LIFE COURSE TRAJECTORIES OF ANTI-CANCER DRUGS THROUGH THE NATIONAL DRUG PRICE NEGOTIATION IN CHINA
Author(s)
Yuanhao Zhu, Bachelor1, Zhihao Zhao, Bachelor1, Miao Lin, PhD2, Siyuan Chen, Bachelor1, MING HU, PhD1;
1West China School of Pharmacy, Sichuan University, Chengdu, China, 2Department of Pharmacy, Naval Medical University, Shanghai, China
1West China School of Pharmacy, Sichuan University, Chengdu, China, 2Department of Pharmacy, Naval Medical University, Shanghai, China
OBJECTIVES: Studies on the global medical insurance access have largely focused on static assessments of negotiation outcomes. This study aims to identify typical patterns and influencing factors within the dynamic life trajectory of anti-cancer drugs, from marketing authorization to inclusion in the National Reimbursement Drug List (NRDL).
METHODS: Anti-cancer drugs included in the 2025 negotiated drugs in China were selected. Data was obtained from Pharmcube and the Nation Healthcare Security Administration (NHSA) website. Descriptive statistical analysis was used to summarize drug characteristics. Social Sequence Analysis was introduced to cluster the life course states of medical insurance admission, with sequence analysis and hierarchical clustering via the Sequenzo tool describing the life trajectory patterns. Regression analysis was employed to identify factors influencing life trajectories.
RESULTS: 118 anti-cancer drugs were analyzed, among which 110 were innovative drugs (93.22%) and 12 had orphan drug designation (10.17%), covering 45 tumour types. 85 listed drugs were approved through the Accelerated Drug Marketing Registration Procedures (ADMRPs). Trajectory patterns were labeled using three components: (i) the timing of marketing authorization (early/middle/late), (ii) the lag from authorization to formal review approval (short/long), and (iii) the lag from approval to NRDL listing (short/long). 229 drug-indication combinations were clustered, recognizing four typical patterns: Middle-Long-Long (marketing authorization - review lag - inclusion lag, n=26), Late-Short-Short (n=92), Early-Long-Short (n=27) and Middle-Short-Long (n=84). The trajectories were associated with multiple factors, including drug category (p=0.0163), age indication (p=0.0001), import status (imported vs. domestic) (p=0.0279), orphan drug designation (p=0.0279) and ADMRP status (p=0.0002).
CONCLUSIONS: This study maps 4 typical patterns for anti-cancer drugs following the negotiated admission in China, from the perspective of drug life cycle. The observed drug and regulatory characteristics relate to the listing speed, and support procedural optimization to shorten the lags displayed in heterogeneous delays before and after the formal review.
METHODS: Anti-cancer drugs included in the 2025 negotiated drugs in China were selected. Data was obtained from Pharmcube and the Nation Healthcare Security Administration (NHSA) website. Descriptive statistical analysis was used to summarize drug characteristics. Social Sequence Analysis was introduced to cluster the life course states of medical insurance admission, with sequence analysis and hierarchical clustering via the Sequenzo tool describing the life trajectory patterns. Regression analysis was employed to identify factors influencing life trajectories.
RESULTS: 118 anti-cancer drugs were analyzed, among which 110 were innovative drugs (93.22%) and 12 had orphan drug designation (10.17%), covering 45 tumour types. 85 listed drugs were approved through the Accelerated Drug Marketing Registration Procedures (ADMRPs). Trajectory patterns were labeled using three components: (i) the timing of marketing authorization (early/middle/late), (ii) the lag from authorization to formal review approval (short/long), and (iii) the lag from approval to NRDL listing (short/long). 229 drug-indication combinations were clustered, recognizing four typical patterns: Middle-Long-Long (marketing authorization - review lag - inclusion lag, n=26), Late-Short-Short (n=92), Early-Long-Short (n=27) and Middle-Short-Long (n=84). The trajectories were associated with multiple factors, including drug category (p=0.0163), age indication (p=0.0001), import status (imported vs. domestic) (p=0.0279), orphan drug designation (p=0.0279) and ADMRP status (p=0.0002).
CONCLUSIONS: This study maps 4 typical patterns for anti-cancer drugs following the negotiated admission in China, from the perspective of drug life cycle. The observed drug and regulatory characteristics relate to the listing speed, and support procedural optimization to shorten the lags displayed in heterogeneous delays before and after the formal review.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
P12
Topic
Health Policy & Regulatory
Topic Subcategory
Reimbursement & Access Policy
Disease
SDC: Oncology