Biomarkers in Parkinson's disease: From pathophysiology to early diagnosis

Chronic nigral neuromodulation aggravates behavioral deficits and synaptic changes in an α-synuclein based rat model for Parkinson's disease

Aggregation of alpha-synuclein (α-SYN) is the pathological hallmark of several diseases named synucleinopathies, including Parkinson's disease (PD), which is the most common neurodegenerative motor disorder. Alpha-SYN has been linked to synaptic function both in physiological and pathological conditions. However, the exact link between neuronal activity, α-SYN toxicity and disease progression in PD is not clear. In this study, we aimed to investigate the effect of chronic neuromodulation in an α-SYN-based rat model for PD using chemogenetics. To do this, we expressed excitatory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) combined with mutant A53T α-SYN, using two different recombinant adeno-associated viral (rAAV) vectors (serotypes 2/7 and 2/8) in rat substantia nigra (SN) and investigated the effect on motor behavior, synapses and neuropathology. We found that chronic neuromodulation aggravates motor deficits induced by α-SYN, without altering dopaminergic neurodegeneration. In addition, neuronal activation led to changes in post-translational modification and subcellular localization of α-SYN, linking neuronal activity to the pathophysiological role of α-SYN in PD.

Regulatory-accepted drug development tools are needed to accelerate innovative CNS disease treatments

Central Nervous System (CNS) diseases represent one of the most challenging therapeutic areas for successful drug approvals. Developing quantitative biomarkers as Drug Development Tools (DDTs) can catalyze the path to innovative treatments, and improve the chances of drug approvals. Drug development and healthcare management requires sensitive, reliable, validated, and regulatory accepted biomarkers and endpoints. This review highlights the regulatory paths and considerations for developing DDTs required to advance biomarker and endpoint use in clinical development (e.g., consensus CDISC [Clinical Data Interchange Standards Consortium] data standards, precompetitive sharing of anonymized patient-level data, and continual alignment with regulators). Summarized is the current landscape of biomarkers in a range of CNS diseases including Alzheimer disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorders, Depression, Huntington's disease, Multiple Sclerosis and Traumatic Brain Injury. Advancing DDTs for these devastating diseases that are both validated and qualified will require an integrated, cross-consortium approach to accelerate the delivery of innovative CNS therapeutics.

Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the PR EDIGT score

Fifty-five years after the concept of dopamine replacement therapy was introduced, Parkinson disease (PD) remains an incurable neurological disorder. To date, no disease-modifying therapeutic has been approved. The inability to predict PD incidence risk in healthy adults is seen as a limitation in drug development, because by the time of clinical diagnosis ≥ 60% of dopamine neurons have been lost. We have designed an incidence prediction model founded on the concept that the pathogenesis of PD is similar to that of many disorders observed in ageing humans, i.e. a complex, multifactorial disease. Our model considers five factors to determine cumulative incidence rates for PD in healthy adults: (i) DNA variants that alter susceptibility (D), e.g. carrying a LRRK2 or GBA risk allele; (ii) Exposure history to select environmental factors including xenobiotics (E); (iii) Gene-environment interactions that initiate pathological tissue responses (I), e.g. a rise in ROS levels, misprocessing of amyloidogenic proteins (foremost, α-synuclein) and dysregulated inflammation; (iv) sex (or gender; G); and importantly, (v) time (T) encompassing ageing-related changes, latency of illness and propagation of disease. We propose that cumulative incidence rates for PD (PR ) can be calculated in healthy adults, using the formula: PR (%) = (E + D + I) × G × T. Here, we demonstrate six case scenarios leading to young-onset parkinsonism (n = 3) and late-onset PD (n = 3). Further development and validation of this prediction model and its scoring system promise to improve subject recruitment in future intervention trials. Such efforts will be aimed at disease prevention through targeted selection of healthy individuals with a higher prediction score for developing PD in the future and at disease modification in subjects that already manifest prodromal signs.