Certis Oncology

CertisAI™ is the first and only patented, commercially available, validated AI/ML platform that predicts therapeutic efficacy by identifying complex gene expression signatures that correlate with drug response. The platform accelerates drug discovery and development by bringing computer-aided precision to IND-enabling, drug repurposing, and label expansion initiatives:

• Select optimal cancer models for in vitro and in vivo validation studies

• Identify gene expression signatures that are predictive of therapeutic response or resistance

• Derive insights into drug mechanisms of action

• Rank cancer types most likely to respond to a lead candidate

• Elucidate synergistic potential or combinatory therapies

• Develop stratification strategies for clinical trial patient selection

• Prioritize candidates in compound libraries

  
  

Leveraging the computational power of CertisAI, cancer researchers can be more confident in strategic preclinical decisions and effectively replace many discovery screening studies with insights derived from in silico models.

CertisAI predictions are based on drug features and the related gene expression profiles of responsive and non-responsive cancer models. Using gene expression as a predictive biomarker is far more nuanced than genetic mutations alone. It leverages the full dynamic range of close to 20,000 genes—all with continuous values of expression. This approach also captures functional gene activity, epigenetic and dynamic genomic changes, and pathway disruptions, offering a systems biology perspective that can be more informative for predicting how a tumor will respond to a particular therapy.

The development of CertisAI involved the integration of several proprietary and publicly available genomic, transcriptomic, and drug response datasets. These data were integrated into a unified training framework through careful mapping, preprocessing, and transformation steps to ensure consistent, high-quality inputs.

Various machine learning algorithms including random forest models, support vector machines, and neural networks, were used to train CertisAI to recognize complex relationships between drug features and the gene expression signatures that correlate with drug response. This pre-built, multi-dimensional, relational map enables CertisAI to predict drug response not only for FDA-approved drugs in the training set, but also for investigational therapies the platform has never before encountered.

CertisAI encompasses a pre-built library of 3.5 million monotherapy predictions for 8,000 FDA-approved and investigational drugs, and 1.9 billion combination therapy predictions for 2,200 FDA-approved drugs. All of Certis’ proprietary PDX and PDX-derived models, as well as the nearly 1,400 models in the Cancer Cell Line Encyclopedia (CCLE) are represented.

AI-Enabled Services:

• Research services that use machine learning (ML) algorithms

• Research services that use large language models (LLM)

• Research services that use natural language processing (NLP)

• Data services that use ML, LLMs or NLP

• Drug response predictions