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Children’s drug research and development incentives and market pricing optimization based on medical imaging

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Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 38944-38956
Autor principal: Mu, Xiaoyan
Otros Autores: Wu, Lin
Publicado:
Nature Publishing Group
Materias:
Side effects
Accuracy
Magnetic resonance imaging
Deep learning
Optimization
Safety
Drug metabolism
Drug development
Pharmaceutical industry
Data analysis
Image processing
Metabolism
Risk assessment
Medical imaging
Research & development > R&D
Efficiency
Machine learning
Quality standards
Drug efficacy
Artificial intelligence
Game theory
Computed tomography
Monte Carlo simulation
Data collection
Information processing
Algorithms
Tumors
Bone diseases
Pediatrics
Neural networks
Social
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2045-2322
DOI:10.1038/s41598-025-22867-6
Fuente:Science Database