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2:50-3:10 pm Future of Diverse Contexts for Multiscale Modeling:
“Computational Systems Pharmacology of Antibody-Drug Conjugates: A Joint Academia-Industry Experience”
Inez Lam, Johns Hopkins University
BIO: Inez Lam received a B.S. in Biomedical Engineering from Johns Hopkins University (JHU) and is currently a Ph.D. Candidate in the Institute for Computational Medicine at JHU. She was selected as one of the inaugural Johns Hopkins-AstraZeneca Scholars, a first-of-its-kind Ph.D. training program aimed at developing researchers at the intersection of academia and industry. Under the mentorship of Dr. Feilim Mac Gabhann of JHU and Dr. Rosalin Arends of AstraZeneca, Inez is building systems pharmacology models of antibody-drug conjugates for cancer therapy. She is also co-founder and Chief Technology Officer of ClearMask, developing transparent surgical masks to improve communication in healthcare.
ABSTRACT: Antibody-Drug Conjugates (ADCs) are engineered immunoconjugate drugs composed of 3 core components: (1) a monoclonal antibody and (2) one or more cytotoxic small molecules (known as warheads), attached via (3) a chemical linker. While ADCs have the potential to assist in the fight against cancer, clinical success has been hindered by a lack of understanding of the mechanisms driving ADC safety and efficacy, and difficulty in optimization of each subunit individually and within the context of the entire ADC. Here, we apply computational systems pharmacology approaches to study ADCs with pyrrolobenzodiazepine (PBD) as the cytotoxic drug entity, combining experimental data and mechanistic modeling to conduct simulations of ADCs in different experimental scenarios relevant to drug development. This computational model is calibrated and validated using in vitro experimental data provided by AstraZeneca for anti-HER2-PBD ADCs, which have antibodies targeting the HER2 antigen and carry PBD payloads with differing properties. This work is critical to understanding how key ADC design characteristics translate to ADC function, enabling the comparison of novel treatment scenarios and the development of better oncology therapies. I first encountered multi-scale modeling in Dr. Feilim Mac Gabhann’s “Systems Pharmacology and Personalized Medicine” class at Johns Hopkins University, and since then have been fascinated by how computational models and techniques can be used to improve human health. As one of the inaugural Johns Hopkins-AstraZeneca Scholars, I will discuss this unique opportunity to apply multi-scale modeling to address scientific questions in both academia and industry.
Comment
wonderful talk - does the model allow for the incorporation of a 'biomaterial delivery vehicle' as well - e.g., a carrier in combination with the ADCs? Particularly in the context of sustained release, localization and related needs, where selective interactions with a biomaterial carrier can be exploited to modulate the release outcomes of the ADCs.
Agreed! While I can't speak on behalf of universities and funding agencies, if both universities and companies are willing to provide support (via funding, resources, project availability, and people), it seems like scaling of these strategies would be highly possible. There are multiple ways this could happen, for instance one university partnering with a few different companies, or vice versa.
Your partnership has some similarity with the academic-industry units in EU’s Innovative Medicines Initiatives. (info below)
With that in mind
Are their plans to expand this to a “precompetitive consortium (or workgroup)” involving multiple academic and industry partners?
Is your partnership planning on working with the FDA on increasing use of these models, incl simulations in the drug/biologic treatment approval process?
IMI sites (https://www.imi.europa.eu/) (example of one workgroups in the past: https://www.imi.europa.eu/projects-results/project-factsheets/newmeds)
A very appropriate talk for St. Patrick's Day
After all Student's T test resulted from such a collaboration between Guinness (the official non-green beer of St. Patrick's Day) and University College London. While an employee of Guinness, William Sealy Gosset went to work with Karl Pearson and published his landmark paper “The Probable Error of a Mean” in 1908.
My question is:
My understanding is that your interaction with industry was happenstance in that you were in academia and selected to be a Johns Hopkins-AstraZeneca Scholar. Has anyone become a Johns Hopkins-AstraZeneca Scholar from the industry side? Does AstraZeneca have position for an undergraduate who upon graduation take a 2 years to work in an AstraZeneca laboratory, show talent and intelligence and subsequently get chosen to be a Johns Hopkins-AstraZeneca Scholar? or is this a one way street?
<p>Thanks for sharing! Currently, I don't know of any plans to create a consortium or workgroup like IMI, but that would be a great move, especially as more programs are established like this in the US. This current program is the first of its kind in the US, but it seems that more and more universities and companies are moving in this direction! While we do not currently have plans to work with the FDA, the FDA does work with companies to review models and simulations during the approval process, so there is potential for this in the future.</p>
Very cool, did not know that - glad to be following in those footsteps!
Currently, there isn't a direct path from industry into this program - however, this shouldn't stop anyone that is interested! In fact, since the program started, I've heard from several people from the industry side who are interested. At its core, this is still a PhD training program, so it is most appropriate for people who want the research training, but with the added exposure to the biotechnology industry. As the program grows and evolves, I can certainly see more pathways opening up, much like the scenarios you mentioned (e.g. an undergraduate obtaining 2 years of industry experience before starting their PhD training with this program).
Fascinating model and system! What is the state of the art for modeling and predicting off-target effects in this system? Thanks! Guy