The CLAIRE COVID-19 initiative: approach, experiences and recommendations.

Ethics Inf Technol. 2021 Feb 09;:1-7

Authors: Bontempi G, Chavarriaga R, eD Canck H, Girardi E, Hoos H, Kilbane-Dawe I, Ball T, Nowé A, Sousa J, Bacciu D, Aldinucci M, eD Domenico M, Saffiotti A, Maratea M

Abstract
A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.

PMID: 33584129 [PubMed - as supplied by publisher]

Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein - Human ACE2 interface.

J Mol Struct. 2021 May 15;1232:130076

Authors: Faria SHDM, Teleschi JG

Abstract
With the advancement of the Covid-19 pandemic, this work aims to find molecules that can inhibit the attraction between the Spike proteins of the SARS-COV-2 virus and human ACE2. The results of molecular docking positioned four molecules at the interaction site Tyr-491(Spike)-Glu-37(ACE2) and one at the site Gly-488(Spike)-Lys-353(ACE2). The QTAIM and IQA data showed that the 1629 molecule had a significant inhibitory effect on the Gly488-Ly353 site, decreasing the Laplacian of the electronic density of the BCP O4-N10. The molecule 2542 showed an inhibitory effect in two regions of interaction of the Tyr491-Glu37 site, acting on the BCPs H30-H33 and O8-H31 while the ligand 2600, in conformation 26, presented a similar effect only on the BCP O8-H31 of that same interactive site. Thus, the data suggest laboratory tests of a combination of molecules that can act at two sites of interaction simultaneously, using the combination of 1629/2542 and 1629/2600 ligands.

PMID: 33583954 [PubMed]

Statins as Potential Chemoprevention or Therapeutic Agents in Cancer: a Model for Evaluating Repurposed Drugs.

Curr Oncol Rep. 2021 Feb 13;23(3):29

Authors: Joharatnam-Hogan N, Alexandre L, Yarmolinsky J, Lake B, Capps N, Martin RM, Ring A, Cafferty F, Langley RE

Abstract
PURPOSE OF REVIEW: Repurposing established medicines for a new therapeutic indication potentially has important global and societal impact. The high costs and slow pace of new drug development have increased interest in more cost-effective repurposed drugs, particularly in the cancer arena. The conventional drug development pathway and evidence framework are not designed for drug repurposing and there is currently no consensus on establishing the evidence base before embarking on a large, resource intensive, potential practice changing phase III randomised controlled trial (RCT). Numerous observational studies have suggested a potential role for statins as a repurposed drug for cancer chemoprevention and therapy, and we review the strength of the cumulative evidence here.
RECENT FINDINGS: In the setting of cancer, a potential repurposed drug, like statins, typically goes through a cyclical history, with initial use for several years in another disease setting, prior to epidemiological research identifying a possible chemo-protective effect. However, further information is required, including review of RCT data in the initial disease setting with exploration of cancer outcomes. Additionally, more contemporary methods should be considered, such as Mendelian randomization and pharmaco-epidemiological research with "target" trial design emulation using electronic health records. Pre-clinical and traditional observational data potentially support the role of statins in the treatment of cancer; however, randomised trial evidence is not supportive. Evaluation of contemporary methods provides little added support for the use of statin therapy in cancer. We provide complementary evidence of alternative study designs to enable a robust critical appraisal from a number of sources of the go/no-go decision for a prospective phase III RCT of statins in the treatment of cancer.

PMID: 33582975 [PubMed - in process]

Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19.

Mol Divers. 2021 Feb 13;:

Authors: Rai H, Barik A, Singh YP, Suresh A, Singh L, Singh G, Nayak UY, Dubey VK, Modi G

Abstract
The importance of the main protease (Mpro) enzyme of SARS-CoV-2 in the digestion of viral polyproteins introduces Mpro as an attractive drug target for antiviral drug design. This study aims to carry out the molecular docking, molecular dynamics studies, and prediction of ADMET properties of selected potential antiviral molecules. The study provides an insight into biomolecular interactions to understand the inhibitory mechanism and the spatial orientation of the tested ligands and further, identification of key amino acid residues within the substrate-binding pocket that can be applied for structure-based drug design. In this regard, we carried out molecular docking studies of chloroquine (CQ), hydroxychloroquine (HCQ), remdesivir (RDV), GS441524, arbidol (ARB), and natural product glycyrrhizin (GA) using AutoDock 4.2 tool. To study the drug-receptor complex's stability, selected docking possesses were further subjected to molecular dynamics studies with Schrodinger software. The prediction of ADMET/toxicity properties was carried out on ADMET Prediction™. The docking studies suggested a potential role played by CYS145, HIS163, and GLU166 in the interaction of molecules within the active site of COVID-19 Mpro. In the docking studies, RDV and GA exhibited superiority in binding with the crystal structure of Mpro over the other selected molecules in this study. Spatial orientations of the molecules at the active site of Mpro exposed the significance of S1-S4 subsites and surrounding amino acid residues. Among GA and RDV, RDV showed better and stable interactions with the protein, which is the reason for the lesser RMSD values for RDV. Overall, the present in silico study indicated the direction to combat COVID-19 using FDA-approved drugs as promising agents, which do not need much toxicity studies and could also serve as starting points for lead optimization in drug discovery.

PMID: 33582935 [PubMed - as supplied by publisher]

InContext: curation of medical context for drug indications.

J Biomed Semantics. 2021 Feb 12;12(1):2

Authors: Moodley K, Rieswijk L, Oprea TI, Dumontier M

Abstract
Accurate and precise information about the therapeutic uses (indications) of a drug is essential for applications in drug repurposing and precision medicine. Leading online drug resources such as DrugCentral and DrugBank provide rich information about various properties of drugs, including their indications. However, because indications in such databases are often partly automatically mined, some may prove to be inaccurate or imprecise. Particularly challenging for text mining methods is the task of distinguishing between general disease mentions in drug product labels and actual indications for the drug. For this, the qualifying medical context of the disease mentions in the text should be studied. Some examples include contraindications, co-prescribed drugs and target patient qualifications. No existing indication curation efforts attempt to capture such information in a precise way. Here we fill this gap by presenting a novel curation protocol for extracting indications and machine processable annotations of contextual information about the therapeutic use of a drug. We implemented the protocol on a reference set of FDA-approved drug product labels on the DailyMed website to curate indications for 150 anti-cancer and cardiovascular drugs. The resulting corpus - InContext - focuses on anti-cancer and cardiovascular drugs because of the heightened societal interest in cancer and heart disease. In order to understand how InContext relates with existing reputable drug indication databases, we analysed it's overlap with a state-of-the-art indications database - LabeledIn - as well as a reputable online drug compendium - DrugCentral. We found that 40% of indications sampled from DrugCentral (and 23% from LabeledIn) respectively, could not be accounted for in InContext. This raises questions about the veracity of indications not appearing in InContext. The additional contextual information curated by InContext about disease mentions in drug SPLs provides a foundation for more precise, structured and formal representations of knowledge related to drug therapeutic use, in order to increase accuracy and agreement of drug indication extraction methods for in silico drug repurposing.

PMID: 33579375 [PubMed - as supplied by publisher]

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/13

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Drug Repurposing for COVID-19 via Knowledge Graph Completion.

J Biomed Inform. 2021 Feb 08;:103696

Authors: Zhang R, Hristovski D, Schutte D, Kastrin A, Fiszman M, Kilicoglu H

Abstract
OBJECTIVE: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.
METHODS: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.
RESULTS: Accuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.
CONCLUSION: We showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.

PMID: 33571675 [PubMed - as supplied by publisher]

Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.

Brief Bioinform. 2021 Feb 11;:

Authors: Pavel A, Del Giudice G, Federico A, Di Lieto A, Kinaret PAS, Serra A, Greco D

Abstract
The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.

PMID: 33569598 [PubMed - as supplied by publisher]

Repurposing Drugs for the Management of Patients with Confirmed Coronavirus Disease 2019 (COVID-19).

Curr Pharm Des. 2021;27(1):115-126

Authors: Lovato ECW, Barboza LN, Wietzikoski S, de Souza ANV, Auth PA, Junior AG, Dos Reis Lívero FA

Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), termed coronavirus disease 2019 (COVID-19) by the World Health Organization, is a newly emerging zoonotic agent that emerged in China in December 2019. No specific treatment for COVID-19 is currently available. Usual palliative treatment includes maintaining hydration and nutrition and controlling fever and cough. The clinical severity and extent of transmission need to be determined, and therapeutic options need to be developed and optimized.
METHODS: The present review discusses the recent repurposing of drugs for COVID-19 treatment.
RESULTS: Several compounds, including remdesivir, lopinavir, ritonavir, interferon-β, ribavirin, chloroquine/ hydroxychloroquine, azithromycin, tocilizumab, and ivermectin, have emerged as promising alternatives. They block the virus from entering host cells, prevent viral replication, and attenuate exacerbation of the host's immune response.
CONCLUSION: Although some evidence indicates the positive actions of different classes of compounds for the treatment of COVID-19, few clinical assays have been established to definitively demonstrate their therapeutic value in humans. Multicenter clinical studies are urgently needed to validate and standardize therapeutic regimens that involve these agents. Although science has not yet presented us with a specific drug against COVID-19, the repurposing of drugs appears to be promising in our fight against this devastating disease.

PMID: 32634080 [PubMed - indexed for MEDLINE]

Repurposing Tyrosine Kinase Inhibitors to Overcome Multidrug Resistance in Cancer: A Focus on Transporters and Lysosomal Sequestration.

Int J Mol Sci. 2020 Apr 30;21(9):

Authors: Krchniakova M, Skoda J, Neradil J, Chlapek P, Veselska R

Abstract
Tyrosine kinase inhibitors (TKIs) are being increasingly used to treat various malignancies. Although they were designed to target aberrant tyrosine kinases, they are also intimately linked with the mechanisms of multidrug resistance (MDR) in cancer cells. MDR-related solute carrier (SLC) and ATB-binding cassette (ABC) transporters are responsible for TKI uptake and efflux, respectively. However, the role of TKIs appears to be dual because they can act as substrates and/or inhibitors of these transporters. In addition, several TKIs have been identified to be sequestered into lysosomes either due to their physiochemical properties or via ABC transporters expressed on the lysosomal membrane. Since the development of MDR represents a great concern in anticancer treatment, it is important to elucidate the interactions of TKIs with MDR-related transporters as well as to improve the properties that would prevent TKIs from diffusing into lysosomes. These findings not only help to avoid MDR, but also help to define the possible impact of combining TKIs with other anticancer drugs, leading to more efficient therapy and fewer adverse effects in patients.

PMID: 32365759 [PubMed - indexed for MEDLINE]

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/11

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/11

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

8 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/10

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

7 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/10

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

ArXiv. 2020 Oct 19:arXiv:2010.09600v2. Preprint.

ABSTRACT

OBJECTIVE: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

METHODS: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.

RESULTS: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed.

CONCLUSION: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.

PMID:33564698 | PMC:PMC7872375

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2021/02/09

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Effective drugs used to combat SARS-CoV-2 infection and the current status of vaccines.

Biomed Pharmacother. 2021 Jan 28;137:111330

Authors: Awadasseid A, Wu Y, Tanaka Y, Zhang W

Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causal factor of the coronavirus disease 2019 (COVID-19). Drug repurposing, portraying patented drugs as a successful drug development technique, could shorten the period and minimize costs relative to de novo drug exploration. Recently several drugs have been used as anti-SARS-CoV-2 such as Remdesivir, Favipiravir, Hydroxychloroquine, Azithromycin, Lopinavir/Ritonavir, Nafamostat mesylate and so on. Despite such efforts, there is currently no successful broad-spectrum antiviral countermeasures to combat SARS-CoV-2 or possibly potential CoVs pandemic. Therefore it is desperately important to recognize and test widely efficient, reliable anti-CoV therapies now and in the future. Remdesivir and Favipiravir were more promising despite having side effects; it had prominent efficacy and efficiency while still not yet approved as the official anti-viral drug for SARS CoV-2. In this review, we summarizes the current drug and vaccine discovery status against SARS-CoV-2, predicting that these efforts will help create effective drugs and vaccines for SARS-CoV-2.

PMID: 33550043 [PubMed - as supplied by publisher]

Clinically relevant cell culture models and their significance in isolation, pathogenesis, vaccine development, repurposing and screening of new drugs for SARS-CoV-2: a systematic review.

Tissue Cell. 2021 Jan 26;70:101497

Authors: Kumar S, Sarma P, Kaur H, Prajapat M, Bhattacharyya A, Avti P, Sehkhar N, Kaur H, Bansal S, Mahendiratta S, Mahalmani VM, Singh H, Prakash A, Kuhad A, Medhi B

Abstract
BACKGROUND: In-Vitro/Cellular evidence is the backbone and vital proof of concept during the development of novel therapeutics as well as drugs repurposing against COVID-19. Choosing an ideal in-vitro model is vital as the virus entry is through ACE2, CD147, and TMPRSS2 dependant and very specific. In this regard, this is the first systematic review addressing the importance of specific cell lines used as potential in-vitro models in the isolation, pathogenesis, and therapeutics for SARS-COV-2.
METHODS: We searched 17 literature databases with appropriate keywords, and identified 1173 non-duplicate studies. In the present study, 71 articles are included after a careful, thorough screening of the titles and their abstracts for possible inclusion using predefined inclusion/exclusion criteria (PRISMA Guidelines).
RESULTS: In the current study, we compiled cell culture-based studies for SARS-CoV-2 and found the best compatible In-Vitro models for SARS-CoV-2 (Vero, VeroE6, HEK293 as well as its variants, Huh-7, Calu-3 2B4, and Caco2). Among other essential cell lines used include LLC-MK2, MDCKII, BHK-21, HepG2, A549,T cell leukemia (MT-2), stems cells based cell line DYR0100for differentiation assays, and embryo-specific NIH3T3 cell line for vaccine production.
CONCLUSION: The Present study provides a detailed summary of all the drugs/compounds screened for drug repurposing and discovery purpose using the in-vitro models for SARS-CoV-2 along with isolation, pathogenesis and vaccine production. This study also suggests that after careful evaluation of all the cell line based studies, Kidney cells (VeroE6, HEK293 along with their clones), liver Huh-7cells, respiratory Calu-3 cells, and intestinal Caco-2 are the most widely used in-vitro models for SARS-CoV-2.

PMID: 33550034 [PubMed - as supplied by publisher]

Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers.

Comput Biol Med. 2021 Jan 28;131:104244

Authors: Sarkar JP, Saha I, Sarkar A, Maulik U

Abstract
Breast cancer is the second leading cancer type among females. In this regard, it is found that microRNAs play an important role by regulating the gene expressions at the post-transcriptional phase. However, identification of the most influencing miRNAs in breast cancer subtypes is a challenging task, while the recent advancement in Next Generation Sequencing techniques allows analyzing high throughput expression data of miRNAs. Thus, we have conducted this research with the help of NGS data of breast cancer in order to identify the most significant miRNA biomarkers. The selected miRNA biomarkers are highly associated with the multiple breast cancer subtypes. For this purpose, a two-phase technique, called Machine Learning Integrated Ensemble of Feature Selection Methods, followed by survival analysis, is proposed. In the first phase, we have selected the best among seven machine learning techniques based on classification accuracy using the entire set of features (in this case miRNAs). Subsequently, eight different feature selection methods are used separately in order to rank the features and validate each set of top features using the selected machine learning technique by considering a multi-class classification task of the breast cancer subtypes. In the second phase, based on the classification accuracy values, the top features from each feature selection method are considered to make an ensemble to provide further categorization of the miRNAs as 8*, 7* up to 1*. The 8* miRNAs provide the highest average classification accuracy of 86% after 10-fold cross-validation. Thereafter, 27 miRNAs are identified from the list that is confined within 8* to 4* miRNAs based on their importance in survival for breast cancer subtypes using Cox regression based survival analysis. Moreover, expression analysis, regulatory network analysis, protein-protein interaction analysis, KEGG pathway and gene ontology enrichment analysis are performed in order to validate biological significance of the proposed solution. Additionally, we have prepared a miRNA-protein-drug interaction network to identify possible drug for the selected miRNAs. Thus, our findings may be considered during a clinical trial for the treatment of breast cancer patients.

PMID: 33550016 [PubMed - as supplied by publisher]

A Multi-Objective Approach for Drug Repurposing in Preeclampsia.

Molecules. 2021 Feb 03;26(4):

Authors: Tejera E, Pérez-Castillo Y, Chamorro A, Cabrera-Andrade A, Sanchez ME

Abstract
Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as "targets". The proteins labeled as "off-targets" were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.

PMID: 33546161 [PubMed - as supplied by publisher]