Test Animals Research Articles

Inconsistent terminology in rodent anatomy hinders appropriate blood sampling in laboratory animal science: A review of blood sampling techniques on the head of mice.

Blood sampling is often performed during animal studies. This is more challenging in mice than in larger animal species owing to their size and lack of blood vessel visibility. Guidelines for blood sampling in mice and papers on animal welfare often refer to the submandibular, cheek, buccal, and anterior facial veins. However, these terms are imprecise. There are at least two different superficial veins that can be used for blood sampling on the head of the mouse; moreover, most studies ignore this distinction. Inaccurate descriptions and even incorrect labelling of the images lead to confusion and unnecessary delays in learning blood collection techniques, as well as stress and pain in animals due to failed attempts. This review explains the most common terminological errors, demonstrates the distinction between bleeding facial and superficial temporal veins using original photographs, and discusses the suitability of sampling from both veins.

Use of alternatives to animal testing for Environmental Safety Assessment (ESA): Report from the 2023 EPAA partners' forum.

Environmental Safety Assessments (ESA) are mandatory for several regulatory purposes and are an important component of stewardship/sustainability initiatives. Fish testing is used for assessing chemical toxicity and bioaccumulation potential; amphibians are included in some jurisdictions and their use is increasing to assess endocrine disruption. Alternative methods are becoming more available, covering the principles of the 3Rs (i.e., replacing, reducing and refining animal tests), but their regulatory incorporation is still limited. A cross-sector review by the European Partnership for Alternative Approaches to Animal Testing (EPAA), discussed the status and priorities for accelerating the adoption of non-animal approaches in ESA. The lack of an internationally agreed definition for "animal testing" was recognized as a challenge. For example, testing with vertebrate embryos up to specific developmental stages is a suitable refinement alternative only in some jurisdictions. Invertebrate testing offers refinement alternatives to develop tiered approaches using vertebrate testing as a last resort. Aquatic ESA was identified as a common need by all sectors and regulatory areas, while terrestrial ESA is particularly relevant for agrochemicals. The standardization and validation of some alternative methods as OECD test guidelines (TGs) for fish acute toxicity and fish bioaccumulation have not yet triggered the expected replacement in regulatory settings. Priority actions in these areas are needed to generate confidence in the regulatory use of the available OECD TGs designed as alternatives, including the identification of applicability domains and guidance/decision-trees for integrating different lines of evidence. Case studies under the OECD Integrated Approaches to Testing and Assessment (IATA) program could facilitate further global regulatory uptake. Replacement of fish chronic toxicity testing is more complex and less advanced. A dual approach was suggested, in the short-term, exploring lines of evidence that, alone or in combination, could identify when further fish testing is not needed. The second phase should focus on the application of the 3Rs in those cases where chronic information is needed. Another area of increasing interest is endocrine disruption. It represents a challenge but also an opportunity for implementing mechanistic non-animal methods, in addition to integrate human and ESA. This requires a step-by-step approach with continuous dialogue to ensure that technical developments will address regulatory needs. The review also agreed that the long-term aspiration is a new ESA paradigm, mapping the protection goals and providing connectivity between the chemical legislation and environmental protection policies.

A proposed screening strategy for evaluating the genotoxicity potential of botanicals and botanical extracts.

Botanicals have long been used to promote health and treat diseases, but the safety of many currently marketed botanicals has not been adequately evaluated. Given the chemical complexity of botanicals, which often contain numerous unknown constituents, and their widespread use, comprehensive toxicity assessments are needed. The Botanical Safety Consortium was established to address this challenge. This international group of experts in toxicology, chemistry, bioinformatics, and pharmacognosy is developing a toolkit of assays to generate reliable toxicological profiles for botanicals. Genotoxicity assessment is especially critical, because, unlike other toxicities, genotoxicity is not adequately identified by adverse event and history-of-use reports, and genotoxicity is directly linked to health consequences such as cancer and birth defects. The Consortium's Genotoxicity Technical Working Group is exploring a genotoxicity testing strategy based on the use of in silico modeling and the bacterial reverse mutation and in vitro micronucleus assays and including several options for additional tests to further characterize genotoxicity and mode of action when indicated. The effectiveness of this testing strategy is being evaluated using 13 well-characterized botanicals with existing toxicological data as case studies. A brief overview of each of these 13 botanicals is provided. The final strategy for developing comprehensive genotoxicity profiles of botanicals will incorporate published genotoxicity data, chemical composition information, in silico and in vitro test data, and human exposure data, reducing the need for animal testing.

A Bioengineered Model of the Human Cornea for Preclinical Assessment of Human Ocular Exposure to Environmental Toxicants.

Here a bioengineered platform is introduced to investigate adverse effects of environmental materials on the human cornea. Using primary cells, this system is capable of reproducing the differentiated corneal epithelium and its underlying stroma in the human eye, which can then be treated with externally applied solid, liquid, or gaseous substances in a controlled manner and under physiologically relevant conditions. The proof-of-principle of how this system can be used to simulate human ocular exposure to different classes of environmental toxicants for direct visualization and quantitative analysis of their potential to induce acute corneal injury and inflammation is demonstrated. This model can also be further engineered to create an electromechanically actuated array of multiple human corneal tissues that can emulate spontaneous eye blinking. Using this advanced system, it is shown that blinking-like mechanical motions may play a protective role against adverse effects of environmental toxicants. This work yields an immediately deployable in vitro technology for screening ocular toxicity of existing and emerging environmental materials of various types and may enable the development of more realistic, human-relevant preclinical toxicology models complementary to traditional animal testing.

Daphnids can safeguard the use of alternative bioassays to the acute fish toxicity test: A focus on neurotoxicity.

Assessment of potential impacts of chemicals on the environment traditionally involves regulatory standard data requirements for acute aquatic toxicity testing using algae, daphnids and fish (e.g., OECD test guidelines (TG) 201, 202, and 203, respectively), representing different trophic levels. In line with the societal goal to replace or reduce vertebrate animal testing, alternative bioassays were developed to replace testing with fish: the fish cell line RTgill-W1 acute toxicity assay (OECD TG249) and the zebrafish embryo acute toxicity test (zFET, OECD TG236). However, previous studies revealed the lower sensitivity of the RTgill-W1 cell line assay and zFET for some neurotoxic chemicals and allyl alcohol, which is presumably biotransformed in fish to the more toxic acrolein (which is predicted well through the cell line assay). To provide an additional alternative to acute fish toxicity, in this study, we analyzed historic ecotoxicity data for fish and daphnids from the EnviroTox Database. We found a considerable variability in acute fish LC50 and acute daphnids EC50 values, particularly for neurotoxic chemicals. Comparing sensitivity of these taxonomic groups according to different neurotoxicity classification schemes indicates that fish rarely represent the most sensitive trophic level of the two. Exceptions here most prominently include a few cyclodiene compounds, which are no longer marketed, and a chemical group that could be identified through structural alerts. Moreover, daphnids are more sensitive than fish to acrolein. This analysis highlights the potential of the Daphnia acute toxicity test, which is usually a standard regulatory data requirement, in safeguarding the environmental protection level provided by the RTgill-W1 cell line assay and the zFET. This research, rooted in decades of efforts to replace the fish acute toxicity test, shifts the focus from predicting fish toxicity 1-to-1 to emphasizing the protectiveness of alternative methods, paving the way for further eliminating vertebrate tests in environmental toxicology.

Comparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction.

Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ethical concerns regarding animal models have fostered the advancement of alternative methods such as in silico and in vitro models. This study aimed to develop deep learning (DL) models based on recurrent neural networks (RNNs) for classifying skin corrosion of chemical compounds based on chemical language notation, molecular substructure, physicochemical properties, and a combination of these three properties called conjoint fingerprints. Simple RNN, long short-term memory, bidirectional long short-term memory (BiLSTM), gated recurrent units, and bidirectional gated recurrent units models, along with 11 molecular features, were employed to generate 55 RNN-based models. Applicability domain and permutation importance analysis were exploited for additional trustable prediction and explanation ability of the models, respectively. Our findings indicate that BiLSTM with conjoint features of MACCS keys and physicochemical descriptors is the most effective model with 84.3% accuracy, 89.8% area under the curve, and 57.6% Matthews correlation coefficient for the external test performance. Furthermore, our model accurately predicted the skin corrosion toxicity of all new and unseen compounds beyond our test set, highlighting prominent classification performance compared to existing skin corrosion models. This finding will contribute to the utilization of DL and conjoint characteristics of molecular structure to enhance the model's predictive capability for skin toxicity assessment.

CTNNB1 syndrome mouse models.

CTNNB1 syndrome is a rare neurodevelopmental disorder, affecting children worldwide with a prevalence of 2.6-3.2 per 100,000 births and often misdiagnosed as cerebral palsy. De novo loss-of-function mutations in the Ctnnb1 gene result in dysfunction of the β-catenin protein, disrupting the canonical Wnt signaling pathway, which plays a key role in cell proliferation, differentiation, and tissue homeostasis. Additionally, these mutations impair the formation of cell junctions, adversely affecting tissue architecture. Motor and speech deficits, cognitive impairment, cardiovascular and visual problems are just some of the key symptoms that occur in CTNNB1 syndrome patients. There is currently no effective treatment option available for patients with CTNNB1 syndrome, with support largely focused on the management of symptoms and physiotherapy, yet recently some therapeutic approaches are being developed. Animal testing is still crucial in the process of new drug development, and mouse models are particularly important. These models provide researchers with new understanding of the disease mechanisms and are invaluable for testing the efficacy and safety of potential treatments. The development of various mouse models with β-catenin loss- and gain-of-function mutations successfully replicates key features of intellectual disability, autism-like behaviors, motor deficits, and more. These models provide a valuable platform for studying disease mechanisms and offer a powerful tool for testing the therapeutic potential and effectiveness of new drug candidates, paving the way for future clinical trials.

Integrating New Approach Methodologies to Address Environmental Pancreatic Toxicity and Metabolic Disorders.

Advancing our understanding of pancreatic toxicity and metabolic disorders caused by environmental exposures requires innovative approaches. The pancreas, a vital organ for glucose regulation, is increasingly recognized as a target of harm from environmental chemicals and dietary factors. Traditional toxicological methods, while foundational, often fail to address the mechanistic complexities of pancreatic dysfunction, particularly under real-world conditions involving multiple exposures. New Approach Methodologies (NAMs)-including high-throughput screening (HTS), OMICS technologies, computational modeling, and advanced in vitro systems-offer transformative tools to tackle these challenges. NAMs enable the identification of mechanistic pathways, improve testing efficiency, and reduce reliance on animal testing. This commentary explores the integration of NAMs into pancreatic toxicity screening, addresses critical gaps in evaluating the cumulative risks of chemical and dietary exposures, and proposes solutions for integrating the pancreas into toxicity screening through NAMs. By highlighting recent advancements and emphasizing their adoption in environmental toxicity assessment frameworks, this work demonstrates the potential of NAMs to revolutionize environmental health research, inspire interdisciplinary collaboration, and protect public health.

Biofabricated 3D Intestinal Models as an Alternative to Animal-Based Approaches for Drug Toxicity Assays.

The main challenge in new drug development is accurately predicting the human response in preclinical models. In this study, we developed three different intestinal barrier models using advanced biofabrication techniques: (i) a manual model containing Caco-2 and HT-29 cells on a collagen bed, (ii) a manual model with a Caco-2/HT-29 layer on a HDFn-laden collagen layer, and (iii) a 3D bioprinted model incorporating both cellular layers. Each model was rigorously tested for its ability to simulate a functional intestinal membrane. All models successfully replicated the structural and functional aspects of the intestinal barrier. The 3D bioprinted intestinal model, however, demonstrated superior epithelial barrier integrity enhanced tight junction formation, microvilli development, and increased mucus production. When subjected to Ibuprofen, the 3D bioprinted model provided a more predictive response, underscoring its potential as a reliable in vitro tool for drug toxicity testing. Our 3D bioprinted intestinal model presents a robust and predictive platform for drug toxicity assessments, significantly reducing the need for animal testing. This model not only aligns with ethical testing protocols but also offers enhanced accuracy in predicting human responses, thereby advancing the field of drug development.

Potentially probiotic NPL 1334 strain of Enterococcus durans benefits rats with diet-induced hypercholesterolemia

PurposeTo study the potential of a candidate probiotic strain belonging to the Enterococcus durans species in alleviating hypercholesterolemia and improving the microbial milieu of rat gut.MethodsA previously isolated and characterized E. durans strain NPL 1334 was further screened in vitro for its bile salt hydrolyzation and cholesterol assimilation ability. An in vivo trial using diet-induced hypercholesterolemic rats was conducted to evaluate the effects of the administered test probiotic strain on the animal’s blood biochemical parameters such as total cholesterol (TC), high-density lipopolysaccharides (HDL), low-density lipopolysaccharides (LDL), triglycerides (TG), on body weight, oxidative stress markers, and its impact on intestinal and fecal microbiota as well as a histopathological examination of the test animal’s livers.ResultsE. durans strain showed good bile salt hydrolyzing ability and ample cholesterol assimilation in vitro. Probiotic-fed hypercholesterolemic rats showed significantly lowered cholesterol, triglyceride and LDL levels. The body weight of probiotic-fed rats was reduced as compared to the control. E. durans also stimulated the growth of beneficial LAB in the intestine of experimental rats and did not harm the liver of the experimental rats.ConclusionE. durans can be a natural therapeutic alternative to manage diet-induced hypercholesterolemia and may eventually enhance anti-cholesterolemic therapies.

PE-GCL: Advancing pesticide ecotoxicity prediction with graph contrastive learning.

Ecotoxicity assessments, which rely on animal testing, face serious challenges, including high costs and ethical concerns. Computational toxicology presents a promising alternative; nevertheless, existing predictive models encounter difficulties such as limited datasets and pronounced overfitting. To address these issues, we propose a framework for predicting pesticide ecotoxicity using graph contrastive learning (PE-GCL). By pre-training on large-scale unlabeled compounds, the PE-GCL captured the intrinsic regulation of molecules. This knowledge is then transferred to specific downstream tasks, thereby enhancing the model generalization in scenarios with small sample sizes. Performance evaluation showed that the PE-GCL outperformed traditional supervised models across most prediction tasks, whereas independent external validation confirmed its superior predictive accuracy for unseen data. Furthermore, interpretability was incorporated to elucidate potential correlations between ecotoxicity and molecular substructures. The trained models were deployed on a publicly accessible web server (https://dpai.ccnu.edu.cn/PERA/) to facilitate the use of the proposed framework.

Comparison of in vitro transcriptomic points of departure with fish acute and chronic toxicity values: A case study of rainbow trout cells exposed to pesticides.

There is growing interest in transcriptomic points of departure (tPOD) values from in vitro experiments as an alternative to animal test method. The study objective was to calculate tPODs in rainbow trout gill cells (RTgill-W1 following OECD 249) exposed to pesticides, and to evaluate how these values compare to fish acute and chronic toxicity data. Cells were exposed to one fungicide (chlorothalonil), ten herbicides (atrazine, glyphosate, imazethapyr, metolachlor, diquat, s-metolachlor, AMPA, dicamba, dimethenamid-P, metribuzin), eight insecticides (chlorpyrifos, diazinon, permethrin, carbaryl, clothianidin, imidacloprid, thiamethoxam, chlorantraniliprole), and OECD 249 positive control 3,4-dichloroaniline. Pesticide concentrations in wells were modeled with IV-MBM EQP v2.1. Sequencing libraries were prepared with UPXome, and tPODs calculated with ExpressAnalyst. The method identified 14,449 unique genes, with 1,115 genes having >5 counts in the 576 samples sequenced. For all chemicals, tPODs were derived and tPOD mode values ranged from 0.0004 to 125µM with an average of 36µM. There were significant correlations between tPOD mode values (x-value) and EC50s from RTgill-W1 cells (y = 0.92x+1.2, R2=0.9, p < 0.00001; n = 11), rainbow trout acute toxicity LC50s (y = 0.81x+0.8, R2=0.63, p < 0.0001; n = 20), fish chronic sub-lethal effect concentrations (y = 0.53x-0.2, R2=0.4, p = 0.009; n = 16) and fish chronic lethal effect concentrations (y = 0.64x-0.023, R2=0.59, p = 0.0013; n = 14). Bland-Altman plot analyses of these comparisons also showed good agreement. Pathway-level benchmark doses were calculated when statistical requirements were met, and only possible for four pesticides. These findings support the notion that tPODs from short-term in vitro studies may be comparable to effect concentration data from in vivo studies of fish exposed for chronic durations.

Cytotoxicity of sub-lethal doses of vanadium pentoxide in male Oryctolagus cuniculus.

Vanadium pentoxide (V2O5) is one of the compounds that have been reported to pose varying degrees of toxicity upon exposure; thus, making it a challenging environmental hazard that affects living organisms. This study investigated the cytotoxicity effects of daily sub-lethal oral doses of V2O5 on the bone marrow of male Oryctolagus cuniculus after 21 days. Male O. cuniculus (n = 60, ∼ 6 week old, 433.45 ± 5.00 g body weight) were simply randomized into four experimental groups and a control with three replicates of four animals each. Based on the estimated 96-h LD50 value of 119.0 mg/kg, sub-lethal doses of V2O5 were prepared as 1 mg/kg, 5 mg/kg, 10 mg/kg and 20 mg/kg, and administered to the test animals daily by oral gavage for 21 days. Vanadium pentoxide induced cytotoxicity in the bone marrow cells (BMCs) of exposed groups, with significant changes in all evaluated haemopoietic bone marrow stem cells (erythrocytes, leukocytes, thrombocytes and plasma cells). There were mixed trends in the values of leucocyte differentials in the exposed animal. Oral exposure to V2O5 exerts cytopathologic effects in the forms of DNA damage on the bone marrow of O. cuniculus. These findings support previous reports on the environmental hazards vanadium pentoxide poses to living organisms.

Smart Polymeric 3D Microscaffolds Hosting Spheroids for Neuronal Research via Quantum Metrology.

Toward the aim of reducing animal testing, innovative in vitro models are required. Here, this study proposes a novel smart polymeric microscaffold to establish an advanced 3D model of dopaminergic neurons. These scaffolds are fabricated with Ormocomp via Two-Photon Polymerization. The scaffolds are further enhanced by functionalization with fluorescent nanodiamonds (FNDs), which can serve as quantum nanosensors for both magnetic and temperature sensing.The material biocompatibility is tested using two different cell lines, SH-SY5Y and A431, with cell viability over 98%. A total of 69% of the FNDs are grafted on the structure compared to those that remained on the glass surface. Cells are tested with the scaffolds in several microenvironments, and the final assembly required for 3D quantum metrology experiments achieved 91% biocompatibility. Subsequently, embryoid bodies containing dopaminergic neurons, the cell type affected by Parkinson's disease (PD), are integrated with FND-functionalized scaffolds. This 3D model is successfully established, demonstrated by strong interactions between dopaminergic neurons and the scaffold, with the directional growth of neurites along the 3D scaffold. Ultimately, this study have developed a 3D platform that enables the readout of signaling in a model that holds great potential for future PD research.

Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach.

Computer-Aided Drug Design (CADD) entails designing molecules that could potentially interact with a specific biomolecular target and promising their potential binding. The stereo- arrangement and stereo-selectivity of small molecules (SMs)--based chemotherapeutic agents significantly influence their therapeutic potential and enhance their therapeutic advantages. CADD has been a well-established field for decades, but recent years have observed a significant shift toward acceptance of computational approaches in both academia and the pharmaceutical industry. Recently, artificial intelligence (AI), bioinformatics, and data science have played a significant role in drug discovery to accelerate the development of effective treatments, reduce expenses, and eliminate the need for animal testing. This shift can be attributed to the availability of extensive data on molecular properties, binding to therapeutic targets, and their 3D structures. Increasing interest from legislators, pharmaceutical companies, and academic and industrial scientists is evidence that AI is reshaping the drug discovery industry. To achieve success in drug discovery, it is necessary to optimize pharmacodynamic, pharmacokinetic, and clinical outcome-related properties. Moreover, the advent of on-demand virtual libraries containing billions of drug-like SMs, coupled with abundant computing capacities, has further facilitated this transition. To fully capitalize on these resources, rapid computational methods are needed for effective ligand screening. This includes structure-based virtual screening (SBVS) of vast chemical spaces, aided by fast iterative screening approaches. At the same time, advances in deep learning (DL) predictions of ligand properties and target activities have become very helpful, as they no longer need information about the structure of the receptor. This study examines recent progress in the drug discovery and development (DDD) approach, their potential to reshape the entire DDD process, and the challenges they face. This review examines the role of artificial intelligence as a fundamental component in drug discovery, particularly focusing on small molecules. It also discusses how AI-driven approaches can expedite the identification of diverse, potent, target-specific, and drug-like ligands for protein targets. This advancement has the potential to make drug discovery more efficient and cost-effective, ultimately facilitating the development of safer and more effective therapeutics.

Industrial Automation using Artificial Intelligence

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&amp;D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Development Validation of UV Spectrophoto Meter Method for Determination of Polyherbal Powder

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&amp;D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Preclinical and Toxicology Assessment of ISFP10, an Inhibitor of Fungal Phosphoglucomutase (PGM).

Previously, the novel small molecule ISFP10 has been shown to inhibit fungal phosphoglucomutase (PGM) activity in Aspergillus fumigatus and Pneumocystis spp. With 50-fold selectivity over the human PGM molecule due to the presence of a unique yet conserved cysteine residue present in a number pathogenic fungal PGMs, use of this compound may provide a novel broad-spectrum approach to treating fungal infections. Accordingly, we sought to determine the tolerability in test animals receiving this compound, as well as the potential antifungal activity of ISFP10 on cultures of the common fungal pathogens Candida albicans and Candida glabrata . C57BL6 mice received once daily intraperitoneal (IP) injections of 100 mL of vehicle control (DMSO) or ISFP10 at a concentration of 10 mg/kg. Body weights were recorded daily for 7 days of treatment. On the final day, mice were weighed and euthanized. Postmortem blood collection was conducted via cardiac puncture and distributed to EDTA and lithium heparin tubes for complete blood count (CBC) and comprehensive blood chemistry panels, respectively. Liver, kidney, and lung tissue were also harvested and placed in 10% formalin for H&E staining and blinded histopathologic scoring. Lung samples were further analyzed for proinflammatory cytokines using enzyme-linked immunosorbent assays (ELISA) and quantitative PCR (qPCR). Furthermore, ISPF10 was tested for antifungal activity via 8-hour growth curve analysis in a concentration-dependent fashion against Candida albicans and Candida glabrata . There was no significant difference in the daily or final body weights of the mice receiving 10 mg/kg of ISFP10 compared to those of the vehicle control group. Extracellular matrix (ECM) transcripts for IL-6 and TNFα were statistically similar via qPCR. ELISA results of proinflammatory cytokines for IL-6 was not significant whereas TNFα levels in lung tissue from the ISFP10 treatment group were significantly reduced, indicating a potential anti-inflammatory effect of ISFP10 at this dosage. Overall, blood chemistry and CBC analysis revealed no overall significant differences between the two groups, except for increased neutrophil counts and decreased potassium levels in samples collected from ISFP10 treated animals compared to the vehicle control group. These laboratory abnormalities were not of clinical significance to the test animals. Blinded histopathological examination revealed no abnormalities or evidence of critical organ toxicity from all groups. Inhibition of C. albicans and C. glabrata culture growth by ISFP10 was concentration-dependent in YPD liquid media containing the ISFP10 compared to vehicle control. Our preliminary testing of ISFP10 revealed no inherent safety or toxicology concerns within the observed parameters. These data further support significant culture suppressive activity against C. albicans and C. glabrata . Taken together, these observations of ISFP10 further indicate that targeting PGM might be a novel and viable therapeutic strategy for serious fungal infections. An inhibitor specific to fungal PGM enzymes, termed ISFP10, was generally well tolerated when administered via intraperitoneal (IP) injection in mice. ISFP10 displays antifungal activity in a concentration-dependent manner against C. albicans and C. glabrata .

Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure.

Testing for developmental toxicity is an integral partof chemical regulations. The applied tests are laborious and costly and require a large number of vertebrate test animals. To reduce animal numbers and associated costs, the zebrafish embryo was proposed as an alternative model. In this study, we investigated the potential of transcriptome analysis in the zebrafish embryo model to support the identification of potential biomarkers for key events in developmental toxicity, using the inhibition of angiogenesis as a proof of principle. Therefore, the effects on the zebrafish transcriptome after exposure to the tyrosine kinase inhibitors, sorafenib (1.3µM and 2.4µM) and SU4312 (1µM, 2µM, and 5µM), and the putative vascular disruptor compound rotenone (25nM and 50nM) were analyzed. An early (2hpf-hours post fertilization) and a late (24hpf) exposure start with a time resolved transcriptome analysis was performed to compare the specificity and sensitivity of the responses with respect to anti-angiogenesis. We also showed that toxicodynamic responses were related to the course of the internal concentrations. To identify differentially expressed genes (DEGs) the time series data were compared by applying generalized additive models (GAMs). We observed mainly unspecific developmental toxicity in the early exposure scenario, while a specific repression of vascular related genes was only partially observed. In contrast, differential expression of vascular-related genes could be identified clearly in the late exposure scenario. Rotenone did not show angiogenesis-specific response on a transcriptomic level, indicating that the observed mild phenotype of angiogenesis inhibition may represent a secondary effect.

Nanoformulation of Polymyxin E Through Complex Coacervation: A Pharmacokinetic Analysis.

Objectives: Polymyxin E (PME), a polymyxin antibiotic, serves as a final resort against antibiotic resistance. Nephrotoxicity is the primary concern when employing PME. To alleviate this issue, researchers have explored strategies including dosing adjustments and innovative formulations. This study employed complex coacervation to create PME nanoformulations, capitalizing on PME's charge properties. The research question and hypothesis posed pertained to whether neutralization of PME's positive charge during formulation would reduce its antibiotic efficacy and alter its tissue distribution and other pharmacokinetic parameters. Our objective was to evaluate the capability of complex coacervation to mitigate the adverse effects of PME while preserving its antibacterial potency and therapeutic effectiveness. Methods: Three negatively charged polyions: potassium sucrose octasulfate, polytamic acid, and sodium hyaluronate, were used for formulation. We performed characterization on the nanocomplex formed by the polyions and PME. The nanoformulations underwent several tests, including minimum inhibitory concentration, in vivo efficacy on an infected mouse model, pharmacokinetic assessments, tissue distribution, and toxicity. Results: the three polyions formed coacervation complexes with PME at varying charge ratios, yielding nanoparticles smaller than 30 nm with low polydispersity (PDI < 0.3). The results demonstrated that complex coacervation-mediated PME nanoformulations exhibited equivalent or superior antibacterial activity, increased maximum tolerant dose, and fewer adverse reactions in animal tests. Conclusions: Utilizing complex coacervation, PME nanoformulations were developed, demonstrating efficacy in the formulation process. Pharmacokinetic assessments revealed absorption and distribution profiles akin to those of standalone PME. The positive charge inherent in PME causing its toxicity was mitigated after complex coacervation.