INTRODUCTION:

The burden of drug safety related to medication is becoming an issue of public healthcare concern^1,2. The absence of drug safety is posing a challenge in drug therapy, leading to several problems resulting in hospitalization and complications of the disease condition.

For instance, in the United States (US) alone, adverse drug reactions (ADRs) have been rated as the fourth to the sixth most significant cause of death in hospitalized patients³. Fortunately, these adverse events related to drug safety can be prevented⁴. Personalizing medications specifically to the patients is an effective approach for prevention of ADRs⁵.

Personalized medicine, which is also known as stratified medicine or precision medicine is a new approach to drug safety whereby drugs or medical decisions or interventions are tailored to the individual patients based on their disease conditions and risk factors⁶. This new approach of ensuring drug safety allows clinicians to predict the most appropriate, efficacious and safe dose of drugs to patients. Also, the personalized medication focuses on customizing a drug dosing according to the genetic make-up of the patient or patient group^{7, 8}.

The practice of personalized medication started with a broader method called pharmacogenomics⁹. This method focused on the effects of medications on human body using genomic profiling¹⁰. Pharmacogenomics has been applied in the past 50 years in many areas including cardiovascular, diabetes, and dose adjustment of warfarin. With advents in research and an increasing need for measures to improve drug safety, new methods have emerged, termed pharmacometabolomics and pharmacometabonomics.

Pharmacometabonomics is a method of ensuring drug safety through prediction of drug effects using pre-dose metabolic profiling of biofluids¹⁰. This method is derived from the brother methods called metabonomics. A method used to evaluate the changes that occurred in the body following drug administration, microbe and genetic alterations, environmental changes, exercise and clinical interventions¹¹. Pharmacometabonomics is a sub-set of metabonomics that is based on the concept that the response of the body to drug administration differs according to their differentiated pre-dose phenotypes¹². Pre-dose phenotypes are influenced by the subjects' genome and by the status of their microbiome and its interaction with their genome¹². The pharmacometabonomics methods are conducted to establish a relationship between the differences in biofluid metabolite profiles of the people before dose administration and their drug effects thereafter (post-drug effects)¹². These methods include the mass spectrometry-, nuclear magnetic resonance (NMR)- detection methods. Subsequently, statistical analysis of metabolite profile is applied to determine the pre-and post-doe drug administration¹².

Pharmacometabonomics has been applied previously in clinical settings. It was first used in 2003, in a large clinical trial involving 100 healthy volunteers to predict drug metabolism (paracetamol) in humans¹³. Consequently, pharmacometabonomics has been rapidly applied in several clinical areas such as (i) prediction of drug efficacy in depressive disorders, psychosis, cholesterol; (ii) prediction of adverse events in cancers, and Non-Steroidal Anti-inflammatory Drugs (NSAIDS); (iii) predictive metabonomics in diabetes onset. Given these demonstrated obstacles and potentials in reaching the central nervous system (CNS) tissue by drugs, pharmacometabonomics could best be applied in neurology to revolutionize drug therapy and patient safety in neurological disorders.

Neurological disorders are diseases affecting both the central and peripheral nervous systems. These diseases consisted of epilepsy¹⁴, Alzheimer’s disease, dementia, stroke, multiple sclerosis, parkinsonism, etc. Safety concerns with neurological disorders are growing due to the nature of the diseases and the high potentials of the neurological drugs to cause adverse events. We, therefore, aimed to summarize and examine the literature regarding the application of pharmacometabonomics in neurology.

METHODS :

We started by conducting a systematic search of the literature in Medline via PubMed, from the inception of the database to April 2020. The search was performed using the following terms as free text (title and abstract) and Medical sub-heading (MeSH), "pharmacometabonomic", "neurology” "central nervous system” "drugs". Other articles were searched from the manual search of the included articles. Other information was retrieved from Google Scholar. The first 20 pages of the Google Scholar search results were retrieved for inclusion. The eligibility criteria were studies that reported the application of pharmacometabonomics in neurological disorders, or drugs used in these disorders.

RESULTS AND DISCUSSION:

A total of 258 peer-reviewed articles were generated from the search of the literature using the electronic databases. After removing duplicates, 169 articles were excluded during the title and abstract screening. Sixty-seven articles were accessed for eligibility, and 59 were removed based on the reasons provided in Figure 1. Two studies were identified through the manual search of the reference list of included articles. Finally, ten articles that fulfilled the study eligibility criteria were included for the review. Figure 1 demonstrates the PRISMA flowchart.

Figure 1. PRISMA flow diagram for study selection

Table 1. Summary of the literature on the clinical application of pharmacometabonomics in neurological disorders

Author	Neurological disorder /Drug	Experiment	Clinical application
Kaddura-Daouk et al., 2011	Major depressive disorder/ Sertraline	LC-ECA and GC–MS	Prediction of drug efficacy	Pre-treatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept
Kaddura-Daouk et al., 2013	Major depressive disorder/Sertraline	LC-ECA and GC–MS	Prediction of drug efficacy	Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo
Ji et al., 2011	Major depressive disorder/serotonin reuptake inhibitors	Serum assays and genotyping tag	Prediction of drug efficacy	Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics
Zhu et al. 2013	Major depressive disorder/ Sertraline	LC-ECA and GC–MS	Prediction of drug efficacy	Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder – a possible role for methoxyindole pathway
Cunningham et al., 2012	Neurological toxicity/Isoniazid	¹H-NMR	Prediction of adverse drug events	Pharmacometabonomic Characterisation of Xenobiotic and Endogenous Metabolic Phenotypes That Account for Inter-individual Variation in Isoniazid-Induced Toxicological Response
Condray et al., 2011	Schizophrenia	LC-ECA	Prediction of drug efficacy	3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia
't Hart et al., 2003	Multiple Sclerosis	¹H-NMR	Prediction of metabolism	¹H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease
Holmes et al. 2006	Schizophrenia	¹H-NMR	Prediction of drug efficacy	Metabolic profiling of CSF: Evidence that early intervention may impact on disease progression and outcome in schizophrenia
Lan et al 2009	Bipolar disorder	¹H-NMR	Investigation of the pathophysiology of bipolar disorder	Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of the bipolar disorder
Chan et al 2011	Schizophrenia/ Fluphenazine	LC-MS and NMR	Understanding the effects of disease and drugs on the brain	Evidence for disease and antipsychotic medication effects in post-mortem brain from schizophrenia patients

LC, liquid chromatography; CEC, coupled with electrochemical coulometric; MS, mass spectrometry; NMR, nuclear magnetic resonance; GC, gas chromatography

Study characteristics:

Four out of the ten studies were on the prediction of drug efficacy for major depressive disorders. Two on the prediction of drug efficacy for schizophrenia. One study investigated the pathophysiology of bipolar disorder, understanding the effects of disease and drugs on the brain.

Application of pharmacometabonomics in multiple sclerosis (MS):

Multiple sclerosis (MS) is an inﬂammatory condition of the brain and spinal cord where myelin sheaths are affected due to unknown cause¹⁵. It is commonly presented as a relapsing or a remitting disorder. The MS-related CNS damage is difﬁcult to assess and monitor due to the complexities of the evolving pathological and physiological processes, leading to challenging therapeutic decisions. The potential application of pharmacometabonomics techniques to monitor the underlying pathology of MS, including investigation of speciﬁc relevant metabolites would be of significant importance in addressing this problem. 't Hart et al. 2003 examined the prediction of metabolism in understanding the pathology of MS¹⁶. NMR spectroscopy was utilized in combination with pattern recognition analysis to characterized chemical patterns of metabolites in the urine of MS patients. They concluded that there is an association between changes in the chemical composition of urine and the development of autoimmune encephalomyelitis¹⁶.

Schizophrenia:

Schizophrenia is a severe mental disorder, in which affected people and their families suffer extensively. Furthermore, it has a tremendous cost to society, in terms of disability-adjusted life-years (DALYs), the total burden of disease was estimated to be 1.1% while the total years lived with disability (YLDs) was determined to be 2.8%¹⁷. In the past decades, there have been spent efforts to reduce adverse effects and enhance the efficacy and of drugs used in schizophrenia. There is progressing advocacy for minimizing the adverse effects of these drugs and refining their mechanisms of action¹⁸. Condray and his colleagues targeted a group of 25 patients with ﬁrst episode of neuroleptic-naive schizophrenia treated with ﬁve different drugs (Tryptophan, Serotonin, 5-hydroxy indole acetic acid, Melatonin and Tryptamine). They proved that the clinical outcome of patients was significantly signiﬁcantly associated with the pre-dose concentrations of 3-hydroxykynurenine (3-OHKY)¹⁹. Over the four weeks treatment period, the study predicted 41%, 38% and 35% of the differences associated with the change in overall symptoms, mood symptoms and psychosis respectively¹⁹. This finding demonstrates the potentials of pharmacometabonomics to deliver specific drug therapy to individual patients with this disorder¹⁹. Holmes et al. 2006 researched to predict the efficacy of drugs in schizophrenia using the ¹H-NMR²⁰. The study showed that the commencement of antipsychotic therapy during a first psychotic episode could affect the response to treatment and/or clinical outcome²⁰. Another study has also demonstrated evidence of the influence of disease and antipsychotic medications in the post-mortem brain of patients with schizophrenia²¹.

Major depressive disorders:

Major depressive disorders (MDD) have been described as a highly heterogeneous disorder associated with difficulties in the specificity of therapy²². There is a growing concern about a new approach to address this challenge. It has been shown that the response of drugs used in MDD can be predicted to improve specificity in treatment. Findings from the previous study have suggested that there was a negative association between a pre-dose glycine plasma concentration and citalopram/escitalopram treatment of patients with MDD²³. Other studies focus on personalizing the delivery of sertraline to patients with MDD^24,25. A study was conducted to investigate the feasibility of pharmacometabonomics in MDD patients taking sertraline²⁴. The outcome of the study shows an achievement of a correct classiﬁcation rate of 81% for sertraline responders compared with achieving 72% for placebo responders. This demonstrates the possibility of personalizing sertraline, with consequent improvement of therapy and reduction of adverse events.

Bipolar disorder:

A bipolar disorder is a neurological condition that is associated with recurrent episodes of mood swings²⁶. The exact cause and pathophysiology are still unclear. Lan et al. 2009, investigated the pathophysiology of bipolar disorder²⁷. Molecular changes associated with the pathophysiology and drug treatment of bipolar disorder were identified by applying the NMR for metabonomic analysis. The results of this study demonstrate a novel vision into the pathophysiology of the disease and could be applied to direct research into innovative therapeutic approaches²⁷.

Neurological toxicity:

The adverse events of some drugs are presented with neurological toxicity due to their effects on the nervous system. Isoniazid is a common drug with neurological toxicity. Pharmacometabonomic methods have been applied previously to predict isoniazid-induced-neurological toxicity. In Cunningham et al.'s study, 2012, xenobiotic and endogenous metabolic phenotypes responsible for inter-individual variation in toxicological response of Isoniazid were characterized²⁸. Considering the common use of the drug in the management of tuberculosis, a screening approach using pharmacometabonomic methods could have translational potential that would enable patient stratification to reduce adverse events.

Future Perspectives:

Characterization of biopharmaceutical properties, toxicity and mechanisms by Pharmacometabonomics methods is now simpler, faster, and less invasive. In clinical practice, pharmacometabonomics has proved its efficiency in the prediction of drug biotransformation, efficacy and toxicity. A growing area for pharmacometabonomics is in the monitoring of surgical patients. This approach has a possible success in guiding surgeons in decision-making by providing quasi-real-time diagnostic information²⁹. This area is currently in its infancy stage. However, it is emerging and progressing. Surgical electrocautery devices could be used for obtaining data from bioﬂuids, tissues or the metabolite-rich smoke to provide biomarkers that would inform decisions on tissue characteristics, viability and physiological state³⁰. Also, these strategies could guide the broader care of patients in pre-operative, peri-operative and post-operative settings³¹.

Challenges of Pharmacometabonomics:

Although pharmacometabonomics has been shown to have promising results in areas such as drug discovery, drug development and as a complementary approach in understanding biological changes after a gene knockout or a drug intervention, however, it has some considerable challenges. Firstly, limitations related to imprecise and inaccurate determination of small molecular metabolites in cell, tissue, organ or whole organisms. Secondly, the need for systematic validation is inevitable, since pharmacometabonomics involves mass spectrometry-based methods, various analytical aspects such as sample preparation/stability, extraction recovery, carryover and matrix effects, choices of internal standards, and quality control³². The third challenge of pharmacometabonomic using NMR-based methods is the extreme complexity of spectrum and the difficulty of interpretation due to a functional group based on multiple peaks per analyte³³. Therefore, for both targeted and non-targeted approaches, the quality of the analytical data is very critical.

CONCLUSION:

Pharmacometabonomics is an emerging field that demonstrated usefulness in predicting drug efficacy, minimizing adverse drug events (neurotoxicity) and understanding the pathophysiology of neurological disorders. In the future, it has the potential to be used in the monitoring of surgical patients to provide quasi-real-time diagnostic information to guide surgeons in decision making. Therefore, more research in this area is needed to better explore its promising potentials.

FUNDING:

The authors received no financial support for the research, authorship, and/or publication of this article.

CONFLICTS OF INTEREST:

No conflict of interest.

ETHICS APPROVAL:

Ethical board approval was not required, since the study did not include human or animal trials.

AUTHOR CONTRIBUTIONS:

The five authors have contributed almost equally to the article; they have contributed to all study tasks from the planning of the work until submission to the journal.

REFERENCES:

1. Jørgensen, J.T., A challenging drug development process in the era of personalized medicine. Drug Discovery Today. 2011. 16(19-20): p. 891-897.

2. Chavhan, A.B. and D.M. Bhoi, Pharmacovigillance: Drug Safety Monitoring. Asian Journal of Pharmacy and Technology. 2019. 9(1): p. 49-52.

3. Lazarou, J., B.H. Pomeranz, and P.N. Corey, Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998. 279(15): p. 1200-1205.

4. Rajput, M.D., Y. Rajput, and L. Rajput, A Review on Adverse Drug Reaction. Asian Journal of Pharmaceutical Research. 2020. 10(3): p. 221-225.

5. Tejas, K. and D. Ganesh, A Review on Orodispersible Tablets: A Novel Approach. Research Journal of Pharmacy and Technology. 2019. 12(8): p. 3993-4001.

6. Pokorska-Bocci, A., et al., 'Personalized medicine': what’s in a name? Personalized Medicine. 2014. 11(2): p. 197-210.

7. Swapnaa, B. and S.V. Kumar, Personalized Medicine-A Novel approach in Cancer Therapy. Research Journal of Pharmacy and Technology. 2017. 10(1): p. 341-345.

8. Radhakrishnan, A., et al., Personalized Nano Delivery Strategy in Treating Uveitis. Research Journal of Pharmacy and Technology. 2019. 12(4): p. 1997-2008.

9. Deepali, G., et al., Pharmacogenomics: An Overview. Research Journal of Pharmacology and Pharmacodynamics. 2009. 1(2): p. 59-65.

10. Clayton, T.A., et al., Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature. 2006. 440(7087): p. 1073-1077.

11. Nicholson, J.K., E. Holmes, and J.C. Lindon, Metabonomic and Metabolomics Techniques and Their Applications in Mammalian Systems. The Handbook of Metabonomics and Metabolomics. 2007: p. 1-34.

12. Everett, J.R., R.L. Loo, and F.S. Pullen, Pharmacometabonomics and personalized medicine. Annals of Clinical Biochemistry. 2013. 50(6): p. 523-545.

13. Wolf, N.I., et al., Severe hypomyelination associated with increased levels of N-acetylaspartylglutamate in CSF. Neurology. 2004. 62(9): p. 1503-1508.

14. Jain, P., et al., Epilepsy: A neurological cramp. Research Journal of Pharmacology and Pharmacodynamics. 2013. 5(1): p. 1-5.

15. Pavithra, C., et al., A Review on Multiple Sclerosis and its Regimens. Research Journal of Pharmacy and Technology. 2020. 13(8): p. 3977-3982.

16. A't Hart, B., et al., 1H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease. Journal of the Neurological Sciences. 2003. 212(1-2): p. 21-30.

17. Pindoria, S. and N. Sinha, Effects of Age on the Quality of Life of Caregivers of Patients with Mental Illness. Research Journal of Humanities and Social Sciences. 2019. 10(2): p. 629-634.

18. Maric, N.P., et al., Improving Current Treatments for Schizophrenia. Drug Dev Res. 2016. 77(7): p. 357-367.

19. Condray, R., et al., 3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia. International Journal of Neuropsychopharmacology. 2011. 14(6): p. 756-767.

20. Holmes, E., et al., Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Medicine. 2006. 3(8).

21. Chan, M.K., et al., Evidence for disease and antipsychotic medication effects in post-mortem brain from schizophrenia patients. Molecular Psychiatry. 2011. 16(12): p. 1189-1202.

22. Paris, J., The mistreatment of major depressive disorder. Can J Psychiatry. 2014. 59(3): p. 148-51.

23. Ji, Y., et al., Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics‐informed pharmacogenomics. Clinical Pharmacology & Therapeutics. 2011. 89(1): p. 97-104.

24. Kaddurah-Daouk, R., et al., Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept. Translational Psychiatry. 2011. 1(7): p. e26-e26.

25. Zhu, H., et al., Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder - possible role for methoxyindole pathway. PLoS One. 2013. 8(7): p. e68283.

26. Chapekar, N.S., S.R. Bavaskar, and F.J. Sayyad, Bipolar Disorder and Role of Lithium in Its Management: An Overview. Research Journal of Pharmacology and Pharmacodynamics. 2010. 2(2): p. 111-116.

27. Lan, M., et al., Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder. Molecular Psychiatry. 2009. 14(3): p. 269-279.

28. Cunningham, K., et al., Pharmacometabonomic characterization of xenobiotic and endogenous metabolic phenotypes that account for inter-individual variation in isoniazid-induced toxicological response. Journal of Proteome Research. 2012. 11(9): p. 4630-4642.

29. Kinross, J.M., et al., Metabolic phenotyping for monitoring surgical patients. The Lancet. 2011. 377(9780): p. 1817-1819.

30. Hinz, K.-P., et al., Characterization of surgical aerosols by the compact single-particle mass spectrometer LAMPAS 3. Analytical and Bioanalytical Chemistry. 2011. 401(10): p. 3165-3172.

31. Mirnezami, R., et al., Implementation of molecular phenotyping approaches in the personalized surgical patient journey. Annals of Surgery. 2012. 255(5): p. 881-889.

32. Koal, T. and H.-P. Deigner, Challenges in mass spectrometry based targeted metabolomics. Current Molecular Medicine. 2010. 10(2): p. 216-226.

33. Wei, R., Metabolomics and its practical value in pharmaceutical industry. Current Drug Metabolism. 2011. 12(4): p. 345-358.

Received on 22.12.2020 Modified on 26.04.2021

Research J. Pharm.and Tech 2022; 15(3):976-980.

DOI: 10.52711/0974-360X.2022.00163

3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia

1H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease

Metabolic profiling of CSF: Evidence that early intervention may impact on disease progression and outcome in schizophrenia

Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of the bipolar disorder

Evidence for disease and antipsychotic medication effects in post-mortem brain from schizophrenia patients

¹H-NMR spectroscopy combined with pattern recognition analysis reveals characteristic chemical patterns in urines of MS patients and non-human primates with MS-like disease