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Development and Validation of the Physiologically Based iDEATM Predictive Model G. Timony, D. A. Norris, G. D. Leesman, R. Retajczyk, S. Tran, Y. Chen, Y. Lee, N. Johnson, J. Castelo, K.-J. Lee, R. J. Christopher, P. Sinko, and G. Grass. Trega Biosciences, 9880 Campus Point Drive, San Diego, CA 92121 Solubility ABSTRACT The equilibrium solubility of each compound was determined in simulated gastric fluid (SGF, pH 1.5) and simulated intestinal fluid (SIF, pH 5.0, 6.5, 7.0, and 7.5). One-half gram of each drug compound was added to 5 ml of SGF or SIF and incubated at 37°C for at least 4 hours to attain saturation equilibrium. The pH was adjusted as necessary to maintain the correct pH. A 1 ml sample was removed, filtered using a 0.45 Fm filter, and analyzed for drug concentration. Solubility was determined for each drug at each pH value in triplicate. Purpose. To develop, optimize, and validate the physiologically-based iDEA predictive model for drug absorption. Methods. The simulation model used permeability (rabbit diffusion chamber or Caco-2 cell), solubility and dose as inputs, and was developed using a database of 56 non-metabolized compounds and a total of 85 drug-dose combinations. Transit through the GI tract was based on dispersed plug flow mixing kinetics. A set of parameters was designed to build a correlation between the in vitro data inputs and the in vivo clinical outcomes so that the model could predict the fraction of dose absorbed into the portal vein (FDp) versus time. Genentech, Parke-Davis, Schering-Plough, SmithKline Beecham, Trega Biosciences, and others provided the in vivo training data. The simulated FDp vs. time profiles, Cmax, and AUC values were compared to observed values that were calculated from oral and intravenous plasma concentration versus time data. Eight compounds from the Biopharmaceutical Classification System (BCS) group were used in the optimization and evaluated as an internal validation set. Three BCS compounds, which were not used in the optimization, were included as an external validation set. The observed FDp’s of the BCS compounds ranged from 47-100%. Results: The compounds in the training set were diverse in terms of structure, solubility (0.05 ng/mL to >100 mg/mL), permeability (rabbit diffusion chamber: 0.059 to 118.010-6 cm/s; Caco-2 Cell 0.150 to 42.5 10-6 cm/s ), and absorption properties. When utilizing rabbit diffusion chamber permeability, the model predicted FDp for the 11 BCS compounds with a mean error of 5.3 8.9%. The observed versus predicted r2 for FDp, Cmax and AUC were 0.92, 0.97, and 0.996, respectively. The model performed equally well when a single Caco-2 cell permeability value was used instead of the rabbit diffusion chamber permeability data. Conclusions. The physiologically-based iDEA predictive model, trained on a diverse set of compound data and using either rabbit diffusion chamber or Caco-2 cell permeability input, was successful in predicting the rate and extent of absorption for a partially external validation set consisting of 11 BCS compounds. Figure 3: General Structure of iDEATM Predictive Model Trns MF The iDEATM predictive model is based on a multi-compartment representation of the human gastro-intestinal tract. The model is physiologic, using human intestinal flow rates, surface areas and luminal pH values gathered from various literature sources. The flow model for the transit of soluble and insoluble drug is based on an approximation of dispersed plug flow (Figure 2). The model accounts for the forward and retrograde movement of the solid and dissolved dosage form, accounts for dissolution of solid dosage form, adjusts solubility according the regional pH, and calculates the compound flux in each region of the intestine (Figure 3). The correlation of in-vitro solubility and permeability to human absorption was achieved by optimizing the value of a series or proprietary adjustment parameters against absorption parameters obtained through the pharmacokinetic analysis of the in-vivo training set data. Diss Trns MB Trns MS01 Trns SM01 Solubility at pH 6.5 Fraction Absorbed (%) Trns SB02 Trns SB Flux 0.01 5 10 15 20 25 30 35 40 45 50 70.0 60.0 50.0 40.0 30.0 20.0 1.0e-5 1.0e-6 Figure 4: iDEATM Predictions for Training Set Compounds 1.0e-5 80 0.0 Compound rank order 70 1.0e-7 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 All Compound Doses rank order 5 10 15 20 25 30 35 40 Compound rank order 45 50 55 5 10 15 20 25 30 35 40 45 50 55 Compound rank order 60 50 40 30 85% w ithin FDp Criterion Mean Error = 11% 10 Pharmacokinetic Analysis 10000 1000 100 Where FDp(t) = fraction dose absorbed to the portal vein at time t, D = dose, FDp = fraction dose absorbed to the portal vein at infinity, t50 is the time for 50% of the dose to be absorbed, and Pec is a parameter related to the slope of the error function. Rabbit Intestinal Permeability Assay Compound permeabilities through duodenum, jejunum, ileum and distal colon were determined in the apical -> basolateral direction using vertical Ussing-type low volume diffusion chambers at 37°C. Donor and receiver chambers were each filled with 1.5 mL of pre-warmed Ringer’s buffer containing 25 mM glucose, pH 7.4. Samples(0.5mL) were collected from the receiver chamber at 30. 45, 60, 75 and 90 minutes after experiment initiation. Removed sample volume was immediately replaced with 0.5 mL Ringer’s buffer containing 25 mM glucose. Compound permeability was calculated using the equation below: V dC A C0 dt Where Pe is the effective permeability in cm/s, V is the receiver chamber volume in milliliters, A is the surface area available for transport (cm2), Co is the donor drug concentration, and dC/dt is the slope of the best fit line through the concentration versus time profile in the receiver chamber. Caco-2 Effective Permeability Assay Effective permeability (Peff) was measured in the apical -> basolateral direction in 20-23 day old caco-2 cell cultures (Passage number 30-40) grown on a filter. The donor side of the chamber was dosed at a concentration of 100 uM in 300 ul of Ringers buffer at pH 7.4 containing a final concentration of 1.0% DMSO. The receiver side of the chamber contained 1200 ul of an identical buffer. Samples were incubated at 37° C, in a 95% humidity chamber containing 5% CO2. 100 ul samples from the receiver side of the chamber were taken at 30, 50, 70, and 90 minutes post experiment initiation. Four replicates of each sample were performed on each day. Bioanalysis was performed using LC-MS, HLPC-UV or LSC. Peff was calculated using the dX / dt following formula: Mean (SD) Solubility (mg/ml) 45.5 (42.1) Rabbit Intestinal Permeability X 106 (cm/sec) 9.3 (7.1) Caco-2 Permeability X 106 (cm/sec) 19 (14) FDp (%) 86.6 ( 19.7) Low 0.01 where X = mass transported, A = surface area and Co = initial donor drug concentration 40 60 70 80 1 90 1 100 10 100 1000 10000 AUC: Known vs Predicted 100000 10000 1000 100 r2 = 0.96 10 1 100000 1 iDEATM Predicted Cmax (ng/mL) FDp: Known vs Predicted >100 100 90 80 70 60 50 40 30 20 10 0 10 100 1000 10000 100000 1000000 iDEATM Predicted AUC (ng/mL*hr) Cmax: Known vs Predicted 100000 Rabbit Permeability Input: Mean Error =5.3% Caco-2 Permeability Input: Mean Error = 5.9% 2 r > 0.89 1000000 10000 1000 Rabbit Permeability Input Caco-2 Permeability Input 100 r2 > 0.97 10 0 20 40 IDEA 0.38 50 1000000 Figure 5: iDEATM Predictions for BCS Compounds High TM 60 Predicted FDp(%) 25.0 80 100 10 100 1000 10000 100000 AUC: Known vs Predicted 100000 10000 Rabbit Permeability Input Caco-2 Permeability Input 1000 r 2 > 0.99 100 100 iDEATM Predicted Cm ax (ng/m L) 1000 10000 100000 1000000 iDEATM Predicted AUC (ng/m L*hr) CONCLUSIONS 0.02 A diverse and unique database of solubility, permeability and human pharmacokinetic data for 56 non-metabolized compounds was assembled to train the iDEATM predictive model. 43.0 The iDEATM predictive model requires only solubility and permeability (rabbit diffusion chamber or caco-2 cell) and dose as inputs. The iDEATM predictive model was constructed using a compartmental framework which incorporates human intestinal physiologic parameters. 0.31cm2 A * C0 * 60 30 Know n Cm ax (ng/m L) Figure 2: Dispersed Plug Flow Model Applied to Human Intestinal Transit of Indocyanine Green 20 iDEATM FDp Predictions Know n FDp (%) t 1 t50 D FDp 2 FDp (t ) 1 erf 1 2 t Pec t50 Table 1: Diversity of the Validation Set: Model Drugs (n=11) from the Biopharmaceutics Classification System 10 r2 = 0.89 10 0 0 Cmax: Known vs Predicted 100000 90 10.0 55 FDp: Known vs Predicted 100 1.0e-6 Observed AUC (ng/m L*hr) 0.1 80.0 Observed Cm ax (ng/m L) 1 90.0 Permeability (cm/s) 10 1.0e-4 Permeability (cm/s) 100 100.0 Trns SA02 Ints A2 Ints A1 1000 20 Best-fit curves were determined for the intravenous and oral plasma concentration data of each drug compound at each dosage level in order to estimate the fraction of dose absorbed to the portal vein (FDp). A two-compartment disposition model with elimination from the central compartment was used to fit the IV and PO curves simultaneously using weighted non-linear regression. The oral input function was fit to the equation below: Flux Trns SA01 Jejunum Permeability Caco-2 Permeability Know n FDp A diverse collection of 56 marketed drugs and drug failures was assembled from consortium members and commercially available substances. All drugs were not metabolized in man or had low hepatic clearance and thus were not subject to significant first pass metabolism. The following pharmacokinetic data was available each of the compounds: Plasma concentration vs time curves following both oral and intravenous administration to healthy human subjects, data from human mass balance studies, and in-vitro metabolic stability data (human hepatocytes). The solubility, permeability and pharmacokinetic properties of the training set were very diverse (Figure 1). Prec Ints S2 RESULTS Fraction of Dose Absorbed (%) iDEATM Peff (cm / sec) Trns SF01 Prec Solubility, pH 6.5 (mg/ml) Selection of Training Set Compounds Trns MS02 Trns SM02 Trns SF Figure 1: In-Vitro and In-Vivo Diversity of Training Set Compounds METHODS Diss Trns MB02 Ints S1 The discovery and development process required to bring new drugs to market is both time consuming and expensive. Although recent practices such as high throughput screening and combinatorial chemistry have increased the number of compounds secured at early phases of drug discovery, this has not translated directly to improvements in the rate or volume of compounds moving ahead into development. A contributing factor is the bottleneck imposed by the need to conduct in-vivo pharmacokinetic evaluations on large numbers of compounds in order to select the subset of compounds with desirable ADME properties in humans. The physiologically-based iDEA predictive model was designed to provide discovery and development scientists with a tool to estimate the rate and extent of absorption of new chemical entities in humans, using only simple in-vitro measurements as inputs, and thereby eliminate the ADME bottleneck. An overview of the development, optimization, and validation of the physiologically-based iDEA predictive model is presented here.. Ints M2 Physiologic Model INTRODUCTION . Pe Trns MF01 Ints M1 Know n AUC (ng/m L*hr) trega 47.2 100 From: N.F.H. Ho, J.Y. Park and W.I. Higuchi. Advancing Quantitative and Mechanistic Approaches in Interfacing Gastrointestinal Drug Absorption Studies in Animals and Humans. In “Animal Models for Oral Drug Delivery in Man” W. Crouthamel and A.L. Sarapu Eds. APHA, 1973. The flow model utilized, based on an approximation of dispersed plug flow, provides an accurate representation of human gastro-intestinal transit. A correlation of in-vitro solubility and permeability to human absorption was achieved by optimizing the value of a series of proprietary adjustment parameters against absorption parameters obtained through the pharmacokinetic analysis of the in-vivo training set data. The iDEATM predictive model accurately predicted the relevant biopharmaceutical outcomes (FDp, Cmax and AUC) for the training set, and for a partially external training set consisting of 11 BCS compounds (See Presentation Number 3512, Leesman et al, for the result of a blinded external validation of the iDEATM predictive model model).