Identifying drugs with disease‐modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology

2020 ◽  
Vol 29 (8) ◽  
pp. 864-872
Author(s):  
Laura C. Maclagan ◽  
Naomi P. Visanji ◽  
Yi Cheng ◽  
Mina Tadrous ◽  
Alix M. B. Lacoste ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Modifying

scholarly journals WHAT’S OLD IS NEW: USING ARTIFICIAL INTELLIGENCE TO ACCELERATE DISCOVERY OF NEW TREATMENTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S16-S17
Author(s):  
Connie Marras ◽  
Laura C Maclagan ◽  
Yi Cheng ◽  
Naomi Visanji ◽  
Mina Tadrous ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Conditional Logistic Regression ◽  
Disease Diagnosis ◽  
Success Rates ◽  
Disease Modifying Drugs ◽  
Logistic Regression Models ◽  
Intelligence System ◽  
Disease Modifying

Abstract Given the high cost of drug development and low success rates, repurposing drugs already proven safe provides a promising avenue for identifying effective therapies with additional indications. The IBM Watson artificial intelligence system was used to search 1.3 million Medline abstracts to prioritize medications that may be potentially disease-modifying in Parkinson’s disease. We assessed patterns of use of the top 50 Watson-ranked drugs among 14,866 adults with Parkinson’s disease aged 70 and older who were matched to persons without Parkinson’s disease on age, sex, and comorbidity. Sociodemographic characteristics, chronic conditions, and use of other medications were compared using standardized differences. Patterns of potentially disease-modifying drug use were examined prior to and following ascertainment of Parkinson’s disease. Preliminary findings from multivariable conditional logistic regression models on the association between previous exposure to potentially disease-modifying drugs and Parkinson’s disease diagnosis will be presented.

Using artificial intelligence to identify anti‐hypertensives as possible disease modifying agents in Parkinson's disease

2020 ◽  
Vol 30 (2) ◽  
pp. 201-209
Author(s):  
Naomi P. Visanji ◽  
Piyush Madan ◽  
Alix M. B. Lacoste ◽  
Italo Buleje ◽  
Yanyan Han ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Modifying ◽  
Disease Modifying Agents

scholarly journals Exenatide once weekly over 2 years as a potential disease-modifying treatment for Parkinson’s disease: protocol for a multicentre, randomised, double blind, parallel group, placebo controlled, phase 3 trial: The ‘Exenatide-PD3’ study

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e047993
Author(s):  
Nirosen Vijiaratnam ◽  
Christine Girges ◽  
Grace Auld ◽  
Marisa Chau ◽  
Kate Maclagan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Primary Outcome ◽  
Phase 3 ◽  
Double Blind ◽  
Treatment Groups ◽  
Link Type ◽  
South Central ◽  
Disease Modifying ◽  
Secondary Outcomes

IntroductionParkinson’s disease (PD) is a common neurodegenerative disorder with substantial morbidity. No disease-modifying treatments currently exist. The glucagon like peptide-1 receptor agonist exenatide has been associated in single-centre studies with reduced motor deterioration over 1 year. The aim of this multicentre UK trial is to confirm whether these previous positive results are maintained in a larger number of participants over 2 years and if effects accumulate with prolonged drug exposure.Methods and analysisThis is a phase 3, multicentre, double-blind, randomised, placebo-controlled trial of exenatide at a dose of 2 mg weekly in 200 participants with mild to moderate PD. Treatment duration is 96 weeks. Randomisation is 1:1, drug to placebo. Assessments are performed at baseline, week 12, 24, 36, 48, 60, 72, 84 and 96 weeks.The primary outcome is the comparison of Movement Disorders Society Unified Parkinson’s Disease Rating Scale part 3 motor subscore in the practically defined OFF medication state at 96 weeks between participants according to treatment allocation. Secondary outcomes will compare the change between groups among other motor, non-motor and cognitive scores. The primary outcome will be reported using descriptive statistics and comparisons between treatment groups using a mixed model, adjusting for baseline scores. Secondary outcomes will be summarised between treatment groups using summary statistics and appropriate statistical tests to assess for significant differences.Ethics and disseminationThis trial has been approved by the South Central-Berkshire Research Ethics Committee and the Health Research Authority. Results will be disseminated in peer-reviewed journals, presented at scientific meetings and to patients in lay-summary format.Trial registration numbersNCT04232969, ISRCTN14552789.

scholarly journals Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

2021 ◽  
pp. 1-6
Author(s):  
Matt Landers ◽  
Suchi Saria ◽  
Alberto J. Espay
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Precision Medicine ◽  
Movement Disorders ◽  
Living Environment ◽  
Patient Status ◽  
Patient Symptom ◽  
Molecular Therapies ◽  
Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

scholarly journals A Preclinical Systematic Review of Ginsenoside-Rg1 in Experimental Parkinson’s Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Liang Song ◽  
Meng-Bei Xu ◽  
Xiao-Li Zhou ◽  
Dao-pei Zhang ◽  
Shu-ling Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Trials ◽  
Quality Score ◽  
Control Group ◽  
Rotarod Test ◽  
Neuroprotective Effects ◽  
Disease Modifying ◽  
Antioxidant Stress ◽  
Th Mrna

To date, no drug has been proven to be neuroprotective or disease-modifying for Parkinson’s disease (PD) in clinical trials. Here, we aimed to assess preclinical evidence of Ginsenosides-Rg1 (G-Rg1), a potential neuroprotectant, for experimental PD and its possible mechanisms. Eligible studies were identified by searching six electronic databases from their inception to August 2016. Twenty-five eligible studies involving 516 animals were identified. The quality score of these studies ranged from 3 to 7. Compared with the control group, two out of the 12 studies of MPTP-induced PD showed significant effects of G-Rg1 for improving the rotarod test (P<0.01), two studies for improving the swim-score values (P<0.01), six studies for improving the level of TH protein expression (P<0.01), and two studies for increasing the expression of TH mRNA in the substantia nigra of mice (P<0.01). The studies reported that G-Rg1 exerted potential neuroprotective effects on PD model through different mechanisms as antineuroinflammatory activities (n=10), antioxidant stress (n=3), and antiapoptosis (n=11). In conclusion, G-Rg1 exerted potential neuroprotective functions against PD largely by antineuroinflammatory, antioxidative, and antiapoptotic effects. G-Rg1 as a promising neuroprotectant for PD needs further confirmation by clinical trials.

scholarly journals Current disease modifying approaches to treat Parkinson’s disease

2015 ◽  
Vol 73 (7) ◽  
pp. 1365-1379 ◽  
Author(s):  
Dan Lindholm ◽  
Johanna Mäkelä ◽  
Valentina Di Liberto ◽  
Giuseppa Mudò ◽  
Natale Belluardo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Modifying

scholarly journals Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease

2020 ◽  
Author(s):  
Daphna Laifenfeld ◽  
Chen Yanover ◽  
Michal Ozery-Flato ◽  
Oded Shaham ◽  
Michal Rozen-Zvi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Trials ◽  
Real World ◽  
Late Onset ◽  
Real World Data ◽  
World Data ◽  
Healthcare Data ◽  
Beneficial Effects ◽  
Disease Modifying

AbstractReal-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21stCentury Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N=88,867) and IBM MarketScan Research Databases (N=106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, for common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Sign in / Sign up

Export Citation Format

Share Document