Pharmacodynamic Drug-Drug Interactions

Interpretable prediction of drug-drug interactions via text embedding in biomedical literature

Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicting the risk of DDIs is crucial for ensuring safe drug use, particularly by identifying the types of DDIs and the mechanisms involved. Therefore, this study used biomedical literature to proposed hierarchical attention-based deep learning models to predict DDIs and their types. The proposed model consists of two components: drug embedding and DDI prediction. The drug embedding module extracts representation vectors that effectively capture drug properties using sentence and sequence embedding methods. For sentence embedding, a pre-trained biomedical language model is used to map drug-related sentences into vector space. For sequence embedding, sentence embedding vectors are sequentially fed into bidirectional long short-term memory with a hierarchical attention network, enabling the analysis of sentences relevant to DDI prediction while accounting for the order of the sentences. Finally, DDI prediction is performed using a deep neural network based on the sequence embedding vectors of a drug pair. Our model achieved high performances in the accuracy (0.85-0.90), AUROC (0.98-0.99), and AUPR (0.63-0.95) performance across 164 DDI types. Additionally, the proposed model showed improvements in up to 11 % in AUROC, and 8 % in AUPR. Furthermore, model interprets predictions by leveraging attention mechanisms and drug similarity. The results indicated that the model considered various factors beyond similarity to predict DDIs. These findings may help prevent unforeseen medical accidents and reduce healthcare costs by predicting detailed drug interaction types.

Pharmacotherapeutic actions related to drug interaction alerts - a questionnaire study among Swedish hospital interns and residents in family medicine

PURPOSE

To explore how hospital interns and residents specialising in family medicine act on drug interaction alerts in a specific patient case, and on interaction alerts in general.

METHODS

A 4-page questionnaire, including a fictional patient case (73-year-old woman; 10 drugs in the medication list triggering 11 drug interaction alerts) and questions regarding the use of interaction alerts in general, was distributed to interns and residents during educational sessions (November‒December 2023). The respondents were instructed to consider what actions they would take "a normal day at work" due to the risk of interactions between the patients' drugs. In the general questions, the respondents were asked how often they access the detailed interaction information (from 1 = never to 5 = always) provided by the knowledge resource, in relation to the alert classification (D = clinically significant, should be avoided; C = clinically significant, can be handled by, e.g., dose adjustment).

RESULTS

The questionnaire was completed by 55 interns and 69 residents (response rate: 98%). In the patient case, the respondents acted on a median of 4 (range: 0‒8) drugs, most often concerning repaglinide (in a D interaction alert with clopidogrel; 96% of the interns and 96% of the residents suggested action), and omeprazole (in three C interaction alerts with citalopram, clopidogrel, and levothyroxine, respectively; 71% and 83% suggested action). Among the respondents who answered the questions about how often (rated 4/5) they access more detailed information about interactions, 56 (59%) did so for D versus 29 (31%) for C alerts (P < 0.001).

CONCLUSION

Physicians act on drug interaction alerts selectively, and the alert classifications seem to guide how they are used.

Comprehensive Cellular Senescence Evaluation to Aid Targeted Therapies

Drug resistance to a single agent is common in cancer-targeted therapies, and rational drug combinations are a promising approach to overcome this challenge. Many Food and Drug Administration-approved drugs can induce cellular senescence, which possesses unique vulnerabilities and molecular signatures. However, there is limited analysis on the effect of the combination of cellular-senescence-inducing drugs and targeted therapy drugs. Here, we conducted a comprehensive evaluation of cellular senescence using 7 senescence-associated gene sets. We quantified the cellular senescence states of ~10,000 tumor samples from The Cancer Genome Atlas and examined their associations with targeted drug responses. Our analysis revealed that tumors with higher cellular senescence scores exhibited increased sensitivity to targeted drugs. As a proof of concept, we experimentally confirmed that etoposide-induced senescence sensitized lung cancer cells to 2 widely used targeted drugs, erlotinib and dasatinib. Furthermore, we identified multiple genes whose dependencies were associated with senescence status across ~1,000 cancer cell lines, suggesting that cellular senescence generates unique vulnerabilities for therapeutic exploitation. Our study provides a comprehensive overview of drug response related to cellular senescence and highlights the potential of combining senescence-inducing agents with targeted therapies to improve treatment outcomes in lung cancer, revealing novel applications of cellular senescence in targeted cancer therapies.

Drug interaction evaluation of the novel phosphodiesterase type 5 inhibitor tunodafil (youkenafil): Effects of tunodafil on omeprazole pharmacokinetics based on CYP2C19 gene polymorphism, and effects of ritonavir on tunodafil pharmacokinetics

PURPOSE

To evaluate the drug-drug interactions (DDI) of tunodafil (youkenafil), a novel phosphodiesterase type 5 inhibitor, its inhibitory effects on CYP450 enzymes in vitro and its clinical trials in combination with ritonavir or omeprazole were conducted.

METHODS

The inhibitory effect of tunodafil on seven major CYP450 enzymes in human liver microsomes was investigated by probe substrate method. The effect of tunodafil on the pharmacokinetics of omeprazole (CYP2C19 substrate) in 40 healthy subjects, who received a single dose of 40 mg omeprazole in combination with tunodafil on the day 8 after taking 100 mg tunodafil daily for 7 days, was assessed based on CYP2C19 genotypes. The clinical DDI of ritonavir (potent CYP3A4 inhibitor) on tunodafil was studied in 28 healthy subjects who received a single dose of 50 mg tunodafil in combination with ritonavir on the day 6 after taking ritonavir twice a day for 5 days.

RESULTS

Tunodafil showed moderate inhibition on CYP2C19 and CYP3A4/5 in vitro. When co-administration omeprazole with tunodafil, the AUC of omeprazole in the Extensive, Intermediate and Poor Metabolizers increased by 26 %, 37 % and 21 %, respectively. After co-administration tunodafil with ritonavir, ritonavir increased the AUC and Cmax of tunodafil in human by about 78- fold and 13-fold respectively.

CONCLUSIONS

Tunodafil slightly increased omeprazole exposure in the Extensive and Intermediate Metabolizers of CYP2C19, but had no significant effect on omeprazole exposure in the Poor Metabolizers. Ritonavir could strongly inhibit the metabolism of tunodafil, and the combination of tunodafil with ritonavir should be prohibited.

Evaluation of Drug-Drug Interactions in Pharmacoepidemiologic Research

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

Alhagi maurorum extract in combination with lytic phage cocktails: a promising therapeutic approach against biofilms of multi-drug resistant P. mirabilis

Antimicrobial resistance (AMR) poses a significant global threat to public health systems, rendering antibiotics ineffective in treating infectious diseases. Combined use of bio compounds, including bacteriophages and plant extracts, is an attractive approach to controlling antibiotic resistance. In this study, the combination of phage cocktail (Isf-Pm1 and Isf-Pm2) and Alhagi maurorum crude extract (AME) was investigated in controlling biofilm-forming multi-drug resistant P. mirabilis isolates, in vitro and a phantom bladder model. The combination of AME and phage cocktails demonstrated no significant disparity in its ability to inhibit quorum sensing (QS) when compared to the individual control of AME alone. Following treatment with the combination of phage cocktail and AME at a 125 μg/mL concentration, the MDR P. mirabilis biofilm biomass was notably reduced by 73% compared to the control (P< 0.0001). The anti-biofilm effect was confirmed by Scanning Electron Microscopy (SEM). Moreover, in a bladder phantom model, there was a considerable decrease in encrustation levels compared to the control. The combined treatment resulted in a 1.85 logarithmic reduction in bacterial adhesion to Vero cells compared to the control. The real-time PCR results indicated significant downregulation of QS- and adhesion-related gens. The phage therapy, combined with AME, holds promising potential in reducing biofilm formation.

A Lipiodol Pickering Emulsion Stabilized by Iron-Doped Carbon Nanozymes for Liver Transarterial Chemoembolization

Transarterial chemoembolization (TACE) utilizing a water-in-oil lipiodol emulsion is a preferable therapeutic strategy for advanced liver cancer in clinical practice. However, the low stability of the lipiodol emulsion and poor efficacy of chemotherapeutic drug seriously undermine the efficiency of TACE. Herein, a novel lobaplatin-loaded lipiodol emulsion (denoted as ICN-LPE) is developed by constructing a lipiodol Pickering emulsion (LPE) stabilized with iron-doped carbon nanozymes (ICN) to mitigate the issue of lipiodol-water separation. This novel emulsion not only solves the instability of conventional lipiodol emulsions, but also facilitates the sustained release of lobaplatin. More importantly, upon entry into tumor cells, ICN catalyze the generation of reactive oxygen species via the Fenton-like reaction while simultaneously consuming intracellular glutathione, thereby inducing tumor cell death via chemodynamic therapy. By integrating chemotherapy and chemodynamic therapy, ICN-LPE demonstrates a synergistic antitumor effect and effectively inhibits tumor growth in a rabbit liver tumor model. Therefore, our ICN-LPE shows an appealing clinical application prospect for TACE.

Advancing analytical and graphical methods for binary and ternary mixtures: The toxic interactions of divalent metal ions in human lung cells

Humans are exposed to various environmental chemicals, particles, and pathogens that can cause adverse health outcomes. These exposures are rarely homogenous but rather complex mixtures in which the components may interact, such as through synergism or antagonism. Toxicologists have conducted preliminary investigations into binary mixtures of two components, but little work has been done to understand mixtures of three or more components. We investigated mixtures of divalent metal ions, quantifying the toxic interactions in a human lung model. Eight metals were chosen: heavy metals cadmium, copper, lead, and tin, as well as transition metals iron, manganese, nickel, and zinc. Human alveolar epithelial cells (A549) were exposed to individual metals and sixteen binary and six ternary combinations. The dose-response was modeled using logistic regression in R to extract LC50 values. Among the individual metals, the highest and lowest toxicity were observed with copper at an LC50 of 102 μM and lead at an LC50 of 5639 μM, respectively. First and second-order interaction coefficients were obtained using machine learning-based linear regression in Python. The resulting second-degree polynomial model formed either a hyperbolic or elliptical conic section, and the positive quadrant was used to produce isobolograms and contour plots. The strongest synergism and antagonism were observed in cadmium-copper and iron-zinc, respectively. A three-way interaction term was added to produce full ternary isobologram surfaces, which, to our knowledge, are a significant first in the toxicology literature.

The Entourage Effect in Cannabis Medicinal Products: A Comprehensive Review

This study explores the complementary or synergistic effects of medicinal cannabis constituents, particularly terpenes, concerning their therapeutic potential, known as the entourage effect. A systematic review of the literature on cannabis "entourage effects" was conducted using the PRISMA model. Two research questions directed the review: (1) What are the physiological effects of terpenes and terpenoids found in cannabis? (2) What are the proven "entourage effects" of terpenes in cannabis? The initial approach involved an exploratory search in electronic databases using predefined keywords and Boolean phrases across PubMed/MEDLINE, Web of Science, and EBSCO databases using Medical Subject Headings (MeSH). Analysis of published studies shows no evidence of neuroprotective or anti-aggregatory effects of α-pinene and β-pinene against β-amyloid-mediated toxicity; however, modest lipid peroxidation inhibition by α-pinene, β pinene, and terpinolene may contribute to the multifaceted neuroprotection properties of these C. sativa L. prevalent monoterpenes and the triterpene friedelin. Myrcene demonstrated anti-inflammatory proprieties topically; however, in combination with CBD, it did not show significant additional differences. Exploratory evidence suggests various therapeutic benefits of terpenes, such as myrcene for relaxation; linalool as a sleep aid and to relieve exhaustion and mental stress; D-limonene as an analgesic; caryophyllene for cold tolerance and analgesia; valencene for cartilage protection; borneol for antinociceptive and anticonvulsant potential; and eucalyptol for muscle pain. While exploratory research suggests terpenes as influencers in the therapeutic benefits of cannabinoids, the potential for synergistic or additive enhancement of cannabinoid efficacy by terpenes remains unproven. Further clinical trials are needed to confirm any terpenes "entourage effects."

IL-17A activates JAK/STAT signaling to affect drug metabolizing enzymes and transporters in HepaRG cells

The founding family member, Interleukin (IL)-17A, is commonly known as IL-17 and has garnered increasingly attention for proinflammatory functions in autoimmune disorders. Although the effects of IL-17A on hepatic important drug-metabolizing enzymes and transporters (DMETs) expression still remain unclear, it is critical to ascertain owing to the well-established alterations of the drug disposition capacity of the liver occurring during immune imbalance. The present study was designed to explore the effects and mechanisms of IL-17A on DMETs mRNA and protein expression in HepaRG cells by real-time quantitative reverse transcription polymerase chain reaction and Western blot, respectively. It is discovered that IL-17A can inhibit most DMETs mRNA expression (drug-metabolizing enzymes of CYP1A2, CYP3A4, CYP2C9, CYP2C19, GSTA1 and UGT1A1 and transporters of NTCP, OCT1, OATP1B1, BCRP and MDR1) as well as the protein expression of CYP3A4 and CYP2C19, via the janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3) signaling pathway. Thus, abnormal regulation of DMETs in IL-17A-mediated immune disorders such as psoriasis may cause alterations in pharmacokinetic processes and may occasionally result in unexpected drug-drug interactions (DDIs) in clinical practice.

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

StructNet-DDI: Molecular Structure Characterization-Based ResNet for Prediction of Drug-Drug Interactions

This study introduces a deep learning framework based on SMILES representations of chemical structures to predict drug-drug interactions (DDIs). The model extracts Morgan fingerprints and key molecular descriptors, transforming them into raw graphical features for input into a modified ResNet18 architecture. The deep residual network, enhanced with regularization techniques, efficiently addresses training issues such as gradient vanishing and exploding, resulting in superior predictive performance. Experimental results show that StructNet-DDI achieved an AUC of 99.7%, an accuracy of 94.4%, and an AUPR of 99.9%, demonstrating the model's effectiveness and reliability. These findings highlight that StructNet-DDI can effectively extract crucial features from molecular structures, offering a simple yet robust tool for DDI prediction.

Drug-drug interactions of plant alkaloids derived from herbal medicines on the phase II UGT enzymes: an introductory review

Herbal medicines are widely used as alternative or complementary therapies to treat and prevent chronic diseases. However, these can lead to drug-drug interactions (DDIs) that affect the glucuronidation reaction of UDP glucuronosyltransferases (UGTs), which convert drugs into metabolites. Plant extracts derived from medicinal herbs contain a diverse array of compounds categorized into different functional groups. While numerous studies have examined the inhibition of UGT enzymes by various herbal compounds, it remains unclear which group of compounds exerts the most significant impact on DDIs in the glucuronidation reaction. Recently, alkaloids derived from medicinal herbs, including kratom (Mitragyna speciosa), have gained attention due to their diverse pharmacological properties. This review primarily focuses on the DDIs of plant alkaloids from medicinal herbs, including kratom on the phase II UGT enzymes. Kratom is a new emerging herbal product in Western countries that is often used to self-treat chronic pain, opioid withdrawal, or as a replacement for prescription and non-prescription opioids. Kratom is well-known for its psychoactive alkaloids, which have a variety of psychopharmacological effects. However, the metabolism mechanism of kratom alkaloids, particularly on the phase II pathway, is still poorly understood. Simultaneously using kratom or other herbal products containing alkaloids with prescribed medicines may have an impact on the drug metabolism involving the phase II UGT enzymes. To ensure the safety and efficacy of treatments, gaining a better understanding of the DDIs when using herbal products with conventional medicine is crucial.

Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

INTRODUCTION

Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

OBJECTIVE

This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

METHODS

The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression).

RESULTS

Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency's (EMA's) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs.

CONCLUSIONS

Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Risk of Fractures With Concomitant Use of Calcium Channel Blockers and Selective Serotonin Reuptake Inhibitors

BACKGROUND

Despite their frequent concurrent use, little is known about the concomitant use of calcium channel blockers (CCBs) and selective serotonin reuptake inhibitors (SSRIs) on fracture risk. We compared risk of fractures in patients concomitantly treated with CCBs and SSRIs versus CCB-only users. We compared risk of fractures among concomitant CCB-SSRI users initiating cytochrome P450 3A4 (CYP3A4)-inhibiting SSRIs versus non-CYP3A4 inhibiting SSRIs.

METHODS

This retrospective cohort study used IBM MarketScan commercial claims and Medicare Supplemental database (2007-2019). We included adults diagnosed with hypertension and depression, newly initiating SSRIs while being treated with CCBs (ie, concomitant CCB-SSRI users) and those who did not (ie, CCB-only users). Primary outcome was the first occurrence of any fracture. We used stabilized inverse probability of treatment weighting (sIPTW) based on propensity scores to balance baseline risk between groups. Cox proportional hazard regression modeling was used to compare fracture risk.

RESULTS

We identified 191 352 concomitant CCB-SSRI and 956 760 CCB-only users (mean age = 56 years, 50.1% males). After sIPTW, compared with CCB-only users, CCBs-SSRIs users had a higher risk of fractures (hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.22-1.66). No difference in the risk of fractures between concomitant users of CCB-CYP3A4-inhibiting SSRIs and those of CCB-non-CYP3A4 inhibiting SSRIs (HR: 1.10, 95% CI: 0.87-1.40) was observed.

CONCLUSION AND RELEVANCE

Short-term concomitant CCB-SSRI use was associated with increased fracture risk. Concomitant CCBs and CYP3A4-inhibiting SSRIs compared with CCBs and non-CYP3A4 inhibiting SSRIs use was not associated with increased risk.

Drug-Drug Interactions in Nosocomial Infections: An Updated Review for Clinicians

Prevention, assessment, and identification of drug-drug interactions (DDIs) represent a challenge for healthcare professionals, especially in nosocomial settings. This narrative review aims to provide a thorough assessment of the most clinically significant DDIs for antibiotics used in healthcare-associated infections. Complex poly-pharmaceutical regimens, targeting multiple pathogens or targeting one pathogen in the presence of another comorbidity, have an increased predisposition to result in life-threatening DDIs. Recognising, assessing, and limiting DDIs in nosocomial infections offers promising opportunities for improving health outcomes. The objective of this review is to provide clinicians with practical advice to prevent or mitigate DDIs, with the aim of increasing the safety and effectiveness of therapy. DDI management is of significant importance for individualising therapy according to the patient, disease status, and associated comorbidities.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Molecular Transformers: Adaptive Multitarget Ligands for Esterase-Induced Transition from Analgesics to Anesthetics

Multitarget strategies are essential in addressing complex diseases, yet developing multitarget-directed ligands (MTDLs) is particularly challenging when aiming to engage multiple therapeutic targets across different tissues. Here, we present a molecular transformer strategy, enhancing traditional MTDLs. By utilizing esterase-driven hydrolysis, this approach mimics the adaptive nature of transformers for enabling molecules to modify their pharmacological effects in response to the biological milieu. By virtual screening and biological evaluation, we identified KGP-25, a novel compound initially targeting the voltage-gated sodium channel 1.8 (Nav1.8) in the peripheral nervous system (PNS) for analgesia, and later the γ-aminobutyric acid subtype A receptor (GABAA) in the central nervous system (CNS) for general anesthesia. Our findings confirm KGP-25's dual efficacy in cellular and animal models, effectively reducing opioid-related side effects. This study validates the molecular transformer approach in drug design and highlights its potential to overcome the limitations of conventional MTDLs, paving new avenues in innovative therapeutic strategies.

Bicalutamide Enhances Conventional Chemotherapy in In Vitro and In Vivo Assays Using Human and Canine Inflammatory Mammary Cancer Cell Lines

Human inflammatory breast cancer (IBC) and canine inflammatory mammary cancer (IMC) are highly aggressive neoplastic diseases that share numerous characteristics. In IBC and IMC, chemotherapy produces a limited pathological response and anti-androgen therapies have been of interest for breast cancer treatment. Therefore, the aim was to evaluate the effect of a therapy based on bicalutamide, a non-steroidal anti-androgen, with doxorubicin and docetaxel chemotherapy on cell proliferation, migration, tumor growth, and steroid-hormone secretion. An IMC-TN cell line, IPC-366, and an IBC-TN cell line, SUM149, were used. In vitro assays revealed that SUM149 exhibited greater sensitivity, reducing cell viability and migration with all tested drugs. In contrast, IPC-366 exhibited only significant in vitro reductions with docetaxel as a single agent or in different combinations. Decreased estrogen levels reduced in vitro tumor growth in both IMC and IBC. Curiously, doxorubicin resulted in low efficacy, especially in IMC. In addition, all drugs reduced the tumor volume in IBC and IMC by increasing intratumoral testosterone (T) levels, which have been related with reduced tumor progression. In conclusion, the addition of bicalutamide to doxorubicin and docetaxel combinations may represent a potential treatment for IMC and IBC.

Understanding Artemisia cina Ethyl Acetate Extract's Anthelmintic Effect on Haemonchus contortus Eggs and L3 Larvae: The Synergism of Peruvin Binary Mixtures

Haemonchus contortus, a blood-feeding parasite in grazing sheep, causes economic losses. Drug resistance necessitates exploring plant-based anthelmintics like Artemisia cina (Asteraceae). The plant, particularly its ethyl acetate extract, shows anthelmintic activity against H. contortus. However, there is limited information on pharmacodynamic interactions in ethyl acetate compounds. The study aims to identify pharmacodynamic interactions in the ethyl acetate extract of A. cina with anthelmintic effects on H. contortus eggs and L3 larvae using binary mixtures. Bioactive compounds were isolated via chromatography and identified using spectroscopic techniques. Pharmacodynamic interactions were assessed through binary mixtures with a main compound. Four bioactive compounds were identified: 1-nonacosanol, hentriacontane, peruvin, and cinic acid. Binary mixtures, with peruvin as the main compound, were performed. Peruvin/1-nonacosanol-hentriacontane and peruvin/cinic acid mixtures demonstrated 1.42-fold and 4.87-fold increased lethal effects in H. contortus L3 infective larvae, respectively, at a 0.50LC25/0.50LC25 concentration. In this work, we determined the synergism between bioactive compounds isolated from the ethyl acetate extract of A. cina and identified unreported compounds for the specie.

Antiviral Use in Mild-to-Moderate SARS-CoV-2 Infections during the Omicron Wave in Geriatric Patients

(1) Background: Geriatric patients are at high risk of complications of Coronavirus disease-2019 (COVID-19) and are good candidates for antiviral drugs. (2) Methods: A retrospective study of electronic health records (EHRs) aiming to describe antiviral (nirmatrelvir and ritonavir (nirmatrelvir/r) or remdesivir) use, drug-drug interactions (DDIs) and adverse drug reactions (ADRs) in elderly patients (75 and over), hospitalized with mild-to-moderate COVID-19 between July 2022 and June 2023. (3) Results: Out of 491 patients (mean age: 86.9 years), 180 (36.7%) received nirmatrelvir/r, 78 (15.9%) received remdesivir, and 233 (47.4%) received no antiviral therapy. No association was found between the choice of antiviral and the demographic or medical data. No serious ADR was observed. Nirmatrelvir/r dosage adjustment was inadequate in 65% of patients with renal impairment. In total, 128 patients (71%) on nirmatrelvir/r had potential pharmacokinetic DDIs, with 43 resulting in a possibly related ADR. In the remdesivir group, pharmacodynamic DDIs were more frequent, with QTc prolongation risk in 56 patients (72%). Only 20 patients underwent follow-up ECG, revealing QTc prolongation in 4. (4) Conclusions: There is an underutilization of antivirals despite their justified indications. Nirmatrelvir/r dosage was rarely adjusted to renal function. Dose adjustments and closer monitoring are needed due to the high risk of drug interactions.

Chronic Lymphocytic Leukemia: Management of Adverse Events in the Era of Targeted Agents

The treatment landscape for CLL has undergone a profound transformation with the advent of targeted agents (TAs) like Bruton's Tyrosine Kinase inhibitors (BTKis) and BCL-2 inhibitors (BCL-2is). These agents target crucial cellular pathways in CLL, offering superior efficacy over traditional chemo-immunotherapy, which has led to improved progression-free and overall survival rates. This advancement promises enhanced disease control and potentially normal life expectancy for many patients. However, the journey is not without challenges, as these TAs are associated with a range of adverse events (AEs) that can impact treatment efficacy and patient quality of life. This review focuses on detailing the various AEs related to TA management in CLL, evaluating their frequency and clinical impact. The aim is to present a comprehensive guide to the effective management of these AEs, ensuring optimal tolerability and efficacy of TAs. By reviewing the existing literature and consolidating findings, we provide insights into AE management, which is crucial for maximizing patient outcomes in CLL therapy.

Synergistic Effect between Eugenol and 1,8-Cineole on Anesthesia in Guppy Fish (Poecilia reticulata)

This study aimed to evaluate the synergistic effect between eugenol and 1,8-cineole on anesthesia in female guppy fish (Poecilia reticulata). Experiment I evaluated the concentrations of 0, 12.5, 25, 50, and 75 mg/L of eugenol and 0, 100, 200, 300, and 400 mg/L of 1,8-cineole for times of induction and recovery from anesthesia. Experiment II divided fish into 16 study groups, combining eugenol and 1,8-cineole in pairs at varying concentrations, based on the dosage of the chemicals in experiment I. The results of the anesthesia showed that eugenol induced fish anesthesia at concentrations of 50 and 70 mg/L, with durations of 256.5 and 171.5 s, respectively. In contrast, 1,8-cineole did not induce fish anesthesia. In combination, using eugenol at 12.5 mg/L along with 1,8-cineole at 400 mg/L resulted in fish anesthesia at a time of 224.5 s. Increasing the eugenol concentration to 25 mg/L, combined with 1,8-cineole at 300 and 400 mg/L, induced fish anesthesia at times of 259.0 and 230.5 s, respectively. For treatments with eugenol at 50 mg/L combined with 1,8-cineole at 100 to 400 mg/L, fish exhibited anesthesia at times of 189.5, 181.5, 166.0, and 157.5 s. In the case of eugenol at 75 mg/L, fish showed anesthesia at times of 175.5, 156.5, 140.5, and 121.5 s, respectively. The testing results revealed that 1,8-cineole as a single treatment could not induce fish anesthesia. However, when supplementing 1,8-cineole in formulations containing eugenol, fish exhibited a significantly faster induction of anesthesia (p < 0.05). Furthermore, all fish that underwent anesthesia were able to fully recover without any mortality. However, the shorter anesthesia duration resulted in a significantly prolonged recovery time. In conclusion, eugenol and 1,8-cineole work better together as anesthetics than when used separately, and demonstrated the safety of using these anesthetic agents on guppy fish.

Unraveling resistance mechanisms in combination therapy: A comprehensive review of recent advances and future directions

Antimicrobial resistance is a global health threat. Misuse and overuse of antimicrobials are the main drivers in developing drug-resistant bacteria. The emergence of the rapid global spread of multi-resistant bacteria requires urgent multisectoral action to generate novel treatment alternatives. Combination therapy offers the potential to exploit synergistic effects for enhanced antibacterial efficacy of drugs. Understanding the complex dynamics and kinetics of drug interactions in combination therapy is crucial. Therefore, this review outlines the current advances in antibiotic resistance's evolutionary and genetic dynamics in combination therapies-exposed bacteria. Moreover, we also discussed four pivotal future research areas to comprehend better the development of antibiotic resistance in bacteria treated with combination strategies.

Emerging therapeutic drug monitoring technologies: considerations and opportunities in precision medicine

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary inter-individually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context.

Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism

Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.

Assessment of potential drug-drug interactions in hospitalized cancer patients

INTRODUCTION

Drug-drug interactions (DDIs) pose a significant threat to patients with cancer, resulting in several adverse events in an oncology setting. Our study aims to identify potential DDIs in inpatient oncology wards, assess their severity, and provide recommendations to avoid these interactions.

MATERIALS AND METHODS

This prospective study was conducted in 79 hospitalized cancer patients over a period of 9 months (from August 2021 to May 2022) at the Amrita Institute of Medical Sciences, Kochi receiving at least two oncological or non-oncological drugs for 5 days.

RESULTS

Significant differences were found in drug count (61.6% vs. 38.4%), hospitalization duration (63.1% vs. 36.9%), and medications for comorbidities (63% vs. 37%) between patients with and without DDIs (p < 0.001, <0.001, and 0.01, respectively). The study identified 321 DDIs, with 14 (4.4%) X interactions, 93 (30%) D interactions, 161 (50%) C interactions, and 53 (15.6%) B interactions. Severity-wise, 76 (23.7%) were major, 190 (59.1%) were moderate, and 55 (17.2%) were minor.

CONCLUSION

Our study showed that drug count, medications for comorbidities, and hospitalization duration significantly increase the risk of DDIs in hospitalized oncology patients. Around 96.4% of recommendations for potential interactions were accepted and implemented, highlighting the huge opportunities and requirements for improvement, implementation, and management of drug interactions in oncology settings.

Discriminative-stimulus effects of cannabidiol oil in Sprague-Dawley rats

Cannabidiol (CBD) is one of the major centrally active phytocannabinoid components of cannabis, and has been approved by the FDA only for the treatment of seizures associated with three rare disorders. It has also been touted as a potential treatment for anxiety in place of more traditional treatments like benzodiazepines. Although there is some evidence of anxiolytic effects of CBD, its suitability as a substitute for benzodiazepines is unknown. This experiment was designed to assess the extent to which CBD shares interoceptive discriminative-stimulus properties with the anxiolytic drug chlordiazepoxide (CDP), a benzodiazepine. In the present experiment, a range of doses (0-1569 mg/kg) of over-the-counter CBD oil was administered (i.g.) in male Sprague-Dawley rats trained to discriminate 5.6 mg/kg CDP from saline. Due to the long time-course effects of CBD, generalization tests were conducted at 90 and 120 min post-CBD administration. The two highest doses of CBD tested (1064 and 1569 mg/kg) were found to partially substitute for 5.6 mg/kg CDP, with mean percent responding on the CDP-associated lever reaching above 20% at time 2 (120 min post-CBD administration), suggesting that high doses of the over-the-counter CBD oils used in this experiment share interoceptive discriminative-stimulus properties to some degree with CDP. These results are novel in comparison to existing research into stimulus effects of CBD, in which substitution for benzodiazepines has not previously been observed.

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network

Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI - a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Multifunctional nanoparticle-mediated combining therapy for human diseases

Combining existing drug therapy is essential in developing new therapeutic agents in disease prevention and treatment. In preclinical investigations, combined effect of certain known drugs has been well established in treating extensive human diseases. Attributed to synergistic effects by targeting various disease pathways and advantages, such as reduced administration dose, decreased toxicity, and alleviated drug resistance, combinatorial treatment is now being pursued by delivering therapeutic agents to combat major clinical illnesses, such as cancer, atherosclerosis, pulmonary hypertension, myocarditis, rheumatoid arthritis, inflammatory bowel disease, metabolic disorders and neurodegenerative diseases. Combinatorial therapy involves combining or co-delivering two or more drugs for treating a specific disease. Nanoparticle (NP)-mediated drug delivery systems, i.e., liposomal NPs, polymeric NPs and nanocrystals, are of great interest in combinatorial therapy for a wide range of disorders due to targeted drug delivery, extended drug release, and higher drug stability to avoid rapid clearance at infected areas. This review summarizes various targets of diseases, preclinical or clinically approved drug combinations and the development of multifunctional NPs for combining therapy and emphasizes combinatorial therapeutic strategies based on drug delivery for treating severe clinical diseases. Ultimately, we discuss the challenging of developing NP-codelivery and translation and provide potential approaches to address the limitations. This review offers a comprehensive overview for recent cutting-edge and challenging in developing NP-mediated combination therapy for human diseases.

Drug-drug interactions involving classic psychedelics: A systematic review

Classic psychedelics, including lysergic acid diethylamide (LSD), psilocybin, mescaline, N,N-dimethyltryptamine (DMT) and 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), are potent psychoactive substances that have been studied for their physiological and psychological effects. However, our understanding of the potential interactions and outcomes when using these substances in combination with other drugs is limited. This systematic review aims to provide a comprehensive overview of the current research on drug-drug interactions between classic psychedelics and other drugs in humans. We conducted a thorough literature search using multiple databases, including PubMed, PsycINFO, Web of Science and other sources to supplement our search for relevant studies. A total of 7102 records were screened, and studies involving human data describing potential interactions (as well as the lack thereof) between classic psychedelics and other drugs were included. In total, we identified 52 studies from 36 reports published before September 2, 2023, encompassing 32 studies on LSD, 10 on psilocybin, 4 on mescaline, 3 on DMT, 2 on 5-MeO-DMT and 1 on ayahuasca. These studies provide insights into the interactions between classic psychedelics and a range of drugs, including antidepressants, antipsychotics, anxiolytics, mood stabilisers, recreational drugs and others. The findings revealed various effects when psychedelics were combined with other drugs, including both attenuated and potentiated effects, as well as instances where no changes were observed. Except for a few case reports, no serious adverse drug events were described in the included studies. An in-depth discussion of the results is presented, along with an exploration of the potential molecular pathways that underlie the observed effects.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

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.

The dual-targeting mechanism of an anti-inflammatory diarylheptanoid from Curcuma zedoaria (Christm.) Roscoe with the capacity for β2-adrenoreceptor agonism and NLRP3 inhibition

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease characterized by symptoms of shortness of breath and chronic inflammation. Curcuma zedoaria (Christm.) Roscoe is a well-documented traditional medical herb that is frequently used in the treatment of COPD. Previously, we identified a diarylheptanoid compound (1-(4-hydroxy-5-methoxyphenyl)-7-(4,5-dihydroxyphenyl)-3,5-dihydroxyheptane; abbreviated as HMDD) from this herb that exhibited potent agonistic activity on β2-adrenergic receptors (β2 adrenoreceptor) that are present on airway smooth muscle cells. In this work, we used chemically synthesized HMDD compound, and confirmed its bioactivity on β2 adrenoreceptors. Then by a proteomics study and anti-inflammatory evaluation detections, we found that HMDD downregulated the nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) signaling pathway and suppressed the NLRP3 receptor expression in RAW264.7 macrophages and in a COPD model in A549 lung carcinoma cells. HMDD also decreased nitric oxide production levels, and impacted other interleukins and the phosphorylation of NF-κB and ERK pathways. We performed molecular docking of HMDD on β2 adrenoreceptor and NLRP3 protein models. This work reports the anti-inflammatory effects of HMDD and suggests a dual-targeting mechanism of β2-adrenoreceptor agonism and NLRP3 inhibition. Such a mechanism scientifically supports the clinical uses of Curcuma zedoaria (Christm.) Roscoe in treating COPD, as it can simultaneously relieve persistent breathlessness and inflammation. HMDD can be considered as a potential non-steroidal anti-inflammatory drug in novel therapy design for the treatment of COPD and other inflammatory diseases.

Drug-drug interactions between COVID-19 therapeutics and psychotropic medications

INTRODUCTION

The coronavirus (COVID-19) pandemic has led to as well as exacerbated mental health disorders, leading to increased use of psychotropic medications. Co-administration of COVID-19 and psychotropic medications may result in drug-drug interactions (DDIs), that may compromise both the safety and efficacy of both medications.

AREAS COVERED

This review provides an update of the current evidence on DDIs between COVID-19 and psychotropic medications. The interactions are categorized into pharmacokinetic, pharmacodynamic, and other relevant types. A thorough literature search was conducted using electronic databases to identify relevant studies, and extract data to highlight potential DDIs, clinical implications, and management strategies.

EXPERT OPINION

Understanding and managing potential DDIs between COVID-19 and psychotropic medications is paramount to ensuring safe and effective treatment of patients with COVID-19 and mental illness. Awareness of the diverse spectrum of DDIs, vigilant monitoring, and judicious dose modifications, while choosing pharmacotherapeutic options with low risk of interaction whenever possible, are necessary. Ongoing and future investigations should continue to review the dynamic landscape of COVID-19 therapeutic modalities and their interactions with psychotropic medications.

Anti-viral effect of usenamine a using SARS-CoV-2 pseudo-typed viruses

The escalating pandemic brought about by the novel SARS-CoV-2 virus is threatening global health, and thus, it is necessary to develop effective antiviral drugs. Usenamine A is a dibenzo-furan derivative separated from lichen Usnea diffracta showing broad-spectrum activity against different viruses. We evaluate that usenamine A has antiviral effects against novel SARS-CoV-2 Delta variant pseudotyped viruses (PVs) in A549 cells. In addition, usenamine A significantly suppresses SARS-CoV-2 PV-induced mitochondrial depolarization, elevated reactive oxygen species (ROS) levels, apoptosis, and inflammation. Usenamine A also causes the SARS-CoV-2 spike protein to become less stable. Thus, usenamine A shows potential as an antiviral drug that can provide protection against COVID-19.

Hydrocodone, Oxycodone, and Morphine Metabolism and Drug-Drug Interactions

Awareness of drug interactions involving opioids is critical for patient treatment as they are common therapeutics used in numerous care settings, including both chronic and disease-related pain. Not only do opioids have narrow therapeutic indexes and are extensively used, but they have the potential to cause severe toxicity. Opioids are the classical pain treatment for patients who suffer from moderate to severe pain. More importantly, opioids are often prescribed in combination with multiple other drugs, especially in patient populations who typically are prescribed a large drug regimen. This review focuses on the current knowledge of common opioid drug-drug interactions (DDIs), focusing specifically on hydrocodone, oxycodone, and morphine DDIs. The DDIs covered in this review include pharmacokinetic DDI arising from enzyme inhibition or induction, primarily due to inhibition of cytochrome p450 enzymes (CYPs). However, opioids such as morphine are metabolized by uridine-5'-diphosphoglucuronosyltransferases (UGTs), principally UGT2B7, and glucuronidation is another important pathway for opioid-drug interactions. This review also covers several pharmacodynamic DDI studies as well as the basics of CYP and UGT metabolism, including detailed opioid metabolism and the potential involvement of metabolizing enzyme gene variation in DDI. Based upon the current literature, further studies are needed to fully investigate and describe the DDI potential with opioids in pain and related disease settings to improve clinical outcomes for patients. SIGNIFICANCE STATEMENT: A review of the literature focusing on drug-drug interactions involving opioids is important because they can be toxic and potentially lethal, occurring through pharmacodynamic interactions as well as pharmacokinetic interactions occurring through inhibition or induction of drug metabolism.

Revealing the dynamic landscape of drug-drug interactions through network analysis

Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60 years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape. Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time. Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.

An update on drug-drug interactions for care of the acutely ill in the era of COVID-19

PURPOSE

To provide key pharmacological concepts underlying drug-drug interactions (DDIs), a decision-making framework, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19.

SUMMARY

DDIs are frequently encountered in the acutely ill. The implications of DDIs include either increased risk of drug toxicity or decreased effectiveness, which may have severe consequences in the acutely ill due to lower physiological and neurocognitive reserves in these patients. In addition, an array of additional therapies and drug classes have been used for COVID-19 that were not typically used in the acute care setting. In this update on DDIs in the acutely ill, we provide key pharmacological concepts underlying DDIs, including a discussion of the gastric environment, the cytochrome P-450 (CYP) isozyme system, transporters, and pharmacodynamics in relation to DDIs. We also provide a decision-making framework that elucidates the identification of DDIs, risk assessment, selection of alternative therapies, and monitoring. Finally, important DDIs pertaining to contemporary acute care clinical practice related to COVID-19 are discussed.

CONCLUSION

Interpreting and managing DDIs should follow a pharmacologically based approach and a systematic decision-making process to optimize patient outcomes.

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Investigating Doxorubicin's mechanism of action in cervical cancer: a convergence of transcriptomic and metabolomic perspectives

Introduction: Cervical cancer remains a significant global health burden, and Doxorubicin is a crucial therapeutic agent against this disease. However, the precise molecular mechanisms responsible for its therapeutic effects are not fully understood. Methods: In this study, we employed a multi-omics approach that combined transcriptomic and metabolomic analyses with cellular and in vivo experiments. The goal was to comprehensively investigate the molecular landscape associated with Doxorubicin treatment in cervical cancer. Results: Our unbiased differential gene expression analysis revealed distinct alterations in gene expression patterns following Doxorubicin treatment. Notably, the ANKRD18B gene exhibited a prominent role in the response to Doxorubicin. Simultaneously, our metabolomic analysis demonstrated significant perturbations in metabolite profiles, with a particular focus on L-Ornithine. The correlation between ANKRD18B gene expression and L-Ornithine levels indicated a tightly controlled gene-metabolite network. These results were further confirmed through rigorous cellular and in vivo experiments, which showed reductions in subcutaneous tumor size and significant changes in ANKRD18B, L-Ornithine, and Doxorubicin concentration. Discussion: The findings of this study underscore the intricate interplay between transcriptomic and metabolomic changes in response to Doxorubicin treatment. These insights could have implications for the development of more effective therapeutic strategies for cervical cancer. The identification of ANKRD18B and L-Ornithine as key components in this process lays the groundwork for future research aiming to unravel the complex molecular networks that underlie Doxorubicin's therapeutic mechanism. While this study provides a solid foundation, it also highlights the necessity for further investigation to fully grasp these interactions and their potential implications for cervical cancer treatment.

Decoding the Postulated Entourage Effect of Medicinal Cannabis: What It Is and What It Isn't

The 'entourage effect' term was originally coined in a pre-clinical study observing endogenous bio-inactive metabolites potentiating the activity of a bioactive endocannabinoid. As a hypothetical afterthought, this was proposed to hold general relevance to the usage of products based on Cannabis sativa L. The term was later juxtaposed to polypharmacy pertaining to full-spectrum medicinal Cannabis products exerting an overall higher effect than the single compounds. Since the emergence of the term, a discussion of its pharmacological foundation and relevance has been ongoing. Advocates suggest that the 'entourage effect' is the reason many patients experience an overall better effect from full-spectrum products. Critics state that the term is unfounded and used primarily for marketing purposes in the Cannabis industry. This scoping review aims to segregate the primary research claiming as well as disputing the existence of the 'entourage effect' from a pharmacological perspective. The literature on this topic is in its infancy. Existing pre-clinical and clinical studies are in general based on simplistic methodologies and show contradictory findings, with the clinical data mostly relying on anecdotal and real-world evidence. We propose that the 'entourage effect' is explained by traditional pharmacological terms pertaining to other plant-based medicinal products and polypharmacy in general (e.g., synergistic interactions and bioenhancement).

Antibiotics and Lipid-Modifying Agents: Potential Drug-Drug Interactions and Their Clinical Implications

Evidence-based prescribing requires taking into consideration the many aspects of optimal drug administration (e.g., dosage, comorbidities, co-administered drugs, etc.). A key issue is the administration of drugs for acute disorders that may potentially interfere with previously prescribed long-term medications. Initiating an antibiotic for an acute bacterial infection constitutes a common example. Hence, appropriate knowledge and awareness of the potential DDIs of antibiotics would lead to proper adjustments, thus preventing over- or under-treatment. For example, some statins, which are the most prescribed lipid-modifying agent (LMA), can lead to clinically important drug-drug interactions (DDIs) with the concurrent administration of antibiotics, e.g., macrolides. This review discusses the clinically significant DDIs of antibiotics associated with co-administrated lipid-lowering therapy and highlights common cases where regimen modifications may or may not be necessary.

Jasminoidin and ursodeoxycholic acid exert synergistic effect against cerebral ischemia-reperfusion injury via Dectin-1-induced NF-κB activation pathway

BACKGROUND

Jasminoidin (JA) and ursodeoxycholic acid (UA) were shown to act synergistically against ischemic stroke (IS) in our previous studies.

PURPOSE

To investigate the holistic synergistic mechanism of JA and UA on cerebral ischemia.

METHODS

Middle cerebral artery obstruction reperfusion (MCAO/R) mice were used to evaluate the efficacy of JA, UA, and JA combined with UA (JU) using neurological function testing and infarct volume examination. High-throughput RNA-seq combined with computational prediction and function-integrated analysis was conducted to gain insight into the comprehensive mechanism of synergy. The core mechanism was validated using western blotting.

RESULTS

JA and UA synergistically reduced cerebral infarct volume and alleviated neurological deficits and pathological changes in MCAO/R mice. A total of 1437, 396, 1080, and 987 differentially expressed genes were identified in the vehicle, JA, UA, and JU groups, respectively. A strong synergistic effect between JA and UA was predicted using chemical similarity analysis, target profile comparison, and semantic similarity analysis. As the 'long-tail' drugs, the top 20 gene ontology (GO) biological processes of JA, UA, and JU groups primarily reflected inflammatory response and regulation of cytokine production, with specific GO terms of JU revealing enhanced regulation on immune response and tumor necrosis factor superfamily cytokine production. Comparably, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling of common targets of JA, UA, and JU focused on extracellular matrix organization and signaling by interleukins, immune system, phagosomes, and lysosomes, which interlock and interweave to produce the synergistic effects of JU. The characteristic signaling pathway identified for JU highlighted the crosstalk between autophagy activation and inflammatory pathways, especially the Dectin-1-induced NF-κB activation pathway, which was validated by in vivo experiments.

CONCLUSIONS

JA and UA can synergistically protect cerebral ischemia-reperfusion injury by attenuating Dectin-1-induced NF-κB activation. The strategy integrating high throughput data with computational models enables ever-finer mapping of 'long-tail' drugs to dynamic variations in condition-specific omics to clarify synergistic mechanisms.

Effects of Natural Product-Derived Compounds on Inflammatory Pain via Regulation of Microglial Activation

Inflammatory pain is a type of pain caused by tissue damage associated with inflammation and is characterized by hypersensitivity to pain and neuroinflammation in the spinal cord. Neuroinflammation is significantly increased by various neurotransmitters and cytokines that are expressed in activated primary afferent neurons, and it plays a pivotal role in the development of inflammatory pain. The activation of microglia and elevated levels of pro-inflammatory cytokines are the hallmark features of neuroinflammation. During the development of neuroinflammation, various intracellular signaling pathways are activated or inhibited in microglia, leading to the regulation of inflammatory proteins and cytokines. Numerous attempts have been conducted to alleviate inflammatory pain by inhibiting microglial activation. Natural products and their compounds have gained attention as potential candidates for suppressing inflammatory pain due to verified safety through centuries of use. Many studies have also shown that natural product-derived compounds have the potential to suppress microglial activation and alleviate inflammatory pain. Herein, we review the literature on inflammatory mediators and intracellular signaling involved in microglial activation in inflammatory pain, as well as natural product-derived compounds that have been found to suppress microglial activation. This review suggests that natural product-derived compounds have the potential to alleviate inflammatory pain through the suppression of microglial activation.

Drug interference with biochemical laboratory tests

Clinical laboratory practice represents an essential part of clinical decision-making, as it influences 60-70% of medical decisions at all levels of health care. Results of biochemical laboratory tests (BLTs) have a key role in establishment of adequate diagnosis as well as in evaluation of treatment progress and outcome. The prevalence of drug-laboratory test interactions (DLTIs) is up to 43% of patients who had laboratory results influenced by drugs. Unrecognized DLTIs may lead to misinterpreted BLTs results, incorrect or delayed diagnosis, extra costs for unnecessary additional tests or inadequate therapy, as all may cause false clinical decisions. The significance of timely and adequate recognition of DLTIs is to prevent common clinical consequences such as incorrectly interpreted test results, delayed or non-treated condition due to erroneous diagnosis or unnecessary extra tests or therapy. Medical professionals should be educated that it is essential to obtain patient data about medications especially for the drugs used in the last 10 days before biological material collection. Our mini-review aims to provide a comprehensive overview of the current state in this important domain of medical biochemistry with detailed analysis of the effect of drugs on BLTs and to give detailed information to medical specialists.

Assessment of community pharmacists' knowledge about drug-drug interactions in Jeddah, Saudi Arabia

Background: Drug-drug interactions (DDIs) have the potential to result in severe adverse drug events and profoundly affect patient outcomes. The pivotal role community pharmacists assume in recognizing and effectively managing these interactions necessitates a comprehensive understanding and heightened awareness of their implications. Such knowledge and awareness among community pharmacists are fundamental for ensuring the delivery of safe and efficacious care to patients. Aim: This study aimed to assess the knowledge of community pharmacists in Jeddah, Saudi Arabia, regarding drug-drug interactions (DDIs). Method: A cross-sectional survey was administered to a cohort of 147 community pharmacists through the utilization of a self-administered questionnaire. The questionnaire encompassed a comprehensive range of 30 multiple-choice questions, encompassing various facets pertaining to drug-drug interactions (DDIs). Results: A total of 147 community pharmacists working in Jeddah City, Saudi Arabia, completed the survey. The majority of them were male (89.1%, n = 131), and had bachelor's degrees in pharmacy. Results showed that the lowest correct response of DDIs was between Theophylline/Omeprazole, while the highest was between amoxicillin and acetaminophen. Results revealed that among the 28 drug pairs, only six pairs were determined correctly by most participants. The study found that majority of the studied community pharmacist could not determine the correct answer on drug-drug interaction knowledge, as also seen with the measured below half mean DDIs knowledge of 38.22 ± 22.0 (min = 0, max = 89.29, median = 35.71). Conclusion: The study highlights the need for ongoing training and education programs for community pharmacists in Saudi Arabia to enhance their knowledge and understanding of DDIs, ultimately leading to improved patient care and safety.