Dataset Information

Population pharmacokinetic modelling to quantify the magnitude of drug-drug interactions between amlodipine and antiretroviral drugs.

ABSTRACT:

Purpose

Drug-drug interactions (DDIs) with antiretroviral drugs (ARVs) represent an important issue in elderly people living with HIV (PLWH). Amlodipine is a commonly prescribed antihypertensive drug metabolized by CYP3A4, thus predisposed to a risk of DDIs. Guidance on the management of DDIs is mostly based on theoretical considerations derived from coadministration with other CYP3A4 inhibitors. This study aimed at characterizing the magnitude of DDIs between amlodipine and ARV drugs in order to establish dosing recommendations.

Methods

A population pharmacokinetic analysis was developed using non-linear mixed effect modelling (NONMEM) and included 163 amlodipine concentrations from 55 PLWH. Various structural and error models were compared to characterize optimally the concentration-time profile of amlodipine. Demographic and clinical characteristics as well as comedications were tested as potential influential covariates. Model-based simulations were performed to compare amlodipine exposure (i.e. area under the curve, AUC) between coadministered ARV drugs.

Results

Amlodipine concentration-time profile was best described using a one-compartment model with first-order absorption and a lag-time. Amlodipine apparent clearance was influenced by both CYP3A4 inhibitors and efavirenz (CYP3A4 inducer). Model-based simulations revealed that amlodipine AUC increased by 96% when coadministered with CYP3A4 inhibitors, while efavirenz decreased drug exposure by 59%.

Conclusion

Coadministered ARV drugs significantly impact amlodipine disposition in PLWH. Clinicians should adjust amlodipine dosage accordingly, by halving the dosage in PLWH receiving ARV with inhibitory properties (mainly ritonavir-boosted darunavir), whereas they should double amlodipine doses when coadministering it with efavirenz, under appropriate monitoring of clinical response and tolerance.

SUBMITTER: Courlet P

PROVIDER: S-EPMC8184532 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Elevated serum creatinine (SCr ) caused by the inhibition of renal transporter(s) may be misinterpreted as kidney injury. The interpretation is more complicated in patients with chronic kidney disease (CKD) due to altered disposition of creatinine and renal transporter inhibitors. A clinical study was conducted in 17 patients with CKD (estimated glomerular filtration rate 15-59 mL/min/1.73 m2 ); changes in SCr were monitored during trimethoprim treatment (100-200 mg/day), administered to prevent recurrent urinary infection, relative to the baseline level. Additional SCr -interaction data with trimethoprim, cimetidine, and famotidine in patients with CKD were collated from the literature. Our published physiologically-based creatinine model was extended to predict the effect of the CKD on SCr and creatinine-drug interaction. The creatinine-CKD model incorporated age/sex-related differences in creatinine synthesis, CKD-related glomerular filtration deterioration; change in transporter activity either proportional or disproportional to glomerular filtration rate (GFR) decline were explored. Optimized models successfully recovered baseline SCr from 64 patients with CKD (geometric mean fold-error of 1.1). Combined with pharmacokinetic models of inhibitors, the creatinine model was used to simulate transporter-mediated creatinine-drug interactions. Use of inhibitor unbound plasma concentrations resulted in 66% of simulated SCr interaction data within the prediction limits, with cimetidine interaction significantly underestimated. Assuming that transporter activity deteriorates disproportional to GFR decline resulted in higher predicted sensitivity to transporter inhibition in patients with CKD relative to healthy patients, consistent with sparse clinical data. For the first time, this novel modelling approach enables quantitative prediction of SCr in CKD and delineation of the effect of disease and renal transporter inhibition in this patient population.

Project description:BACKGROUND AND OBJECTIVES:Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80-90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug-drug interactions (DDIs). This paper describes the development and verification of SimCYP®-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam. METHODS:A PBPK model was optimized in SimCYP® for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model. RESULTS:The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3-6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7-5.4; measured mean AUC ratio = 5.7). CONCLUSIONS:This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients.

Project description:BackgroundDrug interactions between antiretroviral drugs (ARVs) and anthelminthic drugs, ivermectin (IVM) and praziquantel (PZQ) were assessed by investigating their permeation through the Caco-2 cell monolayers in a transwell. The impact of anthelminthics on the transport of ARVs was determined by assessing the apical to basolateral (AP → BL) [passive] and basolateral to apical (BL → AP) [efflux] directions alone, and in presence of an anthelminthic. The reverse was conducted for the assessment of the influence of ARVs on anthelminthics.MethodsSamples from the AP and BL compartments were taken at 60, 120, 180 and 240 min and quantified either by HPLC or radiolabeled assay using a liquid scintillating counter for the respective drugs. Transepithelial resistance (TEER) was used to assess the integrity of the monolayers. The amount of compound transported per second (apparent permeability, Papp) was calculated for both AP to BL (PappAtoB), and BL to AP (PappBtoA) movements. Samples collected after 60 min were used to determine the efflux ratio (ER), quotient of secretory permeability and absorptive permeability (PappBL-AP/PappAP-BL). The reverse, (PappAP-BL/PappBL-AP) constituted the uptake ratio. The impact of SQV, EFV and NVP on the transport of both IVM and PZQ were investigated. The effect of LPV on the transport of IVM was also determined. The influence of IVM on the transport of SQV, NVP, LPV and EFV; as well as the effect PZQ on the transport of SQV of was also investigated, and a two-tailed p value of <0.05 was considered significant.ResultsIVM significantly inhibited the efflux transport (BL → AP movement) of LPV (ER; 6.7 vs. 0.8, p = 0.0038) and SQV (ER; 3.1 vs. 1.2 p = 0.00328); and increased the efflux transport of EFV (ER; 0.7 vs. 0.9, p = 0.031) suggesting the possibility of drug transporter mediated interactions between the two drugs. NVP increased the efflux transport of IVM (ER; 0.8 vs. 1.8, p = 0.0094).ConclusionsThe study provides in vitro evidence of potential interactions between IVM, an anthelminthic drug with antiretroviral drugs; LPV, SQV, NVP and EFV. Further investigations should be conducted to investigate the possibility of in vivo interactions.