Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

Comprehensive Evaluation of OATP- and BCRP-Mediated Drug-Drug Interactions of Methotrexate Using Physiologically-Based Pharmacokinetic Modeling

Methotrexate (MTX) is an antifolate agent widely used for treating conditions such as rheumatoid arthritis and hematologic cancer. This study aimed to quantitatively interpret the drug-drug interactions (DDIs) of MTX mediated by drug transporters using physiologically-based pharmacokinetic (PBPK) modeling. An open-label, randomized, 4-treatment, 6-sequence, 4-period crossover study was conducted to investigate the effects of rifampicin (RFP), an inhibitor of organic anionic transporting peptides (OATP) 1B1/3, and febuxostat (FBX), an inhibitor of breast cancer resistance protein (BCRP), on the pharmacokinetics of MTX in healthy volunteers. PBPK models of MTX, RFP, and FBX were developed based on in vitro and in vivo data, and the performance of the simulation results for final PBPK models was validated in a clinical study. In the clinical study, when MTX was co-administered with RFP or FBX, systemic exposure of MTX increased by 33% and 17%, respectively, compared with that when MTX was administered alone. When MTX was co-administered with RFP and FBX, systemic exposure increased by 52% compared with that when MTX was administered alone. The final PBPK model showed a good prediction performance for the observed clinical data. The PBPK model of MTX was well developed in this study and can be used as a potential mechanistic model to predict and evaluate drug transporter-mediated DDIs of MTX with other drugs.

Applications of model simulation in pharmacological fields and the problems of theoretical reliability

The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

Physiologically-based pharmacokinetic model of sparsentan to evaluate drug-drug interaction potential

Sparsentan is a dual endothelin/angiotensin II receptor antagonist indicated to reduce proteinuria in patients with primary IgA nephropathy at high risk of disease progression. In vitro data indicate that sparsentan is likely to inhibit or induce various CYP enzymes at therapeutic concentrations. Sparsentan as a victim and perpetrator of CYP3A4 mediated drug-drug interactions (DDIs) has been assessed clinically. A mechanistic, bottom-up, physiologically-based pharmacokinetic (PK) model for sparsentan was developed based on in vitro data of drug solubility, formulation dissolution and particle size, drug permeability, inhibition and induction of metabolic enzymes, and P-glycoprotein (P-gp) driven efflux. The model was verified using clinical PK data from healthy adult volunteers administered single and multiple doses in the fasted and fed states for a wide range of sparsentan doses. The model was also verified by simulation of clinically observed DDIs. The verified model was then used to test various DDI simulations of sparsentan as a perpetrator and victim of CYP3A4 using an expanded set of inducers and inhibitors with varying potency. Additional perpetrator and victim DDI simulations were performed using probes for CYP2C9 and CYP2C19. Simulations were conducted to predict the effect of complete inhibition of P-gp inhibition on sparsentan absorption and clearance. The predictive simulations indicated that exposure of sparsentan could increase greater than two-fold if co-administered with a strong CYP3A4 inhibitor, such as itraconazole. Other potential DDI interactions as victim or perpetrator were all within two-fold of control. The effect of complete P-gp inhibition on sparsentan PK was negligible.

MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events

The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration. (i) The global features of drug molecules should be paid attention to, rather than just their local characteristics. (ii) The fusion of multi-source features should also be studied to capture the comprehensive features of the drug. This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better than state-of-the-art models. Especially in cold start scenarios, the model can achieve the best performance. The effectiveness of the model is also supported by the case study on nervous system drugs. The source codes and data are available at https://github.com/BioCenter-SHU/MSFF-MA-DDI.

PBPK modeling to predict the pharmacokinetics of pantoprazole in different CYP2C19 genotypes

Pantoprazole is used to treat gastroesophageal reflux disease (GERD), maintain healing of erosive esophagitis (EE), and control symptoms related to Zollinger-Ellison syndrome (ZES). Pantoprazole is mainly metabolized by cytochrome P450 (CYP) 2C19, converting to 4'-demethyl pantoprazole. CYP2C19 is a genetically polymorphic enzyme, and the genetic polymorphism affects the pharmacokinetics and/or pharmacodynamics of pantoprazole. In this study, we aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of pantoprazole in populations with various CYP2C19 metabolic activities. A comprehensive investigation of previous reports and drug databases was conducted to collect the clinical pharmacogenomic data, physicochemical data, and disposition properties of pantoprazole, and the collected data were used for model establishment. The model was evaluated by comparing the predicted plasma concentration-time profiles and/or pharmacokinetic parameters (AUC and Cmax) with the clinical observation results. The predicted plasma concentration-time profiles in different CYP2C19 phenotypes properly captured the observed profiles. All fold error values for AUC and Cmax were included in the two-fold range. Consequently, the minimal PBPK model for pantoprazole related to CYP2C19 genetic polymorphism was properly established and it can predict the pharmacokinetics of pantoprazole in different CYP2C19 phenotypes. The present model can broaden the insight into the individualized pharmacotherapy for pantoprazole.

The development and impact of an app for a smart drug interaction reminder system

BACKGROUND

Improved access to media and medical knowledge has elicited stronger public health awareness.

OBJECTIVE

This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload.

METHODS

This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction.

RESULTS

The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions.

CONCLUSION

Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model

Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.

Pharmacokinetics during therapeutic hypothermia in neonates: from pathophysiology to translational knowledge and physiologically-based pharmacokinetic (PBPK) modeling

INTRODUCTION

Perinatal asphyxia (PA) still causes significant morbidity and mortality. Therapeutic hypothermia (TH) is the only effective therapy for neonates with moderate to severe hypoxic-ischemic encephalopathy after PA. These neonates need additional pharmacotherapy, and both PA and TH may impact physiology and, consequently, pharmacokinetics (PK) and pharmacodynamics (PD).

AREAS COVERED

This review provides an overview of the available knowledge in PubMed (until November 2022) on the pathophysiology of neonates with PA/TH. In vivo pig models for this setting enable distinguishing the effect of PA versus TH on PK and translating this effect to human neonates. Available asphyxia pig models and methodological considerations are described. A summary of human neonatal PK of supportive pharmacotherapy to improve neurodevelopmental outcomes is provided.

EXPERT OPINION

To support drug development for this population, knowledge from clinical observations (PK data, real-world data on physiology), preclinical (in vitro and in vivo (minipig)) data, and molecular and cellular biology insights can be integrated into a predictive physiologically-based PK (PBPK) framework, as illustrated by the I-PREDICT project (Innovative physiology-based pharmacokinetic model to predict drug exposure in neonates undergoing cooling therapy). Current knowledge, challenges, and expert opinion on the future directions of this research topic are provided.

Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes

BACKGROUND

Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages.

METHODS

Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method.

RESULT

Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred.

CONCLUSION

This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

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.

Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole

Albendazole (ABZ) is a weakly basic drug that undergoes extensive presystemic metabolism after oral administration and converts to its active form albendazole sulfoxide (ABZ_SO). The absorption of albendazole is limited by poor aqueous solubility, and dissolution is the rate-limiting step in the overall exposure of ABZ_SO. In this study, PBPK modeling was used to identify formulation-specific parameters that impact the oral bioavailability of ABZ_SO. In vitro experiments were carried out to determine pH solubility, precipitation kinetics, particle size distribution, and biorelevant solubility. A transfer experiment was conducted to determine the precipitation kinetics. A PBPK model for ABZ and ABZ_SO was developed using the Simcyp™ Simulator based on parameter estimates from in vitro experiments. Sensitivity analyses were performed to assess the impact of physiological parameters and formulation-related parameters on the systemic exposure of ABZ_SO. Model simulations predicted that increased gastric pH significantly reduced ABZ absorption and, subsequently, ABZ_SO systemic exposure. Reducing the particle size below 50 µm did not improve the bioavailability of ABZ. Modeling results illustrated that systemic exposure of ABZ_SO was enhanced by increasing solubility or supersaturation and decreasing the drug precipitation of ABZ at the intestinal pH level. These results were used to identify potential formulation strategies to enhance the oral bioavailability of ABZ_SO.

Clinical Evidence on the Purported Pharmacokinetic Interactions between Corticosteroids and Mycophenolic Acid

Corticosteroids (steroids) are commonly used concurrently with mycophenolic acid (MPA) as the first-line immunosuppression therapy for the prevention of rejection in solid organ transplantations. Steroids are also commonly administered with MPA in various autoimmune disorders such as systemic lupus erythematosus and idiopathic nephrotic syndrome. Despite various review articles having suggested the presence of pharmacokinetic interactions between MPA and steroids, definitive data have not yet been demonstrated. The aim of this Current Opinion is to critically evaluate the available clinical data and propose the optimal study design for characterising the MPA-steroid pharmacokinetic interactions. The PubMed and Embase databases were searched for relevant clinical articles in English as of September 29, 2022, where a total of 8 papers have been identified as supporting and 22 as non-supporting the purported drug interaction. To objectively evaluate the data, novel assessment criteria to effectively diagnose the interaction based on known MPA pharmacology were formulated, including the availability of independent control groups, prednisolone concentrations, MPA metabolite data, unbound MPA concentrations, and the characterisations of entero-hepatic recirculation and MPA renal clearance. Overall, the majority of the identified corticosteroid data were pertaining to prednisone or prednisolone. Our assessment indicated that no conclusive mechanistic data supporting the interaction are available in the current clinical literature, and further studies are required to quantify the effects/mechanisms of steroid-tapering or withdrawal on MPA pharmacokinetics. This current opinion provides justification for further translational investigations, as this particular drug interaction has the potential to exert significant adverse outcomes in patients prescribed MPA.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

A Comparative Analysis of Physiologically Based Pharmacokinetic Models for Human Immunodeficiency Virus and Tuberculosis Infections

Physiologically based pharmacokinetic (PBPK) models have gained in popularity in the last decade in both drug development and regulatory science. PBPK models differ from classical pharmacokinetic models in that they include specific compartments for tissues involved in exposure, toxicity, biotransformation, and clearance processes connected by blood flow. This study aimed to address the gaps between the mathematics and pharmacology framework observed in the literature. These gaps included nonconserved systems of equations and compartment concentration that were not biologically relatable to the tissues of interest. The resulting system of nonlinear differential equations is solved numerically with various methods for benchmarking and comparison. Furthermore, a sensitivity analysis of all parameters were conducted to elucidate the critical parameters of the model. The resulting model was fit to clinical data as a performance benchmark. The clinical data captured the second line of antiretroviral treatment, lopinavir and ritonavir. The model and clinical data correlate well for coadministration of lopinavir/ritonavir with rifampin. Drug-drug interaction was captured between lopinavir and rifampin. This article provides conclusions about the suitability of physiologically based pharmacokinetic models for the prediction of drug-drug interaction and antiretroviral and anti-TB pharmacokinetics.

Complexity and clinical significance of drug-drug interactions (DDIs) in oncology: challenging issues in the care of patients regarding cancer-associated thrombosis (CAT)

Cancer patients have an increased risk of developing venous thromboembolic events. Anticoagulation management includes prophylactic or therapeutic doses of low molecular weight heparins (LMWHs) or direct oral anticoagulants (DOACs). However, the management of thrombosis in patients with cancer is complex due to various individual and disease-related factors, including drug–drug interactions (DDIs). Furthermore, DDIs may impact both, cancer and venous thrombosis, treatment effectiveness and safety; their relevance is highlighted by the advances in cancer therapeutics. Given that these new oncology drugs are extensively used, more attention should be given to monitoring potential DDIs to minimize risks. Recognition of DDIs is of utmost importance in an era of rapid developments in cancer treatments and introduction of novel treatments and protocols. When managing cancer-associated thrombosis (CAT), the concomitant use of a DOAC and a moderate or strong modulator (inhibitor or inducer) of CYP3A4 or a P-glycoprotein (P-gp) is most likely to be associated with significant DDIs. Therefore, LMWHs remain the first-line option for the long-term management of CAT under these circumstances and physicians must consider utilizing LMWHs as first line. This review describes the risk of DDIs and their potential impact and outcomes in patients with cancer associated thrombosis (CAT) receiving anticoagulation.

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

Solute carrier (SLC) transporters present as the loci of important drug-drug interactions (DDIs). Therefore, sponsors generate in vitro half-maximal inhibitory concentration (IC₅₀ ) data and apply regulatory agency-guided "static" methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false-positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (C_max,u )/IC₅₀ ratio ≥ 0.02), investigators have attempted to deploy plasma- and urine-based SLC biomarkers in phase I studies to de-risk DDI and obviate the need for drug probe-based studies. In this regard, it was possible to generate in-house in vitro SLC IC₅₀ data for various clinically (biomarker)-qualified perpetrator drugs, under standard assay conditions, and then estimate "% inhibition" for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUC_inhibitor /AUC_reference ) and % decrease in renal clearance (ΔCL_renal )). After such a "calibration" exercise, it was determined that only compounds with high R values (> 1.5) and C_max,u /IC₅₀ ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCL_renal  > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan-SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.

Can thromboprophylaxis build a link for cancer patients undergoing surgical and/or chemotherapy treatment? The MeTHOS cohort study

BACKGROUND

Patients with active cancer have a 4-sevenfold increased risk for venous thromboembolism (VTE) especially during systematic anticancer treatment. Simultaneously, surgery is an additional risk factor.

METHODS

The Metaxas's Hospital THromboprophylaxis program in Oncological & Surgical Patients (MeTHOS) is a prospective, phase IV, observational, non-interventional cohort study, aiming to record the thromboprophylaxis practice patterns in high-risk active cancer patients undergoing surgical and/or chemotherapy treatment.

RESULTS

We are reporting results from 291 ambulatory patients (median age: 67 years, Q1-Q3: 59-73 years, 54.6% males) who received anti-neoplastic treatment and administered thromboprophylaxis. 59.8% had cardiovascular disease (mostly hypertension), 76.6% were reported as having at least one comorbidity, while 27.5% and 15.8% accumulated two and three comorbidities, respectively. 94.9% of the patients were receiving highly thrombogenic agents such as platinum-based agents, 5-FU, immunotherapy, antiangiogenics/anti-VEGF, or erythropoietin. 26.5% of the patients were initially surgically treated. In terms of anticoagulation, all patients were treated with tinzaparin (fixed dose, 10,000 Anti-Xa IU, OD). The median anticoagulation duration was 6.2 months. Six thrombotic events were observed (2.06%, 95% CI: 0.76-4.43%): 5 were DVT, and one PE. With respect to safety, 7 bleeding events occurred (2.6%, 95% CI: 1.0-5.3%); 6 of them were minor.

CONCLUSIONS

Thromboprophylaxis with LMWH in patients with active cancer and high thrombotic burden was safe and effective. Intermediate dose of tinzaparin seems to be an appropriate agent for cancer-associated thromboprophylaxis management.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: NCT04248348.

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.

Anticoagulation for atrial fibrillation in active cancer (Review)

Atrial fibrillation (AF) may often pre-exist in patients with newly diagnosed cancer or occur with increased frequency shortly after cancer diagnosis. Patients with active cancer and AF have a particularly high risk of thromboembolic complications, as both conditions carry a risk of thrombosis. Thromboembolic risk is determined by several factors, including advanced age, sex (females), cancer histology (adenocarcinomas), location (e.g., pancreas, stomach), advanced stage, anticancer regimens (e.g., platinum compounds, anti-angiogenic therapies, immune modulators), comorbidities (e.g., obesity, kidney disease) and concurrent therapies (e.g., surgery, central catheters). Physicians are often reluctant to prescribe anticoagulants to patients with active cancer and AF, mainly due to fear of bleeding complications, which is partly related to the paucity of evidence in the field. Decision making regarding anticoagulation for the prevention of ischemic stroke and systemic embolism in patients with active cancer and AF may be challenging and should not simply rely on the risk prediction scores used in the general AF population. By contrast, the administration and choice of anticoagulants should be based on the comprehensive, individualized and periodic evaluation of thromboembolic and bleeding risk, drug-drug interactions, patient preferences and access to therapies.

Site-specific protein modification by 3-n-butylphthalide in primary hepatocytes: Covalent protein adducts diminished by glutathione and N-acetylcysteine

AIMS

3-n-Butylphthalide (NBP) is widely used for the treatment of cerebral ischaemic stroke but can causeliver injury in clinical practice. This study aims to elucidate the underlying mechanisms and propose potential preventive strategies.

MAIN METHODS

NBP and its four major metabolites, 3-hydroxy-NBP (3-OH-NBP), 10-hydroxy-NBP, 10-keto-NBP and NBP-11-oic acid, were synthesized and evaluated in primary human or rat hepatocytes (PHHs, PRHs). NBP-related substances or amino acid adducts were identified and semi-quantitated by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). The target proteins and binding sites were identified by shotgun proteomics based on peptide mass fingerprinting coupled with tandem mass spectrometry and verified by molecular docking.

KEY FINDINGS

The toxicity of NBP and its four major metabolites were compared in both PHHs and PRHs, and 3-OH-NBP was found to be the most toxic metabolite. 3-OH-NBP induced remarkable cell death and oxidative stresses in hepatocytes, which correlated well with the levels of glutathione and N-acetylcysteine adducts (3-GSH-NBP and 3-NAC-NBP) in cell supernatants. Additionally, 3-OH-NBP covalently conjugated with intracellular Cys, Lys and Ser, with preferable binding to Cys sites at Myh9 Cys1380, Prdx4 Cys53, Vdac2 Cys48 and Vdac3 Cys36. Furthermore, we found that CYP3A4 induction by rifampicin augmented NBP-induced cell toxicity and supplementing with GSH or NAC alleviated the oxidative stresses and reactive metabolites caused by 3-OH-NBP.

SIGNIFICANCE

Our work suggests that glutathione depletion, mitochondrial injury and covalent protein modification are the main causes of NBP-induced hepatotoxicity, which may be prevented by exogenous GSH or NAC supplementation and avoiding concomitant use of CYP3A4 inducers.

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.

Metabolism‑related pharmacokinetic drug‑drug interactions with poly (ADP‑ribose) polymerase inhibitors (Review)

Poly (ADP‑ribose) polymerase (PARP) inhibitors, including olaparib, niraparib, rucaparib, talazoparib and veliparib, have emerged as one of the most exciting new treatments for solid tumors, particularly in patients with breast‑related cancer antigen 1/2 mutations. Oral administration is convenient and shows favorable compliance with the majority of patients, but it may be affected by numerous factors, including food, metabolic enzymes and transporters. These interactions may be associated with serious adverse drug reactions or may reduce the treatment efficacy of PARP inhibitors. In fact, numerous pharmacokinetic (PK)‑based drug‑drug interactions (DDIs) involve the metabolism of PARP inhibitors, particularly those metabolized via cytochrome P450 enzymes. The present review aims to characterize and summarize the metabolism‑related PK‑based DDIs of PARP inhibitors, and to provide specific recommendations for reducing the risk of clinically significant DDIs.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Analysis of inhibition kinetics of three beverage ingredients, bergamottin, dihydroxybergamottin and resveratrol, on CYP2C9 activity

Some grapefruit juice (GFJ) ingredients and resveratrol, a fruit-derived phytoalexin, are known to inhibit cytochrome P450 (CYP) 2C9. However, their inhibition modes and detailed inhibition kinetics remain undetermined. This study aimed to investigate the inhibitory effects of two GFJ ingredients, bergamottin (BG) and dihydroxybergamottin (DHB), and resveratrol on CYP2C9 activity in vitro. DHB inhibited CYP2C9 activity, as assessed by warfarin 7-hydroxylation, in a preincubation time-dependent manner (i.e., mechanism-based inhibition; MBI), in the same manner as CYP2C19 and CYP3A4. The maximal inactivation rate (k_inact,max) was 0.0638 min^-1 and 0.12- and 0.26-fold of that for CYP2C19 and CYP3A4, respectively. BG showed both MBI and time-independent competitive inhibition. Resveratrol showed non-competitive inhibition with an inhibition constant (K_i) of 3.64 μM. Unlike the inhibition of CYP2C19 and CYP3A4, resveratrol did not induce MBI. These findings are important for estimating the risk of drug interactions between CYP2C9 substrates and some beverages. (146 words).

PBPK Analysis to Study the Impact of Genetic Polymorphism of NAT2 on Drug-Drug Interaction Potential of Isoniazid

PURPOSE

Isoniazid (INH) is prescribed both for the prophylaxis as well as the treatment of tuberculosis. It is primarily metabolized through acetylation by a highly polymorphic enzyme, N-acetyl transferase 2 (NAT2), owing to which significant variable systemic drug levels have been reported among slow and rapid acetylators. Furthermore, many drugs, like phenytoin, diazepam, triazolam, etc., are known to show toxic manifestation when co-administered with INH and it happens prominently among slow acetylators. Additionally, it is revealed in in vitro inhibition studies that INH carries noteworthy potential to inhibit CYP2C19 and CYP3A4 enzymes. However, CYP inhibitory effect of INH gets masked by opposite enzyme-inducing effect of rifampicin, when used in combination. Thus, distinct objective of this study was to fill the knowledge gaps related to gene-drug-drug interactions (DDI) potential of INH when given alone for prophylactic purpose.

METHODS

Whole body-PBPK models of INH were developed and verified for both slow and fast acetylators. The same were then utilized to carry out prospective DDI studies with CYP2C19 and CYP3A4 substrates in both acetylator types.

RESULTS

The results highlighted likelihood of significant higher blood levels of CYP2C19 and CYP3A4 substrate drugs in subjects receiving INH pre-treatment. It was also re-established that interaction was more likely in slow acetylators, as compared to rapid acetylators.

CONCLUSION

The novel outcome of the present study is the indication that prescribers should give careful consideration while advising CYP2C19 and CYP3A4 substrate drugs to subjects who are on prophylaxis INH therapy, and are slow to metabolic acetylation.

Translational aspects of cytochrome P450-mediated drug-drug interactions: A case study with clopidogrel

Multimorbidity, polypharmacotherapy and drug interactions are increasingly common in the ageing population. Many drug-drug interactions (DDIs) are caused by perpetrator drugs inhibiting or inducing cytochrome P450 (CYP) enzymes, resulting in alterations of the plasma concentrations of a victim drug. DDIs can have a major negative health impact, and in the past, unrecognized DDIs have resulted in drug withdrawals from the market. Signals to investigate DDIs may emerge from a variety of sources. Nowadays, standard methods are widely available to identify and characterize the mechanisms of CYP-mediated DDIs in vitro. Clinical pharmacokinetic studies, in turn, provide experimental data on pharmacokinetic outcomes of DDIs. Physiologically based pharmacokinetic (PBPK) modelling utilizing both in vitro and in vivo data is a powerful tool to predict different DDI scenarios. Finally, epidemiological studies can provide estimates on the health outcomes of DDIs. Thus, to fully characterize the mechanisms, clinical effects and implications of CYP-mediated DDIs, translational research approaches are required. This minireview provides an overview of translational approaches to study CYP-mediated DDIs, going beyond regulatory DDI guidelines, and an illustrative case study of how the DDI potential of clopidogrel was unveiled by combining these different methods.

Prediction of Rivaroxaban-Rifampin Interaction After Major Orthopedic Surgery: Physiologically Based Pharmacokinetic Modeling and Simulation

Rivaroxaban is commonly used for the prophylaxis of venous thromboembolism (VTE) for patients undergoing major orthopedic surgery. Rivaroxaban is primarily eliminated by hepatic CYP450 metabolism and renal excretion. Rifampin is a commonly used antibiotic for prosthetic joint infections (PJI) and a potent inducer of CYP450 enzymes. Clinical data about drug-drug interactions of rivaroxaban and rifampin are limited. The present study is to describe DDI of rivaroxaban and rifampin in several prosthetic joint infections patients undergoing major orthopedic surgery. We retrospectively identified six patients concomitantly administered with rivaroxaban and rifampin between 2019 and 2020. Plasma samples of these patients with accurate sampling time were chosen from the biobank and plasma levels of rivaroxaban were measured at each time point. A physiologically based pharmacokinetic model for the rivaroxaban-rifampin interaction was developed to predict the optimal dosing regimen of rivaroxaban in the case of co-medication with rifampin. The model was validated by the observed plasma concentration of rivaroxaban from the above patients. From this model, it could be simulated that when rifampin starts or stops, gradually changing rivaroxaban dose during the first few days would elevate the efficacy and safety of rivaroxaban.

Classification of drugs for evaluating drug interaction in drug development and clinical management

During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro studies. The obtained information should be provided efficiently to medical experts through package inserts and various information materials after the drug's launch. A recently updated Japanese guideline presents general procedures that are considered scientifically valid at the present moment. In this review, we aim to highlight the viewpoints of the Japanese guideline and enumerate drugs that were involved or are anticipated to be involved in evident pharmacokinetic drug interactions and classify them by their clearance pathway and potential intensity based on systematic reviews of the literature. The classification would be informative for designing clinical studies during the development stage, and the appropriate management of drug interactions in clinical practice.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care

Background

This study conducted exploratory research using artificial intelligence methods. The main purpose of this study is to establish an association model between metabolic syndrome and the TCM (traditional Chinese medicine) constitution using the characteristics of individual physical examination data and to provide guidance for medicated diet care.

Methods

Basic demographic and laboratory data were collected from a regional hospital health examination database in northern Taiwan, and artificial intelligence algorithms, such as logistic regression, Bayesian network, and decision tree, were used to analyze and construct the association model between metabolic syndrome and the TCM constitution. Findings. It was found that the phlegm-dampness constitution (90.6%) accounts for the majority of TCM constitution classifications with a high risk of metabolic syndrome, and high cholesterol, blood glucose, and waist circumference were statistically significantly correlated with the phlegm-dampness constitution. This study also found that the age of patients with metabolic syndrome has been advanced, and shift work is one of the risk indicators. Therefore, based on the association model between metabolic syndrome and TCM constitution, in the future, metabolic syndrome can be predicted through the syndrome differentiation of the TCM constitution, and relevant medicated diet care schemes can be recommended for improvement.

Conclusion

In order to increase the public's knowledge and methods for mitigating metabolic syndrome, in the future, nursing staff can provide nonprescription medicated diet-related nursing guidance information via the prediction and assessment of the TCM constitution.

Rapid identification of cytochrome P450 inductive constituents of Shenmai injection by combined pregnane X receptor reporter gene assay and LC-TOF-MS analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Identifying the inductive constituents of cytochrome P450 (CYP) enzymes is important in characterizing the safety of ethnopharmacological herbal preparations.

AIM OF THE STUDY

This study provides a rapid and accurate method for screening CYP inducers in Shenmai injection (SMI), a traditional Chinese medicine.

MATERIALS AND METHODS

We combined a pregnane X receptor (PXR) reporter gene assay and liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) analysis to screen ethanol and aqueous extracts of SMI for CYP-inducing constituents.

RESULTS

The ethanol extract exhibited stronger PXR activity than the aqueous extract. Of the 29 chemical compounds identified, 7 compounds with high relative concentrations in the ethanol extract were further evaluated for PXR activity. The highest activity was exhibited by methyl ophiopogonanone B and ginsenoside F2, indicating that they are CYP inducers.

CONCLUSIONS

The identification method applied in this study was rapid and accurate and is suitable for screening CYP inducers in herbal preparations.

Can Some Anticancer Treatments Preserve the Ovarian Reserve?

BACKGROUND

Preventing premature ovarian failure (POF) is a major challenge in oncology. With conventional regimens, cytotoxicity-associated POF involves primordial follicles (PF) pool depletion by apoptosis or overactivation mechanisms, notably mediated by the ABL/TAp63 and PI3K/Akt/mTOR pathways. New anticancer treatments have been designed to target pathways implicated in tumor growth. Although concerns regarding fertility arise with these targeted therapies, we hypothesized that targeted therapies may exert off-tumor effects on PF that might delay POF. We provide an overview of evidence concerning these off-tumor effects on PF. Limitations and future potential implications of these findings are discussed.

DESIGN

PubMed was searched by combining Boolean operators with the following keywords: fertility, ovarian, follicle, anti-tumoral, cancer, targeted, cytotoxic, and chemotherapy.

RESULTS

Cisplatin-related PF apoptosis via the ABL/TAp63 pathway was targeted with a tyrosine kinase inhibitor, imatinib, in mice, but effects were recently challenged by findings on human ovarian xenografts in mice. In cyclophosphamide-treated mice, PI3K/Akt/mTOR pathway inhibition with mTOR inhibitors and AS101 preserved the PF pool. Proteasome and GSK3 inhibitors were evaluated for direct and indirect follicle DNA damage prevention. Surprisingly, evidence for cytotoxic drug association with PF pool preservation was found. We also describe selected non-anticancer molecules that may minimize gonadotoxicity.

CONCLUSION

Not all anticancer treatments are associated with POF, particularly since the advent of targeted therapies. The feasibility of associating a protective drug targeting PF exhaustion mechanisms with cytotoxic treatments should be evaluated, as a way of decreasing the need for conventional fertility preservation techniques. Further evaluations are required for transfer into clinical practice.

IMPLICATIONS FOR PRACTICE

Anticancer therapies are associated with infertility in 10%-70% of patients, which is the result of primordial follicles pool depletion. Alone or associated with gonadotoxic treatments, some targeted therapies may exert favorable off-targets effects on the primordial follicle pool by slowing down their exhaustion. Current evidence of these effects relies on murine models or human in vitro models. Evaluation of these protective strategies in humans is challenging; however, if these results are confirmed with clinical and biological data, it not only could be a new approach to female fertility preservation but also would change standard fertility strategies.

Physiologically based pharmacokinetic modeling of altered tizanidine systemic exposure by CYP1A2 modulation: Impact of drug-drug interactions and cigarette consumption

Tizanidine is an alpha2-adrenergic agonist, used to treat spasticity associated with multiple sclerosis and spinal injury. Tizanidine is primarily metabolized by CYP1A2 and is considered a sensitive index substrate for this enzyme. The physiologically based pharmacokinetic (PBPK) modeling platform Simcyp® was used to evaluate the impact of CYP1A2 modulation on tizanidine exposure through drug-drug interactions (DDIs) and host-dependent habits (cigarette smoking). A PBPK model was developed to predict tizanidine disposition in healthy volunteers following oral administration. The model was verified based on agreement between model-simulated and clinically observed systemic exposure metrics (C_max, AUC). The model was then used to carry-out DDI simulations to predict alterations in tizanidine systemic exposure when co-administered with various CYP1A2 perpetrators including competitive inhibitors (fluvoxamine, ciprofloxacin), a mechanism-based inhibitor (rofecoxib), and an inducer (rifampin). Additional simulations were performed to evaluate the impact of cigarette smoking on systemic exposure. Under each scenario, the PBPK model was able to capture the observed fold changes in tizanidine C_max and AUC of tizanidine when coadministered with CYP1A2 inhibitors or inducers. These results add to the available research findings in the literature on PBPK predictions of drug-drug interactions and illustrate the potential application in drug development, specifically to support product labeling.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Physiologically Based Pharmacokinetic Modeling Approach to Identify the Drug-Drug Interaction Mechanism of Nifedipine and a Proton Pump Inhibitor, Omeprazole

BACKGROUND AND OBJECTIVES

Proton pump inhibitors (PPIs) can affect the intragastric release of other drugs from their dosage forms by elevating the gastric pH. They may also influence drug absorption and metabolism by interacting with P-glycoprotein or with the cytochrome P450 (CYP) enzyme system. Nifedipine is a Biopharmaceutics Classification System (BCS) class II drug with low solubility across physiologic pH and high permeability. Previous studies have demonstrated that drug-drug interaction (DDI) existed between omeprazole and nifedipine with significantly increased systemic exposure of nifedipine in subjects after pre-treatment for 7 days with omeprazole compared to the subjects without omeprazole treatment. It was shown that omeprazole not only induced an increase in intragastric pH, but also inhibited the CYP3A4 activity, while CYP3A4-mediated oxidation is the main metabolic pathway of nifedipine. The purpose of this study is to apply a physiologically based pharmacokinetic (PBPK) modeling approach to investigate the DDI mechanism for an immediate release formulation of nifedipine with omeprazole.

METHODS

A previously published model for omeprazole was modified to integrate metabolites and to update CYP inhibition based on the most updated published in vitro data. We simulated the nifedipine pharmacokinetics in healthy subjects with or without the multiple-dose pretreatment of omeprazole (20 mg) following oral administrations of immediate-release (IR) (10 mg) nifedipine. Nifedipine solubility at different pHs was used to simulate the nifedipine pharmacokinetics for both clinical arms. Multiple sensitivity analyses were performed to understand the impact of gastric pH and the CYP3A4-mediated gut and liver first pass metabolism on the overall nifedipine pharmacokinetics.

RESULTS

The developed PBPK model properly described the pharmacokinetics of nifedipine and predicted the inhibitory effect of multiple-dose omeprazole on CYP3A4 activity. With the incorporation of the physiologic effect of omeprazole on both gastric pH and CYP3A4 to the PBPK model, the verified PBPK model allows evaluating the impact of the increase in gastric pH and/or CYP3A4 inhibition. The simulated results show that the nifedipine metabolic inhibition by omeprazole may play an important role in the DDI between nifedipine and omeprazole for IR nifedipine formulation.

CONCLUSION

The developed full PBPK model with the capability to simulate DDI by considering gastric pH change and metabolic inhibition provides a mechanistic understanding of the observed DDI of nifedipine with a PPI, omeprazole.

Effects of Dexmedetomidine on the Pharmacokinetics of Parecoxib and Its Metabolite Valdecoxib in Beagles by UPLC-MS/MS

A sensitive and reliable ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was developed for the simultaneous determination of parecoxib and its metabolite valdecoxib in beagles. The effects of dexmedetomidine on the pharmacokinetics of parecoxib and valdecoxib in beagles were studied. The plasma was precipitated by acetonitrile, and the two analytes were separated on an Acquity UPLC BEH C18 column (2.1 mm × 50 mm, 1.7 μm); the mobile phase was acetonitrile and 0.1% formic acid with gradient mode, and the flow rate was 0.4 mL/min. In the negative ion mode, the two analytes and internal standard (IS) were monitored by multiple reaction monitoring (MRM), and the mass transition pairs were as follows: m/z 369.1 → 119.1 for parecoxib, m/z 313.0 → 118.0 for valdecoxib, and m/z 380.0 → 316.0 for celecoxib (IS). Six beagles were designed as a double cycle self-control experiment. In the first cycle, after intramuscular injection of parecoxib 1.33 mg/kg, 1.0 mL blood samples were collected at different times (group A). In the second cycle, the same six beagles were intravenously injected with 2 μg/kg dexmedetomidine for 7 days after one week of washing period. On day 7, after intravenous injection of 2 μg/kg dexmedetomidine for 0.5 hours, 6 beagle dogs were intramuscularly injected with 1.33 mg/kg parecoxib, and blood samples were collected at different time points (group A). The concentration of parecoxib and valdecoxib was detected by UPLC-MS/MS, and the main pharmacokinetic parameters were calculated by DAS 2.0 software. Under the experimental conditions, the method has a good linear relationship for both analytes. The interday and intraday precision was less than 8.07%; the accuracy values were from -1.20% to 2.76%. C_max of parecoxib in group A and group B was 2148.59 ± 406.13 ng/mL and 2100.49 ± 356.94 ng/mL, t_1/2 was 0.85 ± 0.36 h and 0.85 ± 0.36 h, and AUC_(0‐t) was 2429.96 ± 323.22 ng·h/mL and 2506.38 ± 544.83 ng·h/mL, respectively. C_max of valdecoxib in group A and group B was 2059.15 ± 281.86 ng/mL and 2837.39 ± 276.78 ng/mL, t_1/2 was 2.44 ± 1.55 h and 2.91 ± 1.27 h, and AUC_(0‐t) was 4971.61 ± 696.56 ng·h/mL and 6770.65 ± 453.25 ng·h/mL, respectively. There was no significant change in the pharmacokinetics of parecoxib in groups A and B. C_max and AUC_{(0 − ∞)} of valdecoxib in group A were 37.79% and 36.19% higher than those in group B, respectively, and t_1/2 was increased from 2.44 h to 2.91 h. V_z/F and CL_z/F were correspondingly reduced, respectively. The developed UPLC-MS/MS method for simultaneous determination of parecoxib and valdecoxib in beagle plasma was specific, accurate, rapid, and suitable for the pharmacokinetics and drug-drug interactions of parecoxib and valdecoxib. Dexmedetomidine can inhibit the metabolism of valdecoxib in beagles and increase the exposure of valdecoxib, but it does not affect the pharmacokinetics of parecoxib.

Effects of Dexmedetomidine on the Pharmacokinetics of Dezocine, Midazolam and Its Metabolite 1-Hydroxymidazolam in Beagles by UPLC-MS/MS

Objective

We developed and validated a sensitive and reliable UPLC-MS/MS method for simultaneous determination of dezocine (DEZ), midazolam (MDZ) and its metabolite 1-hydroxymidazolam (1-OH-MDZ) in beagle plasma and investigated the effect of dexmedetomidine (DEX) on the pharmacokinetics of DEZ, MDZ and 1-OH-MDZ in beagles.

Materials and Methods

Diazepam was used as the internal standard (IS); the three analytes and IS were extracted by acetonitrile precipitation and separated on an Acquity UPLC BEH C18 column using acetonitrile-0.1% formic acid as mobile phase in gradient mode. In positive ion mode, the three analytes and IS were monitored by multiple reaction monitoring (MRM). Six beagles were designed as a double cycle self-control experiment with 0.15 mg/kg in the first cycle (Group A). After a 1-week washout period, the same six beagles were slowly injected intravenously with 2 µg/kg DEX in the second cycle (Group B), with continuous injection for 7 days. On the seventh day, 0.5 hr after intravenous injection of 2 µg/kg DEX, the six beagles were intramuscularly given with DEZ 0.33 mg/kg and MDZ 0.15 mg/kg.

Results

Under the conditions of this experiment, this method exhibited a good linearity for each analyte. The accuracy and precision were all within the acceptable limits in the bioanalytical method, and the results of recovery, matrix effect and stability have also met the requirements.

Conclusion

The developed UPLC-MS/MS method for simultaneous determination of DEZ, MDZ and 1-OH-MDZ in beagles plasma was accurate, reproducible, specific, and suitable. DEX could inhibit the metabolism of DEZ and MDZ and increase the exposure of DEZ and MDZ in beagles. Therefore, the change of therapeutic effect and the occurrence of adverse reactions caused by drug–drug interaction should be paid attention to when the drugs were used in combination.

Probenecid-Boosted Tenofovir: A Physiologically-Based Pharmacokinetic Model-Informed Strategy for On-Demand HIV Preexposure Prophylaxis

Multiple doses of tenofovir disoproxil fumarate (TDF) together with emtricitabine is effective for HIV preexposure prophylaxis (PrEP). TDF is converted to tenofovir (TFV) in circulation, which is subsequently cleared via tubular secretion by organic ion transporters (OATs; OAT1 and OAT3). Using in vitro kinetic parameters for TFV and the OAT1 and OAT3 inhibitor probenecid, a bottom‐up physiologically‐based pharmacokinetic model was successfully developed for the first time that accurately describes the probenecid–TFV interaction. This model predicted an increase in TFV plasma exposure by 60%, which was within 15% of the observed clinical pharmacokinetic data, and a threefold decrease in renal cells exposure following coadministration of a 600 mg TDF dose with 2 g probenecid. When compared with multiple‐dose regimens, a single‐dose probenecid‐boosted TDF regimen may be effective for HIV PrEP and improve adherence and safety by minimizing TFV‐induced nephrotoxicity by reducing TFV accumulation in renal cells.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Current trends in drug metabolism and pharmacokinetics

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.

Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling

It has been shown that arterial (central) and venous (peripheral) plasma drug concentrations can be very different. While pharmacokinetic studies typically measure drug concentrations from the peripheral vein such as the arm vein, physiologically based pharmacokinetic (PBPK) models generally output simulated concentrations from the central venous compartment that physiologically represents the right atrium, a merge of the superior and inferior vena cava. In this study, a physiologically based peripheral forearm sampling site model was developed and verified using nicotine, ketamine, lidocaine, and fentanyl as model drugs. This verified model allows output of simulated peripheral venous concentrations that can be meaningfully compared with observed pharmacokinetic data from the arm vein. The generalized effect of PBPK model sampling site on simulation output was investigated. Drugs and metabolites with large volumes of distribution showed considerable concentration discrepancy between the simulated central venous compartment and the peripheral arm vein after intravenous or oral administration, resulting in significant differences in values for C _max and time taken to reach C _max (t _max ) In addition, the simulated central venous metabolite profile showed an unexpected profile that was not observed in the peripheral arm vein. Using fentanyl as a model compound, we show that using the wrong sampling site in PBPK models can lead to biased model evaluation and subsequent erroneous model parameter optimization. Such an error in model parameters along with the discrepant sampling site could dramatically mislead the pharmacokinetic prediction in unstudied clinical scenarios, affecting the assessment of drug safety and efficacy. Overall, this study shows that PBPK model publications should specify the model sampling sites and match them with those employed in clinical studies. SIGNIFICANCE STATEMENT: Our study shows that sampling from the central venous compartment (right atrium) during physiologically based pharmacokinetic model development gives rise to biased model evaluation and erroneous model parameterization when observed data are collected from the peripheral arm vein. This can lead to a clinically significant error in predictions of plasma concentration-time profiles in unstudied scenarios. To address this error, we developed and verified a novel peripheral sampling site model to simulate arm vein drug concentrations that can be applied to different drug dosing scenarios.

Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors—ketoconazole, itraconazole, and posaconazole—were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.