Computational Neuropharmacology E-Book
173,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book gives comprehensive insights into the cutting-edge intersection of computational methods and neuropharmacology, making it an essential resource for understanding and advancing medication for neurological and psychiatric disorders.
Computational Neuropharmacology is an in-depth exploration of the convergence of computational methods with neuropharmacology, a science concerned with understanding pharmacological effects on the nervous system. This volume explores the most recent breakthroughs and potential advances in computational neuropharmacology, providing an extensive overview of the computational tools that are transforming medication discovery and development for neurological and psychiatric illnesses. Fundamental principles of computational neuropharmacology, descriptions of molecular-level interactions and their consequences for modern neuropharmacology, and an introduction to theoretical neuroscience are highlighted throughout this resource. Additionally, this study addresses computational attitudes in counseling psychology to improve therapeutic procedures through data-driven insights. Computational psychiatry uses computational technologies to bridge the gap between the molecular basis and clinical symptoms of psychiatric diseases.
This volume covers computational approaches to drug discovery in neurohumoral transmission and signal transduction, Parkinson’s disease, epilepsy, and Alzheimer’s disease, and the use of molecular docking and machine learning in drug development for neurological disorders. It also discusses the use of computational methods to uncover potential treatments for autism spectrum disorder, depression, and anxiety.
Audience
This book is a valuable resource for computer scientists, engineers, researchers, clinicians, and students, providing a detailed understanding of the computational tools that are changing the developing field of neuropharmacology, leading the future of medication discovery and development for neurological and psychiatric illnesses by combining modern computational approaches with neuropharmacological research.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 804
Veröffentlichungsjahr: 2025
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
Part 1: FUNDAMENTALS OF COMPUTATIONAL NEUROPHARMACOLOGY
1 Basic Principles of Computational Neuropharmacology: Neuroscience Meeting Pharmacology
Abbreviations
1.1 Introduction
1.2 Basics of Computational Neuropharmacology
1.3 Multiple Aspects of Computational Neuropharmacology
1.4 Recent Developments in Computational Neuropharmacology
1.5 Limitations of Computational Neuropharmacology
1.6 Conclusion
References
2 Neuropharmacology in the Molecular Epoch
List of Abbreviations
2.1 Introduction
2.2 History of Neuropharmacology
2.3 Neurochemical Interactions
2.4 Molecular Pharmacology of Neuronal Receptors
2.5 Neuropharmacological Drugs
2.6 Impact of Biotechnology of Neuropharmacology
2.7 Future Research and Perspectives
2.8 Conclusion
Acknowledgments
References
3 Basics of Theoretical Neuroscience
List of Abbreviations
3.1 Introduction
3.2 Properties of Neurons and Neuronal Signaling
3.3 Recording Neuronal Responses
3.4 Neural Encoding and Neuronal Decoding
3.5 Neuronal Network Models
3.6 Learning and Synaptic Plasticity
3.7 Conclusion
References
4
In Silico
Modeling of Drug–Receptor Interactions for Rational Drug Design in Neuropharmacology
Abbreviations
4.1 Introduction
4.2 Drug–Receptor Interactions
4.3
In Silico
Methods for Modeling Drug–Receptor Interactions
4.4 Applications of
In Silico
Modeling in Neuropharmacology
4.5 Case Studies
4.6 Conclusion
References
5 Computational Attitudes in Counselling Psychology
List of Abbreviations
5.1 Introduction
5.2 Theoretical Foundations of Computational Attitude
5.3 Empirical Evidence and Efficacy of Computational Counselling
5.4 Ethical and Legal Considerations
5.5 Future Directions and Possibilities
5.6 Conclusion
References
6 Computational Psychiatry: Addressing the Gap Between Pathophysiology and Psychopathology
List of Abbreviations
6.1 Introduction
6.2 Roadmap of Conventional to Modern Evolution Towards Mental (Psychological) Illness
6.3 Pathophysiology of Mental Illness
6.4 Psychopathology
6.5 Computational Psychiatry (CP)
6.6 Computational Psychiatry: An Advanced Version Links Pathology and Psychopathology
6.7 Conclusion
References
7 Computational Neuropharmacology in Psychiatry
List of Abbreviations
7.1 Introduction
7.2 Need for Computational Neuropharmacology in Psychiatry
7.3 Data-Driven Computational Approaches in Psychiatry
7.4 Role of Diagnostic Classification
7.5 Machine Learning and Diagnostic Precision
7.6 The Challenges of Treatment Response Prediction
7.7 Future Implications and Ethical Considerations
7.8 Machine Learning for Informed Decisions
7.9 Network Analysis: Unraveling Symptom Dynamics
7.10 Theory-Driven Computational Approaches: Integrating Knowledge and Data
7.11 Biophysically Realistic Neural Network Models: Bridging the Gap Between Biology and Computation
7.12 Bayesian Models
7.13 Combining Data-Driven and Theory-Driven Computational Approaches
7.14 Conclusion
References
Part 2: CLINICAL ASPECTS OF COMPUTATIONAL NEUROPHARMACOLOGY
8 Computational Attitudes to Drug Discovery in Neurohumoral Transmission and Signal Transduction
Abbreviations
8.1 Introduction
8.2 Neurohumoral Transmission and Signal Transduction
8.3 Computational Approach in Creating Neurohumoral and Synaptic Models
8.4 Primitive Computational Models
8.5 Conclusion
References
9 Computational Attitude to Drug Discovery in Parkinson’s Disease
List of Abbreviations
9.1 Introduction
9.2 PD and Drug Development
9.3 Animal Models and Translational Discovery
9.4 Pathophysiology
9.5 Validated Biomarkers
9.6 Computational Drug Discovery
9.7 Outcomes From Gene Ontology and KEGG Analysis
9.8 Conclusion
Acknowledgments
References
10 Computational Attitudes to Drug Discovery in Epilepsy
List of Abbreviations
10.1 Introduction
10.2 Traditional Drug Discovery Approaches for Epilepsy
10.3 Computer Simulations in Understanding and Optimizing Drug Efficacy
10.4 Development of Computational Models
10.5 Computational Models for Predicting Effects on Seizure Activity
10.6 Data Integration and Analysis in Epilepsy Research
10.7 Challenges and Future Directions
10.8 Conclusion
Acknowledgments
References
11 Computational Attitudes to Drug Discovery in Alzheimer’s Disease
List of Abbreviations
11.1 Introduction
11.2 Alzheimer’s Disease
11.3 Computational Attitudes to Drug Discovery
11.4 Applications of Computational Attitudes to Drug Development Process
11.5 Conclusion
References
12 The Integration of Molecular Docking and Machine Learning in Drug Discovery for Neurological Disorders
Abbreviations
12.1 Introduction
12.2 Neurodegenerative Disease
12.3 Molecular Docking
12.4 Machine Learning in Drug Discovery
12.5 Random Forest
12.6 Naïve Bayesian
12.7 Support Vector Machine
12.8 Conclusion
References
13 Computational Attitudes to Drug Discovery in Autism Spectrum Disorder
List of Abbreviations
13.1 Introduction
13.2 Clinical, Genetic, and Molecular Heterogeneity in Autism Spectrum Disorder
13.3 The Necessity of Drug Discovery
13.4 Computational Model for Drug Discovery
13.5 Importance of Multiomics and Endophenotyping- Based Methods Toward Precision Medicine
13.6 Network-Based Approach for Diseases/Drug Modeling
13.7 Drug Repurposing Candidates for Treatment of ASD Using Bioinformatic Approaches
13.8 Conclusion and Future Prospective
Acknowledgment
References
14 Computational Approaches to Drug Discovery in Depression
List of Abbreviations
14.1 Introduction
14.2 Types of Depressive Disorders
14.3 Hypotheses and Pathways of Depression
14.4 Receptors in Depression
14.5 Computational Approaches to Depression
14.6 Network Pharmacology of Depression
14.7 Conclusion
References
15 Computational Attitudes to Drug Discovery in Anxiety
List of Abbreviations
15.1 Introduction
15.2 Computational Approaches for Drug Discovery
15.3 Ligand-Based Techniques
15.4 Pharmacophore
15.5 Structure-Based Methods for Screening
15.6 AI
15.7 Machine Learning Algorithms for Anxiety Disorder Detection and Prediction
15.8 A Review of the Literature on Machine Learning Approaches for Anxiety-Related Disorders
15.9 Molecular Dynamic Simulation
15.10 Future Prospective
15.11 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Interactions between drugs and neurotransmitter receptors.
Table 1.2 Drugs that communicate with ionic channels [18].
Chapter 2
Table 2.1 Diverse neuronal receptors targeted in neuropharmacology.
Table 2.2 List of various approved neuropharmacological drugs.
Table 2.3 Various biotechnological fields and tools and their impact on neurop...
Chapter 4
Table 4.1 A brief summary of
in silico
techniques used for modeling drug– rece...
Table 4.2 List of various softwares used in
in silico
methods for modeling dru...
Chapter 6
Table 6.1 Inherited deviations allied with psychological disease [43].
Table 6.2 Environmental factors allied with psychological diseases.
Chapter 8
Table 8.1 Neurotransmitters and their role in various neurological disorders.
Table 8.2 Properties of cerebral circuit in NMDA and GABA-A neuronal synapse e...
Chapter 9
Table 9.1 Biomarkers that predict PD progression in morphology and clinical le...
Table 9.2 Biomarkers that predict PD progression in homeostasis level.
Table 9.3 DEGs and their protein classification to predict PD progression base...
Table 9.4 Some common PD related genes and its degree analysis.
Table 9.5 Top 20 biological process from identified 414 genes.
Table 9.6 Molecular function and its strength for 18 filtered genes.
Table 9.7 Top 20 cellular component analysis descriptors from 37 identified ge...
Table 9.8 KEGG analysis of the matched proteins and genes to identify PD targe...
Chapter 10
Table 10.1 Important epilepsy biomarkers and their role in detection of epilep...
Chapter 11
Table 11.1 Drugs being developed for AD (clinicaltrials.gov, accessed January ...
Table 11.2 The role of AI in the development of personalized treatment plans.
Chapter 12
Table 12.1 The role of AI in the development of personalized treatment plans.
Table 12.2 The use of AI in healthcare raises important ethical and privacy co...
Chapter 13
Table 13.1 Databases of primary sequence.
Table 13.2 Tool for phylogenetic analysis.
Table 13.3 Databases for nucleotide sequences.
Table 13.4 Database of genome sequencing.
Table 13.5 Database for protein sequence.
Table 13.6 Databases for molecular interactions.
Table 13.7 Drug target databases.
Table 13.8 Tools for molecular dynamics simulation.
Chapter 14
Table 14.1 Hydrophobic and hydrophilic interactions site binding of 5-HT.
Table 14.2 Antidepressant potency of tryptophan present in banana and pineappl...
Table 14.3 Traditional Chinese medicines investigated for depression using com...
Table 14.4 Important enzymes in depression.
Chapter 15
Table 15.1 Recent advancement on computational approaches.
List of Illustrations
Chapter 1
Figure 1.1 Different targeted approach used in computational neuropharmacology...
Figure 1.2 Use of computational models for the study of drugs and their target...
Chapter 2
Figure 2.1 Representation of mechanism of neuronal communication (or) synapsis...
Figure 2.2 Protein structure of various neurotransmitter receptors (1a: Nicoti...
Chapter 3
Figure 3.1 Key components of neurons.
Figure 3.2 Schematic process of neuronal decoding.
Figure 3.3 Schematic diagram showing spiking neural network model.
Figure 3.4 Schematic diagram showing recurrent neuronal model.
Chapter 4
Figure 4.1 Processes involved in
in silico
drug design.
Figure 4.2 Structure-based drug design workflow for identifying potential drug...
Figure 4.3 Ligand-based drug design workflow for identifying potential drug ca...
Chapter 5
Figure 5.1 The top four computer modelling strategies. The methods can be mixe...
Figure 5.2 (a) The components of Predictive modeling: after deployment the inp...
Figure 5.3 Cognitive models for mental addition fluency [52]. Processing speed...
Figure 5.4 The process model of emotion regulation proposed by gross (1998) [5...
Figure 5.5 Wisconsin Card sorting test, used to detect cognitive deficits, app...
Chapter 6
Figure 6.1 Evolution of mental illness from neuroscience, pathophysiology, psy...
Figure 6.2 Effect of fundamental elements on mental health.
Chapter 7
Figure 7.1 Summary of need for computational neuropharmacology in psychiatry (...
Figure 7.2 Applications of data driven technology and computational tools for ...
Chapter 8
Figure 8.1 Diagrammatic representation of steps involved in excitatory and inh...
Figure 8.2 Diagram of the chemical synapse.
Chapter 9
Figure 9.1 PPI analysis and their descriptors.
Figure 9.2 PPI network of selected genes in Parkinson disease.
Figure 9.3 Linear regression betweenness centrality analysis of screened targe...
Chapter 10
Figure 10.1 Bridging of EEG data in epilepsy drug discovery.
Chapter 11
Figure 11.1 Computational attitudes of drug discovery for AD by different stra...
Figure 11.2 Molecular pathway of dementia and cognitive impairment (AD).
Chapter 12
Figure 12.1 Utilization of AI in computational drug discovery.
Figure 12.2 AI in drug discovery and treatment of neurological disorders.
Chapter 13
Figure 13.1 Application of bioinformatics on biological sciences.
Figure 13.2 Process of drug discovery using computational and system biology a...
Chapter 14
Figure 14.1 Docking targets for anti-depressant drug candidates. AKT1: AKT Ser...
Figure 14.2 Indole and Azaindole aminoquinolines derivatives interacting sites...
Figure 14.3 The 2D co-crystallize ligand of benzodiazepine with its interactin...
Figure 14.4 The 2D interactions of co-crystalize ligand harmine over human MAO...
Figure 14.5 Major metabolite biomarkers in depression.
Chapter 15
Figure 15.1 Flowchart of virtual screening.
Figure 15.2 Workflow of QSAR.
Figure 15.3 Strategies of drug design through molecular docking.
Figure 15.4 Workflow of machine learning.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
Begin Reading
Index
Wiley End User License Agreement
Pages
ii
iii
iv
xix
xx
xxi
xxii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Computational Neuropharmacology
Fundamentals and Clinical Aspects
Edited by
Bhupendra Prajapati
Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Gujarat, India
Department of Industrial Pharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
Alok Tripathi
Era College of Pharmacy, Era University Lucknow, Uttar Pradesh, India
Rishabha Malviya
Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, India
and
Lucy Mohapatra
Amity Institute of Pharmacy, Amity University, Noida, Uttar Pradesh, India
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-24244-3
Front cover image courtesy of Adobe FireflyCover design by Russell Richardson
Foreword
The book Computational Neuropharmacology: Fundamentals and Clinical Aspects explores the synergies between computational science and neuropharmacology. The human brain, with its complexity and ambiguous functions, has long confounded scientists and clinicians, and traditional pharmacological treatments often fall short in treating neurological and psychiatric disorders. Computational neuropharmacology offers more precise, efficient, and personalized therapeutic options.
The book, edited by Dr. Rishabha Malviya and his esteemed team, is a significant contribution to neuropharmacology, offering a comprehensive guide to understanding how computational methods are revolutionizing approaches to neuroscience and pharmacology. It begins with fundamental principles and progresses to specialized topics, providing insights into computational techniques that are transforming drug development, theoretical neuroscience, clinical applications, and psychiatric treatment approaches.
The book’s interdisciplinary approach bridges the gaps between computer science, pharmacology, and neuroscience, providing unique and invaluable information. The integration of molecular docking, machine learning, and in silico modeling demonstrates how these tools collectively enhance the rational design of neuropharmacological agents.
The future of neuropharmacology depends on the seamless integration of computational techniques with traditional methodologies. This book celebrates the remarkable progress made so far and offers a glimpse into the fascinating possibilities that lie ahead. Through this book, researchers can anticipate significant breakthroughs that will enhance their ability to diagnose, treat, and ultimately understand neurological and psychiatric disorders.
This book is more than just an academic publication; it serves as a resource for the future and a source of encouragement. The editors and contributors have developed an excellent resource through this book that will undoubtedly affect the course of neuropharmacology for years to come.
I welcome this opportunity to congratulate Dr. Rishabha Malviya and his team for their remarkable achievement.
Shri Mahendra Patel
Senior General Manager & Head R&D
Aculife Healthcare Private Ltd (Nirma Ltd) Ahmedabad, India
Preface
This book is a detailed exploration of the current state and prospects of computational techniques in neuropharmacology. Its objective is to provide a complete overview of the computational tools currently utilized in the field, spanning from molecular modeling to clinical applications. Beginning with a discussion of the fundamental principles of computational neuropharmacology, it continues into specific chapters, including theoretical neuroscience, in silico modeling, and the use of computational approaches in counseling psychology and psychiatry.
The book’s interdisciplinary approach integrates neuroscience, pharmacology, and computer science, offering researchers, physicians, and students a comprehensive perspective. Its chapters on drug discovery for specific neurological disorders—such as Parkinson’s disease, epilepsy, Alzheimer’s disease, autism spectrum disorder, depression, and anxiety—emphasize personalized approaches and innovative methodologies being developed to address these complex medical conditions. Another key chapter explores the integration of molecular docking and machine learning, providing powerful tools for identifying and optimizing medicinal molecules. Additionally, the book includes thorough case studies and practical examples to help readers acquire the knowledge and skills needed to apply these technologies in their own research and clinical practice.
Computational neuropharmacology will continue to play an important role in identifying and developing novel medications for neurological and psychiatric illnesses. The editors thank the contributing authors for their expertise and dedication, as well as the readers for their interest and participation. In brief, Computational Neuropharmacology: Fundamentals and Clinical Aspects is more than just a collection of chapters; it reflects the persistent search for a better understanding of the brain and its interactions with pharmaceutical drugs.
The editors are grateful to the reviewers who have contributed to improving the quality of the book through their constructive comments. The editors also thank Martin Scrivener and Scrivener Publishing for their support and publication.
Part 1FUNDAMENTALS OF COMPUTATIONAL NEUROPHARMACOLOGY
1Basic Principles of Computational Neuropharmacology: Neuroscience Meeting Pharmacology
Lucy Mohapatra1, Alok S. Tripathi2*, Deepak Mishra1, Alka1,3 and Sambit Kumar Parida4
1Amity Institute of Pharmacy, Lucknow, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India
2Era College of Pharmacy, Era University, Lucknow, Uttar Pradesh, India
3Faculty of Pharmaceutical Sciences, Rama University, Mandhana, Kanpur (Uttar Pradesh), India
4Amity Institute of Pharmacy, Amity University Rajasthan, RIICO Kant Kalwar Industrial Area, Jaipur-Delhi Highway (Main Road), Jaipur, Rajasthan, India
Abstract
The graded features of the brain are protruding in the pharmacologic handling of psychiatric ailments, chiefly directing the receptors that outspread mounting to inherent connectivity within various sections of the brain, inter-provincial connectivity and clinical annotations, including electroencephalogram. The identification and depiction of impending pharmacologic targets in neurology and psychiatry is an elementary enigma at the connection linking medicinal chemistry and the field of neurosciences. Breathtaking novel procedures including proteomics and genomics have encouraged brisk advance, prologue copious inquiries as to the operational importance of drug receptor interactions. A cohesive understanding of neuropharmacological negotiators necessitates linking the breach amid their molecular mechanisms and the biophysical determining factors of neural role as it has been observed that numerous psycho and neuronal active medications characteristically slog in nerve cells by distressing several facets of electrophysiological activities. Computational neuropharmacology lays off a key character in this operable correlation. Vigorous numerical simulations have been established communicating all foremost dynamic rind stuffs under endogenic and exogenic chemical control in the brain. These primarily incorporate voltage-dependent ion channels, GPCRs and neurohumoral transmissions. This chapter describes neuropharmacology from the computational viewpoint and delivers persuasive illustrations of how several compartmental simulations can clarify, elucidate, and forecast the consequence of neuro chemical agonists and antagonists in the brain. It also highlights the recent advancements in the field of computational neuropharmacology to enable understanding the mechanism of action of neuropharmacological intervention on neurones.
Keywords: Neuropharmacology, computational neuroscience, neuroimaging, neurotransmitters, compartmental simulations
Abbreviations
5-HT
5-hydroxytryptamine
Ach
Acetylcholine
ADD
Attention deficit disorder
ADHD
Attention deficit hyperactivity disorder
ALS
Amyotrophic lateral sclerosis
AMPA
α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
CNS
Central nervous system
DA
Dopamine
EEG
Electroencephalography
fMRI
Magnetic resonance imaging
GABA
Gamma-aminobutyric acid.
K
+
Potassium
MEG
Magnetoencephalography
NA
Noradrenaline
Na
+2
Sodium
QSAR
Quantitative Structure-Activity Relationships
QS
Quantitative systems pharmacology
REM
Rapid-eye-movement
SBD
Sleep behavior disorder
1.1 Introduction
Computational neuroscience has the potential to leverage our increasing knowledge about the brain to understand and address brain malfunctions in diseases [1]. In recent years, the lines between imaging and computational neuroscience have blurred, with many practitioners in fMRI and electrophysiology adopting computational approaches due to the statistical and computational skills required for analyzing neuroimaging data [2, 3]. Two key areas in computational neuroscience relevant to neuroimaging are brain activity modelling, which seeks to explain understanding, behaviour, and cognition, as well as biophysical models of neural dynamics, which are inspired by biophysics [4]. Brain diseases affect around 16 million adult Americans, leading to cognitive and behavioural impairments such as memory and motor control issues. As life expectancy increases, the prevalence of this neuropathologist also rises, posing potential social and economic challenges [5]. While pharmaceutical interventions are the most often used therapies for neuropsychiatric patients, numerous neurological and mental illnesses entail unique biological processes, localized parts of the nervous system, and different cellular types [6, 7]. Neuropharmacology aims to understand how therapeutic interventions impact the nervous system, but drugs can have dynamic effects on an organism’s functional phenotypes and may cause side effects. To enhance patient outcomes for neurological illnesses, it is crucial to integrate biomedical and healthcare data and establish treatment pathways supported by precision medicine [8]. Neuroinformatics, Bioinformatics, and Computational Chemistry perform an important function in this context. A special issue of Modern Neuropharmacology focuses on these topics, presenting research and review papers that range from computational and statistical analyses to clinical investigations [9, 10]. The issue covers various subjects, including computer docking investigations, comparative pharmacological assessments, artificial intelligence, molecular modelling, and structure-based drug development as shown in Figure 1.1[11]. It includes research on neuro-disorders such as Alzheimer’s disease, schizophrenia, Parkinson’s disease, depression, epilepsy, dementia, migraine, stroke, Niemann–Pick type C disease, rapid-eye-movement (REM) sleep behavior disorder (SBD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease, highlighting the importance of computational techniques in the research and advancement of therapeutics [11, 12].
Figure 1.1 Different targeted approach used in computational neuropharmacology. The different aspects of Neuropharmacology are covered such as brain cells, cytoarchitecture, behavior and mental disorder in which targeted biomolecules can show action by using a computational approach.
1.2 Basics of Computational Neuropharmacology
Advancements in computational models have the potential to significantly benefit medicinal chemistry, a field that is already extensively utilizing computational techniques in fundamental neuroscience. As we get a greater comprehension of molecular mechanisms and their interactions with biophysical factors influencing neural activity, the process of discovering and characterizing new pharmaceutical are neurological and psychiatric are going to become more effective and successful in its goals [13]. The future of this functional relationship between computational neuroscience and bioinformatics looks promising. One misconception that needs to be addressed is the belief that computer simulations rely on arbitrary assumptions that cannot be verified by the modeler. Most computational models are built on implicit assumptions that underlie the Scientists everywhere use conceptual frameworks. Developing a novel computer model can be a fascinating scientific endeavor, aiming to explore potential explanations for complex experimental data that might otherwise be challenging to understand [14]. Early versions of these models can generate hypotheses that predict how specific situations, such as drug exposure, might affect individuals, even before such predictions have been experimentally validated. When experimental data becomes available for these scenarios, the model can be validated or refined using the new information, leading to the formulation of new scientific hypotheses that can be rigorously tested [15]. Although constructing a completely predictive model for neural processes remained a challenging assignment due to their intricacy and the continuous discovery of new mechanisms, the process of building these models often hints at the existence of novel mechanisms. Importantly, when a model’s predictions are confirmed by experimental results, its applicability for neuropharmacology is enhanced, providing valuable insights for drug development and treatment strategies [16]. Table 1.1. depicts the interactions among drugs and the various neurotransmitters on which they act upon.
Table 1.1 Interactions between drugs and neurotransmitter receptors.
G-Protein family
Brand name of the drug
Region of the brain
Drug action
Condition
References
Acetylcholine (Ach)
Muscarinic 1/5 (Gq)
Donepezil
Cortex, hippocampus
Breakdown inhibitor
Alzheimer
[17]
Nicotinic
*2
HP-184
Hippocampus
Release stimulator (also for 5-HT)
Spinal cord injury
[18]
Nicotine
Thalamus, striatum, anterior cingulate cortex
Agonist
Smoking cessation
[19]
Philanthotoxin
Muscles cells, cortex
Noncompetitive antagonist
Neurodegenerative disease, cognitive disorder
[20]
Dopamine (DA)
d1/5 (Gs)
Adrogolide
Ventral tegmental area, limbic forebrain, nucleus acubens
Agonist
Cocaine addiction
[21]
Bupropion (Zyban, WELLBUTRIN)
Frontal cortex, hippocampus, striatum, thalamus
Reuptake inhibitor
Smoking cessation, depression
[22]
Haloperidol (Haldol)
Mesocotex, limbic system, nigrostriatal pathway
Antagonist (also for d2)
Antipsychotic
[23]
Methylphenidate (Ritalin)
Prefrontal cortex, hippocampus, striatum, hypothalamus
Reuptake inhibitor
ADD, ADHD
[24]
Reserpine
Striatum, frontal cortex
Ruptake inhibitor (also for NE and 5HT)
Antipsychotic
[25]
Gamma-aminobutyric acid (GABA)
GABAA/C
*3
Diazepam (Valium, Diastat)
Cortex, telencephalon, hypothalamus
Agonist
Anxiety, epilepsy
[26]
Methohexital (Brevital Sod)
Thalamus, hippocampus
Agonist
Anestetic
[27]
Sodium valproate (Depakote)
Occipital, parietal, and frontal cortex, accumbens, substantia nigra
Breakdown inhibitor (also NE, DA, and 5-HT modulator)
Mania, migraine, epilepsy, GEFS+ (generalized epilepsy with febrile seizures plus)
[28]
Topiramate (Topamax)
Prefrontal and occipital cortex
Agonist (Na+ blocker)
Epilepsy, SMEI (severe myoclonic epilepsy of infancy)
[29]
GABAB (Gi)
Baclofen (Lioresal)
Hypothalamus, hippocampus/DG, frontal cortex
Agonist
Antispastic agent for multiple sclerosis.
[29]
5-hydroxytryptamine (5-HT)
5-HT1A, B,D (Gi/o)
Eletriptan (Relpax)
Trigeminal sensory nerve-endings, intracranial blood vessels
Agonist
Migraine
[30]
Fluoxetine (Prozac)
Dentate gyrus
Reuptake inhibitor
Depression, obsessive-compulsive disorder
[31]
Vilazodone
Cortex, hippocampus
Partial Agonist (SSRI)
Depression [
51
,
52
]
[32]
5-HT2A,B,C (Gq/11)
Quetiapine (Seroquel)
Prefrontal cortex, striatum, limbic system, anterior pituitary gland
Antagonist
Bipolar disorder, schizophrenia
[33]
TGBA01AD
NA
Reuptake inhibitor (5HT2/1A agonist)
Depression
[34]
5-HT5A (Gi/o)
SGS-518
N/A
Antagonist
Schizophrenia
[35]
5-HT4A, B (Gs)
Benzoate
Frontal cortex, striatum, hippocampus
Antagonist (also for 5HT3)
Neurodegenerative diseases
[36]
5-HT6 (Gq/s)
Olanzapine (Zyprexa)
Frontal cortex, hippocampus
Antagonist (also for 5HT2, DA)
Schizophrenia
[37]
5-HT3
*1
Mirtazapine (Remeron)
Central amygdala, anterior insula, septum
Antagonist
Depression
[38]
Glutamic acid (Glu)
AMPA
*1
S-18986
Frontal cortex, dorsal hippocampus
Modulator
Neurodegeneration, cardiovascular ischemia, cognitive disorder
[39]
Talampanel
Amygdala
Noncompetitive antagonist
Epilepsy, Parkinson
[40]
mGluR1,5 (Gq) mGluR2,3, 4,6,7,8 (Gi/o)
Lamotrigine (Lamictal)
Striatum, thalamus, cortex, hippocampus, amygdala
Release inhibitor (Ca2+ and Ih blocker)
Epilepsy, bipolar disorder.
[41]
LY354740
Striatum, hippocampus, cortex, cerebellum
Agonist
Anxiety
[18]
MPEP
Striatum, thalamus, cortex, amygdala
Antagonist (also for AMPA)
Epilepsy, pain, neurodegenerative diseases, anxiety
[42]
Kainate
*1
LY293558
Hippocampus, lateral and medial habenulae, superior and inferior colli
Antagonist (also for AMPA)
Postoperative pain, anxiety
[43]
NMDA 84]
*2
Aptiganel
Hippocampus, cortex
Antagonist
Neurodegeneration, Parkinson, brain injury, cardiovascular ischemia
[44]
Ifenprodil
Hippocampus/CA3, piriform cortex, amygdala, striatum
Noncompetitive antagonist (Ca2+ blocker)
Schizophrenia
[45]
TAN-950A
Hippocampus/CA1
Agonist
Neurodegenerative disease [
65
,
66
]
[46]
Noradrenaline (NA)
α1A,B,D (Gq) α2A,B,C(Gs) β1,2,3 (Gs)
Atomoxetine (Strattera)
Caudate putamen, cortico (prefrontal ventral and orbital)-baso thalamic loop
Release inhibitor
ADHD
[47]
Dextroamphetamine (Dexedrine)
Insular cortex
Reuptake inhibitor (also for DA and 5-HT)
ADHD
[48]
DOV 21947
Striatum, mesocorticolimbic, cerebrospinal fluid
Reuptake inhibitor (also for DA and 5-HT)
Depression
[49]
All receptors are G-coupled unless noted with* (ionotropic); *1 Monovalent cations; *2 Mono/divalent cations; *3 Chloride.
1.3 Multiple Aspects of Computational Neuropharmacology
Computational psychiatry is a developing field that bridges the gap between psychiatric and computational neuroscience. Computational neuroscience is a discipline of neuroscience that uses theoretical investigation, mathematical representations, and brain abstractions to comprehend the principles underlying the progression, structure, physiology, affective, and cognitive processes of the nervous system. The different aspects of computational neuropharmacology include drug databases, computational fMRI, compartmental simulations of neuronal electrophysiology along with quantitative systems pharmacology and others has been discussed in this section.
1.3.1 Drug Databases
As neuropharmacology expands, medicinal chemists face challenges in gathering comprehensive information on significant molecules within their areas of interest. Given that new drugs target specific brain regions, it is crucial to understand the systems these medications collaborate with each other, as well as their modes of action. Formalizing this information would be extremely beneficial to experimental and theoretical researchers associated with drug discovery, advancement, and interactions [50]. The Neuroscience Database Gateway provides relevant resources, involving datasets of synaptic proteins, GPCRs, and disease-specific neuroinformatic archives and tools. Currently, medicinal chemistry and biomedical literature organize available data primarily by medicine (molecular structure) or disease (pathological state). However, for computational neuropharmacology, a more effective approach would be to group information based on underlying sub-cellular processes [51]. This strategy would allow researchers to explore medication effects at the level of essential chemical components, cell types, and brain regions, enabling the application of new models and simulations with references to quantitative findings in the literature. For instance, the Brain Pharm database focuses on compounds acting on neuronal receptors and signal transduction pathways in both healthy and unhealthy brains, facilitating research on medications for various neurological disorders [52]. This database serves as a source of information that can aid in creating computational neuroscience models relevant to medicinal chemistry. By leveraging this knowledge, researchers can apply it to different categories of dynamical models to demonstrate its potential. The first category involves single-neuron models with compartmentalized electrophysiology and membrane biophysics. The second category comprises computer simulations of enzyme pathways in subcellular compartments of a single neuron. Utilizing this information within these models can offer valuable insights into drug effects and their mechanisms of action, bridging the gap between computational neuroscience and medicinal chemistry as shown in Table 1.2[53].
1.3.2 Computational fMRI
In this section, we present an approach for describing fMRI data using computational models of brain activity and provide an illustrative example highlighting key concepts. Over the course of the past five years, innovative data illustrating philosophy has emerged, moving away from simply modelling noticed neurological signals about experimental variables (e.g., conventional ANOVA models) and towards explaining the data in terms of quantities that the brain must encode, focused on simplified assumptions about brain function [54]. This paradigm often rests on the idea that the brain seeks to optimize various processes. Some researchers propose that perception aims to maximize the reciprocal information among sensory signals and cognitive representations of their sources or minimize the prediction error [55, 56]. Similarly, for motor coordination, many cost functions have been proposed, which the brain strives to minimizes throughout activities [57]. A classic example of this kind of technique is the use of encouraging prediction errors in the development of fMRI regressors. The reward prediction error is a significant latent variable in models of optimal control and reinforcement learning, capturing the discrepancy between anticipated and actual rewards [58, 59]. The value function, representing the expected reward, guides the best behavioural strategy once the mistake in reward estimation is minimized. The underlying functional anatomy of value acquisition along with the brain underpinnings of reward in prediction have been widely researched., emphasizing brain regions like the ventral striatum [60, 61]. A crucial aspect of computational fMRI is the ability to compare various computer models using actual fMRI results. This enables researchers to discern differences in the neurophysiological implementations of different computational approaches and their corresponding neural correlates of assumed mental processes [62]. Additionally, refining neuromeric functions, which translate computational variables to observable neural responses, helps establish a precise mapping from computing modifications to representations of neural networks. To demonstrate how to get psychological states and utilized to explain neuronal activity, we provide a small example in the following paragraphs [63].
Table 1.2 Drugs that communicate with ionic channels [18].
Ionic channel
Family
Drug name
Mechanism of action
Region of brain involved
Neurological condition
Potassium
K
dr
(noninactivating)
Flindokalner
Stimulation
N/A
Cerebrovascular ischemia
K
ir
(inward rectifying)
Spironolactone
K+-sparing diuretic
Lateral septum, hippocampus
Andersen-Tawil syndrome
K
M
(muscarinic dependent)
DMP-543
Inhibitor (Ach release enhancer)
Hippocampus
Alzheimer’s, epilepsy
K
ATP
BAY-X-9227
Stimulation
CNS neurons
Neurodegenerative disease
Sodium
Nap (slowly inactivating)
Topiramate
Inhibitor (GABA-A agonist)
Hippocampus, hypothalamus
Migraine prevention, epilepsy
Navα1.1/1.9
ADCI
Inhibitor (NMDA antagonist)
Hippocampus
Neurodegenerative disease, anticonvulsant
Lithium
Alteration of nerve Na+ transport
Hypothalamus
Bipolar disorder, mood stabilizer
Mixed
Hyperpolarization-activated (NA+ , K+ )
Lamotrigine
Shifts the inactivation curve
Striatum, thalamus, cortex, hippocampus, amygdala
Epilepsy, bipolar disorder
Acetylcholine
Muscarinic1/5 (Gq)
Donepezil
Breakdown inhibitor
Hippocampus
Alzheimer
Nicotinic
HP-184
Release stimulator (also for 5-HT)
Hippocampus
Spinal cord injury
Nicotine
Agonist
Thalamus
Smoking cessation
GABA
GABAB (Gi)
Baclofen
Agonist
Hypothalamus
Antispastic agent for multiple sclerosis
Nor-adrenaline
α1A,B,D (Gq)α2A,B,C (Gs)α1,2,3 (Gs)
DOV 21947
DA and 5-HT reuptake inhibitor
Cerebrospinal fluid
Depression
Atomoxetine
Release inhibitor
Caudate putamen
ADHD
1.3.3 Compartmental Simulations of Neuronal Electrophysiology
Ion channels present in complex dendritic arborizations enable neurons to combine and generate electrical impulses. The changes in dendritic membrane potential over space and time can be estimated through the solution of partially differential equations. However, the realistic complexity of dendrites, including branching, varying diameters, and multiple nonlinear ion channels, makes analytical solutions impractical [64]. To model neurons computationally, researchers use a collection of interconnected, tiny, cylindrical compartments, each representing an isopotential unit with membrane resistance and capacitance, as well as interior resistance or conductivity. These compartments serve as numerical representations of electric circuits. Precise neuronal morphologies are imported into established software environments like NEURON and Genesis from digital reconstructions of actual cells scanned through microscopy [65]. By incorporating membrane and synaptic features, such as the kinetics of voltage- and ligand-gated ionic channels, throughout the arborization of the nerves, researchers can mimic neuronal behavior and match experimental results or test new theories. In this computational framework, researchers can also simulate the impact of medications on neuronal activity by altering neurotransmitter receptors and electric currents. This approach allows for a detailed and flexible exploration of neuronal dynamics and responses to various pharmacological interventions [66].
1.3.4 The Concept of System Thinking Makes an Appearance in Neuropharmacology
It is essential to recognize that most modern medications used in neurology and psychiatry interact with multiple targets, and this is a consequence of their historical origins. The conventional animal screening method for central nervous system (CNS) drug development is no longer a viable or acceptable option for several compelling reasons [67]. Besides the low productivity and moral concerns for animal welfare, there are considerable inter-species discrepancies in gene expression levels of several proteins important for neuropathologies and medication distribution in the brain among rats and humans. These distinctions influence extrapolations from animal models to human brain diseases [68]. Furthermore, the inability of animals to communicate hampers their ability to accurately mimic human neuropsychiatric illnesses, making it challenging to model symptoms such as hallucinations, sadness, or headaches effectively. To overcome these limitations and enhance translation from animal research to the human clinic, the field of systems pharmacology has been growing [69]. Systems pharmacology aims to eliminate barriers by using computer modeling to create multi-target medications that can effectively act on various molecular entities, thereby restoring the disrupted interconnected network caused by specific diseases. This approach seeks to develop drugs from the ground up, designed to target multiple components and pathways, making them more effective in treating complex neurological and psychiatric disorders [70].
1.3.5 Quantitative Systems Pharmacology
The emerging paradigm of quantitative systems pharmacology (QSP) builds upon neurophysiology, pathology, and genomics data used in preclinical animal models but places a strong emphasis on human data. QSP aims to be a valuable translational tool by mathematically modelling actual biological mechanisms in the appropriate medical region, as well as combining preclinical neurophysiology with clinical information on pathology, visualization, and clinical scales [71]. In neuro-psychopharmacology, it is well-established that medications affecting the behavior and functional state of patients with neuro-psychiatric disorders primarily work by altering the effects of synaptic mediator combinations. This aligns well with the systems pharmacology approach, which does not solely focus on one neurotransmitter, as seen in target-oriented drug development programs [72]. Instead, it aims to simultaneously optimize various inhibitory and facilitator effects on multiple targets. While this may seem challenging compared to a singletarget approach, a considerable number of computational methodologies, bioinformatics, and cheminformatics resources have been established and are rapidly evolving to support this systemic approach. A consistent systems biology-based drug discovery method for neuropharmacology could involve several key phases [73]. The first step is to get complete information of the molecular interaction network underlying the specific illness state. This involves delving into the precise biology of the neuropsychiatric disorder at the cellular and circuit level before selecting appropriate targets and developing tailored therapeutic approaches to treat the condition. Multiple approaches that enable integrated biological techniques, such as transcriptomics, proteomics, and metabolomics, can be used to detect and characterize pathway responses in healthy versus sick brain states. Computational tools are then used to find connections in massive biological databases [74]. By integrating human data, computational models, and cutting-edge biological techniques, quantitative systems of pharmacology offer promising avenues for advancing drug discovery and development in the field of neuropharmacology. Figure 1.2. has shown the way to use the computational models in order to study the various drugs and their target.
Figure 1.2 Use of computational models for the study of drugs and their targets. The figure depicts the way of selecting the correct drug for the individual patients by using various computational techniques in which the literature and system response is taken into consideration. The compound characterization is done by the help of High throughput screening where the compounds are examined inside a simulated cellular environment where they need to exhibit their action. Then, the iterative refining is done for the compounds as per the disease that it needs to target are linked together. At last, the standardization of drug is achieved where the effective medicines are finalized for the patient as per their requirement and condition.
1.4 Recent Developments in Computational Neuropharmacology
There are different clinical and nonclinical aspects of Computational Neuropharmacology which are utilized for the therapeutic intervention of different neurological disorders and such technologies are utilized for the future perspective and development of drug regimen for safe and effective use of medicines as per the requirement of the patients. The recent developments in Computational Neuropharmacology has been discussed further in this section.
1.4.1 Compartmental Simulations of Neuronal Electrophysiology
Neurons rely on ion channels to enable the integration and generation of electrical impulses. However, attempting to calculate the intricate and dynamic variation in dendritic membrane potential across space and time purely through solving partial differential equations proves impractical due to the intricate dendritic branching, varying diameters, and the presence of multiple nonlinear ion channels [64]. The estimation of dendritic membrane potential, a pragmatic approach involves conceptualizing the neuron as an assembly of interconnected, minute, uniform cylindrical compartments. In a mathematical framework, these distinct “entities” can be represented by an electrical circuit that comprises both internal characteristics such as longitudinal resistance or conductivity, as well as exterior characteristics such as membrane resistance and capacitance. This depiction takes both the internal and exterior qualities of these units into consideration [65, 66]. This model draws inspiration from specific neuronal structures derived from digitally reconstructed images of actual cells obtained through microscopic scans. These neuronal models are then imported into well-established software platforms tailored for neuron simulation and modeling [18]. The computational model incorporates digitally reconstructed dendritic morphology [75], and integrates key ion currents, including a sodium current, extended rectification potassium currents, A-type potassium currents, and a mixed sodium/potassium current that responds to excessive polarization of the membrane. This comprehensive model has been evaluated against select experimental observations [76].
1.4.2 Quantitative Structure–Activity Relationships (QSAR) for Alzheimer’s Disease Treatment
Alzheimer’s disease poses a significant a public health problem by impairing cognitive functions in patients [77]. To address this issue, Babita and colleagues have harnessed chemoinformatics techniques, exemplifying their use in uncovering the physicochemical attributes of inhibitors for acetylcholine esterase [78]. This investigation has the potential to lay the foundation for future research endeavours aimed at exploring the efficacy of these compounds in preclinical models [79].
1.4.3 Composite Machine Learning Algorithms for Schizophrenia Treatment
The essential inhibitory neurotransmitter gamma-aminobutyric acid (GABA) holds pivotal importance. Sahila et al. used machine learning, computational chemistry, and phytochemistry to identify the chemical characteristics of natural inhibitors aimed at reducing the morbidity and death rates associated with schizophrenia [80]. The pursuit of natural substances as a means of alleviating and managing symptoms offers a pioneering avenue for addressing the intricacies of this neurological affliction.
1.4.4 Discovery and Evaluation of Dual Target Ligands for Parkinson’s Disease
Perez-Castillo and associates present a groundbreaking endeavor in structural bioinformatics, employing mathematical methodologies to derive docking scores that elucidate protein-ligand interactions targeting not one, but two distinct protein targets [81–83]. The generation and assessment of these docking scores hold significant importance in the realm of structure-based drug discovery. This innovative and rigorous study is poised to unlock novel pathways for assessing therapeutic targets that could concurrently influence pleiotropic protein entities, thus enhancing the potential for drug repurposing [84, 85].
1.4.5 Utilizing Structure-Based Drug Design for Neurological Disorder Therapies
The amalgamation of extensive molecular class-specific information has emerged as an asset in the development of models and algorithms for forecasting are elucidating intricate mechanisms like 3D domain switching, a hallmark of neurological disorders such as Alzheimer’s and Parkinson’s diseases [86–88]. Recent endeavors aiming to integrate genetic variances and pharmacological target data have unveiled numerous potential therapeutic links within the domain of neurological disorders [89–91]. A comprehensive overview of contemporary advancements in neurological drug discovery is presented by Aarthy et al. This review probes into the realm of neurological ailments, harnessing the power of structural bioinformatics and cheminformatics methodologies [92]. Diverse neurological conditions, including Alzheimer’s disease, Niemann–Pick type C ailment, REM-SBD, ALS, epilepsy, dementia, migraine, and stroke, are meticulously examined. The review adeptly intertwines clinical, biochemical, and bioinformatics approaches, thereby furnishing a valuable resource for students, researchers, and clinicians seeking holistic insights into the multifaceted landscape of drug development [92].
1.4.6 Structural Modeling of Voltage-Gated Sodium Ion Channel from Anopheles gambiae
Despite sustained global endeavors to combat mosquito-borne infections and the illnesses they propagate, there remains an imperative to identify efficacious vector control strategies [93]. A spectrum of maladies spread via mosquitoes, including malaria, dengue fever, West Nile virus, encephalitis, and Zika fever, manifests neurological repercussions [94, 95]. Gaining insights into the molecular intricacies of vector proteins holds pivotal significance for the creation of repellents, vector-targeting agents, and diverse chemical interventions aimed at curtailing mosquito-borne viral spread [96, 97].
1.5 Limitations of Computational Neuropharmacology
While theory-driven modelling techniques have provided valuable insights into the underlying processes and mechanisms of various illnesses across different research levels, their application has predominantly centralized around clinical issues [98, 99]. Nevertheless, these computational methods come with significant limitations. A prominent drawback is their demand for substantial expertise, often rendering them perplexing and bewildering to nonspecialists [100]. Consequently, there exists a challenge in effectively sharing pertinent information among medical professionals, trial practitioners, experimental researchers, and theoreticians. A potential solution lies in accentuating the utility of computational techniques through their active integration within clinical trials [101, 102]. The convergence of theory-driven and data-driven approaches holds promise, and indeed, the amalgamation of these techniques can yield tangible benefits in practical applications. When the existing knowledge and mechanistic comprehension of a disorder are suitably robust, theory-driven methods offer the capacity to evaluate specific facets relevant to that ailment [103]. This serves to notably curtail data complexity by narrowing down the dataset to pivotal features and parameters. Subsequently, data-driven techniques, often rooted in machine learning, can be applied to this streamlined dataset with heightened dependability and efficiency. This hybrid methodology has been previously deployed in investigating conditions like Huntington’s disease [104] and schizophrenia, culminating in noteworthy enhancements in the categorization of behaviour and subtypes within these disorders. Thus, a computational model emerges as a formidable instrument, transcending mere theory, and holds the potential for integration with clinical tools for patient assessment [105]. For the attainment of a fully applicable model, it becomes imperative to harness each computational approach while mitigating their individual constraints and complexities. Indeed, the intricate nature of mental disorders and the existing gap between biological and phenomenological understanding can be gained from a multi-tier strategy that seamlessly integrates diverse modes of simulation and modelling [106]. This entails a top-down, high-level approach focused on phenotype and symptoms, in conjunction with a bottom-up, biologically oriented model at a granular level.
1.6 Conclusion
Neuropharmacology addresses seriously insufficient medical resources, with neuro-psychic illnesses becoming more common and prevalent in an ageing community, even though inadequate medicinal product is available for serious medical conditions such as stroke and most neurodegenerative diseases, especially Alzheimer’s dementia. Moreover, current neuro-drugs simply reduce or, at most, eradicate some neurologic/psychiatric symptoms without addressing the underlying causes of the disease, which in the case of most neuro-psychic diseases are frequently unknown. This is primarily due to neuroscience’s strong reductionist focus, which has resulted in precise information about the cellular level but only a far poorer grasp of integrative brain functions, which are emergent qualities displayed exclusively at the organismic level. We have shown in this study how computational neuroscience may employ formal models at various levels to give a mechanistic and functional perspective for interpreting psychopathology and its underlying pathophysiology. Different novel models need a high level of competence in a variety of disciplines, including cellular and molecular neuroscience, network neuroscience, cognitive neuroscience, computational neuroscience, psychiatry, psychology, computer science, mathematics, and engineering. This contributes to the vital multidisciplinary topic of computational psychiatry.
References
1. Obermayer, K., Computational Neuroscience and Modeling of Diseases: Do We Need New Paradigms.
Basic Clin. Neurosci.
, 3, 3, 3–4, 2012 Jul 10.
2. Duman, R.S., Neuropharmacology in the next millennium: promise for breakthrough discoveries.
Neuropsychopharmacology
, 20, 2, 97–8, 1999 Feb.
3. Gong, S., Sheng, P., Jin, H., He, H., Qi, E., Chen, W., Dong, Y., Hou, L., Effect of methylphenidate in patients with cancer-related fatigue: a systematic review and meta-analysis.
PLoS One
, 9, 1, e84391, 2014 Jan 8.
4. Green, A.R. and Aronson, J.K., An agenda for UK clinical pharmacology: From basic to clinical neuropharmacology: targetophilia or pharmacodynamics?
Br. J. Clin. Pharmacol.
, 73, 6, 959–67, 2012 Jun.
5. Hill, R.G., Neuropharmacology of the injured spinal cord.
Spinal Cord
, 25, 3, 209–11, 1987 Jun.
6. Margineanu, D.G., Neuropharmacology beyond reductionism–a likely prospect.
Biosystems
, 141, 1–9, 2016 Mar 1.
7. Bielinski, S.J., Olson, J.E., Pathak, J., Weinshilboum, R.M., Wang, L., Lyke, K.J., Ryu, E., Targonski, P.V., Van Norstrand, M.D., Hathcock, M.A., Takahashi, P.Y., Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time—using genomic data to individualize treatment protocol.
Mayo Clin. Proc.
, 89, 1, 25–33, 2014 Jan 1, Elsevier.
8. Overby, C.L., Rasmussen, L.V., Hartzler, A., Connolly, J.J., Peterson, J.F., Hedberg, R.E., Freimuth, R.R., Shirts, B.H., Denny, J.C., Larson, E.B., Chute, C.G., A template for authoring and adapting genomic medicine content in the eMERGE infobutton project.
AMIA Annu. Symp. Proc.
, 2014, 944, 2014, American Medical Informatics Association.
9. Tenenbaum, J.D., Translational bioinformatics: past, present, and future.
Genom. Proteom. Bioinf.
, 14, 1, 31–41, 2016 Feb 1.
10. Polgár, T. and Keseru G, M., Integration of virtual and high throughput screening in lead discovery settings.
Comb. Chem. High Throughput Screen.
, 14, 10, 889–97, 2011 Dec 1.
11. Bajorath, J., Integration of virtual and high-throughput screening.
Nat. Rev. Drug Discov.
, 1, 11, 882–94, 2002 Nov 1.
12. Shameer, K., Nayarisseri, A., Duran, F.X., González-Díaz, H., Improving neuropharmacology using big data, machine learning and computational algorithms.
Curr. Neuropharmacol.
, 15, 8, 1058, 2017 Nov.
13. Migliore, M. and Shepherd, G.M., Emerging rules for the distributions of active dendritic conductances. Nat. Rev. Neurosci., 3, 5, 362–70, 2002 May.
14. Hastings, H.M., Sobel, S.G., Chaterpaul, S., Frank, C., Russell, E., Pekor, J., Chemical amplifiers, in:
Computational Fluid and Solid Mechanics 2003
, pp. 1705–1707, Hempstead-NY, Elsevier Science Ltd, 2003 Jan 1.
15. Hines, M.L. and Carnevale, N.T., The NEURON simulation environment.
Neural Comput.
, 15, 9, 6, 1179–209, 1997 Aug 15.
16. Bower, J.M. and Beeman, D.,
The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System
, Springer Science & Business Media, Boulder, USA, 2012 Dec 6.
17. Fujiki, M., Kobayashi, H., Uchida, S., Inoue, R., Ishii, K., Neuroprotective effect of donepezil, a nicotinic acetylcholine-receptor activator, on cerebral infarction in rats.
Brain Res.
, 1043, 236–41, 2005.
18. Ferrante, M., Blackwell, K.T., Migliore, M., Ascoli, G.A., Computational models of neuronal biophysics and the characterization of potential neuropharmacological targets.
Curr. Med. Chem.
, 15, 24, 2456–71, 2008 Oct 1.
19. Levin, E.D., McClernon, F.J., Rezvani, A.H., Nicotinic effects on cognitive function: behavioral characterization, pharmacological specification, and anatomic localization.
Psychopharmacology
, 184, 523–39, 2006 Feb.
20. Anis, N., Sherby, S., Goodnow, R., Niwa, M., Konno, K., Kallimopoulos, T., Bukownik, R., Nakanishi, K., Usherwood, P., Eldefrawi, A., Structureactivity relationships of philanthotoxin analogs and polyamines on N-methyl-D-aspartate and nicotinic acetylcholine receptors.
J. Pharmacol. Exp. Ther.
, 254, 3, 764–73, 1990 Sep 1.
21. Foster, J.D., Cervinski, M.A., Gorentla, B.K., Vaughan, R.A., Regulation of the dopamine transporter by phosphorylation, in:
Neurotransmitter Transporters
, pp. 197–214, 2006.
22. Vann, R.E., Rosecrans, J.A., James, J.R., Philibin, S.D., Robinson, S.E., Neurochemical and behavioral effects of bupropion and mecamylamine in the presence of nicotine.
Brain Res.
, 1117, 1, 18–24, 2006 Oct 30.
23. Juckel, G., Schlagenhauf, F., Koslowski, M., Wüstenberg, T., Villringer, A., Knutson, B., Wrase, J., Heinz, A., Dysfunction of ventral striatal reward prediction in schizophrenia.
Neuroimage
, 29, 2, 409–16, 2006 Jan 15.
24. Reneman, L., De Bruin, K., Lavalaye, J., Gunning, W.B., Booij, J., Addition of a 5-HT receptor agonist to methylphenidate potentiates the reduction of [123I] FP-CIT binding to dopamine transporters in rat frontal cortex and hippocampus.
Synapse
, 39, 3, 193–200, 2001 Mar 1.
25. O’Leary, O.F., Bechtholt, A.J., Crowley, J.J., Hill, T.E., Page, M.E., Lucki, I., Depletion of serotonin and catecholamines block the acute behavioral response to different classes of antidepressant drugs in the mouse tail suspension test.
Psychopharmacology
, 192, 357–71, 2007 Jun.
26. Vargas, M.L., Abella, C., Hernandez, J., Diazepam increases the hypothalamicpituitary-adrenocortical (HPA) axis activity by a cyclic AMP-dependent mechanism.
Br. J. Pharmacol.
, 133, 8, 1355–61, 2001 Aug.
27. Zhang, L., Zhang, Y., Wennberg, R., Multiple actions of methohexital on hippocampal CA1 and cortical neurons of rat brain slices.
J. Pharmacol. Exp. Ther.
, 286, 3, 1177–82, 1998 Sep 1.
28. Jansen, J.F., Aldenkamp, A.P., Majoie, H.M., Reijs, R.P., de Krom, M.C., Hofman, P.A., Kooi, M.E., Nicolay, K., Backes, W.H., Functional MRI reveals declined prefrontal cortex activation in patients with epilepsy on topiramate therapy.
Epilepsy Behav.
, 9, 1, 181–5, 2006 Aug 1.
29. Obrietan, K. and van den Pol, A., GABAB receptor-mediated regulation of glutamate-activated calcium transients in hypothalamic and cortical neuron development.
J. Neurophysiol.
, 82, 1, 94–102, 1999 Jul 1.
30. Sandrini, G., Perrotta, A., Nappi, G., Eletriptan: a review and new perspectives.
Expert Rev. Neurother.
, 6, 10, 1413–21, 2006 Oct 1.
31. Gourion, D., Perrin, E., Quintin, P., Fluoxetine: an update of its use in major depressive disorder in adults.
L’encephale
, 30, 4, 392–9, 2004 Jul 1.
32. Hughes, Z.A., Starr, K.R., Langmead, C.J., Hill, M., Bartoszyk, G.D., Hagan, J.J., Middlemiss, D.N., Dawson, L.A., Neurochemical evaluation of the novel 5-HT1A receptor partial agonist/serotonin reuptake inhibitor, vilazodone.
Eur. J. Pharmacol.
, 510, 1–2, 49–57, 2005 Mar 7.
33. Gefvert, O., Lundberg, T., Wieselgren, M., Bergström, M., Långström, B., Wiesel, F.A., Lindström, L., D2 and 5HT2A receptor occupancy of different doses of quetiapine in schizophrenia: a PET study.
Eur. Neuropsychopharmacol.
, 11, 2, 105–10, 2001 Apr 1.
34. Szarfman, A., Tonning, J.M., Levine, J.G., Doraiswamy, P.M., Atypical antipsychotics and pituitary tumors: a pharmacovigilance study.
Pharmacother. J. Hum. Pharmacol. Drug Ther.
, 26, 6, 748–58, 2006 Jun.
35. Gaster, L.M. and King, F.D., Serotonin 5-HT3 and 5-HT4 receptor antagonists.
Med. Res. Rev.
, 17, 2, 163–214, 1997 Mar.
35. Yan, Z., Regulation of GABAergic inhibition by serotonin signaling in prefrontal cortex: molecular mechanisms and functional implications.
Mol. Neurobiol.
, 26, 203–16, 2002 Oct.
37. Montgomery, S.A., Baldwin, D.S., Blier, P., Fineberg, N.A., Kasper, S., Lader, M., Lam, R.W., Lépine, J.P., Möller, H.J., Nutt, D.J., Rouillon, F., Which antidepressants have demonstrated superior efficacy? A review of the evidence.
Int. Clin. Psychopharmacol.
, 22, 6, 323–9, 2007 Nov 1.
38. Carrard, A., Elsayed, M., Margineanu, M., Boury-Jamot, B., Fragnière, L., Meylan, E.M., Petit, J.M., Fiumelli, H., Magistretti, P.J., Martin, J.L., Peripheral administration of lactate produces antidepressant-like effects.
Mol. Psychiatry
, 23, 2, 392–9, 2018 Feb.
39. Lee, H.J., Pogatzki-Zahn, E.M., Brennan, T.J., The effect of the AMPA/kainate receptor antagonist LY293558 in a rat model of postoperative pain.
J. Pain
, 7, 10, 768–77, 2006 Oct 1.
40. Hoyte, L., Barber, P.A., Buchan, A.M., Hill, M.D., The rise and fall of NMDA antagonists for ischemic stroke.
Curr. Mol. Med.
, 4, 2, 131–6, 2004 Mar 1.
41. Poolos, N.P., Migliore, M., Johnston, D., Pharmacological upregulation of h-channels reduces the excitability of pyramidal neuron dendrites.
Nat. Neurosci.
, 5, 8, 767–74, 2002 Aug 1.
42. Slassi, A., Isaac, M., Edwards, L., Minidis, A., Wensbo, D., Mattsson, J., Nilsson, K., Raboisson, P., McLeod, D., Stormann, T.M., Hammerland, L.G., Recent advances in non-competitive mGlu5 receptor antagonists and their potential therapeutic applications.
Curr. Top. Med. Chem.
, 5, 9, 897–911, 2005 Aug 1.
43. Jones, N., O’Neill, M.J., Tricklebank, M., Libri, V., Williams, S.C., Examining the neural targets of the AMPA receptor potentiator LY404187 in the rat brain using pharmacological magnetic resonance imaging.
Psychopharmacology
, 180, 4, 743–51, 2005 Aug.
44. Kroppenstedt, S.N., Schneider, G.H., Thomale, U.W., Unterberg, A.W., Neuroprotective Properties of Aptiganel HCL (Cerestat©) following Controlled Cortical Impact Injury, in:
Intracranial Pressure and Neuromonitoring in Brain Injury: Proceedings of the Tenth International ICP Symposium, Williamsburg, Virginia, May 25–29, 1997
, pp. 114–116, Springer Vienna, Berlin, Germany, 1998.
45. Berger, M.L. and Rebernik, P., Zinc and Ifenprodil Allosterically Inhibit Two Separate Polyamine-Sensitive Sites atN-Methyl-d-Aspartate Receptor Complex.
J. Pharmacol. Exp. Ther.
, 289, 3, 1584–91, 1999 Jun 1.
46. Tamura, N., Iwama, T., Itoh, K., Synthesis and glutamate-agonistic acitivity of (S)-2-amino-3-(2, 5-dihydro-5-oxo-3-isoxazolyl)-propanoic acid derivatives.
Chem. Pharm. Bull.
, 40, 2, 381–6, 1992 Feb 25.
47. Pattij, T. and Vanderschuren, L.J., The neuropharmacology of impulsive behaviour.
Trends Pharmacol. Sci.
, 29, 4, 192–9, 2008 Apr 1.
48. Schwarz, A.J., Gozzi, A., Reese, T., Bifone, A.,
In vivo
mapping of functional connectivity in neurotransmitter systems using pharmacological MRI.
Neuroimage
, 34, 4, 1627–36, 2007 Feb 15.
49. Skolnick, P. and Basile, A.S., Triple reuptake inhibitors (“broad spectrum” antidepressants).
CNS Neurol. Disord. Drug Targets (Formerly Curr. Drug Targets-CNS Neurol. Disorders)
, 6, 2, 141–9, 2007 Apr 1.
50. Gardner, D. and Shepherd, G.M., A gateway to the future of neuroinformatics.
Neuroinformatics
, 2, 271–4, 2004 Sep.
51. Zhang, W., Zhang, Y., Zheng, H., Zhang, C., Xiong, W., Olyarchuk, J.G., Walker, M., Xu, W., Zhao, M., Zhao, S., Zhou, Z., SynDB: a Synapse protein DataBase based on synapse ontology.
Nucleic Acids Res.
, 35, suppl_1, D737– 41, 2007 Jan 1.
