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Protein and Peptide Letters 2020Antimicrobial peptides in recent years have gained increased interest among scientists, health professionals and the pharmaceutical companies owing to their therapeutic... (Review)
Review
Antimicrobial peptides in recent years have gained increased interest among scientists, health professionals and the pharmaceutical companies owing to their therapeutic potential. These are low molecular weight proteins with broad range antimicrobial and immuno modulatory activities against infectious bacteria (Gram positive and Gram negative), viruses and fungi. Inability of micro-organisms to develop resistance against most of the antimicrobial peptide has made them as an efficient product which can greatly impact the new era of antimicrobials. In addition to this these peptides also demonstrates increased efficacy, high specificity, decreased drug interaction, low toxicity, biological diversity and direct attacking properties. Pharmaceutical industries are therefore conducting appropriate clinical trials to develop these peptides as potential therapeutic drugs. More than 60 peptide drugs have already reached the market and several hundreds of novel therapeutic peptides are in preclinical and clinical development. Rational designing can be used further to modify the chemical and physical properties of existing peptides. This mini review will discuss the sources, mechanism and recent therapeutic applications of antimicrobial peptides in treatment of infectious diseases.
Topics: Anti-Infective Agents; Antimicrobial Cationic Peptides; Bacteria; Drug Interactions; Drug Resistance, Microbial; Fungi; Humans; Microbial Sensitivity Tests; Molecular Weight; Treatment Outcome; Viruses
PubMed: 31438824
DOI: 10.2174/0929866526666190822165812 -
British Journal of Clinical Pharmacology Nov 2021The objective of this paper is to systematically review the literature on drug-drug interactions with warfarin, with a focus on patient-important clinical outcomes. (Meta-Analysis)
Meta-Analysis Review
AIMS
The objective of this paper is to systematically review the literature on drug-drug interactions with warfarin, with a focus on patient-important clinical outcomes.
METHODS
MEDLINE, EMBASE and the International Pharmaceutical Abstract (IPA) databases were searched from January 2004 to August 2019. We included studies describing drug-drug interactions between warfarin and other drugs. Screening and data extraction were conducted independently and in duplicate. We synthesized pooled odds ratios (OR) with 95% confidence intervals (CIs), comparing warfarin plus another medication to warfarin alone. We assessed the risk of bias at the study level and evaluated the overall certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.
RESULTS
Of 42 013 citations identified, a total of 72 studies reporting on 3 735 775 patients were considered eligible, including 11 randomized clinical trials and 61 observational studies. Increased risk of clinically relevant bleeding when added to warfarin therapy was observed for antiplatelet (AP) regimens (OR = 1.74; 95% CI 1.56-1.94), many antimicrobials (OR = 1.63; 95% CI 1.45-1.83), NSAIDs including COX-2 NSAIDs (OR = 1.83; 95% CI 1.29-2.59), SSRIs (OR = 1.62; 95% CI 1.42-1.85), mirtazapine (OR = 1.75; 95% CI 1.30-2.36), loop diuretics (OR = 1.92; 95% CI 1.29-2.86) among others. We found a protective effect of proton pump inhibitors (PPIs) against warfarin-related gastrointestinal (GI) bleeding (OR = 0.69; 95% CI 0.64-0.73). No significant effect on thromboembolic events or mortality of any drug group used with warfarin was found, including single or dual AP regimens.
CONCLUSIONS
This review found low to moderate certainty evidence supporting the interaction between warfarin and a small group of medications, which result in increased bleeding risk. PPIs are associated with reduced hospitalization for upper GI bleeding for patients taking warfarin. Further studies are required to better understand drug-drug interactions leading to thromboembolic outcomes or death.
Topics: Anticoagulants; Drug Interactions; Gastrointestinal Hemorrhage; Humans; Pharmaceutical Preparations; Randomized Controlled Trials as Topic; Warfarin
PubMed: 33769581
DOI: 10.1111/bcp.14833 -
Classification of drugs for evaluating drug interaction in drug development and clinical management.Drug Metabolism and Pharmacokinetics Dec 2021During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro... (Review)
Review
During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro studies. The obtained information should be provided efficiently to medical experts through package inserts and various information materials after the drug's launch. A recently updated Japanese guideline presents general procedures that are considered scientifically valid at the present moment. In this review, we aim to highlight the viewpoints of the Japanese guideline and enumerate drugs that were involved or are anticipated to be involved in evident pharmacokinetic drug interactions and classify them by their clearance pathway and potential intensity based on systematic reviews of the literature. The classification would be informative for designing clinical studies during the development stage, and the appropriate management of drug interactions in clinical practice.
Topics: Drug Development; Drug Interactions; Pharmaceutical Preparations
PubMed: 34666290
DOI: 10.1016/j.dmpk.2021.100414 -
Clinical Pharmacology and Therapeutics Dec 2022The coronavirus disease 2019 (COVID-19) antiviral nirmatrelvir/ritonavir (Paxlovid) has been granted authorization or approval in several countries for the treatment of... (Review)
Review
The coronavirus disease 2019 (COVID-19) antiviral nirmatrelvir/ritonavir (Paxlovid) has been granted authorization or approval in several countries for the treatment of patients with mild to moderate COVID-19 at high risk of progression to severe disease and with no requirement for supplemental oxygen. Nirmatrelvir/ritonavir will be primarily administered outside the hospital setting as a 5-day course oral treatment. The ritonavir component boosts plasma concentrations of nirmatrelvir through the potent and rapid inhibition of the key drug-metabolizing enzyme cytochrome P450 (CYP) 3A4. Thus nirmatrelvir/ritonavir, even given as a short treatment course, has a high potential to cause harm from drug-drug interactions (DDIs) with other drugs metabolized through this pathway. Options for mitigating risk from DDIs with nirmatrelvir/ritonavir are limited due to the clinical illness, the short window for intervention, and the related difficulty of implementing clinical monitoring or dosage adjustment of the comedication. Pragmatic options are largely confined to preemptive or symptom-driven pausing of the comedication or managing any additional risk through counseling. This review summarizes the effects of ritonavir on drug disposition (i.e., metabolizing enzymes and transporters) and discusses factors determining the likelihood of having a clinically significant DDI. Furthermore, it provides a comprehensive list of comedications likely to be used in COVID-19 patients which are categorized according to their potential DDI risk with nirmatrelvir/ritonavir. It also discusses recommendations for the management of DDIs which balance the risk of harm from DDIs with a short course of ritonavir, against unnecessary denial of nirmatrelvir/ritonavir treatment.
Topics: Humans; Ritonavir; Antiviral Agents; Drug Interactions; COVID-19 Drug Treatment
PubMed: 35567754
DOI: 10.1002/cpt.2646 -
Database : the Journal of Biological... May 2022The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on...
The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug-nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug-interaction research experience and four annotators with less drug-interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus.
Topics: Data Mining; Databases, Factual; Drug Interactions; Humans
PubMed: 35616099
DOI: 10.1093/database/baac031 -
Advances in Clinical and Experimental... Aug 2023The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse...
The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system. To reduce the incidence of ADRs, this study focuses on identifying DDls using a machine-learning approach. Drug-related information was obtained from various free databases, including DrugBank, BioGRID and Comparative Toxicogenomics Database. Eight similarity matrices between drugs were created as covariates in the model in order to assess their infiuence on DDls. Three distinct machine learning algorithms were considered, namely, logistic regression (LR), extreme Gradient Boosting (XGBoost) and neural network (NN). Our study examined 22 notable drugs and their interactions with 841 other drugs from DrugBank. The accuracy of the machine learning approaches ranged from 68% to 78%, while the F1 scores ranged from 78% to 83%. Our study indicates that enzyme and target similarity are the most significant parameters in identifying DDls. Finally, our data-driven approach reveals that machine learning methods can accurately predict DDls and provide additional insights in a timely and cost-effective manner.
Topics: Humans; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Algorithms; Databases, Factual; Machine Learning
PubMed: 37589227
DOI: 10.17219/acem/169852 -
Nucleic Acids Research Jul 2022Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets....
Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.
Topics: Humans; Neoplasms; Antineoplastic Agents; Software; Oncogenes; Algorithms; Mutation; Drug Interactions; Drug Repositioning
PubMed: 35580047
DOI: 10.1093/nar/gkac384 -
Briefings in Bioinformatics Nov 2023In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the... (Review)
Review
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
Topics: Humans; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Databases, Factual; Computer Simulation; Drug Combinations
PubMed: 38113076
DOI: 10.1093/bib/bbad445 -
Cells Oct 2021Human immunodeficiency virus (HIV) affects more than 37 million people globally, and in 2020, more than 680,000 people died from HIV-related causes. Recently, these... (Review)
Review
Human immunodeficiency virus (HIV) affects more than 37 million people globally, and in 2020, more than 680,000 people died from HIV-related causes. Recently, these numbers have decrease substantially and continue to reduce thanks to the use of antiretroviral therapy (ART), thus making HIV a chronic disease state for those dependent on lifelong use of ART. However, patients with HIV have an increased risk of developing some type of cancer compared to patients without HIV. Therefore, treatment of patients who are diagnosed with both HIV and cancer represents a complicated scenario because of the risk associated with drug-drug interaction (DDIs) and related toxicity. Selection of an alternative chemotherapy or ART or temporarily discontinuation of ART constitute a strategy to manage the risk of DDIs. Temporarily withholding ART is the less desirable clinical plan but risks and benefits must be considered in each scenario. In this review we focus on the hepatotoxicity associated with a simultaneous treatment with ART and chemotherapeutic drugs and mechanisms behind. Moreover, we also discuss the effect on the liver caused by the association of immunotherapeutic drugs, which have recently been used in clinical trials and also in HIV patients.
Topics: Antineoplastic Agents; Antiretroviral Therapy, Highly Active; Chemical and Drug Induced Liver Injury; Drug Interactions; HIV Infections; Humans; Immunotherapy
PubMed: 34831094
DOI: 10.3390/cells10112871 -
Clinical Pharmacokinetics Jul 2021Combined antiretroviral treatments have significantly improved the morbidity and mortality related to HIV infection, thus transforming HIV infection into a chronic... (Review)
Review
Combined antiretroviral treatments have significantly improved the morbidity and mortality related to HIV infection, thus transforming HIV infection into a chronic disease; however, the efficacy of antiretroviral treatments is highly dependent on the ability of infected individuals to adhere to life-long drug combination therapies. A major milestone in HIV treatment is the marketing of the long-acting intramuscular antiretroviral drugs cabotegravir and rilpivirine, allowing for infrequent drug administration, with the potential to improve adherence to therapy and treatment satisfaction. Intramuscular administration of cabotegravir and rilpivirine leads to differences in pharmacokinetics and drug-drug interaction (DDI) profiles compared with oral administration. A notable difference is the long elimination half-life with intramuscular administration, which reaches 5.6-11.5 weeks for cabotegravir and 13-28 weeks for rilpivirine, compared with 41 and 45 h, respectively, with their oral administration. Cabotegravir and rilpivirine have a low potential to cause DDIs, however these drugs can be victims of DDIs. Cabotegravir is mainly metabolized by UGT1A1, and rilpivirine is mainly metabolized by CYP3A4, therefore these agents are susceptible to DDIs with inhibitors, and particularly inducers of drug-metabolizing enzymes. Intramuscular administration of cabotegravir and rilpivirine has the advantage of eliminating DDIs occurring at the gastrointestinal level, however interactions can still occur at the hepatic level. This review provides insight on the intramuscular administration of drugs and summarizes the pharmacology of long-acting cabotegravir and rilpivirine. Particular emphasis is placed on DDI profiles after oral and intramuscular administration of these antiretroviral drugs.
Topics: Anti-HIV Agents; Drug Interactions; HIV Infections; HIV-1; Humans; Pharmaceutical Preparations; Pyridones; Rilpivirine
PubMed: 33830459
DOI: 10.1007/s40262-021-01005-1