16 Applications of Dynamic Contrast-Enhanced MRI in Oncology Drug Development

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16 Applications of Dynamic Contrast-Enhanced MRI in Oncology Drug Development

Gordon C. Jayson and John C. Waterton

G. C. Jayson, PhD, FRCP

Cancer Research UK, Department of Medical Oncology, Christie Hospital NHS Trust, Wilmslow Road, Withington, Manchester, M20 6DB, UK

J. C. Waterton, PhD, FRSC

Enabling Science & Technology, AstraZeneca, Alderley Park, Macclesﬁ eld, Cheshire, SK10 4TG, UK

16.1

Introduction

Approximately one third of the American and Euro- pean population will develop cancer at some time in their lives. The incidence of cancer increases with age, and among middle-aged people cancer is the single greatest cause of mortality. Currently the major treatment options are surgery, radiotherapy, cytotoxic chemotherapy and hormonal modulation. Despite the tremendous improvements in cancer treatment over the past few decades, survival rates for many

cancers are still poor and cytotoxic chemotherapy is usually accompanied by signiﬁ cant toxicity. There remains a huge unmet medical need for better cancer therapies.

Entirely novel anti-cancer drugs are under development. With the explosion in our understanding of the molecular biology of cancer, hundreds of potential molecular targets have been identifi ed for anti- cancer drugs beyond the traditional antiproliferative agents. Potential drug targets have been identifi ed in receptors, enzymes and associated biochemical pathways, in areas such as angiogenesis and tumour perfusion, the cell cycle, apoptosis, invasion and growth factors. Targets identifi ed in angiogenesis and the tumour vasculature (Cristofanilli et al.

2002) include the signalling pathways responsible for the growth of new blood vessels, together with factors required for the survival and structural integrity of immature endothelium. Vascular targets are particularly attractive, since effects on a small number of endothelial cells may affect the nutrition of a large number of tumour cells and, because the cancer cell itself is not targeted, the problem of resistance may be reduced. Medicinal chemists and molecular biolo- gists have been highly successful at devising candidate drugs with good activity in preclinical testing against targets in the tumour vasculature. Dozens of such molecules are now in clinical trial, and hundreds more are in pre-clinical evaluation. These candidate drugs include orally bioavailable, rationally designed, small organic molecules, as well as biological agents that would typically require parenteral administration, for example neutralising antibodies.

The clinical development of these newer types of agent poses new challenges. Unlike antiproliferative drugs, they will not inevitably show acute dose-limiting toxicity. In addition the anti-angiogenic compounds are likely to be cytostatic rather than cytotoxic, and thus tumour stabilisation rather than response is a more probable outcome, at least with monotherapy. Considerable ingenuity may be required to devise a development programme that effectively identiﬁ es the best candidate drug molecule

CONTENTS

16.1 Introduction 281

16.2 What Does the Drug Developer Need to Know? 282 16.2.1 Phase III 282

16.2.2 Phase I/II 282

16.3 Considerations in Study Design – Physics, Image Informatics and Validation 283 16.3.1 Which DCE-MRI Parameter to Measure 283 16.3.2 Contrast Media 284

16.3.3 Design of the DCE-MRI Acquisition Protocol 284 16.3.4 Design of the DCE-MRI Analysis Protocol 285 16.3.5 Spatial Heterogeneity Within the Tumour 286 16.3.6 The Validity of the Measurement 287 16.3.7 The Reproducibility and Statistical Power

of the Measurement 287

16.4 Clinical Considerations in Study Design 291 16.5 Clinical Trial Design 292

16.6 DCE-MRI in Comparison with Other Imaging Modalities 293 16.7 Summary 294

References 294 Appendix 296

JACK_16-Jayson.indd 281

JACK_16-Jayson.indd 281 19.10.2004 12:33:14 Uhr19.10.2004 12:33:14 Uhr

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at the optimum dose for full Phase III clinical testing, probably in a variety of solid tumours, at different tumour stages, both in monotherapy and combina- tion therapy. In this chapter we will illustrate the incorporation of DCE-MRI into drug development with examples of anti-angiogenic and anti-vascular agents. For instance a large number of drugs have been targeted against the angiogenic cytokine vascular endothelial growth factor (VEGF) and its signalling pathways. This cytokine is a principal mediator of perfusion and vascular permeability. The ability of DCE-MRI to detect changes in these parameters (Jayson et al. 2002) provides powerful biomarkers for rapid evaluation of the acute pharmacology of these newer agents in clinical trials.

16.2

What Does the Drug Developer Need to Know?

16.2.1 Phase III

The ultimate aim of the drug developer is to gain approval for the molecule from the regulatory agencies in Europe (EMEA), Japan (MHLW), the United States (FDA) and other territories, so that the drug can be made available for oncologists to prescribe for their patients. A key component of the regulatory submission is the ﬁ ndings of prospective randomised phase III clinical trials where the new treatment is compared in a randomised controlled trial against the best available cur- rent therapy, involving typically many hundreds or thousands of patients, in tens or hundreds of centres spread across several different countries.

The most powerful evidence that can be provided to regulators is statistically signiﬁ cant evidence of clinical beneﬁ t, for example improvements in overall survival, progression free survival, or quality of life. Surrogate endpoints can also provide supporting evidence provided they have been validated, i.e. have been shown to be predictive of the clinical outcome of the disease (how a patient feels, functions or survives). While tumour dimensions (Therasse et al. 2000) measured by imaging may be considered a surrogate, no acute biomarker or pharmacodynamic measure from imaging has yet been accepted as a valid surrogate endpoint in oncology. In the future DCE-MRI biomarkers may provide validated surrogate endpoints for use in

phase III, allowing shorter or smaller trials than those that use clinical endpoints such as survival alone, thus permitting regulatory authorities to approve drugs more quickly. However the validation of surrogate endpoints is an immense task (Lesko et al. 2001). The development and validation of DCE-MRI endpoints in this phase III setting has barely begun and is not the focus of this chapter. Given the choice of targets and candidate drugs, one of the greatest challenges for the drug developer is deciding which molecule to take into phase III, at which dose and schedule, in which tumours and perhaps in which combinations. The cost of a failed phase III programme is immense.

Thousands of patients could be exposed to ineffective therapy, while the hundreds of millions of dollars wasted could, instead, have been spent on bringing another, more effective, treatment to patients. A failed phase III programme cannot be repeated.

16.2.2 Phase I/II

Before a molecule enters phase III cancer trials a considerable body of data must be acquired in smaller-scale phase I/II clinical trials, together with toxicology and effi cacy studies in animals, in vitro pharmacology and other studies. At some point in the development of a new drug the compound will be administered to patients for the fi rst time. These phase I clinical trials have several aims, which include identifi cation of the toxicities associated with the agent and the maximum tolerated dose. The aim is to choose doses that can be taken forwards, fi rstly into phase II clinical trials, each of which tests a defi ned dose and schedule in a specifi c clinical situation and ultimately into phase III.

The conventional design of phase I clinical trials is to recruit cohorts of patients who receive the new drug over a few weeks. If that cohort tolerates the drug without developing signifi cant toxicity then a new cohort is treated at a higher dose. Again, if there is no signifi cant toxicity then a further dose is opened and so on until a dose limiting toxicity is identifi ed. But what happens if there is no acute dose limiting toxicity, as is often the case, for example, with some humanised monoclonal antibodies?

In that situation, the maximum tolerated dose and therefore also the doses and schedules for further clinical evaluation cannot be identiﬁ ed. A further issue is that when these apparently non-toxic drugs

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are combined with conventional agents marked toxicities have been recorded in certain situations (Kuenen et al. 2002).

One approach to these problems has been to incorporate biomarkers that rapidly demonstrate a relevant pharmacological action of the drug, into early clinical trials. These are sometimes described as pharmacodynamic endpoints, although strictly that term should be reserved for pharmacological responses coupled to pharmacokinetics of drug that is being administered. For instance agents that inhibit particular DNA repair enzymes can be assessed for their ability to inhibit the biological activity of the target enzyme in tumour biopsy specimens taken from patients on treatment. Alter- natively various imaging biomarkers have been used to characterise the vasculature before and after administration of anti-angiogenic (Jayson et al. 2002; Eder et al. 2002; Medved et al. 2002;

Thomas et al. 2001) and anti-vascular (Dowlati et al. 2002; Evelhoch et al. 2002; Galbraith et al. 2002) agents. These imaging methodologies have the aims of conﬁ rming that the putative drug has appropriate biological activity, identifying the lowest doses that have the required biological effect and more recently, have started to give mechanistic insights into why certain drugs are only partially effective. This is potentially very important as development of an ineffective drug can be halted if the desired effects on the biomarker are not seen in early clinical trials. Ineffective compounds (and worthless drug targets) can be abandoned early after few patient exposures, while for effective compounds a biologically effective dose can be established early for further testing. The principal value of a biomarker (from, for example, DCE-MRI) to the drug developer is thus to help select the right compound, at the best dose in phase I/II trials, and which to abandon.

DCE-MRI, in which imaging is performed at the same time as contrast agent administration, has been widely used in studies to understand the biology of the tumour vasculature. While some consensus rec- ommendations have appeared (Evelhoch et al. 2000;

Leach et al. 2003; Tofts et al. 1999) there remain sig- niﬁ cant issues in translating these approaches into reliable tools which can support robust decision- making in early drug development. The issues will be reviewed in this chapter. The main topics include the design of the DCE-MRI acquisition and analysis protocol, the validity and reproducibility of the measurement and the complexity introduced by tumour heterogeneity.

16.3

Considerations in Study Design – Physics, Image Informatics and Validation

16.3.1

Which DCE-MRI Parameter to Measure

The use of DCE-MRI merely implies that images will be acquired at deﬁ ned intervals during the uptake and elimination of a contrast agent. For an effective compound given at the top of the dose-response curve, the radiologist’s qualitative assessment may reveal an effect of the drug, but this non-quantitative approach lacks statistical power especially in early trials where effects on the tumour, and on the images, at lower doses may be small. For prac- tical drug development some quantitative image analysis is essential. The aim is to provide measurements (or maps) of parameters which reﬂ ect tumour perfusion and which can be analysed to give a quantitative assessment of the pharmacology.

Several analyses, with different levels of analytical complexity, are available to characterise DCE-MRI data. Analysis may simply provide a summary of the MR signal intensity changes or it may attempt to derive underlying physiologic parameters such as blood ﬂ ow by ﬁ tting a pharmacokinetic model of contrast agent uptake and washout to the data.

Note however, that although terms such as “vascular permeability” and “blood ﬂ ow” are commonly encountered in the DCE-MRI literature, an accurate and absolute determination of these parameters (in mm^–2.sec^–1 or ml.g^–1.min^–1) in tumours, from DCE- MRI data, is quite difﬁ cult.

Several semi-quantitative non-pharmacokinetic analyses based on signal intensity changes are available, for example maximum enhancement, gradient or alternatively the time for signal to reach 90% of its maximum value. A limitation of such approaches is that they may be difﬁ cult to compare between centres and between studies because signal intensity changes depend on scanner and pulse sequence in a complex way. In principle, a more robust approach would be to convert signal intensity changes (via effects on T₁ and with simplifying assumptions) to tissue contrast agent concentration. A useful non- pharmacokinetic measurement of such data, which still captures information from the entire uptake- washout curve, is the initial area under the contrast agent time-concentration curve (IAUC). Alterna- tively, pharmacokinetic modelling can be used to estimate the transfer constant (K^trans) for contrast agent movement out of the vasculature and into

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the tissue extravascular extracellular space. Both IAUC and K^trans can be used to derive a “hypervas- cularised tumour volume” by counting the voxels with values above a pre-determined threshold, e.g.

the mean value in a control tissue such as skeletal muscle. These two parameters have the merit that they are measured in absolute units (mM.s and s^-

1 respectively) and so, in principle, should provide data that are comparable between pulse sequences and scanners. Additionally, they have been widely used in animals and humans, and there is now considerable experience on a number of centres on reproducibility and, in animals, there is some evidence of a dose–response relationship (Checkley et al. 2003; Robinson et al. 2003). More complex pharmacokinetic models attempt to separate the contribution of intrinsic physiological parameters such as fl ow, endothelial permeability and vascular volume from the DCE-MRI signal. Such models make greater demands of the image data than K^trans or IAUC analyses either because they require rapid imaging in order to obtain fl ow measurements from initial uptake and are diffi cult to perform in three dimensions, require very high signal-to-noise ratio so are diffi cult to analyse voxelwise, or require the use of investigational high-molecular-weight contrast media (Pradel et al. 2003). In the future complex pharmacokinetic analyses may provide additional information on anti-cancer drug effi cacy in clinical trials but currently they are diffi cult to implement and have not yet been demonstrated to provide additional value in clinical drug development.

A clinical trial should have a clear primary endpoint established prospectively, with known (and good) statistical power and with potential confounds understood and controlled. In particular, it is essential to know that the DCE-MRI technique is robust and reproducible in the proposed setting. It is also important to estimate the expected size of the effect at the top and near the bottom of the dose–

response curve, and for novel drugs this is best measured through animal experiments. It is particularly important to the drug developer that the expected direction of change is certain, otherwise the study has no statistical power. For example, ve, the extravascular extracellular volume fraction, might either increase or decrease with effective antivascular or antiangiogenic cancer therapies, depending for example on changes in tumour oedema. It is therefore of limited value as a primary imaging endpoint, although it may provide interesting mechanistic data.

16.3.2

Contrast Media

Contrast-enhanced MR protocols measure the change in MR signal in the tumour following an intravenous bolus of a contrast medium such as gadoterate (Dotarem), gadodiamide (Omniscan), gadopentetate (Magnevist) or gadoteridol (ProHance). These are all hydrophilic gadolinium chelates with molecular weight around 500 Da. They remain extracellular but rapidly cross permeable endothelia and diffuse through the tumour interstitium. The pharmacokinetics of these agents can be estimated from the model of Tofts et al. (1991), a two-compartment open model with mean distribution and elimination half-lives of the order of 10 and 100 min, respectively, the latter representing renal clearance. These different contrast agents differ in their charge and protein binding and so in principle might show differences in their equilibration between blood plasma and the tumour interstitium. This possibility does not appear to have been exploited in the study of tumour biology or response to therapy; however, it would be prudent in a clinical trial of a new therapeutic agent to ensure that each patient receives the same contrast agent.

Higher molecular weight MR contrast agents such as P792 (Vistarem) (Pradel et al. 2003; Ruehm et al.

2002) offer an interesting alternative as their tissue penetration is modulated by larger gaps in the endothelium than low molecular weight contrast media, and their renal clearance is slower. Potentially evaluation of vascular permeability with high molecular weight compounds might provide a more useful dynamic range of uptake parameters than has been seen with the lower molecular weight compounds.

Protocols combining two contrast media potentially permit the separate contributions of permeability, vascular volume and ﬂ ow to be estimated. However, such agents are themselves currently still in clinical development, and from the point of view of the oncology drug developer, their use remains largely a future prospect, because of ethical and regulatory difﬁ culties in performing trials involving both an investigational diagnostic and an investigational therapeutic drug.

16.3.3

Design of the DCE-MRI Acquisition Protocol

For most studies. it is desirable to have the possibility of implementing the study protocol in a multicentre trial, possibly with different MRI instrumentation

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at each site. Repeat scanning must be possible and acceptable to patients, usually with sufﬁ cient time for contrast medium washout. Techniques must be feasible in the relevant anatomic locations, including maybe locations affected by physiologic motion (e.g. bowel, lung), organs with special patterns of blood supply [e.g. liver (Jackson et al. 2002), lung], and locations suffering magnetic ﬁ eld heterogeneity.

Ideally, techniques should be feasible both in newly presenting disease and also after surgical or radio- or chemotherapeutic intervention.

A choice has to be made between slower 3D and faster 2D protocols. Conventional 3D DCE-MRI protocols normally use T1-weighted MRI, have rela- tively poor time resolution (typically ≥5 s) and are therefore inadequate to measure contrast medium pharmacokinetics during fi rst pass. “Fast” protocols may use T2*-weighted or T1-weighted MRI and have suffi cient time resolution (typically <5 s) to measure contrast medium pharmacokinetics during fi rst pass, allowing blood fl ow and vascular volume to be estimated as well as IAUC or K^trans. Another attrac- tion of these fast acquisition sequences is that they allow the investigator to evaluate drugs in parts of the body usually affected by motion artefact such as the thorax; however, limitations in the gradient per- formance of many older scanners in existence would normally limit them to 2D acquisition.

Typical trials of antivascular and anti-angiogenic agents require comparison of DCE-MRI before and after therapy over periods of hours to weeks (Jayson et al. 2002). In order to obtain valid estimates of tumour response, it is important that the same region of tumour is imaged on each patient visit. For this reason 3D protocols, which provide data over the whole tumour, are often preferred to single slice protocols. Although some MRI instruments now allow fairly “fast” protocols in 3D, some cancer hospitals lack machines with such capability, so that the drug developer must choose between a more complete assessment of perfusion from “fast” 2D protocols, or alternatively, greater conﬁ dence from conventional 3D protocols in which follow-up scans are truly comparable, albeit with some vulnerability to motion artefact. Furthermore, if imaging is to be performed over a few weeks then it is appropriate to study tumours that are not growing too rapidly as comparison of data from different time points becomes very difﬁ cult.

Since contrast medium uptake into the tumour is driven by the plasma concentration, it is desirable that this is standardised, for example by use of a power injector. The analysis should also control

for variations in contrast medium pharmacokinetics (e.g. renal clearance) between patients or in the same patient before and after treatment. Thus it is critical to know the concentration of contrast medium that enters a tumour if the pharmacokinetic analysis is to be accurate and comparable between patients. This input function may be measured from the images.

For “fast” protocols a true arterial input function is required, but for “slow” protocols the venous concentration from, for example the sagittal sinus may provide an acceptable approximation. Alternatively a

“normal” tissue such as skeletal muscle, spleen or the choroid plexus may be employed as a control.

Assuming that contrast agent concentration is to be measured, a valid measurement of T₁ is essential.

Variable ﬂ ip angle spoiled gradient echo approaches are fast and provide good coverage in 3D but are very sensitive to RF inhomogeneity and should ideally be validated in every coil and every relevant spatial location in the scanners used in the study. Saturation- recovery and inversion-recovery sequences are more robust but very time-consuming in 3D.

In summary a number of methodologies have been developed. The more complex protocols, requir- ing more advanced hardware capabilities and more complex analysis algorithms, may provide additional insights into biology but may be less easy to compare between institutions. Thus for phase I trials, where one or two institutions participate, complex analytical techniques can be used. For multicentre trials less demanding technologies should be used but it is essential that adequate standardisation and quality control are performed between centres, perhaps by a specialist imaging contract research organisation, with analysis in a central reading centre, if comparable data are to be obtained.

16.3.4

Design of the DCE-MRI Analysis Protocol

For a DCE-MRI image processing and analysis (informatics) protocol, the steps include the defi nition of the region of interest (ROI) by a process of image segmentation; defi nition of the arterial or vascular input function, calculation of pre-contrast T₁, and calculation of K^trans or IAUC. If the analysis is performed voxelwise, the individual data points may be noisy, leading to fi tting errors in compartmental models;

however, if analysis is conﬁ ned to the total ROI then it is not possible to account for heterogeneity. There are several sources of variation in DCE-MRI of which two major sources of bias and subjectivity must be

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considered. Firstly, if the segmentation is performed on post-contrast images, the ROI may vary with drug effi cacy, which is clearly an undesirable situation. It may therefore be appropriate to defi ne ROIs on co- registered independent contrast images, such as T₂ weighted images, in conjunction with pre-and post- contrast T₁ weighted images. The ROIs should be drawn by a radiologist familiar with oncological MRI who should ideally be blinded to visit and dose. This blinding may be diffi cult to arrange in the context of a phase I trial where it is desirable to analyse each patient’s data in real time: one solution is to perform the measurement unblinded as the trial progresses and then a second blinded read by a different radiologist at the end of the study. If there are multiple lesions, for example multiple hepatic metastases, then either all lesions must be measured or alternatively, index lesion(s) must be identifi ed, with a procedure in place to ensure that the same lesion(s) are measured in follow-up scans.

A second potential source of bias is determination of arrival time of contrast agent in the arteries supplying the tumour: again this should be performed objectively by software or by a blinded radiologist.

16.3.5

Spatial Heterogeneity Within the Tumour

Tumour heterogeneity poses considerable difﬁ culty.

Tumours with large necrotic cores will show very different average values in K^trans in comparison with small well-vascularised tumours, even though values of K^trans in the angiogenic rim may be identi- cal. On the other hand if the core is excluded from the analysis there is a risk of biasing assessments of drug efﬁ cacy. Analysis of a single region of interest covering the entire tumour will obscure differences between rim and core. On the other hand voxelwise analyses may be time consuming, and, if a pharmacokinetic analysis is employed, noisy data may not ﬁ t to a pharmacokinetic curve from the compartmental model, particularly in the presence of motion artefact. Vascular targeting agents in particular have dramatically different effects in different regions of tumour (Fig. 16.1), producing deep reductions in gadolinium uptake in the core while leaving the rim hardly affected. The tools for the statistical analysis of the histograms of parameter values from such maps require further development.

Fig. 16.1. Map of measurement of IAUC measurement in a liver metastasis from colon cancer Magnetic resonance images were used to determine tumour IAUC before (left) and at 6 h, 24 h, and 18 days after (right) the patient was treated with the vascular targeting agent ZD6126. One representative slice from the 3D sets is shown. The areas of red and blue represent high and low IAUC in the tumour, respectively (Evelhoch et al. 2002)

Baseline

6 hours post ZD6126

24 hours post ZD6126

18 days post ZD6126

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Overall the heterogeneity of tumour biology within and between patients has largely not been taken into account when incorporating imaging strategies into early phase clinical trials. This contrasts with the standardisation of normal organ function that are entry criteria for most phase I trials, perhaps explain- ing why we are able to see dose–response effects with regard to toxicity but sometimes not biological or pharmacodynamic phenomena.

16.3.6

The Validity of the Measurement

It is critical to assess the validity of DCEMRI endpoints as biomarkers or surrogates for the desired parameter. Validation has several aspects which should be distinguished.

The strongest validation is validation against out- come (sometimes referred to as predictive validity,

an aspect of criterion validity). This is achieved if we can show, for example that tumours with high K^trans are always associated with a worse prognosis, and that interventions which reduce K^trans in particular patients also improve prognosis in a dose-dependent way in the same patients. If an endpoint has shown predictive validity in a number of large studies, it may be considered by regulatory authorities as a validated surrogate endpoint. A weaker, but still impor- tant, validation is validation against histopathology (sometimes referred to as content validity). This is achieved if we can show, for example, that tumours with high K^trans tend to have a high microvessel density (MVD). Histopathological validation is important if an endpoint is to be employed as a biomarker in Phase I/II. A number of studies have attempted to make these comparisons, and the data are summarised in Table 16.1. Although some studies have not conﬁ rmed that there is a statistical association between particular MRI parameters and the vascular

Table 16.1. Studies assessing validity of DCE-MRI with respect to vascular agents

Tumour MRI parameter p Value

MVD Buadu et al. 1996 Breast Gradient 0.001

Stomper et al. 1997 Breast Time interval 0.02

Hawighorst et al. 1997a,b, 1998 Cervix Amplitude K^trans

<0.001

<0.05

Buckley et al. 1997 Breast Time Interval 0.002

Hulka et al. 1997 Breast K^trans <0.01

Tynninen et al. 1999 Brain Time interval 0.01

Ikeda et al. 1999 Breast K^trans 0.01

Matsubayashi et al. 2000 Breast Time interval <0.001

VEGF Hawighorst 1997, 1998a,b Cervix Amplitude

K^trans

NS NS

Knopp et al. 1999 Breast K^trans 0.05

Matsubayashi et al. 2000 Breast Time interval 0.008

pO₂ Cooper et al. 2000 Cervix Amplitude

Gradient

Amplitude/gradient

0.001 0.071 NS

Metastasis Nagashima et al. 2002 Breast Time interval <0.05

Response Baba et al. 1997 Head and neck Amplitude

Barentsz et al. 1998 Bladder Amplitude <0.05

Hoskin et al. 1999 Head and neck Amplitude

Time interval

0.004 NS

Devries et al. 2001 Rectal Perfusion <0.001

Survival Hawighorst et al. 1999 Cervix K^trans/gradient <0.05

Studies are listed according to the clinical parameter that was compared with the MRI study. Content validity: MVD, microvas- cular density; VEGF, immunohistochemical assessment of amount of VEGF present; pO2, partial pressure of oxygen in tissue.

Predictive validity: metastasis, risk of developing metastasis; response, likelihood of responding to anti-cancer treatment; sur- vival, duration of survival from diagnosis. MRI parameters: gradient, maximum gradient of uptake of gadolinium; time interval, interval from injection to a deﬁ ned percentage of maximum gadolinium uptake; K^trans, transfer constant; amplitude, maximum amount of enhancement by contrast agent; perfusion, semi-quantitative assessment of blood ﬂ ow.

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a b c d or tumour measurement there are sufﬁ cient studies

reported to support the provisional incorporation of MRI into the early clinical evaluation of anti-angiogenic compounds. Nevertheless validation studies are to some extent confounded by the difﬁ culty incurred by comparing histological evaluation, measured at the micrometre level, with DCE-MRI measurements where resolution is measured in millimetres.

A third aspect of validation is the computer systems validation. ICH GCP (good clinical practice) demands that the computer system and software used for the analysis are validated. In this context “validation” refers to the documentation, audit trails and change control procedures used to provide assurance that the analysis is performed correctly on every scan without bugs or errors. Computer systems validation does not of course of itself indicate that the endpoint is appropriate to use.

16.3.7

The Reproducibility and Statistical Power of the Measurement

If there is reliance upon DCE-MRI as a biomarker then it is critical to establish how reproducible the measurement is. For example, if an experimental agent causes a 20% decline in a vascular parameter measured by MRI but the day-to-day variation in that parameter is 25% then there is unlikely to be sufﬁ cient statistical power to determine whether the drug is active. There are many sources of variation in the DCE-MRI experiment due to biological variation, random error, and systematic error. Figure 16.2 shows a hierarchy of sources of variation, from the basic design of the study and tumour type, through to the image informatics. Even this is an oversimpli- ﬁ cation as many of the terms interact. Figure 16.2 represents an enormous notional nested analysis of variance which, although never performed in practice, does provide a framework for thinking about reproducibility and variation. In practice, in any particular study, the components of variation which are of most interest are of course the effect of drug, together with the same-tumour same-analysis different-scan reproducibility (marked with an asterisk in Fig. 16.2). If possible, reproducibility studies for each patient should be built into any study that relies upon DCE-MRI as a biomarker, for example by performing two baseline studies before admin- istering the drug (Leach et al. 2003). It is however important to consider all the factors summarised in Fig. 16.2, both in order to minimise variability and

avoid confounds, and also when comparing values of reproducibility quoted from different studies and centres. The appendix shows a statistical treatment of reproducibility whereby we can calculate statistical power and thus whether a new therapeutic agent has an effect in a single individual or a group beyond the observed day-to-day variation in the technique in the absence of intervention.

Reproducibility studies have been performed for a number of tumours. Data show that these studies can most easily be performed in newly presenting supratentorial glioma, in which the tumour is con- tained in the rigid skull, while studies in the upper abdomen and thorax have been compromised, to some extent, by respiratory motion artefact. For instance we have compared three different analytical techniques in patients with glioma (Fig. 16.3).

These data show that the coefﬁ cients of variation for the gradient, time to 90% saturation and K^trans parameters were 17.9%, 7.1% and 7.7%. Using a two-way mixed effect model, the estimated percentage change needed in these parameters to be 95%

confi dent that a therapeutic effect was in excess of that produced by day-to-day variation, one would need to record 20.2%, 5.2% and 6.2% changes in the parameters, respectively (Jackson et al. 2003). As expected the reproducibility of these studies in the abdomen is lower than that in the brain. When liver metastases were studied over an 8-h period using a fi rst pass method to measure K^trans, the coeffi cient of variation was 11% and the percentage change required to prove drug activity in this setting was 15% (Jackson et al. 2002). Currently techniques are under development to measure vascular parameters through DCE-MRI in the thorax, the major hurdle being the movement associated with breathing and the heart beat.

16.4

Clinical Considerations in Study Design

As one of the principal uses of DCE-MRI has been to investigate changes in the vasculature it is appropriate to consider the results of imaging studies in the context of an anti-angiogenic agent.

Angiogenesis is the process of new blood vessel formation that is disordered in many pathophysio- logical conditions including cancer. In experimental models inhibition of angiogenesis has been associated with tumour growth restraint and even shrink- age while clinical studies of anti-angiogenic agents

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Fig. 16.2. A hierarchical approach to sources of variation in DCE- MRI in cancer. The most commonly considered component of reproducibility is marked with an asterisk

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Fig. 16.3. Reproducibility of different DCE-MRI vascular param- eters. Changes in measured parameters: a Tumour volume (vol).

b MITR, the maximum gradient of uptake of contrast agent. c T90, the time to 90% of maximum contrast agent-induced signal change. d K^trans over the 2-day study period. Symbols represent individual patients. [Reproduced with permission from Jackson et al. (2003)]

have shown some signs of anti-tumour activity. VEGF is one of the most potent angiogenic cytokines and therefore has been selected as the target for a number of anti-angiogenic drugs (Hasan and Jayson 2001).

Strategies for inhibiting this cytokine have involved antibodies against either the cytokine or signalling receptor or small molecule inhibitors of the tyrosine kinase domain of the receptor. K^trans (which depends on the permeability-endothelial surface area prod- uct) has been extensively investigated in the context of VEGF inhibitors, since VEGF controls vascular permeability and therefore measurements of K^trans should reﬂ ect the biological activity of VEGF. Poten- tially therefore, we can use K^trans (and other compartmental modelling parameters) as a biomarker in the evaluation of this class of compound.

One of us has completed a phase I evaluation of a humanised monoclonal antibody in which we moni- tored the distribution of the antibody by labelling the drug with ¹²⁴I and positron emission tomogra-

phy. These data were then related to the DCE-MRI derived measurements of K^trans. The data showed that there was heterogeneity at multiple levels in the tumours including baseline K^trans measurements, intra- and inter-tumoral reductions in K^trans after treatment, drug distribution and drug clearance, even when tumour deposits in the same patient were compared (Jayson et al. 2002). An important implica- tion of these data was that plasma pharmacokinetics were not representative of tumour pharmacokinetics (although they did compare favourably with normal organ clearance) and that imaging, to some extent, can overcome this problem by looking at the tumour directly.

These problems have manifested themselves in other clinical trials where imaging has been used to study vascular parameters (Galbraith et al.

2001, 2002; Herbst et al. 2002). At present the best interpretation of the data is that they show a threshold effect rather than a dose response effect.

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In other words trialists are reporting that as higher doses are given one eventually encounters a dose at which the majority of patients manifest the desired change in the DCE-MRI endpoint. Below that dose the majority of MRI studies do not show the antici- pated change. In the anti-VEGF antibody trial the lowest dose level, 0.3 mg/kg, was significantly infe- rior, in terms of its effect on K^trans, than the three higher doses (1, 3 and 10 mg/kg). However, there was no dose response relationship in the three higher dose levels. In the evaluation of combretastatin A4 phosphate, a vascular targeting agent, the initial report of the data suggested a threshold effect at a minimum of 52 mg/m² (Galbraith et al. 2001). Thus, to date, we have developed methodologies that identify the minimum effective dose rather than the optimum biologically effective dose.

Clearly if we are to use DCE-MRI to guide us in the development of anti-angiogenic and anti-vascular agents we need to modify clinical trial design so that the degree of heterogeneity is addressed.

This might then allow us to identify dose response curves and thus the optimum biologically active dose for further study.

16.5

Clinical Trial Design

Two possible approaches would address the impact of tumour heterogeneity on tumour imaging studies in phase I clinical trials. In addition to the classical cohort based phase I study we could recruit a further cohort of patients who are treated using an intra- patient dose escalation regimen. In this case we could focus on a particular tumour mass in a patient who is then treated with increasing doses of a drug. Imaging studies would be performed at each dose level and the response compared. This approach would control for the biological and functional differences discussed above, although it would be sensitive to a drug that signiﬁ cantly altered tumour behaviour. Evidence in favour of such an approach could be taken from our most recent investigation of the anti-VEGF antibody (Jayson et al. 2002), in which a more profound reduc- tion in K^trans (Fig. 16.4) was recorded when the antibody concentration was higher rather than when the concentration was lower, suggesting that within each patient there was a concentration–response effect.

Whether this would translate into a dose–response

Fig. 16.4. Colour enhanced DCE-MRI measurement of K^trans in a liver metastasis from colon cancer. Magnetic resonance images were used to determine tumour K^trans before (left) and after (right) patients were treated with the humanised anti-vascular endothelial cell growth factor (VEGF) antibody HuMV833. Representative images are shown: Left, the maps showing the K^trans of a metastasis (yellow arrow) in the left lobe of the liver in a patient before receiving HuMV833 (1 mg/kg). Right, the maps showing the K^trans of the same metastasis from the same patients 48 h after receiving HuMV833. The areas of green and blue represent high and low K^trans, respectively. Red and yellow pixels represent artifactually high measurements in the hepatic vein.

[Reproduced with permission from Jayson et al. (2002)]

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effect using the intra-patient dose escalation strategy is unknown.

A second approach to dealing with tumour heterogeneity in imaging studies is the inclusion of larger more precisely defined entry criteria in the hope that this would equalise the biology of the tumours included at baseline. Evidence in favour of this was presented in a recent study (Morgan et al. 2003) in which 26 of 56 patients were selected from two phase I studies of a VEGF receptor tyrosine kinase inhibitor. These patients had colorec- tal cancer metastasis in the liver and, by measur- ing K^trans in these patients, the authors reported a dose–response effect. Whether the selection criteria for this retrospective selection were appropriate will be confirmed in prospective trials ongoing at the moment. However, to date, this is the only trial where a dose–response relationship has been observed.

16.6

DCE-MRI in Comparison with Other Imaging Modalities

From the drug development perspective, DCE-MRI is just one of many ways to provide a biomarker; a signal that provides evidence of an acute, maybe pharmacodynamic, response to the investigational agent. Bio- markers might be sought in principle in bioﬂ uids (e.g. blood, urine), in accessible non-tumour tissue (e.g. skin), from immunohistochemistry of tumour biopsies or from imaging. Imaging has the advantage that pharmacological responses in the tumour itself can be evaluated and perhaps compared with responses in non-target tissue. Unlike biopsy, imaging measurements can be repeated at the same site, although in the case of DCE-MRI normally it would be necessary to wait at least a few hours between examinations for the previous contrast medium dose to be eliminated. Biomarkers have been sought from four major imaging modalities radioisotope imaging (PET, SPECT, scintigraphy), contrast-enhanced ultrasound, contrast-enhanced CT, and magnetic resonance. MR approaches include spectroscopy (MRS)

“conventional” (non-contrast) MRI, arterial spin tagging MRI, as well as contrast-enhanced MRI.

In addition to existing technologies new PET probes are under development and again co-registration methodologies will allow us to relate DCE-MRI parameters to other tumour measurements, princi- pally through PET. These include measurements of

tumour metabolism (¹⁸FDG) (Herbst et al. 2002), tumour DNA synthesis (¹²⁴IUDR) (Blasberg et al.

2000) or tumour apoptosis (annexin V) (Hofstra et al. 2000). The next critical step is to work out how to relate, or co-register, the images so that for instance one could determine the extent to which drug distribution (positron emitting isotope labelled drug) (Jayson et al. 2002) inﬂ uences changes in vascular parameter (DCE-MRI) and vice versa. Certainly the software now exists to co-register these studies and data will emerge shortly.

Although ultrasound methods have been developed for the assessment of vascularity these studies have been hampered by the operator dependency of the study and the difﬁ culty in obtaining images when ﬂ ow rates are very low, which may obscure determination of drug effects.

The incorporation of CT scanners into PET scanners has led to an increased use of dynamic CT in the evaluation of anti-angiogenic and anti-vascular compounds. Early data suggest that the measurements are reproducible and clearly this technology is widely available. A further advantage is that the relationship between signal and contrast agent concentration is linear, unlike MRI. The problem is that early clinical studies of anti-angiogenic compounds require multiple imaging assessments of the tumour. As contrast enhanced dynamic CT necessarily utilises ionising radiation there will be a limit on the number of studies that can be performed in any one patient.

Conventional (non-contrast) MRI can provide measurements of parameters such as mobile proton density (PD), the relaxation times T₁, T₂ or T₂* and the apparent self-diffusion coeffi cient of tissue water (ADC). These parameters have shown interesting responses to intervention in animal and human tumour studies. However, interpretation is diffi cult as they depend in a complex way on tumour oedema, necrosis, interstitial space and deoxyhaemoglobin, and for this reason they are probably insuffi ciently evaluated to use as a primary endpoint in a clini- cal trial. However, parameters such as T₂* and ADC may be useful as secondary endpoints and it may be feasible to measure one or more of these parameters during the precontrast part of a DCE-MRI protocol.

An alternative MRI technique, arterial spin labelling, measures blood ﬂ ow into the tumour. Arte- rial spin labelling is an attractive approach since it does not require the administration of any contrast medium and can be repeated rapidly, since no contrast medium needs to be eliminated. It relies on the inversion of magnetisation in the arteries supplying the tumour. In prospective trials, where patients may

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have tumour in many different anatomic locations it might be difﬁ cult to establish a protocol to provide adequate arterial spin tagging measurements of tumour blood blow in all subjects and perhaps for that reason it has been little used to date.

16.7 Summary

DCE-MRI has been used in the early clinical trial evaluation of a number of anti-angiogenic and anti- vascular compounds. Data have shown that the techniques used are reproducible and have some validity.

Clinical trial results have shown that this methodology can be used to identify the minimum effective dose, but to date there is only limited information to show that this methodology can identify the optimum biologically active dose. A possible explanation for this is that tumour heterogeneity obscures any dose–response relationship and we have suggested two clinical trial design strategies that could be used to circumvent these problems. In the absence of any changes in DCE-MRI one would be reluctant to develop an anti-vascular or anti-angiogenic drug further. Yet the ultimate arbiter for drug development and registration is survival advantage in phase III trials. Thus at present DCE-MRI provides useful information in early clinical trial drug development.

Whether this biomarker information can be incorpo- rated into late drug development strategies remains to be proven.

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