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European Biopharmaceutical Review

Imaging of Relevance

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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Dick Leopold
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Patrick McConville
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