our survey covered all small molecule new molecular entity ( nme ) drugs that are intended for systemic use and were approved by the fda between january 2013 and august 2016 . the new drug application ( nda ) review documents ( drugs@fda , http://www.fda.gov/drugsatfda ) and product labels were examined for pbpkrelated information for the 85 products that met the above criteria . there were a total of 18 products for which pbpk models were considered in the nda review documents ( tables 
 14 ) . in the majority of cases , pbpk models were used for the prediction of the effect of metabolic enzyme mediated drugdrug interactions ( ddis ; figure 
 1a ; tables 
 14 ) . importantly , the frequency of model use is consistent with the perceived level of reliability.1 , 3 , 5 
  overview of physiologically based pharmacokinetic ( pbpk ) information in product labels or us food and drug administration ( fda ) review documents for drugs approved by the fda between january 2013 and august 2016 . ( a ) number of new molecular entities ( nmes ) with information of pbpk for respective areas of applications . ( b ) proportion of product labels / reviews containing pbpk information for drugs in all nmes , anticancer agents , and nmes with breakthrough therapy designation and/or accelerated approval status at the time of approval . seven of eight nmes with pbpk and breakthrough / accelerated approval status were anticancer agents . a key caveat of our survey was that it was limited to publicly available information . the utilization of pbpk models for sponsor 's internal decisionmaking and earlier stage and postapproval regulatory interactions , such as postmarketing requirement ( pmr ) or supplementary nda , were not captured . pbpk models have great potential to influence decisionmaking at different stages of drug development , such as initial dosing recommendations for pediatric clinical trials , design of ddi studies , etc.5 the current evaluation should be interpreted in the context of pbpk use during the final phase in the development of a new drug , because pbpk modeling strategies and the level of model validation can vary at different stages of drug development . this survey also did not capture the potential value of cost and speed savings by using pbpk vs. conducting clinical studies . interestingly , we observed wider acceptance of pbpk models in the field of oncology compared with other therapeutic areas ( figure 
 1b ) . one possible explanation is the difficulty of conducting clinical ddi studies in oncology , due to ( 1 ) shortage of appropriate patient populations or ( 2 ) ethical and safety concerns over exposing healthy volunteers to oncology medications . the first point is supported by the observation that three of eight nmes with pbpk in the nononcology field were for rare diseases ( such as eliglustat , macitentan , and obeticholic acid ) . another possible explanation is that the higher levels of toxicity and narrower therapeutic windows for oncology drugs compared with drugs in other therapeutic areas warrant precise optimization of drug exposure . it is also noteworthy that 7 of 10 anticancer agents were given regulatory incentives to accelerate drug development , either with breakthrough therapy designation or accelerated approval , which may have contributed to a greater reliance on pbpk simulations . pbpk models have been most extensively used for the prediction of the effect of ddi or pharmacogenetic effect on the pharmacokinetics of nme as a victim ( figure 
 1a ) . in particular , the extrapolation of the effect of strong inhibitors or inducers to less potent perpetrators constituted the majority of the applications ( 11 among 13 nmes ; table 
 1 , ids 111 ) , and all applications resulted in labeling recommendations ( table 
 1 ) . in these cases , existing clinical data with strong perpetrator(s ) were used to  anchor  the pbpk model performance , namely by accurately providing fraction metabolized by a particular enzyme , which provides a higher level of confidence in ddi prediction.1 , 5 for example , a fourfold dose reduction of ibrutinib was recommended for patients taking moderate cytochrome p450 ( cyp)3a inhibitors based on the pbpk model validated with clinical ddi data using strong perpetrators . pbpk models in product labels or fda review documents : new drug as a victim of ddis or genetic variations cyp , cytochrome ; ddi , drugdrug interaction ; i d , identification ; nme , new molecular entity ; pbpk , physiologically based pharmacokinetic ; pmc , postmarketing commitment ; pmr , postmarketing requirement ; ugt1a1 , udpglucuronosyltransferase 1a1 . the numbers in the reference column represent pubmed i d ( if physiologically based pharmacokinetic models were published in scientific journals ) . the elimination pathways for all the 11 nmes involve cyp3amediated metabolism , whereas two are metabolized by cyp2d6 in addition to cyp3a . this observation is not surprising , considering that cyp3a and cyp2d6 are involved in metabolism of a large proportion of marketed drugs , and wellestablished probe perpetrators are available for these two enzymes . for two cyp3a and cyp2d6 dual substrates , aripiprazole lauroxil and eliglustat , complex interactions involving the inhibitors of these enzymes and cyp2d6 genotype anchoring of model prediction with both a strong inhibitor and an inducer has been conducted in most cases , but there were two cases in which model validation with inhibitors was used for the prediction of inducer effect ( cobimetinib , panobinostat ) , both of which resulted in a conservative labeling language of avoiding concomitant use with the strong inducers . this may suggest that the required level of pbpk model qualification depends on the context of pbpk application . because modification of recommended dose in the product label application of pbpk,2 the use of a certain amount of clinical pharmacokinetic data as an external validation dataset has generally been required for including dosing recommendations in the product label . the case of belinostat is intriguingalthough there were no external data , the simulation performed by the fda during the nda review resulted in label language recommending a 25% reduction in the starting dose for homozygous carriers of a genetic polymorphism of its major metabolic enzyme , udpglucuronosyltransferase 1a1 . this is likely , in part , because the recommended starting dose is equal to the maximum tolerated dose . pmr to definitively examine the effect of pharmacogenetic alteration on belinostat pharmacokinetics and safety was included in the nda approval . therefore , even when the pbpk approach could not obviate the conduct of a dedicated clinical study , the model can inform the optimal use of medications . the second most frequent use of pbpk modeling is to predict the ddi potency of an nme as a perpetrator ( inhibitor / inducer ; table 
 2 ) . four nmes ( ids 1417 ) have successfully utilized a pbpk modeling approach to demonstrate the lack of a clinically significant effect on the metabolic pathways of interest for the particular drug . one method was to use a negative clinical ddi result with other cyp enzymes as an external validation ( alectinib , panobinostat ) , whereas another was to use a sensitivity analysis on inhibition parameters ( alectinib , canagliflozin ) . in the case of lenvatinib , an external model validation was seemingly not performed , presumably supported by general perception that pbpkbased prediction of mechanismbased inhibition leads to overestimation of ddis.5 these observations based on limited cases suggest that required levels of model validation could be flexible , and that we may expect wider application of pbpk in this category . pbpk models in product labels or fda review documents : new drug as a perpetrator of drugdrug interactions cyp , cytochrome ; ddi , drugdrug interaction ; i d , identification ; mate , multidrug and toxin extrusion transporter ; nme , new molecular entity ; oct2 , organic cation transporter 2 ; pbpk , physiologically based pharmacokinetic ; pmc , postmarketing commitment ; pmr , postmarketing requirement . the numbers in the reference column represent pubmed i d ( if physiologically based pharmacokinetic models were published in scientific journals ) . there were two nmes with pbpk models for which additional dedicated clinical ddi studies with cyp3a substrates were requested as pmr . for ceritinib , in addition , neither of these two nmes had supporting clinical data to verify model performance . further examples will be needed to evaluate the ability of the pbpk approach to quantitatively predict positive ddi effects.1 most of the pbpk applications on absorptionrelated pharmacokinetic changes were exploratory and did not impact the labeling recommendation ( table 
 3 ) . altered panobinostat absorption was not observed  with drugs that elevate the gastric ph in pbpk simulation . pbpk models in product labels or fda review documents : drug absorption ara , acid reducing agents ; auc , area under the curve ; cmax , peak plasma concentration ; fda , us food and drug administration ; i d , identification ; nme , new molecular entity ; pbpk , physiologically based pharmacokinetic ; pgp , pglycoprotein . the numbers in the reference column represent pubmed i d ( if pbpk models were published in scientific journals ) . other pbpk applications observed in the nda review documents include prediction of the effect of hepatic impairment ( hi ; table 
 4 ) , but pmrs were issued for three of four nmes to conduct or complete dedicated clinical studies , presumably because of the low level of reliability.1 in the case of obeticholic acid , the effect of hi on hepatic exposure was explored with pbpk simulations , and this helped to inform the dosing recommendation in patients with hi . pbpk models in product labels or fda review documents : other areas of pbpk applications cyp , cytochrome ; ddi , drugdrug interaction ; hi , hepatic impairment ; i d , identification ; nme , new molecular entity ; oatp , organic aniontransporting polypeptide ; pbpk , physiologically based pharmacokinetic ; pmr , postmarketing requirement . the numbers in the reference column represent pubmed i d ( if physiologically based pharmacokinetic [ pbpk ] models were published in scientific journals ) . the regulatory impacts of pbpkbased predictions have been limited in areas other than metabolismbased ddis and pharmacogenetics , such as in transportermediated ddis or effect of acid reducing agents . accumulated experience of pbpk application in these areas will help to establish the level of confidence necessary to inform regulatory decisions . also , some degree of model validation with clinical data using the nme has generally been required for highimpact regulatory decisions.2 currently , validation of system components , such as hepatic metabolic activity in patients with renal impairment , based on other molecules that share same elimination pathways has not been considered sufficient . such limitations have impeded the application of pbpk models to replace clinical studies for evaluating socalled  intrinsic factors ,  such as in pediatric populations , to evaluate ethnic differences in pharmacokinetics , or in patient populations with organ impairment . the authors believe that future verification efforts of pbpkbased prediction performance with cross learning from other molecules , including both xenobiotics and endogenous biomarkers , will greatly expand the use of pbpk beyond ddi and pharmacogenetic applications and will contribute to acceleration of new drug development .
physiologically based pharmacokinetic ( pbpk ) modeling can be used to predict drug pharmacokinetics in virtual populations using models that integrate understanding of physiological systems . 
 pbpk models have been widely utilized for predicting pharmacokinetics in clinically untested scenarios during drug applications and regulatory reviews in recent years . here , we provide a comprehensive review of the application of pbpk in new drug application ( nda ) review documents from the us food and drug administration ( fda ) in the past 4 years .