FDA scientists from across the agency have contributed to a growing number of peer-reviewed journals on topics related to advancing alternative methods. The list below includes examples of FDA co-authored articles on alternative methods and is updated regularly.
Report of the Assay Guidance Workshop on 3-Dimensional Tissue Models for Antiviral Drug Development, Jordan R, Ford-Scheimer SL, Alarcon RM, Atala A, Borenstein JT, Brimacombe KR, Cherry S, Clevers H, Davis MI, Funnell SGP, Gehrke L, Griffith LG, Grossman AC, Hartung T, Ingber DE, Kleinstreuer NC, Kuo CJ, Lee EM, Mummery CL, Pickett TC, Ramani S, Rosado-Olivieri EA, Struble EB, Wan Z, Williams MS, Hall MD, Ferrer M, Markossian S. The Journal of Infectious Diseases September 2023, jiad334. 10.1093/infdis/jiad334.
This article provides a summary of a recent workshop on 3D tissue models’ utility and limitations in antiviral drug discovery. It highlights recent examples of their use in drug discovery, including when such models predicted some of the outcomes seen in animal models of SARS-CoV-2 infection and in human studies. The article also emphasizes the need for validation and qualification of these assays to support regulatory filings and for close collaboration between stakeholders.
Characterizing On-Chip Angiogenesis Induction in a Microphysiological System as a Functional Measure of Mesenchymal Stromal Cell Bioactivity, Johnny Lam, James Yu, Byungjun Lee, Courtney Campagna, Sanghee Yoo, Kyusuk Baek, Noo Li Jeon, Kyung E. Sung. Advanced Biology. July 2023, 2300094. 10.1002/adbi.202300094.
For cellular therapeutic products, it is critical to develop a reliable analytical tool in order to consistently produce functional cells related to a specified therapeutic indication. The purpose of this study is to provide a novel bioassay for assessing clinically relevant functional activity of cellular therapy products in inducing new blood vessel formation for therapeutic applications such as wound repair or ischemia.
Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021, Qi Liu, Ruihao Huang, Julie Hsieh, Hao Zhu, Mo Tiwari, Guansheng Liu, Daphney Jean, M. Khair ElZarrad, Tala Fakhouri, Steven Berman, Billy Dunn, Matthew C. Diamond, Shiew-Mei Huang. Clinical Pharmacology & Therapeutics. April 2023; 113(4):771-774. 10.1002/cpt.2668.
The US Food and Drug Administration is seeing a rapid increase in the number of submissions that use artificial intelligence to facilitate drug development. Such approaches have been applied to all stages of drug development.
TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes, Li T, Roberts R, Liu Z, Tong W. Chemical Research in Toxicology. June 2023; 36(6):916-925. 10.1021/acs.chemrestox.3c00037.
This article describes the use of mRNA data to create a computer (aka in silico) model that provides insight into potential toxicity of organs, ages and sexes that were not used to train the model. This may enable a more holistic understanding of the potential for a drug to cause injury.
Parallel evaluation of alternative skin barrier models and excised human skin for dermal absorption studies in vitro, Salminen AT, Davis KJ, Felton RP, Nischal N, VonTungeln LS, Beland FA, Derr K, Brown PC, Ferrer M, Katz LM, Kleinstreuer NC, Leshin J, Manga P, Sadrieh N, Xia M, Fitzpatrick SC, Camacho L. Toxicology in Vitro Volume 91, September 2023, 105630 10.1016/j.tiv.2023.105630.
In this study, an experimental workflow is proposed to help compare the performance of alternative skin models with that of excised human skin and understand the potential of each skin model to predict human exposure to topical chemicals of interest to the FDA.
Machine Learning and Deep Learning Promotes Computational Toxicology for Risk Assessment of Chemicals, Kusko R, Hong H. In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 1). Ed. Hong H. Springer, Cham. 2023:1-17. 10.1007/978-3-031-20730-3_1. 10.1007/978-3-031-20730-3_1.
The ever-increasing panoply of potential toxicants precipitates the need for faster and more cost-effective methods for pinpointing toxicants and their methods of toxicity. Machine learning and deep learning approaches present the ability to move beyond simple correlations and instead identify more complex patterns, which is a much closer representation of the biological truth.
Machine Learning for Predicting Organ Toxicity, Liu J, Guo W, Dong F, Patterson TA, Hong H. In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 22). Ed. Hong H. Springer, Cham. 2023:519-537. 10.1007/978-3-031-20730-3_22.
Prediction of organ toxicity using machine learning is a tremendous challenge because of the complicated mechanisms of toxicity yet such an approach has the advantages of efficiency and low cost. This chapter reviews the current advances in this area.
Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals, Chen M, Liu J, Liao T-J, Ashby K, Wu Y, Wu L, Tong W, Hong H. In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 23). Ed. Hong H. Springer, Cham. 2023:541-561. 10.1007/978-3-031-20730-3_23.
Hepatotoxicity presents a significant challenge for drug development and regulatory science. The development of robust predictive models for evaluating the potential of liver injury in humans caused by drug candidates and chemicals is urgently needed. This chapter introduces machine learning technologies, together with recent case studies that exemplify the use of machine learning for resolving practical hepatotoxicity questions.
Gaps and challenges in nonclinical assessments of pharmaceuticals: An FDA/CDER perspective on considerations for development of new approach methodologies,Amy M. Avila, Ilona Bebenek, Donna L. Mendrick, Jackye Peretz, Jia Ya, and Paul C. Brown. Regulatory Toxicology and Pharmacology, Volume 139, March 2023, 105345. 10.1016/j.yrtph.2023.105345.
CDER reviewers identified areas not currently addressed by nonclinical studies. This paper discusses these gaps and where new approach methodologies might be useful.
Machine learning models for rat multigeneration reproductive toxicity prediction, Jie Liu, Wenjing Guo, Fan Dong, Jason Aungst, Suzanne Fitzpatrick, Tucker A. Patterson and Huixiao Hong. Front. Pharmacol., 27 September 2022. 10.3389/fphar.2022.1018226.
This paper reports a computational approach (i.e., machine learning) to predict rat multigeneration reproductive toxicity. The models were evaluated using internal and external validation which showed their capabilities for predicting reproductive toxicity of chemicals. The findings of this study demonstrated that machine learning models have a great potential to serve as alternative approaches to animal toxicity testing.
A microphysiological system-based potency bioassay for the functional quality assessment of mesenchymal stromal cells targeting vasculogenesis, Johnny Lam, Byungjun Lee ,James Yu, Brian J. Kwee, Yangji Kim, Jiho Kim, Yeongmin Choi, Jun Sung Yoon, Youngsoo Kim, Kyusuk Baek, Noo Li Jeon, Kyung E.Sung. Biomaterials 290 (2022) 121826 . 10.1016/j.biomaterials.2022.121826.
In this study, we developed a human blood vessel chips to identify mesenchymal stromal cells (MSCs) that are more likely to stimulate blood vessel regeneration via cell signaling as an alternative to the traditional murine models and in vitro scratch-based wound healing assay. People are interested in using human MSCs to treat patients with vascular diseases. However, the ability of MSCs to stimulate vessel regeneration depends on the cell lines made from donors and the manufacturing conditions used to prepare the cells for therapeutic use. This new assay might be able to help predict which MSCs will effectively stimulate vessel regeneration. This would help manufacturers make safer and more effective therapeutic products that are based on MSCs.
Use of Human iPSC-CMs in Nonclinical Regulatory Studies for Cardiac Safety Assessment, Xi Yang, Alexandre J S Ribeiro, Li Pang, David G Strauss. Toxicological Sciences. 13 September 2022. 10.1093/toxsci/kfac095.
This retrospective review summarized the use of hiPSC-CMs in support of regulatory submissions to the FDA for drug-induced cardiotoxicity detection. The take-home message is that regulatory acceptance of hiPSC-CMs data is dependent on both the context of use and accurate data interpretation.
Effects of Serum and Compound Preparation Methods on Delayed Repolarization Evaluation with Human iPSC-CMs, Wei F, Pence L, Woodling K, Bagam P, Beger R, Gamboa da Costa G, Pang L. Toxicological Sciences. July 2022; 188(1): 48-61. 10.1093/toxsci/kfac043.
This paper discusses some of the technical issues related to the use of alternative models. In this instance the paper looked at the use of human-induced pluripotent stem cell-derived cardiomyocytes used in the CIPA initiative.
Co-Culture of Human Primary Hepatocytes and Nonparenchymal Liver Cells in the Emulate® Liver-Chip for the Study of Drug-Induced Liver Injury, Qiang Shi,Ayesha Arefin,Lijun Ren,Katy S. Papineau,Dustyn A. Barnette,Laura K. Schnackenberg,Jessica J. Hawes,Mark Avigan,Donna L. Mendrick,Lorna Ewart,Janey Ronxhi. Current Protocols 05 July 2022. 10.1002/cpz1.478, This link for FDA Staff only.
Current models are imperfect in identifying all drugs that cause liver injury, particularly those that affect a small percent of patients. (Those are usually found only after 1 million or more patients are exposed.) This publication provides a step-by-step description of the procedures for co-culturing of four major liver cell types of human origin in a recently developed Liver-on-a-Chip system. Our data suggest this new platform better maintains the functions than standard approaches to cultured liver cells, recapitulates some adverse responses observed in humans, and thus might serve as an improved tool for the study of liver adverse events.
Current ecotoxicity testing needs among selected U.S. federal agencies. Regulatory Toxicology and Pharmacology Volume 133, August 2022, 105195. 10.1016/j.yrtph.2022.105195
This review summarizes the ecotoxicity data needs of six U.S. federal agencies and will inform development and implementation of non-animal methods.
Preclinical In Vitro Model to Assess the Changes in Permeability and Cytotoxicity of Polarized Intestinal Epithelial Cells during Exposure Mimicking Oral or Intravenous Routes: An Example of Arsenite Exposure. Parajuli P, Gokulan K, Khare S. International Journal of Molecular Sciences. 2022 May, 23(9), 4851. 10.3390/ijms23094851
Chemicals and drugs can cause different effects on the gut health based on whether they are given by shot in the vein (intravenous exposure) or as a pill that is swallowed (oral exposure). The authors used intestinal cells in culture and found they could assess the difference in the gut barrier function due to the exposure route. This preclinical in vitro model will reduce the use of animals for testing the impact of exposure route.
Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues, Chang X, Palmer J, Lumen A, Lee UJ, Ceger P, Mansouri K, Sprankle C, Donley E, Bell S, Knudsen TB, Wambaugh J, Cook B, Allen D, Kleinstreuer N., Birth Defects Res. May 2022;10.1002/bdr2.2019
IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making, Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M., Toxics 2022, 10(5), 232; 10.3390/toxics10050232
This publication is a compilation of ideas and opinions of scientists from different organizations, including regulatory agencies from across the world, on using alternatives to traditional animal research to evaluate the safety of diverse types of chemicals, such as drugs, food substances, and environmental chemicals. It also highlights challenges and offers suggestions to overcome those challenges involved in use of these newer methods.
Harnessing the Biology of Canine Intestinal Organoids to Heighten Understanding of Inflammatory Bowel Disease Pathogenesis and Accelerate Drug Discovery: A One Health Approach, Kopper JJ, Iennarella-Servantez C, Jergens AE, Sahoo DK, Guillot E, Bourgois-Moche A, Martinez MN, Allenspach K and Mochel JP, Frontiers in Toxicology. 2021 Nov; 3:773953. 10.3389/ftox.2021.773953
Microfluidic Separation of Canine Adipose-Derived Mesenchymal Stromal Cells, Liu Z, Screven R, Yu D, Boxer L, Myers MJ, Han J, Devireddy LR. 2021 Tissue Engineering Part C: Methods Vol. 27, No. 8. 10.1089/ten.tec.2021.0082
Impact of Chronic Tetracycline Exposure on Human Intestinal Microbiota in a Continuous Flow Bioreactor Model, Youngbeom A, Jung JY, Kweon O, Veach BT, Khare S, Gokulan K, Pineiro SA, Cerniglia, CE. Antibiotics 2021, 10(8), 886. 10.3390/antibiotics10080886
AI-based language models powering drug discovery and development, Liu Z, Roberts RA, Lal-Nag M, Chen X, Huang R, Tong W. Drug Discovery Today. 2021 Nov; 26(11):2593-2607. 10.1016/j.drudis.2021.06.009
Perspectives on the evaluation and adoption of complex in vitro models in drug development: Workshop with the FDA and the pharmaceutical industry (IQ MPS Affiliate), ALTEX - Alternatives to animal experimentation. 10.14573/altex.2112203
Effect of ketamine on gene expression in zebrafish embryos, Gu Q, Kanungo J. J Appl Toxicol. 2021 Dec; 41(12):2083-2089. 10.1002/jat.4199
DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation, Li T, Tong W, Roberts R, Liu Z and Thakkar S; Front. Artif. Intell. 2021 Nov 18; 4:757780. 10.3389/frai.2021.757780
Emerging technologies and their impact on regulatory science, Anklam E, Bahl MI, Ball R, Beger RD, Cohen J, Fitzpatrick S, Girard P, Halamoda-Kenzaoui B, Hinton D, Hirose A, Hoeveler A, Honma M, Hugas M, Ishida S, Kass GE, Kojima H, Krefting I, Liachenko S, Liu Y, Masters S, Marx U, McCarthy T, Mercer T, Patri A, Pelaez C, Pirmohamed M, Platz S, Ribeiro AJ, Rodricks JV, Rusyn I, Salek RM, Schoonjans R, Silva P, Svendsen CN, Sumner S, Sung K, Tagle D, Tong L, Tong W, Eijnden-van-Raaij JVD, Vary N, Wang T, Waterton J, Wang M, Wen H, Wishart D, Yuan Y, Slikker W Jr. Exp Biol Med (Maywood). 2021 Nov 16:15353702211052280.10.1177/15353702211052280
Tox-GAN: An AI Approach Alternative to Animal Studies—a Case Study with Toxicogenomics. Chen X, Roberts R, Tong W, Liu Z. Toxicological Sciences. 2021 Dec 31; kfab157 10.1093/toxsci/kfab157
Ex Vivo Human Colon Tissue Exposure to Pristine Graphene Activates Genes Involved in the Binding, Adhesion and Proliferation of Epithelial Cells, Lahiani MH, Gokulan K, Williams K, Khare S. International Journal of Molecular Sciences. 2021; 22(21):11443. 10.3390/ijms222111443
Human transthyretin binding affinity of halogenated thiophenols and halogenated phenols: An in vitro and in silico study, Yang X, Ou W, Zhao S, Wang L, Chen J, Kusko R, Hong H, Liu H. Chemosphere. 2021 Oct; 280:130627. 10.1016/j.chemosphere.2021.130627
Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose, Wu Y, Zhu J, Fu P, Tong W, Hong H, Chen M. International Journal of Environmental Research and Public Health 18:7139. 10.3390/ijerph18137139
Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data, McNally K, Sams C, Hogg A, Lumen A, Loizou G. Front Pharmacol. 12:692442 10.3389/fphar.2021.692442
Human microphysiological systems for drug development, Adrian Roth and MPS-WS Berlin 2019, Science 17 Sep 2021 Vol 373, Issue 6561 pp. 1304-1306. 10.1126/science.abc3734
A robotic system for real-time analysis of inhaled submicron and microparticles, Luis G.Valerio Jr., iScience, 29 September 2021, 103091
Reevaluation of the embryonic stem cell test, RS (2013) Volume 1: Issue 1 | pages 32-49
Transcriptomic time-series analysis of early development in olive from germinated embryos to juvenile tree, BMC Genomics volume 19, Article number: 824 (2018)
Gene expression profiling of cultured mouse testis fragments treated with ethinylestradiol, J. Toxicol. Sci. Vol.44,No.10,667-679, 2019
Evaluation of Culture Time and Media in an In Vitro Testis Organ Culture System, Birth Defects Res. 2017 Apr 17;109(7):465-474.
Evaluation of an in vitro mouse testis organ culture system for assessing male reproductive toxicity, Birth Defects Res. 2019 Jan 15;111(2):70-77
Metabolomics‐based pathway changes in testis fragments treated with ethinylestradiol in vitro, Birth Defects Res. 26 July 2019
Evaluation of pyrrolizidine alkaloid-induced genotoxicity using metabolically competent TK6 cell lines, Food and Chemical Toxicology, Volume 145, November 2020, 111662
Development and Application of TK6-derived Cells Expressing Human Cytochrome P450s for Genotoxicity Testing, Toxicological Sciences, Volume 175, Issue 2, June 2020, Pages 251–265
An FDA/CDER perspective on nonclinical testing strategies: Classical toxicology approaches and new approach methodologies (NAMs), Regul Toxicol Pharmacol. 2020 Jul;114:104662.
Opportunities for use of one species for longer-term toxicology testing during drug development: A cross-industry evaluation, Regul Toxicol Pharmacol. 2020 Jun;113:104624.
Strategies for Rapid Risk Assessment of Color Additives Used in Medical Devices, Toxicol Sci. 2019 Aug 6;kfz179.
An In Vitro Blood Flow Loop System for Evaluating the Thrombogenicity of Medical Devices and Biomaterials, ASAIO J. 2020 Feb;66(2):183-189.
Simultaneous UHPLC-MS/MS Method of Estradiol Metabolites to Support the Evaluation of Phase-2 Metabolic Activity of Induced Pluripotent Stem Cell Derived Hepatocytes, Journal of Chromatography B, Volumes 1126–1127, 15 September 2019, 121765
Liver Microphysiological Systems for Predicting and Evaluating Drug Effects, Clin Pharmacol Ther. 2019 Jul; 106(1): 139–147.
Considerations for an In Vitro, Cell-Based Testing Platform for Detection of Drug-Induced Inotropic Effects in Early Drug Development. Part 2: Designing and Fabricating Microsystems for Assaying Cardiac Contractility With Physiological Relevance Using Human iPSC-Cardiomyocytes, Front Pharmacol. 2019; 10: 934.
Use of high-throughput enzyme-based assay with xenobiotic metabolic capability to evaluate the inhibition of acetylcholinesterase activity by organophosphorous pesticides, Toxicol In Vitro. 2019 Apr;56:93-100.
Assessment of Intestinal absorption of 3-MCPD by Caco-2 cells, Toxicology in Vitro, Volume 67, September 2020, 104887
Biology-inspired microphysiological systems to advance patient benefit and animal welfare in drug development, ALTEX. 2020;37(3):365-394.
Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method, Journal of Pharmacological and Toxicological Methods, Volume 105, September 2020, 106895
Utilization of a model hepatotoxic compound, diglycolic acid, to evaluate liver Organ-Chip performance and in vitro to in vivo concordance, Food and Chemical Toxicology, Volume 146, December 2020, 111850
In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS,Journal of Applied Toxicology, First published: 13 July 2020
Predicting the mutagenic potential of chemicals in tobacco products using in silico toxicology tools, Toxicology Mechanisms and Methods Volume 30, 2020 - Issue 9, Pages 672-678.
Accelerating Innovation and Commercialization Through Standardization of Microfluidic-Based Medical Devices, Lab on a Chip, Issue 1, 2021
Assessment of Flow through Microchannels for Inertia-Based Sorting: Steps toward Microfluidic Medical Devices, Micromachines (Basel). 2020 Oct; 11(10): 886.
Tronolone, J. J., Lam, J., Agrawal, A. & Sung, K. Pumpless, modular, microphysiological systems enabling tunable perfusion for long-term cultivation of endothelialized lumens. Biomed Microdevices 23, 25 (2021).
Ronald L.Wange, Paul C.Brown, Karen L.Davis-Bruno, Implementation of the principles of the 3Rs of animal testing at CDER: Past, present and future, Regulatory Toxicology and Pharmacology, Volume 123, July 2021, 104953
Brian J Kwee and Kyung E Sung, Engineering microenvironments for manufacturing therapeutic cells, Experimental Biology and Medicine 2021; 0: 1–12. DOI: 10.1177/15353702211026922
Knudsen TB, Fitzpatrick SC, De Abrew, et al. Predictive Toxicology for Healthy Children. Toxicol Sci 2021 Apr 12;180(2):198-211.
Richard AM, Huang R, Waidyanatha S, Shinn P, et al. The Tox21 compound library: collaborative chemistry advancing toxicology. Chem Res Toxicol. 2021 Feb 15;34(2):189-216.
Choksi N, Lebrun S, Nguyen M, et al. Validation of the OptiSafe™ eye irritation test. Cutan Ocul Toxicol. 2020 Sep;39(3):180-192.
Kleinstreuer NC, Tong W, et al. Tetko, I V. Computational toxicology. Chem. Res. Toxicol. 2020, 33, 3, 687–688.
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity by Mansouri K, Kleinstreuer N, Abdelaziz AM, et al. Environ Health Perspect 128(2),7 February 2020:027002
An evaluation framework for new approach methodologies (NAMs) for human health safety assessment by Parish ST, Aschner M, Casey W, Corvaro M, Embry MR, Fitzpatrick S, et al. Regul Toxicol Pharmacol. 2020 Apr;112:104592.
Prior H, Baldrick P, Beken S, Booler S, Bower N, Brooker P, Brown P, et al. Opportunities for use of one species for longer-term toxicology testing during drug development: A cross-industry evaluation. Reg Toxicol Pharmacol 113, June 2020:104624.
Rovida C, Barton-Maclaren T, Benfenati E, Caloni F, Chandrasekera C, Chesne C, Cronin MTD, De Knecht J, Dietrich DR, Escher SE, Fitzpatrick S, et al. Internationalization of read-across as a validation new approach method (NAM) for regulatory toxicology. ALTEX. 2020;37(4):579-606.
Choksi NY, Truax J, Layton A, Matheson J, Mattie D, Varney T, Tao J, Yozzo K, McDougal AJ, Merrill J, Lowther D, et al. United States regulatory requirements for skin and eye irritation testing. Cutan Ocul Toxicol. 2019 Jun;38(2):141-155.
Patlewicz G, Lizarraga L, Rua D, Allen DG, Daniel A, Fitzpatrick SC, et al. Exploring current read-across applications and needs among selected U.S. federal agencies. Regul Toxicol Pharmacol. 2019 Aug;106:197-209.
Poston R, Hill R, Allen C, Casey W, Gatewood D, Levis R, et al. Achieving scientific and regulatory success in implementing non-animal approaches to human and veterinary rabies vaccine testing: A NICEATM and IABS workshop report. Biologicals 60, July 2019:8-14.
Strickland J, Daniel AB, Allen D, Aguila C, Ahir S, Bancos S, Craig E, Germolec D, Ghosh C, Hudson NL, Jacobs A, et al. Skin sensitization testing needs and data uses by U.S. regulatory and research agencies. Arch Toxicol. 2019 Feb;93(2):273-291.
Wei Z, Sakamuru S, Zhang L et al. Identification and profiling of environmental chemicals that inhibit the TGFβ/SMAD signaling pathway. Chem. Res. Toxicol. 2019, 32, 12, 2433–2444.
Gene expression analyses reveal potential mechanism of inorganic arsenic-induced apoptosis in zebrafish 10.1002/jat.4520 Inorganic arsenic is a developmental neurotoxicant. This study using zebrafish embryos showed that inorganic arsenic altered dopaminergic neuron development and produced supernumerary motor neurons in the spinal cord via the Sonic hedgehog (Shh) pathway that is essential for motor neuron development. The results may help understand why arsenic-exposed populations suffer from psychiatric disorders and motor neuron disease.
Inorganic arsenic alters the development of dopaminergic neurons but not serotonergic neurons and induces motor neuron development via Sonic hedgehog pathway in zebrafish 10.1016/j.neulet.2022.137042 This study on zebrafish embryos suggests that the p53 pathway is likely responsible for inorganic arsenic-induced apoptosis. Inorganic arsenic significantly reduced global DNA methylation in the zebrafish embryos as has been shown in humans, which may indicate that epigenetic factors could be dysregulated.
QSAR Modeling for Predicting Drug-Induced Liver Injury 10.1016/B978-0-443-15339-6.00009-6 This chapter summarized the development of quantitative structure-activity relationship (QSAR) models for DILI prediction using machine learning (ML) and deep learning (DL) and its progress in the past few years.
Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury 10.1016/B978-0-443-15339-6.00009-6 This chapter summarized the development of quantitative structure-activity relationship (QSAR) models for DILI prediction using machine learning (ML) and deep learning (DL) and its progress in the past few years.
BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk 10.3389/frai.2021.729834 A Natural Language Processing (NLP) approach based on Bidirectional Encoder Representations from Transformers (BERT) was developed. This AI-based model was applied to classify drug-induced liver injury and learn the meanings of complex text in drug labeling documents.
Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI 10.1016/j.jhep.2021.03.021 A statistical model based on drug properties and clinical factors was developed to predict a patient’s possibility to recover from drug-induced liver injury (DILI). These findings in this study offered important insight into factors influencing DILI recovery, which could help improve the clinical management of chronic DILI.
A Review of Feature Reduction Methods for QSAR-Based Toxicity Prediction https://link.springer.com/book/10.1007/978-3-030-16443-0 This book chapter discussed feature reduction methods used for quantitative structure-activity relationship (QSAR) models in toxicity prediction. Different methods for eliminating redundant and irrelevant features to enhance prediction performance, increase interpretability and reduce computational complexity were elaborated.
Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mechanistic Knowledge and Real-World Data https://link.springer.com/chapter/10.1007/978-3-031-20730-3_7 This paper presented a novel approach for developing predictive models for toxicity using in vitro assays and adverse outcome pathway (AOP) networks. By combining AOP networks and Bayesian methods, one can develop accurate and efficient models with a small number of assays.
Statistical methods for exploring spontaneous adverse event reporting databases for drug-host factor interactions 10.1186/s12874-023-01885-w This paper presented a novel statistical framework for identifying drugs or adverse event categories that demonstrated significant disparities regarding certain host factors such as sex and age.
Cannabidiol-induced transcriptomic changes and cellular senescence in human Sertoli cells 10.1093/toxsci/kfac131 The underlying mechanisms by which CBD exerts adverse effects on the male reproductive system have not been elucidated. This study used next-generation sequencing to investigate the molecular changes associated with CBD-induced cytotoxicity in human Sertoli cells. The results contribute insights towards explaining the occurrence of CBD's male reproductive toxicity.
In vitro effects of cannabidiol and its main metabolites in mouse and human Sertoli cells 10.1016/j.fct.2021.112722 Cannabidiol (CBD) has been reported to induce reproductive toxicity in animal models. It remains unknown about its potential reproductive toxicity in humans. This study used mouse and human Sertoli cell models, one of the most important cell types within the testes, to examine the effects of CBD and its main metabolites, 7-carboxy-CBD and 7-hydroxy-CBD. The results highlight potential adverse reproductive consequences caused by CBD and its metabolites.
DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox 10.3389/frai.2022.999289 Causality is essential in regulatory science. DeepCausality integrated a causality specific statistical method into the large language models (LLMs), which demonstrates a potential to supplement the manual-based causality assessment which is time-consuming, labor-intensive, and sometimes even impractical.
Text Fingerprinting and Topic Mining in the Prescription Opioid Use Literature This article applied the well-adapted topic modeling method, Latent Dirichlet Allocation (LDA), to perform text mining on prescription opioid use-related literature. 10.1109/BIBM52615.2021.9669550 The results provided a global systematic literature overview, and the LDA topics further revealed relevant themes such as pain treatment, opioid misuse/abuse, association links between opioid usage and pregnant women/infants.
Race/Ethnicity, Pigmentation, and the Human Skin Barrier – Considerations for Dermal Absorption Studies 10.3389/ftox.2023.1271833 This review details the existing data concerning race, skin pigmentation, and the human skin barrier. It highlights also the research areas that require continued investigation and novel experimental tools, such as reconstructed human pigmented skin models, that may aid to predict better the safety profile of dermatological products in a diverse human