8 Lessons Learned: Resources

Optimizing Preclinical Imaging. In order to study the nature or diseases, such as those affecting the central nervous system, medical practitioners and scientists often use preclinical models and modalities to assist in their diagnosis, frankly, medical practitioners follow certain guidelines to ensure that the imaging recorded from preclinical models can provide a clear and interpretable data across all fields and assist drug manufacturers a better framework to work on how they can conduct their clinical trials and develop their drugs for a certain disease. Prior to engaging in a study, connect preclinical imaging specialists if possible with specialists from elsewhere within the field (preclinical translation and clinical development) to evaluate the translatability of all the components of the model. The relevance of a certain particular disease model and the nature of the disease being studied must always be taken into account because without a clear model to serve as a reference, one will not be able to understand one aspect of the disease being studied.
The Art of Mastering Development
Scaling from rodents up to humans may not be straightforward but the translational aspects of certain parameters-such as metabolites drawn from a magnetic resonance spectroscopy- should be well defined.
The Art of Mastering Development
Modeling paradigms must be examined in junction with the timing and the results of the relevant studies that is related to the imaging endpoints. Always seek for data from imaging groups to determine whether there is a gap within the modeling paradigm and see if the parameters used in the study can be examined by the imaging group or those doing the study. Data from the specific scanners and the representative data and analysis by the team that collects the data using the scanners is critical, also, not that performing the same study with a different scanner can have an adverse change on the interpretation of the outcomes of the study. When studying data, parties must use “test-retest” measures to create the study’s estimates and determine possible variations to the subject, also, the selected animal model and imaging methodologies applied in the study and the type of data analysis selected may influence the overall results of the study thus the necessity of doing multiple retests to get accurate results. Such measures would work wonders for those studying the changes in drug treatments. Studying the possibilities and the limitations of the disease model comes next to proper imagery because this would help the study determine the scope of the study and when a subject should be replaced. These terms can be quite confusing, but they can work hand-in-hand in studies, however, it is important to use the opportunity provided by imaging time per subject to get all the right information and retain high throughput. Of course, do not forget to discuss with experts the methodologies possible for the study to retain the expectations for the study and make it easier to interpret the data coming from imaging studies.