Topical and Transdermal Drug Delivery
Bibliographic record
Abstract
Preface ix About the Editors xi Contributors xiii Part One Current Science, Skin Permeation, and Enhancement Approaches 1. Skin Structure, Function, and Permeation 3 Heather A.E. Benson 2. Passive Skin Permeation Enhancement 23 Majella E. Lane, Paulo Santos, Adam C. Watkinson, and Jonathan Hadgraft 3. Electrical and Physical Methods of Skin Penetration Enhancement 43 Jeffrey E. Grice, Tarl W. Prow, Mark A.F. Kendall, and Michael S. Roberts 4. Clinical Applications of Transdermal Iontophoresis 67 Dhaval R. Kalaria, Sachin Dubey, and Yogeshvar N. Kalia 5. In Vitro Skin Permeation Methodology 85 Barrie Finnin, Kenneth A. Walters, and Thomas J. Franz 6. Skin Permeation Assessment: Tape Stripping 109 Sandra Wiedersberg and Sara Nicoli 7. Skin Permeation Assessment: Microdialysis 131 Rikke Holmgaard, Jesper B. Nielsen, and Eva Benfeldt 8. Skin Permeation: Spectroscopic Methods 155 Jonathan Hadgraft and Majella E. Lane 9. Skin Permeation Assessment in Man: In Vitro In Vivo Correlation 167 Paul A. Lehman, Sam G. Raney, and Thomas J. Franz 10. Risk Assessment 183 Jon R. Heylings Part Two Topical and Transdermal Product Development 11. An Overview of Product Development from Concept to Approval 203 Adam C. Watkinson 12. Regulatory Aspects of Drug Development for Dermal Products 217 William K. Sietsema 13. Toxicological and Pre-clinical Considerations for Novel Excipients and New Chemical Entities 233 Andrew Makin and Jens Thing Mortensen 14. Topical Product Formulation Development 255 Marc B. Brown, Robert Turner, and Sian T. Lim 15. Transdermal Product Formulation Development 287 Kenneth J. Miller 16. Sensitivity and Irritation Testing 309 Belum Viswanath Reddy, Geetanjali Sethi, and Howard I. Maibach 17. New Product Development for Transdermal Drug Delivery: Understanding the Market Opportunity 345 Hugh Alsop 18. Transdermal and Topical Drug Delivery Today 357 Adam C. Watkinson 19. Current and Future Trends: Skin Diseases and Treatment 367 Simon G. Danby, Gordon W. Duff, and Michael J. Cork Index 409
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.077 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".