Becton Dickinson (Ireland)

In silico modeling of subcutaneous injections is a promising tool to derisk and optimize the development of injections systems and improve preliminary understanding of the injectability of drugs. Such modeling can be leveraged early in development, prior to pre-clinical studies, to reduce testing burden and effectively predict aspects such as the time and forces required for full dose delivery into tissue and the tissue-injectate-device interactions. In this work, we address the three main in silico pillars (primary container, injection device, and tissue) that are necessary to simulate a parenteral injection. We showcase how their modeling can be combined and applied to various delivery systems and injection conditions to enable appropriate systems and parameters selection to achieve desired performance. Case studies of viscous drugs in PFS/AI and wearable injection systems with non-rigid component properties are presented to highlight the theoretical treatment of interfaces, the critical inputs required to achieve accurate predictions, the insights gained, and the good agreement between model predictions and experimental results.

BackgroundInfusion failure may have severe consequences for patients receiving critical, short–half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy.ObjectiveThis study aims to identify and rank determinants of the longevity of continuous infusions administered through syringe drivers, using nonlinear predictive models. Additionally, this study aims to evaluate key factors influencing infusion longevity and develop and test a model for predicting the likelihood of achieving successful infusion longevity.MethodsData were extracted from the event logs of smart pumps containing information on care profiles, medication types and concentrations, occlusion alarm settings, and the final infusion cessation cause. These data were then used to fit 5 nonlinear models and evaluate the best explanatory model.ResultsRandom forest was the best-fit predictor, with an F1-score of 80.42, compared to 5 other models (mean F1-score 75.06; range 67.48-79.63). When applied to infusion data in an individual syringe driver data set, the predictor model found that the final medication concentration and medication type were of less significance to infusion longevity compared to the rate and care unit. For low-rate infusions, rates ranging from 2 to 2.8 mL/hr performed best for achieving a balance between infusion longevity and fluid load per infusion, with an occlusion versus no-occlusion ratio of 0.553. Rates between 0.8 and 1.2 mL/hr exhibited the poorest performance with a ratio of 1.604. Higher rates, up to 4 mL/hr, performed better in terms of occlusion versus no-occlusion ratios.ConclusionsThis study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study’s outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.

Abstract Background: Circulating tumour cells (CTCs) are silent precursors of metastatic disease that utilise various mechanisms to survive in circulation and metastasise to distal sites. Classical CTC detection relies on EpCam affinity-based technologies; however, CTCs are highly heterogeneous and often undergo EMT. Recent research highlights the ability of platelets and neutrophils to ‘cloak' CTCs and crosstalk to aid in their proliferation and survival in circulation. Aims: Assess the role of platelets and neutrophils in the characterisation of CTCs. Methods: Cell lines: Breast cancer (MCF-7) and ovarian cancer (SKOV-3, OVCAR3, OAW42) cells were exposed to healthy donor isolated platelets and neutrophils for 24h. EMT and immune evasion gene assays, flow cytometry analysis and cell proliferation assays were performed following co-culture. Cell line-blood spike-in experiments were performed for gating strategy optimisation.Patient samples: Blood specimens were prospectively collected from breast and ovarian cancer patients (central venous and peripheral blood). CTCs and immune cells were then isolated using a ClearCell FX microfluidic device. FX isolated cells were immunophenotyped by either immunofluorescence (IF) or flow cytometry. Single cells were index sorted using BD FACS Melody for subsequent scRNAseq. Results: Platelet cloaking altered EpCam, PLEK2, CCL2 and TWIST1 mRNA expression in MCF-7 cells. Platelet and neutrophil co-culture altered EpCam, PAI-1 and PD-L1 expression in ovarian cancer cell lines. Healthy neutrophils co-cultured with ovarian cancer cells increased cell viability, while induction of NETosis increased cancer cell proliferation. CTCs isolated from peripheral blood in breast and ovarian patients were EpCam/panCK+/CD45- by IF. Immunophenotyped CTCs were identified as EpCam+/E-Cadherin+/CD45-, N-Cadherin+/CD45- and as PD-L1+/EpCam+/E-Cadherin+/CD45- cells by flow cytometry. We also identified EpCam+/CD45+/CD66b+ cells in ovarian central blood. Isolated classical and non-classical CTCs were sorted based on their different immunophenotypes for downstream scRNAseq using BD Precise Whole Transcriptome Assays. Conclusion: Platelets and neutrophils alter the expression of markers used in the identification of CTCs. Neutrophils were found to increase the proliferation of cancer cells as well as increase PD-L1 expression. We identified a heterogeneous population of CTCs across different patients and found that CTCs from central venous blood have increased numbers of non-classical CTCs (CD66b+/EpCam+ cells). Molecular scRNAseq signatures of CTC subsets form the basis for identifying the most clinically relevant CTCs in circulation. Our approach may improve diagnostic accuracy, allow for immunophenotyping of cloaked CTCs, and provide prognostic information. Citation Format: Mark P. Ward, Bashir M. Mohamed, Laura Kane, Mark Bates, Janina Berghoff, Cathy L. Spillane, Tanya Kelly, John Kennedy, Feras A. Saadeh, Karsten Hokamp, Noreen Gleeson, Orla Sheils, Cara Martin, Michael Gallagher, Sean Hannify, Eric P. Dixon, Sharon A. O'Toole, John J. O'Leary. Influence of platelets and neutrophils on circulating tumour cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 764.