Oksana Sirenko

CellXpress.ai: Machine learning powered system for automation of complex 2D and 3D cell culture workflows

Oksana Sirenko, Astrid Michlmayr, Prathyushakrishna Macha, Sandra Grund‐Gröschke et autres

Attrition in the therapeutic pipeline can often be attributed to a lack of translational efficacy from pre-clinical to clinical phases. Organoids show great promise as a game-changer in disease modeling and drug screening, as they better resemble tissue structure and functionality, and …

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Abstract 6419: Automated culture and AI-enabled image analysis of compound responses in patient-derived colorectal cancer organoids

Oksana Sirenko, Prathyushakrishna Macha, Zhisong Tong, Nikki Carter et autres

Abstract Organoids transformed biomedical research by providing physiologically relevant models for cancer studies, essential for investigating disease mechanisms and drug responses. However, manual organoid culture processes are labor-intensive and prone to variability, limiting widespread adoption. Additionally, extracting information from complex biological systems …

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Abstract 4397: Leveraging patient-derived organoids to investigate bystander effects of antibody-drug conjugates in solid tumor backgrounds

Oksana Sirenko, Nikki Carter

Abstract Background: Antibody-drug conjugates (ADCs) have demonstrated significant clinical benefit, yet their efficacy can be influenced by heterogeneous target expression and bystander killing. Understanding these dynamics in physiologically relevant models is critical for optimizing ADC design and patient selection. Methods: We established …

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Abstract 5198: Semi-automated, scaffold-free organoid workflow for the T cell screening assay using AI

Zhisong Tong, Angeline Pei Chiew Lim, Oksana Sirenko, Jia-Yang Chen et autres

Abstract Among the therapies for treating cancer, immunotherapy—especially chimeric antigen receptor (CAR) T-cell therapy—is increasingly popular. CAR T cells are genetically altered T cells that when injecting back into patients, they help arm the immune system to target and destroy cancer cells. …

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Abstract 2060: Automation of 3D cancer spheroid assay for compound screening

Oksana Sirenko, Angeline Pei Chiew Lim, Astrid Michlmayr, Zhisong Tong et autres

Abstract Finding efficient drug combinations to treat cancer patients is critical for therapy success. Accordingly, there is a critical need to develop methods for efficient testing drug efficacy to discover new therapeutic targets. 3D cancer models are highly valuable tools for cancer …

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Abstract 1514: A novel workflow to assess the T-cell and patient-derived organoid interaction

Zhisong Tong, Angeline Pei Chiew Lim, Oksana Sirenko

Abstract Immunotherapy is increasingly popular as a type of cancer treatment. These therapies include the use of Chimeric Antigen Receptor-engineered T-cells (CAR T-cells), tumor-infiltrating lymphocytes (TIL), and other genetically modified T-cells to specifically target the cancer cells. Although much success has been …

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Abstract 4919: AI-enabled hit selection of drug screening on human pancreatic cancer organoids

Zhisong Tong, Angeline Pei Chiew Lim, Marine Meyer, Maria Clapés et autres

Abstract Cancer remains one of the leading causes of death in the 21st century. Despite the latest advances in oncology, most cancer patients lack tailored therapeutic approaches with lasting benefit. Measuring the impact of anticancer compounds and their combinations is only possible …

P16-22: Functional Testing of Human iPSC-derived 3D Neural Spheroids Against a Panel of Neurotoxic Compounds

Ouissame Filali, Scott J. Schachtele, R. Fiene, K. Tomotoshi et autres

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Abstract LB113: Bio-printed 3D cell models and high-content imaging for testing anti-cancer compounds

Prathyushakrishna Macha, John Huang, Zhisong Tong, James B. Hoying et autres

Abstract The discovery and screening of anti-cancer drugs is a vast field that is constantly evolving. More advanced cell-based models are needed to have an efficient way of studying cellular and subcellular effects of compound treatments, especially in early drug discovery. 3D …

Abstract 5362: AI-enabled novel workflow to evaluate T-cell activity in vitro using 3D spheroid models

Zhisong Tong, Oksana Sirenko, Misha Bashkurov, Angeline Pei Chiew Lim

Abstract T-cell therapies are designed to help our immune system eliminate cancer cells. Those include CAR T-cells (Chimeric Antigen Receptor engineered T-cells), tumor infiltrating lymphocytes (TIL), and other genetically modified T-cells. In recent years, the field of cell therapy has started to …

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Evaluating Drug Response in 3D Triple Negative Breast Cancer Tumoroids with High Content Imaging and Analysis

Oksana Sirenko, Courtney K. Brock, Angeline Pei Chiew Lim, Prathyushakrishna Macha et autres

Abstract There is a critical need to develop methods for efficient testing of drug efficacy in patient-derived tumor samples to discover new therapeutics. Two-dimensional (2D) cell culture remains the primary method of drug screening, despite being considered less physiologically relevant than three-dimensional …

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Abstract 180: Automation and high content imaging of 3D triple negative breast cancer patient-derived tumoroids assay for compound screening

Oksana Sirenko, Angeline Pei Chiew Lim, Courtney K. Brock, Katya Nikolov et autres

Abstract Introduction: Currently there are no clinically approved small molecule targeted therapies for triple negative breast cancer, underlining the critical need to discover new therapeutic targets. Patient-derived cell-based 3D cancer models are highly valuable tools for cancer research and drug development. Primary …

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