Overcoming challenges for CD3-bispecific antibody therapy in solid tumors

Simple SummaryCD3-bispecific antibody therapy is a form of immunotherapy that enables soldier cells of the immune system to recognize and kill tumor cells. This type of therapy is currently successfully used in the clinic to treat tumors in the blood and is under investigation for tumors in our organs. The treatment of these solid tumors faces more pronounced hurdles, which affect the safety and efficacy of CD3-bispecific antibody therapy. In this review, we provide a brief status update of this field and identify intrinsic hurdles for solid cancers. Furthermore, we describe potential solutions and combinatorial approaches to overcome these challenges in order to generate safer and more effective therapies.Immunotherapy of cancer with CD3-bispecific antibodies is an approved therapeutic option for some hematological malignancies and is under clinical investigation for solid cancers. However, the treatment of solid tumors faces more pronounced hurdles, such as increased on-target off-tumor toxicities, sparse T-cell infiltration and impaired T-cell quality due to the presence of an immunosuppressive tumor microenvironment, which affect the safety and limit efficacy of CD3-bispecific antibody therapy. In this review, we provide a brief status update of the CD3-bispecific antibody therapy field and identify intrinsic hurdles in solid cancers. Furthermore, we describe potential combinatorial approaches to overcome these challenges in order to generate selective and more effective responses.Experimental cancer immunology and therap

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Risk and Preventive Factors for SUDI: Need We Adjust the Current Prevention Advice in a Low-Incidence Country

Background: The incidence of Sudden Unexpected Death in Infancy (SUDI) is low in the Netherlands, with an incidence rate of 0.18 per 1,000 live births. Therefore, prevention advice may receive less attention, potentially leading to increasing incidence rates. It is currently unknown whether the risks for SUDI changed in the Netherlands, and if other risk factors might be present. The aim of this study was to examine the current risks and preventive factors for SUDI in Dutch infants, in order to determine if it is necessary to adapt the prevention advice toward the current needs. Methods: A case-control study was conducted comparing SUDI cases aged <12 months from 2014–2020 in the Netherlands (n = 47), to a Dutch national survey control group from 2017 including infants <12 months of age (n = 1,192). Results: Elevated risks for several well-known factors were observed, namely: duvet use (aOR = 8.6), mother smoked during pregnancy (aOR = 9.7), or after pregnancy (aOR = 5.4) and the prone sleeping position (aOR = 4.6). Reduced risks were observed for the well-known factors: room-sharing (aOR = 0.3), sleep sack use (aOR = 0.3), breastfeeding (aOR = 0.3), and the use of a pacifier (aOR = 0.4). For infants <4 months, the risk for SUDI was higher when bed-sharing (aOR = 3.3), and lower when room-sharing (aOR = 0.2) compared to older infants. For older infants, the sleep sack was found to be more protective (aOR = 0.2). A high risk for SUDI when bed-sharing was found when mother smoked, smoked during pregnancy, or if the infant did not receive any breastfeeding (respectively aOR = 17.7, aOR = 10.8, aOR = 9.2). Conclusions: Internationally known factors related to the sudden unexpected death of infants were also found in this study. Relatively new findings are related to specific groups of infants, in which the strengths of these risk factors differed. In a low-incidence country like the Netherlands, renewed attention to the current prevention advice is needed. Furthermore, additional attention for prevention measures in low educated groups, and additional advice specifically targeting high-risk groups is recommended

Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings

Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic retinopathy (DR) on a dataset of colour fundus images acquired during routine clinical practice in a tertiary hospital screening program, analyzing the reduction of workload that can be released incorporating this artificial intelligence-based technology. Methods: Evaluation of the software was performed on a dataset of 7195 nonmydriatic fundus images from 6325 eyes of 3189 diabetic patients attending our screening program between February to December of 2019. The software generated a DR severity score for each colour fundus image which was combined into an eye-level score. This score was then compared with a reference standard as set by a human expert using receiver operating characteristic (ROC) curve analysis. Results: The artificial intelligence (AI) software achieved an area under the ROC curve (AUC) value of 0.988 [0.981:0.993] for the detection of referable DR. At the proposed operating point, the sensitivity of the RetCAD software for DR is 90.53% and specificity is 97.13%. A workload reduction of 96% could be achieved at the cost of only 6 false negatives. Conclusions: The AI software correctly identified the vast majority of referable DR cases, with a workload reduction of 96% of the cases that would need to be checked, while missing almost no true cases, so it may therefore be used as an instrument for triage.Comment: 15 pages, 3 figures, 2 table