A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4

A Physiologically Based Pharmacokinetic Approach to Recommend an Individual Dose of Tacrolimus in Adult Heart Transplant Recipients

Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration-time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68-1.22 for Cmax and 0.72-1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

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.

Considering the Oral Bioavailability of Protein Kinase Inhibitors: Essential in Assessing the Extent of Drug-Drug Interaction and Improving Clinical Practice

Protein kinase inhibitors share pharmacokinetic (PK) pathways among themselves. They are all metabolized by several cytochromes P450 (CYP). For most of them, CYP3A4 is the predominant metabolic pathway. However, their oral bioavailability differs. For example, the oral bioavailability of imatinib has been estimated at nearly 100%, but that of ibrutinib averages 3% due to its high hepatic first-pass effect. Overall, the smaller the oral bioavailability, the larger its interindividual PK variability. Indeed, for drugs with low oral bioavailability, the extent of their absorption is an additional cause (along with elimination variability) of differences in drug exposure among patients. The impact of drug-drug interaction (DDI) also differs between drugs with low or high oral bioavailability. We describe and explain why the impact of CYP3A4 inhibitors and inducers is much greater for protein kinase inhibitors with low oral bioavailability. The effect of food on protein kinase inhibitors and DDIs corresponding to plasma protein binding will also be considered. Finally, the benefits of these concepts in clinical practice (including therapeutic drug monitoring) will be discussed. Overall, our main objective was to apply fundamental PK concepts to understanding the main clinical issues of these oral anticancer drugs.

Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.

Impact of pharmacist consultation at clinical trial inclusion: an effective way to reduce drug-drug interactions with oral targeted therapy

PURPOSE

Pharmacist consultation is unfrequently performed in oncology clinical trials that include patients who often have many co-treatments increasing the risk of drug-drug interactions (DDI). The aim of this study was to determine whether best possible medication history (BPMH) by hospital pharmacist at inclusion and therapeutic drug monitoring could be used for DDI risk evaluation and for current oral targeted therapy management.

METHODS

A prospective clinical trial (ALCINA 2, NCT04025541) was carried out in metastatic breast cancer cohort treated by palbociclib to conduct pharmacokinetics-toxicity correlation study. BPMH was prospectively performed by the hospital pharmacist at each trial inclusion, followed by a contact to the patient's community pharmacy to complete the collected data. Pharmacokinetic analysis was performed on blood samples collected at day 15 of cycle 1 of palbociclib treatment.

RESULTS

Pharmacist interventions indicated that at inclusion, current medications were incomplete for 63% of the enrolled patients (32/51). It allowed the real-time management of high-risk DDI detected in third of patients. The palbociclib C_trough geometric median (min-max) was significantly higher in cohort with potential DDI [106 ng/mL (66.7-113)], than cohort without potential DDI [70.1 ng/mL (54.1-89.7)], p = 0.0284.

CONCLUSION

This is the first prospective study evaluating the relevance of proactive BPMH by pharmacist with contact to the community pharmacy during the inclusion step of a clinical trial to ensure the efficacy and safety of the investigated drug. This investigation was thus able to highlight the statistically significant impact of these DDI on palbociclib plasma concentration variation during the clinical trial.

TRIAL REGISTRATION

Clinicaltrials.gov identifier NCT04025541.

Potential drug-drug interactions associated with drugs currently proposed for COVID-19 treatment in patients receiving other treatments

Patients with COVID-19 are sometimes already being treated for one or more other chronic conditions, especially if they are elderly. Introducing a treatment against COVID-19, either on an outpatient basis or during hospitalization for more severe cases, raises the question of potential drug-drug interactions. Here, we analyzed the potential or proven risk of the co-administration of drugs used for the most common chronic diseases and those currently offered as treatment or undergoing therapeutic trials for COVID-19. Practical recommendations are offered, where possible.

Liquid chromatography-tandem mass spectrometric assay for the quantification of CDK4/6 inhibitors in human plasma in a clinical context of drug-drug interaction

The CDK4/6 inhibitors palbociclib and ribociclib are kinase inhibitors used in association with hormonal therapy for the management of patients with metastatic breast cancer. Like most kinase inhibitors, therapeutic drug monitoring may be used for personalize their dosage. To this aim, we developed and validated a sensitive and specific HPLC-MS/MS method for palbociclib and ribociclib quantification in blood samples. We then quantified exposure to palbociclib (plasma trough concentration; C_trough) in a real-life cohort of patients with locally invasive or metastatic breast cancer (n = 18) at day 15 of the first cycle of palbociclib treatment to characterize palbociclib concentration at steady state (Clinicaltrials.gov identifier NCT04025541, IdRCB n° 2018-A00064-51, 03/07/2018). The geometric mean (± standard deviation [min-max]) of palbociclib plasma C_trough was 88.58 ng/mL (± 26.4 [46.5 ng/mL - 133 ng/mL]) at day 15. Some covariates, such as drug-drug interactions, could explain the concentration variations observed in our Caucasian cohort. These first results in real-life settings obtained with our HPLC-MS/MS method give important information on palbociclib monitoring and pharmacokinetic variability.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.