Children’s drug research and development incentives and market pricing optimization based on medical imaging
Guardado en:
| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 38944-38956 |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Publicado: |
Nature Publishing Group
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3269528079 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-22867-6 |2 doi | |
| 035 | |a 3269528079 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Mu, Xiaoyan |u College of Continuing Education, China Pharmaceutical University, 210000, Nanjing, Jiangsu, China (ROR: https://ror.org/01sfm2718) (GRID: grid.254147.1) (ISNI: 0000 0000 9776 7793) | |
| 245 | 1 | |a Children’s drug research and development incentives and market pricing optimization based on medical imaging | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Due to differences in physiological characteristics and drug metabolism between children and adults, drug efficacy evaluation and safety monitoring in pediatric drug development present significant challenges. This paper proposes a data-driven incentive mechanism for pediatric drug development based on medical imaging data. This approach optimizes drug market pricing through precise imaging data, promoting accessibility and R&D efficiency for pediatric drugs. This study first collects multi-source computed tomography (CT), magnetic resonance imaging (MRI), and X-ray data, focusing on images of common pediatric diseases. After data preprocessing, a convolutional neural network (CNN) is used for feature extraction to extract key image information. Image difference methods and a U-Net image segmentation network are then used to evaluate drug efficacy and safety, quantify efficacy changes, and analyze side effects. Next, a drug efficacy-safety evaluation model is developed, and game theory is employed to design a R&D incentive mechanism. Monte Carlo simulation is combined with risk assessment to comprehensively consider factors such as cost, R&D investment, and market demand during the pricing optimization phase. A dynamic pricing strategy is implemented to ensure both economic benefits and social accessibility of the drug. Experiments have shown that the drug has a good development effect, with an average tumor volume reduction of 32.7% (95% CI: 28.4%-36.9%). The drug’s impact on organ volume is within ± 2 cm³, and the market pricing strategy selects a relatively optimal price point. | |
| 653 | |a Side effects | ||
| 653 | |a Accuracy | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Deep learning | ||
| 653 | |a Optimization | ||
| 653 | |a Safety | ||
| 653 | |a Drug metabolism | ||
| 653 | |a Drug development | ||
| 653 | |a Pharmaceutical industry | ||
| 653 | |a Data analysis | ||
| 653 | |a Image processing | ||
| 653 | |a Metabolism | ||
| 653 | |a Risk assessment | ||
| 653 | |a Medical imaging | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Efficiency | ||
| 653 | |a Machine learning | ||
| 653 | |a Quality standards | ||
| 653 | |a Drug efficacy | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Game theory | ||
| 653 | |a Computed tomography | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Data collection | ||
| 653 | |a Information processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Tumors | ||
| 653 | |a Bone diseases | ||
| 653 | |a Pediatrics | ||
| 653 | |a Neural networks | ||
| 653 | |a Social | ||
| 700 | 1 | |a Wu, Lin |u School of International Pharmaceutical Business, China Pharmaceutical University, 211198, Nanjing, Jiangsu, China (ROR: https://ror.org/01sfm2718) (GRID: grid.254147.1) (ISNI: 0000 0000 9776 7793) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 38944-38956 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3269528079/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3269528079/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3269528079/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |