Ravi Chaudhary

Nanoparticles in practice for molecular-imaging applications: An overview

UNLABELLED Nanoparticles (NPs) are playing a progressively more significant role in multimodal and multifunctional molecular imaging. The agents like Superparamagnetic iron oxide (SPIO), manganese oxide (MnO), gold NPs/nanorods and quantum dots (QDs) possess specific properties like paramagnetism, superparamagnetism, surface plasmon resonance (SPR) and photoluminescence respectively. These specific properties make them able for single/multi-modal and single/multi-functional molecular imaging. NPs generally have nanomolar or micromolar sensitivity range and can be detected via imaging instrumentation. The distinctive characteristics of these NPs make them suitable for imaging, therapy and delivery of drugs. Multifunctional nanoparticles (MNPs) can be produced through either modification of shell or surface or by attaching an affinity ligand to the nanoparticles. They are utilized for targeted imaging by magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), positron emission tomography (PET), computed tomography (CT), photo acoustic imaging (PAI), two photon or fluorescent imaging and ultra sound etc. Toxicity factor of NPs is also a very important concern and toxic effect should be eliminated. First generation NPs have been designed, developed and tested in living subjects and few of them are already in clinical use. In near future, molecular imaging will get advanced with multimodality and multifunctionality to detect diseases like cancer, neurodegenerative diseases, cardiac diseases, inflammation, stroke, atherosclerosis and many others in their early stages. In the current review, we discussed single/multifunctional nanoparticles along with molecular imaging modalities. STATEMENT OF SIGNIFICANCE The present article intends to reveal recent avenues for nanomaterials in multimodal and multifunctional molecular imaging through a review of pertinent literatures. The topic emphasises on the distinctive characteristics of nanomaterial which makes them, suitable for biomedical imaging, therapy and delivery of drugs. This review is more informative of indicative technologies which will be helpful in a way to plan, understand and lead the nanotechnology related work.

Current Perspective of Stem Cell Therapy in Neurodegenerative and Metabolic Diseases.

AbstractNeurodegenerative diseases have been an unsolved riddle for quite a while; to date, there are no proper and effective curative treatments and only palliative and symptomatic treatments are available to treat these illnesses. The absence of therapeutic treatments for neurodegenerative ailments has huge economic hit and strain on the society. Pharmacotherapies and various surgical procedures like deep brain stimulation are being given to the patient, but they are only effective for the symptoms and not for the diseases. This paper reviews the recent studies and development of stem cell therapy for neurodegenerative disorders. Stem cell-based treatment is a promising new way to deal with neurodegenerative diseases. Stem cell transplantation can advance useful recuperation by delivering trophic elements that impel survival and recovery of host neurons in animal models and patients with neurodegenerative maladies. Several mechanisms, for example, substitution of lost cells, cell combination, release of neurotrophic factor, proliferation of endogenous stem cell, and transdifferentiation, may clarify positive remedial results. With the current advancements in the stem cell therapies, a new hope for the cure has come out since they have potential to be a cure for the same. This review compiles stem cell therapy recent conceptions in neurodegenerative and neurometabolic diseases and updates in this field. Graphical Absractᅟ

Three-Dimensional (3D) Pharmacophore Modelling-Based Drug Designing by Computational Technique

Three-dimensional (3D) pharmacophore modelling is a modern approach used to elucidate the intermolecular interaction of ligands with the target of interest. In the past few years, pharmacophore models have been developed with chemical features and are intuitively understandable and broadly employed successfully in computational drug discovery by the researchers. The concert and utility of pharmacophore modelling are demarcated by the two major factors; (i) definition and placement of pharmacophoric features and (ii) the arrangement approaches used to overlay the 3D pharmacophore models and small molecules. This chapter provides a brief account of the recent technologies and developed model used in pharmacophores-based drug design.

Bioinformatics Techniques for Drug Discovery

Recent knowledge collected on drug molecules and their intermolecular interactions can be used to predict the mechanisms underlying the human physiological processes. In this scenario, computer-aided drug design (CADD) is commonly employed to facilitate the progression of potential inhibitor identification. Amongst the various computational approaches, pharmacophore modelling is classified as a decent technique to identify the lead inhibitors or drug molecules that fit chemically different structural classes. Besides, biological networks and designed biochemical mathematical models have been employed to explore the pharmacokinetics and pharmacodynamics in biological systems. Moreover, molecular dynamics (MD) simulation, a broadly used computational approach based on Newton’s equation of motion for a given system of atoms, delivers the information about protein–ligand interactions. Additionally, synthetic biology approach has been broadly employed as a precise and vigorous technique to accelerate the genome sequence data and reduction inDNAsynthesis cost. Synthetic biology has been also reported to investigate the biological circuit and behaviour or the role of human physiological system. Prominently, the competences to design potential drugs are highly dependent on the fundamental understanding of drug molecules and their biochemical interactions. In this context, the gap between number of identified hit molecules and authentic or genuine drug molecules can be bridged by utilizing the recent bioinformatics approaches.

Molecular Dynamics Simulation Approach to Investigate Dynamic Behaviour of System Through the Application of Newtonian Mechanics

Molecular dynamics simulations have been successfully incorporated and evolved into a mature technique within a variety of pharmaceutical research programs to study the complex biological and chemical systems. Broadly used in modern drug design, molecular docking methods can be used effectively to understand the macromolecular structure-to-function relationships and ligand conformations adopted within the binding sites of macromolecular targets. Information gathered about the dynamic properties of ligand–receptor binding such as free energy by evaluating critical phenomena involved in the intermolecular recognition process. These results can be employed to shift the usual paradigm of structural bioinformatics from studying single structures to analyse conformational ensembles. Today, as a variety of docking algorithms are available, an understanding of advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this chapter is to examine the current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advancements in the field and role played by integration of structure-and ligand-based methods.

Ligand-Based Approach for In-silico Drug Designing

In this chapter, a brief introduction to ligand-based methodologies employed for designing of drug has been described. Generally, ligand-based approach for drug designing (LB-CADD) technique is employed when biological target structure is not known and hence, this technique is considered as an ancillary approach for the drug designing. The theoretical basis of ligand-based approach involves quantitative structure–activity relationships (QSAR) and biomolecular docking studies. Like molecular descriptors, molecular fingerprint, similarity searches, similarity networks and off-target predictions. Finally, a brief description of the present work is given.

Structure-Based Approach for In-silico Drug Designing

In recent years, research area of structure-based drug design is a rising field that has been used to achieve many successes. Structure-based computer-aided drug design (SB-CADD) depends on the ability to determine and analyse the 3D structures of the target of interest. In other words, a prerequisite for the SB-CADD approach can be defined based on molecule’s ability to interrelate with a specific ligand, that can be a chemical species or biomolecule such as protein, and a desired biological activity based on its ability to favourably interact at a binding site on the selected target. This purposed that the molecules sharing those favourable interactions will reflect the similar biological effects. Therefore, novel ligands can be predicted and concluded by careful analysis of a protein’s binding site. Also, structure-based approach for drug designing allows a rapid selection of potential ligands from different and large compound libraries that can be later validated through modelling/simulation and visualization techniques.

Receptor Thermodynamics of Ligand–Receptor or Ligand–Enzyme Association

Experimental techniques that directly assess the thermodynamics of ligand–receptor or ligand–enzyme association, such as isothermal titration calorimetry, have been improved in recent years and can provide thermodynamic details of the binding process. Parallel to the continuous increase in computational power, several classes of computational methods have been developed that can be used to get a more detail insight into the mode and affinity of compounds (drug) to their target (off). Such methods are affiliated with a qualitative and/or quantitative assessment of binding free energies, and differently trade off speed versus physical accuracy. With the current wealth of available three-dimensional structures of proteins and their complexes with ligands, structure-based drug design studies can be used to identify the key ligand interactions and free energy calculations, and can quantify the thermodynamics of binding between ligand and the target of interest.

Genomics and Proteomics Using Computational Biology

Current functional genomics relies on known and characterised genes, but despite significant efforts in the field of genome annotation, accurate identification and elucidation of protein coding gene structures remains challenging. Methods are limited to computational predictions and transcript-level experimental evidence; hence translation cannot be verified. Proteomic mass spectrometry is a method that enables sequencing of gene product fragments, enabling the validation and refinement of existing gene annotation as well as the elucidation of novel protein coding regions. However, the application of proteomics data to genome annotation is hindered by the lack of suitable tools and methods to achieve automatic data processing and genome mapping at high accuracy and throughput.

Thermodynamic Cycles and Their Application in Protein Targets

A key part of drug design and development is the optimization of molecular interactions between an engineered drug candidate and its binding target. Thermodynamic characterization provides information about the balance of energetic forces driving binding interactions and is essential for understanding and optimizing molecular interactions. Comprehensive thermodynamic evaluation is vital in the drug development process to speed drug development towards an optimal energetic interaction profile while retaining good pharmacological properties. Practical thermodynamic approaches, such as enthalpic optimization, thermodynamic optimization plots and the enthalpic efficiency index, have now been developed to provide proven utility in design process. Improved throughput in calorimetric methods remains essential for even greater integration of thermodynamics into drug design.