Predicting Pharmacokinetic Profiles Using in Silico Derived

Article pubs.acs.org/molecularpharmaceutics

Predicting Pharmacokinetic Profiles Using in Silico Derived Parameters Natalie A. Hosea* and Hannah M. Jones Department of Pharmacokinetic, Dynamics and Metabolism, Pfizer, 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 profiles 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 profiles in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration−time profiles 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, profiles, prediction, modeling, simulation

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INTRODUCTION Predicting human pharmacokinetics (PK) is an important aspect for the discovery and identification of clinical candidates. These activities begin early in the drug discovery process, where predictions can be made across different 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 modifications 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 toxicological 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 profile is also a desired outcome of human PK predictions. GastroPlus (Simulations Plus, Lancas© XXXX American Chemical Society

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 Absorption and Transit” model by Yu and Amidon2 for absorption prediction and a physiologically based pharmacokinetic (PBPK) based model for prediction of disposition. Userdefined 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 reported to accurately predict PK profiles.3−5 As many of the compound specific parameters can be derived from computational 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

A

dx.doi.org/10.1021/mp300482w | Mol. Pharmaceutics XXXX, XXX, XXX−XXX

Molecular Pharmaceutics

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metabolism studies and in vivo preclinical studies to evaluate any extrahepatic routes of CL. Simulations were conducted using in silico estimated compound-specific 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

Such approaches can be used to not only predict human plasma concentration time profiles but can also be applied to predicting profiles in nonclinical species.7−9 In particular, during the course of drug discovery, anticipating dosedependent exposure and dynamic profiles in pharmacological and toxicological species of interest can be advantageous to informing a future study design, aid in prioritizing compounds, and define 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 confidence 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 profiles from in silico and in vitro derived parameter estimates. The first case study compares the accuracy of in silico and measured compounds specific inputs for prediction of human PK profiles 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 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 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 profile, 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, confidence 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 simulation techniques.14−18

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). CL H =

Q H × fub × CLu,int Q H + fub × CLu,int

(1)

,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 nonspecific 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 flux 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 profile. 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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METHODS Case Study 1: Predicting Human Plasma Concentration 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 B



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structures were imported, and parameter inputs were estimated using the ADMET predictor module. For all compounds, the 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 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 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 Concentration Time Profiles for PF-05073992. The aim of case study 3 was to predict the oral PK profile of PF-05073992 (Figure 2) in mouse and compare to observed data. In addition,

Table 1. Predicted and Measured Compound Specific Parameter Estimates for PF-03084014 parameters MW lipophilicity solubility (mg/mL) permeability (human Peff) pKa plasma unbound fraction (fup) mircosomal unbound fraction (fumic) Cblood/Cplasma ratio intrinsic clearance (mL/min·kg) clearance (mL/min·kg)

in silico predicted

in vitro measured

489.7 4.01 (cLogP) 0.32 @ pH 9.7 1.8 6.4, 8.9 0.36% 16%

489.7 2.07 (LogD7.4) 2.2 @ pH 5.3 2.7 5.8, 7.1 2.6% 81%

0.66 123 2.4

0.52 227 4.1

microsomes. The human Peff based on measured Caco2 cell permeability data was closely aligned with the human Peff predicted from the ADMET predictor module. These inputs were subsequently used in GastroPlus to predict the plasma concentration time profiles 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 concentration data (Table 2). Both the predicted profiles (Figure 3)

Figure 2. PF-05073992 structure.

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 profiles in animals to help with dose 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 onecompartmental disposition model. The compound specific parameter estimates for PF-05073992 were either predicted or measured using Pfizer internal models and assays as specified in Table 5. Mouse CLint was unavailable; however, similar 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.

Table 2. Parameter Output from GastroPlus for PF03084014 parameter % absorbed % bioavailability Cmax (ug/mL) Tmax (hr) oral CL (mL/min·kg)

in silico predicted

in vitro measured

observed mean (% CV)

99.9 82 0.50 0.72 4.3

99.9 60 0.63 0.48 6.9

0.460 (27) 1.0 10.4 (26)

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-profile predictability in the absence of errors associated with CL

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RESULTS Case Study 1: Predicting a Human PK Profile for PF03084014 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 C



D

0.54 0.59 4.9 0.48 0.87 0.86 1.6 24 a

Observed values for compounds 1−8 were reported in Jones et al.5 as compounds 6, 8, 9, 10, 11, 12, 13, and 21.

predicted observed

0.54 0.80 9.6 0.80 2.4 0.8 1.6 1.1 3.0 2.7 6.7 0.30 5.7 3.8 4.8 1.7

predicted observed

2.5 2.2 31 0.25 4.0 5.9 5.4 5.4 0.72 0.91 1.27 1.1 0.90 0.65 0.74 0.75

predicted measured

0.70 0.66 2.8 1.0 0.62 0.70 0.87 0.74 30 36 36 21 18 17 7.3 7.6

predicted measured

50 69 45 95 37 4.0 28 0.09 0.040 0.085 3.3 1.7 0.38 0.90 3.3 0.98

predicted scaled

2.6 2.9 3.5 2 1.5 1.6 2.3 0.65 7.0 7.1 11 7.7 7.8 7.5 9.3 8.1

pH predicted

0.0076 1.1 0.52 5.53 0.27 0.48 0.021 0.012 7 7 7 7 7 7 7 7

pH measured

0.28 1.0 94 6 0.5 0.01 2.3 1.1 1.4 0.86 2.5 0.6 1.6 2.1 3.5 3.9

predicted

1 2 3 4 5 6 7 8

measured MW

414.50 337.35 198.27 306.28 441.57 474.59 382.53 412.94

compound

0.60 0.60 0.50 0.50 1.0 2.8 1.3 3.9

permeability (10−4 cm/s) solubility/pH

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 profiles (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 profile 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 profile and resulting exposures were more comparable to the observed data (Figure 5B, Table 4). A similar analysis was done for compound 3; however, no modifications to the in silico parameters would significantly improve the profile (data not shown). One plausible reason driving the inaccuracy in the predicted PK profile 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 first pass-metabolism and consequently an underprediction of the concentration time profile. 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.

LogD

Table 3. Observeda and Predicted Compound Specific Inputs for a Broader Set of Compounds

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.

% plasma unbound (fup)

Cblood/Cplasma ratio

plasma clearance (mL/min·kg)

Vss (L/kg)

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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 PF05073992 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 specific inputs shown in Table 5 were either measured or predicted as indicated. Based on E



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Table 4. Observeda and Predicted Parameter Outputs and Fold Error of Predicted Values Relative to Observed Values compound

a

AUC(0−tlast) (ng·h/mL)

fold error

observed

pred/obs

predicted

Cmax (ng/mL) observed

Tmax (h)

fold error

predicted

fold error

pred/obs

observed

predicted

pred/obs

1 1 with adjusted Peff 1 with adjusted Peff and Sol

909 909 909

18 419 743

0.020 0.46 0.82

180 180 180

1.2 23.0 146

0.007 0.13 0.81

1.0 1.0 1.0

7.3 4.8 1.2

7.3 4.8 1.2

2 2 with adjusted Peff

5336 5336

1192 5426

0.2 1.0

650 650

72 880

0.1 1.4

1.0 1.0

5.3 1.2

5.3 1.2

3 4 5 6 7 8

58 690000 11 1898 26 6.3

5 707000 9 2128 37 4

0.1 1.0 0.8 1.1 1.4 0.6

15 15000 1.2 630 5.2 1.2

2.0 20290 1.0 379 8.1 0.7

0.1 1.4 0.8 0.6 1.6 0.6

1.0 2.0 2.0 0.5 1.5 2.0

0.50 3.2 2.9 1.6 0.79 1.4

0.5 1.6 1.5 3.3 0.5 0.7

Observed 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 profile 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 (○).

target, the desired trough concentration (Cmin) was equivalent 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 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 suggests that free concentrations equivalent to the IC50 at 12 h post dose were not achievable. In addition, the predicted profiles at increasing doses suggested a less than dose proportional increase in Cmax (Figure 6C) and consequently 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, 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

Table 5. Compound Specific Inputs for PF-05073992 parameter MW lipophilicity solubility permeability pKa plasma unbound fraction (fu) Cblood/Cplasma ratio clearance volume of distribution

source

value

comments

calculated LogD (with pH) measured RRCK (MDCK) predicted measured

462.39 3.28

at pH 7.4

0.005 mg/mL 10.8 × 10 −6 cm/s

at pH 7.4 high

4.86 0.007

basic mouse plasma

assumed in vitro

1 15 mL/min·kg

predicted

0.27 L/kg

low CL; extrapolated from RLM scaled to from human in silico predicted

these data, PF-05073992 is characterized as a weak base having low CL and Vss. The predicted plasma-concentration time profile based on these inputs overlaid well with observed concentrations in mouse following a 100 mg/kg orally administered dose (Figure 6A). Based on previous pharmacokinetic-pharmacodynamic (PK-PD) understanding of the F



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Figure 6. Predicted plasma concentration time profiles for PF05073992 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.

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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 G



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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, halflife; F, bioavailability; QH, hepatic blood flow; 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, pharmacokineticpharmacodynamic; PBPK, physiologically based pharmacokinetic

of changing concentrations relative to pharmacological action or simulation of chronic administration. Methods for predicting the plasma concentration profile reported have included compartmental PK models19,21 as well as PBPK models.10,4,5,22 Profile 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 profiles has broad application; such as (1) determining in vitro properties required to obtain the target PK profile, (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 profiles. 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 confirm 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.

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AUTHOR INFORMATION

Corresponding Author

*Pfizer, Inc., Dept. of Pharmacokinetics, Dynamics & Metabolism, 35 Cambridge Park Dr., Cambridge, MA 02140. E-mail: natalie.hosea@pfizer.com; phone: 617-665-7252. Notes

The authors declare the following competing financial interest(s): This work was conducted as an employee of Pfizer, Inc.

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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 Rafidi, Jon Engebretsen, Brenda Ramos, Kathy Zandi, and Naveed Shaik. H



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Predicting Pharmacokinetic Profiles Using in Silico Derived

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