Table 4.

PBPK models in product labels or FDA review documents: Other areas of PBPK applications

ID	NME	Year of approval	Detail of predicted scenarios	Simulation results	Impact / outcome	Referencesa
27	Ceritinib	2014	HI	Minimal effect predicted	No impact on labeling recommendation, PMR to determine HI effect
28	Ibrutinib	2013	HI	Significant overestimation compared to interim clinical data	No impact on labeling recommendation, PMR to complete HI study
29	Obeticholic acid	2016	HIb	Simulated plasma exposure matched observed parent and metabolite pharmacokinetic profile; predicted significantly smaller HI effect on hepatic exposures than plasma exposures	Helped regulatory recommendations of possible up‐titration for HI patients
30	Simeprevir	2013	HI	Significant overestimation compared to interim clinical data	No impact on labeling recommendation, PMR to complete HI study	27896690
			Mechanism of nonlinear pharmacokinetics	Saturation of OATP1B and CYP3A explained observed nonlinearity in exposure	No direct labeling impact, contributed to model development to inform DDI simulation
			Ethnic differences in exposure between whites and Asian	Observed plasma exposure difference reproduced with simulation; hepatic drug exposure simulated in different populations	No direct labeling impact

CYP, cytochrome; DDI, drug‐drug interaction; HI, hepatic impairment; ID, identification; NME, new molecular entity; OATP, organic anion‐transporting polypeptide; PBPK, physiologically based pharmacokinetic; PMR, postmarketing requirement.

^aThe numbers in the Reference column represent PubMed ID (if physiologically based pharmacokinetic [PBPK] models were published in scientific journals). NDA review documents can be found at Drugs@FDA (http://www.fda.gov/drugsatfda).

^bIn‐house custom model built on Phoenix nonlinear mixed effects was used for PBPK simulation. If not specified, Simcyp was used for PBPK simulations.