CONCORD, Mass., April 25, 2018 /PRNewswire/ — Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling, simulation, and analysis to drug research and development, today announced a collaboration with Sanofi Genzyme for the analysis of systems pharmacology pharmacokinetic (PK)/target engagement (TE) models for multi-specific antibody combinations.  Applied BioMath will help assess the risk and feasibility of bispecific combinations and compare them to fixed dose combinations (FDC) for multiple targets.   These analyses will be used to help prioritize the portfolio and provide early screening criteria for Lead generation for targeting immune-modulation targets with biologics for indications in immunology.  

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«We are looking to quickly assess the feasibility of several bispecific combinations, as well as their FDC counterparts, to prioritize our molecules based on our developability requirements,» said Tom O’Shea, Head of DMPK at Sanofi.  «Using Applied BioMath’s approach, we plan to identify critical parameters which will help us prioritize our experiments, provide early indicators of what optimal drug properties would be for each molecule, and ultimately help us determine which molecules are best to pursue. Knowing early on what the optimal affinities, avidities, and half-lives, rather than after phase 1 or phase 2, can potentially save millions of dollars up front, and potentially 100s of millions later.»

«Taking a model-based approach to design multi-specific modalities challenges us to better understand our target antigens,» said Jennifer Fretland, Director of Pharmacokinetics, Sanofi DMPK US.  «By understanding our targets and the interaction of our biotherapeutic with these target antigens, we can prioritize experiments and increase efficiency in discovery.»

Applied BioMath leverages mathematics and high-performance computing to enable massive scale simulations in a very short time. «Model Aided Drug Invention (MADI) allows us to computationally explore numerous scenarios, including best-case and worst-case scenarios, for several molecules in a fraction of the time it would take to do the experiments in the lab,» said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.  «And because our modeling platform was designed specifically for biological modeling, we avoid shortcuts commonly used in other software platforms, which results in fast, accurate predictive analytics. This analysis can help assess risk and prioritize the early portfolio, and help set criteria to develop best-in-class bispecifics at the new targets stage or Lead identification/Lead generation stage, with the goal or reducing late stage attrition rates and helping our partners develop the best possible therapeutics, to ultimately better help patients, as quickly as possible.»

About Applied BioMath

Founded in 2013, Applied BioMath uses mathematical modeling and simulation to provide quantitative and predictive guidance to biotechnology and pharmaceutical companies to help accelerate and de-risk drug research and development.  Their Model-Aided Drug Invention (MADI) approach employs proprietary algorithms and software to support groups worldwide in decision-making from early research through clinical trials.  The Applied BioMath team leverages their decades of expertise in biology, mathematical modeling and analysis, high-performance computing, and industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, patients, and the best path forward into and in the clinic.   For more information about Applied BioMath and its services, visit www.appliedbiomath.com.

Applied BioMath and the Applied BioMath logo are registered trademarks of Applied BioMath, LLC.

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SOURCE Applied BioMath, LLC