Rosa Webinar Series

Webinar Program

A Mechanistic QSP Model of Alzheimer’s Disease Yields Insights into Pathophysiological and Therapeutic Mechanisms

Colleen Witt, PhD, Principal Scientist, Rosa & Co. LLC
Robert Sheehan, PhD, Senior Modeler, Rosa & Co. LLC

Colleen Witt
Dr. Witt has over 25 years of research experience in immunology and neurobiology. Prior to her work with Rosa, Dr. Witt worked as Research Professor at the University of Texas, San Antonio, and served as Director for the core facility of the Specialized Neuroscience Research Program. She has been with Rosa for 9 years and has served as scientific lead on numerous modeling projects focused on disorders of the central and peripheral nervous systems, as well as other non-neuronal inflammatory diseases. Dr. Witt earned her PhD in immunology from the University of Alabama at Birmingham before doing post-doctoral work at the University of California, Berkeley.

Robert Sheehan
Dr. Sheehan has over 10 years of research experience in computational and systems biology. Prior to his work with Rosa, Dr. Sheehan worked as a Postdoctoral Fellow at the Laboratory of Systems Pharmacology at Harvard Medical School. He has been with Rosa for 5 years and has served as modeling lead on numerous projects focused on disorders of the central and peripheral nervous systems, as well as other disease areas including oncology and inflammatory disorders. Dr. Sheehan earned his PhD in computational biology from the University of Pittsburgh.

This Webinar
In this webinar we will discuss results from our paper with colleagues from Roche and Genentech that received the ISoP 2023 Outstanding Research Manuscript Award.

Alzheimer's disease (AD) is an inherently challenging disease to study, given the long time scales and the difficulties in measuring biochemical and physiological changes. QSP can help reduce uncertainty and guide clinical trial design. We developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model includes Aβ production and aggregation, transport of soluble species between brain, cerebrospinal fluid (CSF), and plasma, and pharmacokinetics, transport, and binding dynamics of multiple monoclonal antibodies, measuring response by PET and CSF/plasma biomarkers. Model components were calibrated to internal and literature data, including from clinical trials for anti‐Aβ monoclonal antibodies (mAbs).

In addition to predicting response to therapy, the model supports investigation of pathophysiological mechanisms and dynamics. For example, the model was initially developed for an apolipoprotein E (APOE) ɛ4 allele carrier as the first virtual patient (VP). We then implemented the known mechanistic effects of ApoE4 in the Platform and created an APOE ɛ4 noncarrier VP. We will demonstrate that with no other changes, the noncarrier VP’s predicted total Aβ burden and progression rate were consistent with literature data, suggesting that the known mechanistic ApoE4 effects are sufficient to explain the clinical differences.

In reproducing the impact of therapeutic mAb treatment on removal of aggregated Aβ, the model provided insight into the role of microglial activation. We will demonstrate why, to account for the observed Aβ clearance dynamics, the involvement and contribution to clearance by activated microglia must extend beyond the site of mAb binding.

The model can be used for exploration of other Aβ-directed therapies, or it can be expanded, e.g., to investigate additional AD mechanisms such as Tau pathology.