An innovative non-invasive technique for subcutaneous tumour measurements

Experimental design considerations and statistical analyses in preclinical tumor growth inhibition studies

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.

OBSERVE: guidelines for the refinement of rodent cancer models

Existing guidelines on the preparation (Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE)) and reporting (Animal Research: Reporting of In Vivo Experiments (ARRIVE)) of animal experiments do not provide a clear and standardized approach for refinement during in vivo cancer studies, resulting in the publication of generic methodological sections that poorly reflect the attempts made at accurately monitoring different pathologies. Compliance with the 3Rs guidelines has mainly focused on reduction and replacement; however, refinement has been harder to implement. The Oncology Best-practices: Signs, Endpoints and Refinements for in Vivo Experiments (OBSERVE) guidelines are the result of a European initiative supported by EurOPDX and INFRAFRONTIER, and aim to facilitate the refinement of studies using in vivo cancer models by offering robust and practical recommendations on approaches to research scientists and animal care staff. We listed cancer-specific clinical signs as a reference point and from there developed sets of guidelines for a wide variety of rodent models, including genetically engineered models and patient derived xenografts. In this Consensus Statement, we systematically and comprehensively address refinement and monitoring approaches during the design and execution of murine cancer studies. We elaborate on the appropriate preparation of tumor-initiating biologicals and the refinement of tumor-implantation methods. We describe the clinical signs to monitor associated with tumor growth, the appropriate follow-up of animals tailored to varying clinical signs and humane endpoints, and an overview of severity assessment in relation to clinical signs, implantation method and tumor characteristics. The guidelines provide oncology researchers clear and robust guidance for the refinement of in vivo cancer models.

Harnessing HfO2 Nanoparticles for Wearable Tumor Monitoring and Sonodynamic Therapy in Advancing Cancer Care

Addressing the critical requirement for real-time monitoring of tumor progression in cancer care, this study introduces an innovative wearable platform. This platform employs a thermoplastic polyurethane (TPU) film embedded with hafnium oxide nanoparticles (HfO2 NPs) to facilitate dynamic tracking of tumor growth and regression in real time. Significantly, the synthesized HfO2 NPs exhibit promising characteristics as effective sonosensitizers, holding the potential to efficiently eliminate cancer cells through ultrasound irradiation. The TPU-HfO2 film, acting as a dielectric elastomer (DE) strain sensor, undergoes proportional deformation in response to changes in the tumor volume, thereby influencing its electrical impedance. This distinctive behavior empowers the DE strain sensor to continuously and accurately monitor alterations in tumor volume, determining the optimal timing for initiating HfO2 NP treatment, optimizing dosages, and assessing treatment effectiveness. Seamless integration with a wireless system allows instant transmission of detected electrical impedances to a smartphone for real-time data processing and visualization, enabling immediate patient monitoring and timely intervention by remote medical staff. By combining the dynamic tumor monitoring capabilities of the TPU-HfO2 film with the sonosensitizer potential of HfO2 NPs, this approach propels cancer care into the realm of telemedicine, representing a significant advancement in patient treatment.

'Invisible actors'-How poor methodology reporting compromises mouse models of oncology: A cross-sectional survey

The laboratory mouse is a key player in preclinical oncology research. However, emphasis of techniques reporting at the expense of critical animal-related detail compromises research integrity, animal welfare, and, ultimately, the translation potential of mouse-based oncology models. To evaluate current reporting practices, we performed a cross-sectional survey of 400 preclinical oncology studies using mouse solid-tumour models. Articles published in 2020 were selected from 20 journals that specifically endorsed the ARRIVE (Animal Research: Reporting of In Vivo Experiments) preclinical reporting guidelines. We assessed reporting compliance for 22 items in five domains: ethical oversight assurance, animal signalment, husbandry, welfare, and euthanasia. Data were analysed using hierarchical generalised random-intercept models, clustered on journal. Overall, reporting of animal-related items was poor. Median compliance over all categories was 23%. There was little or no association between extent of reporting compliance and journal or journal impact factor. Age, sex, and source were reported most frequently, but verifiable strain information was reported for <10% of studies. Animal husbandry, housing environment, and welfare items were reported by <5% of studies. Fewer than one in four studies reported analgesia use, humane endpoints, or an identifiable method of euthanasia. Of concern was the poor documentation of ethical oversight information. Fewer than one in four provided verifiable approval information, and almost one in ten reported no information, or information that was demonstrably false. Mice are the "invisible actors" in preclinical oncology research. In spite of widespread endorsement of reporting guidelines, adherence to reporting guidelines on the part of authors is poor and journals fail to enforce guideline reporting standards. In particular, the inadequate reporting of key animal-related items severely restricts the utility and translation potential of mouse models, and results in research waste. Both investigators and journals have the ethical responsibility to ensure animals are not wasted in uninformative research.

Chitosan-Coated-PLGA Nanoparticles Enhance the Antitumor and Antimigration Activity of Stattic – A STAT3 Dimerization Blocker

Purpose

The use of nanocarriers to improve the delivery and efficacy of antimetastatic agents is less explored when compared to cytotoxic agents. This study reports the entrapment of an antimetastatic Signal Transducer and Activator of Transcription 3 (STAT3) dimerization blocker, Stattic (S) into a chitosan-coated-poly(lactic-co-glycolic acid) (C-PLGA) nanocarrier and the improvement on the drug’s physicochemical, in vitro and in vivo antimetastatic properties post entrapment.

Methods

In vitro, physicochemical properties of the Stattic-entrapped C-PLGA nanoparticles (S@C-PLGA) and Stattic-entrapped PLGA nanoparticles (S@PLGA, control) in terms of size, zeta potential, polydispersity index, drug loading, entrapment efficiency, Stattic release in different medium and cytotoxicity were firstly evaluated. The in vitro antimigration properties of the nanoparticles on breast cancer cell lines were then studied by Scratch assay and Transwell assay. Study on the in vivo antitumor efficacy and antimetastatic properties of S@C-PLGA compared to Stattic were then performed on 4T1 tumor bearing mice.

Results

The S@C-PLGA nanoparticles (141.8 ± 2.3 nm) was hemocompatible and exhibited low Stattic release (12%) in plasma. S@C-PLGA also exhibited enhanced in vitro anti-cell migration potency (by >10-fold in MDA-MB-231 and 5-fold in 4T1 cells) and in vivo tumor growth suppression (by 33.6%) in 4T1 murine metastatic mammary tumor bearing mice when compared to that of the Stattic-treated group. Interestingly, the number of lung and liver metastatic foci was found to reduce by 50% and 56.6%, respectively, and the average size of the lung metastatic foci was reduced by 75.4% in 4T1 tumor-bearing mice treated with S@C-PLGA compared to Stattic-treated group (p < 0.001).

Conclusion

These findings suggest the usage of C-PLGA nanocarrier to improve the delivery and efficacy of antimetastatic agents, such as Stattic, in cancer therapy.

Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation

The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.