Assessment Of Drug Interactions Research Articles

Assessment of CYP-Mediated Drug Interactions for Enasidenib Based on a Cocktail Study in Patients with Relapse or Refractory Acute Myeloid Leukemia or Myelodysplastic Syndrome.

As a selective and potent inhibitor targeting the isocitrate dehydrogenase-2 (IDH2) mutant protein, enasidenib obtained approval from the US Food and Drug Administration (FDA) in 2017 for adult patients with acute myeloid leukemia (AML) with an IDH2 mutation. In vitro investigations demonstrated that enasidenib affects various drug metabolic enzymes and transporters. This current investigation aimed to assess enasidenib on the pharmacokinetics (PKs) of CYP substrates, including dextromethorphan (CYP2D6 probe drug), flurbiprofen (CYP2C9 probe drug), midazolam (CYP3A4 probe drug), omeprazole (CYP2C19 probe drug), and pioglitazone (CYP2C8 probe drug), in patients with AML or myelodysplastic syndrome. Results showed that following the co-administration of enasidenib (100 mg, once daily) for 28 days, the PK parameters AUC(0-∞) and Cmax of dextromethorphan increased by 1.37 (90% confidence interval (CI): 0.96, 1.96) and 1.24 (90% CI: 0.94, 1.65)-fold, respectively, compared to dextromethorphan alone. For flurbiprofen, these parameters increased by 1.14 (90%CI: 1.01, 1.29) and 0.97 (90% CI 0.86, 1.08)-fold, respectively, when compared to flurbiprofen alone. Conversely, midazolam exhibited decreases to 0.57 (90% CI 0.34, 0.97) and 0.77 (90% CI 0.39, 1.53)-fold, respectively, in comparison to midazolam alone. The parameters for omeprazole increased by 1.86 (90% CI: 1.33, 2.60) and 1.47 (0.93, 2.31)-fold, respectively, compared to omeprazole alone, while those for pioglitazone decreased to 0.80 (90% CI: 0.62, 1.03) and 0.87 (90% CI: 0.65, 1.16)-fold, respectively, in comparison to pioglitazone alone. These findings provide valuable insights into dose recommendations concerning drugs acting as substrates of CYP2D6, CYP2C9, CYP3A4, CYP2C19, and CYP2C8 when administered concurrently with enasidenib.

A Prospective Study of Disease Patterns and Assessment of Drug Interactions with Prasugrel in Tertiary Care Hospital Patients

This prospective observational study evaluates the prescribing patterns and drug-drug interactions of Prasugrel in hospitalized patients at a tertiary care hospital (BBH). Conducted over six months, it involved 110 inpatients, gathering data from various sources including patient profiles, medicine charts, and lab tests. The study primarily focused on Prasugrel usage, identifying patient demographics and assessing the interactions of Prasugrel with other medications. It found that 64.02% of the patients on Prasugrel therapy were male and 36.08% female, predominantly aged between 55 to 75 years. These patients often had a history of cardiovascular and infectious diseases. The study revealed that 31.36% of patients experienced interactions with Prasugrel, with a higher incidence in males. The most common interactions were with Pantoprazole, Omeprazole, and Aspirin. These interactions were classified as severe (11.81%), moderate (55.05%), and mild (34.13%). Additionally, 17.91% of interactions were defined, 58.45% probable, and 22.62% possible. Prasugrel was mainly prescribed for heart disease, myocardial infarction, and angina. The study suggests cautious use of Pantoprazole with Prasugrel due to potential significant interactions.

Drug-induced cognitive impairment and dementia

One of the reasons for the development or worsening of cognitive impairment (CI) may be the use of a number of drugs: non-steroidal anti-inflammatory drugs, antiarrhythmics, antidepressants, glucocorticosteroids, antitumor drugs and a number of others. The negative effect of drugs on cognitive functions is realized due to many pathophysiological mechanisms: disruption of hormonal regulation, decreased neuronal excitability, increased activity of gamma-aminobutyric acid receptors, decreased cerebral circulation, atrophic changes in the brain; many mechanisms have not been fully established. Risk factors for the development of drug-induced CIs are: old age or childhood, brain damage, chronic diseases, genetic factors, the patient's initial CI, polypharmacy, dose and duration of drug use, acute infectious diseases, metabolic disorders, dehydration, acute urinary retention, etc. To diagnose and differentially diagnose drug-induced CI, it is necessary to establish a connection between the start of taking a suspected drug-inducer and a decrease in cognitive functions. The first step in the treatment of drug-induced CI is the abolition of an inducer drug or a reduction in its dose, in cases where it is impossible to discontinue the drug and there is no replacement, special slow-release dosage forms can be considered. The main measures to prevent drug-induced CI include the use of drugs with the lowest risk of their development, assessment of drug interactions, and the use of modern scales to assess the risk of developing this side-effect (anticholinergic burden scale, etc.).

Vascularized tissue on mesh-assisted platform (VT-MAP): a novel approach for diverse organoid size culture and tailored cancer drug response analysis.

This study presents the vascularized tissue on mesh-assisted platform (VT-MAP), a novel microfluidic in vitro model that uses an open microfluidic principle for cultivating vascularized organoids. Addressing the gap in 3D high-throughput platforms for drug response analysis, the VT-MAP can host tumor clusters of various sizes, allowing for precise, size-dependent drug interaction assessments. Key features include capability for forming versatile co-culture conditions (EC, fibroblasts and colon cancer organoids) that enhance tumor organoid viability and a perfusable vessel network that ensures efficient drug delivery and maintenance of organoid health. The VT-MAP enables the culture and analysis of organoids across a diverse size spectrum, from tens of microns to several millimeters. The VT-MAP addresses the inconsistencies in traditional organoid testing related to organoid size, which significantly impacts drug response and viability. Its ability to handle various organoid sizes leads to results that more accurately reflect patient-derived xenograft (PDX) models and differ markedly from traditional in vitro well plate-based methods. We introduce a novel image analysis algorithm that allows for quantitative analysis of organoid size-dependent drug responses, marking a significant step forward in replicating PDX models. The PDX sample from a positive responder exhibited a significant reduction in cell viability across all organoid sizes when exposed to chemotherapeutic agents (5-FU, oxaliplatin, and irinotecan), as expected for cytotoxic drugs. In sharp contrast, PDX samples of a negative responder showed little to no change in viability in smaller clusters and only a slight reduction in larger clusters. This differential response, accurately replicated in the VT-MAP, underscores its ability to generate data that align with PDX models and in vivo findings. Its capacity to handle various organoid sizes leads to results that more accurately reflect PDX models and differ markedly from traditional in vitro methods. The platform's distinct advantage lies in demonstrating how organoid size can critically influence drug response, revealing insights into cancer biology previously unattainable with conventional techniques.

Assessment of CYP3A-mediated drug interaction via cytokine (IL-6) elevation for mosunetuzumab using physiologically-based pharmacokinetic modeling.

Mosunetuzumab is a CD3/CD20 bispecific antibody. As an on-target effect, transient elevation of interleukin-6 (IL-6) occurs in early treatment cycles. A physiologically-based pharmacokinetic (PBPK) model was developed to assess potential drug interaction caused by IL-6 enzyme suppression on cytochrome P450 3A (CYP3A) during mosunetuzumab treatment. The model's performance in predicting IL-6 CYP3A suppression and subsequent drug-drug interactions (DDIs) was verified using existing clinical data of DDIs caused by chronic and transient IL-6 elevation. Sensitivity analyses were performed for a complete DDI risk assessment. The IL-6 concentration- and time-dependent CYP3A suppression during mosunetuzumab treatment was simulated using PBPK model with incorporation of invitro IL-6 inhibition data. At clinically approved doses/regimens, the DDI at maximum CYP3A suppression was predicted to be a midazolam maximum drug concentration in plasma (Cmax ) and area under the plasma drug concentration-time curve (AUC) ratio of 1.17 and 1.37, respectively. At the 95th percentile of IL-6 concentration level or when gut CYP3A suppression was considered, the predicted DDI risk for mosunetuzumab remained low (<2-fold). The PBPK-based DDI predictions informed the mosunetuzumab product label to monitor, in early cycles, the concentrations and toxicities for sensitive CYP3A substrates with narrow therapeutic windows.

Adherence to epilepsy quality indicators in a tertiary referral center

BackgroundQuality indicators play an important role in healthcare quality and patient safety. The aim of this study is to identify specific clinical pharmacy interventions to improve adherence to quality indicators and minimize risks among patients with epilepsy. Material and methodsA prospective, two-phase, observational study was conducted in a neurology outpatient clinic of a tertiary university hospital. In the first phase of the study, the rate of adherence to the quality indicators was evaluated with a checklist containing the quality indicators. In the second phase of the study, an expert panel meeting was convened to identify clinical pharmacist interventions to reduce the risks associated with non-adherence. The Fine–Kinney method was used to prioritize risks, and adherence rates with each quality improvement indicator (QI) were calculated. ResultsThe study found that adherence rates were highest for QIs involving estimating the number and type of seizures, providing medical treatment or referring patients with evidence of mood disorders to mental healthcare, and co-managing prenatal care for women with epilepsy. The most non-adherence rates were found in QIs involving quality-of-life assessment, daily folate supplementation, and addressing the decreased effectiveness of oral contraception. The annual review of information about educational issues was also poorly provided. An expert panel decided to integrate a clinical pharmacist into the outpatient clinic to improve medication adherence, side-effect assessment, drug interaction assessment, patient education, lifestyle-modification education, depression/suicide-related behavior screening, quality-of-life assessment, and effectiveness evaluation of oral contraceptives for female patients using enzyme-inducing ASM. ConclusionThe study shows that medication adherence, assessment of side effects, drug interactions, and patient education are inadequately provided by neurologists in patients with epilepsy. Clinical pharmacists have a crucial role in reducing potential risks of non-adherence with quality indicators. By integrating clinical pharmacy services into routine epilepsy care processes, the quality of care can be improved. Future studies should focus on implementing these interventions and evaluating their impact on patient outcomes.

Systems Pharmacology and Network Analysis to Advance Pharmacogenomics and Precision Medicine Decisions in Type-2 Diabetes Therapy

Diabetes mellitus type-2 (DMT2) molecular pathophysiology is still challenging since the disease represents a complex, multifactorial metabolic disease caused by polygenic defects and environmental factors. In addition, the resulting secondary organ complications can be affected by various environmental and life-style factors over the years. The metabolic imbalance in DMT2 is manifested by the dysfunction of pancreatic β-cells in secreting insulin and the inability of other tissue cells to respond to insulin and utilize blood glucose. However, over recent years, through the advances in genomics and molecular analysis, several genes and microRNAs have been shown to be correlated as potential biomarkers with DMT2 prognosis, diagnosis, and therapy. Furthermore, drug therapy and clinical pharmacology have benefited from pharmacogenomics in a manner where the molecular knowledge can be translated into clinical information aiming to improve precision and personalized medicine therapeutic methodologies in healthcare. In this work, using systems pharmacology and network analysis approaches, we comprehensively assessed the molecular and genomics data associated with DMT2 to: (a) Better understand miRNA, gene, and drug associations; (b) Create connectivity and interaction maps of practical clinical utility; and (c) Facilitate the application of precision medicine therapeutic decisions in group and individual patients. Moreover, in order for the clinical pharmacology guidelines to be implemented in parallel with the generated molecular data, we also carried out an assessment of drug interactions in specific pharmacological classes that affect DMT2 pharmacotherapy outcomes. Overall, the proposed methodology and the results obtained: (a) Enrich our understanding of DMT2 molecular pathophysiology; (b) Unveil important biomarker and drug-gene pharmacogenomics associations; (c) Help the use of personalized therapy options; and (d) Allow precision medicine concepts to be broadly exploited in new therapeutic developments and within the clinical setting.

Pyrido-Dibemequine Metabolites Exhibit Improved Druglike Features, Inhibit Hemozoin Formation in Plasmodium falciparum, and Synergize with Clinical Antimalarials.

Structural modification of existing chemical scaffolds to afford new molecules able to circumvent drug resistance constitutes one of the rational approaches to antimalarial drug discovery. Previously synthesized compounds based on the 4-aminoquinoline core hybridized with a chemosensitizing dibenzylmethylamine side group showed in vivo efficacy in Plasmodium berghei-infected mice despite low microsomal metabolic stability, suggesting a contribution from their pharmacologically active metabolites. Here, we report on a series of these dibemequine (DBQ) metabolites with low resistance indices against chloroquine-resistant parasites and improved metabolic stability in liver microsomes. The metabolites also exhibit improved pharmacological properties including lower lipophilicity, cytotoxicity, and hERG channel inhibition. Using cellular heme fractionation experiments, we also demonstrate that these derivatives inhibit hemozoin formation by causing a buildup of toxic "free" heme in a similar manner to chloroquine. Finally, assessment of drug interactions also revealed synergy between these derivatives and several clinically relevant antimalarials, thus highlighting their potential interest for further development.

Industry Perspective on Therapeutic Peptide Drug-Drug Interaction Assessments During Drug Development: A European Federation of Pharmaceutical Industries and Associations White Paper.

Drug-drug interaction (DDI) assessments are well defined in health authority guidelines for small molecule drugs, and US Food and Drug Administration (FDA) draft guidance is now available for therapeutic proteins. However, there are currently no regulatory guidelines outlining DDI assessments for therapeutic peptides, which poses significant uncertainty and challenges during drug development for this heterogenous class of molecules. A cross-industry peptide DDI working group consisting of experts from 10 leading companies was formed under the sponsorship of the European Federation of Pharmaceutical Industries and Associations. We aimed to capture the range of DDI studies undertaken for peptide drugs by (i) anonymously surveying relevant companies involved in peptide drug development to better understand DDI study type/timing currently performed and (ii) compiling a database containing in vitro / clinical DDI data from submission packages for recently approved peptide drugs. Our analyses highlight significant gaps and uncertainty in the field. For example, the reported timing of in vitro peptide DDI studies, if performed, vary substantially across responding companies from early research to phase III. Nearly all in vitro cytochrome P450 / transporter inhibition studies reported in the survey were negative. For the few positive hits reported, no clinical follow-up studies were performed, questioning the clinical relevance of these findings. Furthermore, available submission packages reveal DDI likelihood is low for peptides >2kDa, making it reasonable to adopt a risk-based approach during drug development for larger peptides. By benchmarking the landscape of peptide DDI activities across the industry, we set the stage for future discussions with health authorities on harmonizing peptide DDI approaches.

Development of a drug interaction assessment model

Development of a drug interaction assessment model

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Clinical efficacy and safety of cannabidiol for pediatric refractory epilepsy indications: A systematic review and meta-analysis

Antiseizure medications (ASMs) are the mainstay for the treatment of seizure disorders. However, about one-third of people with epilepsy remain refractory to current ASMs. Cannabidiol (CBD) has recently been approved as ASM for three refractory epilepsy syndrome indications in children and adults. In this study, we evaluated the overall clinical potential of an oral CBD to treat refractory epilepsy in patients with Dravet syndrome (DS), Lennox-Gastaut syndrome (LGS), and tuberous sclerosis complex (TSC) through a systematic review and meta-analysis. A comprehensive search of databases was conducted, including randomized controlled trials (RCTs) assessing the effect of CBD in epilepsy patients. The review was conducted as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review focused on RCTs involving patients receiving highly purified oral CBD (Epidiolex, 10 to 50 mg/kg/day) for up to 16 weeks. A subgroup analysis by syndrome and CBD with or without concomitant clobazam was conducted. The key outcomes were reduction in seizure frequency, differences in 50% responder rates, adverse events, and interactions with clobazam as co-therapy. Odds ratio (OR) with 95% confidence interval (CI) were estimated. Of 1183 articles screened, we included 6 RCTs meeting our eligibility criteria. All studies were considered to have a low risk of bias. In the pooled analysis, CBD treatment was found to be more efficacious compared to placebo (OR = 2.45, 95% CI =1.81–3.32, p < 0.01). Subgroup analysis by syndrome demonstrated the odds of ≥50% reduction in seizures with CBD treatment in patients with DS (OR = 2.26, 95% CI:1.38–3.70), LGS (OR = 2.98, 95% CI:1.83–4.85) and TSC (OR = 1.99, 95% CI = 1.06–3.76). Compared with placebo, CBD was associated with increased adverse events (OR = 1.81, 95% CI = 1.33–2.46) such as diarrhea, somnolence, and sedation, and any serious adverse events (OR = 2.86, 95% CI = 1.63–5.05). Other factors, including dosage and clobazam co-therapy, were significantly associated with a greater effect on seizure control and side effects of CBD. In conclusion, the study shows that CBD is highly efficacious both as standalone and adjunct therapy with clobazam for controlling seizures in DS, LGS, and TSC conditions while limiting side effects. Further pharmacodynamic investigation of CBD actions, drug interaction assessments, and therapeutic management guidelines are warranted.

Assessment of pH-shift drug interactions of palbociclib by in vitro micro-dissolution in bio relevant media: An analytical QbD-driven RP-HPLC method optimization

Assessment of pH-shift drug interactions of palbociclib by in vitro micro-dissolution in bio relevant media: An analytical QbD-driven RP-HPLC method optimization

Drug-Induced QTc Prolongation: What We Know and Where We Are Going.

Drug-induced QTc prolongation is a concerning electrocardiogram (ECG) abnormality. This cardiac disturbance carries a 10% risk of sudden cardiac death due to the malignant arrhythmia, Torsades de Pointes. The Arizona Center for Education and Research on Therapeutics (AzCERT) has classified QTc prolonging therapeutic classes, such as antiarrhythmics, antipsychotics, anti-infectives, and others. AzCERT criteria categorize medications into three risk categories: "known," "possible," and "conditional risk" of QTc prolongation and Torsades de Pointes. The list of QTc prolonging medications continues to expand as new drug classes are approved and studied. Risk factors for QTc prolongation can be delineated into modifiable or non-modifiable. A validated risk scoring tool may be utilized to predict the likelihood of prolongation in patients receiving AzCERT classified medication. The resultant risk score may be applied to a clinical decision support system, which offers mitigation strategies. Mitigation strategies including discontinuation of possible offending agents with a selection of an alternative agent, assessment of potential drug interactions or dose adjustments through pharmacokinetic and pharmacodynamic monitoring, and initiation of both ECG and electrolyte monitoring are essential to prevent a drug-induced arrhythmia. The challenges presented by the COVID-19 pandemic have led to the development of innovative continuous monitoring technology, increasing protection for both patients and healthcare workers. Early intervention strategies may reduce adverse events and improve clinical outcomes in patients identified to be at risk of QTc prolongation.

Antifungal Properties of Chemically Defined Propolis from Various Geographical Regions.

Long-term fungal infections that are difficult to treat require new substances for their prevention, treatment, or as adjuvants during antibiotic therapy. Propolis is a very promising source of natural substances that show a wide range of pharmacological properties, including antifungal activity against various fungal strains. The purpose of the literature review was to summarize recent studies (PubMed, Scopus) on progress in evaluating the antifungal activity of chemically defined propolis extracts. During the selection of studies, only those with results of antifungal activity expressed as minimal inhibitory concentration (MIC) and/or minimal fungicidal concentration (MFC) were analyzed. Moreover, plant, animal and environmental factors influencing the chemical composition of propolis are discussed. Mechanisms of antifungal activity of propolis extracts and research trends in the aspect of developing new therapies and the assessment of drug interactions are indicated. The review of the research results shows that there is great progress in the definition of propolis extracts. After comparing the MIC/MFC values, it was assessed that propolis extracts offer a wide range of activity not only against pathogenic Candida strains but also against risky molds; however, the strength of this activity is varied.

Drug Combinations: A New Strategy to Extend Drug Repurposing and Epithelial-Mesenchymal Transition in Breast and Colon Cancer Cells.

Despite the progressive research and recent advances in drug therapy to treat solid tumours, the number of cases and deaths in patients with cancer is still a major health problem. Drug repurposing coupled to drug combination strategies has been gaining interest among the scientific community. Recently, our group proposed novel drug combinations for breast and colon cancer using repurposed drugs from different classes (antimalarial and central nervous system (CNS)) and chemotherapeutic agents such as 5-fluorouracil (5-FU), paclitaxel (PTX), and found promising results. Here, we proposed a novel drug combination using different CNS drugs and doxorubicin (DOX), an antineoplastic used in breast cancer therapy, and studied their anticancer potential in MCF-7 breast cancer cells. Cells were treated with each drug alone and combined with increasing concentrations of DOX and cell viability was evaluated by MTT and SRB assays. Studies were also complemented with morphological evaluation. Assessment of drug interaction was performed using the CompuSyn and SynergyFinder software. We also compiled our previously studied drug pairs and selected the most promising ones for evaluation of the expression of EMT biomarkers (E-cadherin, P-cadherin, vimentin, and β-catenin) by immunohistochemistry (IHC) to assess if these drug combinations affect the expression of these proteins and eventually revert EMT. These results demonstrate that combination of DOX plus fluoxetine, benztropine, and thioridazine at their IC50 can improve the anticancer effect of DOX but to a lesser degree than when combined with PTX (previous results), resulting in most of the drug interactions being antagonist or additive. This suggests that the choice of the antineoplastic drug influences the success of the drug combination. Collectively, these results also allow us to conclude that antimalarial drugs as repurposed drugs have enhanced effects in MCF-7 breast cancer cells, while combination with CNS drugs seems to be more effective in HT-29 colon cancer cells. The IHC results demonstrate that combination treatments increase E-cadherin expression while reducing P-cadherin, vimentin, and β-catenin, suggesting that these treatments could induce EMT reversal. Taken together, these results could provide promising approaches to the design of novel drug combinations to treat breast and colon cancer patients.

Cynomolgus Monkey as an Emerging Animal Model to Study Drug Transporters: In Vitro, In Vivo, In Vitro-to-In Vivo Translation.

Membrane transporters have been recognized as one of the key determinants of pharmacokinetics and are also known to affect the efficacy and toxicity of drugs. Both qualitatively and quantitatively, however, transporter studies conducted using human in vitro systems have not always been predictive. Consequently, researchers have used cynomolgus monkeys as a model to study drug transporters and anticipate their effects in humans. Burgeoning reports of data in the last few years necessitates a comprehensive review on the topic of drug transporters in cynomolgus monkeys that includes cell-based tools, sequence homology, tissue expression, in vitro studies, in vivo studies, and in vitro-to-in vivo extrapolation. This review highlights the state-of-the-art applications of monkey transporter models to support the evaluation of transporter-mediated drug-drug interactions, clearance predictions, and endogenous transporter biomarker identification and validation. The data demonstrate that cynomolgus monkey transporter models, when used appropriately, can be an invaluable tool to support drug discovery and development processes. Most importantly, they enable an early in vitro-to-in vivo extrapolation assessment, which provides additional context to human in vitro data. Additionally, comprehending species similarities and differences in transporter tissue expression and activity is crucial when translating monkey data to humans. The challenges and limitations when applying such models to inform decision-making must also be considered. SIGNIFICANCE STATEMENT: This paper presents a comprehensive review of currently available published reports describing cynomolgus monkey transporter models. The data indicate that Cynomolgus monkeys provide mechanistic insight regarding the role of intestinal, hepatic, and renal transporters in drug and biomarker disposition and drug interactions. The data generated with cynomolgus monkey models provide mechanistic insight into transporter-mediated drug absorption and disposition. They are valuable to human clearance prediction, drug drug interaction assessment, and endogenous biomarker development related to drug transporters.

Analysis between Linagliptin and Azithromycin: In vitro and In vivo Interaction Study.

Linagliptin is prescribed as a dipeptidyl peptidase-4 (DPP-4) inhibitor. Azithromycin is specified as an antibiotic that binds with 23s rRNA of the 50s ribosomal subunit, obstructing the microbial protein synthesis. Our study focuses on the drug-drug interactions of these drugs. The purpose of the study is to understand the bioavailability and physicochemical approaches of Linagliptin and Azithromycin interaction mediated through the strength and nature of the complexation. TheIn vitro assessment of drug interaction was conducted using Ultraviolet-visible spectroscopy (UV/VIS), Ultra-Performance Liquid Chromatography (UPLC), Fourier transform infrared spectroscopy (FT-IR), and Differential scanning calorimetry (DSC), while the Oral Glucose Tolerance Test (OGTT) was performed for theIn vivo assessment of drug interaction in a mouse model. Mild variation in interaction was observed at different pH values at a specific temperature by Job's and Ardon's equations. In UPLC, the drug mixture assessment showed that the area of Linagliptin was 2013793, and the area of Azithromycin was 54631 in 50 mg/l. The height of Linagliptin in the drug mixture was 579234, and that of Azithromycin was 11442. For Azithromycin, the wavelength of 731.02 cm-1, 993.34 cm-1, 1379.10 cm-1, and 1718.58 cm-1 decreased in the mixture. Also, for Linagliptin, the wavelength 1363.67 cm-1, 1473.62 cm-1, and 1718.58 cm-1 decreased in the drug mixture. The melting endotherm was 125.55°C, melting normalized energy was -3.0 W/mg, and 225.75°C with melting normalized energy of -5.5 W/mg of the drug mixture as indicated by DSC. In the OGTT test, the blood glucose level of Linagliptin decreased in the drug mixture by 13.58 % and 57.25%. Hence, the concomitant administration of Linagliptin and Azithromycin simultaneously might reduce the therapeutic effect of the drug complex.

Modeling restoration of gefitinib efficacy by co-administration of MET inhibitors in an EGFR inhibitor-resistant NSCLC xenograft model: A tumor-in-host DEB-based approach.

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non‐small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor‐in‐host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly‐targeted combination therapies. The population DEB‐based tumor growth inhibition (TGI) model well‐described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib‐resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB‐TGI model allowed to capture gefitinib anticancer activity enhanced by the co‐administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model‐based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB‐based tumor‐in‐host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor‐in‐host DEB‐TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.

Assessment of Drug Interactions with Online Electronic Checkers in Multi-Pathological Patients

Introduction: Multi-pathological patients are at high risk of drug interactions and side effects. We aimed to assess the usefulness of 3 online drug interaction checkers. Methods: In a cross-sectional study, carried out in the Medicine Department of Hospital General of Castellon, Spain, in February 2020, we assessed drug interaction detection with 3 online electronic checkers, Drugs.com, Lexicomp®, and Medscape, and compared results obtained with the 3 tools. From every hospitalized patient, we obtained the list of drugs he or she had been taking until admission. Bivariable tests were used for analysis. p values <0.05 were considered significant. Results: We included data from 134 patients; 68 (51%) were male; median (and interquartile range) of their age was 82 (76–88) years. A total of 1,082 substance drugs were entered in the checkers. The number of highest grade interactions found with every program was Drugs.com 85, Lexicomp® 33, and Medscape 67. Positive correlations were found between age and number of drug substances prescribed (p = 0.009) and between number of drug substances prescribed and interactions found with any of the 3 checkers (p < 0.001 in all 3 cases). Regarding highest grade interactions, agreement among all 3 checkers was poor. Conclusions: The 3 online checkers we assessed found a large number of interactions. The 3 programs gave very discrepant results. Impact on Practice Statements: The analyzed programs, Drugs.com, Lexicomp®, and Medscape Interactions, found a large number of drug interactions in the studied patients. The 3 programs gave very discrepant results among them.