Гематологические испытания на предмет злоумышленничества в Китае, 2013-2022:: анализ ландшафта с научно-исследовательской макроуровневой корреляцией.

Introduction

Hematological malignancies are a major threat to public health in Chinese Mainland [ 1 , 2 ], with the total number of patients exceeding 1.21 million in 2021 [ 3 ]. The incidence of multiple myeloma, non‐Hodgkin lymphoma, and other hematological cancers is increasing markedly, creating an urgent need for breakthrough innovations in diagnostics and therapeutics. Hematological disorders, particularly hematological malignancies, have served as a crucial testbed for novel drug development and innovative treatment strategies across the broader healthcare landscape [ 4 , 5 , 6 , 7 , 8 ]. Numerous advanced therapies, such as tumor‐targeted therapy, cellular immunotherapy, and even gene therapy, originated from the treatment of hematological diseases before being extended to wider disease areas, thereby profoundly reshaping the global oncology landscape.

Clinical trials are an indispensable stage for the market entry of innovative drugs and frontier technologies [ 9 ]. As a leading indicator of drug approval and clinical application, the increase in clinical trials directly reflects the robustness of the development pipeline and market scale. Chinese Mainland's clinical trial ecosystem has recently undergone continuous optimisation, from the reform of the review and approval system in 2015 to the newly enacted Regulations on the Administration of Clinical Research and Translational Application of Novel Biomedical Technologies in 2025 [ 10 , 11 , 12 , 13 , 14 ], which provide institutional safeguards for high‐quality clinical translation and mark the country's entry into a new era of standardized clinical research development.

Despite the recognized value of clinical trials in hematological malignancies, substantial knowledge gaps persist. Furthermore, the evolution of the clinical trial landscape and its temporal co‐movement with broader macro‐level indicators remain underexplored.

Given the relatively short history of comprehensive clinical trial registration data in China, conducting large‐sample, confirmatory macroeconomic analyses is not yet feasible. Therefore, this study established a ten‐year baseline from 2013 to 2022 to characterize the clinical trial landscape and descriptively explore its temporal co‐movement with selected indicators of scientific activity and research capacity, disease burden, and socioeconomic context.

Methods

Data Sources

Disease categories for this clinical trial study were defined based on Williams Hematology (10th) [ 15 ] and the World Health Organization Classification of Hematopoietic and Lymphoid Tumors (2016 Edition) [ 16 ]. A systematic search of the Centre for Drug Evaluation (CDE) of the National Medical Products Administration (NMPA) and the Chinese Clinical Trial Registry was conducted using keywords related to “hematological malignancy”. On ClinicalTrials.gov , the search was performed by querying the “Condition or Disease” field with keywords related to “hematological malignancy”, restricted to “Chinese Mainland” as the study location, explicitly excluding trials conducted in the Special Administrative Regions of Hong Kong and Macau or in Taiwan, China. The search was further limited to trials with a first‐posted date between 1 January 2013 and 31 December 2022. The specific search queries are provided in the Data S1 1.1.

Trials were included if they met the following criteria: (1) the intervention specifically targeted hematological malignancies; (2) the trial was registered between 01 January 2013 and 31 December 2022; (3) the study focused on pharmacological agents for hematological malignancies (notably, cell therapies and cancer vaccines were classified as anticancer drugs); (4) the clinical trial sites were located within Chinese Mainland.

The following types of studies were excluded: (1) trials investigating supportive care medications; (2) retrospective registrations for trials initiated before January 1, 2013; (3) studies primarily focusing on predictive or prognostic biomarkers or molecular mechanisms of drug resistance in hematological malignancies. To ensure consistency in data extraction, key trial‐level data for each eligible trial were independently extracted by two reviewers (BXL and ZXH). The two extracted datasets were compared, and any discrepancies or ambiguous classifications were resolved through discussion. If disagreement persisted, the third reviewer (JJG) was consulted, and the final decision was reached by consensus. Overlapping trial registrations across databases were identified during data consolidation and reviewed collectively. Specifically, we employed a multi‐dimensional matching strategy: registrations were first identified by unique trial identifiers (e.g., NCT numbers). For trials lacking consistent identifiers, we performed manual reconciliation based on study title, intervention, target condition, phase, and study location. In cases of duplicate entries across databases, the version with the most comprehensive trial‐level data and the latest update status was retained for final inclusion. Ultimately, 2497 clinical trials were included (Figure S1 ).

Data Extraction

A comprehensive dataset was systematically extracted for each study to facilitate a multi‐dimensional analysis of the clinical trial landscape. This included administrative and temporal identifiers such as trial ID, title, and registration year, alongside operational metrics like trial status and study design. Study design characteristics encompassed study phase (Phase I, Phase II, Phase III, Phase IV, or others), center size (single or multicenter), collaboration model (domestic or international), treatment strategy [cell and gene therapy (CGT) or non‐CGT], and pathological subtypes (myeloid neoplasms, lymphoid neoplasms, histiocytic and dendritic cell neoplasms, or others). Finally, the geographical distribution of lead institutions was documented at the provincial level to assess regional research capacity and the “East–West” developmental gap.

Statistical Analysis

Categorical variables were presented as frequencies and percentages [ n (%)]. Differences between investigator‐initiated trials (IITs) and industry‐sponsored trials (ISTs) were analyzed using the chi‐square test. Joinpoint regression analysis evaluated annual trends in trial numbers and average annual percent change (AAPC). The annual contribution proportion relative to the cumulative total was calculated to quantify the annual cumulative contribution of different trial categories (Data S1 1.2). Paired comparisons between two categories were performed using the Wilcoxon signed‐rank test. Comparisons involving more than two categories were performed using the Friedman test, followed by Bonferroni‐adjusted pairwise comparisons.

The temporal co‐movement between annual clinical trial counts and selected macro‐level indicators was examined descriptively. The indicators were grouped into three domains [ 17 , 18 , 19 ]: (1) Scientific activity and research capacity included: (i) the annual number of institutions in Chinese Mainland with hematological drug research and development (R&D) experience, derived from the integrated clinical trial registration dataset from the CDE, Chinese Clinical Trial Registry, and ClinicalTrials.gov ; (ii) the annual number of Science Citation Index (SCI)‐indexed publications related to hematological malignancies, identified through a Web of Science search restricted to the “Clinical Trial” publication type and to records with affiliations in China; and (iii) the annual number of authorized patents for clinical drugs of hematological malignancies retrieved from the China National Intellectual Property Administration database for Chinese sponsors involved in hematological disease clinical trials. (2) Disease burden was represented by the annual incidence of major hematological malignancies, including leukemia, multiple myeloma, non‐Hodgkin lymphoma, and Hodgkin lymphoma, obtained from the Global Burden of Disease study. (3) Socioeconomic context included annual gross domestic product, annual R&D intensity, and the proportion of the population aged ≥ 65 years, all obtained from the National Bureau of Statistics of China.

Given the potential for strong secular trends in annual clinical trial counts and macro‐level indicators during 2013–2022, analyses based solely on their original annual levels might largely reflect shared temporal trends. We therefore conducted three descriptive analyses. First, Spearman correlation coefficients were calculated for the original annual series. Second, annual trial counts and each macro‐level indicator were natural log‐transformed and separately regressed on calendar year. Spearman correlations were then calculated between the residualized series to assess co‐movement after removal of the linear calendar‐time trend. Third, a first‐difference analysis was performed. Because all annual values were positive, the variables were directly transformed using the natural logarithm, and year‐to‐year changes were calculated as Δlog(X t ) = log(X t ) − log(X t ₋₁). Spearman correlations were then calculated between the first differences in annual trial counts and those in each macro‐level indicator.

The original‐series and detrended analyses were based on 10 annual observations, whereas the first‐difference analyses were based on nine year‐to‐year changes. Given the limited number of observations, all macro‐level analyses were considered descriptive and hypothesis‐generating, and formal statistical significance was not emphasized. The correlation estimates were therefore not interpreted as independent or causal effects. Moreover, because the short time series did not permit reliable estimation of dispersion, standard errors, or temporal‐dependence structures, we did not fit conventional count‐regression or autocorrelation models for the macro‐level analysis.

The reporting of this study followed the STROBE guidelines. A p ‐value < 0.05 was considered statistically significant. For the exploratory macro‐level analyses, p ‐values were not used to support confirmatory inference and were omitted from the primary presentation. Statistical analyses and data visualization were performed using SPSS (version 29.0), Joinpoint (version 5.4.0), and R software (version 4.4.1).

Role of the Funding Source

The funding sources for this study had no role in the study design, data collection, analysis, interpretation, preparation of the report, and the decision to submit the paper for publication. All authors had full access to the data and had final responsibility for the decision to submit the manuscript for publication.

Results

Landscape and Baseline Characteristics of Hematological Malignancy Trials

Of the 2497 trials identified, 1776 (71.1%) were IITs, and 721 (28.9%) were ISTs. Trials predominantly focused on the lymphatic neoplasms (70.6%) and myeloid neoplasms (24.7%). Most were Phase I (49.1%), single center (62.0%), and domestic (93.3%). Compared with IITs, ISTs were more likely to target lymphatic malignancies, use multicenter designs, and focus on CGT (all p < 0.05) (Table 1 ).

Temporal Dynamics and Evolving Landscape of Trial Characteristics

Segmented regression and AAPC analyses revealed a sustained upward trend (AAPC = 23.5%, 95% confidence interval [CI] = 16.0–36.6, p < 0.001). IITs exhibited a faster growth rate (AAPC = 25.6%, 95% CI = 22.3–32.7, p < 0.001) than ISTs (AAPC = 13.1%, 95% CI = 9.1–21.0, p < 0.001). IITs increased rapidly before 2020 and then plateaued (APC = 33.6%, 95% CI = 29.2–51.4, p < 0.001), whereas ISTs increased rapidly from 2015 onwards (APC = 28.9%, 95% CI = 22.1–48.5, p < 0.001) (Figure 1A ).

CGT trials exhibited significant growth from 2013 to 2022 (AAPC = 31.9%), with a particularly rapid expansion before 2019 (APC = 53.0%, 95% CI = 39.6–115.7, p < 0.001), followed by a stabilization in the growth rate. In contrast, non‐CGT trials maintained relatively stable growth throughout the observation period (AAPC = 21.8%, 95% CI = 16.6–30.1, p < 0.001). Regarding center size, the growth of single‐center trials (APC = 23.5%, 95% CI = 20.7–29.8, p < 0.001) outpaced that of multicenter trials (APC = 17.4%, 95% CI = 12.7–30.3, p < 0.001); however, the number of multicenter trials markedly increased after 2016 (APC = 30.0%, 95% CI = 17.2–64.6, p < 0.001). In terms of collaboration models, the number of independent domestic trials increased until 2020 (APC = 31.0%, 95% CI = 26.8–70.6, p < 0.001) and subsequently stabilized. International collaborative trials declined between 2013 and 2015 (APC = −56.8%, 95% CI = −70.5 to −22.3, p < 0.001) but increased rapidly thereafter (APC = 36.7%, 95% CI = 28.7–57.0, p < 0.001) (Figure 1B–D ).

Phase I trials exhibited the most rapid expansion (AAPC = 35.5%, 95% CI = 30.1–52.2, p < 0.001); however, this growth slowed after 2019. Phase II trials showed moderate growth (AAPC = 21.5%, 95% CI = 16.6–29.3, p < 0.001), whereas the number of Phase III trials remained largely stable. Despite a lower baseline, the number of Phase IV trials increased steadily (AAPC = 18.7%, 95% CI = 13.0–34.7, p < 0.001), with a decelerated growth rate observed after 2020. To characterize the evolution across different pathological subtypes, trend analysis by malignancy type demonstrated significant expansions in myeloid (AAPC = 24.2%, 95% CI = 3.2–79.0, p = 0.025), lymphoid (AAPC = 23.7%, 95% CI = 20.1–32.8, p < 0.001), and histiocytic/dendritic cell malignancies (AAPC = 22.7%, 95% CI = 13.7–45.8, p = 0.002), with growth rates moderating after 2017, 2020, and 2020, respectively (Figure 1E,F ).

IITs consistently contributed more annually than ISTs ( p = 0.019). Non‐CGT trials and independent domestic trials also showed significantly higher contribution rates than their counterparts ( p < 0.05) (Figure 2A,B , Figure S2A,B ). Regarding center size, single‐center trials exhibited a marginally higher contribution than multicenter trials; however, the difference was not significant ( p = 0.084). In terms of collaboration models, independent domestic trials contributed significantly more to cumulative growth than international collaborative trials ( p = 0.002; Figure 2C,D , Figure S2C,D ). Pairwise comparisons revealed significant differences between Phase I and Phase IV trials ( p = 0.030), between Phase I and the “Other” phase category ( p < 0.0001), and between Phase II and the “Other” phase category ( p < 0.0001) (Figure 2E , Figure S2E , Table S1 ). Lymphoid trials differed significantly from histiocytic/dendritic ( p < 0.0001) and “Other” ( p = 0.006) trials. Myeloid trials also accounted for a larger share of the expansion than trials targeting histiocytic/dendritic cell neoplasms ( p = 0.002) (Figure 2F , Figure S2F , Table S2 ).

Characterization of Expansion within ISTs

CGT trials within ISTs increased most rapidly (AAPC = 62.0%, 95% CI = 51.8–129.9, p < 0.001), peaking in 2019. Domestic ISTs maintained continuous growth (AAPC = 20.4%, 95% CI = 14.1–31.9, p < 0.001), whereas international collaborative ISTs rebounded sharply in the same period (APC = 37.9%, 95% CI = 27.2–82.3, p < 0.001) (Figure 3 ).

Regarding center size, multicenter ISTs expanded significantly (AAPC = 14.0%, 95% CI = 9.7–22.6, p < 0.001), with a marked acceleration post‐2015 (APC = 31.2%, 95% CI = 23.5–53.9, p < 0.001), whereas single‐center IST numbers remained relatively stable. Stratified analysis by phase revealed a substantial increase in the number of Phase I (AAPC = 32.7%, 95% CI = 22.9–55.3, p < 0.001) and Phase IV ISTs (AAPC = 20.3%, 95% CI = 9.8–39.0, p < 0.001), whereas the number of Phase II–III trials showed no significant variation. Similarly, myeloid and lymphoid ISTs increased steadily (AAPC = 8.5%, 95% CI = 2.9–19.5 and AAPC = 14.2%, 95% CI = 10.6–22.2, respectively; both p < 0.001), with the growth of lymphoid ISTs accelerating significantly after 2015 (APC = 28.0%, 95% CI = 21.9–48.4, p < 0.001) (Figure 3 ).

Paradigm Shift in the IST Therapeutic Ecosystem

Chemical agents constituted 57.7% of the ISTs (Figure 4A ), with small molecules accounting for 79.3% of this category (Figure 4B ). Biopharmaceutical agents comprised 41.7% of the trials (Figure 4A ), of which 67.4% were antibody therapeutics (Figure 4C ). Within the CGT category, 95.1% were classified as gene therapies (Figure 4C ). Small‐molecule drugs represented the largest single therapeutic category among ISTs (Figure 4D ). Antibody drugs have steadily increased since 2019, and antibody–drug conjugates (ADCs) emerged as a significant new category. Between 2013 and 2022, ISTs exhibited a dramatic post‐2015 expansion, suggesting an evolving shift toward a more diversified therapeutic ecosystem led by small‐molecule drugs and targeted biological therapies, including antibodies, CGT, and ADCs (Figure 4D ).

Geospatial Distribution and Regional Research Disparities

The results revealed a pronounced regional concentration, with northern Chinese Mainland (Beijing/Tianjin) and eastern Chinese Mainland (Shanghai/Jiangsu/Zhejiang) forming the largest clusters. The temporal shifts in provincial rankings further illustrated a geographic migration of research excellence; while Beijing and Shanghai remained in the top tier, provinces such as Guangdong and Zhejiang ascended markedly, emerging as vital national growth poles (Figure 5 ). In contrast, the central, western, and northeastern regions each hosted fewer than 100 trials, reflecting a persistent “East‐West gap” and a “North‐heavy” disparity in research resources.

Exploratory Macro‐Level Temporal Co‐Movements

In the original annual series, all selected macro‐level indicators showed strong positive temporal correlations with annual clinical trial counts, with Spearman correlation coefficients ranging from 0.855 to 0.988. These uniformly strong correlations were observed across indicators of scientific activity and research capacity, disease burden, and socioeconomic context (Table 2 ).

After removal of the linear calendar‐time trend, the correlations were substantially attenuated, with detrended coefficients ranging from −0.418 to 0.261. The first‐difference analyses yielded heterogeneous correlations in both direction and magnitude (−0.483 to 0.650), with no consistent pattern across the three macro‐level domains. Although year‐to‐year changes in R&D expenditure intensity showed a moderately positive correlation with changes in trial activity, this estimate was based on only nine annual changes and should be interpreted cautiously. Overall, the attenuation after detrending and the heterogeneous first‐difference results indicate that the strong correlations observed in the original annual series were substantially influenced by shared secular trends. Accordingly, these findings are presented as descriptive temporal co‐movements rather than as evidence of stable independent associations (Table 2 ).

Discussion

This study systematically characterized the temporal evolution and exploratory macro‐level temporal co‐movements of hematological malignancy trials in Chinese Mainland. To the best of our knowledge, this study established the first 10‐year baseline in this field. Together, these findings provide a preliminary framework to inform the strategic positioning of hematological clinical research in Chinese Mainland.

Hematological malignancy trials increased markedly in Chinese Mainland, with an AAPC of 23.5%, substantially exceeding recent global benchmarks [ 20 ]. Notably, IITs predominated, accounting for 71.1% of total trials, markedly higher than the global median of ~47% in oncology IITs [ 20 ]. This dominance, coupled with the greater contribution of IITs to cumulative growth, aligns with Chinese Mainland's long‐standing investigator‐driven innovation paradigm [ 21 , 22 , 23 ]. IITs exhibited an AAPC of 25.6%, with rapid expansion before 2020. In contrast, ISTs showed an inflection point: marginal decline from 2013 to 2015, followed by post‐2015 acceleration, with an APC of 28.9%. The slowdown or plateau observed around 2019–2020 in several trial categories, including IITs, single‐center trials, and domestic trials, may partly reflect the disruptive effects of the COVID‐19 pandemic on clinical trial initiation, site activation, patient recruitment, ethics review, and administrative coordination [ 24 ].

Although Chinese Mainland ranks third globally in registered gene therapy trials [ 21 ], most common CGT modalities remain in early‐phase development, consistent with recent reports [ 25 ]. Moreover, post‐2020 CGT growth plateaued, likely reflecting rational consolidation in these frontier domains, with regulators intensifying oversight of high‐risk trials while preserving investigator autonomy [ 26 ]. The NMPA has initiated a Pharmaceutical Regulatory Science Action Plan, and the CDE issued detailed guidelines for CGT development [ 25 ].

Despite notable progress, systemic challenges persist. First, pronounced geographic disparities exist, with North and East Chinese Mainland accounting for lead trials, whereas central/western provinces and North–East/North–West regions each registered less than 100 trials. Second, collaborative trials represent < 7% of the total number of trials, hindering global simultaneous development and access to cutting‐edge therapies. Third, although CGT trials increased rapidly, their overall proportion remains modest, plateauing post‐2019, indicating untapped potential in frontier technologies. Finally, an imbalance in growth across development phases was observed. Phase I trials had an AAPC of 35.5%, whereas Phase II trials had an AAPC of 21.5%. However, our dataset does not allow direct tracking of individual investigational products across clinical development stages, and therefore true phase‐transition rates cannot be estimated. Future studies using longitudinal trial‐level data are warranted to formally evaluate transition efficiency across development phases.

In the original annual series, clinical trial counts increased in parallel with indicators of scientific activity and research capacity, disease burden, and socioeconomic context. However, these correlations were substantially attenuated after removal of the linear calendar‐time trend, and the first‐difference analyses yielded heterogeneous results in both direction and magnitude. These findings indicate that shared secular trends accounted for a substantial proportion of the apparent associations observed in the original annual data.

Accordingly, the macro‐level indicators examined in this study should not be interpreted as independent determinants of clinical trial expansion. Rather, they are better viewed as contextual characteristics that evolved alongside the broader clinical trial ecosystem during the study period. The parallel increases in scientific output, research capacity, socioeconomic development, disease burden, and trial activity may reflect a broad period of concurrent national development. However, the present ecological data cannot determine whether any individual indicator directly contributed to the expansion of trial activity or quantify its relative importance.

Although year‐to‐year changes in R&D expenditure intensity showed a moderately positive correlation with changes in trial activity, this estimate was based on only nine annual changes and was not accompanied by a consistent pattern across the other indicators. It should therefore be regarded as hypothesis‐generating rather than as evidence of a stable relationship. Overall, the detrended and change‐based analyses support a cautious and primarily descriptive interpretation of the macro‐level findings.

This study has some limitations. First, the short study period and external shocks, particularly the COVID‐19 pandemic, potentially affected trial initiation trends. Although joinpoint regression allows for changes in temporal slopes, the estimated APCs and AAPCs may still be sensitive to year‐to‐year fluctuations, particularly given the short observation period. Therefore, the long‐term temporal trends should be interpreted cautiously. Second, the macro‐level analyses were based on only 10 annual observations, with nine year‐to‐year changes available for the first‐difference analyses. This limited sample size may render the correlation estimates sensitive to fluctuations in individual years and does not permit reliable estimation of dispersion, standard errors, confidence intervals, or temporal autocorrelation structures. Accordingly, conventional regression‐based inference was not used for the macro‐level analyses, and the findings were interpreted as descriptive and hypothesis‐generating rather than confirmatory. Longer time series will be required to determine whether the observed temporal co‐movements persist. Third, although linear detrending and first‐difference analyses were used to assess the influence of shared secular trends, these approaches cannot fully account for nonlinear temporal patterns, structural changes, or external shocks, including regulatory reforms and the COVID‐19 pandemic. Moreover, the ecological study design precludes attribution of trial growth to any individual macro‐level indicator. Therefore, the observed temporal correlations should not be interpreted as independent or causal effects. Despite these limitations, this study provides an initial descriptive baseline for the temporal evolution of hematological malignancy clinical trials in Chinese Mainland.

As the disease burden of hematological malignancies in Chinese Mainland rises, there is an urgent need for coordinated national and regional strategies to strengthen high‐quality clinical research and translational pathways amid the rapid expansion of trial activity. Our findings highlight the need for a shift toward high‐quality clinical research, which could support more effective progression across clinical development phases, accelerate the delivery of innovative therapies, and ultimately improve patient outcomes.

Author Contributions

J.G., Z.H., B.L., J.Q., and M.X. wrote the manuscript. L.Z., L.S., T.C., and J.Q. designed the research. Y.Y., L.L., L.H., S.C., Q.J., Z.S., Q.S., S.M., S.Y., X.W., J.G., Z.H., and B.L. performed the research. Z.H., J.G., B.L., and Y.Y. analyzed the data.

Funding

This work was financially supported in part by the CAMS Innovation Fund for Medical Sciences (CIFMS) (2021‐1‐I2M‐045).

Ethics Statement

As this study involved the analysis of publicly available data, ethical approval was not required. However, the data were handled in accordance with the guidelines provided by ClinicalTrials.gov and the journal's ethical standards.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: cts70708‐sup‐0001‐Supinfo01.docx.

Table S1: Multiple comparison results of annual proportions of hematological malignancy clinical trials by clinical phases relative to the cumulative annual total.

Table S2: Multiple comparison results of annual proportions of hematological malignancy clinical trials by pathological subtype relative to the cumulative annual total.

Figure S1: Flow diagram of the clinical trial selection process.

Figure S2: Cumulative annual trends and composition of hematological malignancy clinical trials in Chinese Mainland (2013–2022).

Acknowledgments

Artificial intelligence–assisted language editing was used to improve the clarity and readability of the manuscript. The authors reviewed and approved all edits and take full responsibility for the final manuscript.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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