Current Topics in Medicinal Chemistry - Volume 19, Issue 6, 2019

Background: Resistance toward chemotherapeutics is one of the main obstacles on the way to effective cancer treatment. Personalization of chemotherapy could improve clinical outcome. However, despite preclinical significance, most of the potential markers have failed to reach clinical practice partially due to the inability of numerous studies to estimate the marker’s impact on resistance properly. Objective: The analysis of drug resistance mechanisms to chemotherapy in cancer cells, and the proposal of study design to identify bona fide markers. Methods: A review of relevant papers in the field. A PubMed search with relevant keywords was used to gather the data. An example of a search request: drug resistance AND cancer AND paclitaxel. Results: We have described a number of drug resistance mechanisms to various chemotherapeutics, as well as markers to underlie the phenomenon. We also proposed a model of a rational-designed study, which could be useful in determining the most promising potential biomarkers. Conclusion: Taking into account the most reasonable biomarkers should dramatically improve clinical outcome by choosing the suitable treatment regimens. However, determining the leading biomarkers, as well as validating of the model, is a work for further investigations.

Background: Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems. Objective: Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically. Methods: Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations. Results: GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools. Conclusion: In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Background: Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer’s diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance. Objective: The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance. Methods: This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance. Results: It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs. Conclusion: Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

Background: In this review, protein-protein interactions (PPIs) were defined, and their behaviors in normal in disease conditions are discussed. Their status at nuclear, molecular and cellular level was underscored, as for their interference in many diseases. Finally, the use of protein nanoscale structures as possible carriers for drugs targeting PPIs was highlighted. Objective: The objective of this review is to suggest a novel approach for targeting PPIs. By using protein nanospheres and nanocapsules, a promising field of study can be emerged. Methods: To solidify this argument, PPIs and their biological significance was discussed, same as their role in hormone signaling. Results: We shed the light on the drugs that targets PPI and we suggested the use of nanovectors to encapsulate these drugs to possibly achieve better results. Conclusion: Protein based nanoparticles, due to their advantages, can be suitable carriers for drugs targeting PPIs. This can open a new opportunity in the emerging field of multifunctional therapeutics.

Background: Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects. Objective: In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer. Methods: Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs. Results: The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges. Conclusion: Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.

Background: Kinases are key modulators in regulating diverse range of cellular activities and are an essential part of the protein-protein interactome. Understanding the interaction of kinases with different substrates and other proteins is vital to decode the cell signaling machinery as well as causative mechanism for disease onset and progression. Objective: The objective of this review is to present all studies on the structure and function of few important kinases and highlight the protein-protein interaction (PPI) mechanism of kinases and the kinase specific interactome databases and how such studies could be utilized to develop anticancer drugs. Methods: The article is a review of the detailed description of the various domains in kinases that are involved in protein-protein interactions and specific inhibitors developed targeting these PPI domains. Results: The review has surfaced in depth the interacting domains in key kinases and their features and the roles of PPI in the human kinome and the various signaling cascades that are involved in certain types of cancer. Conclusion: The insight availed into the mechanism of existing peptide inhibitors and peptidomimetics against kinases will pave way for the design and generation of domain specific peptide inhibitors with better productivity and efficiency and the various software and servers available can be of great use for the identification and analysis of protein-protein interactions.