Open studies

A randomized, parallel group, multicentre, multinational, prospective, open-label exploratory study to evaluate the add-on effect of opicapone 50 mg or levodopa 100 mg as first strategy for the treatment of wearing-off in patients with Parkinson’s Disease.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder characterized by motor and non-motor symptoms with progressive loss of autonomy that negatively impacts quality of life (QoL). The drug levodopa (L-DOPA) has been the gold standard for treatment of PD patients for more than 4 decades. The clinical benefit of L-DOPA progressively reduces, causing the return or a worsening of PD symptoms, a phenomenon called “wearing-off”. Over time, patients experience more and more hours per day in a disabling OFF-state. Patients may benefit from optimised L-DOPA therapy in conjunction with other drugs such as entacapone and tolcapone. Several randomised controlled trials have been conducted with opicapone administered as an adjunctive L-DOPA therapy. Treatment with opicapone was found to be effective in reducing the time patients are in an OFF-state and was well tolerated. As opposed to entacapone and tolcapone, opicapone does not require close laboratory monitoring or multiple oral administrations. Direct comparisons between increased doses of L-DOPA and the adding in of opicapone to treat wearing off have not been performed in previous studies. The goal of this study is to compare the add-on efficacy between opicapone 50 mg and an extra dose of levodopa 100 mg as first strategy for the treatment of wearing-off in subjects with PD.

To identify genetic variants that predispose to or cause Parkinson’s disease or Parkinsonism.  To identify families that may be prepared to participate in translational research related to PD, its aetiology, biology, progression and future treatment.

The registers will offer people with Parkinson’s Disease in Devon and Cornwall the opportunity to participate in future research projects. Patients will be provided with information about the register and asked to provide informed consent to keep their details on a bespoke database and to contact their doctors when necessary to clarify any clinical details and to confirm the diagnosis. Register will be kept up to date on an annual basis using a short questionnaire asking about any change in circumstances. The register will not be research itself, but form a resource whereby investigators in separate research projects with ethical approval will contact DeNDRoN management when looking for participation in such research in Parkinson’s Disease. The register will be overseen by a disease register steering committee. The register will be used to identify people who will then be contacted about possible inclusion in clinical studies.

For more information please contact the Clinical Research Nurse on

Full title: A Randomised Placebo-Controlled Trial of Escitalopram and Nortriptyline with Standard Psychological Care for Depression in Parkinson’s Disease

Summary: Parkinson’s disease (PD) is a progressive neurological disorder that leads to increasing disability and functional decline. Currently, no medications have been shown to halt or delay disease progression. One of the most common complications in patients with this diagnosis is depression, which affects approximately 40% of patients with PD. It is linked to functional impairment, cognitive decline and faster disease progression and is the main determinant of poor quality of life in PD. Psychological therapies are used to treat depressive symptoms (via standard access to appropriate psychological services in the NHS), but often antidepressant medications are required. Despite the high incidence of depression in this population, no conclusive evidence on appropriate choice of antidepressants in PD exists in the NHS; the risk of worsening of parkinsonism and aggravation of non-motor features of PD by antidepressants pose particular challenges in this population.

The aim of the trial is to test the clinical effectiveness and cost-effectiveness of two different types of antidepressants: escitalopram ( a and nortriptyline treatment for depression in PD in addition to standard psychological care. Patients will be randomly assigned to one of the two drugs or a placebo and followed up for one year.

For more information please contact the Clinical Research Nurse on

Full title: CHIEF-PD (CHolinesterase Inhibitor to prEvent Falls in Parkinson’s Disease): A phase 3 randomised double-blind placebo-controlled trial of rivastigmine to prevent falls in Parkinson’s disease

Summary: Parkinson’s disease is a common condition particularly affecting older people. Falls are a very frequent complication of the disease affecting 60% of people with Parkinson’s every year. As the population ages, the number of people living with Parkinson’s disease and the occurrence of complications will increase. The loss of the chemical dopamine in the brain causes walking in Parkinson’s to become slower, unsteady and irregular. People with the condition are therefore at a very high risk of falling. To some extent, people can compensate for these changes by paying more attention to their walking. However, Parkinson’s also diminishes memory and thinking ability. This decreases people’s ability to pay attention to their walking, especially when doing something at the same time.

Cholinesterase inhibitor (ChEis) are drugs that are currently used to treat people with memory problems in Parkinson’s. The effect of these drugs on falls in Parkinson’s has been tested to show that treatment has the potential to almost halve the number of falls.

This trial aims to definitively determine whether cholinesterase inhibitors (ChEi), can prevent falls in Parkinson’s and whether this treatment is cost effective. 600 participants with Parkinson’s disease will be enrolled from hospitals throughout the UK. Participants will be randomly assigned to either receive the drug (ChEi) via a patch or receive a placebo (dummy) treatment via a patch.

Neither the researchers nor the participants will know which group they are in. Participants will take the medication for 12 months and record any falls that they experience in diaries. If successful, this treatment in Parkinson’s disease, it would tackle one of the most disabling complications of the disease and positive findings will provide robust evidence to change clinical practice.

For more information please contact the Clinical Research Nurse on

A nested sub-study within the multicentre, phase III, RCT of the ChEi rivastigmine versus placebo to prevent falls in PD (CPMS 40906).
To determine the effect of cholinesterase inhibitor or placebo treatment on those caring for people with Parkinson’s disease who are enrolled in the phase III CHIEF-PD study.
For purposes of this trial a carer is defined as an individual who undertakes informal or formal care responsibility for the participant.

For more information please contact the Clinical Research Nurse on

Full title: Remote assessment of Parkinsonism supporting ongoing development of interventions in Gaucher’s disease

Summary: This project aims to remotely detect the early signs and symptoms of Parkinson’s disease (PD) in a group of patients who carry a gene (glucocerebrosidase-GBA) which causes a genetic predisposition to it. We aim to detect these signs and symptoms prior to the clinically diagnostic onset of the movement related (motor) symptoms of the disease. We will assess for signs and symptoms including anxiety, depression, bradykinesia (slowness of movement), cognitive decline, reduced sense of smell, abnormal sleep behaviours, constipation and erectile dysfunction associated with the very early stages of PD.

Participants will be required to undertake a questionnaire and a number of interactive assessments, which are designed to identify risk factors and early clinical signs of Parkinson’s. Participants will be genotyped in order to establish genetic factors which determine those who do and do not develop PD.

The answers to this questionnaire/assessments/genotyping will be stored in a secure internet database and will generate a risk score. Blood, urine and cerebrospinal fluid will be analysed to look for biological markers that predict the onset of PD. Under most circumstances the study will be undertaken entirely at the participant’s home, either through an internet portal or with the use of postal assessments. We intend that this study will enable us to understand and describe the early stages of PD in this patient group and provide insights into the genetic risk factors that contribute to it.

We are currently carrying out a research study to develop a computer program that can automatically identify abnormalities on MRI scans of the brain. We are recruiting patients who are booked for a head MRI scan at the hospital. Participation doesn’t require any additional tests or scans, nor any additional visits. If you are interested in taking part and finding out more, please contact or ask a member of staff when you come in for your appointment.

‘Unexpected abnormalities with potential clinical relevance (incidental findings) occur in 2.7% of head MRI studies. There is a wide variation in how incidental findings (IFs) discovered in ‘healthy volunteers’ are managed. Routine reporting of ‘healthy volunteers’ by a radiologist is a challenging logistic and financial burden and in a survey of UK institutions performing research imaging, just 14% of institutions had this as policy. It would be valuable to devise automated strategies to ensure that IFs could be reliably and accurately identified which potentially would remove 90% of scans requiring routine radiological review, thereby increasing the feasibility of implementing a routine reporting strategy. Deep learning is a new technique in computer science that automatically learns hierarchies of relevant features directly from the raw inputs (e.g. MRI) using multilayered neural networks. Previous studies have demonstrated the potential of deep learning methods in the basic interpretation of neuroradiological studies including MRI scans.

We will train a deep learning algorithm on a large database of head MRI studies to recognise studies with abnormalities. The Centre for Neuroimaging Sciences (King’s College London) has acquired and stored more than 20,000 head MRI studies which are available for research purposes. We will train a deep learning algorithm that classifies a subset of these studies as normal or abnormal. We will then test the technique on an independent subset to determine its validity.

Further testing of the algorithm may also be performed (retrospective and prospective) on de-identified clinical King’s Health Partners (KHP) datasets (we will collect > 30,000 MRI head scans); and prospective Centre for Neuroimaging Sciences (King’s College London) datasets.

Convolutional neural networks can provide an appropriate architecture to accurately and reliably categorise head MRI studies into those containing IFs (unexpected abnormalities with potential clinical relevance) and those that do not contain IFs.


Studies in follow up