Characterization of a high throughput human stem cell cardiomyocyte assay to predict drug-induced changes in clinical electrocardiogram parameters

Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accurate diagnosis and medications for effective treatment are lacking, in part due to the historical use of preclinical model systems that do not translate well to the clinic for neurological disorders, such as rodents and heterologous cell lines. Human-induced pluripotent stem cells (hiPSCs) are a promising in vitro system for modeling NDDs, providing opportunities to understand mechanisms driving NDDs in human neurons. Functional assays, including patch clamping, multielectrode array, and imaging-based assays, are popular tools employed with hiPSC disease models for disease investigation. Recent progress in machine learning (ML) algorithms also presents unprecedented opportunities to advance the NDD research process. In this review, we compare two-dimensional and three-dimensional hiPSC formats for disease modeling, discuss the applications of functional assays, and offer insights on incorporating ML into hiPSC-based NDD research and drug screening.

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.

Advances in ion channel high throughput screening: where are we in 2023?

INTRODUCTION

Automated Patch Clamp (APC) technology has become an integral element in ion channel research, drug discovery and development pipelines to overcome the use of the highly time-consuming manual patch clamp (MPC) procedures. This automated technology offers increased throughput and promises a new model in obtaining ion channel recordings, which has significant relevance to the development of novel therapies and safety profiling of candidate therapeutic compounds.

AREAS COVERED

This article reviews the recent innovations in APC technology, including platforms, and highlights how they have facilitated usage in both industry and academia. The review also provides an overview of the ion channel research endeavors and how APC platforms have contributed to the understanding of ion channel research, pharmacological tools and therapeutics. Furthermore, the authors provide their opinion on the challenges and goals for APC technology going forward to accelerate academic research and drug discovery across a host of therapeutic areas.

EXPERT OPINION

It is clear that APC technology has progressed drug discovery programs, specifically in the field of neuroscience and cardiovascular research. The challenge for the future is to keep pace with fundamental research and improve translation of the large datasets obtained.

Evaluation of levocetirizine in beagle dog and cynomolgus monkey telemetry assays: Defining the no QTc effect profile by timepoint and concentration-QTc analysis

In prior clinical studies, levocetirizine (LEVO) has demonstrated no effect on ventricular repolarization (QTc intervals), therefore it is a relevant negative control to assess in nonclinical assays to define low proarrhythmic risk. LEVO was tested in beagle dog and cynomolgus monkey (nonhuman primate [NHP]) telemetry models to understand the nonclinical-clinical translation of this negative control. One oral dose of vehicle, LEVO (10 mg/kg/species) or moxifloxacin (MOXI; 30 mg/kg/dog; 80 mg/kg/NHP) was administered to instrumented animals (N = 8/species) using a cross-over dosing design; MOXI was the in-study positive control. Corrected QT interval values (QTcI) were calculated using an individual animal correction factor. Blood samples were taken for drug exposure during telemetry and for pharmacokinetic (PK) analysis (same animals; different day) for exposure-response (C-QTc) modeling. Statistical analysis of QTc-by-timepoint data showed that LEVO treatment was consistent with vehicle, thus no effect on ventricular repolarization was observed over 24 h in both species. PK analysis indicated that LEVO-maximum concentration levels in dogs (range: 12,300-20,100 ng/ml) and NHPs (range: 4090-12,700 ng/ml) were ≥4-fold higher than supratherapeutic drug levels in clinical QTc studies. Slope analysis values in dogs (0.00019 ms/ng/ml) and NHPs (0.00016 ms/ng/ml) were similar to the human C-QTc relationship and indicated no relationship between QTc intervals and plasma levels of LEVO. MOXI treatment caused QTc interval prolongation (dog: 18 ms; NHP: 29 ms). The characterization of LEVO in these non-rodent telemetry studies further demonstrates the value and impact of the in vivo QTc assay to define a "no QTc effect" profile and support clinical safety assessment.

Use of high throughput ion channel profiling and statistical modeling to predict off-target arrhythmia risk - One pharma's experience and perspective

INTRODUCTION

The use of high throughput patch clamp profiling to determine mixed ion channel-mediated arrhythmia risk was assessed using profiling data generated using proprietary internal and clinical reference compounds. We define the reproducibility of the platform and highlight inherent platform issues. The data generated was used to develop predictive models for cardiac arrhythmia risk, specifically Torsades de Pointes (TdP).

METHODS

A retrospective analysis was performed using profiling data generated over a 3-year period, including patch clamp data from hERG, Ca_v1.2, and Na_v1.5 (peak/late), together with hERG binding.

RESULTS

Assay reproducibility was robust over the 3-year period examined. High throughput hERG patch IC₅₀ values correlated well with GLP-hERG data (Pearson = 0.87). A disconnect between hERG binding and patch was observed for ∼10% compounds and trended with passive cellular permeability. hERG and Ca_v1.2 potency did not correlate for proprietary compounds, with more potent hERG compounds showing selectivity versus Ca_v1.2. For clinical compounds where hERG and Ca_v1.2 activity was more balanced, an analysis of TdP risk versus hERG/Ca_v1.2 ratio demonstrated low TdP probability when the hERG/Ca_v1.2 potency ratios were < 1. Modeling of clinical compound data revealed a lack of impact of the Na_v1.5 (late) current in predicting TdP. Moreover, models using hERG binding data (ROC AUC = 0.876) showed an improved ability to predict TdP risk versus hERG patch clamp (ROC AUC = 0.787).

DISCUSSION

The data highlight the value of high throughput patch clamp data in the prediction of TdP risk, as well as some potential limitations with this approach.

Adventures and Advances in Time Travel With Induced Pluripotent Stem Cells and Automated Patch Clamp

In the Hollywood blockbuster “The Curious Case of Benjamin Button” a fantastical fable unfolds of a man’s life that travels through time reversing the aging process; as the tale progresses, the frail old man becomes a vigorous, vivacious young man, then man becomes boy and boy becomes baby. The reality of cellular time travel, however, is far more wondrous: we now have the ability to both reverse and then forward time on mature cells. Four proteins were found to rewind the molecular clock of adult cells back to their embryonic, “blank canvas” pluripotent stem cell state, allowing these pluripotent stem cells to then be differentiated to fast forward their molecular clocks to the desired adult specialist cell types. These four proteins – the “Yamanaka factors” – form critical elements of this cellular time travel, which deservedly won Shinya Yamanaka the Nobel Prize for his lab’s work discovering them. Human induced pluripotent stem cells (hiPSCs) hold much promise in our understanding of physiology and medicine. They encapsulate the signaling pathways of the desired cell types, such as cardiomyocytes or neurons, and thus act as model cells for defining the critical ion channel activity in healthy and disease states. Since hiPSCs can be derived from any patient, highly specific, personalized (or stratified) physiology, and/or pathophysiology can be defined, leading to exciting developments in personalized medicines and interventions. As such, hiPSC married with high throughput automated patch clamp (APC) ion channel recording platforms provide a foundation for significant physiological, medical and drug discovery advances. This review aims to summarize the current state of affairs of hiPSC and APC: the background and recent advances made; and the pros, cons and challenges of these technologies. Whilst the authors have yet to finalize a fully functional time traveling machine, they will endeavor to provide plausible future projections on where hiPSC and APC are likely to carry us. One future projection the authors are confident in making is the increasing necessity and adoption of these technologies in the discovery of the next blockbuster, this time a life-enhancing ion channel drug, not a fantastical movie.