Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor.

The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Application of Nonhuman Primate Models in the Studies of Pediatric Anesthesia Neurotoxicity.

Numerous animal models have been used to study developmental neurotoxicity associated with short-term or prolonged exposure of common general anesthetics at clinically relevant concentrations. Pediatric anesthesia models using the nonhuman primate (NHP) may more accurately reflect the human condition because of their phylogenetic similarity to humans with regard to reproduction, development, neuroanatomy, and cognition. Although they are not as widely used as other animal models, the contribution of NHP models in the study of anesthetic-induced developmental neurotoxicity has been essential. In this review, we discuss how neonatal NHP animals have been used for modeling pediatric anesthetic exposure; how NHPs have addressed key data gaps and application of the NHP model for the studies of general anesthetic-induced developmental neurotoxicity. The appropriate application and evaluation of the NHP model in the study of general anesthetic-induced developmental neurotoxicity have played a key role in enhancing the understanding and awareness of the potential neurotoxicity associated with pediatric general anesthetics.

Neuroprotective Effects of Carnitine and Its Potential Application to Ameliorate Neurotoxicity.

Carnitine is an essential metabolite that is absorbed from the diet and synthesized in the kidney, liver, and brain. It ferries fatty acids across the mitochondrial membrane to undergo ß-oxidation. Carnitine has been studied as a therapy or protective agent for many neurological diseases and neurotoxicity (e.g., prolonged anesthetic exposure-induced developmental neurotoxicity in preclinical models). Preclinical and clinical data support the notion that carnitine or acetyl carnitine may improve a patient's quality of life through increased mitochondrial respiration, release of neurotransmitters, and global gene expression changes, showing the potential of carnitine beyond its approved use to treat primary and secondary carnitine deficiency. In this review, we summarize the beneficial effects of carnitine or acetyl carnitine on the central nervous system, highlighting protective effects against neurotoxicity-induced damage caused by various chemicals and encouraging a thorough evaluation of carnitine use as a therapy for patients suffering from neurotoxicant exposure.

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations.

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17ß-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

Lipid profiling as an effective approach for identifying biomarkers/adverse events associated with pediatric anesthesia.

Adverse effects related to central nervous system (CNS) function in pediatric populations may, at times, be difficult, if not impossible to evaluate. Prolonged anesthetic exposure affects brain excitability and anesthesia during the most sensitive developmental stages and has been associated with mitochondrial dysfunction, aberrant lipid metabolism and synaptogenesis, subsequent neuronal damage, as well as long-term behavioral deficits. There has been limited research evaluating whether and how anesthetic agents affect cellular lipids, the most abundant components of the brain other than water. Therefore, this review discusses: (1) whether the observed anesthetic-induced changes in lipid profiles seen in preclinical studies represents early signs of neurotoxicity; (2) the potential mechanisms underlying anesthetic-induced brain injury; and (3) whether lipid biomarker(s) identified in preclinical studies can serve as markers for the early clinical detection of anesthetic-induced neurotoxicity.

Lipidomics Reveals Changes in Metabolism, Indicative of Anesthetic-Induced Neurotoxicity in Developing Brains.

Numerous studies have demonstrated that treatment with high dose anesthetics for a prolonged duration induces brain injury in infants. However, whether anesthetic treatment leading to neurotoxicity is associated with alterations in lipid metabolism and homeostasis is still unclear. This review first outlines the lipidomics tools for analysis of lipid molecular species that can inform alterations in lipid species after anesthetic exposure. Then the available data indicating anesthetics cause changes in lipid profiles in the brain and serum of infant monkeys in preclinical studies are summarized, and the potential mechanisms leading to the altered lipid metabolism and their association with anesthetic-induced brain injury are also discussed. Finally, whether lipid changes identified in serum of infant monkeys can serve as indicators for the early detection of anesthetic-induced brain injury is described. We believe extensive studies on alterations in lipids after exposure to anesthetics will allow us to better understand anesthetic-induced neurotoxicity, unravel its underlying biochemical mechanisms, and develop powerful biomarkers for early detection/monitoring of the toxicity.

In Vivo Monitoring of Sevoflurane-induced Adverse Effects in Neonatal Nonhuman Primates Using Small-animal Positron Emission Tomography.

BACKGROUND: Animals exposed to sevoflurane during development sustain neuronal cell death in their developing brains. In vivo micro-positron emission tomography (PET)/computed tomography imaging has been utilized as a minimally invasive method to detect anesthetic-induced neuronal adverse effects in animal studies. METHODS: Neonatal rhesus monkeys (postnatal day 5 or 6, 3 to 6 per group) were exposed for 8 h to 2.5% sevoflurane with or without acetyl-L-carnitine (ALC). Control monkeys were exposed to room air with or without ALC. Physiologic status was monitored throughout exposures. Depth of anesthesia was monitored using quantitative electroencephalography. After the exposure, microPET/computed tomography scans using F-labeled fluoroethoxybenzyl-N-(4-phenoxypyridin-3-yl) acetamide (FEPPA) were performed repeatedly on day 1, 1 and 3 weeks, and 2 and 6 months after exposure. RESULTS: Critical physiologic metrics in neonatal monkeys remained within the normal range during anesthetic exposures. The uptake of [F]-FEPPA in the frontal and temporal lobes was increased significantly 1 day or 1 week after exposure, respectively. Analyses of microPET images recorded 1 day after exposure showed that sevoflurane exposure increased [F]-FEPPA uptake in the frontal lobe from 0.927 ± 0.04 to 1.146 ± 0.04, and in the temporal lobe from 0.859 ± 0.05 to 1.046 ± 0.04 (mean ± SE, P < 0.05). Coadministration of ALC effectively blocked the increase in FEPPA uptake. Sevoflurane-induced adverse effects were confirmed by histopathologic evidence as well. CONCLUSIONS: Sevoflurane-induced general anesthesia during development increases glial activation, which may serve as a surrogate for neurotoxicity in the nonhuman primate brain. ALC is a potential protective agent against some of the adverse effects associated with such exposures.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study.

BACKGROUND: Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade. STUDY DESIGN AND METHOD: Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets. RESULTS: The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0 - 0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112 - 0.220). CONCLUSIONS: The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.

Comparison of the global gene expression of choroid plexus and meninges and associated vasculature under control conditions and after pronounced hyperthermia or amphetamine toxicity.

BACKGROUND: The meninges (arachnoid and pial membranes) and associated vasculature (MAV) and choroid plexus are important in maintaining cerebrospinal fluid (CSF) generation and flow. MAV vasculature was previously observed to be adversely affected by environmentally-induced hyperthermia (EIH) and more so by a neurotoxic amphetamine (AMPH) exposure. Herein, microarray and RT-PCR analysis was used to compare the gene expression profiles between choroid plexus and MAV under control conditions and at 3 hours and 1 day after EIH or AMPH exposure. Since AMPH and EIH are so disruptive to vasculature, genes related to vasculature integrity and function were of interest. RESULTS: Our data shows that, under control conditions, many of the genes with relatively high expression in both the MAV and choroid plexus are also abundant in many epithelial tissues. These genes function in transport of water, ions, and solutes, and likely play a role in CSF regulation. Most genes that help form the blood-brain barrier (BBB) and tight junctions were also highly expressed in MAV but not in choroid plexus. In MAV, exposure to EIH and more so to AMPH decreased the expression of BBB-related genes such as Sox18, Ocln, and Cldn5, but they were much less affected in the choroid plexus. There was a correlation between the genes related to reactive oxidative stress and damage that were significantly altered in the MAV and choroid plexus after either EIH or AMPH. However, AMPH (at 3 hr) significantly affected about 5 times as many genes as EIH in the MAV, while in the choroid plexus EIH affected more genes than AMPH. Several unique genes that are not specifically related to vascular damage increased to a much greater extent after AMPH compared to EIH in the MAV (Lbp, Reg3a, Reg3b, Slc15a1, Sct and Fst) and choroid plexus (Bmp4, Dio2 and Lbp). CONCLUSIONS: Our study indicates that the disruption of choroid plexus function and damage produced by AMPH and EIH is significant, but the changes may not be as pronounced as they are in the MAV, particularly for AMPH. Expression profiles in the MAV and choroid plexus differed to some extent and differences were not restricted to vascular related genes.

Concurrent determination of bisphenol A pharmacokinetics in maternal and fetal rhesus monkeys.

Bisphenol A (BPA) is an important industrial chemical used as the monomer for polycarbonate plastic and in epoxy resins for food can liners. Worldwide biomonitoring studies consistently find a high prevalence of BPA conjugates in urine (>90%) in amounts consistent with aggregate exposure at levels below 1 µg/kg bw/d. The current study used LC/MS/MS to measure concurrently the pharmacokinetics of aglycone (active) and conjugated (inactive) deuterated BPA (d6) in maternal and fetal rhesus monkey serum, amniotic fluid, and placenta following intravenous injection in the dam (100 µg/kg bw). Internal exposures of the fetus to aglycone d6-BPA (serum AUC) were attenuated by maternal, placental, and fetal Phase II metabolism to less than half that in the dam. Levels of aglycone and conjugated d6-BPA measured in whole placenta were consistent with a role in metabolic detoxification. The monotonic elimination of aglycone d6-BPA from the fetal compartment accompanied by persistent conjugate levels provides further evidence arguing against the hypothesis that BPA conjugates are selectively deconjugated by either the placenta or fetus. These results also provide benchmarks to guide the interpretation of human cord blood, amniotic fluid, and placenta sampling and measurement strategies as a basis for estimating fetal exposures to BPA. This study in a non-human primate model provides additional pharmacokinetic data for use in PBPK modeling of perinatal exposures to BPA from food contact, medical devices, and other environmental sources.

Analyzing 3D structures of the SARS-CoV-2 main protease reveals structural features of ligand binding for COVID-19 drug discovery.

The severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease has an essential role in viral replication and has become a major target for coronavirus 2019 (COVID-19) drug development. Various inhibitors have been discovered or designed to bind to the main protease. The availability of more than 550 3D structures of the main protease provides a wealth of structural details on the main protease in both ligand-free and ligand-bound states. Therefore, we examined these structures to ascertain the structural features for the role of the main protease in the cleavage of polyproteins, the alternative conformations during main protease maturation, and ligand interactions in the main protease. The structural features unearthed could promote the development of COVID-19 drugs targeting the SARS-CoV-2 main protease.

Review of machine learning and deep learning models for toxicity prediction.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain.

With advances in pediatric and obstetric surgery, pediatric patients are subject to complex procedures under general anesthesia. The effects of anesthetic exposure on the developing brain may be confounded by several factors including pre-existing disorders and surgery-induced stress. Ketamine, a noncompetitive N-methyl-d-aspartate (NMDA) receptor antagonist, is routinely used as a pediatric general anesthetic. However, controversy remains about whether ketamine exposure may be neuroprotective or induce neuronal degeneration in the developing brain. Here, we report the effects of ketamine exposure on the neonatal nonhuman primate brain under surgical stress. Eight neonatal rhesus monkeys (postnatal days 5-7) were randomly assigned to each of two groups: Group A (n = 4) received 2 mg/kg ketamine via intravenous bolus prior to surgery and a 0.5 mg/kg/h ketamine infusion during surgery in the presence of a standardized pediatric anesthetic regimen; Group B (n = 4) received volumes of normal saline equivalent to those of ketamine given to Group A animals prior to and during surgery, also in the presence of a standardized pediatric anesthetic regimen. Under anesthesia, the surgery consisted of a thoracotomy followed by closing the pleural space and tissue in layers using standard surgical techniques. Vital signs were monitored to be within normal ranges throughout anesthesia. Elevated levels of cytokines interleukin (IL)-8, IL-15, monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein (MIP)-1ß at 6 and 24 h after surgery were detected in ketamine-exposed animals. Fluoro-Jade C staining revealed significantly higher neuronal degeneration in the frontal cortex of ketamine-exposed animals, compared with control animals. Intravenous ketamine administration prior to and throughout surgery in a clinically relevant neonatal primate model appears to elevate cytokine levels and increase neuronal degeneration. Consistent with previous data on the effects of ketamine on the developing brain, the results from the current randomized controlled study in neonatal monkeys undergoing simulated surgery show that ketamine does not provide neuroprotective or anti-inflammatory effects.

Machine learning and deep learning for brain tumor MRI image segmentation.

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Machine learning models for rat multigeneration reproductive toxicity prediction.

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%-61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.