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Winkler D.A.,CSIRO | Winkler D.A.,Monash Institute of Pharmaceutical Sciences | Winkler D.A.,Latrobe Institute for Molecular Science | Winkler D.A.,Flinders University | And 2 more authors.
Pharmacology and Therapeutics | Year: 2016

The noble gases represent an intriguing scientific paradox. They are extremely inert chemically but display a remarkable spectrum of clinically useful biological properties. Despite a relative paucity of knowledge of their mechanisms of action, some of the noble gases have been used successfully in the clinic. Studies with xenon have suggested that the noble gases as a class may exhibit valuable biological properties such as anaesthesia; amelioration of ischemic damage; tissue protection prior to transplantation; analgesic properties; and a potentially wide range of other clinically useful effects. Xenon has been shown to be safe in humans, and has useful pharmacokinetic properties such as rapid onset, fast wash out etc. The main limitations in wider use are that: many of the fundamental biochemical studies are still lacking; the lighter noble gases are likely to manifest their properties only under hyperbaric conditions, impractical in surgery; and administration of xenon using convectional gaseous anaesthesia equipment is inefficient, making its use very expensive. There is nonetheless a significant body of published literature on the biochemical, pharmacological, and clinical properties of noble gases but no comprehensive reviews exist that summarize their properties and the existing knowledge of their models of action at the molecular (atomic) level. This review provides such an up-to-date summary of the extensive, useful biological properties of noble gases as drugs and prospects for wider application of these atoms. © 2016 Published by Elsevier Inc. All rights reserved.


Le T.C.,CSIRO | Ballard M.,CSIRO | Casey P.,CSIRO | Liu M.S.,CSIRO | And 3 more authors.
Molecular Informatics | Year: 2015

Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure - property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than -130°C to over 900°C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials. © 2015 Wiley-VCH Verlag GmbH & Co. KGaA.


Burden F.R.,CSIRO | Burden F.R.,Monash Institute of Pharmaceutical Sciences | Winkler D.A.,CSIRO | Winkler D.A.,Monash Institute of Pharmaceutical Sciences | And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2015

Sparse machine learning methods have provided substantial benefits to quantitative structure property modeling, as they make model interpretation simpler and generate models with improved predictivity. Sparsity is usually induced via Bayesian regularization using sparsity-inducing priors and by the use of expectation maximization algorithms with sparse priors. The focus to date has been on using sparse methods to model continuous data and to carry out sparse feature selection. We describe the relevance vector machine (RVM), a sparse version of the support vector machine (SVM) that is one of the most widely used classification machine learning methods in QSAR and QSPR. We illustrate the superior properties of the RVM by modeling eight data sets using SVM, RVM, and another sparse Bayesian machine learning method, the Bayesian regularized artificial neural network with Laplacian prior (BRANNLP). We show that RVM models are substantially sparser than the SVM models and have similar or superior performance to them. (Figure Presented). © 2015 American Chemical Society.


Winkler D.,CSIRO | Winkler D.,Monash Institute of Pharmaceutical Sciences | Winkler D.,Latrobe Institute for Molecular Science
Australian Journal of Chemistry | Year: 2015

It is clear that the sizes of chemical, 'drug-like', and materials spaces are enormous. If scientists working in established therapeutic, and newly established regenerative medicine fields are to discover better molecules or materials, they must find better ways of probing these enormous spaces. There are essentially five ways that this can be achieved: combinatorial and high throughput synthesis and screening approaches; fragment-based methods; de novo molecular design, design of experiments, diversity libraries; supramolecular approaches; evolutionary approaches. These methods either synthesise materials and screen them more quickly, or constrain chemical spaces using biology or other types of 'fitness functions'. High throughput experimental approaches cannot explore more than a minute part of chemical space. We are nevertheless entering into an era that is data dominated. High throughput experiments, robotics, automated crystallographic beam lines, combinatorial and flow synthesis, high content screening, and the 'omics' technologies are providing a flood of data, and efficient methods for extracting meaning from it are essential. This paper describes how new developments in mathematics have provided excellent, robust computational modelling tools for exploring large chemical spaces, for extracting meaning from large datasets, for designing new bioactive agents and materials, and for making truly predictive, quantitative models of the properties of molecules and materials for use in therapeutic and regenerative medicine. We describe these broadly applicable modelling tools and provide examples of their application to serum free stem cell culture, pathogen resistant polymers for implantable devices, new markers and biological mechanisms derived from mathematical analyses of gene array data, and pharmacokinetically important physicochemical properties of small molecules. We also discuss biologically conserved peptide motifs as a design framework for small molecule drugs and give examples of the application of this concept to drug design. © CSIRO 2015.


Le T.C.,CSIRO | Yan B.,Shandong University | Winkler D.A.,CSIRO | Winkler D.A.,Monash Institute of Pharmaceutical Sciences | And 2 more authors.
Advanced Functional Materials | Year: 2015

Nanomaterials are used increasingly in diagnostics and therapeutics, particularly for malignancies. Efficient targeting of nanoparticles to specific cells is an important requirement for the development of successful nanoparticle-based theranostics and personalized medicines. Gold nanoparticles are surface modified using a library of small organic molecules, and optionally folate, to investigate their ability to target four cell lines from common cancers, three having high levels of folate receptors expression. Uptake of these nanoparticles varies widely with surface chemistriy and cell lines. Sparse machine learning methods are used to computationally model surface chemistry-uptake relationships, to make quantitative predictions of uptake for new nanoparticle surface chemistries, and to elucidate molecular aspects of the interactions. The combination of combinatorial surface chemistry modification and machine learning models will facilitate the rapid development of targeted theranostics. Efficient targeting of nanoparticles to specific cells is an important requirement for the development of successful nanoparticle-based cancer theranostics and personalized medicines. The cancer cell targeting ability of gold nanoparticles coated with a library of small organic molecules plus folate is modeled. Computational models can predict the degree of uptake of the nanoparticles as a function of surface chemistry. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Ebert G.,Walter and Eliza Hall Institute of Medical Research | Ebert G.,University of Melbourne | Preston S.,Walter and Eliza Hall Institute of Medical Research | Preston S.,University of Melbourne | And 25 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

Hepatitis B virus (HBV) infection can result in a spectrum of outcomes from immune-mediated control to disease progression, cirrhosis, and liver cancer. The host molecular pathways that influence and contribute to these outcomes need to be defined. Using an immuno-competent mouse model of chronic HBV infection, we identified some of the host cellular and molecular factors that impact on infection outcomes. Here, we show that cellular inhibitor of apoptosis proteins (cIAPs) attenuate TNF signaling during hepatitis B infection, and they restrict the death of infected hepatocytes, thus allowing viral persistence. Animals with a liver-specific cIAP1 and total cIAP2 deficiency efficiently control HBV infection compared with WT mice. This phenotype was partly recapitulated in mice that were deficient in cIAP2 alone. These results indicate that antagonizing the function of cIAPs may promote the clearance of HBV infection. © 2015, National Academy of Sciences. All rights reserved.


Ebert G.,Walter and Eliza Hall Institute of Medical Research | Ebert G.,University of Melbourne | Allison C.,Walter and Eliza Hall Institute of Medical Research | Allison C.,University of Melbourne | And 18 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

We have shown that cellular inhibitor of apoptosis proteins (cIAPs) impair clearance of hepatitis B virus (HBV) infection by preventing TNF-mediated killing/death of infected cells. A key question, with profound therapeutic implications, is whether this finding can be translated to the development of drugs that promote elimination of infected cells. Drug inhibitors of cIAPs were developed as cancer therapeutics to promote TNF-mediated tumor killing. These drugs are also known as Smac mimetics, because they mimic the action of the endogenous protein Smac/Diablo that antagonizes cIAP function. Here, we show using an immunocompetent mouse model of chronic HBV infection that birinapant and other Smac mimetics are able to rapidly reduce serum HBV DNA and serum HBV surface antigen, and they promote the elimination of hepatocytes containing HBV core antigen. The efficacy of Smac mimetics in treating HBV infection is dependent on their chemistry, host CD4+ T cells, and TNF. Birinapant enhances the ability of entecavir, an antiviral nucleoside analog, to reduce viral DNA production in HBV-infected animals. These results indicate that birinapant and other Smac mimetics may have efficacy in treating HBV infection and perhaps, other intracellular infections. © 2015 National Academy of Sciences. All rights reserved.


PubMed | LaTrobe Institute for Molecular Science, University of Washington, Fred Hutchinson Cancer Research Center and Walter and Eliza Hall Institute of Medical Research
Type: | Journal: eLife | Year: 2016

Many cancers overexpress one or more of the six human pro-survival BCL2 family proteins to evade apoptosis. To determine which BCL2 protein or proteins block apoptosis in different cancers, we computationally designed three-helix bundle protein inhibitors specific for each BCL2 pro-survival protein. Following in vitro optimization, each inhibitor binds its target with high picomolar to low nanomolar affinity and at least 300-fold specificity. Expression of the designed inhibitors in human cancer cell lines revealed unique dependencies on BCL2 proteins for survival which could not be inferred from other BCL2 profiling methods. Our results show that designed inhibitors can be generated for each member of a closely-knit protein family to probe the importance of specific protein-protein interactions in complex biological processes.


PubMed | University of Vic, Peter Doherty Institute, LaTrobe Institute for Molecular Science, Walter and Eliza Hall Institute of Medical Research and TetraLogic Pharmaceuticals
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

Hepatitis B virus (HBV) infection can result in a spectrum of outcomes from immune-mediated control to disease progression, cirrhosis, and liver cancer. The host molecular pathways that influence and contribute to these outcomes need to be defined. Using an immunocompetent mouse model of chronic HBV infection, we identified some of the host cellular and molecular factors that impact on infection outcomes. Here, we show that cellular inhibitor of apoptosis proteins (cIAPs) attenuate TNF signaling during hepatitis B infection, and they restrict the death of infected hepatocytes, thus allowing viral persistence. Animals with a liver-specific cIAP1 and total cIAP2 deficiency efficiently control HBV infection compared with WT mice. This phenotype was partly recapitulated in mice that were deficient in cIAP2 alone. These results indicate that antagonizing the function of cIAPs may promote the clearance of HBV infection.


PubMed | University of Vic, LaTrobe Institute for Molecular Science, Walter and Eliza Hall Institute of Medical Research and TetraLogic Pharmaceuticals
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

We have shown that cellular inhibitor of apoptosis proteins (cIAPs) impair clearance of hepatitis B virus (HBV) infection by preventing TNF-mediated killing/death of infected cells. A key question, with profound therapeutic implications, is whether this finding can be translated to the development of drugs that promote elimination of infected cells. Drug inhibitors of cIAPs were developed as cancer therapeutics to promote TNF-mediated tumor killing. These drugs are also known as Smac mimetics, because they mimic the action of the endogenous protein Smac/Diablo that antagonizes cIAP function. Here, we show using an immunocompetent mouse model of chronic HBV infection that birinapant and other Smac mimetics are able to rapidly reduce serum HBV DNA and serum HBV surface antigen, and they promote the elimination of hepatocytes containing HBV core antigen. The efficacy of Smac mimetics in treating HBV infection is dependent on their chemistry, host CD4(+) T cells, and TNF. Birinapant enhances the ability of entecavir, an antiviral nucleoside analog, to reduce viral DNA production in HBV-infected animals. These results indicate that birinapant and other Smac mimetics may have efficacy in treating HBV infection and perhaps, other intracellular infections.

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