The various imaging modalities have undergone tremendous development over the last two decades; yet significant advances are required to fully exploit their potential for biomedical applications in general and DDD in particular.
Imaging technologies: Diagnostic applications require efficient 3-dimensional coverage of the whole body or selected body regions (e.g., whole brain imaging). For example, whole-body FDG scans allow the detection of the metastatic burden in tumor patients. Similarly, in order to analyze brain connectivity the whole brain has to be scanned with high temporal resolution to enable correlation of signal response in various brain regions and to analyze how drugs might interfere with this network.
Diagnostic specificity and sensitivity can be improved by characterizing a given state using multiple parameters, i.e., by measuring a fingerprint rather than changes in a single parameter. For example, it has been shown that combining various MRI readouts such as water diffusion properties, tissue perfusion status, and microscopic changes as reflected by altered relaxation rates to characterize brain tissue in stroke patients allows predicting the outcome for this patient. Such prognostic data, once validated, could potentially be used to evaluate the efficacy of therapeutic interventions. Multivariate tissue analysis requires the development of hybrid imaging technologies, which, for example, combine structural and physiological or metabolic information (CT/PET, MRI/PET). Combined techniques can also be used to improve the reconstruction of physiological data by using prior structural information. Multimodality imaging strategies combining imaging data at various time and length scales would be crucial in providing information for elucidating the mechanism underlying the signal changes detected macroscopically.
Also the efficiency of imaging procedures should be improved to cope with the increasing number of potential targets that are investigated. For structural phenotyping the time required to acquire a dataset is the rate-limiting step. Hence the application of fast imaging protocols will have a major impact on the throughput achievable. Parallelization of data acquisition enabling the collection of data sets from several animals simultaneously is a strategy that has been recently introduced.
Quantification: A quantitative comparison of efficacy readouts for various drug candidates and placebo groups are essential in DDD. This requires the derivation of quantitative information from imaging data set based on morphometric and densitometric analyses (Section 7.2.1). While such tools are largely available, there is need for further developments. The biomedical researcher is not interested in expressing treatment efficacy in terms of changes in method-specific imaging parameters such as relaxation time, fluorescence intensity, or local activity measures. Results should be provided in terms of physiological parameters such as tissue perfusion, oxygenation levels, or tracer concentrations. Frequently, biological processes are regulated in a discrete manner; a threshold parameter has to be maintained in order to ascertain proper cell function. For example, there is a minimal CBF level to ensure cell survival and there is another higher CBF threshold to allow proper cell function (firing). Hence, expressing perfusion in relation to values in the nonaffected tissue is less relevant in predicting outcome than would be absolute values in relation to these thresholds. Derivation of absolute values of biological parameters from imaging data sets requires a detailed understanding of the underlying biochemical and biophysical processes and the development of sophisticated tissue models.
Target-specific probes: A critical aspect for exploiting the power of molecular imaging applications in DDD is the availability of target-specific imaging probes. Extrapolating past experience with developing generic imaging probes, diagnostics industry seems to hesitate in embarking into molecular imaging agents. Specificity of a potential probe will limit its potential market volume. In this context, pharmaceutical industry will have to play a pioneering role in order to have the agents available to support the DDD programs, following the theranostics concept.
Was this article helpful?