclinical animal models

INTRODUCTION

In preclinical cardiovascular safety pharmacology studies, statistical analysis of the rate corrected QT interval (QTc) is the focus for predicting QTc interval changes in the clinic. Modeling of a concentration/QTc relationship, common clinically, is limited due to minimal pharmacokinetic (PK) data in nonclinical testing. It is possible, however, to relate the average drug plasma concentration from sparse PK samples over specific times to the mean corrected QTc. We hypothesize that averaging drug plasma concentration and the QTc-rate relationship over time provides a simple, accurate concentration-QTc relationship bridging statistical and concentration/QTc modeling.

METHODS

Cardiovascular telemetry studies were conducted in non-human primates (NHP; n = 48) and canines (n = 8). Pharmacokinetic samples were collected on separate study days in both species. Average plasma concentrations for specific intervals (CAverage0-X) were calculated for moxifloxacin in canines and NHP using times corresponding to super-intervals for the QTc data statistical analysis. The QTc effect was calculated for each super-interval using a linear regression correction incorporating QT and HR data from the whole super-interval. The concentration QTc effects were then modeled.

RESULTS

In NHP, a 10.9 ± 0.06 ms (mean ± 95% CI) change in QTc was detected at approximately 1.5× the moxifloxacin plasma concentration that causes a 10 ms QTc change in humans, based on a 0-24 h super-interval. When simulating a drug without QT effects, mock, no effect on QTc was detected at up to 3× the clinical concentration. Similarly, in canines, a 16.6 ± 0.1 ms change was detected at 1.7× critical clinical moxifloxacin concentration, and a 0.04 ± 0.1 ms change was seen for mock.

CONCLUSIONS

While simultaneous PK and QTc data points are preferred, practical constraints and the need for QTc averaging did not prevent concentration-QTc analyses. Utilizing a 0-24 h super-interval method illustrates a simple and effective method to address cardiovascular questions when preclinical drug exposures exceed clinical concentrations.

The New S7B/E14 Q&A Document Provides Additional Opportunities to Replace the Thorough QT Study

Since 2015, concentration-QTc (C-QTc) analysis has been used to exclude the possibility that a drug has a concerning effect on the QTc interval. This has enabled the replacement of the designated thorough QT (TQT) study with serial electrocardiograms (ECGs) in routine clinical pharmacology studies, such as the first-in-human (FIH) study. The E14 revision has led to an increased proportion of FIH studies with the added objective of QT evaluation, with the intention of replacing the TQT study. With the more recent revision of the S7B/E14 Q&A document in February 2022, nonclinical assays/studies can be brought into the process of regulatory decisions at the time of marketing application. If the hERG (human ether-a-go-go-related gene) and the non-rodent in vivo study are conducted according to the described best practices and are negative, the previous requirement that a QTc effect of >10 milliseconds must be excluded in healthy subjects at plasma concentrations 2-fold above what can be seen in patients can be reduced to covering the concentrations seen in patients. For drugs that cannot be safely given in high doses to healthy subjects, ECG evaluation is often performed at the therapeutic dose in patients. If a QTc effect of >10 milliseconds can be excluded, an argument can be made that the drug should be considered as having a low likelihood of proarrhythmic effects due to delayedrepolarization, if supported by negative best practices hERG and in vivo studies. In this article, we describe what clinicians involved in early clinical development need to understand in terms of the hERG and in vivo studies to determine whether these meet best practices and therefore can be used in an integrated clinical/nonclinical QT/QTc risk assessment.

Improving the in Vivo QTc assay: The value of implementing best practices to support an integrated nonclinical-clinical QTc risk assessment and TQT substitute

Recent updates and modifications to the clinical ICH E14 and nonclinical ICH S7B guidelines, which both relate to the evaluation of drug-induced delayed repolarization risk, provide an opportunity for nonclinical in vivo electrocardiographic (ECG) data to directly influence clinical strategies, interpretation, regulatory decision-making and product labeling. This opportunity can be leveraged with more robust nonclinical in vivo QTc datasets based upon consensus standardized protocols and experimental best practices that reduce variability and optimize QTc signal detection, i.e., demonstrate assay sensitivity. The immediate opportunity for such nonclinical studies is when adequate clinical exposures (e.g., supratherapeutic) cannot be safely achieved, or other factors limit the robustness of the clinical QTc evaluation, e.g., the ICH E14 Q5.1 and Q6.1 scenarios. This position paper discusses the regulatory historical evolution and processes leading to this opportunity and details the expectations of future nonclinical in vivo QTc studies of new drug candidates. The conduct of in vivo QTc assays that are consistently designed, executed and analyzed will lead to confident interpretation, and increase their value for clinical QTc risk assessment. Lastly, this paper provides the rationale and basis for our companion article which describes technical details on in vivo QTc best practices and recommendations to achieve the goals of the new ICH E14/S7B Q&As, see Rossman et al., 2023 (this journal).

On the perspective of an aging population and its potential impact on drug attrition and pre-clinical cardiovascular safety assessment

There is no refuting that America's population is growing older: for the first time in US history, by 2034 older adults (defined as >65 years of age) are projected to outnumber children under the age of 18, representing approximately 70 million people or almost 25% of the population (Lloyd-Jones et al., 2010). Described as the "silver tsunami", this flood of older adults is driven by the baby boomers (people born after World War II, from 1946 to 1964): they are now reaching old age, living longer due to significant advances in healthcare coupled with a record low birth rate, resulting in a skewed elderly population demographic. Unfortunately, older adults are also becoming increasingly unhealthy. Many often suffer from several chronic disorders requiring the use of multiple medications at a level higher than any other age group, resulting in an increased risk of drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Indeed, because of age-related changes in pharmacokinetics (PK) and pharmacodynamics (PD), older adults are also more vulnerable to drug toxicity. Prescribed drugs certainly improve a range of health outcomes, but also often cause considerable ADRs, leading to devastating consequences for patients, clinicians, and manufacturers. Therefore, safe and effective pharmacotherapy remains one of the greatest growing challenges in geriatric medicine. In this review we examine the effects of aging and its impact on the increased risk of experiencing ADRs, resulting in devastating consequences for patients and manufacturers. We assess the current regulatory considerations related to the development of drugs for this population and highlight issues, concerns, and propose alternatives to the standard battery of tests focused on assessing cardiovascular (CV) safety in an attempt to develop safer and efficient new drugs for the growing elderly demographic.

Evaluation of moxifloxacin in canine and non-human primate telemetry assays: Comparison of QTc interval prolongation by timepoint and concentration-QTc analysis

The in vivo correct QT (QTc) assay is used by the pharmaceutical industry to characterize the potential for delayed ventricular repolarization and is a core safety assay mentioned in International Conference on Harmonization (ICH) S7B guideline. The typical telemetry study involves a dose‐response analysis of QTc intervals over time using a crossover (CO) design. This method has proven utility but does not include direct integration of pharmacokinetic (PK) data. An alternative approach has been validated and is used routinely in the clinical setting that pairs pharmacodynamic (PD) responses with PK exposure (e.g., concentration‐QTc (C‐QTc) analysis. The goal of our paper was to compare the QTc sensitivity of two experimental approaches in the conscious dog and non‐human primate (NHP) QTc assays. For timepoint analysis, a conventional design using eight animals (8 × 4 CO) to detect moxifloxacin‐induced QTc prolongation was compared to a PK/PD design in a subset (N = 4) of the same animals. The findings demonstrate that both approaches are equally sensitive in detecting threshold QTc prolongation on the order of 10 ms. Both QTc models demonstrated linearity in the QTc prolongation response to moxifloxacin dose escalation (6 to 46 ms). Further, comparison with human QTc findings with moxifloxacin showed agreement and consistent translation across the three species: C‐QTc slope values were 0.7‐ (dog) and 1.2‐ (NHP) fold of the composite human value. In conclusion, our data show that dog and NHP QTc telemetry with an integrated PK arm (C‐QTc) has the potential to supplement clinical evaluation and improve integrated QTc risk assessment.

Population pharmacokinetic/pharmacodynamic modeling of delayed effect of escitalopram-induced QT prolongation

BACKGROUND

A thorough QT study identified that escitalopram-induced QT prolongation was delayed. This study thus aimed to develop a population pharmacokinetic (PK)/pharmacodynamic (PD) model to characterize the relationship between escitalopram concentrations and the delayed effect on QT prolongation.

METHODS

The data of completed subjects who had placebo (n=36) and a single dose of 20 mg escitalopram (n=33) from a previous thorough QT study were used. Population PK/PD analysis was performed by nonlinear mixed-effects modeling. A escitalopram concentration-drug effect model was developed with estimated individual PK and baseline QT parameters. To explain the relationship between escitalopram concentrations and QT prolongation delay, an effect compartment model was utilized.

RESULTS

A two-compartment model with first-order absorption and lag time and first-order elimination adequately described the PK of escitalopram. The circadian rhythm of baseline QT interval was best explained by two harmonic cosine functions. A linear model properly characterized escitalopram-induced QT prolongation. The average estimated maximal QT prolongation was 5.4 ms (range: 1.9-7.6 ms). The equilibrium half-life of delayed QT prolongation was 1.9 h. The drug effect of QTc change compared with that at baseline remained relatively constant from 1.3 to 3.5 ms over 24 h, and the maximum QTc change occurred with a 3-h delay after the time to the maximum plasma concentration.

LIMITATIONS

We did not include genetic polymorphisms, such as CYP2C19, as potential covariates owing to limited information.

CONCLUSIONS

These results may provide useful information on when to monitor electrocardiogram in patients who require intensive care after drug administration.

Exposure-response analysis of drug-induced QT interval prolongation in telemetered monkeys for translational prediction to human

INTRODUCTION

The preclinical in vivo assay for QT prolongation is critical for predicting torsadogenic risk, but still difficult to extrapolate to humans. This study ran preclinical tests in cynomolgus monkeys on seven QT reference drugs containing the drugs used in the IQ-CSRC clinical trial and applied exposure-response (ER) analysis to the data to investigate the potential for translational information on the QT effect.

METHODS

In each of six participating facilities in the J-ICET project, telemetered monkeys were monitored for 24 h following administration of vehicle or 3 doses of test drugs, and pharmacokinetic profiles at the same doses were evaluated separately. An individual rate-corrected QT interval (QTca) was derived and the vehicle-adjusted change in QTca from baseline (∆∆QTca) was calculated. Then the relationship of concentration to QT effect was evaluated by ER analysis.

RESULTS

For QT-positive drugs in the IQ-CSRC study (dofetilide, dolasetron, moxifloxacin, ondansetron, and quinine) and levofloxacin, the slope of the total concentration-QTca effect was significantly positive, and the QT-prolonging effect, taken as the upper bound of the confidence interval for predicted ∆∆QTca, was confirmed to exceed 10 ms. The ER slope of the negative drug levocetirizine was not significantly positive and the QTca effect was below 10 ms at observed peak exposure.

DISCUSSION

Preclinical QT assessment in cynomolgus monkeys combined with ER analysis could identify the small QT effect induced by several QT drugs consistently with the outcomes in humans. Thus, the ER method should be regarded as useful for translational prediction of QT effects in humans.

Challenges in designing and executing clinical trials in a dish studies

The ever-increasing cost of drug discovery and development represents a significant challenge for the pharmaceutical industry and new strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap prior to first human exposures, and to allow earlier decisions to be made on the further development of drugs. A number of studies have demonstrated that various cell types differentiated from human induced pluripotent stem cells (iPSCs) do not just respond similarly to human tissues in general, but rather recapitulate the drug response of their specific donor's, when exposed to the same drug in vivo. This recapitulation opens the doors to Clinical Trials in a Dish (CTiD), a platform which involves testing, in vitro, medical therapies for safety on cells collected from a sample of human patients, before moving into clinical trials. However, the science behind CTiD is complex, and every element of the process from tissue acquisition to data generation must be assessed and designed to meet quality metrics and standards. Without such rigorous assessment and design, the basic scientific integrity of CTiD constructs is likely compromised, and the results questionable. Given the lack of standard process and/or quality metrics in place for the use of stem cell-based products for in vitro testing per se, we discuss here the key elements that one needs to consider when designing, implementing and executing CTiD studies, in order to ensure an approach that will reliably mimic clinical trials, and allow obtaining reproducible and reliable experimental data.

Using vaccine Immunostimulation/Immunodynamic modelling methods to inform vaccine dose decision-making

Unlike drug dose optimisation, mathematical modelling has not been applied to vaccine dose finding. We applied a novel Immunostimulation/Immunodynamic mathematical modelling framework to translate multi-dose TB vaccine immune responses from mice, to predict most immunogenic dose in humans. Data were previously collected on IFN-γ secreting CD4+ T cells over time for novel TB vaccines H56 and H1 adjuvanted with IC31 in mice (1 dose groups (0.1-1.5 and 15 μg H56 + IC31), 45 mice) and humans (1 dose (50 μg H56/H1 + IC31), 18 humans). A two-compartment mathematical model, describing the dynamics of the post-vaccination IFN-γ T cell response, was fitted to mouse and human data, separately, using nonlinear mixed effects methods. We used these fitted models and a vaccine dose allometric scaling assumption, to predict the most immunogenic human dose. Based on the changes in model parameters by mouse H56 + IC31 dose and by varying the H56 dose allometric scaling factor between mouse and humans, we established that, at a late time point (224 days) doses of 0.8-8 μg H56 + IC31 in humans may be the most immunogenic. A 0.8-8 μg of H-series TB vaccines in humans, may be as, or more, immunogenic, as larger doses. The Immunostimulation/Immunodynamic mathematical modelling framework is a novel, and potentially revolutionary tool, to predict most immunogenic vaccine doses, and accelerate vaccine development.

Clinical Trials in a Dish: A Perspective on the Coming Revolution in Drug Development

The pharmaceutical industry is facing unprecedented challenges as the cost of developing new drugs has reached unsustainable levels, fueled in large parts by a high attrition rate in clinical development. Strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap and allow earlier decisions to be made on the continuation or discontinuation of further development of drugs. The discovery and development of human induced pluripotent stem cells (hiPSCs) have opened up new avenues that support the concept of screening for cell-based safety and toxicity at the level of a population. This approach, termed “Clinical Trials in a Dish” (CTiD), allows testing medical therapies for safety or efficacy on cells collected from a representative sample of human patients, before moving into actual clinical trials. It can be applied to the development of drugs for specific populations, and it allows predicting not only the magnitude of effects but also the incidence of patients in a population who will benefit or be harmed by these drugs. This, in turn, can lead to the selection of safer drugs to move into clinical development, resulting in a reduction in attrition. The current article offers a perspective of this new model for “humanized” preclinical drug development.

Complex Bayesian Modeling Workflows Encoding and Execution Made Easy With a Novel WinBUGS Plugin of the Drug Disease Model Resources Interoperability Framework

The Drug Disease Model Resources (DDMoRe) Interoperability Framework (IOF) enables pharmacometric model encoding and execution via Model Description Language (MDL) and R language, through the ddmore package. Through its components and converter plugins, the IOF can execute pharmacometric tasks using different target tools, starting from a single MDL-encoded model. In this article, we present the WinBUGS plugin and show how its integration in the IOF enables an easy implementation of complex Bayesian workflows. We selected a published diabetes-linked study as a real-world example, in which two inter-related models are used to estimate insulin secretion rate in response to a glucose stimulus from intravenous glucose tolerance test (IVGTT) data. This model was implemented following different approaches to propagate uncertainty, via diverse IOF target tools (NONMEM, WinBUGS, PsN, and Xpose). The developed software supports a plethora of pharmacokinetic/pharmacodynamic (PK/PD) modeling features. It provides solutions to reproducibility and interoperability issues in Bayesian modeling, and facilitates the difficult encoding of complex PK/PD models in WinBUGS.

Standard Electrocardiographic Data from Capuchin Monkeys (Cebus apella, Linnaeus, 1758)

Capuchin monkeys are a species of arboreal primate found in all South American countries. These monkeys have been highlighted for their potential for biomedical research due to their anatomic and physiologic similarities and genetic homology with humans. Here we characterized the electrocardiographic tracings from 12 healthy, young capuchin monkeys that were restrained with ketamine and midazolam. All 12 monkeys had normal sinus rhythms. Neither P-wave duration, PR interval, QT interval, nor P- or R-wave amplitude (in millivolts) differed between males and females. The P waves were small, monophasic, and positive in all animals. The QRS complex showed positive polarity in the D1, D2, aVL, aVF, V2, V4, and V10 derivations and negative polarity in the D3, aVR, and rV2 leads. The T wave exhibited a negative polarity only in the aVR derivation in all animals in the study, and no significant difference was present between sexes. The ST segment was isoelectric in both sexes and lacked reductions and elevations. The anesthetic protocol was well tolerated all of the monkeys and allowed for diagnostic-quality acquisition, measurement, and characterization of the electrocardiogram and establishment of the normal electrocardiographic parameters of chemically restrained capuchin monkeys.

Characterizing QT interval prolongation in early clinical development: a case study with methadone

Recently, we have shown how pharmacokinetic–pharmacodynamic (PKPD) modeling can be used to assess the probability of QT interval prolongation both in dogs and humans. A correlation between species has been identified for a drug‐specific parameter, making it possible to prospectively evaluate nonclinical signals. Here, we illustrate how nonclinical data on methadone can be used to support the evaluation of dromotropic drug effects in humans. ECG and drug concentration data from a safety pharmacology study in dogs were analyzed using nonlinear mixed effects modeling. The slope of the PKPD model describing the probability of QT interval prolongation was extrapolated from dogs to humans and subsequently combined with methadone pharmacokinetic data as input for clinical trial simulations. Concentration versus time profiles were simulated for doses between 5 and 500 mg. Predicted peak concentrations in humans were then used as reference value to assess the probability of an increase in QT interval of ≥5 and ≥10 ms. Point estimates for the slope in dogs suggested low probability of ≥10 ms prolongation in humans, whereas an effect of approximately 5 ms increase is predicted when accounting for the 90% credible intervals of the drug‐specific parameter in dogs. Interspecies differences in drug disposition appear to explain the discrepancies between predicted and observed QT prolonging effects in humans. Extrapolation of the effects of racemic compound may not be sufficient to describe the increase in QT interval observed after administration of methadone to patients. Assessment of the contribution of enantioselective metabolism and active metabolites is critical.

Using Data from Macaques To Predict Gamma Interferon Responses after Mycobacterium bovis BCG Vaccination in Humans: a Proof-of-Concept Study of Immunostimulation/Immunodynamic Modeling Methods

Macaques play a central role in the development of human tuberculosis (TB) vaccines. Immune and challenge responses differ across macaque and human subpopulations. We used novel immunostimulation/immunodynamic modeling methods in a proof-of-concept study to determine which macaque subpopulations best predicted immune responses in different human subpopulations. Data on gamma interferon (IFN-γ)-secreting CD4⁺ T cells over time after recent Mycobacterium bovis BCG vaccination were available for 55 humans and 81 macaques. Human population covariates were baseline BCG vaccination status, time since BCG vaccination, gender, and the monocyte/lymphocyte cell count ratio. The macaque population covariate was the colony of origin. A two-compartment mathematical model describing the dynamics of the IFN-γ T cell response after BCG vaccination was calibrated to these data using nonlinear mixed-effects methods. The model was calibrated to macaque and human data separately. The association between subpopulations and the BCG immune response in each species was assessed. The macaque subpopulations that best predicted immune responses in different human subpopulations were identified using Bayesian information criteria. We found that the macaque colony and the human baseline BCG status were significantly (P < 0.05) associated with the BCG-induced immune response. For humans who were BCG naïve at baseline, Indonesian cynomolgus macaques and Indian rhesus macaques best predicted the immune response. For humans who had already been BCG vaccinated at baseline, Mauritian cynomolgus macaques best predicted the immune response. This work suggests that the immune responses of different human populations may be best modeled by different macaque colonies, and it demonstrates the potential utility of immunostimulation/immunodynamic modeling to accelerate TB vaccine development.

Translating QT interval prolongation from conscious dogs to humans

AIM

In spite of screening procedures in early drug development, uncertainty remains about the propensity of new chemical entities (NCEs) to prolong the QT/QTc interval. The evaluation of proarrhythmic activity using a comprehensive in vitro proarrhythmia assay does not fully account for pharmacokinetic-pharmacodynamic (PKPD) differences in vivo. In the present study, we evaluated the correlation between drug-specific parameters describing QT interval prolongation in dogs and in humans.

METHODS

Using estimates of the drug-specific parameter, data on the slopes of the PKPD relationships of nine compounds with varying QT-prolonging effects (cisapride, sotalol, moxifloxacin, carabersat, GSK945237, SB237376 and GSK618334, and two anonymized NCEs) were analysed. Mean slope estimates varied between -0.98 ms μM-1 and 6.1 ms μM-1 in dogs and -10 ms μM-1 and 90 ms μM-1 in humans, indicating a wide range of effects on the QT interval. Linear regression techniques were then applied to characterize the correlation between the parameter estimates across species.

RESULTS

For compounds without a mixed ion channel block, a correlation was observed between the drug-specific parameter in dogs and humans (y = -1.709 + 11.6x; R2  = 0.989). These results show that per unit concentration, the drug effect on the QT interval in humans is 11.6-fold larger than in dogs.

CONCLUSIONS

Together with information about the expected therapeutic exposure, the evidence of a correlation between the compound-specific parameter in dogs and in humans represents an opportunity for translating preclinical safety data before progression into the clinic. Whereas further investigation is required to establish the generalizability of our findings, this approach can be used with clinical trial simulations to predict the probability of QT prolongation in humans.

The TB vaccine H56+IC31 dose-response curve is peaked not saturating: Data generation for new mathematical modelling methods to inform vaccine dose decisions

INTRODUCTION

In vaccine development, dose-response curves are commonly assumed to be saturating. Evidence from tuberculosis (TB) vaccine, H56+IC31 shows this may be incorrect. Mathematical modelling techniques may be useful in efficiently identifying the most immunogenic dose, but model calibration requires longitudinal data across multiple doses and time points.

AIMS

We aimed to (i) generate longitudinal response data in mice for a wide range of H56+IC31 doses for use in future mathematical modelling and (ii) test whether a 'saturating' or 'peaked' dose-response curve, better fit the empirical data.

METHODS

We measured IFN-γ secretion using an ELISPOT assay in the splenocytes of mice who had received doses of 0, 0.1, 0.5, 1, 5 or 15μg H56+IC31. Mice were vaccinated twice (at day 0 and 15) and responses measured for each dose at 8 time points over a 56-day period following first vaccination. Summary measures Area Under the Curve (AUC), peak and day 56 responses were compared between dose groups. Corrected Akaike Information Criteria was used to test which dose-response curve best fitted empirical data, at different time ranges.

RESULTS

(i) All summary measures for dose groups 0.1 and 0.5μg were higher than the control group (p<0.05). The AUC was higher for 0.1 than 15μg dose. (ii) There was strong evidence that the dose-response curve was peaked for all time ranges, and the best dose is likely to be lower than previous empirical experiments have evaluated.

CONCLUSION

These results suggest that the highest, safe dose may not always optimal in terms of immunogenicity, as the dose-response curve may not saturate. Detailed longitudinal dose range data for TB vaccine H56+IC31 reveals response dynamics in mice that should now be used to identify optimal doses for humans using clinical data, using new data collection and mathematical modelling.

The European Citizens' Stop Vivisection Initiative and the revision of Directive

Animal experimentation is presented to the public as an ongoing debate between research scientists on one hand, and the animal protection community on the other. An opportunity to break out of this mindset presented itself in the form of a European Citizens' Initiative, Stop Vivisection, which challenged Directive 2010/63/EU of the European Parliament and of the Council on the protection of animals for scientific purposes. The manifesto of the initiative called upon the European Commission to replace the existing Directive with a new proposal that does away with animal experimentation, and instead makes compulsory the use of human data as a predictive modality for the study of human diseases and responses to drugs. Although the Initiative succeeded in gathering the required one million signatures, the European Commission ultimately rejected the proposal. However, some of the lessons learned from the Initiative may well be relevant to the revision of Directive 2010/63/EU, due to take place by 2017.

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.

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.