Real-Time Pharmacokinetic and Pharmacodynamic Imaging of Immune Checkpoint Blockade In Vivo

Identification: Garris, Christopher


Real-Time Pharmacokinetic and Pharmacodynamic Imaging of Immune Checkpoint Blockade In Vivo
Christopher S. Garris1,2, Sean P. Arlauckas1, Ralph Weissleder1, Mikael J. Pittet1*
1Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; 2Graduate Program in Immunology, Harvard Medical School, Boston, MA 02114, USA
*Corresponding Author
Immune Checkpoint Blockade (ICB) therapies have revolutionized cancer treatment. These therapies act by engaging the immune system to target tumors, however most patients treated with ICB do not respond to therapy. Little is known about how these drugs behave in tumors and why they work or fail against cancer. To address these questions, we developed an in vivo imaging system to track ICB monoclonal antibody (mAb) therapeutics and resolve their pharmacokinetics (PK) and pharmacodynamics (PD) in real-time and at single cell resolution (real-time PK/PD imaging or RPPI). Our initial PK studies using anti-PD-1 mAb identified mAb uptake by PD-1+ tumor-infiltrating CD8+ T cells, as expected, but also showed rapid drug removal from T cells in the presence of tumor-associated macrophages. We found that drug uptake by macrophages depends on Fc:Fc receptor interactions and that neutralizing this pathway could enhance treatment efficacy. Furthermore, our initial PD imaging studies uncovered that successful anti-PD-1 therapy triggers the activation of a distinct population of tumor-infiltrating dendritic cells. These cells are not bound directly by anti-PD-1 mAb and we discovered that their activation requires cytotoxic CD8+ T cells. Additionally, we found that anti-PD-1 therapy's efficacy strictly depends on signals produced by these activated dendritic cells. We propose that the ability to track the dynamics of immunotherapeutics within complex in vivo microenvironments will be important for deciphering drug action mechanisms. Leveraging this knowledge should help not only to engineer better therapeutics but also to select combination therapeutics rationally.
Funding: This work was supported by NIH grants: F31CA196035, T32CA79443 R01AI084880, R01CA206890


Credits: None available.

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