﻿ First, I would like to thank the organizers for the opportunity to present our work from the
Systems Biology Laboratory at the (audio skip) Johns Hopkins School of Medicine.
Here are my disclosures.
Today, I will show you several examples from our recent work.
First, quantitative systems pharmacology for an immuno oncology platform
which are ordinary differential equation based mechanistic compartmental models
of a whole patient (phonetic). Second, I will present a hybrid spatial platform,
spQSP, describing tumor spatial heterogeneity. And third, application of multiplex digital pathology
to aid and model parametrization and validation. We start with the QSP IO platform. I will
also show an example of generating virtual patients and running virtual clinical trials.
Here is a schematic of the platform. It shows the tumor on the right in green with cancer
cells (phonetic), special types of several types of T cells, such as affected (phonetic) T
cells and regulatory T cells, antigen presenting cells, APCs, myeloid derived suppressor cells,
MDSCs, and molecular species such as chemokine CCL2, and the growth factor TGF ß.
Mature APCs are transported through the lymphatic system to the tumor draining lymph nodes.
There, they activate T cells resulting in a factor in regulatory T cells,
those cells in traversing (phonetic) into the central or blood compartment,
shown in the center, circulate and extravasate eventually into the tumor and the other organs
and tissues in the peripheral compartment. In the tumor, these cells interact with cancer cells.
Shown below, the tumor draining lymph node are self (phonetic) cell (phonetic)
receptor ligands (phonetic) interactions that occur in the immunological synapse.
Cancer cells and affected T cells interact with a via receptors PD 1, and ligands PD L1,
and PD L2 activated CD 8 (phonetic) positive T cells interact with mature APCs through
CTLA 4, and CD 8086 (phonetic), and other receptor ligand interactions.
These interactions can be disrupted with an immune checkpoint inhibitor such as Anti PD 1,
Anti PD L1, and Anti CTLA 4. The model typically results in the 100 to 200 ordinary differential
equations describing molecular kinetics at the fundamental level, cell transitions
and transport processes in mechanistic detail, very importantly. Pharmacokinetics (phonetic) of
drugs is included in the model. On the right side, there are several
references to our recent papers on the subject and applications to different types of cancer
and different antibodies. These include bispecific T cell engagers, TCEs. The last reference is for
our mouse model that we constructed to facilitate translation from animal models to human.
Here, I show one application to retrospective analysis of
Phase III clinical trial called Impassion130, of an anti PDL 1 inhibitor atezolizumab in
combination with chemotherapy and nab paclitaxel for triple negative breast cancer. This trial
led to the FDA approval of the combination. In this trial, in this virtual clinical trial,
we generate 900 virtual patients. Each patient is characterized by a set of parameters.
We first simulate response to nab paclitaxel as a single agent. And then, a combination of
nab paclitaxel and atezolizumab following the doses and the regiment in the clinical trial.
The results are presented as spider plots on the left where each line corresponds
to a virtual patient. Shown are 100 randomly selected patients following RECIST criteria.
Patients whose tumors grow over 20 percent represent progressive disease, PD.
Those between 20 percent growth and a 30 percent reduction is stable disease,
SD. And a reduction over 30 percent is partial or complete response, PRCR.
On the right, the results are presented as waterfall plots. Each bar represents a patient.
The results are in quantitative agreement with clinical data for
objective response rate. Looking at the spider plots, we can appreciate the complexity of
predicted responses. For example, we see virtual patients that initially respond to the treatment
and then relapse. Know that the mechanisms for individual virtual patients could be revealed
and interpreted in the model which is very difficult or impossible in a clinical trial.
Now that the model has been validated, we conduct additional simulations to explore
different doses of nab paclitaxel and different sequence of administration. For example,
administering atezolizumab simultaneously with paclitaxel, or delayed by two or four weeks.
The results are shown here for tumor volume at eight weeks. The upper row, CD8 positive
T cell density. The middle row and the regulatory T cell density at eight weeks, the bottom row.
This slows us to explore different scenarios and conduct virtual clinical trials to predict
optimal dose and sequence of drug administration. Interestingly, the simulations showed that more
than doubling the paclitaxel dose does not lead to any efficacy improvement.
Also, of note that the doses and regiment chosen for the clinical trial turned out to be optimal.
QSP models are powerful, and they have been gaining acceptance in industry and regulatory
agencies, as well as academia. However, tumor spatial heterogeneity is a hallmark of cancer,
but the ordinary differential equation based compartmental QSP models do not capture tumor
spatial heterogeneity. A lot of available information such as pathologist specimen
and spatial transcriptomics is not utilized in this model.
To address this issue, we have developed a hybrid spatial QSP platform
combining cold (phonetic) patient QSP modeled with a spatial agent based model, ABM,
describing the tumor. We call the platform Spatial QSP, or spQSP.
This is a schematic of the model. Shown on the left is the four-compartment whole patient
QSP model. On the right, is a zoom tumor volume or region of interest in which we consider
interactions of different certain cell types such as cancer stem like cells (phonetic), progenitor
cells, and synapses (phonetic) cells, as well as different immune cells, and molecular
species such as IL 2, and interferon gamma. Interactions of discreet cells are governed
by experiment based rules, whereas interactions of a description of
molecular species is governed by reaction diffusion partial differential equations.
Here is an example of simulations parameterized for non small cell lung cancer with an
(audio skip) centimeter by one centimeter with the thickness of
0.2 millimeter (phonetic) (audio skip) or better visibility. On the right,
there are a color codes for different cell types. You see the cancer stem like cells, (audio
skip) cancer progenitive cells, synapse (phonetic) cells, and different types of T cells.
Let's look at the tumor in more detail. Here on the left, the (audio skip) two panels follow the
tumor growth, and we zoom into the tumor core (phonetic) on the second panel. On the next
two panels, on the right, we labeled different (phonetic) cell types to mimic multiplex
immunofluorescence and then zoom to look at the cells. Here, we will look (audio skip) enabled
(phonetic) cancer cells cytokine (audio skip) T lymphocytes (phonetic), CTLs (phonetic) and
regulatory T cells, T Reg (phonetic). Unlike pathologist specimen, (audio skip)
history (phonetic) of pathologist specimen (audio skip) with different labels here (audio skip) CD8,
PDL 1 and we also zoom to visualize the invasive front of the tumor.
The right two panels show the distribution of important cytokines IL 2, and interferon gamma.
Note that the ability to visualize and quantify spatial temporal distribution
of cellular (phonetic) and molecular species should significantly enhance the predictive power
of QSP models. We can take full advantage of pathologists' specimens detailing the complex
architecture of the tumor microenvironment.
Here, on this slide, we show an extension of the
spQSP model that incorporates on it, information, specifically, two (audio skip).