Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Non-small cell lung cancer sensitisation to platinum chemotherapy via new thiazole-triazole hybrids acting as dual T-type CCB/MMP-9 inhibitors

Cisplatin remains the unchallenged standard therapy for NSCLC. However, it is not completely curative due to drug resistance and oxidative stress-induced toxicity. Drug resistance is linked to overexpression of matrix metalloproteinases (MMPs) and aberrant calcium signalling. We report synthesis of novel thiazole-triazole hybrids as MMP-9 inhibitors with T-type calcium channel blocking and antioxidant effects to sensitise NSCLC to cisplatin and ameliorate its toxicity. MTT and whole cell patch clamp assays revealed that 6d has a balanced profile of cytotoxicity (IC50 = 21 ± 1 nM, SI = 12.14) and T-type calcium channel blocking activity (⁓60% at 10 μM). It exhibited moderate ROS scavenging activity and nanomolar MMP-9 inhibition (IC50 = 90 ± 7 nM) surpassing NNGH with MMP-9 over -2 and MMP-10 over -13 selectivity. Docking and MDs simulated its receptor binding mode. Combination studies confirmed that 6d synergized with cisplatin (CI = 0.69 ± 0.05) lowering its IC50 by 6.89 folds. Overall, the study introduces potential lead adjuvants for NSCLC platinum-based therapy.

Structure optimization and molecular dynamics studies of new tumor-selective s-triazines targeting DNA and MMP-10/13 for halting colorectal and secondary liver cancers

A series of triazole-tethered triazines bearing pharmacophoric features of DNA-targeting agents and non-hydroxamate MMPs inhibitors were synthesized and screened against HCT-116, Caco-2 cells, and normal colonocytes by MTT assay. 7a and 7g surpassed doxorubicin against HCT-116 cells regarding potency (IC50 = 0.87 and 1.41 nM) and safety (SI = 181.93 and 54.41). 7g was potent against liver cancer (HepG-2; IC50 = 65.08 nM), the main metastatic site of CRC with correlation to MMP-13 expression. Both derivatives induced DNA damage at 2.67 and 1.87 nM, disrupted HCT-116 cell cycle and triggered apoptosis by 33.17% compared to doxorubicin (DNA damage at 0.76 nM and 40.21% apoptosis induction). 7g surpassed NNGH against MMP-10 (IC50 = 0.205 μM) and MMP-13 (IC50 = 0.275 μM) and downregulated HCT-116 VEGF related to CRC progression by 38%. Docking and MDs simulated ligands-receptors binding modes and highlighted SAR. Their ADMET profiles, drug-likeness and possible off-targets were computationally predicted.

Applications, Limitations, and Considerations of Clinical Trials in a Dish

Recent advancements in biotechnology forged the path for clinical trials in dish (CTiDs) to advance as a popular method of experimentation in biomedicine. CTiDs play a fundamental role in translational research through technologies such as induced pluripotent stem cells, whole genome sequencing, and organs-on-a-chip. In this review, we explore advancements that enable these CTiD biotechnologies and their applications in animal testing, disease modeling, and space radiation technologies. Furthermore, this review dissects the advantages and disadvantages of CTiDs, as well as their regulatory considerations. Lastly, we evaluate the challenges that CTiDs pose and the role of CTiDs in future experimentation.

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

The prevalence of antiretroviral drug interactions with other drugs used in women living with HIV and its association with HIV drug change and patient compliance

BACKGROUND

Drug-drug interactions (DDIs) between antiretroviral therapy (ART) and commonly used co-medications in HIV patients, especially women, impact treatment efficacy and patient safety.

OBJECTIVE

This study aimed to study the prevalence and types of drug-drug interactions (DDIs) between antiretroviral therapy drugs (ARTs) and comedications among a female population with HIV. Additionally, the study investigates the association of these DDIs with ART medication changes and treatment adherence.

METHODS

This cross-sectional study included 632 adult women living with HIV (WLHIV). Data was retrospectively extracted from patient files. Drug.com interaction checker website was used to assess DDIs between ART and non-ART medications. Changes to the ART regimen previously attributed to ART side effects or patient non-adherence were considered drug changes.

RESULTS

A total of 429 WLHIV (mean age: 44.05 ± 9.50) were eligible. The prevalence of DDIs between ART and non-ART medications was 21.4%, with 4.7% minor, 18.4% moderate, and 8.9% major interactions. The highest prevalence of DDI was among cardiovascular medication users (71.7%), followed by central nervous system drugs (69.2%). Changing medications resulted in a decrease in DDIs, with significant reductions in total and minor interactions. Participants without DDIs had better adherence to ART. DDI between ART and non-ART medications was significantly associated with ART drug change, even after accounting for side effects attributed to ARTs, indicating an independent twofold association (OR = 1.99, CI 1.04-3.77). Moreover, further adjustments for HIV viral load and CD4 + cell count did not change the significance of the association (OR = 2.01, CI 1.03-3.92).

CONCLUSION

DDIs in WLHIV impact adherence to ART. Altering ART may not be directly related to ART side effects, but rather primarily due to interactions with non-ART medications. Modifying non-ART drug regimens can reduce the likelihood of DDIs.

Deciphering the Intricate Interplay in the Framework of Antibiotic-Drug Interactions: A Narrative Review

Drug interactions are a significant and integral part of the concept of medication-related adverse events, whether referring to potential interactions or those currently observed in real-world conditions. The high global consumption of antibiotics and their pharmacokinetic and pharmacodynamic mechanisms make antibiotic-drug interactions a key element that requires continuous study due to their clinical relevance. In the present work, the current state of knowledge on antibiotic-drug interactions, which are less studied than other drug-drug interactions despite their frequent use in acute settings, has been consolidated and updated. The focus was on the interactions of the commonly used antibiotics in clinical practice, on the characteristics of the geriatric population susceptible to interactions, and on the impact of online drug interaction checkers. Additionally, strategies for optimizing the management of these interactions, including spacing out administrations, monitoring, or avoiding certain combinations, are suggested. Sustained research and careful monitoring are critical for improving antibiotic safety and efficacy, especially in susceptible populations, to enhance precision in managing antibiotic-drug interactions.

Pharmacokinetic interaction of quetiapine and lamotrigine - victim and perpetrator?

OBJECTIVE

We aimed to investigate the ambiguous findings of earlier research regarding the reduction of quetiapine plasma levels when combined with lamotrigine, most likely via UDP-glucuronosyltransferase induction by lamotrigine.

METHODS

One thousand one hundred and fifty samples, divided into four groups of patients receiving either quetiapine immediate- (IR) or extended-release (XR) without or in combination with lamotrigine were compared regarding absolute and dose-adjusted plasma concentrations. Furthermore, samples of intra-individual controls were analyzed.

RESULTS

Patients receiving quetiapine IR in combination with lamotrigine showed 31% lower plasma (p = 0.002) and 23% lower dose-adjusted plasma concentrations (p = 0.004) compared to those receiving IR monotherapy. The proportion of patients with quetiapine plasma concentrations below the lower limit of the therapeutic reference range was 50% and 30% in the combination group and in patients receiving monotherapy, respectively (p = 0.03). However, no significant differences regarding plasma concentration (p = 0.13) and dose-adjusted plasma concentration (p = 0.42) were observed in patients with combination vs. monotherapy with the XR formulation of quetiapine. In the intra-individual controls, no trends could be identified, possibly due to insufficient number of samples (p > 0.05).

CONCLUSIONS

The combination of quetiapine IR with lamotrigine is associated with significantly lower drug concentrations of quetiapine, potentially impacting quetiapine effectiveness. For quetiapine ER, a significant interaction is less likely.

Are there any potential drug-drug interactions with oral inhaler medications?: A retrospective study

Background

Oral inhaler medications (OIMs) are widely used for many respiratory diseases. Although OIMs have minimal systemic effects, they may cause potential drug-drug interactions (pDDIs).Objectives: This study aims to evaluate drug interactions in patients using OIMs.

Methods

This retrospective, and descriptive study was conducted in a community pharmacy in Istanbul (Turkey) between January 1, andMay 312,021. Prescriptions of all asthma and COPD patients aged 18 and over on the specified date were included in the study. Data were collected from the pharmacy information system. Sociodemograhic characteristics were recorded. pDDIs were analyzed via Medscape and Lexicomp drug interaction checker databases. Significant (monitor closely), Serious (use alternative), Contraindicated categories in the Medscape database and D (consider treatment modification) and X (avoid combination) categories in the Lexi-Interact™ database were evaluated as pDDIs. SPSS analysis was performed.

Results

A total of 54 asthma and 42 chronic obstructive pulmonary disease (COPD) patients were included in the study. Most asthma (76%) and COPD (83%) patients were found to have at least one comorbid disease. A total of81 pDDIs were identified in the Medscape database in asthma patients, and 86.5% of them were classified as "monitor closely". A total of 12 drug interactions were detected in the Lexicomp database, with 75% of them were "D" category for asthma patients. In the prescriptions of COPD patients, a total of 162 drug interactions were determined via the Medscape database, with 94.4% classified as "monitor closely". A total of 13 drug interactions were detected in the Lexicomp database, with 61.5% of them falling into the "X" category for COPD patients.

Conclusions

According to the results of this study COPD patients who may be at a high risk of experiencing pDDIs. Healthcare providers should consider the individual patient's clinical profile, including comorbidities and medication regimen, to minimize the risk of pDDIs and optimize treatment outcomes. Further research is needed to elucidate the mechanisms underlying these findings and develop tailored strategies to diminish the risks associated with pDDIs in respiratory disease management.

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population

Drug development teams must evaluate the risk/benefit profile of new drug candidates that perpetrate drug-drug interactions (DDIs). Real-world data (RWD) can inform this decision. The purpose of this study was to develop a predicted impact score for DDIs perpetrated by three hypothetical drug candidates via CYP3A, CYP2D6, or CYP2C9 in type 2 diabetes mellitus (T2DM), obesity, or migraine. Optum Market Clarity was analyzed to estimate use of CYP3A, CYP2D6, or CYP2C9 substrates classified in the University of Washington Drug Interaction Database as moderate sensitive, sensitive, narrow therapeutic index, or QT prolongation. Scoring was based on prevalence of exposure to victim substrates and characteristics (age, polypharmacy, duration of exposure, and number of prescribers) of those exposed. The study population of 14,163,271 adults included 1,579,054 with T2DM, 3,117,753 with obesity, and 410,436 with migraine. For T2DM, 71.3% used CYP3A substrates, 44.3% used CYP2D6 substrates, and 44.3% used CYP2C9 substrates. For obesity, 57.1% used CYP3A substrates, 34.6% used CYP2D6 substrates, and 31.0% used CYP2C9 substrates. For migraine, 64.1% used CYP3A substrates, 44.0% used CYP2D6 substrates, and 28.9% used CYP2C9 substrates. In our analyses, the predicted DDI impact scores were highest for DDIs involving CYP3A, followed by CYP2D6, and CYP2C9 substrates, and highest for T2DM, followed by migraine, and obesity. Insights from RWD can be used to estimate a predicted DDI impact score for pharmacokinetic DDIs perpetrated by new drug candidates currently in development. This score can inform the risk/benefit profile of new drug candidates in a target patient population.

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.

Plasma Concentrations of Rosmarinic Acid in Patients on Antiretroviral Therapy: In Silico Exploration Based on Clinical Data

Rosmarinic acid (RA) is a phenolic compound with antiviral properties, often encountered in dietary supplements and herbal drugs. Data on the pharmacokinetics of RA are lacking in cases of the chronic use of supplements containing this compound, and only limited data on the metabolism and distribution of RA are available. The aim of the study was to investigate the plasma levels of RA after 12 weeks of use and determine potential interactions of RA and selected antiretroviral drugs. Patients infected with human immunodeficiency virus took a supplement containing RA for 12 weeks, after which the RA concentrations in the plasma samples were analyzed. A detailed in silico analysis was conducted in order to elucidate the potential interactions between RA and the drugs efavirenz, darunavir and raltegravir. It was found that RA can be detected in patients' plasma samples, mainly in the form of sulphoglucuronide. The potential interactions are suggested on the level of liver metabolizing enzymes and efflux P-glycoprotein, with RA competing with antiretroviral drugs as a substrate in metabolism and distribution systems. The present study suggests that the simultaneous use of RA and antiretroviral therapy (containing efavirenz, darunavir or raltegravir) may affect the plasma levels of RA after prolonged supplementation.

At the right dose: personalised (N-of-1) dosing for precision oncology

The objective of oncology therapeutics, especially in the age of precision medicine, is to give the right drug(s) to the right patient at the right time. Yet, a major challenge is finding the right dose for each patient. Determining safe and efficacious doses of oncology treatments, especially for novel combination therapies, can be challenging. Moreover, traditionally, dosing cancer drugs is based on giving each patient the same dose (a flat dose) or a dose based on surface area/weight. But patients' ability to tolerate drugs is influenced by additional factors including, but not limited to age, gender, race, comorbidities, organ function, and metabolism. Herein, we present evidence that, in the era of targeted drugs, individualised drug dosing determined by starting at reduced doses and using intrapatient dose escalation can yield safe and effective personalised dosing of novel combinations of approved drugs that have not previously undergone formal phase I trials and can also optimise dosing of tested drug regimens.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine

Drug-drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome P450 (CYP) enzymes, which can be inhibited or induced by co-administered drugs. Studying DDIs involving salbutamol is crucial for optimizing drug therapy and preventing adverse outcomes. Here, we aimed to investigate CYP-mediated DDIs between salbutamol and fluvoxamine through in silico approaches. The physiologically based pharmacokinetic (PBPK) model of salbutamol was developed and validated using available clinical PK data, whereas the PBPK model of fluvoxamine was previously verified by GastroPlus. Salbutamol-fluvoxamine interaction was simulated according to different regimens and patient's characteristics (age and physiological status). The results demonstrated that co-administering salbutamol with fluvoxamine enhanced salbutamol exposure in certain situations, especially when fluvoxamine dosage increased. To sum up, this study demonstrated the utility of PBPK modeling in predicting CYP-mediated DDIs, making it a pioneer in PK DDI research. Furthermore, this study provided insights into the relevance of regular monitoring of patients taking multiple medications, regardless of their characteristics, to prevent adverse outcomes and for the optimization of the therapeutic regimen, in cases where the therapeutic benefit is no longer experienced.

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data

INTRODUCTION

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

METHODS

The area under the curve ratio (AUC_ratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC_ratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

RESULTS

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

CONCLUSION

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

Opinion paper on the systematic application of integrated bioinformatic tools to actuate routine precision medicine in poly-treated patients

Precision Medicine is a reality in selected medical areas, as oncology, or in excellent healthcare structures, but it is still far to reach million patients who could benefit from this medical concept. Here, we sought to highlight how the time is ripe to achieve horizontal delivery to a significant larger audience of patients, represented by the poly-treated patients. Combination therapies are frequent (especially in the elderly, to treat comorbidities) and are related to decreased drug safety and efficacy, disease's exacerbation, additional treatments, hospitalization. But the recent development and validation of bioinformatic tools, aimed to automatic evaluation and optimization of poly-therapies, according to the unique individual characteristics (including genotype), is ready to change the daily approach to pharmacological prescription.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Simultaneous Pharmacokinetic Evaluation of Pantoprazole and Vitamin B Complex for Assessing Drug–Drug Interactions in Healthy Bangladeshi Adults by a Newly Developed and Validated HPLC Method

The present study has been designed to evaluate the impact of the co-administration of pantoprazole (PNT) with vitamin B (VTB) complex (VTB comprising VTB1, VTB6, and VTB12 in this study) on pharmacokinetic behavior. In this study, HPLC-based sensitive and efficient methods for simultaneous determination in human plasma were developed per US-FDA bioanalytical standards. The pharmacokinetic parameters of PNT, VTB1, VTB6, and VTB12 were also evaluated when the medicines were administered alone and co-administered. Following linearity, it was observed that the plasma PNT, VTB1, VTB6, and VTB12 retention times were 6.8 ± 0.2, 2.7 ± 0.1, 5.5 ± 0.2, and 3.8 ± 0.1 min, respectively, over the range of 1−100 μg/mL. For all analytes at the lower limit of quantification and all other values, intra-assay and inter-assay bias were within 15% and 13.5%, respectively. They barely interacted when PNT and VTB samples were evaluated in physical combinations through in vitro tests. Moreover, in the pharmacokinetics study, treatment with VTB did not significantly alter the pharmacokinetic characteristics of PNT. Therefore, the current work’s results might help assess drug–drug interactions that may be applied to bioequivalence studies and therapeutic drug monitoring.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

COVID-19 drugs: potential interaction with ATP-binding cassette transporters P-glycoprotein and breast cancer resistance protein

Background

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2, has resulted in acute respiratory distress, fatal systemic manifestations (extrapulmonary as well as pulmonary), and premature mortality among many patients. Therapy for COVID-19 has focused on the treatment of symptoms and of acute inflammation (cytokine storm) and the prevention of viral infection. Although the mechanism of COVID-19 is not fully understood, potential clinical targets have been identified for pharmacological, immunological, and vaccinal approaches.

Area covered

Pharmacological approaches including drug repositioning have been a priority for initial COVID-19 therapy due to the time-consuming nature of the vaccine development process. COVID-19 drugs have been shown to manage the antiviral infection cycle (cell entry and replication of proteins and genomic RNA) and anti-inflammation. In this review, we evaluated the interaction of current COVID-19 drugs with two ATP-binding cassette transporters [P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP)] and potential drug-drug interactions (DDIs) among COVID-19 drugs, especially those associated with P-gp and BCRP efflux transporters.

Expert opinion

Overall, understanding the pharmacodynamic/pharmacokinetic DDIs of COVID-19 drugs can be useful for pharmacological therapy in COVID-19 patients.

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.

Assessment of drug-drug interaction potential with EDP-305, a farnesoid X receptor agonist, in healthy subjects

EDP-305 is a farnesoid X receptor (FXR) agonist that selectively activates FXR and is a potential treatment for patients with nonalcoholic steatohepatitis (NASH) with liver fibrosis. Results from preclinical studies indicate that CYP3A4 is the primary enzyme involved in EDP-305 metabolism and that EDP-305 has low potential to inhibit or induce cytochrome (CYP) isoenzymes and drug transporters. Four studies were conducted in healthy volunteers to evaluate the drug-drug interaction (DDI) potential of EDP-305 co-administered with drugs known to be substrates for drug metabolizing enzymes or transporters, and to assess the effect of inhibitors and inducers of CYP3A4 on EDP-305. Results suggest caution when substrates of CYP3A4 are administered concomitantly with EDP-305. A potential for increased exposure is apparent when CYP1A2 substrates with a narrow therapeutic index are administered with EDP-305. In contrast, substrates of drug transporters can be administered concomitantly with EDP-305 with a low potential for interactions. Coadministration of EDP-305 and a combined OC had no relevant effects on plasma concentrations of the combined OC. Co-administration of EDP-305 with strong or moderate inhibitors and inducers of CYP3A4 is not recommended. These results indicate low overall likelihood of interaction of EDP-305 and other substrates through CYP mediated interactions. The interaction potential of EDP-305 with drug transporters was low and of unlikely clinical significance. The EDP-305 DDI profile allows for convenient administration in patients with NASH and other patient populations with comorbidities, with minimal dose modification of concomitant medications.

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.

Trends in FDA Transporter-Based Post Marketing Requirements and Commitments Over the Last Decade

Characterizing interactions between new molecular entities (NMEs) and drug transporters is a critical element of drug development that helps in assessing potential transporter-based drug-drug interactions (DDIs). However, not all NME new drug applications (NDAs) include a full characterization of NMEs transporter-based DDI, which necessitates the issuance of post marketing requirement (PMR)/post marketing commitment (PMC) by the US Food and Drug Administration (FDA) to characterize these potential interactions. The objective of this analysis is to identify trends in transporter-based PMRs/PMCs issued by the FDA between 2012 and 2021. A decrease in the number of transporter-based PMRs/PMCs was observed from 2012 to 2016 and an increasing trend in the number of PMRs/PMCs was observed after 2017. The majority of these transporter-based PMRs/PMCs requested clinical evaluation (48%), some requested in vitro assessment (38%), and 2.5% requested modeling and simulation assessment. Most of the PMRs/PMCs requested evaluation of NMEs as perpetrator with the efflux transporters, P-gp and/or BCRP (53%). Forty-eight percent of the PMRs/PMCs were fulfilled with 67% resulted in labelling updates. On average 2.5 years were needed for the information related to PMRs/PMCs to show in NMEs labeling. In conclusion, this analysis highlights the increased emphasis from the FDA on proper characterization of transporter-based DDI and call for the need of early characterization of NMEs-transporters interaction before initial NDA approval.

Physiologically based pharmacokinetic combined JAK2 occupancy modelling to simulate PK and PD of baricitinib with kidney transporter inhibitors and in patients with hepatic/renal impairment

PURPOSE

Our aim is to build a physiologically based pharmacokinetic and JAK2 occupancy model (PBPK-JO) to simultaneously predict pharmacokinetic (PK) and pharmacodynamic (PD) changes of baricitinib (BAR) in healthy humans when co-administrated with kidney transporters OAT3 and MATE2-K inhibitors, and in patients with hepatic and renal impairment.

METHODS

Probenecid and vandetanib were selected as OAT3 and MATE2-K competitive inhibitors, respectively. The PBPK-JO model was built using physicochemical and biochemical properties of BAR, and then verified by observed clinical PK. Finally, the model was applied to determine optimal dosing regimens in various clinical situations.

RESULTS

Here, we have successfully simulated PK and JAK2 occupancy profiles in humans by PBPK-JO model. Moreover, this modelling reproduced every observed PK data, and every mean relative deviation (MRD) was below 2. The simulation suggested that PK of BAR had a significant change (2.22-fold increase), however PD only had a slight increase of 1.14-fold. Additionally, the simulation also suggested that vandetanib was almost unlikely to affect the PK and PD of BAR. In simulations of hepatic and renal impairment patients, the predictions suggested that significant changes in the PK and PD of BAR occurred. However, there was a lower fold increase in JAK2 occupancy than in PK in patients relative to healthy individuals.

CONCLUSION

Administration dose adjustment of BAR when co-administrated with OAT3 inhibitors or in patients with hepatic or renal impairment should combine PK and PD changes of BAR, instead of only considering PK alteration.

ZY12201, A Potent TGR5 Agonist: Identification of a Novel Pan CYP450 Inhibitor Tool Compound for In-Vitro Assessment

BACKGROUND

Identification of clinical drug-drug interaction (DDI) risk is an important aspect of drug discovery and development owing to poly-pharmacy in present-day clinical therapy. Drug metabolizing enzymes (DME) plays important role in the efficacy and safety of drug candidates. Hence evaluation of a New Chemical Entity (NCE) as a victim or perpetrator is very crucial for DDI risk mitigation. ZY12201 (2-((2-(4-(1H-imidazol-1-yl) phenoxy) ethyl) thio)-5-(2-(3, 4- dimethoxy phenyl) propane-2-yl)-1-(4-fluorophenyl)-1H-imidazole) is a novel and potent Takeda-G-protein-receptor-5 (TGR-5) agonist. ZY12201 was evaluated in-vitro to investigate the DDI liabilities.

OBJECTIVE

The key objective was to evaluate the CYP inhibition potential of ZY12201 for an opportunity to use it as a tool compound for pan CYP inhibition activities.

METHOD

In-vitro drug metabolizing enzymes (DME) inhibition potential of ZY12201 was evaluated against major CYP isoforms (1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4/5), aldehyde oxidase (AO), monoamine oxidase (MAO), and flavin-containing monooxygenase (FMO in human liver cytosol/mitochondrial preparation/ microsomes using probe substrates and Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) method.

RESULTS

The study conducted on ZY12201 at 100 µM ZY12201 was found to reduce the metabolism of vanillin (AO probe substrate), tryptamine (MAO probe substrate), and benzydamine (FMO probe substrate) by 49.2%, 14.7%, and 34.9%, respectively. ZY12201 Ki values were 0.38, 0.25, 0.07, 0.01, 0.06, 0.02, 7.13, 0.03 and 0.003 μM for CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4/5 (substrate: testosterone) and CYP3A4/5 (substrate: midazolam), respectively. Time-dependant CYP inhibition potential of ZY12201 was assessed against CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4/5 and no apparent IC50 shift was observed.

CONCLUSIONS

ZY12201, at 100 µM concentration showed low inhibition potential of AO, MAO, and FMO. ZY12201 was found as a potent inhibitor of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4/5 while moderately inhibits to CYP2E1. Inhibition of CYP1A2, CYP2B6, CYP2C19, and CYP2E1 by ZY12201 was competitive, while inhibition of CYP2C8, CYP2C9, CYP2D6, and CYP3A4/5 was of mixed-mode. ZY12201 is a non-time-dependent inhibitor of CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4/5. In summary, the reported Ki values unequivocally support that ZY12201 has a high potential to inhibit all major CYP isoforms. ZY12201 can be effectively used as a tool compound for in-vitro evaluation of CYP-based metabolic contribution to total drug clearance in the lead optimization stage of Drug Discovery Research.

Clinically Significant Cytochrome P450-Mediated Drug-Drug Interactions in Children Admitted to Intensive Care Units

OBJECTIVES

Children admitted to intensive care units (ICUs) often require multiple medications due to the complexity and severity of their disease, which put them at an increased risk for drug interactions. This study examined cytochrome P450-mediated drug-drug interactions (DDIs) based on the Pediatric Intensive Care (PIC) database, with the aim of analyzing the incidence of clinically significant potential drug-drug interactions (pDDIs) and exploring the occurrence of actual adverse reactions.

METHODS

The Lexicomp database was used to screen cytochrome P450-mediated DDI pairings with good levels of reliability and clear clinical phenotypes. Patients exposed to the above drug pairs during the same period were screened in the PIC database. The incidence of clinically significant pDDIs was calculated, and the occurrence of adverse reactions was explored based on laboratory measurements.

RESULTS

In total, 84 (1.21%) of 6920 children who used two or more drugs were exposed to at least one clinically significant pDDI. All pDDIs were based on CYP3A4, with nifedipine + voriconazole (39.60%) being the most common drug pair, and the most frequent being the J02 class of drugs. Based on laboratory measurements, 15 adverse reactions were identified in 12 patients.

CONCLUSIONS

Clinically significant cytochrome P450-mediated pDDIs existed in the children admitted to ICUs, and some of the pDDIs led to adverse clinical outcomes. The use of clinical decision support systems can guide clinical medication use, and clinical monitoring of patients' needs has to be enhanced.

Pharmacokinetic Drug Interaction Study of Sorafenib and Morphine in Rats

A combination of the tyrosine kinase inhibitor—sorafenib—and the opioid analgesic—morphine—can be found in the treatment of cancer patients. Since both are substrates of P-glycoprotein (P-gp), and sorafenib is also an inhibitor of P-gp, their co-administration may affect their pharmacokinetics, and thus the safety and efficacy of cancer therapy. Therefore, the aim of this study was to evaluate the potential pharmacokinetic drug–drug interactions between sorafenib and morphine using an animal model. The rats were divided into three groups that Received: sorafenib and morphine (I_SOR+MF), sorafenib (II_SOR), and morphine (III_MF). Morphine caused a significant increase in maximum plasma concentrations (C_max) and the area under the plasma concentration–time curves (AUC_0–t, and AUC_0–∞) of sorafenib by 108.3 (p = 0.003), 55.9 (p = 0.0115), and 62.7% (p = 0.0115), respectively. Also, the C_max and AUC_0–t of its active metabolite—sorafenib N-oxide—was significantly increased in the presence of morphine (p = 0.0022 and p = 0.0268, respectively). Sorafenib, in turn, caused a significant increase in the C_max of morphine (by 0.5-fold, p = 0.0018). Moreover, in the presence of sorafenib the C_max, AUC_0–t, and AUC_0–∞ of the morphine metabolite M3G increased by 112.62 (p < 0.0001), 46.82 (p = 0.0124), and 46.78% (p = 0.0121), respectively. Observed changes in sorafenib and morphine may be of clinical significance. The increased exposure to both drugs may improve the response to therapy in cancer patients, but on the other hand, increase the risk of adverse effects.

Application of Physiologically Based Pharmacokinetic Modeling to Evaluate the Drug-Drug and Drug-Disease Interactions of Apatinib

Aim: Apatinib is an orally administered vascular epidermal growth factor receptor (VEGFR)-tyrosine kinase inhibitors approved for the treatment of advanced gastric adenocarcinoma or gastric esophageal junction adenocarcinoma. Apatinib is predominantly metabolized by CYP3A4/5, followed by CYP2D6. The present study aimed to evaluate the potential drug–drug interaction (DDI) and drug–disease interaction (DDZI) risks of apatinib in Chinese volunteers.

Methods: Modeling and simulation were conducted using Simcyp Simulator. The input parameters required for modeling were obtained from literature research or experiments. Then, the developed physiologically based pharmacokinetic (PBPK) models were applied to evaluate single-dose DDI potential in Chinese healthy volunteers with weak and moderate CYP3A inhibitors, strong CYP2D6 inhibitors, as well as CYP3A4 inducers. The DDZI potential was also predicted in patients with hepatic or renal impairment.

Results: The developed PBPK models accurately assessed apatinib pharmacokinetics following single-dose administration in Chinese healthy volunteers and cancer patients. The DDI simulation showed 2–4-fold changes in apatinib exposures by moderate CYP3A4 inhibitors and CYP3A4 inducers. A moderate increase of apatinib exposure (1.25–2-fold) was found with strong CYP2D6 inhibitor. In the DDZI simulation with hepatic impairment, the AUC of apatinib was significantly increased by 2.25-fold and 3.04-fold for Child–Pugh B and Child–Pugh C, respectively, with slightly decreased C_max by 1.54 and 1.67-fold, respectively.

Conclusion: The PBPK models developed in the present study would be highly beneficial to quantitatively predict the pharmacokinetic changes of apatinib under different circumstances, which might be difficult to evaluate clinically, so as to avoid some risks in advance.

The role of DMPK science in improving pharmaceutical research and development efficiency

The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.

The Influence of Wuzhi Capsule on the Pharmacokinetics of Cyclophosphamide

BACKGROUND

Cyclophosphamide is approved for the treatment of a variety of tumors, yet the use of cyclophosphamide is limited by kidney and liver toxicity. In the clinic, the Wuzhi capsule is approved to attenuate cyclophosphamide toxicity in the kidney and liver.

OBJECTIVE

We aimed to investigate the effects of the principal ingredients of Wuzhi capsule, schisandrin A (SIA) and schisantherin A (STA), on the pharmacokinetics of cyclophosphamide.

METHODS

The essential pharmacokinetic data and physicochemical parameters of SIA, STA, and cyclophosphamide were collected. Physiologically based pharmacokinetic (PBPK) models of SIA, STA, and cyclophosphamide were built in Simcyp Simulator and verified using published clinical pharmacokinetic data. The verified PBPK models were used to predict potential herb-drug interactions (HDIs) between cyclophosphamide and SIA and STA in cancer patients.

RESULTS

The area under the plasma concentration-time curve (AUC) of cyclophosphamide was increased by 18% and 1% when co-administered with STA and SIA at a single dose, respectively, and increased by 301% and 29% when co-administered with STA and SIA at multiple doses, respectively. The maximum concentration (Cmax) of cyclophosphamide was increased by 75% and 7% when co-administered with STA and SIA at multiple doses, respectively.

CONCLUSION

The AUC and Cmax of cyclophosphamide were increased when cyclophosphamide was combined with the Wuzhi capsule, compared to cyclophosphamide alone. Our study shows that the adverse drug reactions and toxicity of cyclophosphamide should be closely monitored and an effective dosage adjustment of cyclophosphamide may need to be considered when co-administered with the Wuzhi capsule.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy

We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.

Effects of Magnesium, Calcium, and Aluminum Chelation on Fluoroquinolone Absorption Rate and Bioavailability: A Computational Study

Fluoroquinolones (FQs) are a widespread class of broad-spectrum antibiotics prescribed as a first line of defense, and, in some cases, as the only treatment against bacterial infection. However, when administered orally, reduced absorption and bioavailability can occur due to chelation in the gastrointestinal tract (GIT) with multivalent metal cations acquired from diet, coadministered compounds (sucralfate, didanosine), or drug formulation. Predicting the extent to which this interaction reduces in vivo antibiotic absorption and systemic exposure remains desirable yet challenging. In this study, we focus on quinolone interactions with magnesium, calcium and aluminum as found in dietary supplements, antacids (Maalox) orally administered therapies (sucralfate, didanosine). The effect of FQ-metal complexation on absorption rate was investigated through a combined molecular and pharmacokinetic (PK) modeling study. Quantum mechanical calculations elucidated FQ-metal binding energies, which were leveraged to predict the magnitude of reduced bioavailability via a quantitative structure-property relationship (QSPR). This work will help inform clinical FQ formulation design, alert to possible dietary effects, and shed light on drug-drug interactions resulting from coadministration at an earlier stage in the drug development pipeline.