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Three-dimensional (3D) disease models have garnered widespread interest for use in later stages of the drug discovery process, such as preclinical efficacy and toxicology studies, due to their pathophysiologically relevant properties. However, there is a need and opportunity for 3D cancer models to be used earlier in the drug screening process. To meet this need, the 3D models must strike a balance between throughput, which includes scalability and uniformity, and physiological relevance, such as the ability to modulate key attribute of the tumor microenvironment. Here we report the creation of 3D colorectal cancer (CRC) tissue models, referred to as VivoSpheres, and demonstrated their relevance to cancer drug screening. The VivoSphere production platform couples tissue engineering toolkits with microfluidics, enabling the scalable production of engineered cancer microspheres. The model supports the long-term maintenance of the cancer cell phenotype. In a preliminary study, we were able to generate more physiologically relevant drug responses. We formed CRC VivoSpheres by encapsulating HT-29 CRC cells within poly(ethylene glycol)-fibrinogen hydrogel microspheres using our previously developed microfluidic platform. CRC VivoSpheres were rapidly produced with high cell densities (20 × 106 cells/ml) and high uniformity on day 0 with a coefficient of variation (COV) < 7%. This high uniformity was maintained for 15 days (COV ≤ 10%), which is critical for long-term dose studies. The cells maintained high viability and showed high proliferative capability with a significant increase in colony size and expression of Ki67 up to day 29. The encapsulated cells maintained the CRC phenotype over time with the expression of CD44 (cancer stem cell marker) and CK20 (CRC marker). After establishing shipping conditions that maintained cell viability for remote use, the HT-29 VivoSpheres were shipped to the oncology team at Southern Research for drug testing. The CRC VivoSpheres were treated with DMSO, GANT61, and SRI-38832, the latter two of which are GLI1 inhibitors. Phase contrast images and western blot were used to assess the response of CRC VivoSpheres to the treatments. Oncogenic GLI1 transcription activity and NBS1 overexpression have been found to contribute to chemotherapeutic resistance, negating the anti-tumor effects of 5-fluorouracil. While 2D cultured HT-29s responded to treatment with GANT61, HT-29 VivoSpheres continued to express NBS1 following GANT1 treatment, but downregulated NBS1 in response to the GLI1 inhibitor SRI-38832, which is the same response Southern Research has seen in in vivo tumor models. In conclusion, we have developed tissue-engineered 3D CRC models that hold promise for use in drug screening. These models have demonstrated an initial capability to reproduce the CRC phenotype and mimic in vivo drug response. 

Cardiomyocytes (CMs) are heart cells responsible for heart contraction and relaxation. CMs can be derived from human induced pluripotent stem cells (hiPSCs) with high yield and purity. Mature CMs can potentially replace dead and dysfunctional cardiac tissue and be used for screening cardiac drugs and toxins. However, hiPSCs-derived CMs (hiPSC-CMs) are immature, which limits their utilization. Therefore, it is crucial to understand how experimental variables, especially tunable ones, of hiPSC expansion and differentiation phases affect the hiPSC-CM maturity stage. This study applied clustering algorithms to day 30 cardiac differentiation data to investigate if any maturity-related cell features could be related to the experimental variables. The best models were obtained using k-means and Gaussian mixture model clustering algorithms based on the evaluation metrics. They grouped the cells based on eccentricity and elongation. The cosine similarity between the clustering results and the experimental parameters revealed that the Gaussian mixture model results have strong similarities of 0.88, 0.94, and 0.93 with axial ratio, diameter, and cell concentration. 

Abstract There is a need for new in vitro systems that enable pharmaceutical companies to collect more physiologically-relevant information on drug response in a low-cost and high-throughput manner. For this purpose, three-dimensional (3D) spheroidal models have been established as more effective than two-dimensional models. Current commercial techniques, however, rely heavily on self-aggregation of dissociated cells and are unable to replicate key features of the native tumor microenvironment, particularly due to a lack of control over extracellular matrix components and heterogeneity in shape, size, and aggregate forming tendencies. In this study, we overcome these challenges by coupling tissue engineering toolsets with microfluidics technologies to create engineered cancer microspheres. Specifically, we employ biosynthetic hydrogels composed of conjugated poly(ethylene glycol) (PEG) and fibrinogen protein (PEG-Fb) to create engineered breast and colorectal cancer tissue microspheres for 3D culture, tumorigenic characterization, and examination of potential for high-throughput screening (HTS). MCF7 and MDA-MB-231 cell lines were used to create breast cancer microspheres and the HT29 cell line and cells from a stage II patient-derived xenograft (PDX) were encapsulated to produce colorectal cancer (CRC) microspheres. Using our previously developed microfluidic system, highly uniform cancer microspheres (intra-batch coefficient of variation (CV) ≤ 5%, inter-batch CV < 2%) with high cell densities (>20×106 cells/ml) were produced rapidly, which is critical for use in drug testing. Encapsulated cells maintained high viability and displayed cell type-specific differences in morphology, proliferation, metabolic activity, ultrastructure, and overall microsphere size distribution and bulk stiffness. For PDX CRC microspheres, the percentage of human (70%) and CRC (30%) cells was maintained over time and similar to the original PDX tumor, and the mechanical stiffness also exhibited a similar order of magnitude (103 Pa) to the original tumor. The cancer microsphere system was shown to be compatible with an automated liquid handling system for administration of drug compounds; MDA-MB-231 microspheres were distributed in 384 well plates and treated with staurosporine (1 μM) and doxorubicin (10 μM). Expected responses were quantified using CellTiter-Glo® 3D, demonstrating initial applicability to HTS drug discovery. PDX CRC microspheres were treated with Fluorouracil (5FU) (10 to 500 μM) and displayed a decreasing trend in metabolic activity with increasing drug concentration. Providing a more physiologically relevant tumor microenvironment in a high-throughput and low-cost manner, the PF hydrogel-based cancer microspheres could potentially improve the translational success of drug candidates by providing more accurate in vitro prediction of in vivo drug efficacy. Citation Format: Elizabeth A. Lipke, Wen J. Seeto, Yuan Tian, Mohammadjafar Hashemi, Iman Hassani, Benjamin Anbiah, Nicole L. Habbit, Michael W. Greene, Dmitriy Minond, Shantanu Pradhan. Production of cancer tissue-engineered microspheres for high-throughput screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 175. 

Abstract This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.