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Basic 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 a-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...