Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem

J. E. Salazar Jiménez, J. D. Sánchez Carvajal, B. Quiros-Gómez, J. D. Arias-Londoño

2017

Abstract

This work addressed the problem of dimensionality reduction in the drug dissolution profile prediction task. The learning problem is assumed as a multi-output learning task, since dissolution profiles are recorded in non-uniform sampling times, which avoid the use of basic function-on-scalar regression approaches. Ensemblebased tree methods are used for prediction, and also for the selection of the most relevant features, because they are able to deal with high dimensional feature spaces, when the number of training samples is small. All the drugs considered corresponds to rapid release solid oral pharmaceutical forms. Six different feature selection schemes were tested, including sequential feature selection and genetic algorithms, along with a feature scoring procedure, which was proposed in order to get a consensus about the best subset of variables. The performance was evaluated in terms of the similitude factor used in the drug industry for dissolution profile comparison. The feature selection methods were able to reduce the dimensionality of the feature space in 79.2%, without loss in the performance of the prediction system. The results confirm that in the dissolution profile prediction problem, especially for different solid oral pharmaceutical forms, variables from different components and phases of the drug development must be considered.

References

  1. Aguilar, J. (2013). Formulation tools for pharmaceutical development, volume 44. Woodhead Publishing.
  2. Castellano, G. and Fanelli, A. (2000). Variable selection using neural-network models. Neurocomputing, 31(1- 4):1-13.
  3. Contia, S. and O'Hagan, A. (2010). Bayesian emulation of complex multi-output and dynamic computer models. Journal of Statistical Planning and Inference, 140(3):640-651.
  4. Dokoumetzidis, A. and Mahceras, P. (2006). A century of dissolution research: from noyes and whitney to the biopharmaceutics classification system. Int. J. Pharm., 321(1-2):1-11.
  5. FDA, U. (1997). Guidance for industry: Dissolution testing of immediate-release solid oral dosage forms. Food and Drug Administration, Center for Drug Evaluation and Research (CDER).
  6. Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1):3-42.
  7. Ghayas, S., Sheraz, M., Anjum, F., and Baig, M. (2013). Factors influencing the dissolution testing of drugs. Pak. J. Heal. Res., 1(1):1-11.
  8. Gibson, M. (2005). Technlogy Transfer: An international good practice guide for pharmaceuticals and allied industries. DHI Publishing LLC.
  9. Haupt, R. L. and Haupt, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
  10. Ibric, S., Djuris?, J., Parojc?ic, J., and Djuric, Z. (2012). Artificial neural networks in evaluation and optimization of modified release solid dosage forms. Pharmaceutics, 4:531-550.
  11. Mendyk, A., Gres, S., Jachowicz, R., Szlk, J., Polak, S., Winiowska, B., and Kleinebudde, P. (2015). From heuristic to mathematical modeling of drugs dissolution profiles: Application of artificial neural networks and genetic programming. Comput. Math. Methods Med., 2015:1-9.
  12. Moon, S. (2011). Pharmaceutical Production and Related Technology Transfer. World Health Organization.
  13. Qiu, Y. and Zhou, D. (2011). Understanding design and development of modified release solid oral dosage forms. J. Valid. Technol., 17(2):2332.
  14. Reiss, P., Huang, L., and Mennes, M. (2010). Fast functionon-scalar regression with penalized basis expansions. The International Journal of Biostatistics, 6(1):Article 28.
  15. Shao, Q., Rowe, R., and York, P. (2007). Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation. European Journal of Pharmaceutical Sciences: Official Journal of the European Federation for Pharmaceutical Sciences, 31(2):129136.
  16. Shargel, L., Wu-pong, S., and Yu, A. (2007). Applied Biopharmaceutics & Pharmacokinetics. McGraw-Hill's ACCESPHARMACY, 5th edition.
  17. Siepmann, J. and Siepmann, F. (2013). Mathematical modeling of drug dissolution. Int. J. Pharm., 453(1):1224.
  18. Webb, A. R. (2003). Statistical pattern recognition. John Wiley & Sons, 2nd edition.
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Paper Citation


in Harvard Style

Salazar Jiménez J., Sánchez Carvajal J., Quiros-Gómez B. and Arias-Londoño J. (2017). Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 52-58. DOI: 10.5220/0006141800520058


in Bibtex Style

@conference{bioinformatics17,
author={J. E. Salazar Jiménez and J. D. Sánchez Carvajal and B. Quiros-Gómez and J. D. Arias-Londoño},
title={Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={52-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006141800520058},
isbn={978-989-758-214-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem
SN - 978-989-758-214-1
AU - Salazar Jiménez J.
AU - Sánchez Carvajal J.
AU - Quiros-Gómez B.
AU - Arias-Londoño J.
PY - 2017
SP - 52
EP - 58
DO - 10.5220/0006141800520058