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Real‐world data (RWD) and real‐world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post‐approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how this approach can be applied in clinical pharmacology and translational research settings. Therefore, the American Society of Clinical Pharmacology & Therapeutics (ASCPT) held a workshop on March 21st, 2023 entitled "Advancing the Utilization of Real‐World Data (RWD) and Real‐World Evidence (RWE) in Clinical Pharmacology and Translational Research." The work described herein is a summary of the workshop proceedings. [ABSTRACT FROM AUTHOR]

Since the 21st Century Cures Act was signed into law in 2016, real‐world data (RWD) and real‐world evidence (RWE) have attracted great interest from the healthcare ecosystem globally. The potential and capability of RWD/RWE to inform regulatory decisions and clinical drug development have been extensively reviewed and discussed in the literature. However, a comprehensive review of current applications of RWD/RWE in clinical pharmacology, particularly from an industry perspective, is needed to inspire new insights and identify potential future opportunities for clinical pharmacologists to utilize RWD/RWE to address key drug development questions. In this paper, we review the RWD/RWE applications relevant to clinical pharmacology based on recent publications from member companies in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) RWD Working Group, and discuss the future direction of RWE utilization from a clinical pharmacology perspective. A comprehensive review of RWD/RWE use cases is provided and discussed in the following categories of application: drug–drug interaction assessments, dose recommendation for patients with organ impairment, pediatric plan development and study design, model‐informed drug development (e.g., disease progression modeling), prognostic and predictive biomarkers/factors identification, regulatory decisions support (e.g., label expansion), and synthetic/external control generation for rare diseases. Additionally, we describe and discuss common sources of RWD to help guide appropriate data selection to address questions pertaining to clinical pharmacology in drug development and regulatory decision making. [ABSTRACT FROM AUTHOR]

Despite the liver being the primary site for clearance of xenobiotics utilizing a myriad of mechanisms ranging from cytochrome P450 enzyme pathways, glucuronidation, and biliary excretion, there is a dearth of information available as to how the severity of hepatic impairment (HI) can alter drug absorption and disposition (i.e., pharmacokinetics [PK]) as well as their efficacy and safety or pharmacodynamics (PD). In general, regulatory agencies recommend conducting PK studies in subjects with HI when hepatic metabolism/excretion accounts for more than 20% of drug elimination or if the drug has a narrow therapeutic range. In this tutorial, we provide an overview of the global regulatory landscape, clinical measures for hepatic function assessment, methods to stage HI severity, and consequently the impact on labeling. In addition, we provide an in-depth practical guidance for designing and conducting clinical trials for patients with HI and on the application of modeling and simulation strategies in lieu of dedicated trials for dosing recommendations in patients with HI. [ABSTRACT FROM AUTHOR]

Characterizing interactions between new molecular entities (NMEs) and drug transporters is a critical element of drug development that helps in assessing potential transporter‐based drug–drug interactions (DDIs). However, not all NME new drug applications (NDAs) include a full characterization of NMEs transporter‐based DDIs, which necessitates the issuance of post‐marketing requirement (PMR)/post‐marketing commitment (PMC) by the US Food and Drug Administration (FDA) to characterize these potential interactions. The objective of this analysis is to identify trends in transporter‐based PMRs/PMCs issued by the FDA between 2012 and 2021. A decrease in the number of transporter‐based PMRs/PMCs was observed from 2012 to 2016 and an increasing trend in the number of PMRs/PMCs was observed after 2017. The majority of these transporter‐based PMRs/PMCs requested clinical evaluation (48%), some requested in vitro assessment (38%), and 2.5% requested modeling and simulation assessment. Most of the PMRs/PMCs requested evaluation of NMEs as perpetrator with the efflux transporters, P‐gp and/or BCRP (53%). Forty‐eight percent of the PMRs/PMCs were fulfilled with 67% resulted in labeling updates. On average, 2.5 years were needed for the information related to PMRs/PMCs to show in NMEs labeling. In conclusion, this analysis highlights the increased emphasis from the FDA on proper characterization of transporter‐based DDI and call for the need of early characterization of NMEs‐transporters interaction before initial NDA approval. [ABSTRACT FROM AUTHOR]

Drug‐drug interaction (DDI) is an important consideration for clinical decision making in prostate cancer treatment. The objective of this study was to evaluate the effect of enzalutamide, an oral androgen receptor inhibitor, on the pharmacokinetics (PK) of digoxin (P‐glycoprotein [P‐gp] probe substrate) and rosuvastatin (breast cancer resistance protein [BCRP] probe substrate) in men with metastatic castration‐resistant prostate cancer (mCRPC). This was a phase I, open‐label, fixed‐sequence, crossover study (NCT04094519). Eligible men with mCRPC received a single dose of transporter probe cocktail containing 0.25 mg digoxin and 10 mg rosuvastatin plus enzalutamide placebo‐to‐match on day 1. On day 8, patients started 160 mg enzalutamide once daily through day 71. On day 64, patients also received a single dose of the cocktail. The primary end points were digoxin and rosuvastatin plasma maximum concentration (Cmax), area under the concentration‐time curve from the time of dosing to the last measurable concentration (AUClast), and AUC from the time of dosing extrapolated to time infinity (AUCinf). Secondary end points were enzalutamide and N‐desmethyl enzalutamide (metabolite) plasma Cmax, AUC during a dosing interval, where tau is the length of the dosing interval (AUCtau), and concentration immediately prior to dosing at multiple dosing (Ctrough). When administered with enzalutamide, there was a 17% increase in Cmax, 29% increase in AUClast, and 33% increase in AUCinf of plasma digoxin compared to digoxin alone, indicating that enzalutamide is a "mild" inhibitor of P‐gp. No PK interaction was observed between enzalutamide and rosuvastatin (BCRP probe substrate). The PK of enzalutamide and N‐desmethyl enzalutamide were in agreement with previously reported data. The potential for transporter‐mediated DDI between enzalutamide and digoxin and rosuvastatin is low in men with prostate cancer. Therefore, concomitant administration of enzalutamide with medications that are substrates for P‐gp and BCRP does not require dose adjustment in this patient population. [ABSTRACT FROM AUTHOR]

An impaired renal function, including acute and chronic kidney disease and end‐stage renal disease, can be the result of aging, certain disease conditions, the use of some medications, or as a result of smoking. In patients with renal impairment (RI), the pharmacokinetics (PKs) of drugs or drug metabolites may change and result in increased safety risks or decreased efficacy. In order to make specific dose recommendations in the label of drugs for patients with RI, a clinical trial may have to be conducted or, when not feasible, modeling and simulations approaches, such as population PK modeling or physiologically‐based PK modelling may be applied. This tutorial aims to provide an overview of the global regulatory landscape and a practical guidance for successfully designing and conducting clinical RI trials or, alternatively, on applying modeling and simulation tools to come to a dose recommendation for patients with RI in the most efficient manner. [ABSTRACT FROM AUTHOR]

This review aimed to evaluate the clinical success of clopidogrel dosing based on CYP2C19 genotype and to identify the relevant additional factors that may be useful for consideration by the clinician when dosing individuals with clopidogrel. The results indicated that genotype‐guided dosing in individuals with acute coronary syndrome undergoing percutaneous coronary intervention is frequently practiced, although the advantages remain controversial. Demographic factors, such as age, ethnicity, and some comorbidities, such as diabetes mellitus, can potentially contribute to further refinement of clopidogrel dosage but additional clinical studies to guide these practices are required. Drugs that are CYP2C19 or CYP3A4 inhibitors may reduce the effectiveness of clopidogrel and should be carefully considered during co‐administration. In particular, as stated in the clopidogrel label, concomitant use with strong or moderate CYP2C19 inhibitors, such as omeprazole, should be avoided. Increased exposure and response to clopidogrel has been observed in smokers. Noteworthy, a very recent study has shown that smoking cessation in clopidogrel patients may result in reduced response and carries the risk of high on‐clopidogrel platelet reactivity. Recent studies have shown clinically significant increases in exposure to CYP2C8 substrates (repaglinide, dasabuvir, and desloratadine) and a CYP2B6 substrate (s‐sibutramine) following co‐administration with clopidogrel, indicating that therapeutic strategies with clopidogrel should avoid these drugs. [ABSTRACT FROM AUTHOR]

Polymyxin B is used as a last treatment resort for multidrug‐resistant Gram‐negative bacterial infections. The objectives of this study were to examine the population pharmacokinetics of polymyxin B and investigate factor(s) influencing pharmacokinetic variability. Four serial blood samples each were collected from 35 adult patients at steady state. The concentrations of individual polymyxin B components were analyzed using a validated liquid chromatography / tandem mass spectrometry assay and combined to derive total concentrations. A maximum likelihood expectation maximization approach was used to fit the data. Various demographic variables were investigated as potential covariates for clearance and volume of distribution (Vd) using linear regression analysis. A one‐compartment model fit to the data satisfactorily (r2 = 0.96). The best‐fit mean ± SD for clearance and Vd were 2.5 ± 1.1 L/h and 34.3 ± 16.4 L, respectively. Creatinine clearance was found to be a statistically significant covariate of clearance, but the magnitude was deemed clinically insignificant. [ABSTRACT FROM AUTHOR]