Publications by authors named "Gavin Harper"

11 Publications

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Identifying the drivers of patients' reliance on short-acting β2-agonists in asthma.

J Asthma 2020 May 29:1-8. Epub 2020 May 29.

Consulting at McCann Health, Macclesfield, UK.

Background: One of the most commonly observed asthma treatment patterns is the underuse of inhaled corticosteroid (ICS) maintenance therapy when patients are not experiencing symptoms, and the predominant use of short-acting β2-agonists (SABAs) when patients are experiencing symptoms. This multinational study investigated the current beliefs and behaviors related to reliance on reliever inhalers among asthma patients, and the reasons why patients may not adhere to their recommended maintenance controller treatment.

Methods: This was a qualitative research study, in which 80 patients with asthma who were receiving reliever therapy (i.e. SABAs) were interviewed, in-depth, for 60 min. The interview questions focused on the patients' experience of living with asthma and their inhaled treatment regimens.

Results: The key insights identified in the interviews were (a) patients had a strong emotional attachment to SABA relievers driven by their efficacy and success in quickly alleviating asthma symptoms, with the reliever also becoming an emotional support; (b) patients typically did not understand that the frequent use of SABAs indicates poor asthma control; (c) patients had a misperception of ICS, which could lead to a delay in escalation and poor adherence; and (d) severe exacerbations improve adherence to ICS, but only temporarily in many cases.

Conclusion: This study confirmed the poor level of control patients have over their asthma, and how this affects their lifestyle and daily activities. Our results also confirmed that the patients' perception of both the disease and treatment plays a key role in SABA reliance and ICS underuse.
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http://dx.doi.org/10.1080/02770903.2020.1761382DOI Listing
May 2020

Publisher Correction: Recycling lithium-ion batteries from electric vehicles.

Nature 2020 02;578(7794):E20

Faraday Institution, ReLiB Project, University of Birmingham, Birmingham, UK.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41586-019-1862-3DOI Listing
February 2020

Recycling lithium-ion batteries from electric vehicles.

Nature 2019 11 6;575(7781):75-86. Epub 2019 Nov 6.

Faraday Institution, ReLiB Project, University of Birmingham, Birmingham, UK.

Rapid growth in the market for electric vehicles is imperative, to meet global targets for reducing greenhouse gas emissions, to improve air quality in urban centres and to meet the needs of consumers, with whom electric vehicles are increasingly popular. However, growing numbers of electric vehicles present a serious waste-management challenge for recyclers at end-of-life. Nevertheless, spent batteries may also present an opportunity as manufacturers require access to strategic elements and critical materials for key components in electric-vehicle manufacture: recycled lithium-ion batteries from electric vehicles could provide a valuable secondary source of materials. Here we outline and evaluate the current range of approaches to electric-vehicle lithium-ion battery recycling and re-use, and highlight areas for future progress.
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http://dx.doi.org/10.1038/s41586-019-1682-5DOI Listing
November 2019

Emergence of canine parvovirus subtype 2b (CPV-2b) infections in Australian dogs.

Infect Genet Evol 2018 03 16;58:50-55. Epub 2017 Dec 16.

School of Veterinary Science, University of Queensland, Gatton, Queensland 4343, Australia.

Tracing the temporal dynamics of pathogens is crucial for developing strategies to detect and limit disease emergence. Canine parvovirus (CPV-2) is an enteric virus causing morbidity and mortality in dogs around the globe. Previous work in Australia reported that the majority of cases were associated with the CPV-2a subtype, an unexpected finding since CPV-2a was rapidly replaced by another subtype (CPV-2b) in many countries. Using a nine-year dataset of CPV-2 infections from 396 dogs sampled across Australia, we assessed the population dynamics and molecular epidemiology of circulating CPV-2 subtypes. Bayesian phylogenetic Skygrid models and logistic regressions were used to trace the temporal dynamics of CPV-2 infections in dogs sampled from 2007 to 2016. Phylogenetic models indicated that CPV-2a likely emerged in Australia between 1973 and 1988, while CPV-2b likely emerged between 1985 and 1998. Sequences from both subtypes were found in dogs across continental Australia and Tasmania, with no apparent effect of climate variability on subtype occurrence. Both variant subtypes exhibited a classical disease emergence pattern of relatively high rates of evolution during early emergence followed by subsequent decreases in evolutionary rates over time. However, the CPV-2b subtype maintained higher mutation rates than CPV-2a and continued to expand, resulting in an increase in the probability that dogs will carry this subtype over time. Ongoing monitoring programs that provide molecular epidemiology surveillance will be necessary to detect emergence of new variants and make informed recommendations to develop reliable detection and vaccine methods.
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http://dx.doi.org/10.1016/j.meegid.2017.12.013DOI Listing
March 2018

Process validation and screen reproducibility in high-throughput screening.

J Biomol Screen 2009 Jan;14(1):66-76

GlaxoSmithKline R&D Pharmaceuticals, Screening and Compound Profiling, Tres Cantos, Spain.

The use of large-scale compound screening has become a key component of drug discovery projects in both the pharmaceutical and the biotechnological industries. More recently, these activities have also been embraced by the academic community as a major tool for chemical genomic activities. High-throughput screening (HTS) activities constitute a major step in the initial drug discovery efforts and involve the use of large quantities of biological reagents, hundreds of thousands to millions of compounds, and the utilization of expensive equipment. All these factors make it very important to evaluate in advance of the HTS campaign any potential issues related to reproducibility of the experimentation and the quality of the results obtained at the end of these very costly activities. In this article, the authors describe how GlaxoSmithKline (GSK) has addressed the need of a true validation of the HTS process before embarking in full HTS campaigns. They present 2 different aspects of the so-called validation process: (1) optimization of the HTS workflow and its validation as a quality process and (2) the statistical evaluation of the HTS, focusing on the reproducibility of results and the ability to distinguish active from nonactive compounds in a vast collection of samples. The authors describe a variety of reproducibility indexes that are either innovative or have been adapted from generic medical diagnostic screening strategies. In addition, they exemplify how these validation tools have been implemented in a number of case studies at GSK.
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http://dx.doi.org/10.1177/1087057108326664DOI Listing
January 2009

Assessment of chemical coverage of kinome space and its implications for kinase drug discovery.

J Med Chem 2008 Dec;51(24):7898-914

Molecular Discovery Research, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK.

More than 500 compounds chosen to represent kinase inhibitor space have been screened against a panel of over 200 protein kinases. Significant results include the identification of hits against new kinases including PIM1 and MPSK1, and the expansion of the inhibition profiles of several literature compounds. A detailed analysis of the data through the use of affinity fingerprints has produced findings with implications for biological target selection, the choice of tool compounds for target validation, and lead discovery and optimization. In a detailed examination of the tyrosine kinases, interesting relationships have been found between targets and compounds. Taken together, these results show how broad cross-profiling can provide important insights to assist kinase drug discovery.
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http://dx.doi.org/10.1021/jm8011036DOI Listing
December 2008

Evolving interpretable structure-activity relationship models. 2. Using multiobjective optimization to derive multiple models.

J Chem Inf Model 2008 Aug 19;48(8):1558-70. Epub 2008 Jul 19.

Department of Information Studies, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, United Kingdom.

A multiobjective evolutionary algorithm (MOEA) is described for evolving multiple structure-activity relationships (SARs). The SARs are encoded in easy-to-interpret reduced graph queries which describe features that are preferentially present in active compounds compared to inactives. The MOEA addresses a limitation associated with many machine learning methods; that is, the inherent tradeoff that exists in recall and precision which is usually handled by combining the two objectives into a single measure with a consequent loss of control. By simultaneously optimizing recall and precision, the MOEA generates a family of SARs that lie on the precision-recall (PR) curve. The user is then able to select a query with an appropriate balance in the two objectives: for example, a low recall-high precision query may be preferred when establishing the SAR, whereas a high recall-low precision query may be more appropriate in a virtual screening context. Each query on the PR curve aims at capturing the structure-activity information into a single representation, and each can be considered as an alternative (equally valid) solution. We then investigate combining individual queries into teams with the aim of capturing multiple SARs that may exist in a data set, for example, as is commonly seen in high-throughput screening data sets. Team formation is carried out iteratively as a postprocessing step following the evolution of the individual queries. The inclusion of uniqueness as a third objective within the MOEA provides an effective way of ensuring the queries are complementary in the active compounds they describe. Substantial improvements in both recall and precision are seen for some data sets. Furthermore, the resulting queries provide more detailed structure-activity information than is present in a single query.
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http://dx.doi.org/10.1021/ci800051hDOI Listing
August 2008

Evolving interpretable structure-activity relationships. 1. Reduced graph queries.

J Chem Inf Model 2008 Aug 17;48(8):1543-57. Epub 2008 Jul 17.

Department of Information Studies, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, United Kingdom.

A new machine learning method is presented for extracting interpretable structure-activity relationships from screening data. The method is based on an evolutionary algorithm and reduced graphs and aims to evolve a reduced graph query (subgraph) that is present within the active compounds and absent from the inactives. The reduced graph representation enables heterogeneous compounds, such as those found in high-throughput screening data, to be captured in a single representation with the resulting query encoding structure-activity information in a form that is readily interpretable by a chemist. The application of the method is illustrated using data sets extracted from the well-known MDDR data set and GSK in-house screening data. Queries are evolved that are consistent with the known SARs, and they are also shown to be robust when applied to independent sets that were not used in training.
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http://dx.doi.org/10.1021/ci8000502DOI Listing
August 2008

Methods for mining HTS data.

Drug Discov Today 2006 Aug;11(15-16):694-9

GSK, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, United Kingdom.

Data mining is a fast-growing field that is finding application across a wide range of industries. HTS is a crucial part of the drug discovery process at most large pharmaceutical companies. Accurate analysis of HTS data is, therefore, vital to drug discovery. Given the large quantity of data generated during an HTS, and the importance of analyzing those data effectively, it is unsurprising that data-mining techniques are now increasingly applied to HTS data analysis. Taking a broad view of both the HTS process and the data-mining process, we review recent literature that describes the application of data-mining techniques to HTS data.
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http://dx.doi.org/10.1016/j.drudis.2006.06.006DOI Listing
August 2006

Training similarity measures for specific activities: application to reduced graphs.

J Chem Inf Model 2006 Mar-Apr;46(2):577-86

Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, United Kingdom.

Reduced graph representations of chemical structures have been shown to be effective in similarity searching applications where they offer comparable performance to other 2D descriptors in terms of recall experiments. They have also been shown to complement existing descriptors and to offer potential to scaffold hop from one chemical series to another. Various methods have been developed for quantifying the similarity between reduced graphs including fingerprint approaches, graph matching, and an edit distance method. The edit distance approach quantifies the degree of similarity of two reduced graphs based on the number and type of operations required to convert one graph to the other. An attractive feature of the edit distance method is the ability to assign different weights to different operations. For example, the mutation of an aromatic ring node to an acyclic node may be assigned a higher weight than the mutation of an aromatic ring to an aliphatic ring node. In this paper, we describe a genetic algorithm (GA) for training the weights of the different edit distance operations. The method is applied to specific activity classes extracted from the MDDR database to derive activity-class specific weights. The GA-derived weights give substantially improved results in recall experiments as compared to using weights assigned on intuition. Furthermore, such activity specific weights may provide useful structure--activity information for subsequent design efforts. In a virtual screening setting when few active compounds are known, it may be more useful to have weights that perform well across a variety of different activity classes. Thus, the GA is also trained on multiple activity classes simultaneously to derive a generalized set of weights. These more generally applicable weights also represent a substantial improvement on previous work.
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http://dx.doi.org/10.1021/ci050465eDOI Listing
September 2006

Drug rings database with web interface. A tool for identifying alternative chemical rings in lead discovery programs.

J Med Chem 2003 Jul;46(15):3257-74

Medicines Research Centre, GlaxoSmithKline Research and Development, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK.

This paper describes the development of a drug rings database and Web-based search tools. The database contains ring structures from both corporate and commercial databases, along with characteristic descriptors including frequency of occurrence as an indicator of synthetic accessibility and calculated property and geometric parameters. Analysis of the rings in several major databases is described, with illustrations of applications of the database in lead discovery programs where bioisosteres and geometric isosteres are sought.
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http://dx.doi.org/10.1021/jm0300429DOI Listing
July 2003