Can We Panelize Seizure?

Visual detection of seizures in mice using supervised machine learning

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

An integrated approach for early in vitro seizure prediction utilizing hiPSC neurons and human ion channel assays

Seizure liability remains a significant cause of attrition throughout drug development. Advances in stem cell biology coupled with an increased understanding of the role of ion channels in seizure offer an opportunity for a new paradigm in screening. We assessed the activity of 15 pro-seizurogenic compounds (7 CNS active therapies, 4 GABA receptor antagonists, and 4 other reported seizurogenic compounds) using automated electrophysiology against a panel of 14 ion channels (Nav1.1, Nav1.2, Nav1.6, Kv7.2/7.3, Kv7.3/7.5, Kv1.1, Kv4.2, KCa4.1, Kv2.1, Kv3.1, KCa1.1, GABA α1β2γ2, nicotinic α4β2, NMDA 1/2A). These were selected based on linkage to seizure in genetic/pharmacological studies. Fourteen compounds demonstrated at least one "hit" against the seizure panel and 11 compounds inhibited 2 or more ion channels. Next, we assessed the impact of the 15 compounds on electrical signaling using human-induced pluripotent stem cell neurons in microelectrode array (MEA). The CNS active therapies (amoxapine, bupropion, chlorpromazine, clozapine, diphenhydramine, paroxetine, quetiapine) all caused characteristic changes to electrical activity in key parameters indicative of seizure such as network burst frequency and duration. The GABA antagonist picrotoxin increased all parameters, but the antibiotics amoxicillin and enoxacin only showed minimal changes. Acetaminophen, included as a negative control, caused no changes in any of the parameters assessed. Overall, pro-seizurogenic compounds showed a distinct fingerprint in the ion channel/MEA panel. These studies highlight the potential utility of an integrated in vitro approach for early seizure prediction to provide mechanistic information and to support optimal drug design in early development, saving time and resources.

"Appraisal of state-of-the-art" The 2021 Distinguished Service Award of the Safety Pharmacology Society: Reflecting on the past to tackle challenges ahead

This appraisal of state-of-the-art manuscript highlights and expands upon the thoughts conveyed in the lecture of Dr. Jean-Pierre Valentin, recipient of the 2021 Distinguished Service Award of the Safety Pharmacology Society, given on the 2nd December 2021. The article reflects on the strengths, weaknesses, opportunities, and threats that surrounded the evolution of safety and secondary pharmacology over the last 3 decades with a particular emphasis on pharmaceutical drug development delivery, scientific and technological innovation, complexities of regulatory framework and people leadership and development. The article further built on learnings from past experiences to tackle constantly emerging issues and evolving landscape whilst being cognizant of the challenges facing these disciplines in the broader drug development and societal context.

Gaps and challenges in nonclinical assessments of pharmaceuticals: An FDA/CDER perspective on considerations for development of new approach methodologies

Previously, we provided an FDA/CDER perspective on nonclinical testing strategies and briefly discussed the opportunities and challenges of using new approach methodologies (NAMs) in drug development, especially for regulatory purposes. To facilitate the integration of NAMs into nonclinical regulatory testing, we surveyed the CDER Pharmacology/Toxicology community to identify the nonclinical challenges faced by CDER review staff, including gaps and areas of concern underserved by current nonclinical testing approaches, and to understand how development of NAMs with specific contexts of use (COUs) could potentially alleviate them. Survey outcomes were coalesced into CDER-identified needs for which NAMs with specific COUs could potentially be developed to address gaps and challenges in nonclinical safety assessments. We also discussed the current FDA procedure for validation and qualification of NAMs intended to inform regulatory decisions. This manuscript is intended to facilitate productive discussions and collaborations with regulatory, government, and academic stakeholders within the drug development community regarding the development and regulatory use of NAMs and their role in safety and efficacy assessment of pharmaceuticals.

Gene Expression Profile as a Predictor of Seizure Liability

Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.

Assaying Spontaneous Network Activity and Cellular Viability Using Multi-Well Microelectrode Arrays

Microelectrode array (MEA) technology is a neurophysiological method that allows for the measurement of spontaneous or evoked neural activity to determine chemical effects thereon. Following assessment of compound effects on multiple endpoints that evaluate network function, a cell viability endpoint in the same well is determined using a multiplexed approach. Recently, it has become possible to measure electrical impedance of cells attached to the electrodes, where greater impedance indicates greater number of cells attached. This would allow rapid and repeated assessments of cell health as the neural network develops in longer exposure assays without impacting cell health. Typically, the lactate dehydrogenase (LDH) assay for cytotoxity and CellTiter-Blue® (CTB) assay for cell viability are only performed at the end of the chemical exposure period because these assays involve lysing of the cells. Procedures describing the multiplexed methods in acute and network formation screening are included in this chapter.

Raster plots machine learning to predict the seizure liability of drugs and to identify drugs

In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.

Analysis of signal components < 500 Hz in brain organoids coupled to microelectrode arrays: A reliable test-bed for preclinical seizure liability assessment of drugs and screening of antiepileptic drugs

Brain organoids with three-dimensional structure and tissue-like function are highly demanded for brain disease research and drug evaluation. However, to our knowledge, methods for measuring and analyzing brain organoid function have not been developed yet. This study focused on the frequency components of an obtained waveform below 500 Hz using planner microelectrode array (MEA) and evaluated the response to the convulsants pentylenetetrazol (PTZ) and strychnine as well as the antiepileptic drugs (AEDs) perampanel and phenytoin. Sudden and persistent seizure-like firing was observed with PTZ administration, displaying a concentration-dependent periodic activity with the frequency component enhanced even in one oscillation characteristic. On the other hand, in the administration of AEDs, the frequency of oscillation decreased in a concentration-dependent manner and the intensity of the frequency component in one oscillation also decreased. Interestingly, at low doses of phenytoin, a group of synchronized bursts was formed, which was different from the response to the perampanel. Frequency components contained information on cerebral organoid function, and MEA was proven useful in predicting the seizure liability of drugs and evaluating the effect of AEDs with a different mechanism of action. In addition, frequency component analysis of brain organoids using MEA is an important analysis method to perform in vitro to in vivo extrapolation in the future, which will help explore the function of the organoid itself, study human brain developments, and treat various brain diseases.

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Frequency analysis <500 Hz was performed in brain organoids coupled to planner microelectrode arrays (MEA).

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Concentration-dependent changes in frequency components were detected in responses to convulsants and antiepileptic drugs (AEDs).

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Analysis of signal components <500 Hz in brain organoids is a useful method for preclinical seizure liability assessment of drugs and screening of antiepileptic drugs.