A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

PURPOSE: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. EXPERIMENTAL DESIGN: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. RESULTS: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. CONCLUSIONS: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.

Electroanalysis in Pharmacogenomic Studies: Mechanisms of Drug Interaction with DNA.

The review discusses electrochemical methods for analysis of drug interactions with DNA. The electroanalysis method is based on the registration of interaction-induced changes in the electrochemical oxidation potential of heterocyclic nitrogenous bases in the DNA molecule and in the maximum oxidation current amplitude. The mechanisms of DNA-drug interactions can be identified based on the shift in the electrooxidation potential of heterocyclic nitrogenous bases toward more negative (cathodic) or positive (anodic) values. Drug intercalation into DNA shifts the electrochemical oxidation potential to positive values, indicating thermodynamically unfavorable process that hinders oxidation of nitrogenous bases in DNA. The potential shift toward the negative values indicates electrostatic interactions, e.g., drug binding in the DNA minor groove, since this process does not interfere with the electrochemical oxidation of bases. The concentration-dependent decrease in the intensity of electrochemical oxidation of DNA bases allows to quantify the type of interaction and calculate the binding constants.

Predictors of potential clinically harmful drug-drug interactions at the medical wards in the University of Benin Teaching Hospital, Benin City.

Drug-drug interactions are preventable causes of adverse events. Different factors have been recognized as important predictors of drug-drug interactions but few studies have addressed these predictors in patients admitted into medical wards of a tertiary hospital in Nigeria hence this study. This was a retrospective study conducted using case records of patients admitted into the medical wards between January 1 and December 31, 2020. Patients were selected using a systematic random sampling method. Socio-demographic details including age, gender, number of comorbidities, and number of medications prescribed and diagnosis were collected on days 1, 3, and at discharge. Potential drug-drug interactions were checked using Lexi-interact® software. Analysis was set at p < 0.05. A total of 430 case records were included in this study based on the inclusion criteria. Lexi-interact recorded a prevalence of (217) 50.5% on day 1, (146) 34.0% on day 3, and (290) 67.4% at discharge. A significant association (p < 0.05) was found between the potential drug-drug interactions (DDI) and an increased number of medicines prescribed on all the days of admission. Also, patients without certain infectious or parasitic diseases have reduced odds of developing DDI. There is a need for continuous monitoring of medications from admission to discharge especially in the elderly, those on multiple medications, certain infectious or parasitic diseases, and comorbidities as these impact on DDIs.

Perfil de seguridad de nirmatrelvir-ritonavir: evidencia de eventos adversos por interacciones farmacológicas / Safety profile of nirmatrelvir-ritonavir: Evidence of adverse events due to drug-drug interactions

Objetivo: evaluar el perfil de seguridad de nirmatrelvir-ritonavir (NMV-r) en la práctica clínica real y analizar la relevancia clínica de las interacciones farmacológicas en el desarrollo de eventos adversos. Material y métodos: estudio observacional, retrospectivo en el que se evaluaron los datos de seguridad de pacientes tratados con NMV-r entre abril y julio de 2022. Se recopilaron datos demográficos y analíticos antes de comenzar el tratamiento. La duración del seguimiento fue de 28 días y se evaluó el número reacciones adversas reportadas, así como si fueron manejadas de forma ambulatoria o precisaron de asistencia sanitaria especializada y la presencia de deterioro de la función renal y hepática. Se revisó el tratamiento concomitante, identificando interacciones farmacológicas teóricas (IFT) cuya gravedad fue definida mediante la clasificación Lexi-interact. Resultados: el estudio incluyó 146 pacientes, 82 (56,16 %) eran mujeres, cuya mediana de edad fue de 65 años (22-95). El número de IFT detectadas y mantenidas durante el tratamiento con NMV-r fue de 164, siendo el porcentaje de pacientes con al menos una interacción de 62,33%. La mediana de IFT por paciente fue de uno (0-5). En 18 pacientes (11,84%) se reportó al menos un evento adverso (EA). Once EA se relacionaron potencialmente con alguna IFT, 7 pacientes requirieron contacto con asistencia hospitalaria para el manejo del EA, 8 pacientes presentaron deterioro de la función renal y 2 de la función hepática a los 28 días. Los principales grupos de fármacos implicados en la aparición de algún EA fueron los anticoagulantes orales, así como los calcio-antagonistas. Conclusiones: nuestros resultados muestran un elevado número de IFT detectadas entre NMV-r y otros fármacos, aunque la frecuencia de EA asociados fue baja. Este estudio proporciona un mayor conocimiento de los fármacos implicados en dichas interacciones y su potencial relación con la aparición de EA.(AU)

Objective: The aim of the study was to evaluate the safety profile of nirmatrelvir-ritonavir (NMV-r) in real clinical practice and to analyze the clinical relevance of drug-drug interactions in the development of adverse events. Methods: Observational, retrospective study in which safety data of patients treated with NMV-r between April and July 2022 in an outpatient setting were evaluated. The duration of follow-up was 28 days and the number of adverse reactions reported, as well as whether they were managed on an outpatient basis or required health care, and the presence of renal and hepatic function impairment were assessed. Concomitant treatment was reviewed, identifying theoretical drug-drug interactions (TDDIs) whose severity was defined using the Lexi-interact classification. Results: The study included 146 patients, 82 (56,16%) were women, whose median age was 65 years (22-95). The number of TDDIs detected and maintained during treatment with NMV-r was 164, with the percentage of patients with at least one interaction being 62,33%. The median number of TDDIs per patient was 1 (0-5). At least 1 adverse event (AE) was reported in 18 patients (11,84%). Eleven AEs were potentially related to any TDDI. Seven patients required contact with hospital assistance for AE management. Eight patients had impaired renal function and 2 had impaired liver function at 28 days. The main groups of drugs implicated in the occurrence of an AE were oral anticoagulants and calcium antagonists. Conclusions: Our results show a high number of TDDIs detected were detected between NMV-r and other drugs. This study provides greater knowledge of the drugs involved in such interactions and their potential relationship with the occurrence of adverse events.(AU)

Safety profile of nirmatrelvir-ritonavir: Evidence of adverse events due to DDIs / Perfil de seguridad de nirmatrelvir-ritonavir: evidencia de eventos adversos por interacciones farmacológicas

Objetivo: evaluar el perfil de seguridad de nirmatrelvir-ritonavir (NMV-r) en la práctica clínica real y analizar la relevancia clínica de las interacciones farmacológicas en el desarrollo de eventos adversos. Material y métodos: estudio observacional, retrospectivo en el que se evaluaron los datos de seguridad de pacientes tratados con NMV-r entre abril y julio de 2022. Se recopilaron datos demográficos y analíticos antes de comenzar el tratamiento. La duración del seguimiento fue de 28 días y se evaluó el número reacciones adversas reportadas, así como si fueron manejadas de forma ambulatoria o precisaron de asistencia sanitaria especializada y la presencia de deterioro de la función renal y hepática. Se revisó el tratamiento concomitante, identificando interacciones farmacológicas teóricas (IFT) cuya gravedad fue definida mediante la clasificación Lexi-interact. Resultados: el estudio incluyó 146 pacientes, 82 (56,16 %) eran mujeres, cuya mediana de edad fue de 65 años (22-95). El número de IFT detectadas y mantenidas durante el tratamiento con NMV-r fue de 164, siendo el porcentaje de pacientes con al menos una interacción de 62,33%. La mediana de IFT por paciente fue de uno (0-5). En 18 pacientes (11,84%) se reportó al menos un evento adverso (EA). Once EA se relacionaron potencialmente con alguna IFT, 7 pacientes requirieron contacto con asistencia hospitalaria para el manejo del EA, 8 pacientes presentaron deterioro de la función renal y 2 de la función hepática a los 28 días. Los principales grupos de fármacos implicados en la aparición de algún EA fueron los anticoagulantes orales, así como los calcio-antagonistas. Conclusiones: nuestros resultados muestran un elevado número de IFT detectadas entre NMV-r y otros fármacos, aunque la frecuencia de EA asociados fue baja. Este estudio proporciona un mayor conocimiento de los fármacos implicados en dichas interacciones y su potencial relación con la aparición de EA.(AU)

Objective: The aim of the study was to evaluate the safety profile of nirmatrelvir-ritonavir (NMV-r) in real clinical practice and to analyze the clinical relevance of drug-drug interactions in the development of adverse events. Methods: Observational, retrospective study in which safety data of patients treated with NMV-r between April and July 2022 in an outpatient setting were evaluated. The duration of follow-up was 28 days and the number of adverse reactions reported, as well as whether they were managed on an outpatient basis or required health care, and the presence of renal and hepatic function impairment were assessed. Concomitant treatment was reviewed, identifying theoretical drug-drug interactions (TDDIs) whose severity was defined using the Lexi-interact classification. Results: The study included 146 patients, 82 (56,16%) were women, whose median age was 65 years (22-95). The number of TDDIs detected and maintained during treatment with NMV-r was 164, with the percentage of patients with at least one interaction being 62,33%. The median number of TDDIs per patient was 1 (0-5). At least 1 adverse event (AE) was reported in 18 patients (11,84%). Eleven AEs were potentially related to any TDDI. Seven patients required contact with hospital assistance for AE management. Eight patients had impaired renal function and 2 had impaired liver function at 28 days. The main groups of drugs implicated in the occurrence of an AE were oral anticoagulants and calcium antagonists. Conclusions: Our results show a high number of TDDIs detected were detected between NMV-r and other drugs. This study provides greater knowledge of the drugs involved in such interactions and their potential relationship with the occurrence of adverse events.(AU)

Clinical assessment of momelotinib drug-drug interactions via CYP3A metabolism and transporters.

Momelotinib-approved for treatment of myelofibrosis in adults with anemia-and its major active metabolite, M21, were assessed as drug-drug interaction (DDI) victims with a strong cytochrome P450 (CYP) 3A4 inhibitor (multiple-dose ritonavir), an organic anion transporting polypeptide (OATP) 1B1/1B3 inhibitor (single-dose rifampin), and a strong CYP3A4 inducer (multiple-dose rifampin). Momelotinib DDI perpetrator potential (multiple-dose) was evaluated with CYP3A4 and breast cancer resistance protein (BCRP) substrates (midazolam and rosuvastatin, respectively). DDI was assessed from changes in maximum plasma concentration (Cmax), area under the concentration-time curve (AUC), time to reach Cmax, and half-life. The increase in momelotinib (23% Cmax, 14% AUC) or M21 (30% Cmax, 24% AUC) exposure with ritonavir coadministration was not clinically relevant. A moderate increase in momelotinib (40% Cmax, 57% AUC) and minimal change in M21 was observed with single-dose rifampin. A moderate decrease in momelotinib (29% Cmax, 46% AUC) and increase in M21 (31% Cmax, 15% AUC) were observed with multiple-dose rifampin compared with single-dose rifampin. Due to potentially counteracting effects of OATP1B1/1B3 inhibition and CYP3A4 induction, multiple-dose rifampin did not significantly change momelotinib pharmacokinetics compared with momelotinib alone (Cmax no change, 15% AUC decrease). Momelotinib did not alter the pharmacokinetics of midazolam (8% Cmax, 16% AUC decreases) or 1'-hydroxymidazolam (14% Cmax, 16% AUC decreases) but increased rosuvastatin Cmax by 220% and AUC by 170%. Safety findings were mild in this short-term study in healthy volunteers. This analysis suggests that momelotinib interactions with OATP1B1/1B3 inhibitors and BCRP substrates may warrant monitoring for adverse reactions or dose adjustments.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations.

BACKGROUND: The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. METHODS: This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated. RESULTS: A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis. CONCLUSIONS: DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

A simulation-based comparison of drug-drug interaction signal detection methods.

Several statistical methods have been proposed to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance, which mainly relies on spontaneous reporting system database. Most previous studies have applied statistical models to real world data, but it is not clear which method outperforms the others. We aimed to assess the performance of various detection methods by implementing simulations under various conditions. We reviewed proposed approaches to detect signals indicating drug-drug interactions (DDIs) including the Ω shrinkage measure, the chi-square statistic, the proportional reporting ratio, the concomitant signal score, the additive model and the multiplicative model. Under various scenarios, we conducted a simulation study to examine the performances of the methods. We also applied the methods to Korea Adverse Event Reporting System (KAERS) data. Of the six methods considered in the simulation study, the Ω shrinkage measure and the chi-square statistic with threshold = 2 had higher sensitivity for detecting the true signals than the other methods in most scenarios while controlling the false positive rate below 0.05. When applied to the KAERS data, the two methods detected one known DDI for QT prolongation and one unknown (suspected) DDI for hyperkalemia. The performance of various signal detection methods for DDI may vary. It is recommended to use several methods together, rather than just one, to make a reasonable decision.

Opioid-induced respiratory depression suspected of drug interaction in a prostate cancer patient: a case report.

BACKGROUND: Many of the drugs used for the treatment and alleviation of symptoms in cancer patients are known to inhibit or induce cytochrome P450 (CYP). Therefore, it is important to pay attention to the drug interactions of opioid analgesics that are metabolized by CYPs, because for example when using oxycodone metabolized by CYP3A4, it is possible that the effect will be attenuated or enhanced by the concomitant use of drugs that induce or inhibit CYP3A4. Aprepitant, an antiemetic drug used in many patients receiving anticancer drugs, is known as a moderate competitive inhibitor of CYP3A4. We experienced a case of respiratory depression caused by opioids, which was suspected to be caused by a drug interaction with antiemetics especially aprepitant. CASE DESCRIPTION: The patient was a 72-year-old man. He had been treated with continuous oxycodone infusion for perianal pain associated with the rectal invasion of prostate cancer. No comorbidities other than renal dysfunction were observed. Oxycodone treatment was started at 48 mg/day, and was increased to 108 mg/day, and then the pain decreased. Once the pain was controlled, chemotherapy was planned. Antiemetics (dexamethasone, palonosetron, and aprepitant) were administered before anticancer drug administration. Approximately 3 hours after antiemetics administration and before the administration of the anticancer drugs, a ward nurse noticed that oversedation and respiratory depression had occurred. When the patient was called, he immediately woke up and was able to talk normally, so the anticancer drugs were administered as scheduled. About 2 hours after the nurse noticed oversedation, the attending physician reduced the dose of oxycodone infusion to 48 mg/day. After that, his drowsiness persisted, but his respiratory condition improved. Despite reducing the dose of oxycodone to less than half, the pain remained stable at numeric rating scale (NRS) 0-1, without the use of a rescue dose. The patient was discharged from the hospital 36 days after the administration of anticancer drugs, without any problems. CONCLUSIONS: The cause of respiratory depression in this case was thought to be a combination of factors, including drug interactions between oxycodone and antiemetics, and oxycodone accumulation due to renal dysfunction.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

HIV-TB Coinfection: Current Therapeutic Approaches and Drug Interactions.

The co-occurrence of human immunodeficiency virus (HIV) and tuberculosis (TB) infection poses a significant global health challenge. Treatment of HIV and TB co-infection often necessitates combination therapy involving antiretroviral therapy (ART) for HIV and anti-TB medications, which introduces the potential for drug-drug interactions (DDIs). These interactions can significantly impact treatment outcomes, the efficacy of treatment, safety, and overall patient well-being. This review aims to provide a comprehensive analysis of the DDIs between anti-HIV and anti-TB drugs as well as potential adverse effects resulting from the concomitant use of these medications. Furthermore, such findings may be used to develop personalized therapeutic strategies, dose adjustments, or alternative drug choices to minimize the risk of adverse outcomes and ensure the effective management of HIV and TB co-infection.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Clinical Analysis and in Vitro Correlation of BCRP-Mediated Drug-Drug Interaction in the Gastrointestinal Tract.

Breast cancer resistance protein (BCRP) is a drug efflux transporter expressed on the epithelial cells of the small intestine and on the lateral membrane of the bile duct in the liver; and is involved in the efflux of substrate drugs into the gastrointestinal lumen and secretion into bile. Recently, the area under the plasma concentration-time curve (AUC) of rosuvastatin (ROS), a BCRP substrate drug, has been reported to be increased by BCRP inhibitors, and BCRP-mediated drug-drug interaction (DDI) has attracted attention. In this study, we performed a ROS uptake study using human colon cancer-derived Caco-2 cells and confirmed that BCRP inhibitors significantly increased the intracellular accumulation of ROS. The correlation between the cell to medium (C/M) ratio of ROS obtained by the in vitro study and the absorption rate constant (ka) ratio obtained by clinical analysis was examined, and a significant positive correlation was observed. Therefore, it is suggested that the in vitro study using Caco-2 cells could be used to quantitatively estimate BCRP-mediated DDI with ROS in the gastrointestinal tract.

Sources of drug information.

Modern healthcare requires clinicians to navigate through complex drug treatments. This review offers an overview of sources of drug information which can be used for general medication prescription and for challenging patient populations. Key considerations for pregnant or breastfeeding patients, those with renal impairment, and those with liver dysfunction are discussed. We also touch on adverse drug reactions and drug interactions. Finally, information about services from independent regional drug information centers, that can be used by clinicians, are provided.

A case report of drug interaction between co-packaged nirmatrelvir-ritonavir and tacrolimus causing hyponatremia in a lung transplant recipient.

BACKGROUND: Coronavirus disease 2019 (COVID-19) infection in lung transplant recipients can be lethal owing to the use of immunosuppressants. Antiviral agents may be administered to these patients. Co-packaged nirmatrelvir-ritonavir is a new agent currently being used in combination. CASE PRESENTATION: In this report, we present a case of a 64-year-old woman, a lung transplant recipient, who experienced hyponatremia and showed a high serum tacrolimus concentration following the administration of the co-packaged nirmatrelvir-ritonavir combination. CONCLUSION: Although the nirmatrelvir-ritonavir and tacrolimus combination is not contraindicated, other treatment strategies should be considered first, if available, and the dose of tacrolimus should be reduced when using the nirmatrelvir-ritonavir combination. In cases where combination therapy is necessary, serum tacrolimus levels should be closely monitored in lung transplant recipients. Documentation of more such reports is important to identify drug interactions between nirmatrelvir-ritonavir and other agents, with the aim of preventing severe adverse effects.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.