Construction and Analysis of Protein-Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

Computational Investigation of Versatile Activity of Piperine

Piperine is known for its versatile therapeutic activity. It has been used for various disease conditions (e.g., cold, cough, etc.). Piperine is an alkaloid found in black pepper. It possesses various pharmacological actions like anti-inflammatory, anti-oxidant, anti-cholinergic, and anti-cancerous. The above-mentioned properties will be studied by selecting target proteins COX-2 protein, angiotensin converting enzyme, acetylcholineesterases, and survivin using computational docking study. This chapter explains the inhibition property of piperine against selected target protein from the results of docking studies. Based on the docking scores and protein-ligand interactions, piperine was found to bind well in the active site of the selected target proteins. It ensures the binding efficacy of piperine against selected target proteins. Docking scores and protein-ligand interactions plays an important role in its therapeutic activity.

Target Identification of HDAC8 Isoform for the Treatment of Cancer

Target identification has been considered as a chief parameter in drug discovery as it fully characterizes on-target and off-target effects of drug binding. Cell signaling receptors, structural proteins, and post-translational modifications of histones by histone deacetylases are the most widespread targets that are progressively being explored. The FDA approved histone deacetylases inhibitors and the majority of HDACi in and out of clinical trials based on the activities of 11 isoforms of the enzyme in non-selective influence approach. Unfortunately, reported HDACi does not possess a high degree of structural specificity and ultimately lessens the therapeutic index with many dose limiting toxicities. This chapter illustrates how different approaches are incorporated into the novel inhibitors discovery that are truly isoform-selective and which are specifically involved in targeting only a particular isozyme. Further, it highlights the aspects relating to provide a wider therapeutic index with an improved toxicity profile of lead like epigenetic modulators.

Virtual Screening and Its Applications in Drug Discovery Process

Virtual screening plays an important role in the modern drug discovery process. The pharma companies invest huge amounts of money and time in drug discovery and screening. However, at the final stage of clinical trials, several molecules fail, which results in a large financial loss. To overcome this, a virtual screening tool was developed with super predictive power. The virtual screening tool is not only restricted tool small molecules but also to macromolecules such as protein, enzyme, receptors, etc. This gives an insight into structure-based and Ligand-based drug design. VS gives reliable information to direct the process of drug discovery (e.g., when the 3D image of the receptor is known, structure-based drug design is recommended). The pharmacophore-based model is advisable when the information about the receptor or any macromolecule is unknown. In this ADME, parameters such as Log P, bioavailability, and QSAR can be used as filters. This chapter shows both models with various representative examples that facilitate the scientist to use computational screening tools in modern drug discovery processes.

QSAR and Lead Optimization

There are many diseases for which suitable drugs have not been identified. As the population increases and the environment gets polluted, new infections are reported. Random screening of synthesized compounds for biological activity is time consuming. QSAR has a prominent role in drug design and optimization. It is derived from the correlation between the physicochemical properties and biological activity. QSAR equations are generated using statistical methods like regression analysis and genetic function approximation. Both 2D parameters and 3D parameters are involved in generating the equation. Among several QSAR equations generated, the best ones are selected based on statistical parameters. Validation techniques usually verify the predictive power of generated QSAR equations. Once the developed QSAR model is validated to be good, the results of that model may be applied to predict the biological activity of newer analogues. This chapter illustrates the various steps in QSAR and describes the significance of statistical parameters and software used in QSAR.

Lead Optimization in the Drug Discovery Process

Discovering a new drug molecule against disease is the main objective of drug discovery. Lead optimization is one of the important steps and acts as a starting point. Over the years, it has significantly changed the drug discovery process. Its main focus is the development of preclinical candidates from “Hit” or “Lead.” Lead optimization comprises lead selection and optimization, drug candidate confirmation, and preclinical drug characterization. Lead optimization process can improve the effectiveness towards its target potency, selectivity, protein binding, pharmacokinetic parameters, and to develop a good preclinical candidate. Lead optimization from high-throughput screening to identification of clinical drug candidate is a seamless process that draws new techniques for accelerated synthesis, purification, screening from iterative compound libraries, validation, and to deliver clinical drug candidate with limited human resources. In conclusion, lead optimization phase is done under the suggestion that the optimized lead molecule will have activity against a particular disease.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

The Next Generation Sequencing Techniques and Application in Drug Discovery and Development

Next-generation sequencing or massively parallel sequencing describe DNA sequencing, RNA sequencing, or methylation sequencing, which shows its great impact on the life sciences. The recent advances of these parallel sequencing for the generation of huge amounts of data in a very short period of time as well as reducing the computing cost for the same. It plays a major role in the gene expression profiling, chromosome counting, finding out the epigenetic changes, and enabling the future of personalized medicine. Here the authors describe the NGS technologies and its application as well as applying different tools such as TopHat, Bowtie, Cufflinks, Cuffmerge, Cuffdiff for analyzing the high throughput RNA sequencing (RNA-Seq) data.

Semantic Technologies for Medical Knowledge Representation

The ongoing rapid growth of diversity of data and their wide use to solve different complex tasks resulted in a significant number of semantic reference systems enriched with vocabularies, thesauri, terminologies, and ontologies. The extensive use of ontologies stemmed a new approach to build modern intelligent systems in reusing and sharing pieces of declarative knowledge. A lot of effort has been made to produce standard ontologies for medical knowledge representation. This chapter brings an overview of semantic knowledge representation frameworks such as RDF and OWL for developing ontology-based medical systems. The chapter presents the state of the art in ontology resources/systems so that it could be useful for learners and researchers involved in interdisciplinary research areas that include medicine and information technology. Also, a clinical use case is illustrated highlighting the role of ontology in the medical domain.

Quality by Design in Pharmaceutical Formulation

Quality by design (QbD) is a systematic, scientific, risk-based approach to product development and manufacturing process to consistently deliver the quality product. In this chapter, application, benefits, opportunities, regulatory requirements involved in quality by design of pharmaceutical products are discussed. In quality by design approach, during development, the developer defines quality target product profile (QTPP) and identifies critical quality attributes (CQA). Critical process parameters (CPP) of unit operations which impacts critical quality attributes need to be identified to understand the impact of critical material attributes (CMA) on quality attributes of the drug product. Quality by design approach is defined in ICH guidelines Q8 – Pharmaceutical Development, Q9 – Quality Risk Management, Q10 – Pharmaceutical Quality System. This chapter describes the implementation of new concepts in quality by design like design of experiments to achieve design space, control strategy to consistently manufacture quality product throughout the product lifecycle.