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 corporate’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.
Are you able to 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 fee of developing drugs, a 90% failure rate just isn’t sustainable. Imagine how patients would profit if the success rate were even 50%.
CertisOI Assistant immediately addresses two really vital issues that contribute to this failure rate:
- Improved preclinical model selection: Many compounds show promising leads to preclinical studies but fail to show a sufficient therapeutic effect in humans.Most members of the scientific community point to preclinical models as a part of the issue. Selecting preclinical models with the right gene expression signature (and using orthotopic engraftments for pivotal studies) can improve “translation” into the clinic.
- Earlier, higher biomarker identification: Counting on biomarkers that don’t accurately predict therapeutic response can lead to failed clinical trials. CertisOI Assistant is integrated with CertisAI, our patent-pending predictive AI/ML platform, enabling the identification of predictive biomarkers early within the drug development process.
How does the CertisOI Assistant use AI to enhance access to oncology data, and what sets it aside from other AI tools in the sector?
The CertisOI Assistant provides advanced data evaluation 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 big selection of oncology data, including patient information, tumor characteristics, genetic profiles, and drug response predictions. This holistic approach allows for a more comprehensive evaluation than tools specializing in 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 aside 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.
- Give attention to Pre-Clinical Models: The assistant makes a speciality of 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, comparable to 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 rework raw data into meaningful insights. It involves querying a comprehensive oncology dataset, analyzing the info, and presenting the leads to a transparent and interpretable format. Here’s how it really works:
- Data Querying: CertisOI Assistant can access a relational database containing detailed details 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 Evaluation: Once the info is retrieved, CertisOI Assistant can perform various analyses, comparable to identifying common mutations, correlating gene expression with drug sensitivity, or evaluating pharmacology study results. It may well also rank and filter data to spotlight essentially 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 transparent interpretation of the info, 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 concentrate on specific cancer types, genetic markers, or treatment responses, allowing for a highly customized evaluation that aligns with their research objectives.
How does the CertisOI Assistant enhance researchers’ ability to pick out cancer models, design biomarker strategies, or perform in silico validations?
I covered the primary two areas – the cancer model section and biomarker strategy design – on the outset of this interview, so I’ll concentrate on performing in silico validations. CertisOI Assistant provides a virtual environment to check and validate hypotheses related to drug efficacy, goal engagement, and biomarker discovery without the necessity for immediate laboratory experiments. This permits them to rapidly refine their hypotheses and focus experimental efforts on essentially the most promising avenues.
Listed below are a number of examples:
- Drug Response Predictions: Use AI-based predictions for drug response and resistance to evaluate how different models are likely to reply to specific drugs. This will 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 duplicate number variations, to discover potential targets and validate their relevance to the drug’s mechanism of motion. This will help in understanding the molecular basis of drug sensitivity or resistance.
- Biomarker Discovery: Correlate molecular characteristics with drug response predictions to discover potential predictive biomarkers. This will guide the number of patient populations more more likely to profit from a selected therapy.
- Combination Therapy Exploration: Explore drug synergy predictions to discover promising drug mixtures that will enhance therapeutic outcomes. This will provide insights into potential combination strategies that might be further validated experimentally.
- Histological Evaluation: Use histology images to validate the morphological effects of medicine on tumor tissues, providing additional evidence for the drug’s mechanism of motion and potential efficacy.
- Cross-Model Comparisons: Compare different models to know how various genetic backgrounds influence drug response, helping to validate hypotheses concerning the role of specific genes or pathways in silico.
- Virtual Screening: Perform virtual screening of medicine against a big selection of models to prioritize candidates for further experimental validation.
Are you able to share examples of how researchers are anticipated to make use of this tool to enhance their workflows or achieve breakthroughs?
The best example is preclinical model selection. Every preclinical study begins with the number of tumor models. CertisOI Assistant takes the manual effort out of this process and brings great precision to choosing the optimal models for any given study.
One other is developing a biomarker strategy. The standard approach is to hypothesize what biomarker or biomarkers could be linked to the drug’s mechanism of motion after which test those hypotheses in preclinical studies, which is frequently 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 discover and validate more precise, predictive gene expression biomarkers earlier in the event process and with less iteration than the normal workflow—saving time, and money, and improving possibilities for business success.
What sorts of cancer models or datasets does the tool support, and the way does this breadth profit the research community?
The present version of CertisOI gives researchers access to Certis’ rapidly expanding library of PDX and PDX-derived tumor models and the complete 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 evaluation than tools that concentrate on isolated data types.
The CertisOI Assistant is designed to be user-friendly. How do you be sure that it’s accessible to researchers who may not have extensive technical expertise?
Several features make CertisOI Assistant accessible to researchers in any respect levels:
- Intuitive Interface: The interface is designed to be intuitive and straightforward to navigate, allowing users to perform complex queries and analyses while not having to know the underlying technical details.
- Guided Workflows: The assistant provides guided workflows for common research tasks, comparable to querying drug response predictions, analyzing genomic data, and exploring pharmacology studies. This helps users concentrate 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 knowledge they need easier for those without technical expertise. The assistant interprets the queries and translates them into the suitable database queries.
- Comprehensive Documentation: Detailed documentation and tutorials help users understand the right way 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 evaluation, comparable to charts and histology images, allowing users to explore and interpret data visually while not having to put in writing code.
- Responsive Support: Users can access responsive support to help with any questions or issues. This ensures they’ll get help quickly and proceed their research without unnecessary delays.
- Customizable Queries: While the assistant provides default workflows, it also allows for personalization, 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 amongst researchers or institutions?
With CertisOI Assistant, researchers from different teams or institutions can access the identical 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 amongst team members so everyone involved in a project can contribute effectively.
What are the largest challenges in scaling AI adoption in cancer research, and the way can they be addressed?
Significant challenges include data security, data integration, and trust in AI‐based consequence predictions. I’m not an authority on data security or data integration, but great minds are working to resolve those challenges. With respect to trusting AI-generated predictions, we want 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 guage the accuracy of our platform’s predictions. Over time, these tools can even be validated clinically in human patients—but in fact, that may take an amazing deal of money and time, in addition to the willingness to alter the present cancer treatment paradigm. The medical and regulatory community can have to stop counting on how things have at all times been done and embrace the ability of computational analyses to tell decisions.
How do you envision tools just like the CertisOI Assistant shaping the longer term of cancer treatment and precision medicine?
Modern medicine doesn’t yet have an amazing approach to match patients to the perfect treatments. Overall, only 10% of cancer patients experience a clinical profit from treatments matched to tumor DNA mutations. That not only hurts patients’ health, however it also harms them financially. An estimated $2.5 billion —with a B—is wasted on ineffective therapies. It’s a really sad incontrovertible fact that 42% of cancer patients fully deplete their assets by the second 12 months of their diagnosis.
Tools like CertisOI Assistant and CertisAI will help us realize the promise of precision medicine—getting people the optimal treatment for his or her unique type of cancer the primary time, each time…. And to democratize access to more practical, personalized care.
