Concluding remarks
Despite the multiple biomarker candidates for PD and the ongoing
intensive research efforts, there is no single definitive biomarker with
sufficient accuracy or reproducibility that could be used in clinical
practice to diagnose PD, predict the onset of the disease or indicate
response to therapeutic interventions in clinical trials [64]. The
use of a combination of biomarkers, however, could detect multiple
pathological aspects of the disease and result in improved diagnostic
accuracy. Background risk factors (genetic, demographic and
environmental), combined with typical prodromal symptoms and different
biochemical and imaging biomarkers can be used in tandem to improve the
predictive diagnosis of PD [9, 58]. AI-algorithms may be of great
value to define the diagnosis early on if these markers can be properly
validated [11, 65, 66].
More accurate disease subtyping would contribute to the development of
translational disease models and design of successful clinical trials
with stratified inclusion criteria. The failed attempts to find
neuroprotective strategies for PD may stem from the reductionist
approach in the conducted clinical trials which have paid little
attention to the variability of the disease at the individual level
[67]. Development of digital biomarkers may help to address many of
the current diagnostic shortcomings in an economical fashion. They would
allow an objective approach to continuously track fluctuations in motor
and non-motor symptoms during patients’ daily life. The resulting rich
real-world datasets may prove to be highly predictive in assessing
clinical improvement in PD studies and permit personalized therapeutic
adaptations [68].
Ideally, preclinical drug development would already be accompanied with
a reliable and accessible biomarker that could be followed through the
whole process from animal models to clinical trials and regular patient
monitoring. This kind of biomarker would help to predict the effects of
an intervention in a patient population based on preclinical tests in
animal models, and thus, increase the likelihood of successful clinical
translation.
Figure: Summary of biomarker candidates for Parkinson’s disease
and matrices from which they are being analyzed. Aβ, beta-amyloid;
a-syn, alpha-synuclein; GFAP, glial fibrillary acidic protein; ER,
endoplasmic reticulum; NTFs, neurotrophic factors; miRNA, microRNA; MRI,
magnetic resonance imaging; PET, positron emission tomography; SPECT,
single-photon emission computed tomography. Figure created with
BioRender.com.
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