Current Topics in Medicinal Chemistry - Volume 18, Issue 13, 2018

With the advancement of “proteomics” data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.

Antimicrobial resistance is increasing at an alarming rate and the number of new antibiotics developed and approved has decreased in the last decades, basically for economic and regulatory obstacles. Pathogenic bacteria that are resistant to multiple or all available antibiotics are isolated frequently. Hence, new antibacterial agents are urgently needed and antimicrobial peptides are being considered as a potential solution to this important threat. These molecules are small host defense proteins that are part of the immune systems of most living organisms such as plants, bacteria, invertebrates, vertebrates, and mammals. These peptides are found in those parts of organisms that are exposed to pathogens and they are active against multiple organisms such as virus, bacteria, and parasites, among others. This review shows different strategies in the computational design of new antibacterial peptides, the physicochemical properties that are considered as the most relevant for the antibacterial activity and toxicity, and it suggests guidelines in order to help in the finding of new non-toxic antibacterial peptides through the development of computational models.

Screening and identifying a disease-specific novel drug target is the first step towards a rational drug designing approach. Due to the advent of high throughput data generation techniques, the protein search space has now exceeded 24,500 human protein coding genes, which encodes approximately 1804proteins. This work aims at mining out the relationship between target proteins, drugs, and diseases genes through a network-based systems biology approach. A network of all FDA approved drugs, along with their targets were utilized to construct the proposed Drug Target (DT) network. Further, the experimental drugs were mapped into the DT network to infer the functional relationship by utilizing the respective network attributes. Similar to the DT network, a network of disease genes was created through OMIM Gene Map and Morbid Map, to link the binary associations of disorder-disease genes. In the proposed model of Human Interactome Network, shortest path length between the target protein and disease gene was used to infer the correlation between ‘Drug Targets’ and ‘Disease-Gene’. This network-based study will help researchers to analyze, infer and identify disease-specific novel drug targets through harnessing the graph theory based network attributes.

Tuberculosis continues to remain as one of the leading causes of death worldwide, in spite of significant progress being made in the last twenty years through increased compliance to treatment. The current review gives an overview of the recent efforts made in the endeavor to identify novel inhibitors and promising drug targets for Mycobacterium tuberculosis with structure and ligand-based approaches along with bioinformatics studies following complete sequencing of its genome. A large number of these studies target biomolecules in metabolic pathways that are vital for the survival of the microorganism. A discussion on efforts to study metalloproteins as relatively underexplored targets in the context of their druggability is also presented.

Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.

Cancer has become one of the main leading causes of morbidity and mortality worldwide. One of the critical drawbacks of current cancer therapeutics has been the lack of the target-selectivity, as these drugs should have an effect exclusively on cancer cells while not perturbing healthy ones. In addition, their mechanism of action should be sufficiently fast to avoid the invasion of neighbouring healthy tissues by cancer cells. The use of conventional chemotherapeutic agents and other traditional therapies, such as surgery and radiotherapy, leads to off-target interactions with serious side effects. In this respect, recently developed target-selective Antibody-Drug Conjugates (ADCs) are more effective than traditional therapies, presumably due to their modular structures that combine many chemical properties simultaneously. In particular, ADCs are made up of three different units: a highly selective Monoclonal antibody (Mab) which is developed against a tumour-associated antigen, the payload (cytotoxic agent), and the linker. The latter should be stable in circulation while allowing the release of the cytotoxic agent in target cells. The modular nature of these drugs provides a platform to manipulate and improve selectivity and the toxicity of these molecules independently from each other. This in turn leads to generation of second- and third-generation ADCs, which have been more effective than the previous ones in terms of either selectivity or toxicity or both. Development of ADCs with improved efficacy requires knowledge at the atomic level regarding the structure and dynamics of the molecule. As such, we reviewed all the most recent computational methods used to attain all-atom description of the structure, energetics and dynamics of these systems. In particular, this includes homology modelling, molecular docking and refinement, atomistic and coarse-grained molecular dynamics simulations, principal component and cross-correlation analysis. The full characterization of the structure-activity relationship devoted to ADCs is critical for antibody-drug conjugate research and development.

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

Asthma is a complex, heterogeneous, airway inflammatory disorder broadly classified into atopic (IgE mediated) and non-atopic asthma. Monoclonal Antibodies (MAbs) and small chemical Protein- Protein Interaction Modulators (PPIMs) are targeted against interleukins (ILs), which play a critical role in asthma. Many MAbs are targeted against ILs and IgE. Anti IgE MAb (Omalizumab) and Anti IL- 5 MAbs (Mepolizumab, Reslizumab) have only been approved by FDA. Most of the MAbs including Tracolizumab, Lebrikizumab, Anrukinzumab (Anti IL-13 MAb), and Brodalumab (Anti IL-17 MAb) are in different phases of clinical trials. Pascolizumab (Anti IL-4 MAb), however, has failed. These MAbs are expensive and may render adverse immune response. Thus, small chemical modulators targeting ILs and their receptors (IL-Rs) are being exploited computationally and further validated experimentally. The complex ILs and IL-Rs available in PDB are best suited for these types of studies. A large number of small chemical modulators against Protein-Protein Interactions (PPIs) have been compiled in a few databases like TIMBAL, 2P2I DB and IPPIDB. Small chemical libraries are used for virtual screening to find novel modulators targeting IL-R binding interface on IL. Molecular dynamic simulations have been further used for disruption mechanism and kinetic studies. IL-2/IL-2R was targeted with clinically tested small molecule modulators like SP4206, and IL-2 levels were known to increase in non-atopic asthma. In the absence of experimentally known modulators against atopic asthma, computational tools are being explored. For example, IL-33 is a target for atopic asthma where IL-33 and its receptor complex structure is available in PDB. In summary, small chemical modulators against ILs are a complementary approach to MAbs and computational tools have been used for identifying these modulators for asthma.

The serendipitous discovery of covalent inhibitors and their characteristic potency of inducing irreversible and complete inhibition in therapeutic targets have caused a paradigm shift from the use of non-covalent drugs in disease treatment. This has caused a significant evolution in the field of covalent targeting to understand their inhibitory mechanisms and facilitate the systemic design of novel covalent modifiers for ‘undruggable’ targets. Computational techniques have evolved over the years and have significantly contributed to the process of drug discovery by mirroring the pattern of biological occurrences thereby providing insights into the dynamics and conformational transitions associated with biomolecular interactions. Moreover, our previous contributions towards the systematic design of selective covalent modifiers have revealed the various setbacks associated with the use of these conventional techniques in the study of covalent systems, hence there is a need for distinct approaches. In this review, we highlight the modifications and development of computational techniques suitable for covalent systems, their lapses, shortcomings and recent advancements.

One of the fundamental challenges in designing drug molecule against a disease target or protein is to predict binding affinity between target and drug or small molecule. In this review, our focus will be on advancement in the field of protein-small molecule interaction. This review has been divided into four major sections. In the first section, we will cover software developed for protein structure prediction. This will include prediction of binding pockets and post-translation modifications in proteins. In the second section, we will discuss software packages developed for predicting small-molecule interacting residues in a protein. Advances in the field of docking particularly advancement in the knowledgebased force fields will be discussed in the third part of the review. This section will also cover the method developed for predicting affinity between protein and drug molecules. The fourth section of the review will describe miscellaneous techniques used for designing drug molecules, like pharmacophore modelling. Our major emphasis in this review will be on computational tools that are available free for academic use.