Aguilar Formulation Tools for Pharmaceutical Development

1. Auflage 2013
ISBN: 978-1-908818-50-8
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 304 Seiten

Reihe: Woodhead Publishing Series in Biomedicine

A range of new and innovative tools used for preformulation and formulation of medicines help optimize pharmaceutical development projects. Such tools also assist with the performance evaluation of the pharmaceutical process, allowing any potential gaps to be identified. These tools can be applied in both basic research and industrial environment. Formulation tools for pharmaceutical development considers these key research and industrial tools.Nine chapters by leading contributors cover: Artificial neural networks technology to model, understand, and optimize drug formulations; ME_expert 2.0: a heuristic decision support system for microemulsions formulation development; Expert system for the development and formulation of push-pull osmotic pump tablets containing poorly water-soluble drugs; SeDeM Diagram: an expert system for preformulation, characterization and optimization of tables obtained by direct compression; New SeDeM-ODT expert system: an expert system for formulation of orodispersible tablets obtained by direct compression; and 3D-cellular automata in computer-aided design of pharmaceutical formulations: mathematical concept and F-CAD software. - Coverage of artificial intelligence tools, new expert systems, understanding of pharmaceutical processes, robust development of medicines, and new ways to develop medicines - Development of drugs and medicines using mathematical tools - Compilation of expert system developed around the world

Autoren/Hrsg.

Aguilar, J E

Leseproben

List of figures
2.1. Relation between the knowledge space, the design space and the normal operation conditions 9 2.2. Basic comparison between a biological neuronal system and an artificial neural system 12 2.3. Representation of the sigmoid function 13 2.4. Example of how much information cannot solve practical problems 16 2.5. Steps in the search process for the optimal formulation when artificial neural networks and genetic algorithms are coupled 17 2.6. Ishikawa diagram identifying the potential variables that can have an impact on the quality of direct compression tablets 19 2.7. Correlation between experimental values and those predicted by the ANN model for the five outputs studied 23 2.8. 3D plot of percentage of weight lost by friability 24 2.9. 3D plot of percentage of drug dissolved at 30 min predicted by the model 25 2.10. Desirability function for percentage of drug dissolved at 30 min following pharmacopoeia requirements for drug A-based tablets 27 2.11. Comparison between classical set theory and fuzzy set theory to illustrate Zadeh’s example of the ‘tall man’ 28 2.12. The importance of precision and word significance in the real world of the pharmaceutical formulator 29 2.13. Examples of fuzzy sets for continuous variables and categorical variables in the direct compression tablet example 30 2.14. Effect of the studied variables on crushing strength parameter 31 3.1. Typical layout of a multilayer perceptron-artificial neural network (MLP-ANN) 42 3.2. Diagram of the work scheme 45 3.3. Scheme of the data set processing 49 3.4. Ranking of inputs obtained after sensitivity analysis 57 3.5. Prediction of microemulsion region for unknown to artificial neural network quaternary system 60 3.6. Simplistic GUI for version 2.0 63 4.1. Welcome interface of the tool 77 4.2. Interface of projects management 77 4.3. Information input interface for formulation design 78 4.4. Interface for choosing excipients 80 4.5. Interface for displaying the formulation design result 80 4.6. Interface for the input of experimental results 81 4.7. Interface for the experimental result checking 82 4.8. Interface for displaying the finished program 83 4.9. Interface for the release prediction information input 84 4.10. Interface of the release prediction results 85 4.11. An example of troubleshooting 86 4.12. Structure of the tool 87 4.13. Workflow of the tool 88 4.14. Relations of tables in the database 89 4.15. Structure of BP neural networks in this tool 92 4.16. Workflow of core weight modification (auto core weight limit) 96 4.17. Workflow of core weight modification (tooling diameter is selected other than auto) 98 4.18. Workflow of formulation modification 99 4.19. Part of the search tree 102 5.1. Strategy for development 110 5.2. The SeDeM Diagram with 12 parameters 119 5.3. On the right, graph with 8 parameters (maximum reliability), f = 1. In the centre, graph with 12 parameters (n° of parameters in this study), f = 0.952. On the left, graph with eight parameters (minimum reliability), f = 0.900 120 5.4. SeDeM Diagram for API CPSMD0001 122 5.5. Determination using the SeDeM expert system of the percentage of each component required in the final formulation of a tablet by direct compression 126 5.6. SeDeM Diagram for API IBUSDM0001 129 5.7. Green line indicates the excipient that provides suitable dimension to the final mixture with the API (in yellow). Two excipients are shown, both covering the deficiencies of the API 129 5.8. SeDeM Diagram of two batches of ibuprofen 131 5.9. SeDeM Diagram for two kinds of Avicel 131 5.10. SeDeM diagram for disintegrant excipients 132 6.1. Traditional development of ODT against SeDeM-ODT expert system 140 6.2. Diagram of SeDeM-ODT 141 6.3. Development of oral disintegrating tablets using SeDeM-ODT expert system 146 7.1. Generalized plot of equation in a form N/N0 = (1 ? e?kt), where t is time 165 7.2. von Newmann and Moore neighborhood 166 7.3. Example of 2-D cellular automata, a solid gets dissolved by liquid 166 7.4. Evolution of rule 182 cellular automata 168 7.5. Finite-difference 4-dot forward schema to solve 1D diffusion equation 168 7.6. Graphical representation of rule 182 and its binary coding 169 7.7. Numerical solution of the diffusion equation through 1D cellular automata applied rule 182 170 7.8. Growth of particles in a simulated tablet 171 7.9. Left to right: degradation of a porous network (pores depicted as pink) during growth of solid particles (solids are transparent) 172 7.10. Computer-generated tablet and real tablet with leached out API 173 7.11. Particle size distribution of individual particles in a compact with respect to growth iteration 173 7.12. Packing of virtual ‘placeholder’ spheres to find central positions from seeds for further growth of the granules or larger particles of formulation components 175 7.13. Interface of the PAC module with top view of a tablet filled with distributed API cells and surrounded by a steel mantle 176 7.14. Interface of the PAC module with side view of a tablet filled with distributed API cells and surrounded by a steel mantle 176 7.15. Iterations of 3-D CA for ‘growing’ one particle from a seed (Iteration I-IV) 177 7.16. Interface of the PAC module with lateral view of a tablet and particle size distribution plot 178 7.17. Arbitrary simulated formulation release profile with an enlargement of the first 15 minutes 187 7.18. F-CAD-generated release curves for identical formulations, identical porosities, masses, and compact volumes 188 7.19. Release profiles generated for two different unit operations: direct compaction and wet granulation 189 7.20. Experimental and simulated intrinsic dissolution profile of caffeine 190 7.21. Experimental and simulated intrinsic dissolution profile of granulated caffeine 191 7.22. Experimental and simulated dissolution profile of pure caffeine tablets 192 7.23. Experimental and simulated dissolution profiles of Formulation 1.4 193 7.24. Experimental and simulated dissolution profiles of formulation with MCC and Ac-Di-Sol 193 7.25. Experimental and simulated intrinsic dissolution profiles of proquazone 194 7.26. Experimental and simulated dissolution profiles of pure proquazone tablets 194 7.27. Interface tablet designer module 197 7.28. User interface of the discretizer module, showing a round, flat tablet 198 8.1. The OXPIRT process and its...