High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers.

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Alternative lung cell model systems for toxicology testing strategies: Current knowledge and future outlook.

Due to the current relevance of pulmonary toxicology (with focus upon air pollution and the inhalation of hazardous materials), it is important to further develop and implement physiologically relevant models of the entire respiratory tract. Lung model development has the aim to create human relevant systems that may replace animal use whilst balancing cost, laborious nature and regulatory ambition. There is an imperative need to move away from rodent models and implement models that mimic the holistic characteristics important in lung function. The purpose of this review is therefore, to describe and identify the various alternative models that are being applied towards assessing the pulmonary toxicology of inhaled substances, as well as the current and potential developments of various advanced models and how they may be applied towards toxicology testing strategies. These models aim to mimic various regions of the lung, as well as implementing different exposure methods with the addition of various physiologically relevent conditions (such as fluid-flow and dynamic movement). There is further progress in the type of models used with focus on the development of lung-on-a-chip technologies and bioprinting, as well as and the optimization of such models to fill current knowledge gaps within toxicology.

Ninety-eight semesters of cytochrome P450 enzymes and related topics-What have I taught and learned?

This Reflection article begins with my family background and traces my career through elementary and high school, followed by time at the University of Illinois, Vanderbilt University, the University of Michigan, and then for 98 semesters as a Vanderbilt University faculty member. My research career has dealt with aspects of cytochrome P450 enzymes, and the basic biochemistry has had applications in fields as diverse as drug metabolism, toxicology, medicinal chemistry, pharmacogenetics, biological engineering, and bioremediation. I am grateful for the opportunity to work with the Journal of Biological Chemistry not only as an author but also for 34 years as an Editorial Board Member, Associate Editor, Deputy Editor, and interim Editor-in-Chief. Thanks are extended to my family and my mentors, particularly Profs. Harry Broquist and Minor J. Coon, and the more than 170 people who have trained with me. I have never lost the enthusiasm for research that I learned in the summer of 1968 with Harry Broquist, and I have tried to instill this in the many trainees I have worked with. A sentence I use on closing slides is "It's not just a laboratory-it's a fraternity."

E-Cigarette Toxicology.

Since the spread of tobacco from the Americas hundreds of years ago, tobacco cigarettes and, more recently, alternative tobacco products have become global products of nicotine addiction. Within the evolving alternative tobacco product space, electronic cigarette (e-cigarette) vaping has surpassed conventional cigarette smoking among adolescents and young adults in the United States and beyond. This review describes the experimental and clinical evidence of e-cigarette toxicity and deleterious health effects. Adverse health effects related to e-cigarette aerosols are influenced by several factors, including e-liquid components, physical device factors, chemical changes related to heating, and health of the e-cigarette user (e.g., asthmatic). Federal, state, and local regulations have attempted to govern e-cigarette flavors, manufacturing, distribution, and availability, particularly to underaged youths. However, the evolving e-cigarette landscape continues to impede timely toxicological studies and hinder progress made toward our understanding of the long-term health consequence of e-cigarettes.

Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector.

Due to the increasing importance of graphs and graph streams in data representation in today's era, concept drift detection in graph streaming scenarios is more important than ever. Contributions to concept drift detection in graph streams are minimal and practically non-existent in the field of toxicology. This paper applied the discriminative subgraph-based drift detector (DSDD) to graph streams generated from real-world toxicology datasets. We used four toxicology datasets, each of which yielded two graph streams - one with abrupt drift points and one with gradual drift points. We used DSDD both with the standard minimum description length (MDL) heuristic and after replacing MDL with a much simpler heuristic SIZE (number of vertices + number of edges), and applied it to all generated graph streams containing abrupt drift points and gradual drift points for varying window sizes. Following that, we compared and analyzed the results. Finally, we applied a long short-term memory based graph stream classification model to all the generated streams and compared the difference in the performances obtained with and without detecting drift using DSDD. We believe that the results and analysis presented in this paper will provide insight into the task of concept drift detection in the toxicology domain and aid in the application of DSDD in a variety of scenarios.

Dietary modulation of lung lipids influences inflammatory responses to inhaled ozone.

The pulmonary system represents a unique lipidomic environment as it contains cellular membrane-bound lipid species and a specialized reservoir of lipids in the airway epithelial lining fluid. As a major initial point of defense, airway lipids react to inhaled contaminants such as volatile organic compounds, oxides of nitrogen, or ozone (O3), creating lipokine signaling that is crucial for both the initiation and resolution of inflammation within the lung. Dietary modulation of eicosanoids has gained increased attention in recent years for improvements to cardiovascular health. The current study sought to examine how dietary supplementation with eicosanoid precursors (i.e, oils rich in saturated or polyunsaturated fatty acids) might alter the lung lipid composition and subsequently modify the inflammatory response to ozone inhalation. Our study demonstrated that mice fed a diet high in saturated fatty acids resulted in diet-specific changes to lung lipid profiles and increased cellular recruitment to the lung following ozone inhalation. Bioinformatic analysis revealed an ozone-dependent upregulation of several lipid species, including phosphoserine 37:5. Pathway analysis of lipid species revealed the process of lateral diffusion of lipids within membranes to be significantly altered due to ozone exposure. These results show promising data for influencing pulmonary lipidomic profiles via diet, which may provide a pragmatic therapeutic approach to protect against lung inflammation and damage following pulmonary insult.

The novel and potent CD40 agonist KHK2840 augments the antitumor efficacy of anti-PD-1 antibody and paclitaxel.

Lack of tumor-reactive cytotoxic T lymphocytes (CTLs) limits the antitumor efficacy of immune checkpoint inhibitors (ICIs). CD40 agonists have been expected to overcome this limitation by generating tumor-reactive CTLs. However, the clinical efficacy of CD40 agonistic antibodies is not as good as in non-clinical studies. The novel human CD40 (hCD40) agonist KHK2840 is a fully human anti-CD40 IgG2 agonistic antibody that is Fc-engineered to minimize complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity. Compared to other hCD40 agonists, KHK2840 exhibited the most potent hCD40 agonistic signal in tumor-bearing hCD40 transgenic mice and human peripheral blood B cells. Moreover, KHK2840 enhanced the antitumor efficacy of the antiprogrammed cell death 1 antibody and paclitaxel. Comprehensive immune profiling revealed that the antitumor immune response of the triple combination involved tumor-draining lymph nodes in addition to tumor microenvironments. This suggests that a coordinated antitumor immune response between tumors and lymph nodes may underlie the synergistic antitumor efficacy of the triple combination therapy. Finally, a toxicology study in cynomolgus monkeys demonstrated that KHK2840 activated the CD40 signal with tolerable toxicological properties. These results indicate that KHK2840 is a novel and potent hCD40 agonistic antibody for cancer immunotherapy, which is expected to augment the antitumor efficacy of ICIs and chemotherapy.

Nanoparticle Toxicology.

Nanoparticles from natural and anthropogenic sources are abundant in the environment, thus human exposure to nanoparticles is inevitable. Due to this constant exposure, it is critically important to understand the potential acute and chronic adverse effects that nanoparticles may cause to humans. In this review, we explore and highlight the current state of nanotoxicology research with a focus on mechanistic understanding of nanoparticle toxicity at organ, tissue, cell, and biomolecular levels. We discuss nanotoxicity mechanisms, including generation of reactive oxygen species, nanoparticle disintegration, modulation of cell signaling pathways, protein corona formation, and poly(ethylene glycol)-mediated immunogenicity. We conclude with a perspective on potential approaches to advance current understanding of nanoparticle toxicity. Such improved understanding may lead to mitigation strategies that could enable safe application of nanoparticles in humans. Advances in nanotoxicity research will ultimately inform efforts to establish standardized regulatory frameworks with the goal of fully exploiting the potential of nanotechnology while minimizing harm to humans.

Iguratimod, an allosteric inhibitor of macrophage migration inhibitory factor (MIF), prevents mortality and oxidative stress in a murine model of acetaminophen overdose.

BACKGROUND: Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine that has been implicated in multiple inflammatory and non-inflammatory diseases, including liver injury induced by acetaminophen (APAP) overdose. Multiple small molecule inhibitors of MIF have been described, including the clinically available anti-rheumatic drug T-614 (iguratimod); however, this drug's mode of inhibition has not been fully investigated. METHODS: We conducted in vitro testing including kinetic analysis and protein crystallography to elucidate the interactions between MIF and T-614. We also performed in vivo experiments testing the efficacy of T-614 in a murine model of acetaminophen toxicity. We analyzed survival in lethal APAP overdose with and without T-614 and using two different dosing schedules of T-614. We also examined MIF and MIF inhibition effects on hepatic hydrogen peroxide (H2O2) as a surrogate of oxidative stress in non-lethal APAP overdose. RESULTS: Kinetic analysis was consistent with a non-competitive type of inhibition and an inhibition constant (Ki) value of 16 µM. Crystallographic analysis revealed that T-614 binds outside of the tautomerase active site of the MIF trimer, with only the mesyl group of the molecule entering the active site pocket. T-614 improved survival in lethal APAP overdose when given prophylactically, but this protection was not observed when the drug was administered late (6 h after APAP). T-614 also decreased hepatic hydrogen peroxide concentrations during non-lethal APAP overdose in a MIF-dependent fashion. CONCLUSIONS: T-614 is an allosteric inhibitor of MIF that prevented death and decreased hepatic hydrogen peroxide concentrations when given prophylactically in a murine model of acetaminophen overdose. Further studies are needed to elucidate the mechanistic role of MIF in APAP toxicity.

The role of the cytochrome P450 superfamily in the skin.

In mammals, the skin acts as a barrier to prevent harmful environmental stimuli from entering the circulation. CYP450s are involved in drug biotransformation, exogenous and endogenous substrate metabolism, and maintaining the normal physiological function of the skin, as well as facilitating homeostasis of the internal environment. The expression pattern of CYP450s in the skin is tissue-specific and thus differs from the liver and other organs. The development of skin topical medications, and knowledge of the toxicity and side effects of these medications require a detailed understanding of the expression and function of skin-specific CYP450s. Thus, we summarized the expression of CYP450s in the skin, their function in endogenous metabolic physiology, aberrant CYP450 expression in skin diseases and the influence of environmental variables and medications. This information will serve as a crucial foundation for future studies on the skin, as well as for the design and development of new drugs for skin diseases including topical medications.

Sex differences in the neural and behavioral effects of acute high-dose edible cannabis consumption in rats.

The consumption of D9-tetrahydrocannabinol (THC)- or cannabis-containing edibles has increased in recent years; however, the behavioral and neural circuit effects of such consumption remain unknown, especially in the context of ingestion of higher doses resulting in cannabis intoxication. We examined the neural and behavioral effects of acute high-dose edible cannabis consumption (AHDECC). Sprague-Dawley rats (6 males, 7 females) were implanted with electrodes in the prefrontal cortex (PFC), dorsal hippocampus (dHipp), cingulate cortex (Cg), and nucleus accumbens (NAc). Rats were provided access to a mixture of Nutella (6 g/kg) and THC-containing cannabis oil (20 mg/kg) for 10 minutes, during which they voluntarily consumed all of the provided Nutella and THC mixture. Cannabis tetrad and neural oscillations were examined 2, 4, 8, and 24-h after exposure. In another cohort (16 males, 15 females), we examined the effects of AHDECC on learning and prepulse inhibition, and serum and brain THC and 11-hydroxy-THC concentrations. AHDECC resulted in higher brain and serum THC and 11-hydroxy-THC levels in female rats over 24 h. AHDECC also produced: 1) Cg, dHipp, and NAc gamma power suppression, with the suppression being greater in female rats, in a time-dependent manner; 2) hypolocomotion, hypothermia, and anti-nociception in a time-dependent manner; and 3) learning and prepulse inhibition impairments. Additionally, most neural activity and behavior changes appear 2 h post-ingestion, suggesting that interventions around this time might be effective in reversing/reducing the effects of AHDECC. Significance Statement The effects of high-dose edible cannabis on behaviour and neural circuitry are poorly understood. We found that the effects of acute high-dose edible cannabis consumption, which include decreased gamma power, hypothermia, hypolocomotion, analgesia, and learning and information processing impairments, are time- and sex-dependent. Moreover, these effects begin 2 h after AHDECC and last for at least 24 h, suggesting that treatments should target this time window in order to be effective.

Understanding Cisplatin Pharmacokinetics and Toxicodynamics to Predict and Prevent Kidney Injury.

Cisplatin is a common platinum-based chemotherapeutic that induces acute kidney injury (AKI) in about 30% of patients. Pharmacokinetic/toxicodynamic (PKTD) models of cisplatin-induced AKI have been used to understand risk factors and evaluate potential mitigation strategies. While both traditional clinical biomarkers of kidney function [e.g. serum creatinine (SCr), blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), and creatinine clearance (CrCl)] and newer subclinical biomarkers of kidney injury [e.g. urinary kidney injury molecule 1 (KIM-1), beta-2 microglobulin (B2M), neutrophil gelatinase-associated lipocalin (NGAL), calbindin, etc.] can be used to detect cisplatin-induced AKI, published PKTD models are limited to using only traditional clinical biomarkers. Previously identified risk factors for cisplatin nephrotoxicity have included dose, age, sex, race, body surface area, genetics, concomitant medications, and comorbid conditions. However, the relationships between concentrations and PK of platinum and biomarkers of kidney injury have not been well elucidated. This review discusses the evaluation of cisplatin-induced nephrotoxicity in clinical studies, mouse models, and in vitro models, and examines the available human PK and TD data. Improved understanding of the relationships between platinum PK and TD, in the presence of identified risk factors, will enable the prediction and prevention of cisplatin kidney injury. Significance Statement As cisplatin treatment continues to cause AKI in a third of patients, it is critical to improve the understanding of the relationships between platinum PK and nephrotoxicity as assessed by traditional clinical and contemporary subclinical TD markers of kidney injury. Prediction and prevention of cisplatin-induced nephrotoxicity will be advanced by the evolving development of PKTD models that incorporate kidney injury biomarkers with enhanced sensitivity and include covariates that can impact risk of developing cisplatin-induced AKI.

Neonicotinoids differentially modulate nicotinic acetylcholine receptors in immature and antral follicles in the mouse ovary†.

Neonicotinoids are the most widely used insecticides in the world. They are synthetic nicotine derivatives that act as nicotinic acetylcholine receptor agonists. Although parent neonicotinoids have low affinity for the mammalian nicotinic acetylcholine receptor, they can be activated in the environment and the body to positively charged metabolites with high affinity for the mammalian nicotinic acetylcholine receptor. Imidacloprid, the most popular neonicotinoid, and its bioactive metabolite desnitro-imidacloprid differentially interfere with ovarian antral follicle physiology in vitro, but their effects on ovarian nicotinic acetylcholine receptor subunit expression are unknown. Furthermore, ovarian nicotinic acetylcholine receptor subtypes have yet to be characterized in the ovary. Thus, this work tested the hypothesis that ovarian follicles express nicotinic acetylcholine receptors and their expression is differentially modulated by imidacloprid and desnitro-imidacloprid in vitro. We used polymerase chain reaction, RNA in situ hybridization, and immunohistochemistry to identify and localize nicotinic acetylcholine receptor subunits (α2, 4, 5, 6, 7 and ß1, 2, 4) expressed in neonatal ovaries (NO) and antral follicles. Chrnb1 was expressed equally in NO and antral follicles. Chrna2 and Chrnb2 expression was higher in antral follicles compared to NO and Chrna4, Chrna5, Chrna6, Chrna7, and Chrnb4 expression was higher in NO compared to antral follicles. The α subunits were detected throughout the ovary, especially in oocytes and granulosa cells. Imidacloprid and desnitro-imidacloprid dysregulated the expression of multiple nicotinic acetylcholine receptor subunits in NO, but only dysregulated one subunit in antral follicles. These data indicate that mammalian ovaries contain nicotinic acetylcholine receptors, and their susceptibility to imidacloprid and desnitro-imidacloprid exposure varies with the stage of follicle maturity.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Differential Selectivity of Human and Mouse ABCC4/Abcc4 for Arsenic Metabolites.

Millions of people globally are exposed to the proven human carcinogen arsenic at unacceptable levels in drinking water. In contrast, arsenic is a poor rodent carcinogen, requiring >100-fold higher doses for tumour induction, which may be explained by toxicokinetic differences between humans and mice. The human ATP-binding cassette (ABC) transporter hABCC4 mediates the cellular efflux of a diverse array of metabolites, including the GSH conjugate of the highly toxic monomethylarsonous acid (MMAIII), MMA(GS)2, and the major human urinary arsenic metabolite dimethylarsinic acid (DMAV). Our objective was to determine if mouse Abcc4 (mAbcc4) protected against and/or transported the same arsenic species as hABCC4. The anti-ABCC4 antibody M4I-10 epitope was first mapped to an octapeptide (411HVQDFTA418F) present in both hABCC4 and mAbcc4, enabling quantification of relative amounts of hABCC4/mAbcc4. mAbcc4 expressed in HEK293 cells did not protect against any of the six arsenic species tested [arsenite, arsenate, MMAIII, monomethylarsonic acid, dimethylarsinous acid or DMAV], despite displaying remarkable resistance against the antimetabolite 6-mercaptopurine (>9-fold higher than hABCC4). Furthermore, mAbcc4-enriched membrane vesicles prepared from transfected HEK293 cells did not transport MMA(GS)2 or DMAV, despite a >3-fold higher transport activity than hABCC4-enriched vesicles for the prototypic substrate 17ß-estradiol-17-(ß-D-glucuronide). Abcc4(+/+) mouse embryonic fibroblasts (MEFs) were ~3-fold more resistant to arsenate than Abcc4(-/-) MEFs; however, further characterization indicated this was not mAbcc4 mediated. Thus, under the conditions tested, arsenicals are not transported by mAbcc4, and differences between the substrate selectivity of hABCC4 and mAbcc4 seem likely to contribute to differences in human and mouse arsenic toxicokinetics. Significance Statement Toxicokinetics of the carcinogen arsenic differ among animal species. Arsenic methylation is known to contribute to this, whereas arsenic transporters have not been considered. The human ATP-binding cassette transporter hABCC4 is a high affinity transporter of toxicologically important arsenic metabolites. Here we used multiple cell models to demonstrate that mouse Abcc4 does not protect cells against, or transport, any arsenic species tested. Thus, differences between hABCC4 and mAbcc4 substrate selectivity likely contribute to differences in human and mouse arsenic toxicokinetics.

Safety evaluation of Bifidobacterium animalis subsp. lactis BLa80 under in vitro, and in vivo conditions.

Bifidobacterium animalis subsp. lactis BLa80 is a new probiotic strain with extensive applications in food products both domestically and internationally. Given the rising consumption of this probiotic, its safety assessment is increasingly crucial in the food industry. This study evaluates the safety of strain BLa80 using a combination of in vitro and in vivo assays along with genomic analysis. Methods included exposing the strain to artificial gastric and intestinal fluids, as well as a medium containing bile salts, to stimulate human digestive conditions. The strain showed high tolerance to gastric fluid at pH of 2.5 and to 0.3 % bile salts. It maintained a 99.92 % survival rate in intestinal fluid. Additional tests assessed hemolytic activity, antibiotic susceptibility (revealing sensitivity to 7 antibiotics), and biogenic amine production using HPLC-ELSD, confirming the absence of histamine, and other harmful amines. Bile salt hydrolase activity was demonstrated qualitatively, and metabolic byproducts were quantitatively analyzed using a D-/l-lactic acid assay kit, showing that BLa80 produces 1.48 mg/mL of l-lactic acid and no harmful d-lactic acid. Genomic analysis confirmed the absence of virulence or pathogenicity genes, and a 90-day oral toxicity study in rats confirmed no toxic effects at various doses. Overall, these findings support the safety classification of the strain BLa80.