Drug–Drug Interactions—New Approaches and Perspectives

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Biopharmaceutics".

Deadline for manuscript submissions: 10 August 2024 | Viewed by 4265

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Guest Editor
Department I—Drug Analysis, “Victor Babeş” University of Medicine and Pharmacy Timişoara, 300041 Timişoara, Romania
Interests: drug repositioning; network pharmacology; drug–drug interactions; drug–drug similarity networks; drug solubility; drug bioavailability; biopharmacy; drug–cyclodextrin inclusion complexes
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Special Issue Information

Dear Colleagues,

Drug–drug interactions represent a crucial factor affecting pharmacotherapeutic success. Good knowledge of the mechanisms of drug–drug interactions is vital for avoiding them. Accordingly, pharmacists and prescribing doctors must consider the clinical context when assessing drug interactions; the clinical relevance of the same drug combination can differ in distinct pathophysiological states (e.g., extreme ages, pregnancy, diabetes, immune-system-related disorders, and so on). Multiple morbidities in the elderly or specific clinical circumstances that require more than two drugs are conditions for drug–drug interactions that could affect the therapeutic outcome. To evaluate drug–drug interaction in a clinical context, healthcare specialists can use multiple online tools to check drug interactions. However, in practice, they may obtain contradicting results for the same drug combination (An even more unwanted case is when an online drug–drug interaction checker does not include a required drug).

Extensive wet-lab experiments to screen all potential drug–drug interactions will address the information uncertainty in many online check tools (based on existing drug–drug interaction databases). Unfortunately, such an approach would be unfeasible, as covering the entailed search space requires enormous resources. This situation calls for computational methods to predict drug–drug interactions; such tools would substantially narrow the wet-lab experiments search space.

This Special Issue calls for papers presenting multiple aspects of drug interactions relevant to their assessment: mechanisms, evaluation of the clinical relevance, prediction algorithms, electronic alert systems, and the Internet of Medical Things (IoMT) applied in the clinical evaluation of drug–drug and drug–disease interactions.

Dr. Lucreția Udrescu
Guest Editor

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Published Papers (3 papers)

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16 pages, 1021 KiB  
Article
Categorical Analysis of Database Consistency in Reporting Drug–Drug Interactions for Cardiovascular Diseases
by Liana Suciu, Sebastian Mihai Ardelean, Mihai Udrescu, Florina-Diana Goldiş, Daiana Hânda, Maria-Medana Tuică, Sabina-Oana Vasii and Lucreţia Udrescu
Pharmaceutics 2024, 16(3), 339; https://doi.org/10.3390/pharmaceutics16030339 - 28 Feb 2024
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Abstract
Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims [...] Read more.
Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss’ kappa coefficients of κ=0.3363 and κ=0.3572, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss’ kappa coefficients that indicate levels of agreement varying from poor agreement (κ<0) to perfect agreement (κ=1). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset. Full article
(This article belongs to the Special Issue Drug–Drug Interactions—New Approaches and Perspectives)
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23 pages, 6277 KiB  
Article
Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis
by Michael Hecker, Niklas Frahm and Uwe Klaus Zettl
Pharmaceutics 2024, 16(1), 3; https://doi.org/10.3390/pharmaceutics16010003 - 19 Dec 2023
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Abstract
Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and [...] Read more.
Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton’s tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow’s milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs. Full article
(This article belongs to the Special Issue Drug–Drug Interactions—New Approaches and Perspectives)
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40 pages, 1270 KiB  
Systematic Review
Clinical Relevance of Drug Interactions in People Living with Human Immunodeficiency Virus on Antiretroviral Therapy—Update 2022: Systematic Review
by Pedro Amariles, Mónica Rivera-Cadavid and Mauricio Ceballos
Pharmaceutics 2023, 15(10), 2488; https://doi.org/10.3390/pharmaceutics15102488 - 18 Oct 2023
Cited by 1 | Viewed by 1783
Abstract
Background: The clinical outcomes of antiretroviral drugs may be modified through drug interactions; thus, it is important to update the drug interactions in people living with HIV (PLHIV). Aim: To update clinically relevant drug interactions in PLHIV on antiretroviral therapy with novel drug [...] Read more.
Background: The clinical outcomes of antiretroviral drugs may be modified through drug interactions; thus, it is important to update the drug interactions in people living with HIV (PLHIV). Aim: To update clinically relevant drug interactions in PLHIV on antiretroviral therapy with novel drug interactions published from 2017 to 2022. Methods: A systematic review in Medline/PubMed database from July 2017 to December 2022 using the Mesh terms antiretroviral agents and drug interactions or herb–drug interactions or food–drug interactions. Publications with drug interactions in humans, in English or Spanish, and with full-text access were retrieved. The clinical relevance of drug interactions was grouped into five levels according to the gravity and probability of occurrence. Results: A total of 366 articles were identified, with 219 (including 87 citation lists) were included, which allowed for the identification of 471 drug interaction pairs; among them, 291 were systematically reported for the first time. In total 42 (14.4%) and 137 (47.1%) were level one and two, respectively, and 233 (80.1%) pairs were explained with the pharmacokinetic mechanism. Among these 291 pairs, protease inhibitors (PIs) and ritonavir/cobicistat-boosted PIs, as well as integrase strand transfer inhibitors (InSTIs), with 70 (24.1%) and 65 (22.3%) drug interaction pairs of levels one and two, respectively, were more frequent. Conclusions: In PLHIV on antiretroviral therapy, we identify 291 drug interaction pairs systematically reported for the first time, with 179 (61.5%) being assessed as clinically relevant (levels one and two). The pharmacokinetic mechanism was the most frequently identified. PIs, ritonavir/cobicistat-boosted PIs, and InSTIs were the antiretroviral groups with the highest number of clinically relevant drug interaction pairs (levels one and two). Full article
(This article belongs to the Special Issue Drug–Drug Interactions—New Approaches and Perspectives)
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