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

Using artificial intelligence to identify drugs for repurposing to treat l-DOPA-induced dyskinesia

Repurposing regulatory agency-approved molecules, with proven safety in humans, is an attractive option for developing new treatments for disease. We identified and assessed the efficacy of 3 drugs predicted by an in silico screen as having the potential to treat l-DOPA-induced dyskinesia (LID) in Parkinson's disease. We analysed ∼1.3 million Medline abstracts using natural language processing and ranked 3539 existing drugs based on predicted ability to reduce LID. 3 drugs from the top 5% of the 3539 candidates; lorcaserin, acamprosate and ganaxolone, were prioritized for preclinical testing based on i) having a novel mechanism of action, ii) having not been previously validated for the treatment of LID, iii) being blood-brain-barrier penetrant and orally bioavailable and iv) being clinical trial ready. We assessed the efficacy of acamprosate, ganaxolone and lorcaserin in a rodent model of l-DOPA-induced hyperactivity, with lorcaserin affording a 58% reduction in rotational asymmetry (P < 0.05) compared to vehicle. Acamprosate and ganaxolone failed to demonstrate efficacy. Lorcaserin, a 5HT2C agonist, was then further tested in MPTP lesioned dyskinetic macaques where it afforded an 82% reduction in LID (P < 0.05), unfortunately accompanied by a significant increase in parkinsonian disability. In conclusion, although our data do not support the repurposing of lorcaserin, acamprosate or ganaxolone per se for LID, we demonstrate value of an in silico approach to identify candidate molecules which, in combination with an in vivo screen, can facilitate clinical development decisions. The present study adds to a growing literature in support of this paradigm shifting approach in the repurposing pipeline.

The role of the brain renin-angiotensin system in Parkinson´s disease

The renin-angiotensin system (RAS) was classically considered a circulating hormonal system that regulates blood pressure. However, different tissues and organs, including the brain, have a local paracrine RAS. Mutual regulation between the dopaminergic system and RAS has been observed in several tissues. Dysregulation of these interactions leads to renal and cardiovascular diseases, as well as progression of dopaminergic neuron degeneration in a major brain center of dopamine/angiotensin interaction such as the nigrostriatal system. A decrease in the dopaminergic function induces upregulation of the angiotensin type-1 (AT1) receptor activity, leading to recovery of dopamine levels. However, AT1 receptor overactivity in dopaminergic neurons and microglial cells upregulates the cellular NADPH-oxidase-superoxide axis and Ca2+ release, which mediate several key events in oxidative stress, neuroinflammation, and α-synuclein aggregation, involved in Parkinson's disease (PD) pathogenesis. An intraneuronal antioxidative/anti-inflammatory RAS counteracts the effects of the pro-oxidative AT1 receptor overactivity. Consistent with this, an imbalance in RAS activity towards the pro-oxidative/pro-inflammatory AT1 receptor axis has been observed in the substantia nigra and striatum of several animal models of high vulnerability to dopaminergic degeneration. Interestingly, autoantibodies against angiotensin-converting enzyme 2 and AT1 receptors are increased in PD models and PD patients and contribute to blood-brain barrier (BBB) dysregulation and nigrostriatal pro-inflammatory RAS upregulation. Therapeutic strategies addressed to the modulation of brain RAS, by AT1 receptor blockers (ARBs) and/or activation of the antioxidative axis (AT2, Mas receptors), may be neuroprotective for individuals with a high risk of developing PD or in prodromal stages of PD to reduce progression of the disease.

Association of antihypertensives and Parkinson's disease in a primary care population matched for underlying diagnosis

PURPOSE

To examine the association of several antihypertensive medication classes with incidence of Parkinson's disease (PD), taking into account possible underlying conditions.

METHODS

In a case-control study based on a large primary care database and including 21,981 PD cases and 21,981 non-PD controls matched for age, sex, and possible treatment indications associations with different antihypertensive medication groups, including diuretics, betablockers, calcium channel blockers, angiotensin-converting enzyme inhibitors and angiotensin-II receptor-blockers and PD were examined.

RESULTS

Antihypertensive medications overall were associated with a lower risk of subsequent diagnosis of PD (OR: 0.94, 95% CI 0.90-0.97), with the negative association most significant for medications acting on the renin-angiotensin-aldosterone system. A positive association with diagnosis of PD was only seen for betablockers and restricted to those with relatively young age and not in those with longer treatment duration.

CONCLUSION

When taking into account underlying diagnoses, antihypertensive medications overall were associated with a reduced incidence of PD.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

An AI-guided screen identifies probucol as an enhancer of mitophagy through modulation of lipid droplets

Failures in mitophagy, a process by which damaged mitochondria are cleared, results in neurodegeneration, while enhancing mitophagy promotes the survival of dopaminergic neurons. Using an artificial intelligence platform, we employed a natural language processing approach to evaluate the semantic similarity of candidate molecules to a set of well-established mitophagy enhancers. Top candidates were screened in a cell-based mitochondrial clearance assay. Probucol, a lipid-lowering drug, was validated across several orthogonal mitophagy assays. In vivo, probucol improved survival, locomotor function, and dopaminergic neuron loss in zebrafish and fly models of mitochondrial damage. Probucol functioned independently of PINK1/Parkin, but its effects on mitophagy and in vivo depended on ABCA1, which negatively regulated mitophagy following mitochondrial damage. Autophagosome and lysosomal markers were elevated by probucol treatment in addition to increased contact between lipid droplets (LDs) and mitochondria. Conversely, LD expansion, which occurs following mitochondrial damage, was suppressed by probucol and probucol-mediated mitophagy enhancement required LDs. Probucol-mediated LD dynamics changes may prime the cell for a more efficient mitophagic response to mitochondrial damage.

Identifying Protective Drugs for Parkinson's Disease in Health-Care Databases Using Machine Learning

BACKGROUND

Available treatments for Parkinson's disease (PD) are only partially or transiently effective. Identifying existing molecules that may present a therapeutic or preventive benefit for PD (drug repositioning) is thus of utmost interest.

OBJECTIVE

We aimed at detecting potentially protective associations between marketed drugs and PD through a large-scale automated screening strategy.

METHODS

We implemented a machine learning (ML) algorithm combining subsampling and lasso logistic regression in a case-control study nested in the French national health data system. Our study population comprised 40,760 incident PD patients identified by a validated algorithm during 2016 to 2018 and 176,395 controls of similar age, sex, and region of residence, all followed since 2006. Drug exposure was defined at the chemical subgroup level, then at the substance level of the Anatomical Therapeutic Chemical (ATC) classification considering the frequency of prescriptions over a 2-year period starting 10 years before the index date to limit reverse causation bias. Sensitivity analyses were conducted using a more specific definition of PD status.

RESULTS

Six drug subgroups were detected by our algorithm among the 374 screened. Sulfonamide diuretics (ATC-C03CA), in particular furosemide (C03CA01), showed the most robust signal. Other signals included adrenergics in combination with anticholinergics (R03AL) and insulins and analogues (A10AD).

CONCLUSIONS

We identified several signals that deserve to be confirmed in large studies with appropriate consideration of the potential for reverse causation. Our results illustrate the value of ML-based signal detection algorithms for identifying drugs inversely associated with PD risk in health-care databases. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Angiotensin type-1 receptor and ACE2 autoantibodies in Parkinson´s disease

The role of autoimmunity in neurodegeneration has been increasingly suggested. The renin-angiotensin system (RAS) autoantibodies play a major role in several peripheral inflammatory processes. Dysregulation of brain RAS has been involved in neuroinflammation and neurodegeneration. We aimed to know whether angiotensin type-1 receptor (AT1) autoantibodies (AT1 agonists) and angiotensin-converting enzyme 2 (ACE2) autoantibodies (ACE2 antagonists) may be involved in Parkinson's disease (PD) progression and constitute a new therapeutical target. Both AT1 and ACE2 serum autoantibodies were higher in a group of 117 PD patients than in a group of 106 controls. Serum AT1 autoantibodies correlated with several cytokines, particularly Tumor Necrosis Factor Ligand Superfamily Member 14 (TNFSF14, LIGHT), and 27-hydroxycholesterol levels. Serum ACE2 autoantibodies correlated with AT1 autoantibodies. Both autoantibodies were found in cerebrospinal fluid (CSF) of four PD patients with CSF samples. Consistent with the observations in patients, experimental dopaminergic degeneration, induced by 6-hydroxydopamine, increased levels of autoantibodies in serum and CSF in rats, as well as LIGHT levels and transglutaminase activity in rat substantia nigra. In cultures, administration of AT1 autoantibodies enhanced dopaminergic neuron degeneration and increased levels of neuroinflammation markers, which was inhibited by the AT1 antagonist candesartan. The results suggest dysregulation of RAS autoantibodies as a new mechanism that can contribute to PD progression. Therapeutical strategies blocking the production, or the effects of these autoantibodies may be useful for PD treatment, and the results further support repurposing AT1 blockers (ARBs) as treatment against PD progression.

Alpha-Synuclein Targeting Therapeutics for Parkinson's Disease and Related Synucleinopathies

α-Synuclein (asyn) is a key pathogenetic factor in a group of neurodegenerative diseases generically known as synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Although the initial triggers of pathology and progression are unclear, multiple lines of evidence support therapeutic targeting of asyn in order to limit its prion-like misfolding. Here, we review recent pre-clinical and clinical work that offers promising treatment strategies to sequester, degrade, or silence asyn expression as a means to reduce the levels of seed or substrate. These diverse approaches include removal of aggregated asyn with passive or active immunization or by expression of vectorized antibodies, modulating kinetics of misfolding with small molecule anti-aggregants, lowering asyn gene expression by antisense oligonucleotides or inhibitory RNA, and pharmacological activation of asyn degradation pathways. We also discuss recent technological advances in combining low intensity focused ultrasound with intravenous microbubbles to transiently increase blood-brain barrier permeability for improved brain delivery and target engagement of these large molecule anti-asyn biologics.

The use of real-world data in drug repurposing

Drug repurposing, or repositioning, is to identify new uses for existing drugs. Significantly reducing the costs and time-to-market of a medication, drug repurposing has been an alternative tool to accelerate drug development process. On the other hand, 'real world data (RWD)' has been also increasingly used to support drug development process owing to its better representing actual pattern of drug treatment and outcome in real world. In the healthcare domain, RWD refers to data collected from sources other than traditional clinical trials; for example, in electronic health records or claims and billing data. With the enactment of the 21st Century Cures Act, which encourages the use of RWD in drug development and repurposing as well, such increasing trend in RWD use will be expedited. In this context, this review provides an overview of recent progresses in the area of drug repurposing where RWD was used by firstly introducing the increasing trend and regulatory change in the use of RWD in drug development, secondly reviewing published works using RWD in drug repurposing, classifying them in the repurposing strategy, and lastly addressing limitations and advantages of RWDs.