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Pharmacokinetic drug interactions precipitated by botanical and other natural products (NPs) remain critically understudied. Investigating these complex interactions is fraught with difficulties due to the methodologic and technical challenges associated with the inherently complex chemistries and product variability of NPs. This knowledge gap is perpetuated by a continuing absence of a harmonized framework regarding the design of clinical pharmacokinetic studies of NPs and NP-drug interactions. Accordingly, this Recommended Approach, the fourth in a series of Recommended Approaches released by the Center of Excellence for Natural Product Drug Interaction Research (NaPDI Center), provides recommendations for the design of clinical pharmacokinetic studies involving NPs. Building on prior Recommended Approaches and data generated from the NaPDI Center, such a framework is presented for the design of (1) phase 0 studies to assess the pharmacokinetics of an NP and (2) clinical pharmacokinetic NP-drug interaction studies. Suggestions for NP sourcing, dosing, study design, participant selection, sampling periods, and data analysis are presented. With the intent to begin addressing the gap between regulatory agencies' guidance documents about drug-drug interactions and contemporary NPDI research, the objective of this Recommended Approach is to propose methods for the design of clinical pharmacokinetic studies of NPs and NP-drug interactions., (© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The popularity of botanical and other purported medicinal natural products (NPs) continues to grow, especially among patients with chronic illnesses and patients managed on complex prescription drug regimens. With few exceptions, the risk of a given NP to precipitate a clinically significant pharmacokinetic NP-drug interaction (NPDI) remains understudied or unknown. Application of static or dynamic mathematical models to predict and/or simulate NPDIs can provide critical information about the potential clinical significance of these complex interactions. However, methods used to conduct such predictions or simulations are highly variable. Additionally, published reports using mathematical models to interrogate NPDIs are not always sufficiently detailed to ensure reproducibility. Consequently, guidelines are needed to inform the conduct and reporting of these modeling efforts. This recommended approach from the Center of Excellence for Natural Product Drug Interaction Research describes a systematic method for using mathematical models to interpret the interaction risk of NPs as precipitants of potential clinically significant pharmacokinetic NPDIs. A framework for developing and applying pharmacokinetic NPDI models is presented with the aim of promoting accuracy, reproducibility, and generalizability in the literature. SIGNIFICANCE STATEMENT: Many natural products (NPs) contain phytoconstituents that can increase or decrease systemic or tissue exposure to, and potentially the efficacy of, a pharmaceutical drug; however, no regulatory agency guidelines exist to assist in predicting the risk of these complex interactions. This recommended approach from a multi-institutional consortium designated by National Institutes of Health as the Center of Excellence for Natural Product Drug Interaction Research provides a framework for modeling pharmacokinetic NP-drug interactions., Competing Interests: The authors have no financial conflicts of interest to disclose., (Copyright © 2021 by The American Society for Pharmacology and Experimental Therapeutics.)

This white paper provides updated International Transporter Consortium (ITC) recommendations on transporters that are important in drug development following the 3 rd ITC workshop. New additions include prospective evaluation of organic cation transporter 1 (OCT1) and retrospective evaluation of organic anion transporting polypeptide (OATP)2B1 because of their important roles in drug absorption, disposition, and effects. For the first time, the ITC underscores the importance of transporters involved in drug-induced vitamin deficiency (THTR2) and those involved in the disposition of biomarkers of organ function (OAT2 and bile acid transporters)., (© 2018 American Society for Clinical Pharmacology and Therapeutics.)

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development., (© 2018 American Society for Clinical Pharmacology and Therapeutics.)

For dual transporter-enzyme substrate drugs, the extended clearance model can be used to predict the rate-determining step(s) (RDS) of a drug and hence predict its drug-drug interaction (DDI) liabilities (i.e., transport, metabolism, or both). If the RDS of the hepatic clearance of the drug is sinusoidal uptake clearance (CL s in ), even if the drug is eliminated mainly by hepatic metabolism, its DDI liability (as viewed from changes to systemic drug concentrations) is expected to be inhibition or induction of uptake transporters but not hepatic enzymes; however, this is true only if the condition required to maintain CL s in as the RDS is maintained. Here, we illustrate through theoretical simulations that the RDS condition may be violated in the presence of a DDI. That is, the RDS of a drug can switch from CL s in to all hepatobiliary clearances [i.e., metabolic/biliary clearance (CL met + bile ) and CL s in ], leading to unexpected systemic DDIs, such as metabolic DDIs, when only transporter DDIs were anticipated. As expected, these analyses revealed that the RDS switch depends on the ratio of CL met + bile to sinusoidal efflux clearance (CL s ef ). Additional analyses revealed that for intravenously administered drugs, the RDS switch also depends on the magnitude of CL s in We analyzed published in vitro quantified hepatobiliary clearances and observed that most drugs have a CL met + bile /CL s ef ratio < 4; hence, in practice, the magnitude of CL s in must be considered when establishing the RDS. These analyses provide insights previously not appreciated and a theoretical framework to predict DDI liabilities for drugs that are dual transporter-enzyme substrates., (Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.)

Subcellular fractionation of tissue homogenate provides enriched in vitro models (e.g., microsomes, cytosol, or membranes), which are routinely used in the drug metabolism or transporter activity and protein abundance studies. However, batch-to-batch or interlaboratory variability in the recovery, enrichment, and purity of the subcellular fractions can affect performance of in vitro models leading to inaccurate in vitro to in vivo extrapolation (IVIVE) of drug clearance. To evaluate the quality of subcellular fractions, we developed a simple, targeted, and sensitive LC-MS/MS proteomics-based strategy, which relies on determination of protein markers of various cellular organelles, i.e., plasma membrane, cytosol, nuclei, mitochondria, endoplasmic reticulum (ER), lysosomes, peroxisomes, cytoskeleton, and exosomes. Application of the quantitative proteomics method confirmed a significant effect of processing variables (i.e., homogenization method and centrifugation speed) on the recovery, enrichment, and purity of isolated proteins in microsomes and cytosol. Particularly, markers of endoplasmic reticulum lumen and mitochondrial lumen were enriched in the cytosolic fractions as a result of their release during homogenization. Similarly, the relative recovery and composition of the total membrane fraction isolated from cell vs tissue samples was quantitatively different and should be considered in IVIVE. Further, analysis of exosomes isolated from sandwich-cultured hepatocyte media showed the effect of culture duration on compositions of purified exosomes. Therefore, the quantitative proteomics-based strategy developed here can be applied for efficient and simultaneous determination of multiple protein markers of various cellular organelles when compared to antibody- or activity-based assays and can be used for quality control of subcellular fractionation procedures including in vitro model development for drug metabolism and transport studies.

Pharmacokinetic interactions between natural products (NPs) and conventional medications (prescription and nonprescription) are a longstanding but understudied problem in contemporary pharmacotherapy. Consequently, there are no established methods for selecting and prioritizing commercially available NPs to evaluate as precipitants of NP-drug interactions (NPDIs). As such, NPDI discovery remains largely a retrospective, bedside-to-bench process. This Recommended Approach, developed by the Center of Excellence for Natural Product Drug Interaction Research (NaPDI Center), describes a systematic method for selecting NPs to evaluate as precipitants of potential clinically significant pharmacokinetic NPDIs. Guided information-gathering tools were used to score, rank, and triage NPs from an initial list of 47 candidates. Triaging was based on the presence and/or absence of an NPDI identified in a clinical study (≥20% or <20% change in the object drug area under the concentration vs. time curve, respectively), as well as mechanistic and descriptive in vitro and clinical data. A qualitative decision-making tool, termed the fulcrum model, was developed and applied to 11 high-priority NPs for rigorous study of NPDI risk. Application of this approach produced a final list of five high-priority NPs, four of which are currently under investigation by the NaPDI Center., (Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.)

Determining fetal drug exposure (except at the time of birth) is not possible for both logistical and ethical reasons. Therefore, we developed a novel maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) model to predict fetal exposure to drugs and populated this model with gestational age-dependent changes in maternal-fetal physiology. Then, we used this m-f-PBPK to: 1) perform a series of sensitivity analyses to quantitatively demonstrate the impact of fetoplacental metabolism and placental transport on fetal drug exposure for various drug-dosing regimens administered to the mother; 2) predict the impact of gestational age on fetal drug exposure; and 3) demonstrate that a single umbilical venous (UV)/maternal plasma (MP) ratio (even after multiple-dose oral administration to steady state) does not necessarily reflect fetal drug exposure. In addition, we verified the implementation of this m-f-PBPK model by comparing the predicted UV/MP and fetal/MP AUC ratios with those predicted at steady state after an intravenous infusion. Our simulations yielded novel insights into the quantitative contribution of fetoplacental metabolism and/or placental transport on gestational age-dependent fetal drug exposure. Through sensitivity analyses, we demonstrated that the UV/MP ratio does not measure the extent of fetal drug exposure unless obtained at steady state after an intravenous infusion or when there is little or no fluctuation in MP drug concentrations after multiple-dose oral administration. The proposed m-f-PBPK model can be used to predict fetal exposure to drugs across gestational ages and therefore provide the necessary information to assess the risk of drug toxicity to the fetus., (Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.)

Protein expression of renal uptake and efflux transporters was quantified by quantitative targeted proteomics using the surrogate peptide approach. Renal uptake transporters assessed in this study included organic anion transporters (OAT1-OAT4), organic cation transporter 2 (OCT2), organic/carnitine cation transporters (OCTN1 and OCTN2), and sodium-glucose transporter 2 (SGLT2); efflux transporters included P-glycoprotein, breast cancer resistance protein, multidrug resistance proteins (MRP2 and MRP4), and multidrug and toxin extrusion proteins (MATE1 and MATE2-K). Total membrane was isolated from the cortex of human kidneys (N = 41). The isolated membranes were digested by trypsin and the digest was subjected to liquid chromatography-tandem mass spectrometry analysis. The mean expression of surrogate peptides was as follows (given with the standard deviation, in picomoles per milligram of total membrane protein): OAT1 (5.3 ± 1.9), OAT2 (0.9 ± 0.3), OAT3 (3.5 ± 1.6), OAT4 (0.5 ± 0.2), OCT2 (7.4 ± 2.8), OCTN1 (1.3 ± 0.6), OCTN2 (0.6 ± 0.2), P-glycoprotein (2.1 ± 0.8), MRP2 (1.4 ± 0.6), MRP4 (0.9 ± 0.6), MATE1 (5.1 ± 2.3), and SGLT2 (3.7 ± 1.8). Breast cancer resistance protein (BCRP) and MATE2-K proteins were detectable but were below the lower limit of quantification. Interestingly, the protein expression of OAT1 and OAT3 was significantly correlated (r > 0.8). A significant correlation was also observed between expression of multiple other drug transporters, such as OATs/OCT2 or OCTN1/OCTN2, and SGLT2/OCTNs, OCT, OATs, and MRP2. These renal transporter data should be useful in deriving in vitro to in vivo scaling factors to accurately predict renal clearance and kidney epithelial cell exposure to drugs or their metabolites., (Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.)

The well-stirred hepatic clearance model (WSHM) has been expanded to include drug transporters (i.e., extended clearance model [ECM]). However, the consequences of this expansion in understanding when transporters vs. metabolic enzymes will affect the pharmacokinetic (PK) and pharmacodynamic (PD) of drugs remains opaque. Identifying the rate-determining step(s) in systemic or tissue drug PK/PD will allow accurate predictions of drug PK/PD and drug-drug interactions (DDIs). Here, we clarify the implications of the ECM on PK/PD of drugs., (© 2016 ASCPT.)

Organic anion-transporting polypeptide (OATP) 1A2 has the potential to be a target for central nervous system drug delivery due to its luminal localization at the human blood-brain barrier and broad substrate specificity. We found OATP1A2 mRNA expression in the human brain to be comparable to breast cancer resistance protein and OATP2B1 and much higher than P-glycoprotein (P-gp), and confirmed greater expression in the brain relative to other tissues. The goal of this study was to establish a model system to explore OATP1A2-mediated transcellular transport of substrate drugs and the interplay with P-gp. In vitro (human embryonic kidney 293 cells stably expressing Oatp1a4, the closest murine isoform) and in vivo (naïve and Oatp1a4 knock-out mice) studies with OATP1A2 substrate triptan drugs demonstrated that these drugs were not Oatp1a4 substrates. This species difference demonstrates that the rodent is not a good model to investigate the active brain uptake of potential OATP1A2 substrates. Thus, we constructed a novel OATP1A2 expressing Madin-Darby canine kidney (MDCK) II wild type and an MDCKII-multidrug resistance protein 1 (MDR1) system using BacMam virus transduction. The spatial expression pattern of OATP1A2 after transduction in MDCKII-MDR1 cells was superimposed to P-gp, confirming apical membrane localization. OATP1A2-mediated uptake of zolmitriptan, rosuvastatin, and fexofenadine across monolayers increased with increasing OATP1A2 protein expression. OATP1A2 counteracted P-gp efflux for cosubstrates zolmitriptan and fexofenadine. A three-compartment model incorporating OATP1A2-mediated influx was used to quantitatively describe the time- and concentration-dependent apical-to-basolateral transcellular transport of rosuvastatin across OATP1A2 expressing the MDCKII monolayer. This novel, simple and versatile experimental system is useful for understanding the contribution of OATP1A2-mediated transcellular transport across barriers, such as the blood-brain barrier., (Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.)

The fraction of drug transported (ft) into or out of a tissue is a concept useful to understand the impact of transporters on absorption, distribution, metabolism, and excretion (ADME) and tissue distribution of a drug. Here, ft is utilized to explain the impact of transporters on central nervous system (CNS) distribution of drugs, drug interactions (DDI), and to predict the unbound brain concentration (Cu,b) of the drug. The latter is important to ascertain if Cu,b is sufficient for efficacy., (© 2015 American Society for Clinical Pharmacology and Therapeutics.)

Drug transporter expression in tissues (in vivo) usually differs from that in cell lines used to measure transporter activity (in vitro). Therefore, quantification of transporter expression in tissues and cell lines is important to develop scaling factor for in vitro to in vivo extrapolation (IVIVE) of transporter-mediated drug disposition. Since traditional immunoquantification methods are semiquantitative, targeted proteomics is now emerging as a superior method to quantify proteins, including membrane transporters. This superiority is derived from the selectivity, precision, accuracy, and speed of analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. Moreover, LC-MS/MS proteomics has broader applicability because it does not require selective antibodies for individual proteins. There are a number of recent research and review papers that discuss the use of LC-MS/MS for transporter quantification. Here, we have compiled from the literature various elements of MRM proteomics to provide a comprehensive systematic strategy to quantify drug transporters. This review emphasizes practical aspects and challenges in surrogate peptide selection, peptide qualification, peptide synthesis and characterization, membrane protein isolation, protein digestion, sample preparation, LC-MS/MS parameter optimization, method validation, and sample analysis. In particular, bioinformatic tools used in method development and sample analysis are discussed in detail. Various pre-analytical and analytical sources of variability that should be considered during transporter quantification are highlighted. All these steps are illustrated using P-glycoprotein (P-gp) as a case example. Greater use of quantitative transporter proteomics will lead to a better understanding of the role of drug transporters in drug disposition.

Pregnant women and their fetuses are orphan populations with respect to the safety and efficacy of drugs. Physiological and absorption, distribution, metabolism, and excretion (ADME) changes during pregnancy can significantly affect drug pharmacokinetics (PK) and may necessitate dose adjustment. Here, the specific aspects related to the design, execution, and analysis of clinical studies in pregnant women are discussed, underlining the unmet need for top-down pharmacometrics analyses and bottom-up modeling approaches. The modeling tools that support data analysis for the pregnancy population are reviewed, with a focus on physiologically based pharmacokinetics (PBPK) and population pharmacokinetics (POP-PK). By integrating physiological data, preclinical data, and clinical data (e.g., via POP-PK) to quantify anticipated changes in the PK of drugs during pregnancy, the PBPK approach allows extrapolation beyond the previously studied model drugs to other drugs with well-characterized ADME characteristics. Such a systems pharmacology approach can identify drugs whose PK may be altered during pregnancy, guide rational PK study design, and support dose adjustment for pregnant women.

Many mathematical models for in vitro to in vivo prediction of drug-drug interactions (DDIs) of orally administered victim drugs have been developed. However, to date, none of these models have been applicable to all intravenously administered victim drugs. We derived and conducted a sensitivity/error analysis of a modification to the existing multiple mode interaction prediction model such that it is applicable to all intravenously administered victim drugs. Using this model we showed that ignoring the hepatic extraction ratio (EH) (as low as 0.3) of intravenously administered victim drugs can result in 1) substantial underestimation of f(m, CYPi) (the fraction of hepatic clearance of the victim drug via a given enzymatic pathway) and 2) error in dissecting the contribution of intestinal and hepatic components of DDIs for orally administered drugs. Using this model we describe DDI boundaries (degree of inhibition or induction) at which ignoring the EH of commonly used victim drugs results in ≥30% error in the predicted area under the concentration-time curve (AUC) ratio or contribution of intestinal interaction to a DDI (CYP3A probes only). For the most widely used victim drug midazolam, these boundaries for AUC ratio are net inhibition (I/K(i) or λ/k(deg)) ≥1.3 or fold induction ≥2.1; for intestinal contribution the boundaries are 0.37 and 1.5, respectively. To accurately predict the intravenous AUC ratio, intestinal contribution, or f(m, CYPi) 1) for all induction DDIs irrespective of EH of the victim drug and 2) for modest to potent inhibition DDIs even when the EH is moderate (≥0.3), we propose that our model be used.

The interdependence of both transport and metabolism on the disposition of drugs has recently gained heightened attention in the literature, and has been termed the "interplay of transport and metabolism". Such "interplay" is observed when inhibition of biliary clearance of a drug results in an "apparent" increase in the metabolic clearance of the drug or vice versa. In this manuscript, we derived and explored through simulations a physiological-based pharmacokinetic model that integrates both transport and metabolism and explains the "apparent" dependence of hepatic clearance on both these processes. In addition, we show that the phenomenon of hepatic "transport-metabolism interplay" is a result of using the plasma concentration as a point of reference when calculating metabolic or biliary clearance, and this interplay is maximal when the drug is actively transported into the hepatocytes (i.e., hepatocyte sinusoidal influx clearance is greater than the sinusoidal efflux clearance). When the hepatic drug concentration is used as a reference point to calculate metabolic or biliary clearance, this interplay ceases to exist. A mechanistic understanding of this interplay phenomenon can be used to explain the somewhat paradoxical results that may be observed in drug-drug interaction studies when a drug is cleared by both metabolism and biliary excretion. That is, when one of these two pathways is inhibited, the other pathway appears to be induced or activated. This interplay results in an increase in hepatic drug concentrations and therefore has implications for the hepatic efficacy and toxicity of a drug.

Using liquid chromatography-mass spectrometry (LC-MS), two sensitive cocktail assays were developed, one to simultaneously determine activities of the cytochrome P450 enzymes, CYP1A2 (phenacitin), 2B6 (bupropion), 2C8 (amodiaquine) and 2C19 (omperazole), and the other to determine simultaneously activities of CYP3A4/5 (testosterone), 2C9 (tolbutamide) and 2D6 (dextromethorphan). These cocktail assays are sensitive, require only a small amount of microsomal protein, employ selective and high turnover CYP substrates and do not require post-incubation extraction. In each of these cocktails, no interactions were observed between the substrates. Combining the two cocktails into a single cocktail resulted in significant inhibition of CYP2D6 by amiodiaquine. These assays were used successfully to determine induction of CYP enzymes in microsomes isolated from human hepatocytes treated (72 h) with or without the prototypic inducer, rifampin., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

Any treatment of a pregnant woman with medication (drugs) de facto results in the treatment of her unborn child, even when her unborn child is not the target of drug therapy. This is because, in most instances, the placenta is not a complete barrier to the passage of drugs from the maternal to the fetal compartment. This barrier is in part due to the presence of various efflux transporters in the placenta. The placenta is also richly endowed with influx transporters. In this article, we will review the physiological characteristics of the placenta and how it functions as a barrier to passage of drugs into the fetal compartment. In addition, we will review placental transporters that are important in modulating the exposure of the fetus to drugs and, therefore, the efficacy and toxicity of such drugs towards the fetus.

1 Two methods of estimating the average plasma drug concentration at plateau Cav are considered. 2 Given an acceptable error in the estimate of Cav, a range of sampling times, rather than a single time, during the dosing interval, can be calculated. This range depends on the variability of the half-life within the patient population, the dosing interval, the error in assay measurement and the therapeutic index of the drug. 3 When the minimum plasma drug concentration is monitored, an estimate of the individual's half-life is needed, unless the dosing interval is short. Then the population half-life may be used to obtain a reasonable estimate of Cav.

The "single-point, single-dose" method for predicting individual maintenance dosage is examined. Data available in the literature on nortriptyline is used to illustrate the method. Optimal use of the method requires knowledge of the pharmacokinetics, especially the elimination rate constant, within the patient population requiring the drug. The method is applicable to intravenous and extravascular administration, when absorption is rapid relative to elimination, for those drugs whose disposition kinetics can be described by a linear one-compartment model. As a reasonable approximation, the optimal sampling time after the single test dose is 1.5 times the population half-life. Error in chemical analysis needs to be considered when applying the method. One method of evaluating the prediction and subsequent adjustment of dosage is discussed.

Three models, linked in series, can be used to analyze combined pharmacokinetic (PK) and pharmacodynamic (PD) data arising from non--steady-state experiments. A PK model relates dose to plasma drug concentration (Cp); a link model relates Cp to drug concentration at the effect site (Ce); and a PD model relates Ce to drug effect (E). All three submodels can be stated parametrically. Recently the use of a nonparametric PD submodel has been proposed (CLIN PHARMACOL THER 1984;35:733-41). In this article we use an extended nonparametric approach that represents both the PK and PD models nonparametrically, but retains a parametric link model. Cp data from several PK models and E data from several PD models were simulated. After the addition of noise to both the Cp and E data, they were analyzed by both the parametric and extended nonparametric methods. The methods were compared by how well they estimated the PD model. To assess robustness, the effect of misspecification of the PK submodel on the goodness of estimation of both methods was also compared. In the absence of model misspecification, the parametric method usually estimates the PD model better than the nonparametric method. However, this difference in the performances diminishes and even reverses when the PK model is misspecified. Because one can rarely be certain that model misspecification is absent, the nonparametric approach may offer a distinct advantage for routine analysis of PK/PD data.