Yes, InnoSer offers fully integrated PDO (Patient-Derived Organoid) and PDX (Patient-Derived Xenograft) services to support translational oncology research. By combining in vitro PDO models with in vivo PDX studies, you can gain a comprehensive understanding of tumor biology and drug responses. 

Advantages of integrated PDO/PDX services: 

• Efficient screening of drug candidates in PDOs before selecting for PDX validation using in vivo mouse models of cancer 

• Reduction in animal usage, improving ethical and cost-effectiveness considerations 

• Our integrated workflows allows for a seamless transition from in vitro to in vivo models, ensuring that drug responses observed in organoids are further validated in patient-derived xenografts which can be implanted into immunodeficient or humanized mouse models  

Reach out to InnoSer’s team to learn how our integrated PDO/PDX services can advance your oncology research. 

InnSer’s organoid high-throughput phenotypic screening services accelerate the drug discovery process by enabling researchers to screen hundreds of compounds rapidly and efficiently. By implementing phenotypic screening, we can visualise how these patient-relevant models respond to a given therapy, providing more in-depth insights in its mechanism of action. This approach enhances the identification of high-potential lead compounds by using human-relevant models, improving the likelihood of translation of novel therapeutics to the clinic. Organoids closely replicate key characteristics of their corresponding parent tumors, including: 

• Expression of cancer markers 

• Morphology similar to primary tumors 

• Transcriptomic profiling 

• Mutational landscape 

• Inter- and intra-patient response heterogeneity 

This is because organoids faithfully replicate important characteristics of the corresponding parent tumors, exemplified by the expression of cancer markers, morphology, transcriptomic profiling and mutational landscape similar to primary tumors. Research has shown that organoids can faithfully replicate drug responses, as well as help optimize treatment strategies for patients on individual basis.  

Key advantages of including InnoSer’s organoid high-throughput screening services in your drug development journey include:  

• Identify lead compounds with higher translational potential 

• Obtain insights into sub-clonal responses on single-organoid level  

• Predict drug responses with unmatched accuracy  

• Optimize personalized treatment strategies  

• Uncover mechanism of action (MoA) and/or drug-resistance pathways of your lead compounds 

• Identify biomarkers for response for better patient stratification.  

• Analyse drug synergies with your lead compounds to discover more effective treatment combinations  

Reach out to InnoSer’s team to learn more about how you can leverage our combined expertise to streamline your drug discovery screening efforts.  

Sub-clonal responses in cancer refer to the genetic diversity within a tumor, where distinct populations of cells, or sub-clones, exhibit varying characteristics and responses to treatment, contributing to tumor progression and therapy resistance. Therefore, one of critical challenges in treating cancers that show high intra-tumoral heterogeneity – such as pancreatic ductal adenocarcinoma (PDAC) – is understanding how intrinsic sub-clonal heterogeneity affects the drug response heterogeneity.  

To guide the development of novel therapeutics, InnoSer’s platform leverages the expertise of DrugVisionAI and Orbits platforms, enabling single-organoid resolution analyses to assess responses across resistant, intermediate, and sensitive subclones, providing critical insights for personalized treatment strategies. As the name suggests, wide-field imaging excels at capturing the entire structure of patient-derived organoids, offering a comprehensive view of tumor responses to therapeutics.  

Indeed, a study performed by DrugVisionAI”s team has shown that a combination of normalized drug screening metric (NDR) with the dynamic quantification of single organoid responses (Orbits) can detect patient-specific and intra-tumoral sub-clonal sensitivities to different standard-of-care therapies, correlating to the matched clinical patient response to therapy. Leveraging the automated high-throughput single-organoid screening process together with Orbits’ AI workflows will allow you to evaluate the efficacy of novel therapeutics on multiple subclones but can also help you evaluate pro-invasive properties of tumoroid sub-clones.  

To learn more about the methods and results of this research in more detail, view the research paper by Le Compte et al., (2023).  

Current high-throughput organoid drug screening systems rely on gold-standard measurements that leverage fluorescent labels to track biological processes during live cell imaging. Fluorescent labels, used to track cells, cellular structures and biological processes during live-cell imaging, are known to affect basal cellular activity via inducing oxidative stress through the process of photoxicity. Therefore, these reagents can skew experimental results and confound the effects of drugs in a screening. 

InnoSer’s platform for high-throughput organoid screening leverages label-free image analyses using AI image segmentation techniques developed by Orbits. Indeed, a study by Orbits’ team (Deben et al., 2022) has shown that the imaging-based method monitors patient-derived organoids with high accuracy and can even distinguish between cytostatic and cytotoxic responses.  

To learn more about the methods and results of this research in more detail, view the research paper by Deben et al., (2022). 

Distinguishing between cytotoxic (cell-killing) and cytostatic (growth-inhibiting) effects of your lead compound is the key to help you understand your lead compounds’ mechanism of action (MoA), optimize treatment strategies, minimize side effects, and develop personalized therapies to effectively manage tumor growth and resistance. In high throughput organoid screening, traditionally used so-called “bulk” assays that rely on quantification of ATP in a cell suspension fail to capture the intra-tumoral heterogeneity and the drug response heterogeneity that impacts drug efficacy, MoA and resistance.  

InnoSer’s platform leverages the methodologies developed by DrugVisionAi and Orbits’ to accuarately measure both cytostatic and cytotoxic effects by combining wide-field imaging with a novel organoid growth rate metric (normalized organoid growth rate: NOGR). This approach was demonstrated in studies such as Deben et al., 2024, further validating the reliability of the screening methods. 

With the ability to test multiple drugs simultaneously, InnoSer’s high-throughput organoid drug screening platform can help you identify synergistic drug combinations that may enhance treatment efficacy. This helps accelerate the discovery of novel therapies combination, whilst simultaneously leveraging the power of drug repurposing, that could be more effective in treating different types of cancers.  

Leveraging the power of label-free live-cell imaging and automated AI cell segmentation analyses, InnoSer implements DrugVision.AI and Orbits’ workflows to enable identifications of synergistic drug combinations. In an example study performed by Deben et al., (2024), a synergy between Auranofin, a drug repurposing candidate for cancer, and various anticancer agents in normal lung organoids and lung and pancreatic cancer organoids was shown. 

NOGR (Normalized Organoid Growth Rate) is a drug response metric used in InnoSer’s high-throughput organoid screening studies. NOGR was developed to better capture the complex reactions of organoids to treatments, offering a more accurate reflection of drug efficacy compared to traditional measures like relative viability (RV). Unlike RV, which can be influenced by variations in cell seeding density and cannot differentiate between cytostatic (growth-inhibiting) and cytotoxic (cell-killing) effects, NOGR takes into account dynamic changes in growth rate, providing a more biologically relevant measure of drug response. Similarly, traditional metrics like Normalized Growth Rate Inhibition (GR) and Normalized Drug Response (NDR) have been used to assess organoid responses to anticancer treatments but face limitations in accurately quantifying cytostatic and cytotoxic effects across varying growth rates. 

By using live-cell imaging capabilities of DrugVisionAi combined with Orbit’s automated AI segmenting workflow analyses, NOGR overcomes the limitations of other methods, including the sensitivity to cell division rates, to offer a more consistent and reliable metric for assessing drug sensitivity and resistance in patient-derived organoid models.  

This is thanks to the label-free image analysis workflows that allow a precise segmentation of organoids to track their growth rates over time. To learn more about how NOGR was developed in detail, view paper by Deben et al., (2024).