Biopharmaceutics & Drug Disposition最新文献

Predicting the brain penetration of drugs has been notoriously difficult; however, recently, permeability-limited brain models have been constructed. Lead optimization for central nervous system compounds often focuses on compounds that have low transporter efflux, where passive permeability could be a main driver in determining cerebrospinal fluid (CSF)/brain concentrations. The main objective of this study was to evaluate the translatability of passive permeability data generated from different in vitro systems and its impact on the prediction of human CSF/brain concentrations using physiologically-based pharmacokinetic (PBPK) modeling. In vitro data were generated using gMDCK and parallel artificial membrane permeability assay-blood–brain barrier for comparison and predictions using a quantitative structure-activity relationship model were also evaluated. PBPK modeling was then performed for seven compounds with moderate-high permeability and a range of efflux in vitro, and the CSF/brain mass concentrations and Kpuu were reasonably predicted. This work provides the first step of a promising approach using bottom-up PBPK modeling for CSF/brain penetration prediction to support lead optimization and clinical candidate selection.

As an analog of clopidogrel and prasugrel, vicagrel is completely hydrolyzed to intermediate thiolactone metabolite 2-oxo-clopidogrel (also the precursor of active thiol metabolite H4) in human intestine, predominantly by AADAC and CES2; however, other unknown vicagrel hydrolases remain to be identified. In this study, recombinant human Raf kinase inhibitor protein (rhRKIP) and pooled human intestinal S9 (HIS9) fractions and microsome (HIM) preparations were used as the different enzyme sources; prasugrel as a probe drug for RKIP (a positive control), vicagrel as a substrate drug of interest, and the rate of the formation of thiolactone metabolites 2-oxo-clopidogrel and R95913 as metrics of hydrolase activity examined, respectively. In addition, an IC₅₀ value of inhibition of rhRKIP-catalyzed vicagrel hydrolysis by locostatin was measured, and five classical esterase inhibitors with distinct esterase selectivity were used to dissect the involvement of multiple hydrolases in vicagrel hydrolysis. The results showed that rhRKIP hydrolyzed vicagrel in vitro, with the values of K_m, V_max, and CL_int measured as 20.04 ± 1.99 μM, 434.60 ± 12.46 nM/min/mg protein, and 21.69 ± 0.28 ml/min/mg protein, respectively, and that an IC₅₀ value of locostatin was estimated as 1.24 ± 0.04 mM for rhRKIP. In addition to locostatin, eserine and vinblastine strongly suppressed vicagrel hydrolysis in HIM. It is concluded that RKIP can catalyze the hydrolysis of vicagrel in the human intestine, and that vicagrel can be hydrolyzed by multiple hydrolases, such as RKIP, AADAC, and CES2, concomitantly.

Two-thirds of patients with type 2 diabetes mellitus have hypertension, and thus the combination of two or more drugs to treat these diseases is common. It has been shown that the combination of metformin and enalapril has beneficial effects, but few studies have evaluated the interactions between these two drugs. This study investigated the effects of enalapril on the pharmacokinetics and urinary excretion of metformin in rats, with a focus on transporter-mediated drug interactions. Rats were dosed orally with metformin alone (100 mg/kg) or in combination with enalapril (4 mg/kg). The concentration of metformin was measured by high performance liquid chromatography and the level of organic cation transporters (rOCTs) and multidrug and toxin excretion protein 1 (rMATE1), which mediate the uptake and efflux of metformin, respectively, were evaluated by immunoblotting. After single and 7-day dosing, the plasma concentration of metformin in the co-administration group was significantly lower than that in the metformin-only group, and the CL/F and urinary excretion were increased in the co-administration group. Enalapril did not affect the K_p of metformin but reduced renal slice-uptake of metformin. The expression of rMATE1 was increased, whereas rOCT2 expression was decreased in rat kidney. Importantly, long-term co-administration of metformin and enalapril markedly decreased the level of lactic acid and uric acid in the blood. Enalapril increases the urinary excretion of metformin through the up-regulation of rMATE1. This reveals a new mechanism of drug interactions and provides a basis for drug dosage adjustment when these drugs are co-administered.

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug–drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Considerable advances have been made in the research and development of oligonucleotide therapeutics (OTs) for treating central nervous system (CNS) diseases, such as psychiatric and neurodegenerative disorders, because of their promising mode of action. However, due to the tight barrier function and complex physiological structure of the CNS, the efficient delivery of OTs to target the brain has been a major challenge, and intensive efforts have been made to overcome this limitation. In this review, we summarize the representative methodologies and current knowledge of biodistribution, along with the pharmacokinetic/pharmacodynamic (PK/PD) relationship of OTs in the CNS, which are critical elements for the successful development of OTs for CNS diseases. First, quantitative bioanalysis methods and imaging-based approaches for the evaluation of OT biodistribution are summarized. Next, information available on the biodistribution profile, distribution pathways, quantitative PK/PD modeling, and simulation of OTs following intrathecal or intracerebroventricular administration are reviewed. Finally, the latest knowledge on the drug delivery systems to the brain via intranasal or systemic administration as noninvasive routes for improved patient quality of life is reviewed. The aim of this review is to enrich research on the successful development of OTs by clarifying OT distribution profiles and pathways to the target brain regions or cells, and by identifying points that need further investigation for a mechanistic approach to generate efficient OTs.

Renal impairment can affect the elimination of hepatically metabolized drugs. Bexarotene (BXT) used for cutaneous T-cell lymphoma is highly bound in plasma and metabolized by CYP3A4. The BXT European Medicine Agency and Food and Drug Administration packages recommended the evaluation of renal impairment on BXT metabolism. The plasma protein binding of BXT can be changed in patients with renal dysfunction due to hypoalbuminemia and accumulation of uremic toxins. In vitro, microsomal stability and plasma protein binding studies were pursued. A preclinical pharmacokinetic study was pursued in control, chronic kidney disease (CKD), and acute kidney injury (AKI) rats. A BXT physiologically based pharmacokinetic (PBPK) model that utilized in vitro–in vivo extrapolation of metabolism was established and verified in healthy rats, customized to CKD and AKI rats, and extrapolated to healthy human subjects and those with CKD stages 3, 4, and 5. In vitro studies showed that AKI and CKD significantly increased the BXT fraction unbound in plasma (from 0.011 to 0.018 and 0.022, respectively) and decreased intrinsic clearance (from 4.1 to 2.5, and 2.2 mL/min/g liver, respectively). This could explain the reduced in vivo clearance observed in CKD rats (from 0.4 to 0.28 L/h/kg) and the 1.3-fold increase in BXT exposure. Changes in BXT disposition in AKI rats were not straightforward due to simultaneous changes in BXT distribution. The human PBPK model predicted an increased BXT exposure by 2-fold in CKD patients, suggesting the need for dose reduction and drug monitoring. The reduced BXT metabolism due to renal impairment is especially relevant in cancer patients with CKD.

Lisinopril is an antihypertensive drug with poor intestinal permeability. Enhancement of intestinal absorption depends on a clear understanding of the permeation pathways and absorption mechanisms. Unfortunately, these are not fully elucidated for lisinopril. Accordingly, the aim was to determine lisinopril permeation pathways and obstacles limiting membrane transport with subsequent nomination of appropriate permeation enhancers. This employed an in situ rabbit intestinal perfusion technique, which revealed site-dependent absorptive clearance (PeA/L) from a lisinopril simple solution (5 μg/ml), with paracellular absorption playing a role. Regional drug permeability ranked as colon> duodenum> jejunum> ileum opposing intestinal expression rank of P-glycoprotein (P-gp) efflux transporters. Duodenal and jejunal perfusion of a higher lisinopril concentration (50 μg/ml) reflected saturable absorption, suggesting carrier-mediated transport. The effect of piperine and verapamil as P-gp inhibitors on intestinal absorption of lisinopril was investigated. Coperfusion with either piperine or verapamil significantly enhanced lisinopril absorption, with enhancement being dominant in the ileum segment. This supported the contribution of P-gp transporters to poor lisinopril permeability. On the other hand, coperfusion of lisinopril with zinc acetate dihydrate significantly multiplied lisinopril PeA/L by 2.3- and 6.6-fold in duodenum and ileum segments, respectively, through magnifying intestinal water flux. The study explored the barriers limiting lisinopril intestinal absorption. Moreover, the study exposed clinically relevant lisinopril interactions with common coadministered cargos that should be considered for an appropriate lisinopril regimen. However, this requires further in vivo verification.

Many mothers need to take some medications during breastfeeding, which may carry a risk to breastfed infants. Thus, determining the amount of a drug transferred into breast milk is critical for risk–benefit analysis of breastfeeding. Breast cancer resistance protein (BCRP), an efflux transporter which usually protects the body from environmental and dietary toxins, was reported to be highly expressed in lactating mammary glands. In this study, we developed a mechanistic lactation physiologically based pharmacokinetic (PBPK) modeling approach incorporating BCRP mediated transport kinetics to simulate the concentration–time profiles of five BCRP drug substrates (acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin) in nursing women’s plasma and milk. Due to the lack of certain physiological parameters and scaling factors in nursing women, we combine the bottom up and top down PBPK modeling approaches together with literature reported data to optimize and determine a set of parameters that are applicable for all five drugs. The predictive performance of the PBPK models was assessed by comparing predicted pharmacokinetic profiles and the milk-to-plasma (M/P) ratio with clinically reported data. The predicted M/P ratios for acyclovir, bupropion, cimetidine, ciprofloxacin, and nitrofurantoin were 2.48, 3.70, 3.55, 1.21, and 5.78, which were all within 1.5-fold of the observed values. These PBPK models are useful to predict the PK profiles of those five drugs in the milk for different dosing regimens. Furthermore, the approach proposed in this study will be applicable to predict pharmacokinetics of other transporter substrates in the milk.

Medication use during breastfeeding can be a matter of concern due to unintended infant exposure to drugs through breast milk. The available information relating to the safety of most medications is limited and may vary. More precise information is needed regarding the safety to the newborn or infants of the medications taken by the mother during breastfeeding. Physiologically based Pharmacokinetic Model (PBPK) approaches can be utilized to predict the drug exposure in the milk of breastfeeding women and can act as a supporting tool in the risk assessment of feeding infants. This study aims to assess the predictive performance of an integrated ‘log transformed phase-distribution’ lactation model within a PBPK platform. The model utilizes the physicochemical properties of four basic drugs, namely tramadol, venlafaxine, fluoxetine, and paroxetine, and analyses the milk compositions to predict the milk-to-plasma (M/P) ratio. The M/P prediction model was incorporated within the Simcyp Simulator V20 to predict the milk exposure and to estimate the likely infant dose for these drugs. The PBPK models adequately predicted the maternal plasma exposure, M/P ratio, and the infant daily dose to within two-fold of the clinically observed values for all four compounds. Integration of the lactation model within PBPK models facilitates the prediction of drug exposure in breast milk. The developed model can inform the design of lactation studies and assist with the neonatal risk assessment after maternal exposure to such environmental chemicals or basic drugs which diffuse passively into the milk.