Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks

European Journal of Pharmaceutical Sciences

Volumn 41, Issue 5, 2010, Pages 707-715

  de Matas, Marcel ^a   Shao, Qun ^a   Biddiscombe, Martyn F ^b   Meah, Sally ^b   Chrystyn, Henry ^c   Usmani, Omar S ^b  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

^b ROYAL BROMPTON HOSPITAL   (United Kingdom)

^c UNIVERSITY OF HUDDERSFIELD   (United Kingdom)

Author keywords

Artifical neural networks; Asthma; Bronchodilator response; In vitro in vivo correlations; Modelling; Patients; Prediction

Indexed keywords

BRONCHODILATING AGENT; SALBUTAMOL SULFATE;

ADULT; AEROSOL; ARTICLE; ARTIFICIAL NEURAL NETWORK; ASTHMA; BRONCHODILATATION; CLINICAL ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; CORRELATIONAL STUDY; FEMALE; FORCED EXPIRATORY VOLUME; HUMAN; IN VITRO STUDY; IN VIVO STUDY; MALE; METERED DOSE INHALER; MODEL; PARTICLE SIZE; PREDICTION; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; TREATMENT OUTCOME;

ADMINISTRATION, INHALATION; ADULT; AEROSOLS; ALBUTEROL; ASTHMA; BRONCHODILATOR AGENTS; FEMALE; FORCED EXPIRATORY VOLUME; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PARTICLE SIZE; TIME FACTORS; TREATMENT OUTCOME; VITAL CAPACITY;

EID: 78349310984     PISSN: 09280987     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejps.2010.09.018     Document Type: Article

References (32)

1. Barros M.J., Rees P.J. Bronchodilator responses to salbutamol followed by ipratropium bromide in partially reversible airflow obstruction. Respir. Med. 1990, 84(5):371-375.
2. Bennett W.D., Scheuch G., Zeman K.L., Brown J.S., Kim C., Heyder J., Stalhofen W. Bronchial airway deposition and retention of particles in inhaled boluses: effect of anatomic dead space. J. Appl. Physiol. 1998, 85(2):685-694.
3. Biddiscombe M.F., Usmani O.S., Barnes P.J. A system for the production and delivery of monodisperse salbutamol aerosols to the lungs. Int. J. Pharm. 2003, 254:243-253.
4. Bourquin J., Schmidli H., Hoogevest P.V., Leuenberger H. Application of artificial neural networks (ANN) in the development of solid dosage forms. Pharm. Dev. Technol. 1997, 2:111-121.
5. de Matas M., Shao Q., Silkstone V., Chrystyn H. Evaluation of an in-vitro in-vivo correlation for nebulizer delivery using artificial neural networks. J. Pharm. Sci. 2007, 96(12):3293-3303.
6. de Matas M., Shao Q., Richardson C.H., Chrystyn H. Evaluation of in-vitro in-vivo correlations for dry powder inhaler delivery using artificial neural networks. Eur. J. Pharm. Sci. 2008, 33(1):80-90.
7. Gerrity T.R., Lee P.S., Hass F.J., Marinelli A., Werner P., Lourenco R.V. Calculated deposition of inhaled particles in the airway generations of normal subjects. J. Appl. Physiol. 1979, 47:867-873.
8. Gonda I. Systemic delivery of drugs to humans via inhalation. J. Aerosol Med. 2006, 19:47-53.
9. Heyder J., Gebhart J., Rudolph G., Schiller C.F., Stalhofen W. Deposition of particles in the human respiratory tract in the size range 0.005-15μm. J. Aerosol Sci. 1986, 17(5):811-825.
10. Hussain, A.S., 2005. Engineering a proactive decision system for pharmaceutical quality: integrating science of design, process analytical technology and quality system. Presentation at IFPAC conference. (accessed 16.03.10.). http://www.fda.gov/cder/OPS/hussain_1_2005.pdf.
11. Jain R.K., Pendharkar U., Srivastava J.K., Dave R.K. Simulation of ozonation process for treatment of industrial effluents using artificial neural networks and regression analysis. Asian J. Microbiol. Biotech. Environ. Sci. 2009, 11(2):387-394.
12. Jain R.K., Pendharkar U., Srivastava J.K., Dave R.K. ANN and regression modelling of dissolved solids (DS) removal from industrial effluents. Pollut. Res. 2009, 28(2):165-170.
13. Liggett S.B. Polymorphisms of the β2-adrenergic receptor and asthma. Am. J. Respir. Crit. Care Med. 1997, 156:S156-S162.
14. Liggett S.B. Genetic variability of the β2-adrenergic receptor and asthma exacerbations. Thorax 2006, 61:925-927.
15. Martin C.J., Das S., Young A.C. Measurements of dead space volume. J. Appl. Physiol. 1979, 47(2):319-324.
16. Martonen T.B., Katz I. Deposition patterns of polydisperse aerosols within human lungs. J. Aerosol Med. 1993, 6(4):251-273.
17. Martonen T.B., Quan L., Zhang Z., Musante C.J. Flow simulation in the human upper respiratory tract. Cell Biochem. Biophys. 2002, 37(1):27-36.
18. Morrow P.E. An evaluation of the physical properties of monodisperse and heterodisperse aerosols used in the assessment of bronchial function. Chest 1981, 80:809-813.
19. Nazir J., Barlow D.J., Lawrence M.J., Richardson C.J., Shrubb I. Artificial neural network prediction of aerosol deposition in human lungs. Pharm. Res. 2002, 19(8):1130-1136.
20. Nazir J., Barlow D.J., Lawrence M.J., Shrubb I. Artificial neural network prediction of the patterns of polydisperse aerosols within the human lungs. J. Pharm. Sci. 2005, 94(9):1986-1997.
21. Newman S.P., Chan H.-K. In vitro/in vivo comparisons in pulmonary drug delivery. J. Aerosol Med. 2008, 21(1):1-8.
22. Niu T., Rogus J.J., Chen C., Wang B., Yang J., Fang Z., Weiss S.T., Xu X. Familial aggregation of bronchodilator response: a community-based study. Am. J. Respir. Crit. Care Med. 2000, 162(5):1833-1837.
23. Numa A.H., Newth C.J. Anatomic dead space in infants and children. J. Appl. Physiol. 1996, 80(5):1485-1489.
24. Shao Q., Rowe R.C., York P. Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur. J. Pharm. Sci. 2006, 28(5):394-404.
25. Shao Q., Rowe R.C., York P. Data mining of fractured experimental data using neurofuzzy logic - discovering and integrating knowledge hidden in multiple formulation databases for fluid-bed granulation process. J. Pharm. Sci. 2008, 97(6):2091-2101.
26. Srichana T., Suedee R., Muanpanarai D., Tanmanee N. The study of in vitro-in vivo correlation: pharmacokinetics and pharmacodynamics of albuterol dry powder inhalers. J. Pharm. Sci. 2005, 94(1):220-230.
27. Ullah M., Newman G.B., Saunders K.B. Influence of age on response to ipratropium and salbutamol in asthma. Thorax 1981, 36:523-529.
28. Usmani O.S., Biddiscombe M.F., Nightingale J.A., Underwood S.R., Barnes P.J. Effects of bronchodilator particle size in asthmatic patients using monodisperse aerosols. J. Appl. Physiol. 2003, 95(5):2106-2112.
29. Usmani O.S., Biddiscombe M.F., Underwood S.R., Barnes P.J. Characterization of the generation of radiolabeled monodisperse albuterol particles using the spinning-top aerosol generator. J. Nucl. Med. 2004, 45:69-73.
30. Usmani, O.S., 2005. Assessing the lung deposition and clinical response in asthmatic subjects as a function of beta-adrenergic aerosol particle size. Thesis submitted to the University of London for the degree of Doctor of Philosophy in the Faculty of Medicine, Department of Thoracic Medicine, National Heart and Lung Institute, Imperial College London.
31. Usmani O.S., Biddiscombe M.F., Barnes P.J. Regional lung deposition and bronchodilator response as a function of β2-agonist particle size. Am. J. Respir. Crit. Care Med. 2005, 172(12):1497-1504.
32. Zhou P., Tian F., Lv F., Shang Z. Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of Escherichia coli proteome. J. Chromatogr. A 2009, 1216(15):3107-3116.