PDX Mouse Clinical Trials

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.

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.

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.

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.

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.