AI and Machine Learning with Advanced Preclinical Cancer Models for Improved Clinical Translation

Life Sciences, Preclinical,
  • Wednesday, October 04, 2023

In recent years, the biopharmaceutical industry has become keenly interested in adopting artificial intelligence (AI) and machine learning platforms. These AI platforms can be used for streamlining R&D efforts, reducing discovery timelines and costs, and improving efficiency. At the same time, the demand for predictive and robust preclinical models to minimize translational failures in oncology is at an all-time high.  

As researchers look to leverage genotypic features of models for early identification of predictive biomarkers, understand complex immune interactions, and elucidate combination therapies’ mechanisms of action, it’s becoming apparent that the fight against cancer will be won at the intersection of biology, biochemistry, and computer science. 

During this webinar, Michael Boice, PhD, will look at how in silico predictions of efficacy, when validated in more clinically relevant preclinical models, can bring greater certainty to early decision-making, and improve translation to the clinic. He also will demonstrate how in vivo validation studies can accelerate AI and machine learning, rapidly improving the accuracy of compound-specific in silico models for biomarker optimization. 

Photo Credit: Nik Spencer/Nature; Reference: The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network.
Pan-Cancer Analysis of Whole Genomes. Nature (Special Issue). Feb 5, 2020.


Michael Boice, Certis Oncology Solutions Inc.

Michael Boice, PhD, Senior Director, Scientific Engagement and Key Accounts, Certis Oncology Solutions Inc.

Michael Boice, PhD, has over 20 years of experience in translational oncology drug development, from bench science to business development. As Senior Director of Scientific Engagement and Key Accounts at Certis Oncology Solutions, Michael works directly with biopharmaceutical clients to enhance their preclinical programs through a collaborative approach to scientific problem-solving and optimized study designs.

Michael’s areas of scientific expertise include the development of novel oncology therapeutics, functional genomics and rare cancers. He holds a PhD in Pharmacology from Weill Cornell Graduate School of Medical Sciences, department of Pharmacology Memorial Sloan Kettering Cancer Center, Cancer Biology and Genetics.

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Long H. Do, PhD, Certis Oncology Solutions Inc.

Long H. Do, PhD, Director, Bioinformatics, Certis Oncology Solutions Inc.

Featured CertisAI™ Expert Panelist for the Q&A Period

Long H. Do, PhD, has over 10 years of experience advancing the application of bioinformatics and machine learning in preclinical drug development. He has helped advance many candidates through the drug discovery pipeline, overseeing bioinformatics departments supporting IND-enabling programs and successful enrollment into clinical trials, including a first-in-class osteoarthritis candidate (currently Phase III) and a colorectal/non-small cell lung cancer candidate (Phase I). His expertise in drug development includes the identification of mechanisms of action, pharmacodynamic response (both preclinical and clinical), machine learning-based biomarker discovery for patient selection and high throughput virtual screening to find new candidate compounds.

As Director of Bioinformatics and Data Science at Certis Oncology Solutions in San Diego, California, Dr. Do leads a team of scientists responsible for the company’s bioinformatics initiatives, including the development of machine learning algorithms to identify predictive and prognostic biomarkers, analysis pipelines for next-generation sequencing data, cloud-based high performance compute clusters and client-facing web-based applications. Dr. Do also leads Certis’ AI-directed precision oncology initiatives to guide functional testing and personalize treatments for patients diagnosed with recurrent, resistant and otherwise recalcitrant cancers.

Dr. Do holds a Bachelor of Arts degree in Molecular and Cellular Biology from the University of California, Berkeley, and a PhD in Biology with a specialization in Bioinformatics from the University of California, San Diego. He completed postdoctoral research at the University of California, San Diego, and served as a Lecturer in Bioinformatics at the same institution.

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Who Should Attend?

This webinar will appeal to professionals interested in advancing translational science and improving clinical success in the following roles:

  • Pharmaceutical Scientist
  • Cancer Researcher

What You Will Learn

During this 50-minute presentation, you will learn how an AI-enabled preclinical strategy can:

  • Identify biomarkers that are predictive of therapeutic response
  • Elucidate the synergistic potential of combinatory therapies
  • Optimize preclinical development using in vitro and in vivo models developed from the same tumor tissue

Xtalks Partner

Certis Oncology

Certis Oncology Solutions is a life science technology company committed to accelerating the development of new cancer therapies and realizing the promise of precision oncology. Our product is Oncology Intelligence™ — highly predictive therapeutic response data derived from advanced biological models of cancer and enhanced with CertisAI™ Predictive Oncology Intelligence. Certis partners with therapeutics developers to help close the problematic translation gap between preclinical studies and clinical trials. Through more clinically relevant, well-characterized and annotated cancer models, advanced imaging technology, including x-ray irradiation with image-guided radiation therapy, and thoughtfully designed custom studies, our proprietary platform brings greater certainty to go/no-go drug development decisions.

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