Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
373
causing motor fluctuations. Inhibiting COMT enhances levodopa’s bioavailability and prolongs
its therapeutic effect, reducing motor fluctuations and improving symptom control. Tolcapone
and entacapone are FDA-approved COMT inhibitors used in combination with levodopa therapy.
By optimizing levodopa pharmacokinetics, COMT inhibition offers symptomatic relief and
enhances patient quality of life in PD. Targeting COMT represents a valuable strategy for managing
motor complications and optimizing levodopa therapy in PD patients [48].
3) LRRK2
LRRK2 has emerged as a significant enzymatic target in PD research. Mutations in the LRRK2
gene are associated with both familial and sporadic forms of PD. LRRK2 is a kinase enzyme
implicated in various cellular processes, including vesicle trafficking, cytoskeletal dynamics,
and autophagy. Dysregulation of LRRK2 activity leads to aberrant phosphorylation events and
cellular dysfunction, contributing to PD pathology. Targeting LRRK2 with small-molecule
inhibitors is a promising therapeutic approach, aiming to modulate its kinase activity and miti-
gate neurodegeneration in PD. Developing effective LRRK2 inhibitors holds potential for
disease-modifying treatments in PD management [49].
4) GCase (glucocerebrosidase)
GCase plays a role in lysosomal function and alpha-synuclein metabolism. Mutations in the
GBA1 gene, encoding, are a significant genetic risk factor for PD. Reduced GCase activity leads
to impaired degradation of alpha-synuclein, promoting its aggregation and contributing to PD
pathogenesis. Therapeutic strategies aimed at enhancing GCase activity or promoting lysoso-
mal function hold promise for slowing disease progression and alleviating PD symptoms.
Investigating GCase as an enzymatic target offers insights into novel approaches for tackling
alpha-synuclein pathology and advancing PD treatment options [50].
5) PARP-1 [poly(ADP-ribose) polymerase-1]
PARP-1 is involved in DNA repair, inflammation, and cell death pathways implicated in PD
pathology. Dysregulated PARP-1 activity exacerbates oxidative stress and neuroinflammation,
Monoamine oxidase B (MAO-B)
Poly (ADP-Ribose) Polymerase-1 (PARP-1)
PTEN-Induced Kinase 1 (PINK1)
Parkinson protein 7 (DJ-1)
Nuclear factor erythroid 2-related factor
2: (Nrf2)
Catechol-O-methyltransferase (COMT)
Leucine-rich repeat Kinase 2 (LRRK-2)
Glucocerebrosidase (GCase)
Increase in GCase activity to address
alpha-synuclein accumulation
Inhibition of MAO B increase
dopamine level which
improves motor symptoms
Inhibition of PARP-1 for
neuroprotection in PD
Enhancing PINK1 to treat
mitochondrial dysfunction in
PD
Increasing or stabilizing DJ-1
function for neuroprotection in PD
Activating Nrf2 to enhance the
cellular defense against oxidative
stress
Inhibition of COMT helps to
prolong the effects of levodopa
Inhibitors targeting LRRK2 kinase
activity as disease modifying therapy
Figure 16.3 Enzymatic targets for Parkinson’s disease and their mechanism.
374
contributing to DA neuron degeneration. Inhibiting PARP-1 shows promise in preclinical
studies for neuroprotection and alleviating PD symptoms. Targeting PARP-1 offers a potential
therapeutic strategy for slowing disease progression and enhancing neuroprotection in
PD. Further exploration of PARP-1 as an enzymatic target provides avenues for developing
novel treatments that address underlying molecular mechanisms and improve outcomes for PD
patients [51].
6) PINK1
PINK1 plays a crucial role in mitochondrial quality control and mitophagy, processes essential
for maintaining cellular homeostasis and preventing neurodegeneration. Mutations in the
PINK1 gene are linked to familial forms of PD. Dysfunctional PINK1 leads to mitochondrial
dysfunction and accumulation of damaged mitochondria, contributing to PD pathology.
Therapeutic strategies aimed at modulating PINK1 activity or enhancing mitophagy hold
promise for protecting DA neurons and slowing disease progression in PD [52].
7) DJ-1 (Parkinson protein 7)
DJ-1, also known as PARK7, mutations in the DJ-1 gene are linked to familial forms of
PD. Although DJ-1’s precise role in PD remains elusive, it is believed to play a neuroprotective
role against oxidative stress and mitochondrial dysfunction. DJ-1 is involved in various cellular
processes, including antioxidative defense mechanisms and mitochondrial homeostasis.
Therapeutic strategies targeting DJ-1 aim to enhance its neuroprotective functions or restore its
activity in PD [53].
8) Nrf2
Nrf2 is a transcription factor that regulates the expression of antioxidant and detoxifying
enzymes, playing a crucial role in cellular defense against oxidative stress. Dysregulation of
Nrf2 signaling is implicated in PD pathology, with decreased Nrf2 activity observed in
affected brain regions. Therapeutic approaches aimed at enhancing Nrf2 activation offer
promise for mitigating oxidative damage, reducing neuroinflammation, and promoting
neuronal survival in PD. Targeting Nrf2 represents a potential strategy for developing
disease-modifying treatments that address underlying mechanisms of neurodegeneration
in PD [46].
These enzymatic targets are part of ongoing research efforts aimed at developing disease-
modifying therapies for PD. It is important to note that while progress has been made, many
potential treatments are still in preclinical or early clinical stages, and further research is needed
to determine their safety and efficacy in human patients.
16.4.5 Current Therapeutic Approaches to Treat PD
Several therapeutic approaches are used to manage the symptoms of PD. It is important to note
that ongoing research and developments may bring new treatments, and individualized manage-
ment plans are tailored by healthcare professionals based on the specific needs of each patient.
Drugs for treating motor and nonmotor symptoms and disease-modifying therapy of PD are given
in Table 16.1. The common therapeutic approaches include the following:
1) Drugs to treat motor symptoms of PD
● Dopamine precursor-levodopa (-DOPA): It is a precursor to dopamine, a neurotransmitter
that is lacking in PD patients’ brains. In the brain, levodopa gets converted into dopamine, which
reduces motor symptoms. On the other hand, prolonged usage could result in dyskinesias and
motor irregularities [54].
375
● Dopamine agonists: These drugs function in the brain similarly to dopamine. They can be
used alone or in conjunction with levodopa as monotherapy. Ropinirole and pramipexole are
two examples [55].
● MAO-B inhibitors: By preventing dopamine from being broken down, MAO-B inhibitors
like rasagiline and selegiline raise dopamine levels in the brain. They are typically applied to
PD in its early stages [56].
● COMT inhibitors: Because they stop levodopa from breaking down in the peripheral tissues,
entacapone and other COMT inhibitors prolong the effects of the drug. They frequently work
in combination with levodopa [57].
● Anticholinergic medications: These drugs, such as trihexyphenidyl and benztropine, help
control tremors and rigidity by modulating the activity of acetylcholine, another
neurotransmitter [58].
2) Drugs to treat non-motor symptoms of PD
● Cholinesterase inhibitors: Medications such as rivastigmine may be prescribed to address
cognitive impairment in PD, especially if there are symptoms of dementia [59].
● Antidepressants: Depression and anxiety are prevalent nonmotor symptoms in PD and can
be treated with selective serotonin reuptake inhibitors (SSRIs) like sertraline or ventlafaxine
or duloxetine or serotonin–norepinephrine reuptake inhibitors (SNRIs) like sertraline [60].
●
Antipsychotics: Traditional antipsychotics are usually not prescribed due to their potential
to worsen motor symptoms while atypical antipsychotics like quetiapine may be used
cautiously to treat psychosis, hallucinations, or severe behavioral disturbances [61].
● Antiemetics: Ondansetron and metoclopramide are specifically designed to prevent or
relieve drug-induced nausea and vomiting [62].
● Antimuscarinic agents: Antimuscarinic medications like tolterodine and oxybutynin can
help to reduce bladder spasms and may be used to treat overactive bladder symptoms, which
may help with symptoms of urinary urgency and frequency [63].
3) Disease-modifying therapies to treat PD
● Deep brain stimulation (DBS): DBS entails implanting electrodes into particular
movement-regulating brain areas. These electrodes are attached to a stimulator that modifies
Table 16.1 Current treatment strategies for Parkinson’s disease.
S. no.
Drugs to treat motor symptoms of PD
Drugs to treat nonmotor
symptoms of PD
Disease-modifying
therapiesCategory Examples Category Examples
1 Dopamine precursors Levodopa Cholinesterase
inhibitors
Rivastigmine Deep brain
stimulation (DBS)
2 Dopamine agonist Pramipexole,
ropinirole
Antidepressants Fluoxetine,
Sertraline
Physical and
occupational therapy
3 MAO-B inhibitor Selegiline,
rasagiline
Antipsychotics Quetiapine Speech and
swallowing therapy
4 COMT inhibitors Entacapone Antiemetics Ondansetron,
metoclopramide
Exercise programs
5 Anticholinergic drugs Trihexyphenidyl,
benztropine
Antimuscarinic
agents
Tolterodine,
oxybutynin
Diet and nutrition
376
aberrant neural activity by delivering electrical impulses. In cases of advanced PD, DBS is
frequently taken into consideration [64].
● Physical and occupational therapy: Physical therapy helps maintain mobility and improve
balance, while occupational therapy focuses on adapting daily activities to the individual’s
capabilities [65].
● Speech and swallowing therapy: Speech therapy helps individuals with PD overcome
speech and swallowing difficulties that may arise as the disease progresses [66].
● Exercise programs: Frequent physical activity, such as strength training and aerobics, has
been demonstrated to improve mobility and general well-being in people with PD [67].
●
Diet and nutrition: A balanced diet, along with adequate fluid intake, is important for
managing PD symptoms and potential medication side effects [68].
Research is ongoing to develop disease-modifying therapies that can slow or halt the progression
of PD. Additionally, emerging therapies, including gene therapies and novel drug candidates, are
being explored in clinical trials.
16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
There were several limitations associated with existing treatments for PD. Keep in mind that
there may have been advancements or changes since then. Here are some of the common
limitations:
1) Symptomatic relief only
The majority of PD medications now in use concentrate on symptom relief rather than treating
the disease’s fundamental cause. Although drugs like levodopa can effectively relieve motor
symptoms, the disease’s course is neither slowed down nor stopped by them.
2) Motor fluctuations and dyskinesias
Levodopa is a vital drug for treating PD symptoms; however, prolonged use of it might cause
dyskinesias and motor irregularities. Patients may have periods of decreased pharmaceutical
efficacy, which could result in “off” periods when symptoms reappear or “on” periods when
they have dyskinesias, or uncontrollable movements.
3) Limited efficacy in nonmotor symptoms
Numerous nonmotor symptoms, including mood disorders, cognitive decline, and autonomic
dysfunction, are linked to PD. These elements of the condition may not be adequately addressed
by current treatments, and patients may need to take extra drugs or undergo other therapies to
control their symptoms.
4) Disease progression
Although the current medications can temporarily alleviate symptoms, PD cannot be stopped
from progressing. This emphasizes the need for medications that alter the course of disease and
slow down or stop the brain’s DA neurons from degenerating.
5) Side effects
PD treatments come with a number of adverse effects, and different people may react differ-
ently to different medications. It might be difficult to weigh the advantages against any possible
drawbacks, particularly as the illness worsens. Some common side effects of drugs used to treat
motor symptoms of PD are given in Table 16.2.
6) Limited treatment options for advanced PD
When DBS becomes less successful in treating symptoms, solutions such as medication may be
considered in advanced stages of PD. But not every patient is a good fit for these treatments, and
there could be dangers involved.
377
7) Individual variability
As a diverse disorder, Parkinson’s affects people differently in how it shows up. There is no one-
size-fits-all method of therapy, and determining the right combination of drugs and therapies
can be difficult and time-consuming [69].
Researchers and clinicians continue to explore new treatment avenues, including gene therapies,
neuroprotective agents, and interventions targeting specific pathways involved in PD.
16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
Unmet needs in PD therapeutics include a lack of disease-modifying treatments to slow or halt
progression. Nonmotor symptoms such as cognitive issues and mood disorders require targeted
interventions. Improved formulations for levodopa, addressing motor fluctuations and dyskinesias, are
essential. Biomarkers for early diagnosis and personalized treatments based on individual variability
are crucial. Neuroprotective therapies, a deeper understanding of PD subtypes, and symptomatic relief
without side effects remain pressing challenges. Integrating patient-reported outcomes into clinical
assessments and ensuring global access to therapies are vital for comprehensive PD care. Ongoing
research aims to address these gaps and enhance overall management [70].
16.4.8 Significance of Computational Approaches in Parkinson’s Disease
● CADD approaches to overcome therapeutic challenges in PD
CADD is a powerful approach that can contribute to overcoming therapeutic challenges in PD
in several ways.
● Target identification and validation
CADD can assist in identifying and validating potential drug targets by analyzing the complex
molecular interactions involved in PD pathogenesis. This aids in the selection of targets with the
highest therapeutic potential.
● Virtual screening
Through virtual screening, CADD helps in identifying potential drug candidates by analyzing
large chemical databases. This accelerates the drug discovery process, allowing for the efficient
identification of molecules that may modulate specific targets associated with PD.
● Predicting drug–target interactions
CADD employs computational models to predict the interactions between drugs and their target
proteins. This helps in understanding how potential therapeutics may affect specific molecular
pathways involved in PD.
Table 16.2 Side effects of drugs used to treat motor symptoms.
S. no. Drugs Examples Side effects
1 Dopamine precursors Carbidopa/levodopa Shortness of breath, numbness
2 Dopamine agonist Bromocriptine, Pramipexole
Ropinirole
Blurred vision
Chest pain or discomfort, insomnia
3 MAO-B inhibitor Rasagiline, Selegiline, Safinamide Sudden increase in blood pressure,
depression
4 COMT inhibitors Tolcapone, Entacapone Dyskinesia, confusion, hallucinations
5 Anticholinergic drugs Trihexyphenidyl and Benztropine Drowsiness, irregular heartbeat
378
● Optimizing drug candidates
CADD assists in optimizing lead compounds by predicting their pharmacokinetic
properties, bioavailability, and potential side effects. This reduces the likelihood of failure
in later stages of drug development and ensures the selection of compounds with better
chances of success.
● Understanding molecular mechanisms
CADD provides insights into the detailed molecular mechanisms underlying PD. This under-
standing is crucial for designing drugs that can specifically target the pathological processes
involved in the disease [71].
● Repurposing existing drugs
CADD allows for the systematic exploration of existing drug databases to identify potential
candidates that can be repurposed for PD treatment. This strategy may accelerate the development
of new therapies by leveraging the safety profiles of already-approved drugs.
● Personalized medicine
CADD can contribute to the development of personalized medicine by analyzing individual
genetic and molecular profiles. Tailoring treatments to the specific characteristics of each patient
may enhance therapeutic efficacy and minimize side effects.
● Simulating drug effects
Computational simulations can predict the effects of potential drugs on cellular and molecular
systems. This aids in understanding how drugs may influence complex biological processes,
allowing for more informed decision-making in drug development.
● Drug delivery optimization
CADD can assist in optimizing drug delivery methods, including overcoming the blood–brain
barrier, to ensure that therapeutic agents reach the target areas in the brain effectively.
By integrating these CADD approaches into the drug discovery and development process,
researchers can streamline the identification and optimization of potential therapeutics for
PD. This computational approach enhances the efficiency of drug discovery, potentially leading
to the development of more effective and targeted treatments for PD [72].
16.4.9 Use of Computational Tools in Identifying Biomarkers
Computational tools play a crucial role in identifying biomarkers for PD, contributing to early
diagnosis and disease monitoring. Various approaches leverage advanced technologies:
● Bioinformatics and data analysis
Computational tools analyze large-scale biological data, including genomics, transcriptomics,
and proteomics, to identify potential biomarkers associated with PD. This aids in understanding
the molecular mechanisms and pathways involved in the disease.
● ML algorithms
ML models can analyze complex datasets to identify patterns and correlations that may not be
apparent through traditional methods. This enables the discovery of potential biomarkers and
the development of predictive models for early PD diagnosis.
● Imaging analysis
Computational tools are used to process and analyze medical imaging data, such as functional
MRI (fMRI) and positron emission tomography (PET) scans. This assists in identifying structural
and functional changes in the brain that may serve as biomarkers for PD.
379
● Network analysis
Computational tools evaluate biological networks to identify key nodes or pathways associated
with PD. This systems biology approach helps uncover molecular interactions and potential bio-
markers linked to disease progression.
● Artificial intelligence (AI) in wearable technology
AI-driven analysis of data from wearable devices, such as smartwatches or activity trackers, can
provide continuous monitoring of motor symptoms and aid in the identification of biomarkers
related to movement patterns and fluctuations.
● Clinical data integration
Computational tools integrate clinical data from various sources, including electronic health
records and patient databases. This comprehensive analysis helps identify correlations between
patient characteristics, treatment responses, and disease progression, facilitating the discovery
of relevant biomarkers.
● Predictive modeling
Computational models predict the risk of developing PD based on various risk factors, genetics,
and environmental exposures. This aids in identifying individuals at higher risk, enabling early
intervention strategies.
● Biochemical pathway analysis
Computational tools assess biochemical pathways associated with PD, which help to identify potential
biomarkers related to specific metabolic or signaling processes that may be dysregulated in the disease.
Integration of these computational approaches enhances the identification and validation of
biomarkers, ultimately contributing to a more comprehensive understanding of PD pathophysiol-
ogy. These tools expedite the translation of molecular insights into clinical applications, fostering
advancements in early diagnosis, personalized treatment strategies, and the development of
disease-modifying therapies for PD [73–75].
16.4.10 Neuroprotective Strategies Through Computational Insights
16.4.10.1 Computational Models for Neuroprotection
Computational models have been developed to study neuroprotection in PD, aiming to understand
the mechanisms behind neurodegeneration and identify potential therapeutic targets. Some of
these models include:
● PD signaling cascade planned and implemented using a systems biology approach: this
work focused on the relationship between alpha-synuclein and protein kinase A (PKA) and used
nanoparticle-mediated suppression of the disease [76].
● A computational model of PD-related DA cell loss because of excitotoxicity induced by
glutamate: This model examined the relationship between the subthalamic nucleus (STN) and
SNc, demonstrating how the loss of DA cells is aggravated when SNc cells are initially lost. This
process is known as a positive-feedback loop [77].
●
A multiscale computational model of levodopa-induced toxicity in PD: It is a multiscale
computational model. The SNc-striatum system was the subject of this model, which modeled
striatal neurons at the spiking level and SNc neurons at the biophysical level. The toxicity of
-DOPA in SNc, which is brought on by an energy deficit, was captured by the model. This
model may provide insight into possible treatment targets and assist in understanding the pro-
cess underlying neurodegeneration in PD [77].
380
These computational models contribute to the understanding of PD mechanisms and provide
insights into potential therapeutic targets. However, more research is needed to develop effective
treatments for PD.
16.4.11 Examples of Computational Successes in Parkinson’s Disease
Drug Development
Computational approaches have played a significant role in various aspects of PD drug develop-
ment, offering insights into disease mechanisms, identifying potential drug candidates, and opti-
mizing treatment strategies. Here are a few examples of computational successes in PD drug
development:
1) Target identification and validation
LRRK2 inhibitors: Computational modeling and bioinformatics analyses have been crucial in
identifying LRRK2 as a potential target for PD. Inhibitors targeting LRRK2 have been explored
for their neuroprotective effects, and computational tools helped in validating their therapeutic
potential [78].
2) Drug repurposing
Isradipine: Computational analyses and virtual screening have been used to repurpose exist-
ing drugs for PD. Isradipine, originally an antihypertensive medication, showed neuroprotec-
tive effects in preclinical models of PD, and computational methods played a role in identifying
its potential [79].
3) Alpha-synuclein aggregation inhibitors
Virtual screening: Computational methods, such as molecular docking and dynamics simula-
tions, have been employed in virtual screening to identify small molecules capable of inhibiting
the aggregation of alpha-synuclein, a key pathological feature in PD. These approaches aid in
the discovery of potential disease-modifying agents [80].
4) Deep learning in biomarker discovery
Biomarker identification: Deep learning algorithms applied to multiomics data have shown
promise in identifying biomarkers associated with PD. These computational approaches
enhance our understanding of disease progression and may aid in developing diagnostic and
prognostic markers [81].
5) Personalized medicine
Genomic medicine: Computational tools analyze genetic data to identify patient-specific
variations that may influence drug responses. This personalized medicine approach aims to
tailor treatments based on individual genetic profiles, optimizing therapeutic
outcomes [71].
6) Drug-induced neuroprotection
Network pharmacology: Computational network pharmacology approaches help elucidate
the complex interactions between drugs and biological pathways. This has been applied to iden-
tify compounds that may induce neuroprotection by modulating multiple targets involved in
PD pathogenesis [82].
7) Optimizing clinical trials
Clinical trial simulation: Computational modeling and simulation are used to optimize clini-
cal trial design, including patient selection criteria, dosage regimens, and expected outcomes.
This helps streamline the drug development process and enhance the chances of success in
clinical trials [83].
381
16.4.12 Future Directions and Innovations in Computational Methods
for Parkinson’s Disease
Some potential future directions and innovations in computational methods for PD are as follows:
1) ML and AI-based diagnostics
ML algorithms, particularly deep learning models, can analyze large datasets such as neuroim-
aging, genetic, and clinical data to identify patterns associated with PD. These models can assist
in early diagnosis, prognosis prediction, and subtype classification of PD [84].
2) Wearable technology integration
Wearable sensors, such as smartwatches and accelerometers, provide continuous monitoring of
movement patterns and symptoms in PD patients. Integrating data from wearable devices with
computational algorithms can offer insights into disease progression, medication response, and
motor fluctuations, enabling personalized treatment strategies [85].
3) Multimodal data fusion
Integrating heterogeneous data sources, including genetic, imaging, clinical, and environmen-
tal data, can provide a more comprehensive understanding of PD etiology and progression.
Computational methods for multimodal data fusion, such as fusion algorithms and network-
based approaches, can uncover complex relationships between different data modalities and
also identify biomarkers for early detection and disease monitoring [86].
4) Predictive modeling of disease progression
Computational models, including mathematical modeling and simulation techniques, can sim-
ulate the progression of PD based on individual patient characteristics and disease-related fac-
tors. These predictive models can aid clinicians in treatment planning, clinical trial design, and
assessing the long-term impact of interventions [87].
5) Network analysis of brain connectivity
Graph theory-based approaches can analyze structural and functional brain networks derived
from neuroimaging data to characterize alterations in brain connectivity associated with
PD. Network-based biomarkers can help to identify disease-related changes in brain organiza-
tion and facilitate the development of targeted therapies aimed at modulating dysfunctional
neural circuits [88].
6) Personalized treatment optimization
Computational methods, such as reinforcement learning and optimization algorithms, can
optimize treatment strategies for individual PD patients based on their clinical profiles, medica-
tion responses, and disease progression trajectories. Personalized therapeutic interventions,
including medication dosing schedules and DBS parameters, can improve symptom manage-
ment and enhance the quality of life for PD patients [89].
7) Data sharing and collaboration platforms
Collaborative platforms and data repositories enable researchers to share and analyze large-scale
datasets, fostering interdisciplinary collaboration and accelerating scientific discoveries in PD
research. Open-access data initiatives and standardized data formats facilitate the development
and validation of computational methods across different research groups and institutions [90].
Overall, the integration of computational methods with advances in technology and data
analytics holds great promise for advancing our understanding of PD pathophysiology, improv-
ing diagnostic accuracy, and optimizing personalized treatment approaches. Continued inno-
vation and collaboration in this field are essential for translating computational research
findings into clinical practice and ultimately improving outcomes for PD patients.
382
16.5 Conclusion
In conclusion, the rational design of drugs for neurodegenerative disorders represents a promising
avenue for addressing the urgent need for effective therapies. By leveraging computational meth-
ods such as molecular docking, QSAR modeling, and network pharmacology analysis, researchers
can identify and validate novel drug targets, design small molecules or biologics with improved
potency and selectivity, and repurpose existing drugs for therapeutic intervention. The use of
CADD approaches enables researchers to understand the intricate molecular mechanisms under-
lying neurodegenerative disorders, allowing for the targeted design of therapeutic agents that can
modulate specific biological targets implicated in disease pathology. By leveraging the vast amount
of available structural and chemical data, CADD facilitates the rational selection and optimization
of lead compounds, thereby increasing the likelihood of successful clinical translation. Additionally,
ML algorithms facilitate the analysis of large-scale omics data and clinical datasets to identify dis-
ease biomarkers, predict drug responses, and optimize treatment strategies. Overall, the rational
design of drugs for neurodegenerative disorders holds great promise for accelerating the drug dis-
covery process, advancing personalized medicine approaches, and ultimately improving patient
outcomes in the treatment of these debilitating conditions.
References
1 Lamptey, R.N.L., Chaulagain, B., Trivedi, R. et al. (2022). A review of the common
neurodegenerative disorders: current therapeutic approaches and the potential role of
nanotherapeutics. International Journal of Molecular Sciences 23: 1851. https://doi.org/10.3390/
ijms230318512.
2 Breijyeh, Z. and Karaman, R. (2020). Comprehensive review on Alzheimer’s disease: causes and
treatment. Molecules 25: 5789. https://doi.org/10.3390/molecules25245789.
3 Goyal, V. and Radhakrishnan, D. (2018). Parkinson’s disease: a review. Neurology India 66: 26.
https://doi.org/10.4103/0028-3886.226451.
4 McColgan, P. and Tabrizi, S.J. (2017). Huntington’s disease: a clinical review. European Journal of
Neurology 25: 24–34. https://doi.org/10.1111/ene.13413.
5 Masrori, P. and Van Damme, P. (2020). Amyotrophic lateral sclerosis: a clinical review. European
Journal of Neurology 27: 1918–1929. https://doi.org/10.1111/ene.14393.
6 Dobson, R. and Giovannoni, G. (2018). Multiple sclerosis – a review. European Journal of
Neurology 26: 27–40. https://doi.org/10.1111/ene.13819.
7 Sivasathiaseelan, H., Marshall, C., Agustus, J. et al. (2019). Frontotemporal dementia: a clinical
review. Seminars in Neurology 39: 251–263. https://doi.org/10.1055/s-0039-1683379.
8 Nabi, M. and Tabassum, N. (2022). Role of environmental toxicants on neurodegenerative
disorders. Frontiers in Toxicology 4: https://doi.org/10.3389/ftox.2022.837579.
9 Niranjan, R. (2018). Recent advances in the mechanisms of neuroinflammation and their roles in
neurodegeneration. Neurochemistry International 120: 13–20. https://doi.org/10.1016/j.neuint.
2018.07.003.
10 Kwon, H.S. and Koh, S.H. (2020). Neuroinflammation in neurodegenerative disorders: the roles of
microglia and astrocytes. Translational Neurodegeneration 9: https://doi.org/10.1186/s40035-
020-00221-2.
11 Zhu, W.M., Neuhaus, A., Beard, D.J. et al. (2022). Neurovascular coupling mechanisms in health
and neurovascular uncoupling in Alzheimer’s disease. Brain 145: 2276–2292. https://doi.org/
10.1093/brain/awac174.