Predicting Pharmacokinetic Profiles Using in Silico Derived Parameters

Natalie A. Hosea* and Hannah M. Jones
Department of Pharmacokinetic, Dynamics and Metabolism, Pfi zer, Inc., Cambridge, Massachusetts 02140, United States

ABSTRACT: Human pharmacokinetic (PK) predictions play a critical role in assessing the quality of potential clinical candidates where the accurate estimation of clearance, volume of distribution, bioavailability, and the plasma-concentration- time profi les are the desired end points. While many methods for conducting predictions utilize in vivo data, predictions can be conducted successfully from in vitro or in silico data, applying modeling and simulation techniques. This approach can be facilitated using commercially available prediction software such as GastroPlus which has been reported to
accurately predict the oral PK profile of small drug-like molecules. Herein, case studies are described where GastroPlus modeling and simulation was employed using in silico or in vitro data to predict PK profi les in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration-time profi les with in silico derived as well as in vitro measured parameters and hence predicting PK profiles with minimal data. The applicability of this approach can provide key information enabling decisions on either dose selection, chemistry strategy to improve compounds, or clinical protocol design, thus demonstrating the value of modeling and simulation in both early discovery and exploratory development for predicting absorption and disposition profiles.
KEYWORDS: pharmacokinetic, profi les, prediction, modeling, simulation
■ INTRODUCTION
Predicting human pharmacokinetics (PK) is an important aspect for the discovery and identifi cation of clinical candidates. These activities begin early in the drug discovery process, where predictions can be made across diff erent compounds to determine potential liabilities within a chemical series or even to evaluate the PK characteristics of virtual compounds before they are synthesized. As the project moves to the lead optimization stage, predictions serve to rank order compounds for further testing, aid in the dose selection in nonclinical in vivo pharmacology studies, establish structure-activity relationships for structural modifi cations intended to improve the properties and to support clinical candidate selection. Ultimately once a candidate is selected, human PK predictions are used to estimate the dose-dependent changes in exposure related to an anticipated pharmacological response and potential toxicolog- ical findings.
Across the spectrum of early discovery to the clinical setting, a common set of PK parameters are predicted: clearance (CL), volume of distribution at steady state (Vss), the fraction absorbed ( fa), the rate of absorption (ka), and subsequently bioavailability (F) for oral administered compounds. While single point estimates can provide a facile way of comparing compounds and prioritizing those for future evaluation, these provide little information about the dynamic changes in compound concentrations. Hence predicting the plasma concentration time profi le is also a desired outcome of human PK predictions. GastroPlus (Simulations Plus, Lancas-

ter, CA) is a mechanistically based simulation software package that simulates PK in human and animals based on preinstalled human and animal physiological parameters. GastroPlus utilizes the “Advanced Compartmental Absorption and Transit model” (ACAT) model,1 derived from the “Compartmental Absorp- tion and Transit” model by Yu and Amidon2 for absorption prediction and a physiologically based pharmacokinetic (PBPK) based model for prediction of disposition. User- defi ned compound specific properties such as molecular weight, lipophilicity, solubility, permeability, pKa, unbound fraction in plasma, blood-to-plasma concentration ratio, and CL can be used as input into GastroPlus. Such approaches have been
3-5
reported to accurately predict PK profi les. As many of the compound specifi c parameters can be derived from computa- tional approaches (in silico) or alternatively measured in vitro, predictions using such software can be easily conducted with minimal data at early stages. These models can be continually validated and refined as more data and ADME understanding becomes available throughout the lifecycle of the project.6 In this sense, the model built can evolve with the compound of interest.

Special Issue: Predictive DMPK: In Silico ADME Predictions in Drug Discovery
Received: August 30, 2012
Revised: February 19, 2013 Accepted: February 21, 2013
© XXXX American Chemical Society A dx.doi.org/10.1021/mp300482w | Mol. Pharmaceutics XXXX, XXX, XXX-XXX
Such approaches can be used to not only predict human plasma concentration time profi les but can also be applied to
7-9
predicting profi les in nonclinical species. In particular, during the course of drug discovery, anticipating dose- dependent exposure and dynamic profi les in pharmacological and toxicological species of interest can be advantageous to informing a future study design, aid in prioritizing compounds, and defi ne criteria for compound optimization and selection for in vivo testing. As with human, physiological parameters for mice, rats, dogs, and monkeys are imbedded within the program and adaptable if needed. While there is less precedence for using GastroPlus for this application, predicting nonclinical data has been shown to serve as a basis for building confi dence in the model’s applicability to predict human parameters.5,10
Herein, we describe three case studies to illustrate the utility of GastroPlus in predicting PK profi les from in silico and in vitro derived parameter estimates. The first case study compares the accuracy of in silico and measured compounds specifi c inputs for prediction of human PK profi les for PF-03084014,11,12 while the second case study extends the analysis to a broader set of compounds. The third case study predicts the PK profile for another compound, PF-05073992,13 in mice across a wide range of doses along with an analysis of critical parameters

metabolism studies and in vivo preclinical studies to evaluate any extrahepatic routes of CL. Simulations were conducted using in silico estimated compound-specifi c parameters as well as measured parameters as inputs (Table 1). For in silico based simulations, the ADMET predictor module within GastroPlus was used to predict all compound relevant parameters based on the compound structure for PF-03084014 (Figure 1) with the

 

 

 
Figure 1. PF-03084014 structure.

exception of the intrinsic clearance (CLint) which was derived from an internal (Pfizer) in silico prediction model of intrinsic clearance (cCLint) for human liver microsomal CLint. Measured parameter estimates described in Table 1 were derived internally using standard methodologies described previously.19 All CLint values were scaled according to the well-stirred model (eq 1).

hindering oral exposure. These examples are meant to demonstrate the “fit for purpose” application of modeling and simulation and PBPK techniques early in drug discovery to

CL
Q H × fub × CL
=
H Q H + fub × CL
u,int
u,int
(1)

drive decision making. At this early stage of discovery, many assumptions are made which would require further validation as the project progresses. The overall conclusion of this work demonstrates the utility of using in silico derived parameter estimates at a minimum and in vitro measure estimates in GastroPlus to provide a satisfactory prediction of the plasma concentration time profi le, illustrating the feasibility of conducting these predictions early on in discovery with minimal data. It should be emphasized however that such simulations are dependent on the quality of the inputs and mechanistic understanding of the processes driving PK. Hence, confi dence in such early simulations will improve over the project life cycle as more data and understanding becomes available. A number of more detailed case studies are available in the literature illustrating the value of such modeling and
14-18
simulation techniques.
■ METHODS
Case Study 1: Predicting Human Plasma Concen- tration Time Profiles for PF-03084014. The aim of case study 1 was to predict the human PK profile of PF-03084014 and compare the use of in silico derived and measured compound specific parameters to in vivo observations. The case study demonstrates the feasibility of predicting a human oral PK profile with minimal data.
All modeling was performed in GastroPlus (version 7) in a similar manner as previously described.5 For a detailed description and thorough understanding of the use of GastroPlus, refer to http://www.simulations-plus.com/. The rate and extent of absorption was predicted using the ACAT model1 set to the human physiological fasted condition. For the disposition, a human PBPK model was used where each tissue was assumed to be perfusion rate limited, and the liver was considered to be the only tissue to eliminate the compound. Hepatic P450 mediated metabolism was anticipated to be the primary route of CL for PF-03084014 based on in vitro
,where Q is the hepatic blood flow of 20 mL/min·kg for humans and 90 mL/min·kg for mice, CLint is the intrinsic microsomal CL either predicted from in silico or measured, fub is the fraction unbound in plasma/blood-plasma concentration ratio, CLu,int is CLint/fumic, and fumic is the unbound fraction in microsomes.
The Vss was predicted from Kp values for the PBPK model which were estimated from equations derived from Poulin and Theil20 equations within GastroPlus. These equations assume the compound distributes homogenously into the tissue and plasma by passive diffusion and accounts for both nonspecifi c binding to lipids and plasma proteins estimated by lipophilicity data and plasma protein binding, respectively. This model was chosen based on good correlation of predicted Vss values for rat and dog using this approach. Given this compound is only weakly basic, prediction accuracy using equations derived from Poulin and Theil20 were comparable to other methods.
Human effective permeability (Peff) for the measured inputs was derived from a measured apical to basolateral fl ux in Caco2 cells and a calibration data set within GastroPlus. Human PK simulations were performed at a dose of 95 mg as an oral suspension.
Case Study 2: Prediction of Human Concentration Time Profiles for a Broader Set of Compounds. The aim of case study 2 was to expand the evaluation of using in silico derived parameters across a broader set of compounds to predict a mean human oral plasma concentration time profi le. The case study assesses the feasibility of predicting a human oral PK profile with minimal data.
The modeling was conducted as described in case study 1 with the exception that the human observed CL was used as an input rather than the predicted value. The compound sets #1- 8, shown in Table 3, were previously evaluated and reported5 using GastroPlus with measured parameter inputs; these correspond to compound numbers 6, 8, 9, 10, 11, 12, 13, and 21, respectively. For the analysis herein, the compound
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structures were imported, and parameter inputs were estimated using the ADMET predictor module. For all compounds, the

Table 1. Predicted and Measured Compound Specifi c Parameter Estimates for PF-03084014

dose formulation was set to suspension, and the observed CL was incorporated in the PBPK model in either the liver tissue for those primary cleared by P450 mediated metabolism and in the kidney tissue for those with the primary route of CL as
MW lipophilicity
parameters
in silico predicted in vitro measured
489.7 489.7
4.01 (cLogP) 2.07 (LogD7.4)

renal. The method of Vss estimation was as reported in Jones et al.5
For compound 1 and 2, additional simulations were conducted where the in silico predicted permeability and/or
solubility (mg/mL) permeability (human Peff) pKa
plasma unbound fraction (fup)
0.32 @ pH 9.7 2.2 @ pH 5.3
1.8 2.7
6.4, 8.9 5.8, 7.1
0.36% 2.6%

solubility were adjusted to measured values. These simulations were then compared to those conducted with only in silico derived parameter inputs to identify plausible reasons for the poor prediction with purely in silico derived parameter inputs.
Case Study 3: Prediction of Mouse Plasma Concen-
mircosomal unbound fraction
(fumic) Cblood/Cplasma ratio
intrinsic clearance (mL/min·kg) clearance (mL/min·kg)
16%

0.66
123
2.4
81%

0.52
227
4.1

tration Time Profiles for PF-05073992. The aim of case study 3 was to predict the oral PK profi le of PF-05073992 (Figure 2) in mouse and compare to observed data. In addition,
microsomes. The human Peff based on measured Caco2 cell permeability data was closely aligned with the human Peff predicted from the ADMET predictor module.

 

 

 

Figure 2. PF-05073992 structure.
These inputs were subsequently used in GastroPlus to predict the plasma concentration time profi les following oral administration of a 95 mg dose. The ADMET predicted parameters using the Poulin and Theil homogeneous PBPK model20 estimated a Vss of 8.8 L/kg whereas the measured parameters estimated a Vss of 1.8 L/kg. The predicted plasma concentration time profiles and PK parameters Cmax, Tmax, and oral CL (CLpo) were compared to mean observed concen- tration data (Table 2). Both the predicted profi les (Figure 3)

simulations were performed over a broad dose range to

determine feasibility in achieving suitable exposure for a pharmacological testing. The case study demonstrates the utility of predicting PK profi les in animals to help with dose
Table 2. Parameter Output from GastroPlus for PF- 03084014

selection and compound prioritization for in vivo testing. For more detailed information regarding PF-05073992, refer to Guo et al.13
Modeling was performed as described for case study 1, however, with a mouse physiological ACAT model and a one- compartmental disposition model. The compound specific parameter estimates for PF-05073992 were either predicted or measured using Pfi zer internal models and assays as specifi ed in Table 5. Mouse CLint was unavailable; however, similar

parameter
% absorbed
% bioavailability Cmax (ug/mL) Tmax (hr)
oral CL
(mL/min·kg)
in silico predicted
99.9
82
0.50
0.72
4.3
in vitro measured
99.9
60
0.63
0.48
6.9
observed mean (%
CV)
0.460 (27) 1.0
10.4 (26)

compounds were primarily metabolized by P450 enzymes. Given rat microsomal data was available, mouse CLint was estimated from rat in vitro microsomal CLint assuming a similar hepatic extraction ratio of 17% and was therefore scaled to mouse hepatic blood flow of 90 mL/min·kg. The mouse Vss was estimated from an in silico predicted human Vss (0.92 L/kg) assuming equivalent unbound Vss values.
■ RESULTS
Case Study 1: Predicting a Human PK Profile for PF- 03084014 from in Silico and Measured Parameter Estimates. PF-03084014 (Figure 1) was used in this case study to evaluate the utility of GastroPlus to predict a human plasma concentration time profiles from in silico derived parameter inputs. The compound structure for PF-03084014 was imported into GastroPlus and the ADMET predictor module was used to predict the compound specific properties (Table 1). Measured parameters for LogD, solubility, human Peff, pKa, fup, fumic, blood-plasma concentration ratio, and microsomal CLint are shown in Table 1. Overall, the total predicted CL from in silico-derived approaches was comparable to those from measured parameters, despite differences in predicted and measured nonspecific binding in plasma and
from in silico inputs and in vitro measured inputs accurately predict the Cmax and Tmax while both similarly overpredict the exposure in the terminal phase. For this example, we relied on in vivo data to better understand and validate the CL mechanism and Vss prediction. However, in a “real life” situation in the very early stages of drug discovery, to perform these predictions using purely in silico data, this knowledge would have to be gained from compounds with similar properties in the same series.
Case Study 2: Predictability of Using in Silico Derived Parameters Across a Broader Set of Compounds. Although GastroPlus successfully predicted the human concentration time profile using in silico parameter inputs for PF-03084014, predictability across a broader set of compounds has the utility to assess the scope of application. To this end, a set of known compounds shown in Table 3 from a previous analysis reported by Jones et al.5 were included in the assessment of using in silico derived parameter inputs. Upon importing the structures into GastroPlus, the ADMET predictor module predicted the relevant inputs. Given observed CL was available for the compounds, this was incorporated in lieu of a predicted CL to allow assessment of PK-profi le predictability in the absence of errors associated with CL
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Figure 3. Predicted and observed human concentration time profiles for PF-03084014 from in silico inputs using ADMET predictor (A) and from measured in vitro parameters (B) as listed in Table 1.
prediction and/or knowledge of CL mechanism. Generally speaking, the parameters predicted by the ADMET predictor were in close alignment to the measured values with the exception of the permeability for compounds 1 and 2 and as well as the solubility of compound 1. The resulting simulations from in silico predicted inputs were compared to those with measured inputs and mean observed human concentrations (Figure 4). Overall, the predicted profi les (Figure 4) and resulting parameters (Table 4) for compounds 4-8 using in silico derived inputs were comparable to the observed data. However, for compounds 1-3, the use of in silico parameters under predicted the observed data. For compound #1, adjustment of the permeability and solubility to the measured values was needed to provide an adequate prediction of the human profi le and exposure (Figure 5A, Table 4). Upon adjustment of the permeability for compound 2’s in silico predicted value of 0.085 to the value derived from measured data (2.9), the profi le and resulting exposures were more comparable to the observed data (Figure 5B, Table 4). A similar analysis was done for compound 3; however, no modifi cations to the in silico parameters would signifi cantly improve the profi le (data not shown). One plausible reason driving the inaccuracy in the predicted PK profi le is the CL route. While compound 3 was reported as being primarily cleared by P450 mediated metabolism, the F of 75% suggests there may be nonhepatic routes of CL. Given the CL value is assigned to the relevant tissue in the PBPK model, the CL inputs for compound 3 may be incorrect leading to overprediction of fi rst pass-metabolism and consequently an underprediction of the concentration time profi le. The analysis of this expanded set of compounds illustrates the need to proceed with caution especially when in silico rather than measured inputs are utilized and when there is no opportunity to validate the inputs and assumptions with in vivo properties.

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Figure 4. Predicted and observed human concentration time profiles for compounds 1-8 (Table 3). Predicted profile with in silico derived inputs (), predicted profile with measured inputs (—), and mean observation (○).
Case Study 3: Predicting a Mouse PK Profile for PF- 05073992 and Identification of Limiting Factors to Oral Exposure. Predicting PK profiles for preclinical species can help predict dose-dependent exposure and consequently aid in
dose selection for pharmacological or toxicological evaluation. As such, prediction of the mouse PK profile for PF-05072992 was conducted. The compound specifi c inputs shown in Table 5 were either measured or predicted as indicated. Based on
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Table 4. Observeda and Predicted Parameter Outputs and Fold Error of Predicted Values Relative to Observed Values
AUC(0-tlast) (ng·h/mL) fold error Cmax (ng/mL) fold error Tmax (h) fold error
compound observed predicted pred/obs observed predicted pred/obs observed predicted pred/obs
1 909 18 0.020 180 1.2 0.007 1.0 7.3 7.3
1 with adjusted Peff 909 419 0.46 180 23.0 0.13 1.0 4.8 4.8
1with adjusted Peff and Sol 909 743 0.82 180 146 0.81 1.0 1.2 1.2

2 5336 1192 0.2 650 72 0.1 1.0 5.3 5.3
2with adjusted Peff 5336 5426 1.0 650 880 1.4 1.0 1.2 1.2

3 58 5 0.1 15 2.0 0.1 1.0 0.50 0.5
4 690000 707000 1.0 15000 20290 1.4 2.0 3.2 1.6
5 11 9 0.8 1.2 1.0 0.8 2.0 2.9 1.5
6 1898 2128 1.1 630 379 0.6 0.5 1.6 3.3
7 26 37 1.4 5.2 8.1 1.6 1.5 0.79 0.5
8 6.3 4 0.6 1.2 0.7 0.6 2.0 1.4 0.7
aObserved values for compounds 1-8 were reported in Jones et al.5 as compounds 6, 8, 9, 10, 11, 12, 13, and 21.

 

 

 

 

 

 

 

 

Figure 5. Predicted and observed human concentration time profiles for compound 1 (A) and compound 2 (B). Predicted profi le with in silico derived inputs (solid bold line), predicted profile with measured inputs (—), predicted profile with in silico inputs except for scaled permeability from measured inputs (dashed bold line), predicted profile with in silico input except for scaled permeability from measured values and measured solubility (••), and mean observation (○).

Table 5. Compound Specifi c Inputs for PF-05073992 target, the desired trough concentration (Cmin) was equivalent
parameter MW

source
calculated 462.39

value

comments
to in vitro pharmacological free IC50 of 48 nM for PF-05073992 (corrected for nonspecific binding determined in the assay media). For this particular mouse model, dosing frequencies

lipophilicity solubility
permeability
LogD 3.28 at pH 7.4
(with pH)
measured 0.005 mg/mL at pH 7.4
RRCK 10.8 × 10 -6 cm/s high
(MDCK)
more than twice daily were not feasible. Oral administration of a 100 mg/kg dose was not predicted to provide sufficient exposure. Hence, simulations were conducted at higher doses (Figure 6B). Even at higher doses (e.g., 400 mg/kg), the model

pKa
plasma unbound
fraction (fu)
predicted 4.86
measured 0.007
basic
mouse plasma
suggests that free concentrations equivalent to the IC50 at 12 h post dose were not achievable. In addition, the predicted profi les at increasing doses suggested a less than dose

Cblood/Cplasma ratio assumed 1 proportional increase in Cmax (Figure 6C) and consequently

clearance

volume of
distribution
in vitro

predicted
15 mL/min·kg

0.27 L/kg
low CL; extrapolated from RLM
scaled to from human in silico predicted
exposure. Given our understanding of the PK-PD properties of this target with previous compounds that had similar pharmacology, the predicted profiles suggested PF-05073992 was not suitable for pharmacological testing and was hence moved to a lower priority relative to other compounds.
To provide direction toward optimizing the oral exposure,

these data, PF-05073992 is characterized as a weak base having low CL and Vss. The predicted plasma-concentration time profi le based on these inputs overlaid well with observed concentrations in mouse following a 100 mg/kg orally administered dose (Figure 6A). Based on previous pharmaco- kinetic-pharmacodynamic (PK-PD) understanding of the
the “Parameter Sensitivity Analysis” (PSA) module was used to predict the impact of the solubility, permeability, and dose of PF-05073992 on oral exposure. PSA for CL was not included in the analysis given a compartmental model was employed and did not allow for sensitivity analysis of changes in first pass extraction. However, given the low hepatic clearance was
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Figure 6. Predicted plasma concentration time profiles for PF- 05073992 in mouse shown as unbound plasma concentrations (nM) relative to observed concentrations (A) and simulated profiles (B) with predicted Cmax (C) over the dose range of 25-400 mg/kg.

Figure 7. Parameter sensitivity analysis of clearance, permeability, and solubility on bioavailability (A) and absorption (B) for PF-05073992.
predicted, low oral exposure due to clearance is unlikely. The

analysis shows little impact of changing permeability on F with a solubility of 0.005 mg/mL, where changes in solubility had a dramatic impact, indicating PF-05073992’s oral F and absorption are limited by the low solubility. An increase in solubility of 10-fold is predicted to increase F to a suitable level. Additionally, the bioavailability and absorption decreased with increasing dose, a consistent observation as shown in Figure 7A and B.
■ DISCUSSION
Prediction of PK properties is a common practice for estimating suitability of potential drug candidates. Single-point estimates of CL, Vss, and F help to determine overall exposure and effective half-life.19 A limitation of this approach however is an understanding of the dynamic changes in compound concentrations over the dosing interval and hence association
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of changing concentrations relative to pharmacological action or simulation of chronic administration. Methods for predicting the plasma concentration profi le reported have included
10,4,5,22
compartmental PK models19,21 as well as PBPK models.
Profi le predictions using compartmental PK models are convenient; however, extrapolation and assumptions about distributional kinetics relative to animals are necessary.21 On the contrary, PBPK models have been demonstrated to facilitate cross species extrapolation and more accurately predict plasma concentration time profiles.5,10 Regardless of the PK model used, predicting PK profi les has broad application; such as (1) determining in vitro properties required to obtain the target PK profi le, (2) ranking ordering compounds in discovery most likely to result in a desired exposure profile in animals or humans, (3) assessing the effect of food on absorption in humans, (4) estimating local gut concentrations to assess potential DDI, and (5) determine attributes governing F and required to obtain a desired profile.
The cases studies described herein are examples of where GastroPlus has been able to utilize in silico derived parameter estimates to adequately predict the observed clinical profile or combine with measured parameters to predict mouse PK profi les. In practice, as more in silico derived parameters are included as inputs for the model relative to the number of measured parameters, more assumptions are required. As with all modeling and simulation, the most closely aligned parameter set with in vivo measured values will likely provide the best prediction. Hence, when inputs other than those from observed data are utilized, caution should be taken to ensure the inputs most closely reflect anticipated in vivo properties. An approach when utilizing in silico predicted inputs is to confi rm a correlation of in silico to measured values for representatives of similar chemical matter. Once correlations are established, in silico derived inputs can enable PK predictions in the early phase of drug discovery when minimal measured data and consequently aid in anticipating PK characteristics, identifying potential liabilities within a chemical series and identifying appropriate human relevant tools for optimizing against this liability. Hence, these predictions can be used to gain early understanding of PK issues on newly synthesized or virtually designed compounds and aid in prioritizing compounds for future evaluation.
■ AUTHOR INFORMATION
Corresponding Author
*Pfi zer, Inc., Dept. of Pharmacokinetics, Dynamics &
Metabolism, 35 Cambridge Park Dr., Cambridge, MA 02140. E-mail: [email protected]; phone: 617-665-7252.
Notes
The authors declare the following competing fi nancial interest(s): This work was conducted as an employee of Pfi zer, Inc.
■ ACKNOWLEDGMENTS
We thank project team members associated with the case studies, Alex Guo, Angelica Linton, Susan Kemphart, Mason Pairish, Martha Ornelas, Hovik Gukasyan, Minerva Batugo, Andrea Shen, Robert Hunter, David Paterson, Andrea Fanjul, David Briere, Manli Shi, Kris Rafi di, Jon Engebretsen, Brenda Ramos, Kathy Zandi, and Naveed Shaik.
■ ABBREVIATIONS
ADME, absorption distribution metabolism and elimination; CL, systemic clearance; CLpo, oral clearance; CLH, hepatic clearance; Vss, volume of distribution at steady-state; t1/2, half- life; F, bioavailability; QH, hepatic blood fl ow; fa, fraction absorbed; ka, rate of absorption; fub, free fraction in blood; fup, free fraction in plasma; fuinc, free fraction in microsomes or hepatocytes; CLint, total intrinsic clearance; CLu,int, unbound intrinsic clearance; HLM, human liver microsomes; IV, intravenous; PK, pharmacokinetic; PK-PD, pharmacokinetic- pharmacodynamic; PBPK, physiologically based pharmacoki- netic
■ REFERENCES
(1)Agoram, B.; Woltosz, W. S.; Bolger, M. B. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Delivery Rev. 2001, 50 (Suppl 1), S41-67.
(2)Yu, L. X.; Amidon, G. L. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharmaceutics 1999, 186 (2), 119-25.
(3)Jones, H. M.; Parrott, N.; Ohlenbusch, G.; Lave, T. Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling. Clin. Pharmacokinet. 2006, 45 (12), 1213-26.
(4)De Buck, S. S.; Sinha, V. K.; Fenu, L. A.; Nijsen, M. J.; Mackie, C. E.; Gilissen, R. A. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab. Dispos. 2007, 35 (10), 1766-80.
(5)Jones, H. M.; Gardner, I. B.; Collard, W. T.; Stanley, P. J.; Oxley, P.; Hosea, N. A.; Plowchalk, D.; Gernhardt, S.; Lin, J.; Dickins, M.; Rahavendran, S. R.; Jones, B. C.; Watson, K. J.; Pertinez, H.; Kumar, V.; Cole, S. Simulation of human intravenous and oral pharmacoki- netics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin. Pharmacokinet. 2011, 50 (5), 331- 47.
(6)Rowland, M.; Benet, L. Z. Lead PK commentary: Predicting human pharmacokinetics. J. Pharm. Sci. 2011, 100, 4047-9.
(7)Germani, M.; Crivori, P.; Rocchetti, M.; Burton, P. S.; Wilson, A. G.; Smith, M. E.; Poggesi, I. Evaluation of a basic physiologically based pharmacokinetic model for simulating the first-time-in-animal study. Eur. J. Pharm. Sci. 2007, 31 (3-4), 190-201.
(8)De Buck, S. S.; Sinha, V. K.; Fenu, L. A.; Gilissen, R. A.; Mackie, C. E.; Nijsen, M. J. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab. Dispos. 2007, 35 (4), 649-59.
(9)Parrott, N.; Paquereau, N.; Coassolo, P.; Lave, T. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J. Pharm. Sci. 2005, 94 (10), 2327-43.
(10)Jones, H. M.; Parrott, N.; Jorga, K.; Lave, T. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin. Pharmacokinetics 2006, 45 (5), 511-42.
(11)Wei, P.; Walls, M.; Qiu, M.; Ding, R.; Denlinger, R. H.; Wong, A.; Tsaparikos, K.; Jani, J. P.; Hosea, N.; Sands, M.; Randolph, S.; Smeal, T. Evaluation of selective gamma-secretase inhibitor PF- 03084014 for its antitumor efficacy and gastrointestinal safety to guide optimal clinical trial design. Mol. Cancer Ther. 2010, 9 (6), 1618-28.
(12)Brodney, M. A.; Auperin, D. D.; Becker, S. L.; Bronk, B. S.; Brown, T. M.; Coffman, K. J.; Finley, J. E.; Hicks, C. D.; Karmilowicz, M. J.; Lanz, T. A.; Liston, D.; Liu, X.; Martin, B. A.; Nelson, R. B.; Nolan, C. E.; Oborski, C. E.; Parker, C. P.; Richter, K. E.; Pozdnyakov, N.; Sahagan, B. G.; Schachter, J. B.; Sokolowski, S. A.; Tate, B.; Wood, D. E.; Wood, K. M.; Van Deusen, J. W.; Zhang, L. Design, synthesis, and in vivo characterization of a novel series of tetralin amino imidazoles as gamma-secretase inhibitors: discovery of PF-3084014. Bioorg. Med. Chem. Lett. 2011, 21 (9), 2637-40.
H dx.doi.org/10.1021/mp300482w | Mol. Pharmaceutics XXXX, XXX, XXX-XXX
(13)Guo, C.; Pairish, M.; Linton, A.; Kephart, S.; Ornelas, M.; Nagata, A.; Burke, B.; Dong, L.; Engebretsen, J.; Fanjul, A. N. Design of oxobenzimidazoles and oxindoles as novel androgen receptor antagonists. Bioorg. Med. Chem. Lett. 2012, 22 (7), 2572-8.
(14)Chen, Y.; Jin, J. Y.; Mukadam, S.; Malhi, V.; Kenny, J. R. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm. Drug Dispos. 2012, 33 (2), 85-98.Nirogacestat
(15)Jones, H. M.; Dickins, M.; Youdim, K.; Gosset, J. R.; Attkins, N. J.; Hay, T. L.; Gurrell, I. K.; Logan, Y. R.; Bungay, P. J.; Jones, B. C.; Gardner, I. B. Application of PBPK modelling in drug discovery and development at Pfizer. Xenobiotica 2012, 42 (1), 94-106.Nirogacestat
(16)Yamazaki, S.; Skaptason, J.; Romero, D.; Vekich, S.; Jones, H. M.; Tan, W.; Wilner, K. D.; Koudriakova, T. Prediction of oral pharmacokinetics of cMet kinase inhibitors in humans: physiologically based pharmacokinetic model versus traditional one-compartment model. Drug Metab. Dispos. 2011, 39 (3), 383-93.
(17)Bungay, P. J.; Tweedy, S.; Howe, D. C.; Gibson, K. R.; Jones, H. M.; Mount, N. M. Preclinical and clinical pharmacokinetics of PF- 02413873, a nonsteroidal progesterone receptor antagonist. Drug Metab. Dispos. 2011, 39 (8), 1396-405.
(18)Jones, H. M.; Gardner, I. B.; Watson, K. J. Modelling and PBPK simulation in drug discovery. AAPS J. 2009, 11 (1), 155-66.
(19)Hosea, N. A.; Collard, W. T.; Cole, S.; Maurer, T. S.; Fang, R. X.; Jones, H.; Kakar, S. M.; Nakai, Y.; Smith, B. J.; Webster, R.; Beaumont, K. Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction ap- proaches. J. Clin. Pharmacol. 2009, 49 (5), 513-33.
(20)Poulin, P.; Theil, F. P. A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically- based pharmacokinetic models in drug discovery. J. Pharm. Sci. 2000, 89 (1), 16-35.
(21)Boxenbaum, H. Interspecies pharmacokinetic scaling and the evolutionary-comparative paradigm. Drug Metab. Rev. 1984, 15 (5-6), 1071-121.
(22)Jamei, M.; Dickinson, G. L.; Rostami-Hodjegan, A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab. Pharmacokinet. 2009, 24 (1), 53-75.

 

 

 

 

 

 

 

 

 

 

 

 

 
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