8
views
0
recommends
+1 Recommend
1 collections
0
shares
• Record: found
• Abstract: found
• Article: found
Is Open Access

# EDITORIAL: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms

Current Neuropharmacology

Bentham Science Publishers

Bookmark
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

### Abstract

1. Introduction Drugs perform dynamic role in perturbing the functional phenotypes of organism; for example, a drug with the vasorelaxant role may have an impact on the nervous system however poses side effects of fatigue [1]. The principal aim of neuro-pharmacology is to explain the impact of therapeutic interventions on the nervous systems [2]. Integrating the continuum of biomedical and healthcare data types forms key factor in understanding therapeutics in neurology and its associated side effect [3-5]. Utilizing the biomedical and healthcare data and implementing precision medicine-aided clinical pathways are anticipated to improve patient outcomes suffering from neurological disorders [6, 7]. In the given context, our special issue of the Current Neuropharmacology emphasizes “Neuroinformatics, Bioinformatics, and Computational Chemistry, for Neuropharmacology” . Our special issue brings an exciting volume of research and review articles that include representative results all the way from application of computational and statistical research to clinical research. Our special issue covers a broad range of topics including structure-based drug discovery, machine learning, molecular modeling, comparative pharmacological evaluations, dual-drug targeting methods and computational docking studies. The special issue covers research on diverse range of  neuro-disorders per se Alzheimer’s disease, schizophrenia, Parkinson’s disease, depression, epilepsy, dementia, migraine and stroke, Niemann-Pick type C disease, rapid-eye-movement (REM) sleep behavior disorder (RBD), amyotrophic lateral sclerosis (ALS) and Huntington’s disease. Multiple research articles in this special issue have used various translational bioinformatics and chemoinformatics approaches and thus provide the collective value of computational approaches in therapeutic discovery and development [8-12]. 2. Quantitative Structure-Activity Relationships (QSAR) of acetylcholine esterase (ACHE) inhibitors to treat Alzheimer’s disease Alzheimer’s disease is a major public health challenge that affects the cognitive ability of the patients [13]. In this study, Babita et al. present an example of the application of chemoinformatics tools to identify physicochemical properties of AChE inhibitors [14]. This research could inspire several follow-up studies to evaluate these compounds in preclinical models [15]. 3. Application of composite machine learning algorithms to evaluate chemical features of herbal inhibitors for the treatment of Schizophrenia The gamma-amino butyric acid (GABA) is a key inhibitory neurotransmitter. In this study, Sahila et al. combine machine learning, computational chemistry and phytochemistry to ascertain the chemical properties of natural inhibitors to treat Schizophrenia. Schizophrenia is a disease with high morbidity and mortality rate and developing natural compounds to alleviate and manage the symptoms would be an innovative approach to address the complex neurological condition [16]. 4. Comparative efficacy of aripiprazole and risperidone in Schizophrenia In this study, Sajeevet et al. provide compelling insights  into the efficacy of two prominent monotherapies for schizophrenia from a single-center in India. It is further interesting as the study is from the Southeast Asia region and thus adds to the global catalog of pharmacological evaluation of available therapeutic efficacies for available neuro- pharmacological therapies [16, 17]. Similarly, independent post-marketing comparative efficacy studies would further help to evaluate the frontline treatments would also contribute to establish personalized, precision therapies for neuro- psychiatric illness [17-20]. 5. Structure based discovery and evaluation of dual target ligands for Parkinson’s disease Perez-Castillo et al. present an innovative structural bioinformatics study that uses mathematical approaches to derive docking scores to understand protein-ligand interaction to target not one, but dual targets. Derivation and biophysical interpretation of docking scores is a relevant theme in structure-based drug discovery [21-23]. This innovative and challenging research will open new avenues for evaluating drug targets that could target pleiotropic protein targets and could also improve drug repositioning [24-26]. 6. Antidepressant drug targets of ursolic acid Singla and Dubey’s research leverage bioinformatics and chemoinformatics tools to determine neurological targets of ursolic acid. The antidepressant role of ursolic acid is known for a while, in this study authors provides complementary evidence using computational studies [27]. 7. Chemoinformatics evaluation of tar- geting monoamine oxidase B adenosine and A(2A) receptor (MAO-B/A2AAR) using chromone derivatives Chromones (1-benzopyran-4-ones) are a natural product with therapeutic implications in cancer, diabetes, cancer  and inflammatory diseases. In this study, Cruz-Monteagudo presents quantitative chemistry evaluations to assess the effect of chromone derivatives for dual targeting MAO-B/A2AAR [28]. 8. Leveraging structure-based drug designing to develop therapies to target neuro-logical disorders Combining a large amount of molecular class specific data has been useful to develop predictive models and algorithms to understand mechanisms like 3D domain swapping, a hallmark feature of neurological diseases including Alzheimer’s and Parkinson's disease [29-32]. Recent efforts in integrating genetic variants and drug target data have revealed several novel therapeutic associations for neurological disorder [33-35]. Aarthy et al. provide an overview of several recent advances in neurological drug discovery. Authors here discuss the application of structural bioinformatics, chemo- informatics methods to neurological disorders. Authors further discuss different neurological disease modalities including Alzheimer’s disease, Niemann-Pick type C disease, REM-RBD, ALS, epilepsy, dementia, migraine, and stroke. The review offers an excellent balance between the clinical, biochemical and bioinformatics methods and thus could help students, researchers and clinicians to understand various inter-disciplinary aspects of drug development [36]. 9. Structural modeling of voltage-gated sodium ion channel from Anopheles gambiae Irrespective of global public health efforts to control malaria and other infectious diseases transmitted by mosquitoes, we are still in search for developing efficient vector controlling measures. The majority of diseases transmitted by mosquito including malaria, dengue, West Nile virus, encephalitis and Zika fever have complications that affect the neurological systems [37-39]. Understanding the molecular role of the vector proteins is critical to developing repellents, vector controlling agents and other chemical agents to control mosquitoes that spread viral diseases [40, 41]. In their research paper, Rithvik and Sowdhamini present results from a challenging task of modeling an ion channel from Anopheles gambiae. The findings surge in the development of novel anti-infectious agents that combat mosquito borne diseases including Zika virus pathogenicity [42]. We would like to thank all the authors from various countries including India, Portugal, Ecuador, Cuba, Chile and Spain. Our special thanks to reviewers from Japan, France, USA, Spain, Portugal and India for their contributions to our special issue and help transcending the boundaries of research. We hope several of these authors would further initiate collaborative projects to develop diagnostic, therapeutic interventions to target neurological diseases.

### Most cited references40

• Record: found
• Abstract: found

### Clinical exome sequencing for genetic identification of rare Mendelian disorders.

(2014)
Clinical exome sequencing (CES) is rapidly becoming a common molecular diagnostic test for individuals with rare genetic disorders. To report on initial clinical indications for CES referrals and molecular diagnostic rates for different indications and for different test types. Clinical exome sequencing was performed on 814 consecutive patients with undiagnosed, suspected genetic conditions at the University of California, Los Angeles, Clinical Genomics Center between January 2012 and August 2014. Clinical exome sequencing was conducted as trio-CES (both parents and their affected child sequenced simultaneously) to effectively detect de novo and compound heterozygous variants or as proband-CES (only the affected individual sequenced) when parental samples were not available. Clinical indications for CES requests, molecular diagnostic rates of CES overall and for phenotypic subgroups, and differences in molecular diagnostic rates between trio-CES and proband-CES. Of the 814 cases, the overall molecular diagnosis rate was 26% (213 of 814; 95% CI, 23%-29%). The molecular diagnosis rate for trio-CES was 31% (127 of 410 cases; 95% CI, 27%-36%) and 22% (74 of 338 cases; 95% CI, 18%-27%) for proband-CES. In cases of developmental delay in children (<5 years, n = 138), the molecular diagnosis rate was 41% (45 of 109; 95% CI, 32%-51%) for trio-CES cases and 9% (2 of 23, 95% CI, 1%-28%) for proband-CES cases. The significantly higher diagnostic yield (P value = .002; odds ratio, 7.4 [95% CI, 1.6-33.1]) of trio-CES was due to the identification of de novo and compound heterozygous variants. In this sample of patients with undiagnosed, suspected genetic conditions, trio-CES was associated with higher molecular diagnostic yield than proband-CES or traditional molecular diagnostic methods. Additional studies designed to validate these findings and to explore the effect of this approach on clinical and economic outcomes are warranted.
Bookmark
• Record: found
• Abstract: found

### Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset.

(1998)
The goal of this study was to project the future prevalence and incidence of Alzheimer's disease in the United States and the potential impact of interventions to delay disease onset. The numbers of individuals in the United States with Alzheimer's disease and the numbers of newly diagnosed cases that can be expected over the next 50 years were estimated from a model that used age-specific incidence rates summarized from several epidemiological studies, US mortality rates, and US Bureau of the Census projections. in 1997, the prevalence of Alzheimer's disease in the United States was 2.32 million (range: 1.09 to 4.58 million); of these individuals, 68% were female. It is projected that the prevalence will nearly quadruple in the next 50 years, by which time approximately 1 in 45 Americans will be afflicted with the disease. Currently, the annual number of new incident cases in 360,000. If interventions could delay onset of the disease by 2 years, after 50 years there would be nearly 2 million fewer cases than projected; if onset could be delayed by 1 year, there would be nearly 800,000 fewer prevalent cases. As the US population ages, Alzheimer's disease will become an enormous public health problem. interventions that could delay disease onset even modestly would have a major public health impact.
Bookmark
• Record: found
• Abstract: found

### Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders.

(2014)
Neurodevelopmental disorders (NDDs) affect more than 3% of children and are attributable to single-gene mutations at more than 1000 loci. Traditional methods yield molecular diagnoses in less than one-half of children with NDD. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) can enable diagnosis of NDD, but their clinical and cost-effectiveness are unknown. One hundred families with 119 children affected by NDD received diagnostic WGS and/or WES of parent-child trios, wherein the sequencing approach was guided by acuity of illness. Forty-five percent received molecular diagnoses. An accelerated sequencing modality, rapid WGS, yielded diagnoses in 73% of families with acutely ill children (11 of 15). Forty percent of families with children with nonacute NDD, followed in ambulatory care clinics (34 of 85), received diagnoses: 33 by WES and 1 by staged WES then WGS. The cost of prior negative tests in the nonacute patients was $19,100 per family, suggesting sequencing to be cost-effective at up to$7640 per family. A change in clinical care or impression of the pathophysiology was reported in 49% of newly diagnosed families. If WES or WGS had been performed at symptom onset, genomic diagnoses may have been made 77 months earlier than occurred in this study. It is suggested that initial diagnostic evaluation of children with NDD should include trio WGS or WES, with extension of accelerated sequencing modalities to high-acuity patients. Copyright © 2014, American Association for the Advancement of Science.
Bookmark

### Author and article information

###### Journal
Curr Neuropharmacol
Curr Neuropharmacol
CN
Current Neuropharmacology
Bentham Science Publishers
1570-159X
1875-6190
November 2017
November 2017
: 15
: 8
: 1058-1061
###### Affiliations
[1 ]Institute of Next Generation Healthcare (INGH), Icahn Institute of Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, USA;
[2 ]Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-, India;
[3 ]In silico Research Laboratory, Legene Biosciences, Vijaynagar, Indore-, India;
[4 ]IMQ Zorrotzaurre Clinic, Ballets Olaeta Kalea, 4, Spain;
[5 ]Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain;
[6 ]IKERBASQUE, Basque Foundation for Science , , Spain.
###### Author notes
[* ]Address correspondence to this author at the Institute of Next Generation Healthcare (INGH), Icahn Institute of Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, Manhattan, NY USA; Tel: +1-646-267-6448; E-mail: shameer.khader@ 123456mssm.edu
[^]

Current address: Healthcare Transformation Services, Philips Healthcare, 2 Canal Park, Cambridge, MA 02141

CN-15-1058
10.2174/1570159X1508171114113425
5725537
29199918