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Torimitsu S, Nakazawa A, Flavel A, Swift L, Makino Y, Iwase H, Franklin D. Estimation of ancestry from cranial measurements based on MDCT data acquired in a Japanese and Western Australian population. Int J Legal Med 2024; 138:1193-1203. [PMID: 38252284 PMCID: PMC11003893 DOI: 10.1007/s00414-024-03159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
The estimation of ancestry is important not only towards establishing identity but also as a required precursor to facilitating the accurate estimation of other attributes such as sex, age at death, and stature. The present study aims to analyze morphological variation in the crania of Japanese and Western Australian individuals and test predictive models based on machine learning for their potential forensic application. The Japanese and Western Australian samples comprise computed tomography (CT) scans of 230 (111 female; 119 male) and 225 adult individuals (112 female; 113 male), respectively. A total of 18 measurements were calculated, and machine learning methods (random forest modeling, RFM; support vector machine, SVM) were used to classify ancestry. The two-way unisex model achieved an overall accuracy of 93.2% for RFM and 97.1% for SVM, respectively. The four-way sex and ancestry model demonstrated an overall classification accuracy of 84.0% for RFM and 93.0% for SVM. The sex-specific models were most accurate in the female samples (♀ 95.1% for RFM and 100% for SVM; ♂91.4% for RFM and 97.4% for SVM). Our findings suggest that cranial measurements acquired in CT images can be used to accurately classify Japanese and Western Australian individuals into their respective population. This is the first study to assess the feasibility of ancestry estimation using three-dimensional CT images of the skull.
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Affiliation(s)
- Suguru Torimitsu
- Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia.
- Department of Forensic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.
| | - Akari Nakazawa
- Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia
- Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
| | - Ambika Flavel
- Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia
| | - Lauren Swift
- Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia
| | - Yohsuke Makino
- Department of Forensic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Hirotaro Iwase
- Department of Forensic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Daniel Franklin
- Centre for Forensic Anthropology, University of Western Australia, Crawley, WA, 6009, Australia
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Houssein EH, Hosney ME, Mohamed WM, Ali AA, Younis EMG. Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data. Neural Comput Appl 2022; 35:5251-5275. [PMID: 36340595 PMCID: PMC9628476 DOI: 10.1007/s00521-022-07916-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.
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Affiliation(s)
- Essam H. Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Mosa E. Hosney
- Faculty of Computers and Information, Luxor University, Luxor, Egypt
| | - Waleed M. Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Eman M. G. Younis
- Faculty of Computers and Information, Minia University, Minia, Egypt
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Đuriš J, Kurćubić I, Ibrić S. Review of machine learning algorithms' application in pharmaceutical technology. ARHIV ZA FARMACIJU 2021. [DOI: 10.5937/arhfarm71-32499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.
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Cheruvu HS, Liu X, Grice JE, Roberts MS. Modeling percutaneous absorption for successful drug discovery and development. Expert Opin Drug Discov 2020; 15:1181-1198. [DOI: 10.1080/17460441.2020.1781085] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Hanumanth Srikanth Cheruvu
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Xin Liu
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Jeffrey E. Grice
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Michael S. Roberts
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
- University of South Australia School of Pharmacy and Medical Sciences, The Queen Elizabeth Hospital, Adelaide, Australia
- Therapeutics Research Centre, Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, Australia
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Houssein EH, Hosney ME, Oliva D, Mohamed WM, Hassaballah M. A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106656] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Ashrafi P, Sun Y, Davey N, Wilkinson SC, Moss GP. The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability. ACTA ACUST UNITED AC 2019; 72:197-208. [PMID: 31724749 DOI: 10.1111/jphp.13203] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 10/26/2019] [Indexed: 11/28/2022]
Abstract
OBJECTIVES The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp ) and choice of diffusion cell on model quality and performance. METHODS Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C. KEY FINDINGS Using data from flow-through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow-through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures. CONCLUSIONS This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz-type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.
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Affiliation(s)
- Parivash Ashrafi
- The School of Computing, University of Hertfordshire, Hatfield, UK
| | - Yi Sun
- The School of Computing, University of Hertfordshire, Hatfield, UK
| | - Neil Davey
- The School of Computing, University of Hertfordshire, Hatfield, UK
| | - Simon C Wilkinson
- Wolfson Unit, Medical School, Medical Toxicology Centre, University of Newcastle-upon-Tyne, Newcastle-upon-Tyne, UK
| | - Gary P Moss
- The School of Pharmacy, Keele University, Keele, UK
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Pecoraro B, Tutone M, Hoffman E, Hutter V, Almerico AM, Traynor M. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies. J Chem Inf Model 2019; 59:1759-1771. [PMID: 30658035 DOI: 10.1021/acs.jcim.8b00934] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.
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Affiliation(s)
- Beatrice Pecoraro
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Marco Tutone
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Ewelina Hoffman
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Victoria Hutter
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Anna Maria Almerico
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Matthew Traynor
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
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Ashrafi P, Sun Y, Davey N, Adams RG, Wilkinson SC, Moss GP. Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression. J Pharm Pharmacol 2018; 70:361-373. [PMID: 29341138 DOI: 10.1111/jphp.12863] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 11/22/2017] [Indexed: 11/30/2022]
Abstract
OBJECTIVES The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. METHODS Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. KEY FINDINGS The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. CONCLUSIONS The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.
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Affiliation(s)
- Parivash Ashrafi
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Yi Sun
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Neil Davey
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Roderick G Adams
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Simon C Wilkinson
- Medical Toxicology Centre, Wolfson Unit, Medical School, University of Newcastle-upon-Tyne, Newcastle upon Tyne, UK
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10
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Ekins S. The Next Era: Deep Learning in Pharmaceutical Research. Pharm Res 2016; 33:2594-603. [PMID: 27599991 DOI: 10.1007/s11095-016-2029-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/23/2016] [Indexed: 01/22/2023]
Abstract
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina, 27526, USA. .,Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA.
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11
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Williams FM, Rothe H, Barrett G, Chiodini A, Whyte J, Cronin MT, Monteiro-Riviere NA, Plautz J, Roper C, Westerhout J, Yang C, Guy RH. Assessing the safety of cosmetic chemicals: Consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern). Regul Toxicol Pharmacol 2016; 76:174-86. [DOI: 10.1016/j.yrtph.2016.01.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 01/06/2016] [Accepted: 01/07/2016] [Indexed: 12/01/2022]
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12
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Shah A, Sun Y, Adams RG, Davey N, Wilkinson SC, Moss GP. Support vector regression to estimate the permeability enhancement of potential transdermal enhancers. J Pharm Pharmacol 2016; 68:170-84. [DOI: 10.1111/jphp.12508] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 11/19/2015] [Indexed: 11/30/2022]
Abstract
Abstract
Objectives
Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations.
Methods
The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability.
Key findings
A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer.
Conclusions
Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a ‘mixed-methods’ approach may be best in optimising predictive models.
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Affiliation(s)
- Alpa Shah
- Department of Software Engineering and IT, Ecole de Technologie Superieure, Montreal, QC, Canada
| | - Yi Sun
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Rod G Adams
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Neil Davey
- School of Computer Science, University of Hertfordshire, Hatfield, UK
| | | | - Gary P Moss
- Medical Toxicology Centre, Wolfson Unit, Medical School, University of Newcastle-upon-Tyne, Newcastle-upon-Tyne, UK
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Ashrafi P, Moss GP, Wilkinson SC, Davey N, Sun Y. The application of machine learning to the modelling of percutaneous absorption: an overview and guide. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:181-204. [PMID: 25783869 DOI: 10.1080/1062936x.2015.1018941] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail - and providing the detailed coding for - the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.
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Affiliation(s)
- P Ashrafi
- a School of Computer Science , University of Hertfordshire , Hatfield , UK
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Johnston BA, Coghill D, Matthews K, Steele JD. Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study. J Psychopharmacol 2015; 29:24-30. [PMID: 25237119 DOI: 10.1177/0269881114548438] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH.
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Affiliation(s)
- Blair A Johnston
- Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - David Coghill
- Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Keith Matthews
- Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - J Douglas Steele
- Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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Waters LJ, Shahzad Y, Stephenson J. Modelling skin permeability with micellar liquid chromatography. Eur J Pharm Sci 2013; 50:335-40. [DOI: 10.1016/j.ejps.2013.08.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Revised: 07/16/2013] [Accepted: 08/02/2013] [Indexed: 11/16/2022]
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Hathout RM. Using principal component analysis in studying the transdermal delivery of a lipophilic drug from soft nano-colloidal carriers to develop a quantitative composition effect permeability relationship. Pharm Dev Technol 2013; 19:598-604. [PMID: 23879693 DOI: 10.3109/10837450.2013.813544] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The aim of principal component analysis is to reduce the dimensionality of the data while retaining its variation. Obtaining a vector component representing the most important variation amongst the data and summarizing the factors are usually needed to achieve a new descriptor for the system. This can be used to elaborate certain properties related to the components used in formulating drug delivery systems. To this end, it is possible to develop what exclusively can be called quantitative composition effect permeability relationship. In this study, fundamental features of the Fourier transform infrared spectroscopy together with the degree of saturation of a model drug, testosterone hormone, were used as initial dimensions and their extent of change were utilized as original variables to generate a correlation matrix. The principal component (PC) with the largest eigen value was selected for regression analysis to provide a quantitative model relating the effects of different compositions with the enhanced penetration of the model lipophilic drug from microemulsions. A strong correlation (r = 0.90) was obtained between the main PC derived data and the observed permeability coefficient results which warrants the use of this analyzing method in optimizing different drug delivery systems.
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Affiliation(s)
- Rania M Hathout
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University , Cairo , Egypt
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Moss GP, Wilkinson SC, Sun Y. Mathematical modelling of percutaneous absorption. Curr Opin Colloid Interface Sci 2012. [DOI: 10.1016/j.cocis.2012.01.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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