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European Biopharmaceutical Review
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The vast majority of cancer drug candidates that enter clinical trials
fail, due either to lack of efficacy or higher than expected toxicity.
Poorly predictive tumour models used for the preclinical selection of
drug candidates typically take the blame.
A wide variety of
cancer models are used in the various stages of the discovery process,
from initial target discovery and validation through selection of
clinical candidates, to biomarker validation and modelling of clinical
trial scenarios. These models include transplantable or
chemicallyinduced syngeneic tumours in mice and rats, human tumour cell
lines/xenografts, genetically engineered mouse models (GEMs), and
patient-derived xenografts (PDX). Each has its advantages in selected
phases of the discovery process, but none possess all of the attributes
to fully mimic clinical use.
Clinical Relevance
The
bulk of the drug discovery process today employs the use of
longestablished human tumour cell lines grown as xenografts in
immune-deficient mice, and this model has become much maligned for its
apparent lack of predictive power. Nevertheless, many analyses have
concluded that a significant correlation exists between the finding of
meaningful activity in the preclinical models and the eventual clinical
success of the same compounds (1).
Regardless of the predictive
power of these models, a significant effort has been made to generate
more clinically relevant models in the hope that predictive power will
be improved. In general, the newer models being established seek to
improve clinical relevance by:
- Better expression of clinically relevant genetic drivers and stem cell populations
- Localisation of the tumours in environments/locations that result in relevant stromal interactions and drug exposures
- The
generation of a truly malignant phenotype, including the ability to
invade neighbouring tissue and metastasize to distant sites with
relevant physiologic consequences
- Evaluating human cancers in intact mice by so-called humanisation of the immune system
Model Formats
Key
models promoted as more clinically relevant include orthotopic implants
of human tumour xenografts, PDX and GEMs. All three of these formats
are typically more prone to invade and metastasize, thus better
modelling malignant disease.
Orthotopic xenografts,
regardless of the source of the tumour sample, place the tumour in sites
typically encountered in the clinic – usually in the organ in which it
arises – providing more relevant drug exposures, environments and
stromal interactions. (2,3).
GEMs are precisely established
with carefully chosen genetic drivers that promote the formation of
tumours in relevant environments (4). PDX models show strong ability to
recapitulate the treatment outcomes of the patients from which they are
derived; they are also more clinically relevant in terms of maintenance
of relevant histology, stem cell populations, genetic drivers and
heterogeneity – especially in an orthotopic format –but, until recently,
have not been widely available. Orthotopic PDX models are arguably the
most clinically relevant models available today (5).
All of
these model formats are showing promise in terms of recapitulating
clinical experience, but they share a common property that hinders
efficient and large-scale use in development: the tumours and response
to treatment are difficult to diagnose and measure.
Overcoming Barriers
Fortunately,
the advancement of in vivo imaging technologies, in parallel with these
more relevant models, has enabled non-invasive detection and
quantification of tumour burden. The application of imaging technologies
makes these models much more robust and efficient, with more clinically
relevant end-points, and thus more appropriate for broad application in
the discovery process (6,7).
Orthotopic models have the
advantage of a known location, at least for the primary tumour. GEM
models, on the other hand, are stochastic, even if organ specific, and
can vary in precise location, time of formation and multiplicity.
Nevertheless,
several imaging technologies are applicable and can be honed to deal
with both model types. Traditional and clinically translatable
anatomical imaging techniques such as magnetic resonance imaging (MRI),
computed tomography (CT) and ultrasound can be used to noninvasively
locate and quantify tumour burdens in preclinical animal models.
Furthermore, they can be used to assess tumour progression over time,
response to therapy, and eventual relapse or emergence of resistance.
MRI and CT, in particular, offer high spatial resolution and accurate
determination of tumour volume. These modalities are most easily applied
when the location of the tumour is already known, and therefore are
used most with orthotopic models or GEM models with specific organ
tropism.
Tracking Metastasis
A hallmark of
cancer is metastatic spread. This can be assessed non-invasively in
preclinical studies by MRI or CT imaging, if the target tissues for
metastasis of the particular model have been predetermined. CT imaging
is particularly useful for the assessment of lung metastases, where
incidence, multiplicity and mean (as well as individual) lesion sizes
can be simultaneously determined. However, other imaging modalities
receive greater use for this application. These include positron
emission tomography (PET), fluorescence molecular tomagraphy (FMT) and
bioluminescence imaging (BLI). Despite having relatively poor spatial
resolution, these techniques have high sensitivity, and allow whole body
imaging for detecting and tracking primary tumours and metastases.
If
used appropriately, these modalities generate signals that are
biomarkers for tumour burden. All three of these technologies involve
the use of imaging molecules or reporters that are colocalised and/or
activated by tumour cells in a specific fashion. FMT is a particularly
flexible and efficient means for assessing tumour burden through the use
of relatively inexpensive fluorescent molecules that can be targeted
to, or activated by, a variety of tumours.
The use of BLI
requires modification of the tumour model prior to implantation of the
model by transduction with a bioluminescent protein. Care must be taken
to ensure that the transduction process does not modify important
properties of the tumour, such as driver mutations and gene expression
profiles; however, sensitivity for detection and throughput are both
high for this approach. Dependent on the promoter used for expression of
the bioluminescent protein, the signal can be proportional to viable
tumour burden – a more specific measure of burden and response than
tumour volume.
Biologics Tracking
Another
recently evolving application of imaging in the preclinical setting is
biodistribution imaging, particularly for targeted biological or
nanoparticle therapies. PET and single-photon emission computed
tomography imaging (SPECT), which utilise radiolabelling of the
therapeutic with shortlived isotopes, can be used to quantify the
targeting of the drug candidate, its affinity for the tumour tissue, and
pharmacokinetic and residence/ clearance profiles in the whole body.
Alternatively, fluorescent tags can be used in connection with FMT for
the same purpose. These applications generate a non-invasive counterpart
of real-time whole body autoradiography, albeit with lower spatial
resolution.
Blending Technologies
The application
of widely available and well-validated non-invasive imaging
technologies can dramatically enhance the use of our most appropriate
tumour models. These models have clinically relevant genetic and stem
cell profiles, and exhibit truly malignant disease with relevant stromal
and paracrine interactions.
Serial time course imaging
generates rich datasets with high statistical power that deliver truly
quantitative readouts of efficacy, and with fewer animals. Clinically
relevant endpoints, such as tumour response rates, and progressionfree
survival are also uniquely obtainable – further enhancing the clinical
relevance of the test.
Thus, the marriage of recent developments
in tumour models with emerging imaging technologies has enabled the
efficient and robust use of the most clinically relevant and, therefore,
the most predictive models. This unique blending of emerging
technologies shows promise to enhance the pace of desperately needed
cancer drug approvals.
References
1. Johnson JI, Decker S, Zaharevitz D, Rubinstein LV et al, Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials, British Journal of Cancer 84(10): pp1,424-1,431, 2001
2. Bibby MC, Orthotopic models of cancer for preclinical drug evaluation: advantages and disadvantages, Eur J Cancer 40(6): pp852-857, 2004
3. Killion JJ, Radinsky R and Fidler IJ, Orthotopic models are necessary to predict therapy of transplantable tumors in mice, Cancer Metastasis Rev 17(3): pp279-284, 1999
4. Walrath JC, Hawes JJ, Van Dyke T and Reilly KM, Genetically engineered mouse models in cancer research, Adv Cancer Res 106: pp113-164, 2010
5. Fiebig HH and Burger AM, Patientderived tumor models and explants, in Tumor Models in Cancer Research, Teicher BA (ed), Springer Science, pp167-193, 2011
6. Kaimal V, Leopold WR and McConville P, Imaging efficacy in tumor models, in Tumor Models in Cancer Research, Teicher BA (ed), Springer Science, pp215-241, 2011
7. Lister D, Leopold WR and McConville P, Imaging the laboratory mouse in vivo, in The Laboratory Mouse, Hedrich HJ (ed), Elsevier, pp761-780, 2012
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Dick Leopold is the Vice President of Oncology at Molecular Imaging, Inc, of which he was a co-founder. He previously led the cancer drug discovery effort at Parke-Davis and Pfizer Ann Arbor. During his tenure, the Parke-Davis/Pfizer programme promoted more than 15 novel anti-cancer drug candidates to clinical trials, many of which were first in class. Dick is an Adjunct Professor of Medicinal Chemistry at the University of Michigan and a former member of the Purdue University Cancer Center external advisory committee. He gained his PhD in Oncology from University of Wisconsin Medical School.
Patrick McConville is the Chief Scientific Officer and a co-founder of Molecular Imaging, Inc, and has over 10 years of experience in multimodality, small animal imaging, with a drug discovery focus. Patrick is the author of more than 20 peer-reviewed publications and book chapters, and the holder of a patent. He received his PhD in Medical Physics from Queensland University of Technology. After postdoctoral work at the US National Institutes of Health, he established the core multimodality imaging centre at Molecular Imaging, Inc, and directed implementation, optimisation and validation of in vivo imaging protocols across imaging modalities and therapeutic areas.
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