Novel treatments in nanomedicine:
Developments and challenges
Kristofer J Thurecht
Centre for Advanced Imaging and
Australian Institute for Bioengineering and Nanotechnology,
University of Queensland, Australia.
ARC Centre of Excellence in Convergent Bio-Nano Science
and Technology
Cyclops Grand Challenge Workshop
What’s wrong with nanomedicines?
Delivery efficiency [%ID]
Development of nanomedicines is a
key component of next generation
therapeutics, but scientists still don’t
fully understand how key parameters
drive efficacy
No. Data Sets
1. Median 0.7% Injected dose reached
tumour
2. This number has not changed in 10
years!
The problem: There is no internal measure of predicting efficacy for individual
patients; all patients get a nominal dose and treatment regime
Chan et al. 2016, Nature Reviews “Analysis of Nanoparticle delivery to tumours”
Beyond targeting, addressing efficacy
Challenges in Nanomedicine translation – towards closed
loop therapeutics and personalisation of medicine
PSMA Positive
x10
PSMA Negative
3
Volume of Distribution (mL/cm 3)
What factors affect how a nanomedicine gets
to a tumour?
- Accumulation, targeting, biodistribution
- Immune response (both innate and
adaptive immune system)
VOI Activity (kBq/mL)
250
200
150
100
50
0
0
1000
2000
3000
4000
5000
6000
7000
-4
PSMA Positive
PSMA Negative
*
2
1
0
Time (Seconds)
Theranostics
What factors affect efficacy of therapeutic
once nanomedicine arrives at the tumour?
Wirelessly
relay distribution
information back to clinician in real-time, allowing
- Intra-tumour
analysis and comparison with big datasets and drive therapeutic
decisions
Dynamic PET
Liver
Heart
First 2 hours following injection of nanomedicine
Kidney
2 hrs
Imaging nanomedicine aggregation in tumour
VOI activity
PSMA + tumour (targeted)
PSMA+
tumour
PSMAtumour
PSMA – tumour (EPR Effect)
0
7000
Time / s
Time-activity curves for tumours
Dynamic PET with CT
Our Approach for optimising treatment: Two compartment
model for tumour aggregation of nanomedicines.
Nanomedicine distribution in tumour – targeted vs EPR effect
2 Compartment Model for Distribution
Reference Tissue Model
Accounts for differences in extravasation for two tumours
specific vs non-specific binding
PSMA Positive
250
x10
PSMA Negative
Volume of Distribution (mL/cm 3)
3
-4
PSMA Positive
PSMA Negative
*
VOI Activity (kBq/mL)
200
Targeting
increases accumulation by ~ 2-fold
150
2
For
individual patients, the degree of
100
1
accumulation
can be determined and therapeutic
50
strategy
modelled
0
0
1000
2000
3000
4000
5000
6000
7000
Time (Seconds)
Accumulation
in tumour: k1/k2
Bound vs unbound; k3/k4
0
Determination of binding potential
Strategy 2: Feedback loop on therapeutic
response, rather than delivery
Variable patient response to
treatment and dose
Theranostic materials that are
activated by response (apoptosis)
Tumour
Polymer
Blood
vessel
Strategy 2: Feedback loop on therapeutic
response, rather than delivery
Switchable theranostics –
Combined 19F MRI/1H MRI
Multimodal Imaging – looking at
multiple responses simultaneously
Tumour
Polymer
Blood
vessel
Summary
New approaches to nanomedicines offers a route for
developing internal feedback mechanism for therapeutic
efficacy; theranostics
Challenges lie in detection, and choosing suitable response
probes; subsequent decision making can be guided by
feedback loop to established data sets.
With suitable modelling, these systems offer a realtime
validation of individualised treatments.