Certis Oncology Solutions, led by Peter Ellman, President and CEO, is a life science technology company dedicated to realizing the promise of precision oncology. The company’s product is Oncology Intelligence® — highly predictive therapeutic response data derived from advanced cancer models. Certis partners with physician-scientists and industry researchers to expand access to precision oncology and address the critical translation gap between preclinical studies and clinical trials.
Can you describe the broader problem in oncology research that the CertisOI Assistant is addressing?
The failure rate of oncology investigational drug candidates is high. It was recently reported that in 2023, 90% of oncology programs ultimately failed. That figure is a remarkable improvement over the historical trend, which hovered around 96% until 2022. Considering the cost of developing drugs, a 90% failure rate is not sustainable. Imagine how patients would benefit if the success rate were even 50%.
CertisOI Assistant immediately addresses two really important issues that contribute to this failure rate:
- Improved preclinical model selection: Many compounds show promising results in preclinical studies but fail to demonstrate a sufficient therapeutic effect in humans.Most members of the scientific community point to preclinical models as part of the problem. Choosing preclinical models with the correct gene expression signature (and using orthotopic engraftments for pivotal studies) can improve “translation” into the clinic.
- Earlier, better biomarker identification: Relying on biomarkers that do not accurately predict therapeutic response can result in failed clinical trials. CertisOI Assistant is integrated with CertisAI, our patent-pending predictive AI/ML platform, enabling the identification of predictive biomarkers early in the drug development process.
How does the CertisOI Assistant use AI to improve access to oncology data, and what sets it apart from other AI tools in the field?
The CertisOI Assistant provides advanced data analysis and predictive modeling capabilities through an easy-to-use, natural language interface. It stands out in several ways:
- Comprehensive Dataset Integration: The assistant integrates a wide range of oncology data, including patient information, tumor characteristics, genetic profiles, and drug response predictions. This holistic approach allows for a more comprehensive analysis than tools focusing on isolated data types.
- AI-Based Predictions: The assistant employs AI algorithms to predict drug response and resistance, offering insights into which treatments will likely be effective for specific cancer models. This predictive capability is crucial for personalized medicine and sets it apart from tools that rely solely on historical data.
- User-Friendly Interface: By providing an intuitive interface for querying and analyzing complex datasets, the assistant makes it easier for researchers to access and interpret oncology data without requiring advanced technical skills.
- Focus on Pre-Clinical Models: The assistant specializes in pre-clinical oncology research, particularly PDX and cell line models, offering unique insights into early-stage drug development and tumor biology.
- Interactive Visualizations: The assistant supports interactive visualizations, such as pharmacology and tumor growth studies, enabling researchers to explore data more engaging and informatively.
How does the tool transform complex data into actionable insights, especially for researchers working on drug sensitivity or genomic data?
CertisOI Assistant leverages a structured workflow to transform raw data into meaningful insights. It involves querying a comprehensive oncology dataset, analyzing the data, and presenting the results in a clear and interpretable format. Here’s how it works:
- Data Querying: CertisOI Assistant can access a relational database containing detailed information about oncology models, including patient data, tumor characteristics, genomic data, and drug response predictions. It uses SQL-like queries to extract relevant data based on the researcher’s specific needs.
- Data Analysis: Once the data is retrieved, CertisOI Assistant can perform various analyses, such as identifying common mutations, correlating gene expression with drug sensitivity, or evaluating pharmacology study results. It can also rank and filter data to highlight the most significant findings.
- Visualization: The assistant can present data in tabular formats, generate interactive charts for pharmacology and tumor growth studies, and display histology images. This visualization helps researchers quickly grasp complex data patterns and relationships.
- Interpretation and Insights: By providing a clear interpretation of the data, including predictions for drug sensitivity or resistance, CertisOI Assistant helps researchers make informed decisions about potential therapeutic strategies or further experimental directions.
- Customization and Flexibility: Researchers can tailor their queries to focus on specific cancer types, genetic markers, or treatment responses, allowing for a highly customized analysis that aligns with their research objectives.
How does the CertisOI Assistant enhance researchers’ ability to select cancer models, design biomarker strategies, or perform in silico validations?
I covered the first two areas – the cancer model section and biomarker strategy design – at the outset of this interview, so I’ll focus on performing in silico validations. CertisOI Assistant provides a virtual environment to test and validate hypotheses related to drug efficacy, target engagement, and biomarker discovery without the need for immediate laboratory experiments. This allows them to rapidly refine their hypotheses and focus experimental efforts on the most promising avenues.
Here are a few examples:
- Drug Response Predictions: Use AI-based predictions for drug response and resistance to assess how different models are likely to respond to specific drugs. This can help validate the potential efficacy of a drug in silico before moving to in vitro or in vivo studies.
- Genomic and Molecular Profiling: Analyze the genomic data, including mutations, gene expression, and copy number variations, to identify potential targets and validate their relevance to the drug’s mechanism of action. This can help in understanding the molecular basis of drug sensitivity or resistance.
- Biomarker Discovery: Correlate molecular characteristics with drug response predictions to identify potential predictive biomarkers. This can guide the selection of patient populations more likely to benefit from a particular therapy.
- Combination Therapy Exploration: Explore drug synergy predictions to identify promising drug combinations that may enhance therapeutic outcomes. This can provide insights into potential combination strategies that can be further validated experimentally.
- Histological Analysis: Use histology images to validate the morphological effects of drugs on tumor tissues, providing additional evidence for the drug’s mechanism of action and potential efficacy.
- Cross-Model Comparisons: Compare different models to understand how various genetic backgrounds influence drug response, helping to validate hypotheses about the role of specific genes or pathways in silico.
- Virtual Screening: Perform virtual screening of drugs against a wide range of models to prioritize candidates for further experimental validation.
Can you share examples of how researchers are anticipated to use this tool to improve their workflows or achieve breakthroughs?
The simplest example is preclinical model selection. Every preclinical study begins with the selection of tumor models. CertisOI Assistant takes the manual effort out of this process and brings great precision to selecting the optimal models for any given study.
Another is developing a biomarker strategy. The traditional approach is to hypothesize what biomarker or biomarkers might be linked to the drug’s mechanism of action and then test those hypotheses in preclinical studies, which is usually an iterative process. If preclinical data is promising, researchers must validate predictive biomarkers in human clinical trials—and as discussed, the failure rate is high.
The CertisOI Assistant helps researchers identify and validate more precise, predictive gene expression biomarkers earlier in the development process and with less iteration than the traditional workflow—saving time, and money, and improving chances for commercial success.
What kinds of cancer models or datasets does the tool support, and how does this breadth benefit the research community?
The current version of CertisOI gives researchers access to Certis’ rapidly expanding library of PDX and PDX-derived tumor models and the entire Cancer Cell Line Encyclopedia (CCLE) of models. The platform’s algorithms also draw on data from Genomics of Drug Sensitivity in Cancer (GDSC), International Cancer Genome Consortium (ICGC), CI ALMANAC, O’Neil, and other datasets. This holistic approach to data integration allows for a more comprehensive analysis than tools that focus on isolated data types.
The CertisOI Assistant is designed to be user-friendly. How do you ensure that it is accessible to researchers who may not have extensive technical expertise?
Several features make CertisOI Assistant accessible to researchers at all levels:
- Intuitive Interface: The interface is designed to be intuitive and easy to navigate, allowing users to perform complex queries and analyses without needing to understand the underlying technical details.
- Guided Workflows: The assistant provides guided workflows for common research tasks, such as querying drug response predictions, analyzing genomic data, and exploring pharmacology studies. This helps users focus on their research questions without getting bogged down in technical complexities.
- Natural Language Processing: Users can interact with the assistant using natural language queries, making accessing the information they need easier for those without technical expertise. The assistant interprets the queries and translates them into the appropriate database queries.
- Comprehensive Documentation: Detailed documentation and tutorials help users understand how to use the assistant effectively. This includes step-by-step guides, examples, and explanations of key concepts.
- Interactive Visualizations: The assistant provides interactive visualizations for data analysis, such as charts and histology images, allowing users to explore and interpret data visually without needing to write code.
- Responsive Support: Users can access responsive support to assist with any questions or issues. This ensures they can get help quickly and continue their research without unnecessary delays.
- Customizable Queries: While the assistant provides default workflows, it also allows for customization, enabling users to tailor queries to their specific research needs without requiring deep technical knowledge.
Collaboration is a key aspect of research. How does the CertisOI Assistant facilitate teamwork among researchers or institutions?
With CertisOI Assistant, researchers from different teams or institutions can access the same dataset and tools, allowing them to work collaboratively on shared projects or research questions. The platform also makes it easy to download and share data queries, results, and insights among team members so everyone involved in a project can contribute effectively.
What are the biggest challenges in scaling AI adoption in cancer research, and how can they be addressed?
Significant challenges include data security, data integration, and trust in AI‐based outcome predictions. I am not an expert on data security or data integration, but great minds are working to solve those challenges. With respect to trusting AI-generated predictions, we need efficient and credible ways to validate those predictions.
Certis has taken a two-pronged approach to this: in silico validation via internal, cross-validation studies, and in vivo validation—performing studies in clinically relevant mouse models to evaluate the accuracy of our platform’s predictions. Over time, these tools will also be validated clinically in human patients—but of course, that will take a great deal of time and money, as well as the willingness to change the current cancer treatment paradigm. The medical and regulatory community will have to stop relying on how things have always been done and embrace the power of computational analyses to inform decisions.
How do you envision tools like the CertisOI Assistant shaping the future of cancer treatment and precision medicine?
Modern medicine doesn’t yet have a great way to match patients to the ideal treatments. Overall, only 10% of cancer patients experience a clinical benefit from treatments matched to tumor DNA mutations. That not only hurts patients’ health, but it also harms them financially. An estimated $2.5 billion —with a B—is wasted on ineffective therapies. It is a very sad fact that 42% of cancer patients fully deplete their assets by the second year of their diagnosis.
Tools like CertisOI Assistant and CertisAI will help us realize the promise of precision medicine—getting people the optimal treatment for their unique form of cancer the first time, every time…. And to democratize access to more effective, personalized care.
Thank you for the great interview, readers who wish to learn more should visit Certis Oncology Solutions.
The post Peter Ellman, President and CEO of Certis Oncology Solutions – Interview Series appeared first on Unite.AI.
