, 1995). Permeability was considered high if the calculated fraction absorbed was equal or greater than 0.9, and a value below 0.9 was considered as low permeability ( U.S. Food and Drug Administration, 2000). The fraction absorbed was calculated employing Eq. (4) ( Amidon et al., 1995 and Sinko et al., 1991) equation(4) fa=1-e-2PeffRTSIwhere R is the mean radius of the small intestine (1.75 cm) and TSI is the mean transit time in the small intestine (3.32 h) KRX-0401 ( Lennernäs et al., 1992 and Yu et al., 1996). Data analysis was carried out using Matlab 2013a (The Mathworks Inc., Natick, MA, USA). The analysis was

focused on the impact of the release rate constant (krel), and the drug specific parameters on the simulation outcome (fa, Fg and AUC). Several scenarios were evaluated for the impact of both CYP3A4 and P-gp clearance employing a “one-at-a-time” method, i.e., fixing most of the parameters and varying the parameters of interest. These were accomplished by either fixing Vmax,CYP3A4/Jmax,P-gp, and varying Km

(CYP3A4/P-gp) or vice versa. The scenarios evaluated are described in Table 1. Amongst the scenarios described in Table 1, the cases in which a CR formulation showed higher relative bioavailability (Frel) than the corresponding IR formulation were investigated in further detail. Frel was calculated using Eq. (5) equation(5) Frel=AUCMRAUCIR×100where Raf inhibitor AUCIR was the AUC of the IR formulation with a krel of 4.6 h−1 and AUCMR was the AUC of any of the other formulations evaluated. The simulations were compared, in terms of release characteristics, relative bioavailability and metabolic clearance, with the observed data derived from the literature search. The latter was performed only for compounds with similar physicochemical properties as the simulated compounds and for those for which the main metabolic enzyme was CYP3A4, i.e., the CYP3A4 is responsible for 50% or more of the compound’s metabolic clearance (fmCYP3A4 ⩾ 0.5). Whenever possible the release characteristics of the literature compounds were derived from the in vitro

release profiles where the corresponding see more krel was estimated according to its t90 (Eq. (6)) otherwise these were approximated based on the information described in the product label and/or clinical studies. With regards to the metabolic clearance, in order to avoid any possible underpredictions resulting from the use of the mean in vitro metabolic data ( Hallifax et al., 2010 and Hallifax and Houston, 2012) the intrinsic metabolic clearance in HLM was back calculated from the in vivo systemic clearance employing either the well-stirred model ( Rowland et al., 1973) or the dispersion model ( Roberts and Rowland, 1986). The details of the calculations are described in the Supplementary Material. equation(6) krel=ln10t90 The literature survey was successful in retrieving and identifying 17 studies of 11 different compounds that met the inclusion criteria (Fig. 2).