Virtual clinical QT exposure-response studies - A translational computational approach

BACKGROUND AND PURPOSE

A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the proarrhythmic potential of drug candidates. While current computational methodologies focus on predicting the incidence of proarrhythmic events after drug administration, the objective of this study is to predict concentration-response relationships of QTc as a clinical endpoint.

EXPERIMENTAL APPROACH

Full heart computational models reproducing human cardiac populations were created to predict the concentration-response relationship of changes in the QT interval as recommended for clinical trials. The concentration-response relationship of the QT-interval prolongation obtained from the computational cardiac population was compared against the relationship from clinical trial data for a set of well-characterized compounds: moxifloxacin, dofetilide, verapamil, and ondansetron.

KEY RESULTS

Computationally derived concentration-response relationships of QT interval changes for three of the four drugs had slopes within the confidence interval of clinical trials (dofetilide, moxifloxacin and verapamil) when compared to placebo-corrected concentration-ΔQT and concentration-ΔQT regressions. Moxifloxacin showed a higher intercept, outside the confidence interval of the clinical data, demonstrating that in this example, the standard linear regression does not appropriately capture the concentration-response results at very low concentrations. The concentrations corresponding to a mean QTc prolongation of 10 ms were consistently lower in the computational model than in clinical data. The critical concentration varied within an approximate ratio of 0.5 (moxifloxacin and ondansetron) and 1 times (dofetilide, verapamil) the critical concentration observed in human clinical trials. Notably, no other in silico methodology can approximate the human critical concentration values for a QT interval prolongation of 10 ms.

CONCLUSION AND IMPLICATIONS

Computational concentration-response modelling of a virtual population of high-resolution, 3-dimensional cardiac models can provide comparable information to clinical data and could be used to complement pre-clinical and clinical safety packages. It provides access to an unlimited exposure range to support trial design and can improve the understanding of pre-clinical-clinical translation.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

Plasma Protein Binding Evaluations of Per- and Polyfluoroalkyl Substances for Category-Based Toxicokinetic Assessment

New approach methodologies (NAMs) that make use of in vitro screening and in silico approaches to inform chemical evaluations rely on in vitro toxicokinetic (TK) data to translate in vitro bioactive concentrations to exposure metrics reflective of administered dose. With 1364 per- and polyfluoroalkyl substances (PFAS) identified as of interest under Section 8 of the U.S. Toxic Substances Control Act (TSCA) and concern over the lack of knowledge regarding environmental persistence, human health, and ecological effects, the utility of NAMs to understand potential toxicities and toxicokinetics across these data-poor compounds is being evaluated. To address the TK data deficiency, 71 PFAS selected to span a wide range of functional groups and physico-chemical properties were evaluated for in vitro human plasma protein binding (PPB) by ultracentrifugation with liquid chromatography-mass spectrometry analysis. For the 67 PFAS successfully evaluated by ultracentrifugation, fraction unbound in plasma (fup) ranged from less than 0.0001 (pentadecafluorooctanoyl chloride) to 0.7302 (tetrafluorosuccinic acid), with over half of the PFAS showing PPB exceeding 99.5% (fup < 0.005). Category-based evaluations revealed that perfluoroalkanoyl chlorides and perfluorinated carboxylates (PFCAs) with 6-10 carbons were the highest bound, with similar median values for alkyl, ether, and polyether PFCAs. Interestingly, binding was lower for the PFCAs with a carbon chain length of ≥11. Lower binding also was noted for fluorotelomer carboxylic acids when compared to their carbon-equivalent perfluoroalkyl acids. Comparisons of the fup value derived using two PPB methods, ultracentrifugation or rapid equilibrium dialysis (RED), revealed RED failure for a subset of PFAS of high mass and/or predicted octanol-water partition coefficients exceeding 4 due to failure to achieve equilibrium. Bayesian modeling was used to provide uncertainty bounds around fup point estimates for incorporation into TK modeling. This PFAS PPB evaluation and grouping exercise across 67 structures greatly expand our current knowledge and will aid in PFAS NAM development.

Multiparameter Optimization of Trypanocidal Cruzain Inhibitors With In Vivo Activity and Favorable Pharmacokinetics

Cruzain, the main cysteine protease of Trypanosoma cruzi, plays key roles in all stages of the parasite's life cycle, including nutrition acquisition, differentiation, evasion of the host immune system, and invasion of host cells. Thus, inhibition of this validated target may lead to the development of novel drugs for the treatment of Chagas disease. In this study, a multiparameter optimization (MPO) approach, molecular modeling, and structure-activity relationships (SARs) were employed for the identification of new benzimidazole derivatives as potent competitive inhibitors of cruzain with trypanocidal activity and suitable pharmacokinetics. Extensive pharmacokinetic studies enabled the identification of metabolically stable and permeable compounds with high selectivity indices. CYP3A4 was found to be involved in the main metabolic pathway, and the identification of metabolic soft spots provided insights into molecular optimization. Compound 28, which showed a promising trade-off between pharmacodynamics and pharmacokinetics, caused no acute toxicity and reduced parasite burden both in vitro and in vivo.

Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans

The extent of plasma protein binding is an important compound-specific property that influences a compound's pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7 % than other methods. For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

Characterization of plasma protein binding in two mouse models of humanized liver, PXB mouse and humanized TK-NOG mouse

The unbound fractions in plasma (f _up) in two mouse models of humanized liver mice, PXB and humanized TK-NOG mice, were compared with human f _up values using equilibrium dialysis method. A good relationship between f _up values obtained from PXB mice and humans was observed; the f _up of 34/39 compounds (87.2%) in PXB mice were within 3-fold of human f _up. In contrast, a weak correlation was observed between human and humanized TK-NOG mouse f _up values; the f _up of 15/24 compounds (62.5%) in humanized TK-NOG mice were within 3-fold of human f _up. As different profiles of plasma protein binding (PPB) profiles were observed between PXB and humanized TK-NOG mice, f _up evaluation is necessary in each mouse model to utilize these humanized liver mice for pharmacological, drug-drug interaction (DDI), and toxicity studies. The unbound fraction in the mixed plasma of human and SCID mouse plasma (85:15) was well correlated with f _up in PXB mice (38/39 compounds within a 3-fold). Thus, this artificial PXB mouse plasma could be used to evaluate PPB.

Use of a Bile Salt Export Pump Knockdown Rat Susceptibility Model to Interrogate Mechanism of Drug-Induced Liver Toxicity

Inhibition of the bile salt export pump (BSEP) may be associated with clinical drug-induced liver injury, but is poorly predicted by preclinical animal models. Here we present the development of a novel rat model using siRNA knockdown (KD) of Bsep that displayed differentially enhanced hepatotoxicity to 8 Bsep inhibitors and not to 3 Bsep noninhibitors when administered at maximally tolerated doses for 7 days. Bsep KD alone resulted in 3- and 4.5-fold increases in liver and plasma levels, respectively, of the sum of the 3 most prevalent taurine conjugated bile acids (T3-BA), approximately 90% decrease in plasma and liver glycocholic acid, and a distinct bile acid regulating gene expression pattern, without resulting in hepatotoxicity. Among the Bsep inhibitors, only asunaprevir and TAK-875 resulted in serum transaminase and total bilirubin increases associated with increases in plasma T3-BA that were enhanced by Bsep KD. Benzbromarone, lopinavir, and simeprevir caused smaller increases in plasma T3-BA, but did not result in hepatotoxicity in Bsep KD rats. Bosentan, cyclosporine A, and ritonavir, however, showed no enhancement of T3-BA in plasma in Bsep KD rats, as well as Bsep noninhibitors acetaminophen, MK-0974, or clarithromycin. T3-BA findings were further strengthened through monitoring TCA-d4 converted from cholic acid-d4 overcoming interanimal variability in endogenous bile acids. Bsep KD also altered liver and/or plasma levels of asunaprevir, TAK-875, TAK-875 acyl-glucuronide, benzbromarone, and bosentan. The Bsep KD rat model has revealed differences in the effects on bile acid homeostasis among Bsep inhibitors that can best be monitored using measures of T3-BA and TCA-d4 in plasma. However, the phenotype caused by Bsep inhibition is complex due to the involvement of several compensatory mechanisms.

Current status and future directions of high-throughput ADME screening in drug discovery

During the last decade high-throughput in vitro absorption, distribution, metabolism and excretion (HT-ADME) screening has become an essential part of any drug discovery effort of synthetic molecules. The conduct of HT-ADME screening has been "industrialized" due to the extensive development of software and automation tools in cell culture, assay incubation, sample analysis and data analysis. The HT-ADME assay portfolio continues to expand in emerging areas such as drug-transporter interactions, early soft spot identification, and ADME screening of peptide drug candidates. Additionally, thanks to the very large and high-quality HT-ADME data sets available in many biopharma companies, in silico prediction of ADME properties using machine learning has also gained much momentum in recent years. In this review, we discuss the current state-of-the-art practices in HT-ADME screening including assay portfolio, assay automation, sample analysis, data processing, and prediction model building. In addition, we also offer perspectives in future development of this exciting field.

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.

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hERG potency/safety margin is a widely used nonclinical cardiac safety strategy.

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A new regulatory paradigm promotes the integration of nonclinical and clinical data.

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Lack of uncertainty quantification hindered using hERG potency in the new paradigm.

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A systematic method was established to address this limitation.

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Analysis supports using different safety margin thresholds in different context.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

A Comparison of LC-MS/MS and a Fully Integrated Autosampler/Solid-Phase Extraction System for the Analysis of Protein Binding Samples

A new analysis approach was evaluated for measuring plasma protein binding (PPB) of small molecules using the Agilent RapidFire high-throughput system coupled with a Sciex API 4000 mass spectrometer (RF-MS/MS). Thirty-three proprietary and 12 literature compounds were subjected to rapid equilibrium dialysis (RED) and evaluated in parallel using RF-MS/MS at 16.4 s/sample and traditional liquid chromatography–tandem mass spectrometry (LC-MS/MS) at 3.5 min/sample, thus making the RF-MS/MS analysis over 12 times faster than LC-MS/MS. The high-throughput analysis method that was developed demonstrated excellent correlation with the traditional LC-MS/MS analysis method with an r2 value of 0.96. The RF-MS/MS analysis method was implemented to increase sample throughput, decrease turnaround time for PPB data, and decrease time burden on existing LC-MS/MS instruments.