Boston - Please join us on Wednesday, July 19th, 2017 for an evening of sharing, learning, and socializing with fellow Boston QSP members. Hear from our great speaker lineup at the Havana room. Then enjoy great ambience and conversation with members at Venture Café over some selected craft beers and food from a small business. RSVP is required. The event is sponsored by Pfizer.
5:30-6:00PM - Andreas Raue, PhD, Merrimack Pharmaceuticals followed by Q&A (Havana Room)
6:00PM - 6:30PM - Pinky Dua, PhD, Pfizer, Inc. followed by Q&A (Havana Room)
6:30 pm-7:30 pm -Reception @ Venture Cafe (a few steps down the hallway from Havana Room).
Venue: Cambridge Innovation Center, 1 Broadway, Cambridge.
RSVP here. RSVP capped off at 100. Please do not hesitate to RSVP on the waiting list if the RSVP is full as some guests "un-RSVP" as the event gets closer.
Speakers and details
Andreas Raue, PhD, Head of the Stabilized Immuno-Ligand Program, Merrimack Pharmaceuticals.
Dr. Andreas Raue applies computational modeling and analysis to develop new therapeutic strategies for cancer treatment. Dr. Raue received his PhD in theoretical physics from the University of Freiburg in 2013. He is founder and lead developer of Data2Dynamics, an open-source modeling software package for differential equation models for MATLAB. He was awarded the Reinhart Heinrich Doctoral Thesis Award from the European Society for Mathematical and Theoretical Biology (ESMTB) in 2013, as well as the MTZ-Award for Medical Systems Biology in 2014.
How can we make use of machine learning in QSP applications?
In this presentation, I will discuss how machine learning algorithms can be used in combination with more traditional mechanistic models to answer questions in drug development. I will illustrate this with results from a hybrid modeling approach that can significantly improve prediction of responder cell lines to receptor tyrosine kinase (RTK) targeted antibodies.
RTKs are high-affinity cell surface receptors for growth factors that are frequently deregulated in cancer. Signaling through these receptors has been associated with increasing cancer cell proliferation and resistance to cytotoxic therapies. To block this detrimental signaling, a series of antibodies against RTKs has been developed.
A key challenge in clinical studies is the optimal stratification of patients. For a RTK targeted antibody, the detection of the respective growth factor in the tumor microenvironment is an important bio-marker. Beyond the physical presence of the growth factor, the decision if a cancer cell will respond to growth factor induced signals is governed by complex intra-cellular signaling networks.
I will discuss different approaches to predict cellular responses and highlight a combination of mechanistic modeling based on ordinary differential equations with a decision tree algorithm. The models are trained on in vitro drug response screens. Their mechanistic parts are trained on quantitative data from signal transduction studies as well as RNAseq data for cellular characterization.
Pinky Dua, PhD, Senior Director of Clinical Pharmacology and Head of Clinical Pharmacology for the Rare Disease Research Unit. Pfizer, Inc.
Dr. Pinky Dua has over 10 years experience in the pharmaceutical industry and has strong scientific and technical background with proven track record in quantitative modeling and simulation for drug discovery and development. She has worked across a wide variety of therapeutic areas – Pain, Neurology, Psychiatry, Respiratory, GI, and Rare Diseases. Dr. Dua has a PhD in Chemical Engineering from Imperial College London, has published over 20 journal and conference papers and 6 book chapters. She has delivered invited/plenary talks at international pharmaceutical conferences and at academic institutions.
A Quantitative Systems Pharmacology Model of the Nerve Growth Factor (NGF) Pathway to Aid Drug Discovery and Development
The nerve growth factor (NGF) pathway has been shown to play a key role in pain treatment. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. The model was further extended to include the role of the p75 receptor, to gain understanding of the effect of p75 on the dynamics of NGF signal transduction. Specifically, models were developed for the so-called heterodimer and for the ligand-passing hypotheses. The model was used to compare the effect of inhibition of NGF and TrkA and its implication for drug discovery and development for pain treatment. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.
About Boston QSP
Boston QSP is a 501(c)(3) non-profit organization whose mission is foster the sharing of QSP knowledge, challenges, solutions, and opportunities to advance the field as an interdisciplinary community in Boston.