Search

Purpose. To determine the human jejunal permeability of cimetidine and ranitidine using a regional jejunal perfusion approach, and to integrate such determinations with previous efforts to establish a baseline correlation between permeability and fraction dose absorbed in humans for soluble drugs.

Physiologically based pharmacokinetic (PBPK) modeling and simulation is a tool that can help predict the pharmacokinetics of drugs in humans and evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure. The use of this tool is increasing at all stages of the drug development process. This report reviews recent instances of the use of PBPK in decision-making during regulatory review. The examples are based on Center for Drug Evaluation and Research reviews of several submissions for investigational new drugs (INDs) and new drug applications (NDAs) received between July 2008 and June 2010. The use of PBPK modeling and simulation facilitated the following types of decisions: the need to conduct specific clinical pharmacology studies, specific study designs, and appropriate labeling language. The report also discusses the challenges encountered when PBPK modeling and simulation were used in these cases and recommends approaches to facilitating full utilization of this tool.

Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA. Correspondence: L Lesko (lawrence.lesko@fda.hhs.gov), I Zineh (Issam.Zineh@fda.hhs.gov), or S-M Huang (ShiewMei.Huang@fda.hhs.gov)doi:10.1038/clpt.2010.229

The US FDA encourages the integration of biomarkers in drug development and their appropriate use in clinical practice. It is believed that this approach will help alleviate stagnation and foster innovation in the development of new medical products, and, ultimately, lead to more personalized medicine. To facilitate the use of biomarkers in drug development and clinical practice, the FDA organized workshops, issued guidances, established a voluntary submission process, developed online educational tools and, most importantly, strives to ensure the integration of this information into drug labels, for example, via the update of existing labels, or the inclusion of appropriate language in new drug labels. A pilot process has been set up to qualify novel biomarkers that are not associated with specific drug products, but are of more common use (e.g., biomarkers for drug safety). In addition, the FDA has initiated the creation of various consortia that are working towards the identification and characterization of exploratory biomarkers in order to qualify them for a specific use.

Over the past 10 years, the US FDA has become a strong pharmacogenomics advocate as part of its mission to both protect and advance public health by enabling innovations that make medicines safer to use and more effective. The agency has evolved its advocacy cautiously on a foundation of science-based information from novel programs, such as the Voluntary Genomics Data Submission initiative, and on careful regulatory assessment of the extraordinary advances in clinical pharmacogenomics that have supported the update of drug labels with genetic information. This commentary goes into detail on the evolution of these achievements. However, many challenges remain for pharmacogenomics, and they will continue to evolve, and all stakeholders must work together. As the decade draws to a close, we have presented four major areas that need to be addressed collectively to assure that pharmacogenomics continues to mature over the next 10 years into a science that is essential to the practice of medicine.

The calcineurin inhibitors-cyclosporine and tacrolimus-are the mainstay of immunosuppressive therapy in solid organ transplantation. These drugs produce severe adverse drug effects (ADEs) such as nephrotoxicity, posttransplantation diabetes mellitus, and hypertension. Accumulated evidence suggests that the development of type 2 diabetes, hypertension, and renal failure may be associated with specific DNA genotypes. In this review, the genes involved with the development of these disease processes are compared with those implicated in calcineurin inhibitor-induced ADEs. The renin-angiotensin system genes, cytokine-encoding genes, and plasminogen activator inhibitor type 1 genes have been implicated in calcineurin inhibitor-induced nephrotoxicity, as well as in development of renal failure. A number of genes are implicated in contributing to diabetes, and these include the vitamin D receptor gene, VDR; hepatocyte nuclear factor genes, HNF; transcription factor 7-like 2 gene, TCF7L2; angiotensin-converting enzyme gene, ACE; cytokines; peroxisome proliferator-activated receptor gamma gene, PPARG; and others. Studies have suggested that the VDR, PPARG, HNF1A, and adenosine 5'-triphosphate-binding cassette ABCC8 (which encodes the sulfonylurea receptor) genes are associated with calcineurin inhibitor-induced diabetes. The genes encoding for the angiotensin-converting enzyme, endothelial constitutive nitric oxide synthase, and cytochrome P450 3A isoenzyme have been involved in the development of hypertension and in calcineurin inhibitor-induced hypertension. The genetic study of disease states can be the stepping stones for thoroughly understanding the genetic basis of ADEs. Gene polymorphisms are implicated in the development of diseases and corresponding disease-like ADEs. The disease-associated genes provide candidate genes for exploring ADEs and may provide genomic biomarkers for assessing the risk for developing severe calcineurin inhibitor-related ADEs as well as for developing preventive strategies.

Personalized medicine is health care that tailors interventions to individual variation in risk and treatment response. Although medicine has long strived to achieve this goal, advances in genomics promise to facilitate this process. Relevant to present-day practice is the use of genomic information to classify individuals according to disease susceptibility or expected responsiveness to a pharmacologic treatment and to provide targeted interventions. A symposium at the annual meeting of the Society for Medical Decision Making on 23 October 2007 highlighted the challenges and opportunities posed in translating advances in molecular medicine into clinical practice. A panel of US experts in medical practice, regulatory policy, technology assessment, and the financing and organization of medical innovation was asked to discuss the current state of practice and research on personalized medicine as it relates to their own field. This article reports on the issues raised, discusses potential approaches to meet these challenges, and proposes directions for future work. The case of genetic testing to inform dosing with warfarin, an anticoagulant, is used to illustrate differing perspectives on evidence and decision making for personalized medicine.

The effect of post maturational aging on the in vivo disposition of theophylline was examined in the Mongolian gerbils (Meriones unguiculatus) aged 30-39 (old), 12-18 (middle-aged) and 3 (young) months following a 20 mg/kg i.p. dose. Biotransformation of theophylline was also examined in liver microsomes from non-induced and 3-methylcholanthrene induced gerbils. Analysis of theophylline plasma kinetics showed decreased clearance, increased half-life and increased volume of distribution in old vs. young animals. Clearance to the 1,3-dimethyluric acid metabolite was similar for all age groups, while clearance to the 1-methyluric acid metabolite was significantly lower in the middle-aged group compared to that of young and old gerbils. Urinary recovery of 1-methylurate was increased in old vs. young and middle-aged animals while recovery of theophylline was decreased. 3-Methylcholanthrene induction resulted in decreased recovery of theophylline and increased recovery of 1,3-dimethylurate and 1-methylurate in young and middle-aged gerbils compared to non-induced controls. Decreased microsomal protein content was observed in old vs. young and middle-aged gerbils and an age-related decrease in cytochrome P-450 content (nmol P-450/g liver) was also observed. The rate of dimethylurate formation was decreased 37% in microsomes from old vs. young and middle-aged gerbils. 3-Methylcholanthrene administration resulted in a 2- and 1.5-fold increase in the rate of 1,3-dimethylurate formation in young and middle-aged gerbils, respectively. The results of these experiments indicate that the Mongolian gerbil may be useful for the study of the biochemical mechanisms underlying age-related changes in the biotransformation and kinetics of theophylline.

Many factors can affect a patient's response to a drug. These include intrinsic factors such as age, gender, race/ethnicity, genetics, disease states, organ dysfunctions, and other physiological changes, including pregnancy, lactation, and extrinsic factors such as smoking, diet (food, juice, dietary supplements), and concomitant medications (ICH E5, 1998 and 2004). The interplay of genotypes of the enzymes, transporters and receptors, among other factors (such as concomitant medications and disease states), can affect the risk/benefit ratio for individual patients. This commentary discusses when the genomic information should be obtained during drug development and when it is to be assimilated into labeling and standards of care that can be used to "individualize" drug therapy and become one of the pillars of "personalized medicine."

Warfarin is a challenging drug to accurately dose, both initially and for maintenance, because of its narrow therapeutic range, wide interpatient variability, and long list of factors that can influence dosing. Two million people in the United States are initiated on warfarin therapy annually, and this number is steadily increasing because of the increase in number of eligible patients. Recently, warfarin was reported to be the fourth leading cause of adverse events. The U.S. Food and Drug Administration recognizes that the adverse event rate of warfarin can be improved through better initial dosing, because many of the serious adverse events of warfarin occur soon after starting treatment. A substantial number of studies demonstrate that common variants of two genes, VKORC1 and CYP2C9, along with other nongenetic factors, correlate significantly with warfarin dosing. The genotypes of VKORC1 and CYP2C9 alone account for nearly 3 times more of the variability ( approximately 30%) in warfarin dosing than do age, weight, gender, and other clinical factors combined ( approximately 12%). Therefore, the purpose of this report is to review the current recommendations for warfarin therapy that involve genetic testing.

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.

Quantitative disease-drug-trial models allow learning from prior experience and summarize the knowledge in a ready to apply format. Employing these models to plan future development is proposed as a powerful solution to improve pharmaceutical R&D productivity. The disease and trial models are, to a large extent, independent of the product, but the drug model is not. The goals are to apply the disease and trial models to future development and regulatory decisions, and publicly share them. We propose working definitions of these models, describe the various subcomponents, provide examples, and discuss the challenges and potential solutions for developing such models. Building useful disease-drug-trial models is a challenging task and cannot be achieved by any single organization. It requires a consorted effort by industry, academic, and regulatory scientists. We also describe the strategic goals of the FDA Pharmacometrics group.

Predicting clinically significant drug interactions during drug development is a challenge for the pharmaceutical industry and regulatory agencies. Since the publication of the US Food and Drug Administration's (FDA's) first in vitro and in vivo drug interaction guidance documents in 1997 and 1999, researchers and clinicians have gained a better understanding of drug interactions. This knowledge has enabled the FDA and the industry to progress and begin to overcome these challenges. The FDA has continued its efforts to evaluate methodologies to study drug interactions and communicate recommendations regarding the conduct of drug interaction studies, particularly for CYP-based and transporter-based drug interactions, to the pharmaceutical industry. A drug interaction Web site was established to document the FDA's current understanding of drug interactions (http://www.fda.gov/cder/drug/drugInteractions/default.htm). This report provides an overview of the evolution of the drug interaction guidances, includes a synopsis of the steps taken by the FDA to revise the original drug interaction guidance documents, and summarizes and highlights updated sections in the current guidance document, Drug Interaction Studies-Study Design, Data Analysis, and Implications for Dosing and Labeling.

The drug development process is dependent upon having established end points for measuring drug efficacy and adverse effects. New drug development in organ transplantation suffers from having end points which are either outdated or which do not serve the purpose of addressing the current critical drug therapy problems. Numerous biomarkers have been examined in organ transplantation, but almost all would be classified as exploratory for drug development purposes. Some of the possible pathways out of this dilemma include investigator- or consortium-initiated research that would qualify the biomarkers as either probable or known valid biomarkers, help in identification of new end points in transplantation and their associated biomarkers, co-development of a new biomarker and drug for transplantation and the use of new clinical trial design methods which facilitate enriched or stratified transplant patient populations. With new biomarkers and new study design methodologies for drug development, improvement in the drug development process for transplantation is a real possibility that the transplant clinical and research community can help to bring about.

Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005-2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Publisher Summary This chapter discusses the role of the Food and Drug Administration (FDA) in guiding drug development. The FDA guides drug development in many different ways such as (1) by interpreting laws, rules, and regulations; (2) by disseminating policy statements that may otherwise be vague or unclear; (3) by providing advice and sharing experiences and expertise in face-to-face meetings and through written agreements and letters; (4) by domestic and international guidances; (5) by planned telephone conferences and/or scheduled videoconferences; and (6) by the FDA web site. The FDA also guides drug development by holding closed or open advisory committee meetings. These meetings facilitate the regulatory review and FDA approval process by bringing together external experts to assess data, recommend need for new studies, and address specific questions formulated by the FDA to help resolve scientific or clinical problems related to the drug development process or a specific product approval. The most widespread, effective, and important way that the FDA communicates with sponsors and guides drug development is through guidances issued either by the FDA or by the International Conference on Harmonization (ICH). The FDA should take the lead in developing a national Critical Path Opportunities List to bring concrete focus to these tasks and prepare for the challenges in drug development in the future.

Pharmacogenomics and pharmacogenetics provide methodologies that can lead to DNA-based tests to improve drug selection, identify optimal dosing, maximize drug efficacy or minimize the risk of toxicity. Rapid advances in basic research have identified many opportunities for the development of 'personalized' treatments for individuals and/or subsets of patients defined by genetic and/or genomic tests. However, the integration of these tests into routine clinical practice remains a major multidisciplinary challenge, and even for well-established biomarkers there has been little progress. Here, we consider this challenge from a regulatory perspective, highlighting recent initiatives from the FDA that aim to facilitate the integration of pharmacogenetics and pharmacogenomics into drug development and clinical practice.

Early identification of toxicologic side effects of a drug candidate is critical to an efficient drug discovery and development process. Toxicogenomics, the marriage of data-rich genomics approaches with traditional toxicologic end point evaluation combined with increasingly powerful in silico modeling approaches, promises to accelerate this process. The advent of parallel experimental platforms, for example, DNA microarrays, has enabled us to gain insight into complex biologic responses to drugs. The challenge is to analyze and correctly interpret these large data sets. Currently, no common standards exist for such data even though attempts are being made to streamline and standardize the presentation of the information. These efforts include ArrayExpress infrastructure for microarray data (http://www.ebi.ac.uk/arrayexpress), Minimum Information About a Microarray Experiment (http://www.mged. org/Workgroups/MIAME/miame.html), and MicroArray Gene Expression (MAGE) markup language (http://www.mged.org; http://www.omg.org/technology/documents/formal/gene_ expression.htm).

Serious drug-drug interactions have contributed to recent U.S. market withdrawals and also recent nonapprovals of a few new molecular entities. Many of these interactions involved the inhibition or induction of metabolizing enzymes and efflux transporters, resulting in altered systemic exposure and adverse drug reactions or loss of efficacy. In addition to drug-drug interactions, drug-dietary supplement and drug-citrus fruit interactions, among others, could also cause adverse drug reactions or loss of efficacy and are important issues to consider in the evaluation of new drug candidates. This commentary reviews (1). the current understanding of the mechanistic basis of these interactions, (2). issues to consider in the interpretation of study results, and (3). recent labeling examples to illustrate the translation of study results to information useful for patients and health care providers.

Although women have been included in drug development in numbers predicted by their disease prevalence whenever this has been examined, the conduct of gender-specific analysis has become standard only in the last decade; these are often referenced in labeling. Pharmacokinetic (PK) differences, by far the most common known gender differences, are readily assessed because gender-specific PK data are now regularly obtained. The chapter discusses the current status of participation of women in clinical trials of new medical products and gender differences and the current status of clinical pharmacology/biopharmaceutic studies of new drugs and gender differences The exclusion of women from early trials in life-threatening conditions because of their childbearing potential was barred by regulation in 1993. During the last 2 decades, the FDA has encouraged inclusion in clinical trials of a reasonable representation of both genders, of people with ethnic profiles similar to the population that will be using the product, and of people at the extremes of age. Current practice should ensure the detection of large gender differences in response, but subtle differences may be undetected until they are specifically sought. The profound gender effects of the selective serotonin reuptake inhibitor (SSRI) antidepressants on sexual function in women were significantly underestimated for years because usual clinical trials do not assess this function well. The greater susceptibility of women to drugs that cause TdP arrhythmias also took many years to become recognized, as did the potential of CYP3A inducers to render oral contraceptives ineffective.

The use of pharmacogenetics and pharmacogenomics in the drug development process, and in the assessment of such data submitted to regulatory agencies by industry, has generated significant enthusiasm as well as important reservations within the scientific and medical communities. This situation has arisen because of the increasing number of exploratory and confirmatory investigations into variations in RNA expression patterns and DNA sequences being conducted in the preclinical and clinical phases of drug development, and the uncertainty surrounding the acceptance of these data by regulatory agencies. This report summarizes the outcome of a workshop cosponsored by the Food and Drug Administration (FDA), the Pharmacogenetics Working Group (PWG), the Pharmaceutical Research and Manufacturers of America (PhRMA), and the PhRMA Preclinical Safety Committee (DruSafe). The specific aim of the workshop was to identify key issues associated with the application of pharmacogenetics and pharmacogenomics, including the feasibility of a regulatory "safe harbor" for exploratory genome-based data, and to provide a forum for industry-regulatory agency dialogue on these important issues.

A number of published reports have addressed the fundamental problems that affect the pharmaceutical and biotechnology industries today (1, 2). The cost of drug development continues to increase, the time from drug discovery to peak sales is thought to be too long, and the amount of data and information arising from clinical trials is increasing exponentially, thereby complicating its interpretation. The solution to these problems is not always clear or simple. Increasingly, the pharmaceutical industry is looking toward new paradigms of drug development to deal with these issues, and in almost all cases, new technology is viewed as one way to overcome the challenges and the obstacles to achieving a more affordable, efficient, and informative drug development process. Among the technology-enabling solutions for which there are high expectations in the next 5-10 years include such platforms as pharmacogenomics, biomarkers, bioinformatics, and modeling and simulation (M&S). This chapter will focus only on the regulatory perspectives of M&S, or more specifically clinical trial simulation (CTS).

The current BSC guidance issued by the FDA allows for biowaivers based on conservative criteria. Possible new criteria and class boundaries are proposed for additional biowaivers based on the underlying physiology of the gastrointestinal tract. The proposed changes in new class boundaries for solubility and permeability are as follows: 1. Narrow the required solubility pH range from 1.0-7.5 to 1.0-6.8. 2. Reduce the high permeability requirement from 90% to 85%. The following new criterion and potential biowaiver extension require more research: 1. Define a new intermediate permeability class boundary. 2. Allow biowaivers for highly soluble and intermediately permeable drugs in IR solid oral dosage forms with no less than 85% dissolved in 15 min in all physiologically relevant dissolution media, provided these IR products contain only known excipients that do not affect the oral drug absorption. The following areas require more extensive research: 1. Increase the dose volume for solubility classification to 500 mL. 2. Include bile salt in the solubility measurement. 3. Use the intrinsic dissolution method for solubility classification. 4. Define an intermediate solubility class for BCS Class II drugs. 5. Include surfactants in in vitro dissolution testing.

Background St John's wort is a popular over-the-counter dietary supplement and herbal remedy that has been implicated in drug interactions with several substrates of P-glycoprotein. The effect of St John's wort on P-glycoprotein activity in vivo was examined with use of fexofenadine as selective probe drug. Methods A 3-period, open-label, fixed-schedule study design was used. Fexofenadine, 60 mg, was administered orally before administration of St John's wort, with a single dose of St John's wort (900 mg), and after 2 weeks of treatment with St John's wort (300 mg 3 times a day) to determine P-glycoprotein activity. Results A single dose of St John's wort significantly (P < .05) increased the maximum plasma concentration of fexofenadine by 45% and significantly (P < .05) decreased the oral clearance by 20%, with no change in half-life or renal clearance. Long-term administration of St John's wort did not cause a significant change in fexofenadine disposition relative to the untreated phase. Compared with the single-dose treatment phase, long-term St John's wort caused a significant 35% decrease (P < .05) in maximum plasma concentration and a significant 47% increase (P < .05) in fexofenadine oral clearance. Conclusions A single dose of St John's wort resulted in a significant inhibition of intestinal P-glycoprotein. Long-term treatment with St John's wort reversed the changes in fexofenadine disposition observed with single-dose administration. Clinical Pharmacology & Therapeutics (2002) 71, 414–420; doi: 10.1067/mcp.2002.124080

The purpose of this human intestinal perfusion study (in vivo) was twofold. Firstly, we aimed to determine the effective in vivo jejunal permeability (P(eff)) of amoxicillin and to classify it according to the Biopharmaceutics Classification System (BCS). Secondly, we investigated the acute effect of amiloride on the jejunal P(eff) of amoxicillin. Amoxicillin, a beta-lactam antibiotic, has been reported to be absorbed across the intestinal mucosa by both passive diffusion and active transport. A regional single-pass perfusion of the jejunum was performed using a Loc-I-Gut perfusion tube in 14 healthy volunteers. Each perfusion lasted for 200 min and was divided into two periods of 100 min each. The concentration of amoxicillin entering the jejunal segment was 300 mg/l in both periods, and amiloride, an inhibitor of the Na+/H+ exchanger, was added at 25 mg/l in period 2. The concentrations of amoxicillin and amiloride in the outlet jejunal perfusate were measured with two different HPLC-methods. Antipyrine and [14C]PEG 4000 were added as internal standards to the perfusion solution. Amiloride had no significant effect on the jejunal P(eff) of amoxicillin. The human in vivo jejunal P(eff) for amoxicillin was 0.34+/-0.11 x 10(-4) and 0.46+/-0.12 x 10(-4) cm/s in periods 1 and 2, respectively. The high jejunal P(eff) for amiloride was 1.63+/-0.51 x 10(-4) cm/s which predicts an intestinal absorption of more than 90%. Following the BCS amoxicillin was classified as a low P(eff) drug, and amiloride had no acute effect on the in vivo jejunal P(eff) of amoxicillin.

In recent years, pediatric drug development has been encouraged by regulatory authorities in light of the insufficient number of drugs, doses and formulations available for application in children and high off-label use of adult medicines.1 The US Food and Drug Administration Safety and Innovation Act (FDASIA) 2012 requires sponsors to submit a pediatric drug development plan to the regulatory agency at the end of phase II studies2 while a Pediatric Investigation Plan (PIP) is required earlier in Europe at the end of Phase I studies.3 Although the establishment of pediatric development plans is in many cases challenging due to the lack of data and an incomplete understanding of the drug's pharmacokinetics (PK) and pharmacodynamics (PD) in children, these regulatory initiatives prompted the early use of predictive models to support pediatric drug development programs.4,5,6,7 These models frequently employ simple allometric functions to scale a PK parameter (Y), such as clearance or volume of distribution, from adults to children using a body-weight (BW)–based power function (Y = a·BWb) with a coefficient (a) and an exponent (b).8,9 Although this scaling approach has found wide application for small molecules,9 it has its limitations for capturing highly non-linear processes, such as target-mediated drug disposition (TMDD) of monoclonal antibodies (mAbs)10,11 or maturational changes in mAb disposition from birth.12 Therapeutic mAbs are cleared via multiple routes, which can be divided into mAb-specific and mAb non-specific elimination routes. Non-specific mAb elimination is primarily mediated by intracellular catabolism following fluid phase and receptor-mediated endocytosis,8 which is typically non-saturable at therapeutic doses (i.e., linear PK). Interactions between the mAb and its specific target, on the other hand, can lead to saturable and thus non-linear PK. Low mAb concentration relative to the concentration of target result in rapid elimination.13 Once mAb concentrations increase, more targets are occupied, which results in a non-linear decrease in clearance. At very high mAb concentrations, when targets are completely saturated, clearance can be considered linear again.14 As such, BW-based allometric scaling may be sufficient to scale adult doses to children for mAbs with linear kinetics except for low weight children,15,16 but may less accurately predict pediatric dosing regimens for mAbs that show non-linear kinetics, i.e., that employ TMDD. Literature adult data suggests that PK parameters for many mAbs change in a less than BW-proportional manner as respective allometric exponents for clearance and volume of distribution were estimated to range from 0.3 to 0.7 (refs. 17,18) There are only a few examples, where strong BW effects were observed as indicated by exponents greater than 0.75.17 It should be noted that allometric exponents determined for within species scaling are typically smaller than those observed for between species scaling, which is likely the result of a narrower BW range in adults for a given species (approximately two- to threefold).17 Since the respective BW range in children is wider than that in adults, further research is necessary to determine appropriate allometric exponents in children.17 For example, clearance exponents were estimated to be 0.823 (ref. 19) for canakinumab in systemic juvenile idiopathic arthritis (SJIA) patients, whereas a value of 0.75 was found appropriate for palivizumab and infliximab when scaling from adults to children.15,20 Yet, few studies systematically explored the entire range of allometric exponents to determine an optimal value for CL and Vss across mAbs. There is also limited information on whether or not BW-based dosing strategies allow to reliably predict pediatric doses that result in comparable systemic drug exposure (AUC0–infinity) in children and adults for mAbs that exhibit TMDD. The uncertainty around how to optimally scale adult doses to children for mAbs with non-linear kinetics is reflected in the lack of approved pediatric dosing regimens as compared to mAb with linear kinetics (Table 1). Table 1 Overview of approved antibody-based therapeutic proteins for application in adults and children The objective of our study was therefore to evaluate the impact of differences in target expression between adults and children on pediatric dosing for mAbs exhibiting non-linear kinetics via simulations.

Rivastigmine is a cholinersterase inhibitor approved recently for the treatment of Alzheimer's disease (AD). The objective of this study is to characterize the pharmacokinetics-pharmacodynamics of rivastigmine in patients with AD. Eighteen AD patients received doses ranging from 1 to 6 mg bid for about 11 weeks. Rivastigmine and its active (major) metabolite (ZNS 114-666, also called NAP 226-90), plasma, and cerebrospinal fluid (CSF) concentrations were determined together with the AChE activity and computerized neuropsychological test battery (CNTB) scores. Nonlinear mixed-effects modeling of pharmacokinetic and pharmacodynamic data was conducted using NONMEM. Rivastigmine and its metabolite exhibited dose-disproportional pharmacokinetics. The apparent clearance and volume of distribution (plasma) of rivastigmine were estimated to be 120 L/h and 236 L, respectively. The relative bioavailability at the 6 mg dose was about 140%. The metabolite had a clearance of about 100 L/h and a volume of distribution of 256 L. The kinetics of the parent and metabolite in CSF showed an equilibration half-life of about 0.2 and 0.5 hours, respectively. The metabolite levels in CSF correlated very well with the acetylcholinesterase inhibition, with a ZNS 114-666 concentration of about 5.4 microg/L required for half-maximal inhibition of acetylcholinesterase activity. No statistically significant correlation of the CNTB scores with enzyme inhibition, parent or metabolite concentration (plasma/CSF), or rivastigmine dose could be established. The PK-PD model presented in this study can provide valuable information to optimize the drug development of rivastigmine and other related compounds and also in rationalizing dosing recommendations.

Objective: The US Food and Drug Administration (FDA) currently uses bioequivalence (BE) limits for fasting BE studies that are based on the 90% confidence interval for the ratio of difference of the test and reference products C max and AUC falling within 80% to 125%. The FDA has also proposed that BE limits be used similarly for AUC and C max measurements from fed BE studies. In some cases, regulatory agencies have considered a wider BE limit for C max , because of the typically higher variability of C max compared to AUC. We investigated the consequences of changing from an 80%/ 125% limit for both pharmacokinetic measures to one that uses a limit of 80%/125% for AUC and 70%/143% for C max . Methods: We computed the sample sizes required for BE studies using 80%/ 125% for AUC and 70%/143% for C max as BE limits. We also determined the range of the ratios of C max and AUC values in a study that could meet the 70%/143% and 80%/125% BE limits. Results: The sample size for the study, in order to have adequate power with 80%/125% for AUC and 70%/143% for C max , will be determined primarily by the intrasubject variability of AUC, though with a substantial proportion of studies (about one third) still determined by the variability of C max . The ratio of mean C max values that can pass a wider 70%/143% BE limit could easily be as high as 128%. Conclusion: Without further scientific or clinical rationale, we find it difficult to justify widening the bioequivalence limit for C max to 70%/143% for either fasting or fed BE studies.

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.

Ninety-eight new molecular entities applications approved between 1987 to 1991 (period I) and 193 applications for new molecular entities between 1992 to 1997 (period II) were surveyed for drug-drug interaction studies. In period I (used as a comparator), 32 applications contained drug-drug interaction studies for a total of 117 studies. In period II, 106 applications reported drug-drug interaction studies, and the number of studies per new molecular entity ranged from 0 to 15. Most studies (77%) were performed in healthy subjects, with 44% using crossover designs, 7% using parallel designs, and the remaining using fixed sequence designs. The most common dosing scheme for new molecular entities/interacting drug was multiple dose (47%), whereas single dose/multiple dose was used in 31% of studies, and single dose/single dose was used in 18% of studies. Of the 540 drug-drug interaction studies submitted in period II, 80 (15%) resulted in clinically significant labeling statements. Submissions for new molecular entities to the Center for Drug Evaluation and Research divisions most likely to include drug-drug interaction studies were neuropharmacology, cardiorenal, antiviral, and antiinfective drugs. Some drug classes such as oncology drug products and radioimaging products were least likely to include drug-drug interaction studies in their submissions. We conclude that the use of drug-drug interaction studies in the drug development process has increased between the two periods.

The objective of this report is to provide a regulatory perspective on the quality of pharmacokinetic studies in renal impairment (RI) studies submitted in support of new drug applications (NDAs) or supplements to NDAs (sNDAs) submitted to the Food and Drug Administration (FDA). Fifty-one NDA and 20 sNDA submissions reviewed between 1996 and 1997 by the Office of Clinical Pharmacology and Biopharmaceutics were evaluated for the following: (1) whether an RI study was conducted; (2) contribution of the renal clearance to the overall clearance in subjects without renal impairment; (3) degree of plasma protein binding (%PB) in subjects without renal impairment; (4) dose proportionality of single and multiple doses; (5) study design, including dosing regimen; (6) definition of renal impairment; (7) stratification of renal functions; (8) number of subjects/group; (9) data analysis and interpretation; and (10) impact on labeling. Results of the analysis indicated that 67% of the NDAs and 30% of supplemental NDAs contained RI-studies (34/51 for NDAs and 6/20 for sNDAs). No obvious differences in the pharmacokinetic characteristics (e.g., percentage excreted unchanged in urine and %PB) were observed between drugs for which RI studies were conducted versus those not conducted. Most studies conducted were designed as single dose (70%). Seventy-five percent of the studies used doses within the therapeutic dosage range of the drug. The measured 24-hour creatinine clearance was most often used to assess the renal function. Stratification of renal function ranged from one to five groups, with 6 to 8 subjects enrolled per group. In most studies conducted (38/40), data were analyzed by point estimate using ANOVA. Results of RI studies were adequately reflected in the labeling. The survey reveals that RI study design can be improved for regulatory review purposes. In part based on this analysis, the FDA has prepared a guidance that provides recommendations on the design, analysis, and impact on dosing and labeling for RI studies to include recommendations on when RI studies do not need to be performed. The guidance proposes an equivalence approach with confidence intervals, as opposed to a point estimate approach, to assess the impact of RI on systemic exposure measures.

This report investigates the quality and quantity of drug-drug interaction studies in recent new drug applications (NDAs). Eighty-nine studies contained in 14 NDAs submitted between December 1995 and November 1996 to the U.S. Food and Drug Administration (FDA) were reviewed. The results indicated that the median number of clinical drug-drug interaction studies per NDA was 6, almost double that of a 1994-1995 survey. In vitro metabolism data were present in 70% of the submissions. More than 50% of the submissions contained interaction studies using a battery of drugs (cimetidine, digoxin, or warfarin) without optimal use of the in vitro metabolism or in vivo mass balance data. Various study designs using a median number of 12 subjects were employed in the evaluation of drug-drug interactions. Some of the important study design factors such as dose size, dosing regimen, dosing duration, and timing of coadministration were considered, although not consistently, by the sponsors in their study design. Seventy-five percent of the studies used normal, healthy male subjects, and 25% used patients for whom the new molecular entities were intended. In 33% of the studies, female subjects were also recruited. Although the majority (80%) of the submissions still used p-values to determine the significance of drug interactions, 30% used a more relevant equivalence approach with 90% confidence intervals for key pharmacokinetic and/or pharmacodynamic parameter ratios to assess the extent of drug interactions. Overall, 82% of the studies concluded no interaction. Although population pharmacokinetic analysis can be a useful tool in studying drug-drug interactions, only 21% of the submissions used this approach. In summary, this assessment reveals that the quantity and quality of drug-drug interaction studies in NDAs have improved over the years. These improvements, as well as others that can be implemented, should result in more informative labeling and better patient care. FDA guidance for industry dealing with the design, analysis, and labeling language of in vivo metabolic drug-drug interactions has been developed to assist sponsors and FDA reviewers with these issues.

The application of population approaches to drug development is recommended in several US Food and Drug Administration (FDA) guidance documents. Population pharmacokinetic (and pharmacodynamic) techniques enable identification of the sources of inter- and intra-individual variability that impinge upon drug safety and efficacy. This article briefly discusses the 2-stage approach to the estimation of population pharmacokinetic parameters, which requires serial multiple measurements on each participant, and comprehensively reviews the nonlinear mixed-effects modelling approach, which can be applied in situations where extensive sampling is not done on all or any of the participants. Certain preliminary information, such as the compartment model used in describing the pharmacokinetics of the drug, is required for a population pharmacokinetic study. The practical design considerations of the location of sampling times, number of samples/participants and the need to sample an individual more than once should be borne in mind. Simulation may be useful for choosing the study design that will best meet study objectives. The objectives of the population pharmacokinetic study can be secondary to the objectives of the primary clinical study (in which case an add-on population pharmacokinetic protocol may be needed) or primary (when a stand-alone protocol is required). Having protocols for population pharmacokinetic studies is an integral part of 'good pharmacometric practice'. Real-time data assembly and analysis permit an ongoing evaluation of site compliance with the study protocol and provide the opportunity to correct violations of study procedures. Adequate policies and procedures should be in place for study blind maintenance. Real-time data assembly creates the opportunity for detecting and correcting errors in concentration-time data, drug administration history and covariate data. Population pharmacokinetic analyses may be undertaken in 3 interwoven steps: exploratory data analysis, model development and model validation (i.e. predictive performance). Documentation for regulatory purposes should include a complete inventory of key runs in the analyses undertaken (with flow diagrams if possible), accompanied by articulation of objectives, assumptions and hypotheses. Use of diagnostic analyses of goodness of fit as evidence of reliability of results is advised. Finally, the use of stability testing or model validation may be warranted to support label claims. The opinions expressed in this article were revised by incorporating comments from various sources and published by the FDA as 'Guidance for Industry: Population Pharmacokinetics' (see the FDA home page http:/(/)www.fda.gov for further information).

A total of 194 new molecular entities approved by the Food and Drug Administration between 1992 and 1997 were surveyed to determine the role of in vitro metabolic interactions in the conduct of drug-drug interaction studies and to examine the methods used in these studies. Approximately 30% of the submissions were found to have in vitro metabolism-based interaction studies, most of which were inhibitory in nature. Chemical inhibition was the most commonly used approach in studying drug interactions in vitro. In this article, an attempt to assess the quality of the chemical inhibition approach was made. Four areas were found to be often overlooked: (1) incubation time and concentrations of the drug, (2) the difference between inhibition constant (ki) and 50% inhibitory concentration (IC50) values, (3) the substrate-dependent inhibition potential, and (4) the metabolic genotype or phenotype of the liver donor. We discuss the pitfalls in estimating drug interactions when these four areas are overlooked. Clinical Pharmacology & Therapeutics (1999) 66, 9–15