Evidence map›Paper›PMID 33928468›Full record

ReviewInvestigational new drugs2021

The drug lag issue: a 20-year review of China.

Xiaoyu Li, Yue Yang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Investigational new drugs, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xiaoyu LiInternational Food & Drug Policy and Law Research Center, School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China.
Yue YangInternational Food & Drug Policy and Law Research Center, School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China. yanghappy@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-6102-9240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has been criticized for the long drug delay for a long time. There was little understanding of Chinese drug lag formation from the investigational new drug (IND) submission to the new drug application (NDA) approval. Therefore, we analyzed the problem of drug lag in China cumulating from the clinical trial starting lag to the lags formed during the regulatory process and discerned the key underlying factors. After investigating the availability in China of new molecular entities (NMEs) approved by the Food and Drug Administration (FDA) between 1999 and 2019, we find that even though cutting regulatory process could reduce the approval lag, the clinical trial starting time in China is more important in drug lag reduction than shortening development time and review time. The reduction of the regulatory process also needs continuous efforts by defining the clinical value based on the medical needs, regulatory procedure harmonization, and intensive discussions between applicants and regulators during the drug development process. Meanwhile, proactive approaches should be taken to encourage developing the first generics in China. More importantly, enhancing domestic research and development capabilities is still the key to cutting the drug lag. Moreover, the China National Medical Product Administration (NMPA) should attach importance to the accumulation of regulation experience on innovative drugs and transform the style of regulating generics to new drugs.

Indexed as

Drugs, InvestigationalChinaClinical Trials as TopicDrug ApprovalTime FactorsUnited StatesUnited States Food and Drug AdministrationDrugs, InvestigationalApproval lagChinaClinical trial starting lagNMERegulatory processUnited States

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.