bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.09.12.675933

Methamphetamine and α-pyrrolidinopentiophenone (α-PVP) Intravenous Self-Administration in Female and Male Rats

Abstract

BackgroundStimulant drug users vary in their substance of choice and may, in some cases, switch up their preferred substance based on availability, cost or other factors. Poly-substance use is rarely assessed in rodent models of drug seeking and this study determined if training drug alters the apparent reinforcing properties of methamphetamine (MA) and -pyrrolidinopentiophenone (-PVP). MethodsFemale and male Wistar rats (N=8 per group) were trained in the intravenous self-administration (IVSA) of -PVP or MA. The impact of dose substitution (0.0125, 0.0250, 0.100, 0.300 mg/kg/infusion) for each training drug was then assessed in all groups under FR and Progressive Ratio schedules of reinforcement. ResultsMale and female rats obtained similar numbers of infusions of MA (0.05 mg/kg/infusion) and of -PVP (0.05 mg/kg/infusion) during acquisition, however more infusions of -PVP than of MA were obtained by each sex. Mean lever discrimination ratios exceeded 80% on the drug-associated lever within 5 training sessions for -PVP groups but were not consistently at this level for either MA group. Drug potency was similar across groups but was less effective in the MA-trained males. ConclusionsInterpretations of sex differences in the acquisition of drug IVSA require caution when dose is not varied across or within group. This study also further confirms that the apparent efficacy of a drug as a reinforcer depends at least partially on the behavioral antecedents, including the identity of the drug used for initial IVSA acquisition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gutierrez, A., Grant, Y., Vandewater, S. A., Taffe, M. A.. 2025-09-17. Methamphetamine and α-pyrrolidinopentiophenone (α-PVP) Intravenous Self-Administration in Female and Male Rats. https://doi.org/10.1101/2025.09.12.675933

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Translational Pharmacokinetics and Pharmacodynamics of a Cationic mRNA-Lipid Nanoparticle from Mice to Non-Human Primates

Cationic lipid nanoparticles have demonstrated unique potential for extrahepatic mRNA delivery, particularly enabling selective targeting of the pulmonary endothelium. However, their translational development has been hampered by reports of infusion-related immune reactions and innate immune system activation, most notably transient complement activation. Here, we present a case study illustrating the discovery and translational advancement of a selected cationic LNP into non-human primates (NHPs) for initial pharmacokinetic assessment and evaluation of potential immunostimulatory side effects. We show surface charge dependent organ-selective expression of reporter mRNAs from different LNPs in vivo. An mRNA encoding the Tie2 agonist COMP-Angl, was formulated with LNP002, and respective pharmacokinetic and pharmacodynamic readouts were analyzed in two independent non-human primate studies. Notably, dose-dependent transient complement activation could be abrogated by extending the infusion time. Finally, we identified the blood-borne pharmacodynamic biomarker PDGFB for LNP002/mRNA-76 treatment reflecting activated Tie2-signalling in healthy pulmonary endothelium in vivo supported by single cell sequencing and cluster-alignment of downstream effector genes with the same spatial profile as the delivered mRNA.

pharmacology and toxicology↗

Cytotoxic Effects of Multiple Pesticides and their Mixtures on Caco-2 Cells Evaluated by Using MTT and Trypan Blue Assays

BACKGROUND: Pesticides are extensively used in agriculture, raising concerns about their potential impact on human health through dietary and environmental exposure. OBJECTIVES: This study evaluated the in vitro cytotoxicity of ten commonly used pesticides and their mixtures (lambda-cyhalothrin, cypermethrin, deltamethrin, tebuconazole, glyphosate, acetamiprid, cyprodinil, piperonyl butoxide, fluopyram, and imazalil) on human intestinal Caco-2 cells. METHODS: Cytotoxicity was assessed using the MTT assay, as a measure of metabolic activity, and the trypan blue exclusion test, as an indicator of cell membrane integrity. FINDINGS: Results showed that high concentrations (100 mg/L) of all pesticides significantly reduced cell viability and vitality. Notably, glyphosate and tebuconazole exhibited significant toxicity even at lower concentrations, respectively 0.1 mg/L and 10 mg/L. Combination treatments (Top 3 and Top 8 pesticide mixtures) retained the cytotoxic effects observed for individual compounds, showing additive (non-synergistic) effects. CONCLUSIONS: Overall, these findings indicate that certain pesticides-based herbicides can exert cytotoxic effects on intestinal cells even at relatively low concentrations and highlight the importance of using the component-based approach in mixture risk assessment for humans. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

pharmacology and toxicology↗

Assessing chemical toxicity across Eukaryota using multimodal transformers

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

pharmacology and toxicology↗