Dataset Information

Races of small molecule clinical trials for the treatment of COVID-19: An up-to-date comprehensive review.

ABSTRACT: The coronavirus disease-19 (COVID-19) pandemic has become a global threat since its first outbreak at the end of 2019. Several review articles have been published recently, focusing on the aspects of target biology, drug repurposing, and mechanisms of action (MOAs) for potential treatment. This review gathers all small molecules currently in active clinical trials, categorizes them into six sub-classes, and summarizes their clinical progress. The aim is to provide the researchers from both pharmaceutical industries and academic institutes with the handful information and dataset to accelerate their research programs in searching effective small molecule therapy for treatment of COVID-19.

SUBMITTER: Hu S

PROVIDER: S-EPMC8653368 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Races of small molecule clinical trials for the treatment of COVID-19: An up-to-date comprehensive review.

Hu Suwen S Jiang Songwei S Qi Xiang X Bai Renren R Ye Xiang-Yang XY Xie Tian T

Drug development research 20211111 1

The coronavirus disease-19 (COVID-19) pandemic has become a global threat since its first outbreak at the end of 2019. Several review articles have been published recently, focusing on the aspects of target biology, drug repurposing, and mechanisms of action (MOAs) for potential treatment. This review gathers all small molecules currently in active clinical trials, categorizes them into six sub-classes, and summarizes their clinical progress. The aim is to provide the researchers from both pharm ...[more]

PMID: 34762760

Similar Datasets

Project description:BackgroundCOVID-19 led to a rapid acceleration in the number of systematic reviews. Readers need to know how up to date evidence is when selecting reviews to inform decisions. This cross-sectional study aimed to evaluate how easily the currency of COVID-19 systematic reviews published early in the pandemic could be determined and how up to date these reviews were at the time of publication.MethodsWe searched for systematic reviews and meta-analyses relevant to COVID-19 added to PubMed in July 2020 and January 2021, including any that were first published as preprints. We extracted data on the date of search, number of included studies, and date first published online. For the search date, we noted the format of the date and where in the review this was reported. A sample of non-COVID-19 systematic reviews from November 2020 served as a comparator.ResultsWe identified 246 systematic reviews on COVID-19. In the abstract of these reviews, just over half (57%) reported the search date (day/month/year or month/year) while 43% failed to report any date. When the full text was considered, the search date was missing from 6% of reviews. The median time from last search to publication online was 91 days (IQR 63-130). Time from search to publication was similar for the subset of 15 rapid or living reviews (92 days) but shorter for the 29 reviews published as preprints (37 days). The median number of studies or publications included per review was 23 (IQR 12-40). In the sample of 290 non-COVID SRs, around two-thirds (65%) reported the search date while a third (34%) did not include any date in the abstract. The median time from search to publication online was 253 days (IQR 153-381) and each review included a median of 12 studies (IQR 8-21).ConclusionsDespite the context of the pandemic and the need to easily ascertain the currency of systematic reviews, reporting of the search date information for COVID-19 reviews was inadequate. Adherence to reporting guidelines would improve the transparency and usefulness of systematic reviews to users.

Dataset Information

Races of small molecule clinical trials for the treatment of COVID-19: An up-to-date comprehensive review.

Publications

Races of small molecule clinical trials for the treatment of COVID-19: An up-to-date comprehensive review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets