Rational drug design

Response surface methodology in docking study of small molecule BACE-1 inhibitors

Computational evaluation of ligand-receptor binding via docking strategy is a well established approach in structure-based drug design. This technique has been applied frequently in developing molecules of biological interest. However, any procedure would require an optimization set up to be more efficient, economic and time-saving. Advantages of modern statistical optimization methods over conventional one-factor-at-a-time studies have been well revealed. The optimization by experimental design provides a combination of factor levels simultaneously satisfying the requirements considered for each of the responses and factors. In this study, response surface method was applied to optimize the prominent factors (number of genetic algorithm runs, population size, maximum number of evaluations, torsion degrees for ligand and number of rotatable bonds in ligand) in AutoDock4.2-based binding study of small molecule β-secretase inhibitors as anti-alzheimer agents. Results revealed that a number of rotatable bonds in ligand and maximum number of docking evaluations were determinant variables affecting docking outputs. The interference between torsion degrees for ligand and number of genetic algorithm runs for docking procedure was found to be the significant interaction term in our model. Optimized docking outputs exhibited a high correlation with experimental fluorescence resonance energy transfer-based IC₅₀s for β-secretase inhibitors (R² = 0.9133).

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Modeling of molecular interaction between apoptin, BCR-Abl and CrkL--an alternative approach to conventional rational drug design

In this study we have calculated a 3D structure of apoptin and through modeling and docking approaches, we show its interaction with Bcr-Abl oncoprotein and its downstream signaling components, following which we confirm some of the newly-found interactions by biochemical methods. Bcr-Abl oncoprotein is aberrantly expressed in chronic myelogenous leukaemia (CML). It has several distinct functional domains in addition to the Abl kinase domain. The SH3 and SH2 domains cooperatively play important roles in autoinhibiting its kinase activity. Adapter molecules such as Grb2 and CrkL interact with proline-rich region and activate multiple Bcr-Abl downstream signaling pathways that contribute to growth and survival. Therefore, the oncogenic effect of Bcr-Abl could be inhibited by the interaction of small molecules with these domains. Apoptin is a viral protein with well-documented cancer-selective cytotoxicity. Apoptin attributes such as SH2-like sequence similarity with CrkL SH2 domain, unique SH3 domain binding sequence, presence of proline-rich segments, and its nuclear affinity render the molecule capable of interaction with Bcr-Abl. Despite almost two decades of research, the mode of apoptin's action remains elusive because 3D structure of apoptin is unavailable. We performed in silico three-dimensional modeling of apoptin, molecular docking experiments between apoptin model and the known structure of Bcr-Abl, and the 3D structures of SH2 domains of CrkL and Bcr-Abl. We also biochemically validated some of the interactions that were first predicted in silico. This structure-property relationship of apoptin may help in unlocking its cancer-selective toxic properties. Moreover, such models will guide us in developing of a new class of potent apoptin-like molecules with greater selectivity and potency.

Targeted therapies for hepatocellular carcinoma

Unlike most solid tumors, the incidence and mortality of hepatocellular carcinoma (HCC) have increased in the United States and Europe in the past decade. Most patients are diagnosed at advanced stages, so there is an urgent need for new systemic therapies. Sorafenib, a tyrosine kinase inhibitor (TKI), has shown clinical efficacy in patients with HCC. Studies in patients with lung, breast, or colorectal cancers have indicated that the genetic heterogeneity of cancer cells within a tumor affect its response to therapeutics designed to target specific molecules. When tumor progression requires alterations in specific oncogenes (oncogene addiction), drugs that selectively block their products might slow tumor growth. However, no specific oncogene addictions are yet known to be implicated in HCC progression, so it is important to improve our understanding of its molecular pathogenesis. There are currently many clinical trials evaluating TKIs for HCC, including those tested in combination with (eg, erlotinib) or compared with (eg, linifanib) sorafenib as a first-line therapy. For patients who do not respond or are intolerant to sorafenib, TKIs such as brivanib, everolimus, and monoclonal antibodies (eg, ramucirumab) are being tested as second-line therapies. There are early stage trials investigating the efficacy for up to 60 reagents for HCC. Together, these studies might change the management strategy for HCC, and combination therapies might be developed for patients with advanced HCC. Identification of oncogenes that mediate tumor progression, and trials that monitor their products as biomarkers, might lead to personalized therapy; reagents that interfere with signaling pathways required for HCC progression might be used to treat selected populations, and thereby maximize the efficacy and cost benefit.

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Antileishmanial and antitrypanosomal activities of the 8-aminoquinoline tafenoquine

The 8-aminoquinoline tafenoquine showed significant in vitro activity against Leishmania species, including L. donovani amastigotes in macrophages, with 50% inhibitory concentrations (IC(50)s) between 0.1 and 4.0 μM for both pentavalent antimony (SbV)-sensitive and SbV-resistant strains and by oral administration in BALB/c mice, with 50% effective dose (ED(50)) values of 1.2 to 3.5 mg/kg for 5 days. Tafenoquine was less active against intracellular Trypanosoma cruzi amastigotes, with an IC(50) of 21.9 μM.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

Current trends in antimicrobial agent research: chemo- and bioinformatics approaches

Databases and chemo- and bioinformatics tools that contain genomic, proteomic and functional information have become indispensable for antimicrobial drug research. The combination of chemoinformatics tools, bioinformatics tools and relational databases provides means of analyzing, linking and comparing online search results. The development of computational tools feeds on a diversity of disciplines, including mathematics, statistics, computer science, information technology and molecular biology. The computational approach to antimicrobial agent discovery and design encompasses genomics, molecular simulation and dynamics, molecular docking, structural and/or functional class prediction, and quantitative structure-activity relationships. This article reviews progress in the development of computational methods, tools and databases used for organizing and extracting biological meaning from antimicrobial research.

Current strategies to target p53 in cancer

Tumor suppressor p53 is a transcription factor that guards the genome stability and normal cell growth. Stresses like DNA damage, oncogenic assault will turn on p53 function which leads to cell cycle arrest for DNA repair, senescence for permanent growth arrest or apoptosis for programmed cell death. At the late stage of cancer progression, p53 is hijacked in all forms of tumors either trapped in the negative regulator such as MDM2/viral proteins or directly mutated/deleted. Re-introduction of a functional p53 alone has been proven to induce tumor regression robustly. Also, an active p53 pathway is essential for effective chemo- or radio-therapy. The emerging cyclotherapy in which p53 acts as a chemoprotectant of normal tissues further expands the utility of p53 activators. Functionally, it is unquestionable that drugging p53 will render tumor-specific intervention. One direct method is to deliver the functional wild-type (wt) p53 to tumors via gene therapy. The small molecule strategies consist of activation of p53 family member such as p73, manipulating p53 posttranslational modulators to increase wt p53 protein levels, protein-protein interaction inhibitors to free wt p53 from MDM2 or viral protein, and restoring p53 function to mutant p53 by direct modulation of its conformation. Although most of the current pre-clinical leads are in microM range and need further optimization, the success in proving that small molecules can reactivate p53 marks the beginning of the clinical development of p53-based cancer therapy.