Use of functional imaging across clinical phases in CNS drug development

Hogg

Experimental Dermatology

et al. 2017

Section: Assessment Of Treatment Strategies Using Fmrimentioning

confidence: 99%

Functional magnetic resonance imaging in dermatology: The skin, the brain and the invisible

Hogg

Experimental Dermatology

et al. 2017

“…As highlighted by Borsook et al (2013), misclassification of study subjects (i.e. incorrect diagnosis, a mild form of the disease under investigation, challenging comorbities) is a major cause of failure in CNS trials.…”

Section: Who Should I Recruit?-personalised Medicine Approachesmentioning

confidence: 99%

“…Magnetic resonance imaging (MRI)-based techniques hold great potential offering high spatial and temporal resolution in a non-invasive manner and obviating the need for radiolabeled ligands for the specific target of interest. However, several operational and analytical issues need to be considered and clarified before realising the potential of neuroimaging for drug development (Borsook et al 2010;Schwarz et al 2011a, b;Wise and Preston 2010).…”

Section: Introductionmentioning

confidence: 99%

The role of machine learning in neuroimaging for drug discovery and development

2015

Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.

“…In early-Phase clinical studies, fMRI methods can provide a means to detect a functional CNS effect of pharmacological treatment in brain regions appropriate to the mechanism and/or target population of the compound [9,28–30]. Although it is not technically a marker of target engagement (i.e., of pharmacological agent binding to a target site), an fMRI signal can provide indirect evidence of target engagement if a biologically plausible link can be established between the fMRI response and the molecular target [31–33].…”

Section: Introduction and Scopementioning

confidence: 99%

The role of fMRI in drug development

Carmichael

Schwarz

Chatham

et al. 2018

Drug Discovery Today

Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.