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PDX Mouse Clinical Trials

Translational Models, Predictive Biomarkers, New Indications, Deeper Drug Insights

The Challenge: Enhancing Clinical Translation in Oncology Drug Development

Many oncology therapies fail in clinical trials—especially in Phase II—due to a lack of efficacy. This underscores the need for more predictive preclinical models that better mirror human responses and improve clinical trial success rates.

Our Solution: PDX Mouse Clinical Trials (MCTs)

(Previously known as HuTrial)

Patient-Derived Xenograft (PDX) Models are transforming preclinical oncology research by offering more clinically relevant data. By preserving the key features of patient tumors, PDX models provide a powerful tool for drug development and clinical translation. These models help bridge the gap between preclinical studies and clinical success, offering oncology drug developers a reliable pathway to more accurate predictions and outcomes.

HuBase™: The World’s Largest Collection of Clinically Relevant PDX Models

Access 2,500+ global PDX models. Tailor your selections based on indication, drug responses, patient histories, and multiomics data for precision in your studies.

Figure. A representative PDX Mouse Clinical Trial DesignFigure. A representative PDX Mouse Clinical Trial Design

Why Choose PDX Mouse Clinical Trials?

Enhancing Oncology Drug Development with Predictive Models

PDX Mouse Clinical Trials offer a unique advantage for oncology drug developers by providing:

  • Better Clinical Translation: PDX models closely mimic human tumor biology, maintaining key features such as the genetic and phenotypic characteristics of patient tumors, leading to more accurate predictions of clinical outcomes.
  • Predictive Biomarker Discovery: These models help identify biomarkers for patient stratification, enabling more personalized treatment strategies and increasing the likelihood of clinical trial success.
  • Exploration of New Indications: PDX models allow for the evaluation of therapies across diverse tumor types, uncovering potential new therapeutic applications and expanding indications.
  • Deeper Drug Response Insights: Gain a deeper understanding of drug efficacy, mechanisms of action (MoA), and resistance mechanisms. PDX models are invaluable for optimizing drug combinations and reducing attrition in clinical development.

Why Choose Crown Bioscience for PDX Mouse Clinical Trials?

The PDX Expert:

  • Largest Commercial PDX Collection and Quality
    • Better representation of patient populations
    • > 500 live PDX models for fast track service
    • Patented NGS QC Method ensures the highest PDX model quality
  • Accurate Biomarker Assay & Data Interpretation
    • Our specialized methodologies ensure precise biomarker analysis, even with complex mixed tumor tissues in PDXs1-3

MCT Data Science Expert Support:

  • Bioinformatics Expertise
    • Expert design of optimized, cost-efficient, data-driven MCTs4
  • Tailored Bioinformatics Consultation
    • Receive biostatistical and bioinformatics support, including model selection and study design consultation.
  • End-to-End Service
    • Complete support from trial design to data analysis, ensuring seamless execution
Crown Bioscience for PDX Mouse Clinical Trials Fig. End-to-end MCT Data Science Expert Support

Rely on the Experts for Accurate PDX Biomarker Insights

PDX models are among the most predictive of patient tumors, but they come with unique challenges. After human tumor fragments are engrafted into immunodeficient mice, human stroma is quickly replaced by mouse cells, including immune components and blood vessels. This can complicate biomarker analysis and data interpretation, potentially leading to misleading results if human and mouse signals aren’t properly distinguished1,2.

With our deep expertise and a proven track record of publications1-3, Crown Bioscience leads the field in overcoming these challenges, delivering reliable, actionable insights that drive successful oncology research.

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Delivering Comprehensive Solutions in PDX Mouse Clinical Trials

Our PDX Mouse Clinical Trials are designed to maximize the impact of your oncology research by offering:

  • Optimized Clinical Trial Designs: Tailored to maximize predictive power and ensure clinical relevance, setting your studies up for success in oncology drug development.
  • Integrated Data Reports: Our comprehensive reports combine biomarker discovery, PK/PD analysis, and bioinformatics to provide a holistic view of your study's outcomes.
  • Seamless Service: From trial initiation to final report, our dedicated team ensures consistency, quality, and efficiency at every stage, empowering you to confidently advance in your drug development journey.
Step by Step Guide for Mouse Clinical TrialFigure. Step by Step Guide for Mouse Clinical Trial

Applications of PDX Mouse Clinical Trials in Oncology

PDX Mouse Clinical Trials support diverse oncology research areas, helping to drive better clinical translation and drug development outcomes:

  • Biomarker Discovery & Validation: Identify biomarkers for personalized treatment strategies, improving patient stratification and increasing clinical trial success.
  • Clinical Stratification: Generate precise data to support targeted therapies, improving patient selection and enhancing treatment efficacy.
  • Exploring New Indications: Investigate therapies across various cancer types, discovering new therapeutic uses and expanding treatment possibilities.
  • Targeted Research: Focus on specific genetic mutations or cancer drivers to refine therapeutic approaches and advance precision medicine.
  • Drug Combination Strategies: Evaluate drug combinations to overcome resistance and optimize treatment responses, backed by our innovative in vivo synergy assessments for enhanced 

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FAQ

Can Crown Bioscience help select the best PDX models?

Absolutely! During the scoping phase, we provide complimentary support for study design and model selection, taking a data-driven approach to identify the ideal models. We leverage multi-omics data—including genomics, transcriptomics, proteomics, and pathway activity—to find the best fit for your study.

  • Gene Expression and Mutation Data: For example, if you’re testing an EGFR inhibitor, we can help filter cancer types by high EGFR expression.
  • Protein Expression: For markers like NECTIN4, where gene and protein expression may not correlate, we use our PDX proteomics data to identify models based on protein levels.
  • Omics-Derived and Pathway Activity Analysis: When gene or protein filtering alone doesn’t suffice, we can apply custom omics-derived methods, such as pathway activity scores, to refine model selection for pathways like HER2 signaling.
How is the number of PDX models and animals determined for MCT?

For multi-PDX MCT studies, we use Linear Mixed Models (LMM) to generate power curves that estimate the number of models and animals based on drug potency. You can explore our published work for additional insights (Guo et al., BMC Cancer 2019).

For single PDX model studies, we apply our proprietary algorithm for sample size calculations. This tool uses historical standard-of-care (SOC) data to determine the necessary number of animals, considering metrics like Tumor Growth Inhibition (TGI) and the desired statistical power.

For more details or free access to our web-based sample size calculator, please contact us.

How are baseline factors balanced in group allocations for MCT?

Ensuring balanced group allocation is critical for reliable results. Randomization alone can lead to imbalances, so we use a proprietary algorithm that distributes baseline covariates—such as tumor size and body weight—equally across groups, minimizing bias in complex trials.

For more details or free access to this tool, please contact us.

How is drug efficacy analyzed in MCT?

We employ advanced metrics to provide a comprehensive view of drug efficacy, going beyond single time-point measurements.

  • eGR (Exponential Growth Rate): This in-house metric analyzes the full tumor growth dataset, offering a holistic view of treatment impact. For more details, refer to our published work (Guo et al., BMC Cancer 2019).
  • Linear Mixed Model (LMM): LMM enables us to analyze growth rates across the full dataset, accounting for inter-mouse variability to give a clearer view of treatment effects.
What support is provided during MCT?

We support you throughout your MCT study, ensuring robust analysis and reliable results.

  • Outlier Detection: Advanced methods are used to identify and address outliers, ensuring the data accurately reflects the treatment’s impact.
  • Design Optimization: Based on real-time data, we can recommend study adjustments to enhance quality, such as modifying model numbers.
  • Interim Reporting: Stay updated with real-time progress through interim reports available on our platform.
How are study results delivered?

Results are delivered in a traditional report format, and we also provide an interactive webpage, enabling you to explore the data dynamically in a user-friendly environment.

  

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References

1. Wubin Qian, Xiaobo Chen, Yanghui Sheng, Likun Zhang, Jingjing Wang, Zhenzhen Song, Qi-Xiang Li, Sheng Guo; Tumor Purity in Preclinical Mouse Tumor Models. Cancer Research Communications 2 May 2022; 2 (5): 353–365. https://doi.org/10.1158/2767-9764.CRC-21-0126

2. Zhaomei Shi, Binchen Mao, Xiaobo Chen, Piliang Hao, Sheng Guo; Mouse Stromal Cells Confound Proteomic Characterization and Quantification of Xenograft Models. Cancer Research Communications 1 February 2023; 3 (2): 202–214. https://doi.org/10.1158/2767-9764.CRC-22-0431

3. Huajun Zhou, Binchen Mao, Sheng Guo; Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies. Cancer Research Communications 2024; https://doi.org/10.1158/2767-9764.CRC-24-0059

4. Sheng Guo, Xiaoqian Jiang, Binchen Mao, Qi-Xiang Li. The design, analysis and application of mouse clinical trials in oncology drug development. BMC Cancer 19, 718 (2019). https://doi.org/10.1186/s12885-019-5907-7

5. Binchen Mao, Sheng Guo; Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models. Cancer Research Communications 2 October 2023; 3 (10): 2146–2157. https://doi.org/10.1158/2767-9764.CRC-23-0243