AD is an intricate neurological ailment that is affected by numerous hereditary variables (Zs-Nagy, 2010; Montine et al., 2012). The APOE gene is a highly influential genetic factor in determining the risk and course of AD (Shanker et al., 2010; Lovestone and Harrington, 2006; Scheltens and Van der Flier, 2013). The APOE ε4 allele is specifically linked to a higher likelihood of developing AD and a less favourable reaction to cholinesterase inhibitors, which are routinely employed for AD symptom management (Giri et al., 2016; Van Cauwenberghe et al., 2016; Braak and Del Tredici, 2015; Spillantini et al., 1997; Stewart et al., 1997). For instance, pharmacogenomic research has demonstrated that patients with the APOE ε4 allele may need modified doses or other treatment approaches to achieve the best clinical results. Recent research has emphasized the significance of other genetic variables in AD, including the TREM2 gene, which plays a role in the functioning of microglia and the inflammatory response. TREM2 variants have been linked to a higher likelihood of developing late-onset AD and may impact how individuals respond to anti-inflammatory treatments (Wyss-Coray and Rogers, 2012; Guerreiro et al., 2013; Van Dongen and Van Gool, 2004). Moreover, variations in the BDNF gene, responsible for encoding brain-derived neurotrophic factors, have been associated with cognitive deterioration and the effectiveness of treatment in patients with AD (Wright and Van der Flier, 2014; Lim et al., 2014; Jagust et al., 2018; Venegas and Heneka, 2017). Considering these genetic characteristics, pharmacogenomic methods can enhance treatment strategies and results for people with AD.

PD is mainly characterized by the deterioration of dopaminergic neurones in the substantia nigra, resulting in both motor and non-motor symptoms (Irwin et al., 2013). The presence of genetic variants in the CYP2D6 gene, which is responsible for producing an enzyme involved in the metabolism of drugs, has a significant impact on how individuals respond to dopaminergic medications like levodopa and dopamine agonists (Nalls et al., 2019). Individuals with specific CYP2D6 polymorphisms may exhibit variations in the metabolism of various medications, resulting in altered effectiveness and the probability of experiencing adverse reactions (Wang et al., 2006). Pharmacogenomic testing for CYP2D6 enables the customization of treatment programs, guaranteeing that patients are administered the smost suitable and efficient medicine. Additional genetic factors that impact PD include abnormalities in the GBA gene (Thomas et al., 2004). These mutations are linked to Gaucher disease and elevate the likelihood of developing PD. Mutations in the GBA gene can impact how PD patients respond to dopaminergic treatments and their susceptibility to cognitive deterioration (Sidransky et al., 2009; Uddin et al., 2020)). Additionally, variations in the COMT gene, responsible for encoding catechol-O-methyltransferase, can impact how levodopa is metabolized and the likelihood of experiencing motor problems (Vilar Dde et al., 2014; Taymans et al., 2014). Moreover, the discovery of COMT polymorphisms enables targeted adjunct therapies with COMT inhibitors for improved treatment outcomes. Pharmacogenomics also holds promise for more sophisticated interventions through deep brain stimulation (DBS) by evaluating individual genetic responses to dopaminergic treatments (Mehanna et al., 2017). Pharmacogenomics can improve PD management and patient outcomes by using these genetic findings (Beach et al., 2008).

Huntington’s disease is an inherited neurological ailment that occurs due to increased CAG repeats in the HTT gene. This genetic mutation results in the synthesis of an altered form of the huntingtin protein, which forms clumps and leads to the demise of nerve cells. The genetic foundation of HD presents a distinct objective for pharmacogenomic therapies (Karran and De Strooper, 2016). Genetic profiling can be utilized to forecast the effectiveness of medications intended to decrease the amounts of mutant huntingtin or to regulate its harmful impacts (Ross and Tabrizi, 2011). Pharmacogenomic strategies in HD concentrate on creating treatments that address the fundamental genetic abnormality. Advancements in CRISPR-Cas9 have demonstrated success in reducing toxic proteins in HD models, targeting the HTT gene. Antisense oligonucleotides (ASOs) have been specifically engineered to decrease the activity of the mutant HTT gene, which could potentially decelerate the advancement of the disease (Tabrizi et al., 2019). Furthermore, researchers are investigating the possible use of small molecule inhibitors to regulate the function of the proteasome or autophagy pathways as potential therapies for HD (Gao et al., 2018). Pharmacogenomics can expedite the creation of precise treatments for HD by utilizing genetic knowledge to target the underlying cause.

Amyotrophic lateral sclerosis (ALS) is a degenerative condition affecting the motor neurones, characterized by a multifaceted genetic makeup—mutations in genes such as SOD1, C9orf72, TARDBP, and FUS cause ALS. Pharmacogenomic methods can enhance the effectiveness of current medications, including riluzole and edaravone, by considering these genetic characteristics. Patients with specific genetic alterations may exhibit differential responses to particular treatments, necessitating alternate therapeutic procedures for those who do not respond favourably (Renton et al., 2014). Recent breakthroughs in ALS research have discovered supplementary genetic variations that impact the likelihood and development of the disease (Mehta et al., 2020). For instance, mutations in the ATXN2 gene, which is connected to spinocerebellar ataxia type 2, have been related to a higher likelihood of developing ALS and could impact the effectiveness of treatment (Elden et al., 2010; Tanzi and Bertram, 2005). Furthermore, alterations in the UBQLN2 gene, responsible for producing ubiquitin-2, have been linked to ALS and frontotemporal dementia (Wesnes and Edgar, 2014; Deng et al., 2011; Snow and Shapiro, 2021; Winblad et al., 2016). In ALS, pharmacogenomics facilitates precise drug selection, particularly with riluzole, where polymorphisms in the ABCB1 gene affect treatment efficacy (Woolley et al., 2020). Pharmacogenomics can enhance the management of ALS and improve therapy outcomes by integrating these genetic findings.

Genome-wide association studies have played a crucial role in finding genetic variations linked to the way drugs affect neurodegenerative illnesses. By examining the genetic composition of extensive groups of patients, GWAS can reveal prevalent and uncommon genetic variations that impact how individuals react to different therapies. These findings can inform the development of personalized medicines and clinical decision-making (Klein et al., 2005). GWAS have discovered many genomic regions linked to neurodegenerative illnesses and how individuals respond to treatment. For instance, GWAS have shown genetic variations in the LRRK2 gene linked to the likelihood of developing PD and the responsiveness to treatment (Nalls et al., 2019). In addition, GWAS have discovered genetic variations in the CR1 and BIN1 genes that are associated with an increased risk of AD and may also affect how individuals respond to anti-amyloid medications (Lambert et al., 2013; Hardy, 2009; Selkoe and Hardy, 2016). Using GWAS discoveries, Pharmacogenomics can improve the accuracy of treatment approaches for neurodegenerative illnesses.

Next-generation sequencing (NGS) methods allow for thorough genetic profiling by sequencing the complete genomes or exomes (Johnson et al., 2008). NGS can detect both prevalent and infrequent genetic variations that affect how drugs are processed, their effectiveness, and their safety. This efficient method is progressively becoming more feasible in clinical environments, enabling the development of more accurate and tailored treatment strategies founded on an individual’s distinct genetic makeup (Goodwin et al., 2016). NGS has transformed the study of pharmacogenomics, allowing for the discovery of new genetic variations that impact medication response. NGS has revealed uncommon variations in the TREM2 gene linked to the risk of AD and could impact the effectiveness of anti-inflammatory treatments (Guerreiro et al., 2013). In addition, NGS has detected genetic variations in the GCH1 gene that impact the reaction to levodopa in individuals with PD (Kuilenburg et al., 2016). Pharmacogenomics can offer more precise and individualized treatment suggestions by employing NGS.

CRISPR-Cas9 is an innovative gene-editing technique that enables accurate alterations of genomic sequences. This technology is crucial for researching gene-drug interactions and formulating gene-based treatments for neurodegenerative illnesses. CRISPR-Cas9 can rectify genetic flaws or regulate gene expression by focusing on specific genetic mutations, which could lead to personalized therapeutic interventions (Doudna and Charpentier, 2014). CRISPR-Cas9 has been utilized to create gene-based treatments for neurodegenerative illnesses by explicitly targeting the genetic abnormalities responsible for the condition. CRISPR-Cas9 has been used to specifically hinder the expression of the mutant HTT gene in HD mice, resulting in decreased quantities of harmful huntingtin protein and an enhancement in neuronal function (Yang et al., 2020). In addition, the CRISPR-Cas9 system has been used to rectify SOD1 mutations in models of ALS, thereby reinstating standard protein functionality and enhancing the survival of motor neurones (Gaj et al., 2017). Pharmacogenomics can expedite the creation of groundbreaking and tailored treatments for neurodegenerative illnesses by utilizing CRISPR-Cas9.

Pharmacogenomic testing can analyze genes such as APOE and BDNF to determine the appropriate selection and dosage of cholinesterase inhibitors and NMDA receptor antagonists in AD (Oddo and LaFerla, 2006; Miller and Seeley, 2013). Patients with the APOE ε4 allele may experience improved therapeutic results using alternative medicines or modified dosages. Genetic testing to create individualized treatment approaches can enhance the control of symptoms and decelerate the advancement of AD (Yeung and Wong, 2020; Timmerman et al., 2020; Masters et al., 2015). Pharmacogenomics can provide valuable information for utilizing new therapeutic strategies in AD (Maki and Holsinger, 2010; Oddo and LaFerla, 2006). For example, using monoclonal antibodies or small molecule inhibitors to target amyloid-beta and tau pathology may significantly impact patients with specific genetic profiles, as Cummings et al., 2019 suggested (Zheng and Koo, 2011; Zhang et al., 2007; Yan and Stern, 2014; Trojanowski and Lee, 2000; Mattsson et al., 2011; Trojanowski and Lee, 2005; Wang and Mandelkow, 2016; Zhou and Lee, 2011). In addition, pharmacogenomic knowledge can be used to direct the administration of neuroprotective substances, such as antioxidants and anti-inflammatory medications, to decelerate disease advancement and enhance cognitive abilities (Querfurth and LaFerla, 2010; Aisen et al., 2016; Panza et al., 2006; Yang et al., 2020). Pharmacogenomics can improve the accuracy and effectiveness of AD treatments by incorporating genetic testing into clinical practice (Cummings et al., 2015; O’Brien, and Wong, 2011).

Genetic testing can identify polymorphisms in CYP2D6, COMT, and other essential genes. This information can be used to customize dopaminergic therapy for individuals with Parkinson’s disease. Clinicians can optimize drug dosages and choose the most suitable treatments to reduce side effects and improve effectiveness by comprehending an individual’s genetic profile (Mammen and Fernandez, 2009). Implementing a customized strategy can significantly enhance the wellbeing of individuals with PD (Kang et al., 2020; Okun, 2012). Pharmacogenomics can also provide valuable insights for the use of modern treatments in PD, such as DBS and gene therapy. Genetic testing can determine which patients are more likely to have positive effects from deep brain stimulation (DBS) by evaluating their responsiveness to dopaminergic medications (Mehanna et al., 2017). In addition, gene therapy methods that focus on particular genetic alterations, such as the use of AAV to deliver GDNF or Nurr1, have the potential to offer neuroprotection and enhance motor function in individuals with Parkinson’s disease (Axelsen and Woldbye, 2018). By integrating pharmacogenomic knowledge, medical professionals can enhance treatment approaches and results for PD patients.

Targeted medicines, such as antisense oligonucleotides (ASOs) and small-molecule inhibitors, can be customized to match the genetic profiles of individuals with Huntington’s disease (HD). Genetic testing can ascertain the people likely to derive advantages from these medications, enabling a more individualized and efficacious strategy for managing HD. This technique aims to decrease the production of mutant huntingtin protein and alleviate its detrimental impact on neurones (Tabrizi et al., 2019). Pharmacogenomics can also guide using additional therapeutic strategies for Huntington’s disease, including neuroprotective medicines and treatments to alleviate symptoms. Genetic testing can determine the appropriate use of drugs that affect neurotransmitter systems, including dopamine antagonists and glutamate modulators, to enhance both motor and mental symptoms (Frank, 2014). In addition, the use of pharmacogenomic information can aid in creating combination medicines that specifically target various pathways implicated in the development of Huntington’s disease (Smith et al., 2014). Pharmacogenomics can improve the accuracy and effectiveness of HD treatments by incorporating genetic testing into clinical practice.

Genetic factors should be considered to optimize the utilization of current medications for ALS, such as riluzole and edaravone, through pharmacogenomic insights. Patients with specific genetic alterations may exhibit enhanced responsiveness to particular medicines, but different therapeutic approaches may be necessary for others. Genetic testing can guide the selection and dosage of these medications, leading to enhanced therapeutic results and a decrease in the likelihood of adverse side effects (Taylor et al., 2016). Pharmacogenomics can provide valuable insights into the application of innovative treatment methods in ALS, including gene therapy and stem cell therapy (Guerini et al., 2015). One possible approach to address the C9orf72 mutation is to use ASOs or CRISPR-Cas9 to target it specifically. This can decrease the creation of harmful RNA foci and dipeptide repeat proteins, which may slow the disease’s progression (DeJesus-Hernandez et al., 2011). In addition, stem cell therapy methods that administer neurotrophic factors or substitute impaired motor neurones can offer neuroprotection and enhance motor function in individuals with ALS By integrating pharmacogenomic knowledge, medical practitioners can enhance treatment approaches and results for ALS patients.

Implementing pharmacogenomics raises several ethical and legal concerns, such as genetic privacy, informed consent, and the possibility of discrimination based on genetic data. Guaranteeing fair access to personalized treatment and safeguarding patient privacy is crucial for the success of pharmacogenomics. In order to promote the widespread adoption of pharmacogenomic techniques, policymakers and healthcare professionals need to address these issues and work towards building trust (Burke et al., 2010). In order to tackle these difficulties, it is imperative to establish solid regulatory frameworks and rules to control the utilization of genetic information in clinical practice. These frameworks must guarantee that genetic testing is carried out in an ethically sound manner, with enough measures in place to respect patient privacy and prevent discrimination. In addition, it is crucial to promote public education and participation to increase understanding of pharmacogenomics’s advantages and potential drawbacks. This will facilitate informed decision-making and acceptance among patients and healthcare providers (McGuire et al., 2008; Strobel et al., 2004).

Integrating genetic, clinical, and pharmacological data is a substantial barrier (Galvin et al., 2001; Pardridge, 2005). However, it is crucial for the advancement of pharmacogenomics. Advanced bioinformatics tools and machine learning techniques are essential for effectively organizing and analyzing this intricate data. These technologies can aid in identifying patterns and correlations, enabling the creation of personalized treatment programs based on a thorough comprehension of an individual’s genetic and clinical profile (Kohane, 2015). Effective data integration requires interdisciplinary collaboration among geneticists, doctors, bioinformaticians, and data scientists. In addition, it is crucial to build standardized protocols and data-sharing frameworks to enable the seamless transmission of genetic and clinical information among various healthcare settings (Zucker and Regehr, 2002). Pharmacogenomics can optimize the use of genetic information to enhance patient care by utilizing advanced data analytics and promoting collaboration (Johnson et al., 2013).

Conventional clinical trial designs may not be appropriate for pharmacogenomic research, as they frequently overlook the genetic diversity among participants. Researchers are currently investigating adaptive trial designs and N-of-1 trials, explicitly targeting individual patients, to assess the effectiveness of personalized treatments. These novel methodologies can offer more pertinent and precise data, directing the advancement and execution of pharmacogenomic treatments (Woodcock and LaVange, 2017). Adaptive trial designs permit adjustments to the trial protocol based on interim data analysis, enabling researchers to modify treatment regimens and optimize patient results in real time. In contrast, N-of-1 trials specifically target individual patients and use multiple assessments to evaluate the effectiveness of various treatments. These methods can offer helpful knowledge about the effectiveness and safety of personalized treatments, making it easier to create customized treatment strategies for neurodegenerative diseases (Lillie et al., 2011). The results of Genetic variants and their impact on drug response in neurodegenerative diseases are shown in Table 1, including pathways and genes in Figure 1. Different pharmacological tools are shown in Table 2 and Figure 2.

Pharmacogenomics is becoming a crucial tool in the treatment of neurodegenerative diseases, allowing for more personalized and precise therapies. In AD, PD, HD, and ALS, genetic variations significantly influence drug responses, highlighting the need for personalized treatment approaches (Swen et al., 2011). However, translating these findings into clinical practice presents challenges, particularly in identifying reliable genetic markers and managing polygenic influences on disease and treatment outcomes.