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Results for “pharmacology and toxicology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity

Adverse outcome pathways (AOPs) describe mechanisms of toxicity by connecting molecular events with outcomes at higher levels of biological organization. Computational AOPs (cAOPs), constructed using existing toxicological data, can accelerate the early stages of AOP development, which is often a time-consuming and resource-intensive process. In this study, an integrative network-based framework was developed to construct cAOPs, with a particular focus on elucidating the toxicity of organic mercury in fish. First, 124 organic mercury compounds, corresponding fish-specific toxicity endpoints, and proteins were curated from CTD and ECOTOX. Next, molecular docking investigations were performed to determine novel molecular interactions with 16 zebrafish protein orthologs. Subsequently, the toxicity endpoints, including the identified molecular interactions, were standardized and harmonized using data from AOP-Wiki, Gene Ontology, and MeSH. These endpoints were integrated with event relationship information from AOP-Wiki and published literature to construct an organic mercury-associated toxicity network comprising 197 nodes and 243 edges. Thereafter, node- and edge-level filtration criteria were designed based on AOP definitions and utilized to identify biologically relevant pathways within the toxicity network. Further, these pathways were ranked based on their novelty with respect to existing AOPs within AOP-Wiki. Finally, based on extensive evidence from published literature, four top-ranked novel pathways that describe binding with glutathione peroxidase or alterations in metallothionein levels leading to neurologic manifestations or dysbiosis in fish were proposed as cAOPs. Overall, this study presents an integrative network-based framework for constructing cAOPs applicable to diverse contaminants and species, supporting New Approach Methodologies for toxicological risk assessment.

pharmacology and toxicology↗

bioRxiv2026

High-throughput Virtual Screen of Endocrine-disrupting Chemicals Identifies Disruptors of EGFR Signaling

Chemical exposures during pregnancy are linked to an increased risk of pregnancy complications that contribute significantly to maternal and infant morbidity and mortality and can lead to long term health consequences for both the mother and the offspring. The placenta, a central regulator of pregnancy health, is a direct target of environmental toxicants. Epidermal growth factor receptor (EGFR), highly expressed in the placenta, regulates proliferation, migration, invasion, fusion, and cellular bioenergetics. To identify compounds of environmental concern with potential for EGFR-disrupting activity, we optimized a high-throughput virtual screening protocol for the identification of EGFR inhibitors and achieved enrichment factors of EF1% = 10.09, EF5% = 3.86, and EF10% = 3.0 in a benchmarking dataset. We applied this protocol to screen the Collaborative Estrogen Receptor Activity Prediction Project database, finding that top-scoring compounds were enriched for aromatic and fused-ring chemical classes, including dyes. Kinase activity assays revealed that two out of thirteen selected compounds, Vat Red 32 and Reactive Red 136, inhibited EGFR kinase activity with micromolar IC50 values. Additionally, pose refinement with molecular dynamics simulations characterized the binding interactions of Reactive Red 136 within the EGFR kinase domain, and functional assays in HTR-8/SVneo placental trophoblast cells showed that Reactive Red 136, but not Vat Red 32, partially attenuated EGF-mediated cell migration despite both compounds inhibiting EGFR kinase activity. Together, this study has generated an enriched dataset of candidate environmental EGFR modulators, with experimental validation confirming enrichment for EGFR-disrupting activity among the selected compounds. These results provide a valuable resource for toxicological studies.

pharmacology and toxicology↗

bioRxiv2020

Effect of pesticides and metals on zebrafish embryo development and larval locomotor activity

The zebrafish has been widely used as a predictive model in safety and toxicology. Low cost high-throughput screening can be achieved with this model, and the genome contains orthologues of the majority of human disease genes. However, previous studies indicate that the predictivity of the zebrafish model in toxicology varies between compound and compound class. We examined this issue by screening 24 compounds from two different compound classes, metals and biocides (pesticides/insecticides) for toxicity in the zebrafish model and looked at the effects on hatching, morphology and predictivity for mammalian toxicity. Wild-type zebrafish embryos were exposed to test compounds in 96-well plates for 96 hours starting at 24 hours post fertilization. Hatching was either delayed or accelerated depending on the compound. Three types of alteration in behavioural responses were noted: (i) hypoactivity; (ii) hyperactivity; and (iii) biphasic response (a dose-dependent shift between hypo- and hyperactivity). LC50 of compounds was calculated and compared to published LD50 values in rodents. The zebrafish-rodent values were poorly correlated for both metals and biocides. We conclude that, although the zebrafish is a good model for some aspects of toxicology, its predictivity for mammalian toxicity needs to be determined per compound class.

pharmacology and toxicology↗

bioRxiv2023

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies

Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems. Author SummaryWe demonstrate that a deep autoencoder using raw behavioral tracking data from zebrafish toxicity screens outperforms conventional statistical methods, resulting in a comprehensive evaluation of behavioral data. Our models can accurately distinguish between normal and abnormal behavior with near-complete overlap with existing statistical approaches, with many chemicals detectable at lower concentrations than with conventional statistical tests; this is a crucial finding for the protection of public health. Our deep learning models enable the identification of new substances capable of inducing aberrant behavior, and we generated new data to demonstrate the reproducibility of these results. Thus, neurodevelopmentally active chemicals identified by our deep autoencoder models may represent previously undetectable signals of subtle individual response differences. Our method elegantly accounts for the high degree of behavioral variability associated with the genetic diversity found in a highly outbred population, as is typical for zebrafish research, thereby making it applicable to multiple laboratories. Utilizing the vast quantities of control data generated during high-throughput screening is one of the most innovative aspects of this study and to our knowledge is the first study to explicitly develop a deep autoencoder model for anomaly detection in large-scale toxicological behavior studies.

pharmacology and toxicology↗

bioRxiv2023

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs), using a data-centric approach. By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

pharmacology and toxicology↗

bioRxiv2019

Variability in Human Drug Metabolizing Cytochrome P450 CYP2C9, CYP2C19 and CYP3A5 Activities Caused by Genetic Variations in Cytochrome P450 Oxidoreductase

A broad spectrum of human diseases are caused by mutations in the NADPH cytochrome P450 oxidoreductase (POR). Cytochrome P450 proteins perform several reactions, including the metabolism of steroids, drugs, and other xenobiotics. In 2004 the first human patients with defects in POR were reported, and over 250 variations in POR are known. Information about the effects of POR variants on drug metabolizing enzymes is limited and has not received much attention. By analyzing the POR sequences from genomics databases, we identified potentially disease-causing variations and characterized these by in vitro functional studies using recombinant proteins. Proteins were expressed in bacteria and purified for activity assays. Activities of cytochrome P450 enzymes were tested in vitro using liposomes prepared with lipids into which P450 and P450 reductase proteins were embedded. Here we are reporting the effect of POR variants on drug metabolizing enzymes CYP2C9, CYP2C19, and CYP3A5 which are responsible for the metabolism of many drugs. POR Variants A115V, T142A, A281T, P284L, A287P, and Y607C inhibited activities of all P450 proteins tested. Interestingly, the POR variant Q153R showed a reduction of 20-50% activities with CYP2C9 and CYP2C19 but had a 400% increased activity with CYP3A5. The A287P is most common POR mutation found in patients of European origin, and significantly inhibited drug metabolism activities which has important consequences for monitoring and treatment of patients. In vitro, functional assays using recombinant proteins provide a useful model for establishing the metabolic effect of genetic mutations. Our results indicate that detailed knowledge about POR variants is necessary for correct diagnosis and treatment options for persons with POR deficiency and the role of changes in drug metabolism and toxicology due to variations in POR needs to be addressed.

pharmacology and toxicology↗

bioRxiv2026

Network Pharmacology-Guided Discovery of Fungal Autophagy Modulators for Tauopathies: Structural and Proteomic Evidence

Autophagic clearance of hyperphosphorylated tau is impaired in tauopathies, leading to the progressive accumulation of toxic tau species. Fungal metabolites provide a rich yet largely untapped source of neuroactive molecules with therapeutic potential. Here, we investigated metabolites from Lions Mane (Hericium erinaceus), Magic Mushrooms (Psilocybe spp.), and Ergot fungi (Claviceps spp.) using a computational drug-discovery workflow. We characterised their structural diversity, predicted blood-brain barrier permeability and toxicological properties, and integrated network pharmacology with protein-protein interaction and functional enrichment analyses to identify autophagy-related targets. Peroxisome proliferator-activated receptor gamma (PPARG), glycogen synthase kinase-3 beta (GSK3B), and casein kinase 2 alpha 1 (CSNK2A1) emerged as the three most promising candidates, given their complementary roles linking autophagy and tau pathology and their attractiveness as targets for multi-target drug discovery. Their interactions with fungal metabolites were evaluated by molecular docking, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring, molecular dynamics simulations, complemented by machine-learning quantitative structure-activity relationship (QSAR) modelling as an additional, ligand-based line of evidence. Molecular dynamics and MM/GBSA analyses confirmed stable, target-specific binding for Corallocin A and Erinacerin M (PPARG), Chaetopyranin and Ergocryptine (GSK3B), and Hericioic Acid D and Isohericerin (CSNK2A1), alongside Emodin, a reference compound with previously reported activity against all three targets. QSAR predictions were informative primarily for the PPARG candidates, which fell within the models applicability domain; predictions for the remaining candidates fell outside their respective models applicability domains and were therefore not interpretable as evidence for or against their prioritisation. Reanalysis of an independent hippocampal proteomic dataset from Alzheimers disease patients showed CSNK2A1 protein levels to be significantly altered in the CA3 subfield, providing an additional, correlative line of support for this target; PPARG and GSK3B showed no significant changes in protein abundance, which does not preclude their functional involvement given their extensive post-translational regulation. Overall, these findings identify fungal metabolites as promising multi-target autophagy modulator candidates and provide a systematic computational strategy for prioritising them for experimental validation in tauopathies.

pharmacology and toxicology↗

bioRxiv2026

Human CYP1A2 metabolism of organophosphorus pesticides and nerve agent surrogates

CYP1A2 contributes 13-15% of hepatic CYP450 content and plays a critical role in the metabolism of approximately 10% of pharmaceutical drugs in the human body. CYP1A2 has been previously reported to bioactivate and detoxify both the thion (P=S) and oxon (P=O) forms of organophosphorus (OP) compounds. In this study, we determined the capacity of CYP1A2 to degrade OP compounds using LC-MS/MS, as well as the inhibition of its activity by OP compounds using the previously developed pFluor50 assay. CYP1A2 was selective in metabolizing thions over oxons, and several OP compounds inhibited its activity in a time- and dose-dependent manner. To further investigate the mechanism of CYP1A2-OP interactions, molecular docking studies were performed using a crystal structure of CYP1A2 (PDB ID: 2HI4). Linear correlation analysis between CYP1A2 inhibition at 10 M with 30 minute preincubation and the % OP compound remaining at 2 hours (R = -0.71, P<0.001) showed a moderate but significant correlation. Based on these findings, CYP1A2 was identified as a potential bioscavenger for OP compound metabolism, supporting the future integration of experimental and in silico data for developing artificial intelligence (AI) / machine learning (ML) models for prediction of CYP1A2-mediated metabolism of existing and emerging OP compounds, including those of toxicological and chemical warfare relevance.

pharmacology and toxicology↗

bioRxiv2026

Comparative metabolism of the Alternaria toxins altenuene and tentoxin in rat and human primary hepatocytes

The ubiquitously occurring food contaminants altenuene (ALT) and tentoxin (TEN) are recognized as emerging Alternaria mycotoxins, yet substantial data gaps remain when it comes to their toxicological behavior and toxicokinetic characteristics. This study aimed to compare and generate quantitative data on their hepatic metabolism and to obtain semi-quantitative insights into their metabolite profiles. To this end, primary rat and human hepatocytes were incubated with 10 {micro}M ALT or TEN over multiple time points up to 4 h. Both substrate depletion and metabolite identification revealed pronounced interspecies differences. The extent of ALT metabolism was significant, with an 88% and 57% decrease in rat and human hepatocytes after 4 h, respectively. In contrast, TEN showed extensive biotransformation in rats (67%) but only modest turnover in humans (27%) over the same period. Hepatocellular clearances were consistently higher for ALT than TEN, with hepatic extraction ratios indicating intermediate extraction for ALT and low extraction for TEN. High-resolution mass spectrometry combined with targeted analysis of selected metabolites annotated phase II conjugation as the predominant metabolic pathway for ALT and phase I oxidative metabolism for TEN, including mono- and double-metabolized species for the latter. Overall, these results provide a comprehensive characterization of ALT- and TEN-metabolism in hepatocytes, offering a foundation for future studies on their toxicological relevance and impact on human health.

pharmacology and toxicology↗

bioRxiv2026

Species-dependent clearance of alternariol monomethyl ether, tenuazonic acid and altertoxin II in rat and human primary hepatocytes

The ubiquitously occurring food contaminants alternariol monomethyl ether (AME), tenuazonic acid (TeA) and altertoxin II (ATX-II) are recognized as emerging Alternaria mycotoxins, yet substantial data gaps remain regarding their toxicokinetic characteristics. The hepatic metabolism of these three substances was investigated in primary rat (PRH) and human (PHH) hepatocytes by monitoring parent compound depletion and, where applicable, metabolite formation. AME was initially evaluated at 5 {micro}M and subsequently investigated over a concentration range of 0.75-8 {micro}M (0.75, 1.5, 3, and 8 {micro}M), whereas TeA was assessed at 5 {micro}M, and ATX-II was assessed at 0.22 and 1.11 {micro}M, with additional short-term experiments conducted over 30 min for AME and ATX-II, and over 10 min for concentration-dependent AME depletion. For AME, time-dependent clearance was further evaluated in PRHs at two hepatocyte densities, 0.25 and 0.5 million cells/mL. In PRHs, AME metabolism followed Michaelis-Menten kinetics (Vmax = 150.9 pmol{middle dot}min-{superscript 1}{middle dot}10- cells, Km = 1.18 {micro}M), whereas no reliable kinetic model could be established for PHHs. In contrast, TeA exhibited high metabolic stability, with only 9-10% depletion after 4 h, indicating negligible hepatic clearance in both species. ATX-II was also rapidly depleted and became undetectable within 30 min, accompanied by transient formation of altertoxin I (ATX-I), which was more pronounced in PHHs than in PRHs. Substrate depletion revealed pronounced interspecies differences in hepatic clearance capacity and stability. Overall, these findings provide comparative insights in primary human and rat hepatocyte systems, offering a foundation for future studies on their toxicological relevance and impact on human health.

pharmacology and toxicology↗

bioRxiv2022

Mechanisms of Antimicrobial Agent Cetylpyridinium Chloride Mitochondrial Toxicity in Rodent and Primary Human Cells: Super-resolution Microscopy Reveals Nanostructural Disruption

People are exposed to high concentrations of antibacterial agent cetylpyridinium chloride (CPC) via personal care and food products, despite little information regarding CPC effects on eukaryotes. CPC is used as an antibacterial agent via a detergent mechanism when above [~]600- 900 M. While three previous studies suggested CPC mitochondrial toxicity, this phenomenon is not well-studied. Here, we show that low-micromolar CPC inhibits mitochondrial ATP production in primary human keratinocytes, mouse NIH-3T3 fibroblasts, and rat RBL-2H3 immune mast cells, in galactose media, which causes cells to produce ATP via mitochondria. ATP inhibition via CPC (EC50 1.7LJ{micro}M) is nearly as potent as that caused by canonical mitotoxicant CCCP (EC50 1.2LJ{micro}M). CPC inhibition of oxygen consumption rate (OCR) tracks with that of ATP: OCR is halved due to 1.75 M CPC in RBL-2H3 cells and 1.25 M in primary human keratinocytes. Here we demonstrate that CPC is more potent as a mitotoxicant than as an immune mast cell signaling inhibitor, an effect published previously. Mitochondrial [Ca2+] changes can cause mitochondrial dysfunction. Here we show, using a novel plate reader assay with reporter CEPIA2mt, that CPC causes mitochondrial Ca2+ efflux from mast cells via an ATP-inhibition mechanism. Using super-resolution microscopy (fluorescence photoactivation localization) in live cells, we have discovered that CPC causes mitochondrial nanostructural defects in fibroblasts, including the formation of spherical structures with donut-like cross section, as quantified by novel Fourier transform analysis. This work reveals CPC as a mitotoxicant despite widespread use, highlighting the importance of further research into its toxicological safety.

pharmacology and toxicology↗

bioRxiv2026

MAOMAO: An Ontology-Guided FAIR Resource for Harmonized Peptide Toxicity Data

Peptide toxicity is a critical safety and developability parameter in peptide discovery and therapeutic development, yet relevant information remains fragmented across databases, literature resources, and curated datasets. Here, we present MAOMAO, an ontology-guided FAIR-oriented resource that integrates and harmonizes peptide toxicity data from 54 sources. MAOMAO contains 71,701 unique peptide sequences across seven toxicity-related endpoints, represented as 501,907 sequence endpoint combinations with endpoint-specific evidence states and explicit encoding of unavailable information. The resource combines standardized terminology, a hierarchical toxicity vocabulary, evidence-aware state resolution, provenance-aware metadata, 41 physicochemical descriptors, 10 protein language model representations, and a one-hot baseline. It provides endpoint-specific benchmark partitions across splitting strategies and random seeds, reusable with numerical representations. MAOMAO establishes a reusable framework for peptide toxicity research and data-driven toxicology.

pharmacology and toxicology↗

bioRxiv2021

NeurotoxKb: compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals

Exposure to environmental neurotoxicants is a significant concern due to their potential to cause permanent or irreversible damage to the human nervous system. Here, we present the first dedicated knowledgebase, NeurotoxKb, on environmental neurotoxicants specific to mammals. Using a detailed workflow, we have compiled 475 potential non-biogenic neurotoxicants from 835 published studies with evidence of neurotoxicity specific to mammals. A unique feature of NeurotoxKb is the manual curation effort to compile and standardize the observed neurotoxic effects for the potential neurotoxicants from 835 published studies. For the 475 potential neurotoxicants, we have compiled diverse information such as chemical structures, environmental sources, chemical classification, physicochemical properties, molecular descriptors, predicted ADMET properties, and target human genes. To better understand the prospect of human exposure, we have explored the presence of potential neurotoxicants in external exposomes via two different analyses. By analyzing 55 chemical lists representing global regulations and guidelines, we reveal potential neurotoxicants both in regular use and produced in high volume. By analyzing human biospecimens, we reveal potential neurotoxicants detected in them. Lastly, a construction of the chemical similarity network and ensuing analysis revealed the diversity of the toxicological space of 475 potential neurotoxicants. NeurotoxKb is accessible online at: https://cb.imsc.res.in/neurotoxkb/.

pharmacology and toxicology↗

bioRxiv2023

Machine learning models for prediction of xenobiotic chemicals with high propensity to transfer into human milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via transfer of environmental chemicals from maternal exposome is a significant concern for infant health. Machine learning based predictive toxicology models can be valuable in predicting chemicals with high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated dataset to date of 375 chemicals with known Milk to Plasma concentration (M/P) ratios. Our Support Vector Machine (SVM) based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and external test dataset. Specifically, the SVM based classifier on (internal) test data achieved a classification accuracy of 77.33%, specificity of 84%, sensitivity of 64%, and F-score of 65.31%. When evaluated on an external test dataset, our SVM based classifier is found to be generalizable with sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. We have made our complete workflow, train and test datasets, and computer codes for the classification and regression models publicly available via a dedicated GitHub repository. Overall, this study attests the immense potential of predictive computational toxicology models in characterizing the myriad chemicals in the human exposome.

pharmacology and toxicology↗

bioRxiv2026

Organophosphorus pesticide and nerve agent surrogate metabolism by human CYP3A4

Of the cytochrome P450 enzymes, CYP3A4 is the most abundant isoform in the human liver, and this enzyme plays a dominant role in the metabolism of a wide range of clinical drugs and xenobiotics. Previous studies have demonstrated that CYP3A4 participates in the oxidative metabolism of several organophosphorus (OP) pesticides involving both thion (P=S) and oxon (P=O) forms. In the present study, we evaluated the capacity of CYP3A4 to metabolize a structurally diverse set of OP compounds using LC-MS/MS methods and assessed their potential to inhibit CYP3A4 activity using previously developed pFlour50 fluorogenic assay. Our results demonstrate that CYP3A4 preferentially metabolizes thions, as compared to oxons, and several OP compounds were also found to inhibit CYP3A4 activity in a time-dependent manner. To gain further mechanistic structural insight into the CYP3A4-OP interactions, molecular docking studies were performed using a crystal structure of CYP3A4 (PDB ID: 3NXU). Linear correlation analysis between in silico parameters like molecular weight or binding energy correlated with experimental data including inhibition data for 10 or 30 minutes or the LC-MS/MS data showing the degradation at 1 or 2 hours showed moderate but significant correlation. Soman surrogate PiMP, and cyclosarin surrogate CMP, were both effectively metabolized by CYP3A4, while docking of these surrogates and authentic agents with CYP3A4 receptor revealed very similar binding poses and interactions. Collectively, these findings highlight the important role of CYP3A4 in OP metabolism and support the potential of integrating experimental and in silico data to predict CYP3A4-mediated metabolism of existing and emerging OP compounds, including those of toxicological and chemical warfare relevance.

pharmacology and toxicology↗

bioRxiv2024

Dose range-finding toxicity study in rats with recombinant human lactoferrin produced in Komagataella phaffii

The oral toxicity of recombinant human lactoferrin (Helaina rhLF, Effera) produced in Komagataella phaffii was investigated in adult Sprague-Dawley rats by once daily oral gavage for 14 consecutive days. The study used groups of 3-6 rats/sex/dose. The vehicle control group received sodium citrate buffer and the test groups received daily doses of 200, 1000 and 2000 mg of rhLF per kg body weight. Bovine LF at 2000 mg/kg body weight per day was used as a comparative control. Clinical observations, body weight, hematology, clinical chemistry, iron parameters, immunophenotyping, and gross examination at necropsy were used as criteria for detecting the effects of treatment in all groups and to inform dose levels for future toxicology studies. Quantitative LF levels were also analyzed as an indication of bioavailability. Overall, administration of Helaina rhLF by once daily oral gavage for 14 days was well tolerated in rats at levels up to 2000 mg/kg/day, or 400x Helainas intended commercial use, and indicating that a high dose of 2000 mg/kg/day is appropriate for future definitive toxicology studies.

pharmacology and toxicology↗

bioRxiv2025

A Bayesian Neural Ordinary Differential Equations Framework to Study the Effects of Chemical Mixtures on Survival

In the field of Plant Protection Products (PPP), the combination of multiple active ingredients is a common strategy to improve the efficacy against target species. However, these mixtures may expose non-target species to untested chemical combinations due to varied application and degradation pathways. Developing a method to predict the effects of such mixtures without extensive testing is crucial for enhancing risk assessment for non-target organisms. Toxicokinetic (TK) and toxicodynamic (TD) models currently provide predictive power for the toxicity of mixture over time, utilizing the concentration addition and independent action principles. However, their capacity to capture complex, non-linear interactions -- such as synergistic and antagonistic effects -- remains limited. To overcome these limitations, we introduce a new methodology that integrates TKTD models using Ordinary Differential Equations (ODE) with neural networks (NN), offering a robust framework for modeling complex substance interactions. The ODE component encodes fundamental biological principles that constrain the neural network to biologically plausible solutions. Bayesian inference further refines the model by addressing uncertainties in data, model parameters, and biological processes, while also allowing quantification of prediction uncertainty, highlighting gaps in experimental data that warrant further investigation. This hybrid model was evaluated across 99 acute toxicity studies that included various PPP mixtures, testing its ability to identify and forecast deviations from expected mixture behaviors. Our findings highlight the potential of combining mechanistic models with machine learning techniques to advance predictive accuracy in environmental toxicology, providing a framework to anticipate the combined effects of untested chemical mixtures and inform risk assessment practices.

pharmacology and toxicology↗

bioRxiv2026

Chili Pepper Flavourants in 'Heat" Oral Nicotine Pouches Marketed as Unflavoured in United States Jurisdictions Restricting Flavoured Tobacco Products

Background: In the United States, several states have restricted sales of flavoured tobacco products, including popular menthol- and mint-flavoured Oral Nicotine Pouches (ONP). In response, tobacco companies introduced "unflavoured" ONP containing odorless synthetic cooling agents. Since these, in turn, have become targets of legislative bans, the tobacco industry may seek out flavourants with other sensory effects to increase the appeal of "unflavoured" ONP. Methods: Online merchants were searched for "unflavored" ONP marketed to consumers in jurisdictions with flavour bans. Sensory effects of aqueous extracts from identified "heat", "spicy" and "unflavoured" ONP were analyzed by Ca2+ microfluorimetry in HEK293 cells expressing the human heat/chili pepper flavourant (capsaicinoid) receptor, hTRPV1. ONP were analyzed for capsaicinoids and sweeteners by Liquid Chromatography/Mass Spectrometry (LC/MS). Results: A new category of "heat" or "spicy" ONP was identified, including products marketed as "unflavoured". Extracts from all these ONP robustly activated TRPV1, with "unflavoured" Lucy Heat the most potent. Chemical analysis demonstrated that Lucy Heat contained the synthetic capsaicinoid nonivamide at high levels (~675 microgram/pouch), while others contained mixtures of capsaicinoids (5-25 microgram/pouch) combined with other characterizing flavours (tropical, fruit). All tested ONP contained sweeteners. Conclusions: The tobacco industry continues to probe regulatory loopholes by claiming that newly introduced capsaicinoid flavourants and sweeteners in ONP do not represent characterizing flavours. This is contradicted by industry and regulatory determinations assigning characterizing flavour properties to these additives. The toxicological health risks of repeated capsaicinoid exposures due to ONP use, in combination with nicotine and other constituents, need to be assessed.

pharmacology and toxicology↗