Statin Drug-Drug Interactions: Pharmacokinetic Basis of FDA Labeling Recommendations and Comparison Across Common Tertiary Clinical Resources.

Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)-based drug-drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK-based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding "Dose Adjustment" and "Avoid Coadministration" recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource.

Membrane transporters in drug development and as determinants of precision medicine.

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Combined Oral Contraceptives As Victims of Drug Interactions.

Combined oral contraceptives (COCs) are widely used in women of reproductive age in the United States. Metabolism plays an important role in the elimination of estrogens and progestins contained in COCs. It is unavoidable that a woman using COCs may need to take another drug to treat a disease. If the concurrently used drug induces enzymes responsible for the metabolism of progestins and/or estrogens, unintended pregnancy or irregular bleeding may occur. If the concurrent drug inhibits the metabolism of these exogenous hormones, there may be an increased safety risk such as thrombosis. Therefore, for an investigational drug intended to be used in women with reproductive potential, evaluating its effects on the pharmacokinetics of COCs is important to determine if additional labeling is necessary for managing drug-drug interactions (DDIs) between the concomitant product and the COCs. It is challenging to determine when this clinical drug interaction study is needed, whether an observed exposure change of progestin/estrogen is clinically meaningful, and if the results of a clinical drug interaction study with one COC can predict exposure changes of unstudied COCs to inform labeling. In this review, we summarize the current understanding of metabolic pathways of estrogens and progestins contained in commonly used COCs and known interactions of these COCs as victim drugs and we discuss possible mechanisms of interactions for unexpected results. We also discuss recent advances, knowledge gaps, and future perspectives on this important topic. The review will enhance the understanding of DDIs with COCs and improve the safe and effective use of COCs. SIGNIFICANCE STATEMENT: This minireview provides an overview of the metabolic pathways of ethinyl estradiol and progestins contained in commonly used combined oral contraceptives (COCs) and significant drug interactions of these COCs as victims. It also discusses recent advances, knowledge gaps, future perspectives, and potential mechanisms for unexpected results of clinical drug interaction studies of COCs. This minireview will help the reader understand considerations when evaluating the drug interaction potential with COCs for drugs that are expected to be used concurrently.

Clinical pharmacology information in regulatory submissions and labeling: A comparative analysis of orphan and non-orphan drugs approved by the FDA.

Clinical pharmacology is an integral discipline supporting the development, regulatory evaluation, and clinical use of drugs for the treatment of both common and rare diseases. Here, we evaluated the recommendations and information available from select clinical pharmacology studies in the therapeutic product labeling of new molecular entities (NMEs) approved from 2017 to 2019 for both common and rare diseases. A total of 151 NMEs, including 72 orphan and 79 non-orphan drugs, were analyzed for recommendations and information available related to food-drug interaction, drug-drug interaction, renal impairment, hepatic impairment, QT assessment, and human radiolabeled mass balance studies using data collected from the original labeling and other regulatory documents. The analysis showed no statistically significant difference in the recommendations between orphan and non-orphan drugs except for renal impairment related recommendations in section 8 of the labeling. Although not significant, fewer hepatic impairment labeling recommendations were available for orphan drugs when compared with non-orphan drugs. At the time of initial approval, 79 postmarketing requirements (PMRs) and postmarketing commitments (PMCs) for 33 orphan drugs and 39 PMRs and PMCs for 19 non-orphan drugs were established; with most difference observed for drug-drug interaction, hepatic impairment, and QT assessment. Overall, although there was a trend for more labeling recommendations and fewer postmarketing studies and clinical trials for non-orphan drugs, there appeared to be no substantial differences in how these select clinical pharmacology studies are leveraged during the development and approval of orphan and non-orphan drugs.

Using Endogenous Biomarkers to Derisk Assessment of Transporter-Mediated Drug-Drug Interactions: A Scientific Perspective.

Comprehensive characterization of transporter mediated drug-drug interactions (DDIs) is important to formulate clinical management strategies and ensure the safe and effective use of concomitantly administered drugs. The potential of a drug to inhibit transporters is predicted by comparing the ratio of the relevant concentration (depending on the transporter) and the half maximum inhibitory concentration to a predefined "cutoff" value. If the ratio is greater than the cutoff value, modeling approaches such as physiologically based pharmacokinetic modeling or a clinical DDI trial may be recommended. Because false-positive (in vitro data suggest the potential for a DDI, whereas no significant DDI is observed in vivo) and false-negative (in vitro data does not suggest the potential for a DDI, whereas significant DDI is observed in vivo) outcomes have been observed, there is interest in exploring additional approaches to facilitate prediction of transporter-mediated DDIs. The idea of assessing changes in the concentration of endogenous biomarkers (which are substrates of clinically relevant transporters) to gain insight on the potential for a drug to inhibit transporter activity has received widespread attention. This brief report describes how endogenous biomarkers may help to expand the DDI assessment toolkit, highlights some current knowledge gaps, and outlines a conceptual framework that may complement the current paradigm of predicting the potential for transporter-mediated DDIs.

Current Perspective on Residual Knowledge Gaps in the Assessment of Transporter-Mediated Drug Interactions.

Assessment of transporter-mediated drug-drug interaction (DDI) is integral to drug development. A risk-based approach leveraging in vitro, in vivo, and in silico information is used to evaluate the DDI liability of drugs and inform the instructions of use. While tremendous advances have been made in recent decades, there are knowledge gaps warranting further research. Herein, we focus on select areas to advance assessment of DDI potential for drugs as substrates, inhibitors, or inducers of certain transporters.

Clinical Relevance of Hepatic and Renal P-gp/BCRP Inhibition of Drugs: An International Transporter Consortium Perspective.

The role of P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) in drug-drug interactions (DDIs) and limiting drug absorption as well as restricting the brain penetration of drugs with certain physicochemical properties is well known. P-gp/BCRP inhibition by drugs in the gut has been reported to increase the systemic exposure to substrate drugs. A previous International Transporter Consortium (ITC) perspective discussed the feasibility of P-gp/BCRP inhibition at the blood-brain barrier and its implications. This ITC perspective elaborates and discusses specifically the hepatic and renal P-gp/BCRP (referred as systemic) inhibition of drugs and whether there is any consequence for substrate drug disposition. This perspective summarizes the clinical evidence-based recommendations regarding systemic P-gp and BCRP inhibition of drugs with a focus on biliary and active renal excretion pathways. Approaches to assess the clinical relevance of systemic P-gp and BCRP inhibition in the liver and kidneys included (i) curation of DDIs involving intravenously administered substrates or inhibitors; (ii) in vitro-to-in vivo extrapolation of P-gp-mediated DDIs at the systemic level; and (iii) curation of drugs with information available about the contribution of biliary excretion and related DDIs. Based on the totality of evidence reported to date, this perspective supports limited clinical DDI risk upon P-gp or BCRP inhibition in the liver or kidneys.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective.

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

Application of Solubility and Dissolution Profile Comparison for Prediction of Gastric pH-Mediated Drug-Drug Interactions.

The objective of this study was to assess how solubility and dissolution profile comparisons under different pH conditions can be used to predict gastric pH-mediated drug-drug interaction (DDI) potential. We collected information for new molecular entities (NMEs) approved from 2003 to 2019 by the U.S. Food and Drug Administration (FDA) that had dedicated clinical DDI studies with acid-reducing agents (ARAs). Among these, 67 NMEs with solubility under different pHs and dissolution profiles generated in pH 1.2, 4.5, and 6.8 aqueous media were included for analysis. Similarity factor (f2) was used to compare dissolution profiles at different pHs for pH-mediated DDI prediction (e.g., f2<50 predicts positive DDI). Prediction accuracy was calculated based on the outcome comparison between predicted and observed DDIs. Based on dissolution profile comparisons and observed DDI data, weak base drugs (WBDs) (n = 49) showed 72.5% prediction accuracy under the fasted conditions, and 66.7% prediction accuracy under fed conditions. While using solubility and clinical dose for prediction, the prediction accuracy was 80% under fasted conditions and 66.7% under fed conditions, respectively. Comparison of dissolution profiles generated at pH 1.2, 4.5, and 6.8 can be used to predict gastric pH-mediated DDI potential for WBDs. It demonstrated comparable prediction accuracy under both fasted and fed conditions when compared to the prediction using solubility and clinical dose. Furthermore, dissolution profile comparison could add an additional understanding of possible impact of pH change on the release behavior of the drug product. Graphical abstract.

Exploring the Relationship of Drug BCS Classification, Food Effect, and Gastric pH-Dependent Drug Interactions.

Food effect (FE) and gastric pH-dependent drug-drug interactions (DDIs) are both absorption-related. Here, we evaluated if Biopharmaceutics Classification System (BCS) classes may be correlated with FE or pH-dependent DDIs. Trends in FE data were investigated for 170 drugs with clinical FE studies from the literature and new drugs approved from 2013 to 2019 by US Food and Drug Administration. A subset of 38 drugs was also evaluated to determine whether FE results can inform the need for a gastric pH-dependent DDI study. The results of FE studies were defined as no effect (AUC ratio 0.80-1.25), increased exposure (AUC ratio ≥1.25), or decreased exposure (AUC ratio ≤0.8). Drugs with significantly increased exposure FE (AUC ratio ≥2.0; N=14) were BCS Class 2 or 4, while drugs with significantly decreased exposure FE (AUC ratio ≤0.5; N=2) were BCS Class 1/3 or 3. The lack of FE was aligned with the lack of a pH-dependent DDI for all 7 BCS Class 1 or 3 drugs as expected. For the 13 BCS Class 2 or 4 weak base drugs with an increased exposure FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). Among the 16 BCS Class 2 or 4 weak base drugs with no FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). FE appears to have limited correlation with BCS classes except for BCS Class 1 drugs, confirming that multiple physiological mechanisms can impact FE. Lack of FE does not indicate absence of pH-dependent DDI for BCS Class 2 or 4 drugs. Graphical Abstract.

Enable a Facile Size Re-distribution of MBE-Grown Ga-Droplets via In Situ Pulsed Laser Shooting.

A MBE-prepared Gallium (Ga)-droplet surface on GaAs (001) substrate is in situ irradiated by a single shot of UV pulsed laser. It demonstrates that laser shooting can facilely re-adjust the size of Ga-droplet and a special Ga-droplet of extremely broad size-distribution with width from 16 to 230 nm and height from 1 to 42 nm are successfully obtained. Due to the energetic inhomogeneity across the laser spot, the modification of droplet as a function of irradiation intensity (IRIT) can be straightly investigated on one sample and the correlated mechanisms are clarified. Systematically, the laser resizing can be perceived as: for low irradiation level, laser heating only expands droplets to make mergences among them, so in this stage, the droplet size distribution is solely shifted to the large side; for high irradiation level, laser irradiation not only causes thermal expansion but also thermal evaporation of Ga atom which makes the size-shift move to both sides. All of these size-shifts on Ga-droplets can be strongly controlled by applying different laser IRIT that enables a more designable droplet epitaxy in the future.

How Science Is Driving Regulatory Guidances.

This chapter provides regulatory perspectives on how to translate in vitro drug metabolism findings into in vivo drug-drug interaction (DDI) predictions and how this affects the decision of conducting in vivo DDI evaluation. The chapter delineates rationale and analyses that have supported the recommendations in the U.S. Food and Drug Administration (FDA) DDI guidances in terms of in vitro-in vivo extrapolation of cytochrome P450 (CYP) inhibition-mediated DDI potential for investigational new drugs and their metabolites as substrates or inhibitors. The chapter also describes the framework and considerations to assess UDP-glucuronosyltransferase (UGT) inhibition-mediated DDI potential for drugs as substrates or inhibitors. The limitations of decision criteria and further improvements needed are also discussed. Case examples are provided throughout the chapter to illustrate how decision criteria have been utilized to evaluate in vivo DDI potential from in vitro data.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018.

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUCM /AUCP ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUCM /AUCP  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUCM /AUCP  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (Cmax ) divided by inhibition constant (Ki ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUCM /AUCP cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

Intestinal P-gp and Putative Hepatic OATP1B Induction: International Transporter Consortium Perspective on Drug Development Implications.

There is an increasing interest in transporter induction (i.e., decreased systemic drug exposure due to increased efflux-limited absorption or transporter-mediated clearance) as a mechanism of drug-drug interactions (DDIs), although evidence of clinical relevance is still evolving. Intestinal P-glycoprotein (P-gp) and hepatic organic anion transporting polypeptides 1B (OATP1B) can be important determinants of drug absorption and disposition, as well as targets for DDIs. Current data indicate that intestinal P-gp protein levels can be induced up to threefold to fourfold in humans primarily with pregnane X receptor (PXR) activators, and that this induction can decrease the systemic exposure of drugs with P-gp efflux-limited absorption (e.g., ≤ 67% decrease in the exposure of total dabigatran following rifampin multiple oral dosing). Evaluation of the clinical relevance of P-gp induction as a DDI mechanism must consider the induction potential of the perpetrator drug for P-gp and attenuation of exposure of the victim drug in the context of its therapeutic window. Practical drug development recommendations are provided herein. Reports are contradictory on OATP1B induction by PXR activators in human hepatocytes and liver biopsies. Some clinical investigations demonstrated that rifampin pretreatment decreased exposure of OATP1B substrates, while other studies found no differences, and the potential involvement of other mechanisms in these observed DDIs cannot be definitively ruled out. Thus, further studies are needed to understand hepatic OATP1B induction and potential involvement of other mechanisms contributing to reduced exposure of OATP1B substrates. This review critically summarizes the state-of-the-art on intestinal P-gp and hepatic OATP1B induction, and highlights implications for drug development.

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine-Drug Interactions.

Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter-mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional-OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine-drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra-individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady-state effect regardless of OCT2 directionality assumptions. However, only the bidirectional-OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations.

Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics-informed in vitro-in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics-informed bottom-up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine-trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle-out optimization of mechanistic models.

Roles of Renal Drug Transporter in Drug Disposition and Renal Toxicity.

The kidney plays an important role in maintaining total body homeostasis and eliminating toxic xenobiotics and metabolites. Numerous drugs and their metabolites are ultimately eliminated in the urine. The reabsorption and secretion functions of the nephron are mediated by a variety of transporters located in the basolateral and luminal membranes of the tubular cells. In the past decade, many studies indicated that transporters play important roles in drug pharmacokinetics and demonstrated the impact of renal transporters on the disposition of drugs, drug-drug interactions, and nephrotoxicities. Here, we focus on several important renal transporters and their roles in drug elimination and disposition, drug-induced nephrotoxicities and potential clinical solutions.

Liver Microphysiological Systems for Predicting and Evaluating Drug Effects.

Liver plays a major role in drug metabolism and is one of the main sites of drug adverse effects. Microphysiological systems (MPS), also known as organs-on-a-chip, are a class of microfluidic platforms that recreate properties of tissue microenvironments. Among different properties, the liver microenvironment is three-dimensional, fluid flows around its cells, and different cell types regulate its function. Liver MPS aim to recreate these properties and enable drug testing and measurement of functional endpoints. Tests with these systems have demonstrated their potential for predicting clinical drug effects. Properties of liver MPS that improve the physiology of cell culture are reviewed, specifically focusing on the importance of recreating a physiological microenvironment to evaluate and model drug effects. Advances in modeling hepatic function by leveraging MPS are addressed, noting the need for standardization in the use, quality control, and interpretation of data from these systems.