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Exposure Matching of Pediatric Anti-infective Drugs: Review of Drugs Submitted to the Food and Drug Administration for Pediatric Approval

      Abstract

      Purpose

      Over the last decade, few new antibiotics have been approved by the Food and Drug Administration (FDA) for pediatric use. For most anti-infective agents, including antibiotics, extrapolation of efficacy from adults to children is possible if the disease and therapeutic exposures are similar between the 2 populations. This approach reduces the number of studies required in children, but relies heavily on exposure matching between children and adults. Failures in exposure matching can lead to delays in pediatric approvals of new anti-infective agents. We sought to determine the extent of exposure matching, defined by a comparison of area under the concentration-time curve, between children and adults, for anti-infective drug products submitted to the FDA for approval.

      Methods

      We reviewed anti-infective submissions to the FDA (2002–2014) for pediatric indication. We included drug products administered via oral, intravenous, or intramuscular administration routes, and those with AUC estimates for children in available FDA reports. Our main outcome of interest was the proportion of drugs with median (or mean) pediatric AUC within 20% of the median (or mean) reported adult value.

      Findings

      We identified 29 drug products that met inclusion criteria, 14 (48%) of which had mean (or median) AUCs of all submitted age groups within 20% of that in adults. Only route of administration and drug class were associated with pediatric AUC within 20% of adult AUC.

      Implications

      Future research is needed to define criteria for and predictors of successful exposure matching of anti-infectives between children and adults.

      Key words

      Introduction

      In the United States, recent legislation has aimed to address deficiencies in pediatric drug product labeling. Through the Best Pharmaceuticals for Children Act, sponsors are provided incentives to submit pediatric pharmacokinetic (PK) and pharmacodynamic (PD) studies of drugs in children.
      Committee on Pediatric Studies Conducted Under the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA), Board on Health Sciences Policy, Institute of Medicine, Policy Framework for BPCA and PREA.
      These incentives have resulted in an expansion of pediatric clinical research, submission of pediatric data to the Food and Drug Administration (FDA), and pediatric labeling changes, including 466 changes after the initial Best Pharmaceuticals for Children Act renewal in 2002.

      Food and Drug Administration. New pediatric labeling information database. FDA website. www.accessdata.fda.gov/scripts/sda/sdNavigation.cfm?sd=labelingdatabase. Accessed November 17, 2014.

      Despite the large number of label changes, a substantial number of pediatric clinical trials have failed to meet their proposed objectives. In one analysis of 158 studies conducted to obtain pediatric drug labels, 18% of drug products were not labeled for pediatric use, and 6% were labeled for pediatric use but not for the studied indication.
      • Burckart G.J.
      • Momper J.D.
      Pediatric clinical pharmacology in regulatory and drug development sciences: lessons learned and the path forward.
      Potential reasons for these failures include lack of appropriate dosing range, lack of efficacy, lack of pediatric formulation, trial design failure, insufficient sample size, and poor dose selection.
      • Burckart G.J.
      • Momper J.D.
      Pediatric clinical pharmacology in regulatory and drug development sciences: lessons learned and the path forward.
      To improve the success of pediatric submissions and to ensure appropriate allocation of limited resources, the FDA developed a pediatric study planning and extrapolation algorithm that provides guidance for industry about the necessary studies required for drug approval for a pediatric indication.

      US Food and Drug Administration. General clinical pharmacology considerations for pediatric studies for drugs and biological products: guidance for industry. FDA website. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm425885.pdf. Accessed December 26, 2014.

      According to this algorithm, if the disease progression and response to intervention are expected to be similar between children and adults, required clinical trials are limited to those that evaluate the PK and safety profile of the drugs. This is true for most anti-infective agents. Therefore, the goal of PK studies for anti-infectives is to identify doses in children that result in drug exposures found to be efficacious in adults. Achieving similar drug exposures between children and adults (ie, exposure matching) is paramount to extrapolation of efficacy from adult studies.
      Given the need to achieve exposure matching between children and adults, one of the most important reasons for failure to obtain pediatric approval for anti-infectives is poor dose selection that occurs early in the drug development process.
      • Benjamin Jr, D.K.
      • Smith P.B.
      • Jadhav P.
      • et al.
      Pediatric antihypertensive trial failures: analysis of end points and dose range.
      Therefore, pediatric drug development may have improved efficiency and success, as well as decreased costs if doses for early-phase trials are more appropriately selected.
      Available methods for dose selection using adult data include population PK models, which incorporate allometric scaling, maturational and organ functions, and physiologically based PK modeling, among others. It is unclear in public reviews which of these methods, if any, sponsors are using for pediatric drug development. Further, although several FDA guidance documents discuss approaches for matching systemic exposure between 2 populations,

      Food and Drug Administration. Draft guidance for industry. Bioavailability and bioequivalence studies submitted in NDAs or INDs—general consideration 2014. FDA website. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM389370.pdf. Accessed March 16, 2015.

      US Food and Drug Administration. Draft guidance for industry. Drug interaction studies: study design, data analysis, implications for dosing, and labeling recommendations 2012. FDA website. www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm292362.pdf. Accessed March 16, 2015.

      US Food and Drug Administration. Draft guidance for industry. Pharmacokinetics in patients with impaired renal function—study design, data analysis, and impact on dosing and labeling 2010. FDA website. www.fda.gov/downloads/Drugs/Guidances/UCM204959.pdf. Accessed March 16, 2015.

      US Food and Drug Administration. Draft guidance for industry. Pharmacokinetics in patients with impaired hepatic function: study design, data analysis, and impact on dosing and labeling, 2003. FDA website. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/%20ucm072123.pdf. Accessed March 16, 2015.

      US Food and Drug Administration. Draft guidance for industry. Clinical pharmacology data to support a demonstration of biosimilarity to a reference product. FDA website. www.fda.gov/downloads/drugs/%20guidancecomplianceregulatoryinformation/guidances/ucm397017.pdf. Accessed March 16, 2015.

      there is no well-accepted approach for evaluating matching between adult and pediatric populations. We sought to describe the extent to which area under the concentration versus time curve, a well-accepted measure of mean drug concentration over a time period,

      US Food and Drug Administration. Guidance for industry. Exposure-response relationships—study design, data analysis, and regulatory applications. FDA website. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm072109.pdf. Accessed March 16, 2015.

      was comparable between children and adults using publicly available data.

      Methods

      Data Sources

      Since 2002, medical, clinical pharmacology, and statistical review documents are regularly published on the FDA website for each pediatric submission.

      US Food and Drug Administration. Medical, statistical, and clinical pharmacology reviews of pediatric studies conducted under Section 505A and 505B of the Federal Food, Drug, and Cosmetic Act (the Act), as amended by the FDA Amendments Act of 2007 (FDAAA). FDA website. www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ucm049872.htm. Accessed November 15, 2014.

      The medical and clinical pharmacology review documents describe or summarize studies that were conducted to obtain pediatric approval.
      We identified all anti-infective drug products submitted to the FDA for a pediatric indication and available online in the FDA websites from January 1, 2002 through August 28, 2014. We reviewed clinical pharmacology and medical review reports for each anti-infective submission. If available documentation on the FDA website was incomplete, we retrieved publications from PubMed related to the referenced studies in the FDA reports.
      We extracted anti-infective characteristics including known pathways of elimination from the FDA product label or drug reference applications (ie, Micromedex, Lexicomp, DailyMed [http://dailymed.nlm.nih.gov]).

      Inclusion and Exclusion Criteria

      The unit of observation to identify exposure matching between children and adults was the drug submitted for pediatric approval. We included an anti-infective in our analysis if it met the following criteria: (1) oral, intravenous, or intramuscular administration; and (2) inclusion of AUC estimates in available reports for pediatric age groups of interest. Submissions for a new physical design of a previously approved drug product (eg, new scored tablet, previously unscored) were not eligible for inclusion.
      The unit of observation to identify anti-infective trial characteristics associated with exposure matching was a trial submitted to support an application. We included an anti-infective trial in our analysis if the trial was the first to describe a drug’s PK and PD properties in a pediatric age group.

      Definitions and Outcomes

      We used AUC0−∞ or AUC0−t available in drug submissions to determine exposure matching between children and adults. We preferentially extracted median values if available for both children and adults, followed by geometric mean, and then arithmetic mean.
      For individual drugs, we described exposure matching between children and adults by the following criteria: (1) median (or mean) pediatric AUC within 20% of the reported adult value; (2) median (or mean) pediatric AUC within 50% of the reported adult value; or (3) ≥75% of pediatric AUC ranges within the reported adult range. Pediatric exposure was defined as “within 20% of the median (or mean) adult exposure” if the ratio of the pediatric to adult AUC was 0.8 to 1.2 for all age groups in which sponsors were seeking approval. For example, the sponsor for caspofungin sought approval for ages 3 months to 17 years. We extracted median values to calculate the AUCchild/AUCadult ratio for each of the following age groups: 3–24 months, 2–11 years, and 12–17 years. If a single value of AUCchild/AUCadult ratio in any of these age groups did not meet our predefined criteria (0.8–1.2), we classified the drug as not being within 20% of the adult median exposure. Similarly, we calculated exposure matching within 50% using an AUCchild/AUCadult ratio of 0.5 to 1.5.
      Pediatric AUC ranges were (1) defined as the median and observed range of values if available for both children and adults for a given drug, or (2) calculated as the mean AUC ± 2 SDs for a given age group if median and range were not available. The default adult AUC was obtained from the reported value in healthy volunteers unless medical and PK reports specifically noted that exposure from patients was used as the target for exposure matching. If adult exposure was not reported in the FDA documents, pediatric exposures were compared with the most liberal (values closes to pediatric values) data in healthy adults identified in the FDA product label. We determined the percentage of the AUC range for each age group (eg, 3–24 months, 2–11 years, and 12–17 years for caspofungin) that was within the calculated AUC range for adults. We summarized drug exposure matching according to whether ≥75% of the AUC ranges for the entire pediatric age range was within the calculated adult range. Each age group was weighted according to the number of years it contributed to the overall drug submission. For example, the caspofungin 2- to 11-year-old age group accounted for approximately 60% of the total age submission (10 years in this range/17 total years in the submission = 0.60). The AUC range for this age group was 0.70 of the adult range, and the weighted value was 0.42 (0.70 × 0.60). The sum of weighted values for all age groups submitted (3–24 months, 2–11 years, and 12–17 years) determined whether caspofungin was classified as having ≥75% of the pediatric AUC range within the adult range.
      We also directly compared exposure matching by AUCchild/AUCadult ratio and range of AUCchild/AUCadult ratios for each anti-infective trial that determined drug exposure in the following FDA-defined pediatric age groups: neonates (<1 month), infants (1 month to <2 years), children (2 to <12 years), and adolescents (12 to 16 years). We presented the data in graphical form for each age group.

      Data Collection

      We collected specific application, drug product, and trial details for each anti-infective. In addition to exposure-matching data, we extracted the following details from the FDA reports: (1) sponsor requests and FDA approval of new or expanded anti-infective indications for all age groups of interest; (2) routes of administration and elimination; (3) anti-infective class (antiviral, antibacterial, antifungal); (4) year of pediatric anti-infective approval; (5) drug development phase of clinical trial in which sponsors collected qualifying PK and PD data; (6) PK data analysis methods; (7) extrapolation of efficacy from studies in adults; and (8) method the sponsors used to determine dose in the PK and PD clinical trial. For the method used to determine dose, we specifically noted whether before trial initiation, a sponsor explicitly mentioned or described use of the following methods: (1) scaled the adult dose to children using a body size measure (eg, linear or allometric) or applied a maturational function; (2) developed a strategy to evaluate multiple different doses and dose combinations within one study to determine the correct pediatric dose; or (3) used data from a previous pilot study of PK and PD properties in children.

      Statistical Analyses

      We used standard summary statistics, including percentages, means, and ranges, to describe the study variables. Fisher’s exact tests were used to compare exposure matching by anti-infective and trial characteristics. We used linear regression and Cochran−Armitage tests for trend to identify directionality of significant results. We used STATA software, version 13.1 (StataCorp LP, College Station, Texas) for all statistical analyses and considered a P value <0.05 statistically significant.

      Results

      We reviewed 55 applications for 51 anti-infectives submitted to the FDA for approval (Figure 1). For 4 of the submitted anti-infectives (8%), we retrieved publications from PubMed regarding trials conducted and submitted to the FDA. Of the 51 evaluated anti-infectives, 12 (24%) were topical formulations and 10 had limited AUC information specifically available for the population of interest. Of the 10 drugs excluded for limited AUC information, 5 (50%) were antivirals. The remaining 29 anti-infectives (54%) met our criteria for analysis. Sponsors submitted applications to solely obtain a new pediatric indication for 20 of 29 anti-infectives and to solely expand the pediatric indication for 6 of 29 anti-infectives. Darunavir and tenofovir each had one submission for a new pediatric indication and a second to expand the pediatric indication, while atazanavir had 2 submissions, each to expand the pediatric indication. The majority of anti-infectives were antivirals (21 of 29 [72%]), indicated for HIV (14 of 29 [48%]), and administered orally (23 of 29 [79%]) (Table I).
      Figure 1.
      Figure 1Flow chart of drug inclusion in the final analysis. PK = pharmacokinetic.
      Table IIncluded drugs and drug and study characteristics.
      DrugDrug ClassRoute of AdministrationPredominant Route of EliminationPediatric Trial PhaseAges Studied, yAges Approved, y
      AdefovirAntiviralOralRenal unchanged
      Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      1/212–1712–17
      Amoxicillin clavulanate XRAntibacterialOralRenal unchanged17–157–15
      Atazanavir
      Two applications submitted to expand the pediatric indication.
      ,
      Atazanavir without ritonavir evaluated separately from atazanavir with ritonavir.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1/2; 3b6–17; 0.25–56–17; 0.25–5
      AzithromycinAntibacterialOralOther10.5–160.5–16
      Caspofungin
      Ages studied and ages approved do not match.
      AntifungalIntravenousHepatic then renal10.25–110.25–17
      Darunavir
      One new application submitted; a second application to expand the pediatric indication.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1,23–173–17
      DolutegravirAntiviralOralFeces unchanged1/212–1712–17
      Efavirenz
      Purpose of the application was to expand the pediatric indication.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1/20.25–20.25–2
      Emtricitabine
      Purpose of the application was to expand the pediatric indication.
      AntiviralOralRenal unchanged
      Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      10–0.250–0.25
      EntecavirAntiviralOralRenal unchanged22-152–15
      ErtapenemAntibacterialIntravenousHepatic then renal10.25–170.25–17
      EtravirineAntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      16–176–17
      FamciclovirAntiviralOralHepatic then renal1/20.083–12None
      Fosamprenavir
      Purpose of the application was to expand the pediatric indication.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      20.08–50.08–5
      LevofloxacinAntibacterialOral and IntravenousRenal unchanged10.5–170.5–17
      LinezolidAntibacterialIntravenousHepatic then renal10–170–17
      Lopinavir/ritonavir
      Purpose of the application was to expand the pediatric indication.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1/20.04–0.5; 13–170.04–0.5; 13–17
      MicafunginAntifungalIntravenousHepatic then renal10.33–160.33–16
      Nelfinavir
      Ages studied and ages approved do not match.
      ,
      Purpose of the application was to expand the pediatric indication.
      AntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      10–1None
      OseltamivirAntiviralOralHepatic then renal11–171–17
      Pegylated interferon alfa-2bAntiviralSubcutaneousHepatic then renal1/33–173–17
      RaltegravirAntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1/22–172–17
      RitonavirAntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      1/20.08–10.08–1
      Tenofovir
      One new application submitted; a second application to expand the pediatric indication.
      AntiviralOralRenal unchanged
      Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      32–172–17
      TerbinafineAntifungalOralHepatic then renal1/24–174–17
      TipranavirAntiviralOralHepatic then fecal
      Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      When administered with ritonavir, there is minimal metabolism.
      1/22–172–17
      ValacyclovirAntiviralOralRenal unchanged
      Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      11–171–17
      ValgancyclovirAntiviralOralHepatic then renal
      Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      10.33–160.33–16
      XR = extended release.
      low asterisk Administered as prodrug and active metabolite eliminated in the urine, largely unchanged.
      Two applications submitted to expand the pediatric indication.
      Atazanavir without ritonavir evaluated separately from atazanavir with ritonavir.
      § Ages studied and ages approved do not match.
      One new application submitted; a second application to expand the pediatric indication.
      Purpose of the application was to expand the pediatric indication.
      ** Some hepatic metabolism with primary excretion into feces, also unchanged drug eliminated in feces.
      †† When administered with ritonavir, there is minimal metabolism.
      Among the 29 anti-infectives, exposure matching by median (or mean) AUC was variable. Considering initial PK trials for anti-infectives included in this study, 14 anti-infectives (48%) had a median (or mean) AUC of all submitted age groups within 20% of that in adults, while 22 (76%) had a median (or mean) AUC within 50% of that in adults (Table II). Route of administration and anti-infective class were the only statistically significant differences between matched and unmatched drugs within 20% (Table III). All anti-infectives that had a median (or mean) AUC within 20% were orally administered, and 13 of 14 (93%) were antivirals. All anti-infectives that sought approval for adolescents only (4 drugs) matched adult and pediatric median (or mean) exposures within 20%.
      Table IIComparison of median (or mean) AUC and AUC ranges for anti-infectives in children and adults.
      DrugWithin 20% of Adult Median
      Mean values used if medians were unavailable.
      Within 50% of Adult Median
      Mean values used if medians were unavailable.
      % Pediatric Range Within Adult Range
      Presented as a range of percentages (eg, 78–100) to account for variability in each age groups of interest for a specific drug. For example, 0 to 4-year-olds for drug A may have a pediatric range entirely within the adult range (ie, 100%), but only 78% of the range for 5- to 8-year-olds is within the adult range. This is listed as 78–100.
      AdefovirYesYes78–100
      Amoxicillin clavulanate XRNoYes40–81
      Atazanavir without ritonavirNoNo71–100
      Atazanavir with ritonavirYesYes100
      AzithromycinNoNo42–100
      CaspofunginNoYes60–73
      DarunavirYesYesNA
      DolutegravirYesYes93
      EfavirenzYesYes94
      EmtricitabineYesYes43–79
      EntecavirYesYes81–100
      ErtapenemNoYes3–8
      EtravirineNoYes97–100
      FamciclovirNoNo0–100
      FosamprenavirNoYes31–45
      LevofloxacinNoNo0–100
      LinezolidNoNo0–44
      Lopinavir/ritonavirNoNo53–100
      MicafunginNoNo28–49
      NelfinavirYesYes66–90
      OseltamivirYesYes85–100
      Pegylated interferon alfa-2b + ribavirinNoYes52–56
      RaltegravirYesYes23–61
      RitonavirYesYes44–67
      TenofovirYesYes73–100
      TerbinafineYesYes88–100
      TipranavirNoYes59–93
      ValacyclovirYesYes71–100
      ValgancyclovirNoYes55–100
      NA = not available; XR = extended release.
      low asterisk Mean values used if medians were unavailable.
      Presented as a range of percentages (eg, 78–100) to account for variability in each age groups of interest for a specific drug. For example, 0 to 4-year-olds for drug A may have a pediatric range entirely within the adult range (ie, 100%), but only 78% of the range for 5- to 8-year-olds is within the adult range. This is listed as 78–100.
      Table IIIDrug characteristics associated with median pediatric AUC within 20% of median adult AUC. Data are given as n (%).
      Mean values used if medians were unavailable.
      CharacteristicAUCchild/AUCadult Ratio
      0.8–1.2 (n = 14)<0.8 or >1.2 (n = 15)
      Route of administration
       Oral14 (100)9 (60)
       Intravenous03 (20)
       Both02 (13)
       Other01 (7)
      Route of elimination
       Renal unchanged5 (36)2 (13)
       Hepatic then renal2 (14)7 (47)
       Hepatic then fecal7 (50)5 (33)
       Other01 (7)
      Indication
       HIV9 (64)5 (33)
       Intra-abdominal1 (7)1 (7)
       Systemic3(21)5 (33)
       Candidiasis02 (13)
       Other1 (7)2 (13)
      Drug class
       Antiviral13 (93)8 (53)
       Antibacterial05 (33)
       Antifungal1 (7)2 (13)
      Age range
       Only 12–17 y4 (29)0
       >2 y5 (36)5 (33)
       >28 d3 (21)8 (53)
       >Birth2 (14)2 (13)
      Year approved
       ≥20088 (57)13 (87)
       <20086 (43)2 (13)
      Approved indication13 (93)13 (87)
      low asterisk Mean values used if medians were unavailable.
      Exposure matching by AUC range criteria was also variable. Range data were available or able to be calculated for all anti-infectives except darunavir. Fourteen (48%) drugs had one or more submitted age groups in which the pediatric AUC range was completely within the published adult range. Fifteen anti-infectives (52%) had ≥75% of the pediatric AUC ranges within the adult range. Compared to antibacterial drug class and intravenous route of administration, antiviral drug class and oral route of administration were indicative of exposure matching by the 75% range criteria. Eleven anti-infectives (38%) were within 20% of the adult mean (or median) and met the 75% range criteria.
      The variability in exposure matching by our criteria is magnified when the drugs are classified by age according to the following groups: neonates (<30 days), infants (1 month to <2 years), children (2–11 years), and adolescents (12–16 years) (Figure 2).
      Figure 2.
      Figure 2Comparison of pediatric with adult area under the concentration-time curve without outliers: age (A) <30 days, (B) 1 month to <2 years, (C) 2 to 11 years, and (D) 12 to 16 years. Drug products were considered outliers and excluded from the figure if the range of ratios for the drug product was >2.5 SD of the range of ratios for the age group of interest. Multiple studies for a single drug will have the same identifying number (eg, 3 studies supporting the 2- to 11-year-old age group for a drug will be located on the x-axis at 7.0, 7.4, and 7.8). Blue dots represent the AUCchild/AUCadult median (or mean) ratio. The solid black line represents the equivalence median (or mean) AUC between children and adults (ratio = 1). Horizontal dashed lines represent the upper and lower bounds of a normalized adult AUC range. Ratios and ranges that fall within the dashed lines indicate pediatric AUC values within the adult range.
      Of the 29 anti-infectives included in our analysis, all but 3 (atazanavir without ritonavir from 2007 submission, famciclovir, and nelfinavir) received FDA approval for the entire age range for which sponsors were seeking approval. Among the anti-infectives that were approved, mean (or median) AUCs for children were within 20% of adult AUCs for 46% of anti-infectives. Similarly, ≥75% of the child AUC range was within the adult range for 54% of approved anti-infectives. Of the anti-infectives that were approved, 12 of 26 (46%) relied on some extrapolation of efficacy from adult studies. Of these, 5 (42%) anti-infectives matched exposure by mean AUC 20% or range criteria.
      Finally, we examined trial characteristics that might influence the similarity between pediatric and adult exposures. In total, we analyzed 39 trials. Noncompartmental analysis was the predominant method of PK analysis (23 of 39 trials [59%]). To determine the dose studied in the pediatric trial, sponsors commonly scaled by body size or applied an age-dependent organ function (eg, creatinine clearance) to the adult dose (20 of 39 [51%]). Of these 20 studies, 6 (30%) scaled the adult dose using body weight, 9 (45%) used body surface area, 4 (25%) used the equivalent adult dose in adolescent populations, and 1 (5%) used an age-dependent maturational factor to determine the dose. Phase of trial, method of PK analysis, and overall method of initial dose determination (eg, application of a body size measure or applied maturational function, strategy to evaluate doses within one study, or use of pilot data) were not associated with exposure matching. However, all trials that used body weight−based dosing met mean AUC 20% criteria, and 78% of trials that used body surface area did not meet this criteria (P = 0.007).

      Discussion

      According to our results, differences in exposure matching by AUC occurred in the initial pediatric PK studies of ≥50% of the anti-infectives evaluated in this study. Because this study focuses on the early stages of pediatric drug submission, we cannot comment on exposure matching for the FDA-approved dose. However, FDA approval occurred for most drugs in our study. The observed discrepancy between exposure matching in initial PK studies and drug approval could be because (1) anti-infective approval does not rely on information obtained from exposure matching in early PK studies alone, or on information included in publicly available reports; (2) anti-infective approval does not rely on our definition of exposure matching; (3) exposure measures other than AUC were used for exposure matching; (4) anti-infective approval relies on exposure matching information in some pediatric subpopulations to grant approval for the whole pediatric population; or (5) pediatric exposures should be different from those in adults, given discrepant therapeutic indices or possibilities for drug resistance between the 2 populations.
      • Burckart G.J.
      • Momper J.D.
      Pediatric clinical pharmacology in regulatory and drug development sciences: lessons learned and the path forward.
      • Momper J.D.
      • Mulugeta Y.
      • Burckart G.J.
      Failed pediatric drug development trials.
      It is likely that exposure matching does not rely on our definition; to date, criteria for exposure matching between children and adults for pediatric drug approvals have not been defined in the United States.

      Food and Drug Administration. Draft guidance for industry. Bioavailability and bioequivalence studies submitted in NDAs or INDs—general consideration 2014. FDA website. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM389370.pdf. Accessed March 16, 2015.

      Instead, sponsors define their own targets for exposure matching for each drug product. The result of this method is variability in the exposure measure of interest and variability in acceptable parameters for exposure matching. For example, this variability is evident in the submissions for adefovir and fosempranavir. For adefovir exposure in children, sponsors targeted the adult Cmax (18.4 ng/mL) and AUC0−∞ (230.3 ng/h/mL); however, the sponsor did not explicitly define the minimum and maximum acceptable values of Cmax or AUC0−∞. In contrast, the sponsor more clearly defined the targets for fosemprenavir, ranging from the 25th percentile for concentration at a defined time point (Cτ) to the 95th percentile for AUC observed in healthy adults.
      In contrast to the variable definitions for exposure matching observed in submissions to the FDA, we defined specific criteria that we applied to all anti-infective submissions. In defining these criteria, we considered PK and drug exposure variability. Exposure matching using summary data alone (eg, median or mean AUC for a given population) is risky in children because it oversimplifies the population under study. Most pediatric early-phase studies are small, are conducted in patients with the disease, and may include a relatively wide age range. These factors all increase the variability in observed exposures. Therefore, it is possible for a drug in children to have, on average, very similar exposures to adults, but in reality have only toxic or subtherapeutic exposure ranges. This discrepancy between mean exposures and ranges of exposures may be even more pronounced if you examine exposure matching by age, as depicted in Figure 2.
      Possible discrepancy between mean values and ranges, as well as variability in the extent of exposure matching by our criteria, suggests the need to consider several key factors in the development of formal exposure matching criteria for children and adults. These factors include (1) the ideal primary exposure measure for exposure matching analysis (this will likely vary by drug), (2) use of individual compared with summary data to calculate exposure matching, (3) the need for varying criteria by pediatric age group based on underlying variability in drug disposition, and (4) consideration of a drug’s therapeutic index.
      Although it is clear that the development of defined criteria for exposure matching between children and adults is necessary, it is less clear what drug or trial characteristics influence exposure matching according to the defined criteria. We anticipated that age of the population of interest (eg, only adolescents or only children and adolescents compared with neonates) and method of dose determination would greatly influence the extent of exposure matching. In a recent analysis, 87 of 92 (95%) drug products submitted to the FDA had recommendations for equivalent dosing of adolescent and adult patients.
      • Momper J.D.
      • Mulugeta Y.
      • Green D.J.
      • et al.
      Adolescent dosing and labeling since the Food and Drug Administration Amendments Act of 2007.
      However, age was not a statistically significant determinant of exposure matching by our criteria.
      Method of dose determination was not associated with exposure matching by our criteria, despite known developmental changes in body composition and maturation in organ function that occur with age and should influence method of dose determination.
      • Kearns G.L.
      • Abdel-Rahman S.M.
      • Alander S.W.
      • et al.
      Developmental pharmacology—drug disposition, action, and therapy in infants and children.
      • van den Anker J.N.
      • Schwab M.
      • Kearns G.L.
      Developmental pharmacokinetics.
      For example, scaling of adult dosing to pediatric patients using allometric principles can account for differences in developmental PK for children older than 2 years of age; however, empirical models using maturational functions or physiologic models based on in vitro data are typically needed to describe drug disposition in younger children.
      • Holford N.
      • Heo Y.A.
      • Anderson B.
      A pharmacokinetic standard for babies and adults.
      • Barrett J.S.
      • Della Casa Alberighi O.
      • Laer S.
      • Meibohm B.
      Physiologically based pharmacokinetic (PBPK) modeling in children.
      Reasons for lack of association between age or method of dose determination and exposure matching may include: (1) limited number of anti-infectives included in our analysis (29 of 51 met inclusion and exclusion criteria); (2) skewed distribution of age groups of interest among anti-infectives (eg, 4 studies including neonates and 4 studies that included only adolescents); and (3) sponsors’ use of maturational factors or physiologic principles in appropriate populations.
      The limitations of our study include (1) a limited number of anti-infectives that met inclusion and exclusion criteria; (2) availability of predominantly HIV drugs for analysis, where concerns about drug toxicity may disproportionately influence dose selection in early-phase trials; (3) limited ability to capture additional factors (eg, study sample size and variation in ages) that potentially influence successful exposure matching; (4) ability to extract only information that was publicly available, which may have varied by drug; (5) lack of consistency in the content and structure of documents submitted to the FDA; (6) access to summary-level data and not patient-level data; (7) inclusion of AUC as the only measure of successful exposure matching; and (8) inability to determine clinical relevance of our defined criteria for exposure matching.
      Access to only summary-level data and inclusion of AUC data only are likely the most important of these limitations. Use of summary data mandated calculations to obtain AUC ranges for children and adults; however, this method might not have reflected actual observed minimum and maximum AUC values. Further, although AUC is one of the most widely available PK parameters for anti-infectives and other drug classes, it is unknown whether AUC is the best PK parameter to determine exposure matching.

      Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL.pdf. Accessed December 26, 2014.

      For many anti-infectives, PK and PD indices that have been predictive of antimicrobial efficacy include Cmax and MIC, AUC and MIC, and time above MIC.
      • Mueller M.
      • de la Pena A.
      • Derendorf H.
      Issues in pharmacokinetics and pharmacodynamics of anti-infective agents: kill curves versus MIC.
      Given the specific interest in extrapolation of efficacy from adult to pediatric populations, such parameters with proven association to efficacy may be more relevant than AUC alone.

      Conclusions

      Despite certain limitations, our study highlights several areas for improvement in exposure matching of adult to pediatric anti-infectives. These areas for improvement include the need to (1) define criteria for successful exposure matching that accounts for PK variability in pediatric populations; (2) design trials accounting for developmental changes that affect drug disposition; (3) improve granularity and standardization of sponsor submissions to the FDA; and (4) concentrate resources on exposure matching and reduce studies of pediatric efficacy for drugs that may extrapolate efficacy from adult data. The ability to address these areas in future studies may increase the likelihood of success in obtaining a pediatric indication, improve the breadth of pediatric labeling, and help to conserve limited financial resources that are currently available for research in pediatric therapeutics.

      Conflict Of Interest

      Drs. Smith and Benjamin receive research support from Cempra Pharmaceuticals (subaward to HHS0100201300009C) and industry for neonatal and pediatric drug development (www.dcri.duke.edu/research/coi.jsp). Dr. Wolkowiez has received support from industry for drug development in adults and children (www.dcri.duke.edu/research/coi.jsp).
      These sponsors had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.

      Role of the Funding Sources

      The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

      Disclaimer

      The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the FDA.
      Dr. Zimmerman collected the data for the study and wrote the first draft of the manuscript. Dr. Putera collected data for the study. Dr. Hornik assisted with data analysis and review of the manuscript. Drs. Smith, Benjamin, Burckhart, and Mulugeta reviewed the manuscript for completeness and accuracy. Drs. Gonzalez and Cohen-Wolkowiez designed the study and assisted with drafting and reviewing the manuscript.

      Acknowledgments

      Dr. Zimmerman is funded by grant KL2TR001115-03 from the Duke Clinical and Translational Science Awards. Dr. Hornik receives salary support for research from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under award number UL1TR001117. Dr. Smith receives salary support for research from the NIH and the National Center for Advancing Translational Sciences of the NIH (award UL1TR001117), the National Institute for Child Health and Human Development (NICHD) (contract HHSN275201000003I and grant 1R01-HD081044-01), and the Food and Drug Administration (grant 1R18-FD005292-01). Dr. Benjamin receives support from the NIH (award 2K24HD058735-06, National Center for Advancing Translational Sciences award UL1TR001117, NICHD contract HHSN275201000003I, and National Institute of Allergy and Infectious Diseases contract HHSN272201500006I). Dr. Cohen-Wolkowiez receives support for research from the NIH (grant 1R01-HD076676-01A1), the National Center for Advancing Translational Sciences (award UL1TR001117), the National Institute of Allergy and Infectious Diseases (contracts HHSN272201500006I and HHSN272201300017I), the NICHD (contract HHSN275201000003I), the Food and Drug Administration (grant 1U01FD004858-01), the Biomedical Advanced Research and Development Authority (contract HHSO100201300009C), and the nonprofit organization Thrasher Research Fund (www.thrasherresearch.org). Dr. Gonzalez is funded by K23HD083465 from the NICHD and by the nonprofit Thrasher Research Fund (www.thrasherresearch.org).

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