Best practice and reproducible science are required to advance artificial intelligence in real-world applications.

Drug-induced liver injury (DILI) is one of the most significant concerns in medical practice but yet it still cannot be fully recapitulated with existing in vivo, in vitro and in silico approaches. To address this challenge, Chen et al. [ 1] developed a deep learning-based DILI prediction model based on chemical structure information alone. The reported model yielded an outstanding prediction performance (i.e. 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913 for AUC, accuracy, recall, precision, F1-score and specificity, respectively, on a test set), far outperforming all publicly available and similar in silico DILI models. This extraordinary model performance is counter-intuitive to what we know about the underlying biology of DILI and the principles and hypothesis behind this type of in silico approach. In this Letter to the Editor, we raise awareness of several issues concerning data curation, model validation and comparison practices, and data and model reproducibility.

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.

Drug-induced liver injury (DILI) is the most frequently reported single cause of safety-related withdrawal of marketed drugs. It is essential to identify drugs with DILI potential at the early stages of drug development. In this study, we describe a deep learning-powered DILI (DeepDILI) prediction model created by combining model-level representation generated by conventional machine learning (ML) algorithms with a deep learning framework based on Mold2 descriptors. We conducted a comprehensive evaluation of the proposed DeepDILI model performance by posing several critical questions: (1) Could the DILI potential of newly approved drugs be predicted by accumulated knowledge of early approved ones? (2) is model-level representation more informative than molecule-based representation for DILI prediction? and (3) could improved model explainability be established? For question 1, we developed the DeepDILI model using drugs approved before 1997 to predict the DILI potential of those approved thereafter. As a result, the DeepDILI model outperformed the five conventional ML algorithms and two state-of-the-art ensemble methods with a Matthews correlation coefficient (MCC) value of 0.331. For question 2, we demonstrated that the DeepDILI model's performance was significantly improved (i.e., a MCC improvement of 25.86% in test set) compared with deep neural networks based on molecule-based representation. For question 3, we found 21 chemical descriptors that were enriched, suggesting a strong association with DILI outcome. Furthermore, we found that the DeepDILI model has more discrimination power to identify the DILI potential of drugs belonging to the World Health Organization therapeutic category of 'alimentary tract and metabolism'. Moreover, the DeepDILI model based on Mold2 descriptors outperformed the ones with Mol2vec and MACCS descriptors. Finally, the DeepDILI model was applied to the recent real-world problem of predicting any DILI concern for potential COVID-19 treatments from repositioning drug candidates. Altogether, this developed DeepDILI model could serve as a promising tool for screening for DILI risk of compounds in the preclinical setting, and the DeepDILI model is publicly available through https://github.com/TingLi2016/DeepDILI.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment.

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?

In vitro toxicogenomics (TGx) has the potential to replace or supplement animal studies. However, TGx studies often suffer from a limited sample size and cell types. Meanwhile, transcriptomic data have been generated for tens of thousands of compounds using cancer cell lines mainly for drug efficacy screening. Here, we asked the question of whether these types of transcriptomic data can be used to support toxicity assessment. We compared transcriptomic profiles from three cancer lines (HL60, MCF7, and PC3) from the CMap data set with those using primary hepatocytes or in vivo repeated dose studies from the Open TG-GATEs database by using our previously reported pair ranking (PRank) method. We observed an encouraging similarity between HL60 and human primary hepatocytes (PRank score = 0.70), suggesting the two cellular assays could be potentially interchangeable. When the analysis was limited to drug-induced liver injury (DILI)-related compounds or genes, the cancer cell lines exhibited promise in DILI assessment in comparison with conventional TGx systems (i.e., human primary hepatocytes or rat in vivo repeated dose). Also, some toxicity-related pathways, such as PPAR signaling pathways and fatty acid-related pathways, were preserved across various assay systems, indicating the assay transferability is biological process-specific. Furthermore, we established a potential application of transcriptomic profiles of cancer cell lines for studying immune-related biological processes involving some specific cell types. Moreover, if PRank analysis was focused on only landmark genes from L1000 or S1500+, the advantage of cancer cell lines over the TGx studies was limited. In conclusion, repurposing of existing cancer-related transcript profiling data has great potential for toxicity assessment, particularly in predicting DILI.

Regulatory landscape of dietary supplements and herbal medicines from a global perspective.

The number of Individuals that use dietary supplements and herbal medicine products are continuous to increase in many countries. The context of usage of a dietary supplement varies widely from country-to-country; in some countries supplement use is just limited to general health and well-being while others permit use for medicinal purposes. To date, there is little consensus from country to country on the scope, requirements, definition, or even the terminology in which dietary supplement and herbal medicines categories could be classified. Transparent science-based quality standards for the ingredients across these regulatory frameworks/definitions becomes even more important given the international supply chain. Meanwhile, there has been a rapid advancement in emerging technologies and data science applied to the field. This review was conceived at the Global Summit on Regulatory Sciences that took place in Beijing on September 2018 (GSRS2018) which is organized by Global Coalition for Regulatory Science Research (GCRSR) that consists of the global regulatory agencies from over ten countries including the European Union. This review summarizes a significant portion of discussions relating to a longitudinal comparison of the status for dietary supplements and herbal medicines among the different national jurisdictions and to the extent of how new tools and methodologies can improve the regulatory application.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA.

BACKGROUND: Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. RESULTS: This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). CONCLUSIONS: This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Emerging technologies for food and drug safety.

Emerging technologies are playing a major role in the generation of new approaches to assess the safety of both foods and drugs. However, the integration of emerging technologies in the regulatory decision-making process requires rigorous assessment and consensus amongst international partners and research communities. To that end, the Global Coalition for Regulatory Science Research (GCRSR) in partnership with the Brazilian Health Surveillance Agency (ANVISA) hosted the seventh Global Summit on Regulatory Science (GSRS17) in Brasilia, Brazil on September 18-20, 2017 to discuss the role of new approaches in regulatory science with a specific emphasis on applications in food and medical product safety. The global regulatory landscape concerning the application of new technologies was assessed in several countries worldwide. Challenges and issues were discussed in the context of developing an international consensus for objective criteria in the development, application and review of emerging technologies. The need for advanced approaches to allow for faster, less expensive and more predictive methodologies was elaborated. In addition, the strengths and weaknesses of each new approach was discussed. And finally, the need for standards and reproducible approaches was reviewed to enhance the application of the emerging technologies to improve food and drug safety. The overarching goal of GSRS17 was to provide a venue where regulators and researchers meet to develop collaborations addressing the most pressing scientific challenges and facilitate the adoption of novel technical innovations to advance the field of regulatory science.

Scaling bioinformatics applications on HPC.

BACKGROUND: Recent breakthroughs in molecular biology and next generation sequencing technologies have led to the expenential growh of the sequence databases. Researchrs use BLAST for processing these sequences. However traditional software parallelization techniques (threads, message passing interface) applied in newer versios of BLAST are not adequate for processing these sequences in timely manner. METHODS: A new method for array job parallelization has been developed which offers O(T) theoretical speed-up in comparison to multi-threading and MPI techniques. Here T is the number of array job tasks. (The number of CPUs that will be used to complete the job equals the product of T multiplied by the number of CPUs used by a single task.) The approach is based on segmentation of both input datasets to the BLAST process, combining partial solutions published earlier (Dhanker and Gupta, Int J Comput Sci Inf Technol_5:4818-4820, 2014), (Grant et al., Bioinformatics_18:765-766, 2002), (Mathog, Bioinformatics_19:1865-1866, 2003). It is accordingly referred to as a "dual segmentation" method. In order to implement the new method, the BLAST source code was modified to allow the researcher to pass to the program the number of records (effective number of sequences) in the original database. The team also developed methods to manage and consolidate the large number of partial results that get produced. Dual segmentation allows for massive parallelization, which lifts the scaling ceiling in exciting ways. RESULTS: BLAST jobs that hitherto failed or slogged inefficiently to completion now finish with speeds that characteristically reduce wallclock time from 27 days on 40 CPUs to a single day using 4104 tasks, each task utilizing eight CPUs and taking less than 7 minutes to complete. CONCLUSIONS: The massive increase in the number of tasks when running an analysis job with dual segmentation reduces the size, scope and execution time of each task. Besides significant speed of completion, additional benefits include fine-grained checkpointing and increased flexibility of job submission. "Trickling in" a swarm of individual small tasks tempers competition for CPU time in the shared HPC environment, and jobs submitted during quiet periods can complete in extraordinarily short time frames. The smaller task size also allows the use of older and less powerful hardware. The CDRH workhorse cluster was commissioned in 2010, yet its eight-core CPUs with only 24GB RAM work well in 2017 for these dual segmentation jobs. Finally, these techniques are excitingly friendly to budget conscious scientific research organizations where probabilistic algorithms such as BLAST might discourage attempts at greater certainty because single runs represent a major resource drain. If a job that used to take 24 days can now be completed in less than an hour or on a space available basis (which is the case at CDRH), repeated runs for more exhaustive analyses can be usefully contemplated.

Succinamide derivatives of melampomagnolide B and their anti-cancer activities.

A series of succinamide derivatives of melampomagnolide B have been synthesized by coupling MMB monosuccinate (2) with various heterocyclic amines to afford compounds 3a-3l. MMB monosuccinate was also reacted with terminal diaminoalkanes to afford dimeric succinamido analogs of MMB (4a-4h). These succinamide analogs of MMB were evaluated for their anti-cancer activity against a panel of sixty human cancer cell lines. Analogs 3d-3i and dimers 4f-4g exhibited promising anti-cancer activity with GI50 values ranging from 0.28 to 33.5µM against most of the cell lines in the panel. The dimeric analogs 4f and 4g were identified as lead compounds with GI50 values in the nanomolar range (GI50=280-980nM) against several cell lines in the panel; i.e. leukemia cell lines CCRF-CEM, HL-60(TB), K-562, MOLT-4, RPMI-8226 and SR; and solid tumor cell lines NCI-H522 (non-small cell lung cancer), SW-620 and HCT-116 (colon cancer), LOX IMVI (melanoma), RXF 393 (renal cancer), and MCF7, BT-549 and MDA-MB-468 (breast cancer). Succinamide analogs 3a, 3c-3l and 4b-4h were also evaluated for their apoptotic activity against M9-ENL1 acute myelogenous leukemia cells; compounds 3h-3j and 4g were equipotent with parthenolide, exhibiting LC50 values in the range 4.1-8.1µM. Molecular docking studies indicate that these molecules interact covalently with the highly conserved Cys-46 residue of the N-terminal lobe (1-109) of human IKKß to inhibit the NFκB transcription factor complex, resulting in down-regulation of anti-apoptotic genes under NFκB control.

PEGylation of a High-Affinity Anti-(+)Methamphetamine Single Chain Antibody Fragment Extends Functional Half-Life by Reducing Clearance.

PURPOSE: Methamphetamine (METH) abuse is a worldwide drug problem, yet no FDA-approved pharmacological treatments are available for METH abuse. Therefore, we produced an anti-METH single chain antibody fragment (scFv7F9Cys) as a pharmacological treatment for METH abuse. ScFv's have a short half-life due to their small size, limiting their clinical use. Thus, we examined the pharmacokinetic effects of conjugating poly(ethylene) glycol (-PEG) to scFv7F9Cys to extend its functional half-life. METHODS: The affinity of scFv7F9Cys and PEG conjugates to METH was determined in vitro via equilibrium dialysis saturation binding. Pharmacokinetic and parameters of scFv7F9Cys and scFv7F9Cys-PEG20K (30 mg/kg i.v. each) and their ability to bind METH in vivo were determined in male Sprague-Dawley rats receiving a subcutaneous infusion of METH (3.2 mg/kg/day). RESULTS: Of three PEGylated conjugates, scFv7F9Cys-PEG20K was determined the most viable therapeutic candidate. PEGylation of scFv7F9Cys did not alter METH binding functionality in vitro, and produced a 27-fold increase in the in vivo half-life of the antibody fragment. Furthermore, total METH serum concentrations increased following scFv7F9Cys or scFv7F9Cys-PEG20K administration, with scFv7F9Cys-PEG20K producing significantly longer changes in METH distribution than scFv7F9Cys. CONCLUSIONS: PEGylation of scFv7F9Cys significantly increase the functional half-life of scFv7F9Cys, suggesting it may be a long-lasting pharmacological treatment option for METH abuse.

Heteroaromatic analogs of the resveratrol analog DMU-212 as potent anti-cancer agents.

Heteroaromatic analogs of DMU-212 (8-15) have been synthesized and evaluated for their anti-cancer activity against a panel of 60 human cancer cell lines. These novel analogs contain a trans-3,4,5-trimethoxystyryl moiety attached to the C2 position of indole, benzofuran, benzothiazole or benzothiophene ring (8, 11, 13 and 14, respectively) and showed potent growth inhibition in 85% of the cancer cell lines examined, with GI50 values <1 µM. Interestingly, trans-3,4- and trans-3,5-dimethoxystyryl DMU-212 analogs 9, 10, 12 and 15 exhibited significantly less growth inhibition than their 3,4,5-trimethoxystyryl counterparts, suggesting that the trans-3,4,5-trimethoxystyryl moiety is an essential structural element for the potent anti-cancer activity of these heterocyclic DMU-212 analogs. Molecular modeling studies showed that the four most active compounds (8, 11, 13 and 14) all bind to the colchicine binding site on tubulin, and that their binding modes are similar to that of DMU-212.

Molecular dynamics guided design of tocoflexol: a new radioprotectant tocotrienol with enhanced bioavailability.

There is a pressing need to develop safe and effective radioprotector/radiomitigator agents for use in accidental or terrorist-initiated radiological emergencies. Naturally occurring vitamin E family constituents, termed tocols, that include the tocotrienols, are known to have radiation-protection properties. These agents, which work through multiple mechanisms, are promising radioprotectant agents having minimal toxicity. Although α-tocopherol (AT) is the most commonly studied form of vitamin E, the tocotrienols are more potent than AT in providing radioprotection and radiomitigation. Unfortunately, despite their very significant radioprotectant activity, tocotrienols have very short plasma half-lives and require dosing at very high levels to achieve necessary therapeutic benefits. Thus, it would be highly desirable to develop new vitamin E analogues with improved pharmacokinetic properties, specifically increased elimination half-life and increased area under the plasma level versus time curve. The short elimination half-life of the tocotrienols is related to their low affinity for the α-tocopherol transfer protein (ATTP), the protein responsible for maintaining the plasma level of the tocols. Tocotrienols have less affinity for ATTP than does AT, and thus have a longer residence time in the liver, putting them at higher risk for metabolism and biliary excretion. We hypothesized that the low-binding affinity of tocotrienols to ATTP is due to the relatively more rigid tail structure of the tocotrienols in comparison with that of the tocopherols. Therefore, compounds with a more flexible tail would have better binding to ATTP and consequently would have longer elimination half-life and, consequently, an increased exposure to drug, as measured by area under the plasma drug level versus time curve (AUC). This represents an enhanced residence of drug in the systemic circulation. Based on this hypothesis, we developed a new class of vitamin E analogues, the tocoflexols, which maintain the superior bioactivity of the tocotrienols with the potential to achieve the longer half-life and larger AUC of the tocopherols.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Tocotrienols Provide Radioprotection to Multiple Organ Systems through Complementary Mechanisms of Antioxidant and Signaling Effects.

Tocotrienols have powerful radioprotective properties in multiple organ systems and are promising candidates for development as clinically effective radiation countermeasures. To facilitate their development as clinical radiation countermeasures, it is crucial to understand the mechanisms behind their powerful multi-organ radioprotective properties. In this context, their antioxidant effects are recognized for directly preventing oxidative damage to cellular biomolecules from ionizing radiation. However, there is a growing body of evidence indicating that the radioprotective mechanism of action for tocotrienols extends beyond their antioxidant properties. This raises a new pharmacological paradigm that tocotrienols are uniquely efficacious radioprotectors due to a synergistic combination of antioxidant and other signaling effects. In this review, we have covered the wide range of multi-organ radioprotective effects observed for tocotrienols and the mechanisms underlying it. These radioprotective effects for tocotrienols can be characterized as (1) direct cytoprotective effects, characteristic of the classic antioxidant properties, and (2) other effects that modulate a wide array of critical signaling factors involved in radiation injury.

Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation.

[This corrects the article DOI: 10.3389/frai.2021.757780.].