Int J Mol Sci. 2022 Dec 9;23(24):15655. doi: 10.3390/ijms232415655.

ABSTRACT

Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity.

PMID:36555297 | PMC:PMC9779353 | DOI:10.3390/ijms232415655

Int J Mol Sci. 2022 Dec 7;23(24):15480. doi: 10.3390/ijms232415480.

ABSTRACT

Experimental findings for SARS-CoV-2 related to the glycan biochemistry of coronaviruses indicate that attachments from spike protein to glycoconjugates on the surfaces of red blood cells (RBCs), other blood cells and endothelial cells are key to the infectivity and morbidity of COVID-19. To provide further insight into these glycan attachments and their potential clinical relevance, the classic hemagglutination (HA) assay was applied using spike protein from the Wuhan, Alpha, Delta and Omicron B.1.1.529 lineages of SARS-CoV-2 mixed with human RBCs. The electrostatic potential of the central region of spike protein from these four lineages was studied through molecular modeling simulations. Inhibition of spike protein-induced HA was tested using the macrocyclic lactone ivermectin (IVM), which is indicated to bind strongly to SARS-CoV-2 spike protein glycan sites. The results of these experiments were, first, that spike protein from these four lineages of SARS-CoV-2 induced HA. Omicron induced HA at a significantly lower threshold concentration of spike protein than the three prior lineages and was much more electropositive on its central spike protein region. IVM blocked HA when added to RBCs prior to spike protein and reversed HA when added afterward. These results validate and extend prior findings on the role of glycan bindings of viral spike protein in COVID-19. They furthermore suggest therapeutic options using competitive glycan-binding agents such as IVM and may help elucidate rare serious adverse effects (AEs) associated with COVID-19 mRNA vaccines, which use spike protein as the generated antigen.

PMID:36555121 | PMC:PMC9779393 | DOI:10.3390/ijms232415480

Int J Mol Sci. 2022 Dec 7;23(24):15470. doi: 10.3390/ijms232415470.

ABSTRACT

Cannabis contains over 500 distinct compounds, which include cannabinoids, terpenoids, and flavonoids. However, very few of these compounds have been studied for their beneficial effects. There is an emerging concept that the constituents of the cannabis plant may work in concert to achieve better therapeutic benefits. This study is aimed at determining if the combination of a minor cannabinoid (cannabidiol, CBD) and a terpene (beta-caryophyllene, BCP) works in concert and if this has any therapeutic value. We used an inflammatory pain model (formalin) in mice to test for any functionality of CBD and BCP in combination. First, we determined the analgesic effect of CBD and BCP individually by establishing dose-response studies. Second, we tested the analgesic effect of fixed-ratio combinations and monitored any adverse effects. Finally, we determined the effect of this combination on inflammation. The combination of CBD and BCP produces a synergistic analgesic effect. This effect was without the cannabinoid receptor-1 side effects. The analgesic effect of CBD and BCP in combination involves an inflammatory mechanism. The combination of these two constituents of the cannabis plant, CBD and BCP, works in concert to produce a therapeutic effect with safety profiles through an inflammatory mechanism.

PMID:36555111 | PMC:PMC9779834 | DOI:10.3390/ijms232415470

Drug Ther Bull. 2022 Dec 23:dtb-2022-000075. doi: 10.1136/dtb.2022.000075. Online ahead of print.

ABSTRACT

Overview of: European Medicines Agency Pharmacovigilance Risk Assessment Committee. Codeine with ibuprofen: PRAC adds warning for serious renal and gastrointestinal harms. September 2022.

PMID:36564156 | DOI:10.1136/dtb.2022.000075

Mol Biol Rep. 2022 Dec 23. doi: 10.1007/s11033-022-08158-7. Online ahead of print.

ABSTRACT

BACKGROUND: Anti-tuberculosis drug-induced liver injury (AT-DILI) is one of the most common side effects in TB patients during treatment. The prime cause of liver injury during TB treatment is reported to be isoniazid and its metabolites. Different factors influenced the development of AT-DILI, and genetic factors are one of the major factors.

METHODS AND RESULTS: Polymorphisms in drug metabolism genes like NAT2, CYP2E1, PXR, and GST have been reported to be associated with AT-DILI, and they are one of the major areas of focus at present. Attempts are met in this review to analyse the different markers in these drug metabolism genes for their association with AT-DILI.

CONCLUSION: A better understanding of the polymorphisms in these genes and their functional effects will give better insights into the development of AT-DILI, and it could facilitate in designing and developing more effective personalized treatment for TB.

PMID:36562936 | DOI:10.1007/s11033-022-08158-7

Vaccines (Basel). 2022 Dec 13;10(12):2137. doi: 10.3390/vaccines10122137.

NO ABSTRACT

PMID:36560547 | DOI:10.3390/vaccines10122137

Pharmaceuticals (Basel). 2022 Nov 22;15(12):1444. doi: 10.3390/ph15121444.

NO ABSTRACT

PMID:36558895 | DOI:10.3390/ph15121444

Medicina (Kaunas). 2022 Dec 15;58(12):1848. doi: 10.3390/medicina58121848.

NO ABSTRACT

PMID:36557050 | DOI:10.3390/medicina58121848

J Clin Med. 2022 Dec 19;11(24):7526. doi: 10.3390/jcm11247526.

NO ABSTRACT

PMID:36556142 | DOI:10.3390/jcm11247526

Healthcare (Basel). 2022 Dec 13;10(12):2523. doi: 10.3390/healthcare10122523.

NO ABSTRACT

PMID:36554047 | DOI:10.3390/healthcare10122523

Medicine (Baltimore). 2022 Dec 16;101(50):e31888. doi: 10.1097/MD.0000000000031888.

NO ABSTRACT

PMID:36550840 | DOI:10.1097/MD.0000000000031888

BMC Med Inform Decis Mak. 2022 Dec 22;22(1):338. doi: 10.1186/s12911-022-02085-0.

ABSTRACT

INTRODUCTION: Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year.

OBJECTIVES: The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness.

METHODS: The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results.

CONCLUSIONS: Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection.

PMID:36550485 | PMC:PMC9773506 | DOI:10.1186/s12911-022-02085-0

BMJ Case Rep. 2022 Dec 22;15(12):e252052. doi: 10.1136/bcr-2022-252052.

ABSTRACT

Fluoroquinolones are commonly used antimicrobials with multiple known adverse effects, yet overdose events are rarely reported. Here, we report a case of a previously healthy middle-aged woman who unintentionally ingested 7 g of levofloxacin in one dose. Thereafter, she presented to the emergency department with hemiparesis concerning for ischaemic stroke and was administered tissue plasminogen activator. Her brain imaging showed no ischaemic injury and her symptoms resolved within 24 hours; this is consistent with a transient ischaemic attack. Our case highlights potential adverse effects of an acute overdose of levofloxacin that has not previously been well described.

PMID:36549757 | DOI:10.1136/bcr-2022-252052

BMJ Open. 2022 Dec 22;12(12):e061476. doi: 10.1136/bmjopen-2022-061476.

ABSTRACT

OBJECTIVES: To assess the risk of new-onset or worsening hyperglycaemia, hypertension, weight gain and hyperlipidaemia with systemic corticosteroid therapy (CST) as reported in published randomised control trial (RCT) studies.

DATA SOURCES: Literature search using MEDLINE, EMBASE, Cochrane library, Web of Science and Scopus STUDY ELIGIBILITY CRITERIA: Published articles on results of RCT with a systemic CST arm with numerical data presented on adverse effect (AE).

PARTICIPANTS AND INTERVENTIONS: Reports of hyperglycaemia, hypertension, weight gain and hyperlipidaemia associated with systemic CST in patients or healthy volunteer's ≥17 years of age.

STUDY APPRAISAL METHODS: Risk of bias tool, assessment at the level of AE and key study characteristics.

RESULTS: A total of 5446 articles were screened to include 118 studies with 152 systemic CST arms (total participants=17 113 among which 8569 participants treated with CST). Pooled prevalence of hyperglycaemia in the CST arms within the studies was 10% (95% CI 7% to 14%), with the highest prevalence in respiratory illnesses at 22% (95% CI 9% to 35%). Pooled prevalence of severe hyperglycaemia, hypertension, weight gain and hyperlipidaemia within the corticosteroid arms was 5% (95% CI 2% to 9%), 6% (95% CI 4% to 8%), 13% (95% CI 8% to 18%), 8% (95% CI 4% to 17%), respectively. CST was significantly associated hyperglycaemia, hypertension and weight gain as noted in double-blinded placebo-controlled parallel-arms studies: OR of 2.13 (95% CI 1.66 to 2.72), 1.68 (95% CI 0.96 to 2.95) and 5.20 (95% CI 2.10 to 12.90), respectively. Intravenous therapy posed higher risk than oral therapy: OR of 2.39 (95% CI 1.16 to 4.91).

LIMITATIONS: There was significant heterogeneity in the AE definitions and quality of AE reporting in the primary studies and patient populations in the studies. The impact of cumulative dose effect on incidental AE could not be calculated.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Systemic CST use is associated with increased risk of metabolic AEs, which differs for each disease group and route of administration.

PROSPERO REGISTRATION NUMBER: CRD42020161270.

PMID:36549729 | PMC:PMC9772659 | DOI:10.1136/bmjopen-2022-061476

Chin Med. 2022 Dec 22;17(1):142. doi: 10.1186/s13020-022-00692-7.

NO ABSTRACT

PMID:36550503 | DOI:10.1186/s13020-022-00692-7

Oncology (Williston Park). 2022 Dec 20;36(12):732-738. doi: 10.46883/2022.25920981.

NO ABSTRACT

PMID:36548097 | DOI:10.46883/2022.25920981

Ther Adv Drug Saf. 2022 Dec 16;13:20420986221143266. doi: 10.1177/20420986221143266. eCollection 2022.

NO ABSTRACT

PMID:36545565 | PMC:PMC9761248 | DOI:10.1177/20420986221143266

Addict Health. 2022 Apr;14(2):138-151. doi: 10.22122/AHJ.2022.196156.1266.

NO ABSTRACT

PMID:36544515 | PMC:PMC9743819 | DOI:10.22122/AHJ.2022.196156.1266

Drug Ther Bull. 2023 Jan;61(1):3. doi: 10.1136/dtb.2022.000072.

NO ABSTRACT

PMID:36543344 | DOI:10.1136/dtb.2022.000072

Drug Ther Bull. 2023 Jan;61(1):6. doi: 10.1136/dtb.2022.000073.

NO ABSTRACT

PMID:36543342 | DOI:10.1136/dtb.2022.000073