Computational exploration of Mechanistic Determinants of Antibody-drug Conjugate Pharmacokinetics
Published on 10th July
Applied BioMath, an industry-leader in applying mechanistic modeling to drug research and development to help biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in their R&D, earlier today announced their involvement in the upcoming Antibody Protein Therapeutics Summit on July 11-12, 2017 in Baltimore, MD.
During the meeting in Balitmore Jennifer Park, PhD, Associate Director of Business Development at Applied BioMath, will present a poster entitled “Computational exploration of mechanistic determinants of antibody drug-conjugate pharmacokinetics using quantitative systems pharmacology modeling strategies.”
Quantitative systems pharmacology
Quantitative systems pharmacology or QSP is defined as an approach to translational medicine that combines computational and experimental methods to elucidate, validate and apply new pharmacological concepts to the development and use of small molecule and biologic drugs.
Applied BioMath’s poster highlights how their quantitative systems pharmacology or QSP approach provides biological insights into the impact of drug-to-antibody ratio and the resulting changes in molecular properties on overall pharmacokinetics (PK) and relative payload disposition as observed in pre-clinical and clinical studies.
This approach, based on research led by Anna Katharina Wilkins, PhD, Principal Scientist at Applied BioMath, explores the PK of antibody-drug conjugate therapeutics which typically show a discrepancy between the PK of total antibody and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed to deconjugation, however recent evidence suggests that the underlying mechanisms may be more complex.
“This antibody-drug conjugate study is a great example of how QSP modeling approaches provide insights, some of which are counterintuitive, into complex therapeutic platforms. These insights help our collaborators accelerate the development of potentially best-in-class therapeutics and quickly get drug candidates into the clinic, with higher likelihoods of testing proof of clinical concept, ultimately to help patients,” noted John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.
Applied BioMath is well versed in the use of QSP modeling approaches for ADC therapeutics. Their inaugural presentation of QSP approaches for ADC research received top honors at World ADC 2016.
Recently they announced their ADC collaboration with Zymeworks for Clinical Trial Support in Oncology involving quantitative systems pharmacology support for antibody drug conjugate therapeutics intended for the treatment of cancer.
As part of their collaboration, the company has supported Zymeworks previously for GLP toxicology studies and first-in-human dose predictions for IND filings. This extension of their collaboration will update the previously built QSP model with new clinical data to analyze and predict dose escalations and inform internal decisions about next steps in clinical trials.
“One of the benefits of our modeling approach is that we can easily evolve our model as new data is produced to continue to support the project team throughout R&D,” explained John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.
“We prioritize an accurate representation of the disease biology specifically for this reason, so that the model translates well from early development to the clinic,” he added.
Commenting on the collaboration, Gerry Rowse, PhD, Senior Director, Therapeutics Preclinical Development, Zymeworks noted: “We are pleased to extend our collaboration with Applied BioMath.”
“The mechanistic modeling work the Applied BioMath team continues to perform significantly contributes to our R&D efforts and decision making processes. We look forward to continuing our working relationship with them as we advance our programs into clinical trials,” he concluded.
Last editorial review: July 10, 2017
Featured Image: Baltimore Courtesy: © 2017. Fotolia. Used with permission.
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