One of the most effective ways in which regulatory agencies communicate with sponsors and guide drug development is through the issuance of guidances or guidelines. These can be issued domestically in a given region such as the United States by the Food and Drug Administration (FDA) or internationally through the International Conference on Harmonization. Currently, there are over 400 final or draft guidances that can be found through the FDA website. The development of guidances proceeds through a process known as Good Guidance Practices, which is intended to assure that there is an appropriate level of meaningful public participation in the development of guidance. In the past 10 years, clinical pharmacology guidances covering important areas have been issued, including pharmacokinetic data in patients with renal and hepatic impairment, dose-response studies, and drug-drug interactions.

In the future, biomarkers will play an increasingly important role in all phases of drug development, including regulatory review. However, only a few of these biomarkers will become established well enough to serve in regulatory decision making as surrogate endpoints, thereby substituting for traditional clinical endpoints. Even generally accepted surrogate endpoints are unlikely to capture all the therapeutic benefits and potential adverse effects a drug will have in a diverse patient population. Accordingly, combinations of biomarkers probably will be needed to provide a more complete characterization of the spectrum of pharmacologic response. In the future, pharmacogenomic approaches, including those based on differential expression of gene arrays, will provide panels of relevant biomarkers that can be expected to transform the drug development process.

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015

The US Food and Drug Administration (FDA) is currently developing a guidance for industry to replace a previous guidance, "Pharmacokinetics in Patients With Impaired Renal Function--Study Design, Data Analysis, and Impact on Dosing and Labeling" (renal guidance) issued in May 1998. The impact of the 1998 renal guidance was assessed following a survey of 94 new drug applications (NDAs) for small-molecule new molecular entities (NMEs) approved over the past 5 years (2003-2007). The survey results indicate that 57% of these NDAs included renal impairment study data, that 44% of those with renal data included evaluation in patients on hemodialysis, and that 41% of those with renal data resulted in recommendation of dose adjustment in renal impairment. In addition, the survey results provided evidence that renal impairment can affect the pharmacokinetics of drugs that are predominantly eliminated by nonrenal processes such as metabolism and/or active transport. The latter finding supports our updated recommendation to evaluate pharmacokinetic/pharmacodynamic alterations in renal impairment for those drugs that are mainly eliminated by nonrenal processes, in addition to those that are mainly excreted unchanged by the kidney.

Many intrinsic and extrinsic factors can affect an individual patient's drug exposure and response. The US Food and Drug Administration (FDA) has published a number of guidances that recommend how and when to evaluate these factors during drug development. The most recent FDA draft guidance on drug interactions provides advice for in vitro and in vivo drug interaction studies, including suggestions for study design, dosing strategies and analysis, and interpretation of data for medical product labels. The draft guidance updated the FDA's recommendations on the evaluation of important cytochrome P450 (CYP) enzyme- and transporter-based drug interactions during drug development.

No abstract

Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0–28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12–17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug- and system-specific information of adults and children through PBPK modeling.

To optimize drug therapy for individuals, it is critical to understand how various intrinsic (e.g., age, gender, race, genetics, organ impairment) and extrinsic factors (e.g., diet, smoking, concomitantly administered drugs) affect drug exposure and response.(1) Up to now, it has been far easier to discover effects on exposure caused by these factors, and the US Food and Drug Administration (FDA) has published several guidance documents with recommendations on how to evaluate these factors during drug development.