A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm.

INTRODUCTION: hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. METHODS: A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. RESULTS: A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. DISCUSSION: This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

Considerations for assessing the potential effects of antidiabetes drugs on cardiac ventricular repolarization: A report from the Cardiac Safety Research Consortium.

Thorough QT studies conducted according to the International Council on Harmonisation E14 guideline are required for new nonantiarrhythmic drugs to assess the potential to prolong ventricular repolarization. Special considerations may be needed for conducting such studies with antidiabetes drugs as changes in blood glucose and other physiologic parameters affected by antidiabetes drugs may prolong the QT interval and thus confound QT/corrected QT assessments. This review discusses potential mechanisms for QT/corrected QT interval prolongation with antidiabetes drugs and offers practical considerations for assessing antidiabetes drugs in thorough QT studies. This article represents collaborative discussions among key stakeholders from academia, industry, and regulatory agencies participating in the Cardiac Safety Research Consortium. It does not represent regulatory policy.

Gastrointestinal Safety Pharmacology in Drug Discovery and Development.

Although the basic structure of the gastrointestinal tract (GIT) is similar across species, there are significant differences in the anatomy, physiology, and biochemistry between humans and laboratory animals, which should be taken into account when conducting a gastrointestinal (GI) assessment. Historically, the percentage of cases of drug attrition associated with GI-related adverse effects is small; however, this incidence has increased over the last few years. Drug-related GI effects are very diverse, usually functional in nature, and not limited to a single pharmacological class. The most common GI signs are nausea and vomiting, diarrhea, constipation, and gastric ulceration. Despite being generally not life-threatening, they can greatly affect patient compliance and quality of life. There is therefore a real need for improved and/or more extensive GI screening of candidate drugs in preclinical development, which may help to better predict clinical effects. Models to identify drug effects on GI function cover GI motility, nausea and emesis liability, secretory function (mainly gastric secretion), and absorption aspects. Both in vitro and in vivo assessments are described in this chapter. Drug-induced effects on GI function can be assessed in stand-alone safety pharmacology studies or as endpoints integrated into toxicology studies. In silico approaches are also being developed, such as the gut-on-a-chip model, but await further optimization and validation before routine use in drug development. GI injuries are still in their infancy with regard to biomarkers, probably due to their greater diversity. Nevertheless, several potential blood, stool, and breath biomarkers have been investigated. However, additional validation studies are necessary to assess the relevance of these biomarkers and their predictive value for GI injuries.

The Efficacy, Effectiveness, and Efficiency of Integrated QTc Assessment: Rationalizing Approaches to New Drug Modalities.

After nearly 3 decades of regulatory activity concerning new drugs' potential for delayed cardiac repolarization an integrated risk assessment paradigm for small molecule drugs has been established. Regulatory guidance also suggests that for large, targeted proteins and monoclonal antibodies no quantitative clinical QTc assessment is necessary. The expansion of new drug modalities prompts the question: "Should these new modalities be treated like small molecule drugs or like monoclonal antibodies?"

A comparison of the performance of contemporary, historical, and cross-lab controls in QTc assessment in conscious nonhuman primates.

Cardiovascular safety pharmacology and toxicology studies include vehicle control animals in most studies. Electrocardiogram data on common vehicles is accumulated relatively quickly. In the interests of the 3Rs principles it may be useful to use this historical information to reduce the use of animals or to refine the sensitivity of studies. We used implanted telemetry data from a large nonhuman primate (NHP) cardiovascular study (n = 48) evaluating the effect of moxifloxacin. We extracted 24 animals to conduct a n = 3/sex/group analysis. The remaining 24 animals were used to generate 1000 unique combinations of 3 male and 3 female NHP to act as control groups for the three treated groups in the n = 3/sex/group analysis. The distribution of treatment effects, median minimum detectable difference (MDD) values were gathered from the 1000 studies. These represent contemporary controls. Data were available from 42 NHP from 3 other studies in the same laboratory using the same technology. These were used to generate 1000 unique combinations of 6, 12, 18, 24 and 36 NHP to act as historical control animals for the 18 animals in the treated groups of the moxifloxacin study. Data from an additional laboratory were also available for 20 NHP. The QT, RR and QT-RR data from the three sources were comparable. However, differences in the time course of QTc effect in the vehicle data from the two laboratories meant that it was not possible to use cross-lab controls. In the case of historical controls from the same laboratory, these could be used in place of the contemporary controls in determining a treatment's effect. There appeared to be an advantage in using larger (≥18) group sizes for historical controls. These data support the opportunity of using historical controls to reduce the number of animals used in new cardiovascular studies.

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.

Supporting an integrated QTc risk assessment using the hERG margin distributions for three positive control agents derived from multiple laboratories and on multiple occasions.

BACKGROUND: Determination of a drug's potency in blocking the hERG channel is an established safety pharmacology study. Best practice guidelines have been published for reliable assessment of hERG potency. In addition, a set of plasma concentration and plasma protein binding fraction data were provided as denominators for margin calculations. The aims of the current analysis were five-fold: provide data allowing creation of consistent denominators for the hERG margin distributions of the key reference agents, explore the variation in hERG margins within and across laboratories, provide a hERG margin to 10 ms QTc prolongation based on several newer studies, provide information to use these analyses for reference purposes, and provide recommended hERG margin 'cut-off' values. METHODS: The analyses used 12 hERG IC50 'best practice' data sets (for the 3 reference agents). A group of 5 data sets came from a single laboratory. The other 7 data sets were collected by 6 different laboratories. RESULTS: The denominator exposure distributions were consistent with the ICH E14/S7B Training Materials. The inter-occasion and inter-laboratory variability in hERG IC50 values were comparable. Inter-drug differences were most important in determining the pooled margin variability. The combined data provided a robust hERG margin reference based on best practice guidelines and consistent exposure denominators. The sensitivity of hERG margin thresholds were consistent with the sensitivity described over the course of the last two decades. CONCLUSION: The current data provide further insight into the sensitivity of the 30-fold hERG margin 'cut-off' used for two decades. Using similar hERG assessments and these analyses, a future researcher can use a hERG margin threshold to support a negative QTc integrated risk assessment.

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.

Comparing the sensitivity of cross-over and parallel study designs for QTc assessment: An analysis based on a single large study of moxifloxacin in 48 nonhuman primates.

The cardiovascular safety pharmacology (SP) study conducted to satisfy ICH S7A and S7B has commonly used a cross-over study design where each animal receives all treatments. In an increasing number of cases, cross-over designs are not possible and parallel studies have to be used. These can seldom be as large as 8 animals/treatment to match an n = 8 cross-over. Animals in parallel designs receive only one treatment. Parallel studies will have a different sensitivity to detect changes. This sensitivity is a critical question in using nonclinical QTc evaluations to support an integrated proarrhythmic risk assessment under the newly released ICH E14/S7B Q&As. The current analysis used a study large enough (n = 48) to be analyzed both as a parallel and as a cross-over design to directly compare the performance of the two experimental designs coupled to different statistical models, while all other study conduct aspects were the same. A total of 48 nonhuman primates (NHP) received 2 different treatments twice: vehicle, moxifloxacin (80 mg/kg), vehicle, moxifloxacin (80 mg/kg). Post-dose QTc interval data were recorded for 48 h for each treatment. Data were analyzed using 12 animals randomly selected for each treatment in a parallel design or as an n = 48 animal cross-over study. Different statistical models were used. The primary endpoint was the residual deviation (sigma) from the models applied to hourly time intervals. The sigma was used to determine the minimal detectable difference (MDD) for the study design-statistical model combination. Two statistical models were applicable to either study design. They gave similar sigma and resulting MDD values. In cross-over designs, the individual animal identification (ID) can be used in the statistical model. This enabled the smallest MDD value. Simple statistical models for analysis were identified: Treatment + Baseline for parallel designs and Treatment + ID for cross-over designs. The statistical sensitivity of NHP parallel study designs is reasonable (MDD for n = 6 of 12.7 ms), and in combination with testing exposures higher than likely to be necessary in man could be used in an integrated risk assessment. Where sensitivity of the NHP in vivo QTc assessment is critical, the cross-over design enabled a higher sensitivity (MDD 12.2 ms for n = 4; 8 ms for n = 8).

The "One-Step" approach for QT analysis increases the sensitivity of nonclinical QTc analysis.

Whether a compound prolongs cardiac repolarization independent of changes in beat rate is a critical question in drug research and development. Current practice is to resolve this in two steps. First, the QT interval is corrected for the influence of rate and then statistical significance is tested. There is renewed interest in improving the sensitivity of nonclinical corrected QT interval (QTc) assessment with modern studies having greater data density than previously utilized. The current analyses examine the effects of moxifloxacin or vehicle on the QT interval in nonhuman primates (NHPs) using a previously described one-step method. The primary end point is the statistical sensitivity of the assessment. Publications suggest that for a four animal crossover (4 × 4) in NHPs the minimal detectable difference (MDD) is greater than or equal to 10 ms, whereas in an eight animal crossover the MDD is ~6.5 ms. Using the one-step method, the MDD for the four animal NHP assessments was 3 ms. In addition, the one-step model accounted for day-to-day differences in the heart rate and QT-rate slope as well as drug-induced changes in these parameters. This method provides an increase in the sensitivity and reduces the number of animals necessary for detecting potential QT change and represents "best practice" in nonclinical QTc assessment in safety pharmacology studies.

Comparison of validity of standard nonclinical group size selection versus standard clinical group sizes for nonhuman primate QTc prolongation evaluation.

The number of animals used in a nonhuman primate (NHP) in vivo QTc assessment conducted as part of the safety pharmacology (SP) studies on a potential new drug is relatively small (4-8 subjects). The number is much smaller than the number of healthy volunteers in a conventional thorough QT (TQT) study (40-60 volunteers). How is it possible that such small studies could offer an equivalent sensitivity in an integrated nonclinical and clinical cardiac repolarization risk assessment? This study provided the opportunity to empirically demonstrate in a large number of NHPs the performance of a nonclinical evaluation at a similar size to a TQT study. By contrasting an analysis mimicking the sampling and aggregation of QTc interval data in a manner which is TQT-like with a more conventional SP-like analysis it was demonstrated that the SP-like analysis was more sensitive. In prospective power calculations 80% power at p = 0.05 can be achieved for a 5 ms QTc change with only n = 8 NHPs using the SP-like analysis and in a group of only 4 NHPs 80% power to detect 10 ms could be achieved. By contrast groups of 24 NHPs would be required to achieve 80% power to detect 5 ms using the TQT-like sampling and aggregation approach. Overall, this study has demonstrated that smaller safety pharmacology in vivo QTc assessments using all the available data in larger data aggregates can achieve sensitivity comparable to a human TQT study.

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).

Best practice considerations for nonclinical in vivo cardiovascular telemetry studies in non-rodent species: Delivering high quality QTc data to support ICH E14/S7B Q&As.

The ICH E14/S7B Questions and Answers (Q&As) guideline introduces the concept of a "double negative" nonclinical scenario (negative hERG assay and negative in vivo QTc study) to demonstrate that a drug does not produce a clinically relevant QT prolongation (i.e., no QT liability). This nonclinical "double negative" data package, along with negative Phase 1 clinical QTc data, may be sufficient to substitute for a clinical Thorough QT (TQT) study in some specific cases. While standalone GLP in vivo cardiovascular studies in non-rodent species are standard practice during nonclinical drug development for small molecule programs, a variety of approaches to the design, conduct, analysis and interpretation are utilized across pharmaceutical companies and contract research organizations (CROs) that may, in some cases, negatively impact the stringent sensitivity needed to fulfill the new Q&As. Subject matter experts from both Pharma and CROs have collaborated to recommend best practices for more robust nonclinical cardiovascular telemetry studies in non-rodent species, with input from clinical and regulatory experts. The aim was to increase consistency and harmonization across the industry and to ensure delivery of high quality nonclinical QTc data to meet the proposed sensitivities defined within the revised ICH E14/S7B Q&As guideline (Q&As 5.1 and 6.1). The detailed best practice recommendations presented here cover the design and execution of the safety pharmacology cardiovascular study, including optimal methods for acquiring, analyzing, reporting, and interpreting the resulting QTc and pharmacokinetic data to allow for direct comparison to clinical exposures and assessment of safety margin for QTc prolongation.

QT Ratio: A simple solution to individual QT correction.

Preclinical risk assessment of drug-induced arrhythmias is critical for drug development and relies on heart rate corrected QT interval (QT) prolongation as a biomarker for arrhythmia risk. However, the methods used to correct QT vary in complexity and don't account for all changes in the QT-rate relationship. Thus, we developed the novel Ratio QT correction method which characterizes that relationship at each timepoint using the ratio between QT, adjusted for a species-specific constant, and rate (RR interval). This ratio represents the slope between the intercept and the datapoint being corrected, which is then used in a linear equation like individual methods. A unique correction coefficient for each datapoint avoids assuming static relationships. We hypothesize that the simple and dynamic nature of the Ratio method will provide more consistent rate correction and error reduction compared to Bazett's and individual regression methods. Comparisons were made using ECG data from non-human primates (NHPs) treated with dofetilide or moxifloxacin, separated into small groups (n = 4). The methods were compared based on corrected QT vs RR slopes, standard error, and minimal detectable difference (MDD) for each method. The Ratio method resulted in smaller corrected QT-rate relationship slopes than Bazett's, more closely matching those of individual methods. It produced similar or lower MDDs compared to individual and Bazett's correction, respectively, with more consistent reduction in standard error. This simple and effective method has the potential for easy translatability across species.

Improving corrected QT; Why individual correction is not enough.

The use of QT-prolongation as a biomarker for arrhythmia risk requires that researchers correct the QT-interval (QT) to control for the influence of heart rate (HR). QT correction methods can vary but most used are the universal correction methods, such as Bazett's or Van de Water's, which use a single correction formula to correct QT-intervals in all the subjects of a study. Such methods fail to account for differences in the QT/HR relationship between subjects or over time, instead relying on the assumption that this relationship is consistent. To address these changes in rate relationships, we test the effectiveness of linear and non-linear individual correction methods. We hypothesize that individual correction methods that account for additional influences on the rate relationship will result in more effective and consistent correction. To increase the scope of this study we use bootstrap sampling on ECG recordings from non-human primates and beagle canines dosed with vehicle control. We then compare linear and non-linear individual correction methods through their ability to reduce HR correlation and standard deviation of corrected QT values. From these results, we conclude that individual correction methods based on post-treatment data are most effective with the linear methods being the best option for most cases in both primates and canines. We also conclude that the non-linear methods are more effective in canines than primates and that accounting for light status can improve correction while examining the data from the light periods separately. Individual correction requires careful consideration of inter-subject and intra-subject variabilities.

Time for a Fully Integrated Nonclinical-Clinical Risk Assessment to Streamline QT Prolongation Liability Determinations: A Pharma Industry Perspective.

Defining an appropriate and efficient assessment of drug-induced corrected QT interval (QTc) prolongation (a surrogate marker of torsades de pointes arrhythmia) remains a concern of drug developers and regulators worldwide. In use for over 15 years, the nonclinical International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B and clinical ICH E14 guidances describe three core assays (S7B: in vitro hERG current & in vivo QTc studies; E14: thorough QT study) that are used to assess the potential of drugs to cause delayed ventricular repolarization. Incorporating these assays during nonclinical or human testing of novel compounds has led to a low prevalence of QTc-prolonging drugs in clinical trials and no new drugs having been removed from the marketplace due to unexpected QTc prolongation. Despite this success, nonclinical evaluations of delayed repolarization still minimally influence ICH E14-based strategies for assessing clinical QTc prolongation and defining proarrhythmic risk. In particular, the value of ICH S7B-based "double-negative" nonclinical findings (low risk for hERG block and in vivo QTc prolongation at relevant clinical exposures) is underappreciated. These nonclinical data have additional value in assessing the risk of clinical QTc prolongation when clinical evaluations are limited by heart rate changes, low drug exposures, or high-dose safety considerations. The time has come to meaningfully merge nonclinical and clinical data to enable a more comprehensive, but flexible, clinical risk assessment strategy for QTc monitoring discussed in updated ICH E14 Questions and Answers. Implementing a fully integrated nonclinical/clinical risk assessment for compounds with double-negative nonclinical findings in the context of a low prevalence of clinical QTc prolongation would relieve the burden of unnecessary clinical QTc studies and streamline drug development.

Improving prediction of torsadogenic risk in the CiPA in silico model by appropriately accounting for clinical exposure.

Any adverse event is reliant on three properties: the appropriate pharmacology to trigger the event, the appropriate exposure of compound, and intrinsic patient factors. Each alone is necessary but insufficient to predict the event. The Comprehensive in vitro Proarrhythmia Assessment (CiPA) initiative attempts to predict the risk of torsade de pointes (TdP) by focusing on an in-silico model with thresholds determined at modest multiples of the therapeutic exposure for the parent molecule. This emphasizes the pharmacologic properties necessary for TdP but does not account for situations where clinical exposure may be higher, or where hERG potassium channel active metabolites are involved. Could accounting for clinical worst-case scenarios and metabolites, as is already standard practice in thorough QTc studies, improve the prediction algorithm? Terfenadine, a drug classed as "Intermediate" risk by CiPA, was assessed differently in the in-silico model validation. The clinical concentration of terfenadine used for the model was the exposure in the presence of metabolic inhibition representing a 14 to 40-fold increase in exposure compared to the therapeutic plasma concentration. However, several other "Intermediate" risk compounds are also known to be sensitive to metabolic inhibition and/or to have therapeutically active major metabolites, some of which are known to block hERG. Risperidone and astemizole are relevant examples. If only parent exposure is used to calculate a therapeutic window, risperidone has a relatively large multiple between clinical exposure and the hERG potency. Using this exposure of risperidone, the drug borders the "Intermediate" and "Low/No" risk categories for the CiPA in-silico model's TdP metric. The desmethyl metabolite of astemizole likely contributes significantly to the effects on cardiac repolarization, being equipotent on hERG but circulating at much higher levels than parent. Recalculating the TdP metric and margin values for terfenadine, risperidone and astemizole using the unbound concentration normally associated with treatment and a clinical worst case changes the qNet metric to higher risk values and illustrates the potential benefit to the algorithm of consistently using a clinical high exposure scenario accounting for all "hERG-active species". This exercise suggests repeating the model qualification accounting for clinical exposures and metabolites under 'stressed' scenarios would improve prediction of the TdP risk.

A Bayesian approach to toxicological testing.

INTRODUCTION: Testing for toxicities is an important activity in drug development. In an ideal world the tests applied would be definitive. In reality this is seldom the case. There are two types of power associated with a test. A test's discriminatory power is characterized by its sensitivity and specificity and tells the investigator the probability of obtaining a test positive in the presence (sensitivity) or a test negative in the absence (specificity) of the toxicity. A test's discriminatory power is an attribute of the test itself. The investigator is, however, more interested in a test's predictive power, which is the probability that the toxicity is present or absent in a novel molecule given the test result. A test's predictive power is a consequence of the test's discriminatory power and the context of its application. Unlike its discriminatory power, the predictive power of a test is not 'fixed' and varies with testing context. This means that tests and test context must be taken together to enable an investigator to achieve their desired predictive power. Our intent is to illustrate a broadly applicable approach to testing schemes designed to maximize a test's positive or negative predictive power. Rather than hypothetical tests and toxicities, we use as examples tests available for the prediction of a substance's liability to cause the cardiac arrhythmia torsade de pointes. METHODS: Owing to intense focus over the last two decades, the discriminatory powers of a number of tests for predicting a torsade de pointes liability are publicly available. Having randomly chosen an initial test (random although plausible as an early screening assessment), the inter-relationship between the prevalence of torsadogenic liability and the discriminatory power of potential follow-on tests were explored in a probability framework, based on Bayes Theorem, to show how testing schemes could be developed based on odds and likelihood ratios. Uncertainty around the prevalence of torsade liability and the discriminatory power of a test were addressed by varying these values and examining their impact on the test's predictive power. RESULTS: Overall, the analysis demonstrates that tests can be strategically combined to reach a desired level of predictive power. This is generally more easily achieved for negative predictive power given a low prevalence of the toxicity under scrutiny. For this work, we used a base prevalence of 10% for a substance to carry a tordsadogenic liability. Given uncertainty around a test's discriminatory power, a probabilistic rather than deterministic approach was recommended. Such an approach necessarily requires the investigator to define distributions around test characteristics as well as their desired probability of attaining a given predictive power. CONCLUSIONS: The proposed approach is easily implemented deterministically since values of the discriminatory power of the tests are readily and publicly available. The probabilistic implementation is also easily implemented, but requires that the uncertainty around the test performance and prevalence, and the targets for probability of attaining the desired predictive value all be made explicit rather than remain implicit as is often the case in 'integrated risk assessment' or 'totality of evidence' presentations. This general approach could form a basis for testing and decision-making that can be communicated and discussed in a consistent manner between scientists as well as between sponsors and regulators.

Revisiting the hERG safety margin after 20 years of routine hERG screening.

INTRODUCTION: It has been two decades since screening new molecules and potential clinical drug candidates against the hERG potassium channel became a routine part of safety pharmacology. The earliest heuristic for what was an adequate safety margin to separate molecules with a potential liability to cause the arrhythmia torsade de pointes (TdP) from those with no such liability emerged in 2002 and was determined to be a hERG IC50 value 30-fold above the therapeutic free plasma concentration (Webster, Leishman, & Walker, 2002). In the intervening years nonclinical and clinical ICH guidance has been introduced and intense scrutiny has been applied to the QT interval of the electrocardiogram in animals and man. Has the 30-fold heuristic stood the test of time? METHODS: The hERG margins between the IC50 value and the therapeutic unbound plasma concentrations were examined for 367 compounds. These margins were compared against the categories used by www.CredibleMeds.com to classify a drug's TdP risk. A subset of 336 of these drugs were compared against their US product labels with respect to black box warnings on QTc prolongation or TdP, warnings and precautions on QTc or TdP, and QTc language in the clinical pharmacology section. RESULTS: Against the CredibleMeds classification the means of the margins for Known, Conditional, or Possible Risk of TdP, and Not Listed (presumably no TdP liability) were 4.8, 28, 71 and 339, respectively. Against the US label language the means of margins for black boxes and warnings were 3.1 and 26, respectively. The average margins associated with, positive QTc outcome, negative QTc outcome and no QTc language were 16, 479 and 204, respectively. Based on ROC curves the optimal hERG margin thresholds to separate "Known risk of TdP" from "Not Listed" and, QTc prolongation positive from QTc negative were 37- and 50-fold, respectively. CONCLUSIONS: The observed optimal margin of 50-fold in the current study is not appreciably different from a previously reported 45-fold optimal margin (Gintant, 2011). The margin falls between the margins for negative (QTc outcome or no QTc language) and positive (positive QTc outcome, warnings or black boxes) compounds. The observed optimal margin of 37-fold in the current study is not appreciably different from the commonly used 30-fold optimal margin (Webster et al., 2002). This margin falls between those for drugs with a known or conditional TdP risk and those where it is at best a possible risk, and from the 240 drugs not listed on www.CredibleMeds.com. It is expected that there would be a small numerical difference (e.g. 37 vs. 50, or as previously published 30 vs. 45) between optimal cut-offs for the TdP liability and QTc prolongation predictions since some QTc positive drugs are described on CredibleMeds.com as having only a "Possible Risk of TdP" as they are not associated with TdP when used as directed. The fact that the margins in each category form distributions is also expected given biologic variability. However, we argue that a more consistent manner of assessing hERG potency and evaluating relevant exposures would be likely to reduce the spread in these distributions and make margins even more useful as a decision-making data point.