In Vitro Drug 
Interaction Studies  
Cytochrome P450 
Enzyme- and 
Transporter-Mediated 
Drug Interactions 
Guidance for Industry 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
 
January 2020 
Clinical Pharmacology 
 
    
In Vitro Drug 
Interaction Studies 
Cytochrome P450 
Enzyme- and 
Transporter-Mediated 
Drug Interactions 
Guidance for Industry 
 
Additional copies are available from: 
Office of Communications, Division of Drug Information  
Center for Drug Evaluation and Research 
Food and Drug Administration 
10001 New Hampshire Ave., Hillandale Bldg., 4th Floor  
Silver Spring, MD 20993-0002  
Phone: 855-543-3784 or 301-796-3400; Fax: 301-431-6353 
Email: druginfo@fda.hhs.gov  
https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
 
January 2020 
Clinical Pharmacology 
 
  Contains Nonbinding Recommendations 
 
TABLE OF CONTENTS 
 
 
 
I.  INTRODUCTION ...................................................................................................... 1 
II.  BACKGROUND ........................................................................................................ 2 
III.  EVALUATING METABOLISM-MEDIATED DRUG INTERACTIONS................ 2 
A.  Determining if the Investigational Drug is a Substrate of Metabolizing Enzymes ............... 3 
B.  Determining if the Investigational Drug is an Inhibitor of Metabolizing Enzymes .............. 3 
C.  Determining if the Investigational Drug is an Inducer of Metabolizing Enzymes ................ 5 
IV.  EVALUATING TRANSPORTER-MEDIATED DRUG INTERACTIONS ............. 8 
A.  Determining if the Investigational Drug is a Substrate of the Transporters P-gp and BCRP 9 
B.  Determining if the Investigational Drug is a Substrate of the Hepatic Transporters 
OATP1B1 and OATP1B3.....................................................................................................10 
C.  Determining if the Investigational Drug is a Substrate of the Renal Transporters OAT, 
OCT, and MATE ................................................................................................................11 
D.  Determining if the Investigational Drug is an Inhibitor of a Transporter..........................11 
E.  Determining if the Investigational Drug is an Inducer of a Transporter ...........................14 
V.  EVALUATING THE DDI POTENTIAL OF METABOLITES .............................. 14 
A.  Metabolite as a Substrate..............................................................................................14 
B.  Metabolite as an Inhibitor ............................................................................................15 
VI.  LABELING RECOMMENDATIONS ..................................................................... 16 
VII.  APPENDICES .......................................................................................................... 17 
A.  Evaluating Metabolism-Based Drug Interactions In Vitro ...............................................17 
B.  Evaluating Transporter-Mediated Drug Interactions In Vitro .........................................23 
C.  Using Model-Based Predictions to Determine a Drugs Potential to Cause DDIs ...............27 
VIII.  ABBREVIATIONS AND ACRONYMS .................................................................. 35 
IX.  REFERENCES......................................................................................................... 37 
i  Contains Nonbinding Recommendations 
 
In Vitro Drug Interaction Studies  Cytochrome P450 Enzyme- 
and Transporter-Mediated  Drug Interactions 
Guidance for Industry1 
 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or Agency) on 
this topic.  It does not establish any rights for any person and is not binding on FDA or the public.  You 
can use an alternative approach if it satisfies the requirements of the applicable statutes and regulations.  
To discuss an alternative approach, contact the FDA office responsible for this guidance as listed on the 
title page.   
 
 
 
 
I.  INTRODUCTION  
 
This final guidance is intended to help drug developers plan and evaluate studies to determine 
the drug-drug interaction (DDI) potential of an investigational  drug product.2  The final guidance 
focuses on in vitro approaches to evaluate the interaction potential  between investigational  drugs 
with cytochrome P450 enzymes (CYPs) and transporters as well as how in vitro results can 
inform future clinical  DDI studies.  The appendices of this guidance include considerations  when 
choosing in vitro experimental systems, key issues regarding in vitro experimental conditions, 
and more detailed explanations  regarding model-based DDI prediction strategies.  See section 
VIII for a list of terms used in this guidance and their definitions.   Note that at this time, the in 
vitro methods to evaluate the induction  of P-gp and other transporters are not well established; 
therefore, recommendations for the in vitro evaluation of investigational  drugs as transporter 
inducers are not provided. 
 
If an in vitro assessment suggests that the sponsor should conduct a clinical  DDI study, the 
sponsor should refer to the January 2020 final FDA guidance for industry entitled Clinical Drug 
Interaction Studies Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions.3  
Together, these two final guidances describe a systematic, risk-based approach to assessing the 
DDI potential  of investigational  drugs and making recommendations  to mitigate DDIs.  
 
In general, FDAs guidance documents do not establish legally  enforceable responsibilities.   
Instead, guidances describe the Agencys current thinking  on a topic and should be viewed only 
as recommendations,  unless specific regulatory or statutory requirements are cited.  The use of 
                                              
1 This guidance has been prepared by the Office of Clinical Pharmacology, Office of Translational Sciences in the 
Center for Drug Evaluation and Research at the Food and Drug Administration.  
 
2 Only small molecule drugs are covered in this guidance.  Interactions involving biologics (therapeutic proteins) are 
beyond the scope of this guidance. 
 
3 We update guidances periodically.  For the most recent version of a guidance, check the FDA guidance web page at 
https://www.fda.gov/RegulatoryInformation/Guidances/default.htm. 
  1  Contains Nonbinding Recommendations 
 
the word should in Agency guidances means that something  is suggested or recommended, but 
not required.  
 
 
II.  BACKGROUND 
 
Evaluating  the DDI potential of an investigational  new drug involves:   (1) identifying  the 
principal  routes of the drugs elimination;  (2) estimating  the contribution  of enzymes and 
transporters to the drugs disposition;  and (3) characterizing the effect of the drug on enzymes 
and transporters.  This evaluation often starts with in vitro experiments to identify  potential 
factors influencing  drug disposition  to elucidate potential DDI mechanisms and to yield kinetic 
parameters for use in further studies.  Results of in vitro experiments, along with clinical 
pharmacokinetic  (PK) data, provide mechanistic information  that can inform the need for and 
proper design of potential future clinical  studies.   
 
Various modeling  approaches can help translate in vitro observations into in vivo predictions of 
potential clinical  DDIs.  For example, when evaluating the drug as a perpetrator of a metabolism-
mediated DDI, basic models (Einolf 2007; Einolf,  Chen, et al. 2014; Vieira, Kirby, et al. 2014), 
static mechanistic models (Einolf 2007; Fahmi, Hurst, et al. 2009; Einolf,  Chen, et al. 2014), or 
dynamic mechanistic models including  physiologically-based  pharmacokinetic (PBPK) models 
(Zhao, Zhang, et al. 2011; Zhao, Rowland, et al. 2012; Jones, Chen, et al. 2015; Wagner, Zhao, 
et al. 2015; September 2018 FDA guidance for industry Physiologically Based Pharmacokinetic 
Analyses  Format and Content) can help guide decisions on when and how to conduct a 
clinical  DDI study.   
 
This guidance outlines  a general framework for conducting in vitro experiments and interpreting 
in vitro study results to determine the potential for clinical DDIs.  The recommendations in this 
guidance are based on current scientific understanding.  The recommendations outlined  here may 
be periodically  updated as the scientific field of DDIs evolves and matures.  Refer to the 
appendices for general considerations regarding in vitro systems to evaluate DDIs for drug 
development and regulatory purposes. 
 
III.  EVALUATING METABOLISM-MEDIATED DRUG INTERACTIONS 
 
Many drugs undergo metabolism as a major mechanism of bioactivation  (e.g., in the case of 
prodrugs) or clearance from the body.  Drugs can be metabolized  in several organs, including but 
not limited  to, the liver, kidney,  gut wall, and lung; however, drug metabolism  primarily  occurs 
in the liver and intestine.  These organs express a wide variety of drug metabolizing  enzymes and 
are responsible for the biotransformation  of many drugs.  Hepatic metabolism  occurs primarily 
through the CYP family of enzymes located in the hepatic endoplasmic  reticulum but can also 
occur through non-CYP enzymes, including  Phase II glucuronosyl-  and sulfo-transferases.  
Sponsors should examine the potential for interactions between these metabolizing  enzymes and 
investigational  drugs by initiating  in vitro metabolic studies before first-in-human  studies to 
inform the need for and design of clinical PK studies.  We recommend that the sponsor conducts 
the following  in vitro studies to evaluate the potential  for metabolism-mediated  drug interactions. 
 
  2  Contains Nonbinding Recommendations 
 
A.  Determining if the Investigational Drug is a Substrate of Metabolizing   
Enzymes 
 
1.  Conducting In Vitro Studies 
 
The sponsor should routinely  evaluate CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, 
CYP2D6, and CYP3A using in vitro phenotyping  experiments to determine which enzymes 
metabolize  the investigational  drug.  If the investigational  drug is not found to undergo 
significant  in vivo metabolism  by these major CYP enzymes, the sponsor should then determine 
what additional  enzymes contribute to the metabolism  of the investigational  drug.  These 
additional  enzymes include but are not limited  to: 
 
  CYP enzymes including  CYP2A6, CYP2J2, CYP4F2, and CYP2E1 
 
  Other Phase I enzymes including  aldehyde oxidase (AO), carboxylesterase (CES), 
monoamine  oxidase (MAO), flavin monooxygenase  (FMO), xanthine oxidase (XO), and 
alcohol/aldehyde  dehydrogenase (ADH/ALDH) 
 
  Phase II enzymes including  UDP glucuronosyl  transferases (UGTs) and sulfotransferases 
(SULTs) 
 
2.  Data Analysis and Interpretation 
 
The contribution  of a specific metabolizing  enzyme to an investigational  drugs clearance is 
considered significant  if the enzyme is responsible for > 25% of the drugs elimination  based on 
the in vitro phenotyping  studies and human PK data.  Under these circumstances, the sponsor 
should conduct clinical  DDI studies using strong index inhibitors  and/or inducers of the enzyme 
(see the January 2020 FDA final guidance for industry entitled Clinical Drug Interaction Studies 
Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions).  
 
B.  Determining if the Investigational Drug is an Inhibitor of Metabolizing 
Enzymes 
 
1.  Conducting In Vitro Studies 
 
The sponsor should evaluate an investigational  drugs potential to inhibit  CYP1A2, CYP2B6, 
CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A in both a reversible manner (i.e., 
reversible inhibition)  and time-dependent manner (i.e., time-dependent inhibition  (TDI)). 
  
2.  Data Analysis and Interpretation 
 
For basic models, the sponsor should calculate the ratio of intrinsic  clearance values of a probe 
substrate for an enzymatic pathway in the absence and in the presence of the interacting drug. 
This ratio is referred to as R  for reversible inhibition.   For CYP3A, R  should also be 
1 1,gut
calculated as shown in Figure 1.  
 
  3  Contains Nonbinding Recommendations 
 
Figure 1:  Equations to Calculate the R value for Basic Models of Reversible Inhibition 
(Vieira, Kirby, et al. 2014) 
 
R  = 1 + (I  / K )  
1 max,u i,u
 
R  = 1 + (I  / K ) 
1,gut gut i,u
 
I  is the maximal unbound plasma concentration of the interacting drug at steady state.* 
max,u
I  is the intestinal luminal concentration of the interacting drug calculated as the dose/250 mL. 
gut
K  is the unbound inhibition constant determined in vitro. 
i,u
 
Note:  I and K need to be expressed in the same unit (e.g., in a molar concentration unit). 
i
 
*Considering uncertainties in the protein binding measurements, the unbound fraction in plasma should be set to 1% 
(fraction unbound in the plasma (f )= 0.01) if experimentally determined to be < 1%. 
u,p  
 
For basic models of TDI, the sponsor should calculate R  as described in Figure 2. 
2
 
Figure 2:  Equations to Calculate the R value for Basic Models of TDI (Yang, Liao, et al. 
2008; Grimm, Einolf, et al. 2009; Vieira, Kirby, et al. 2014) 
 
R  = (k  + k ) / k   
2 obs deg deg
 
Where k  = (k  50  I )  / (K   + 50  I ) 
obs inact   max,u I,u max,u
               
k is the observed (apparent first order) inactivation rate of the affected enzyme. 
obs 
k  is the apparent first-order degradation rate constant of the affected enzyme.  
deg
K  is the unbound inhibitor concentration causing half-maximal inactivation.  
I,u
k  is the maximal inactivation rate constant. 
inact
I  is the maximal unbound plasma concentration of the interacting drug at steady state.* 
max,u
 
Note:  I and K  need to be expressed in the same unit (e.g., in a molar concentration unit). 
I
 
*Considering uncertainties in the protein binding measurements, the unbound fraction in plasma should be set to 1% 
(fraction unbound in the plasma (f )= 0.01) if experimentally determined to be < 1%. 
u,p  
 
If R   1.02, R   1.25 (Vieira, Kirby et al. 2014) or the R   11 (Tachibana, Kato, et al. 
1 2 1,gut 
2009; Vieira, Kirby, et al. 2014), the sponsor should further investigate the DDI potential by 
either using mechanistic models (see appendix, section VII.C) or conducting a clinical  DDI study 
with a sensitive index substrate.  If the predicted ratio of area under the plasma concentration-
time curve (AUCR) of a sensitive index substrate in the presence and absence of an 
investigational  drug is  1.25 based on static mechanistic models or dynamic mechanistic models 
(e.g., PBPK models) (see appendix, section VII.C.1), the sponsor should conduct a clinical  DDI 
study using a sensitive index substrate.  
 
When static mechanistic models or dynamic models (see appendix,  section VII.C.1) are used for 
predicting  DDIs caused by enzyme inhibition,  the models should include the inhibition 
mechanism only (i.e., the model should not include concurrent induction  predictions for an 
  4  Contains Nonbinding Recommendations 
 
investigational  drug that is hypothesized  to be both an inducer and inhibitor)  to assess the 
potential of the investigational  drug to inhibit  metabolizing  enzymes.   
 
C.  Determining if the Investigational Drug is an Inducer of Metabolizing 
Enzymes 
 
1.  Conducting In Vitro Studies 
 
The sponsor should evaluate the potential of an investigational  drug to induce CYP1A2, 
CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4.  Initially,  sponsors can conduct 
experiments to evaluate CYP1A2, CYP2B6, and CYP3A4 only.  If no induction  of CYP3A4 
enzymes is observed, evaluating the induction potential  of CYP2C enzymes is not necessary 
because both CYP3A4 and CYP2C enzymes are induced via activation of the pregnane X 
receptor (PXR).  If the investigational  drug induces CYP3A4 and the results suggest that a 
clinical  study is warranted, the sponsor should evaluate the potential  of the investigational  drug 
to induce CYP2C.  However, a negative in vivo study with a CYP3A sensitive substrate can be 
used to rule out induction  potential of an investigational  drug on CYP2C enzymes, as long as the 
potential of CYP3A inhibition  by the drug and its metabolite(s) can be excluded.   
 
2.  Data Analysis and Interpretation 
 
The induction  results should be evaluated separately for each donor.  If the result from at least 
one donor exceeds the pre-defined threshold,  the sponsor should consider the drug to have 
induction  potential  and conduct a follow-up  evaluation.  Several basic methods can assess the 
potential of an investigational  drug to induce metabolizing  enzymes (Fahmi, Kish, et al. 2010; 
Fahmi and Ripp 2010; Einolf,  Chen, et al. 2014; Kenny, Ramsden, et al. 2018).  Three are 
described in detail below: 
 
1. Fold-change method:  The sponsor can examine the fold-change in CYP enzyme mRNA 
levels when incubated with the investigational  drug by using a cutoff determined from 
known positive and negative controls to calibrate the system.  For example, a drug is 
interpreted as an inducer if:  (1) it increased mRNA expression of a CYP enzyme in a 
concentration-dependent  manner; and (2) the fold change of CYP mRNA expression relative 
to the vehicle control is  2-fold at the expected hepatic concentrations of the drug.  Expected 
drug concentrations in the liver can be calculated by assuming a certain fold of I  (e.g., 
max,u
30-fold of mean unbound  maximal steady-state plasma concentration at therapeutic dose).  
Considering  uncertainties in the protein binding  measurements, the unbound fraction in 
plasma should be set to 1% (f  = 0.01) if experimentally  determined to be < 1% when 
u,p
cacluating I . 
max,u
 
However, the induction  potential should not be ruled out for an investigational  drug that 
increases CYP enzyme mRNA less than 2-fold the of vehicle control, if the increase is more 
than 20% of the response of the positive control.  Further evaluation is recommended when 
there is an inconclusive  finding. 
 
  5  Contains Nonbinding Recommendations 
 
To calculate the percent of the response to the positive  control, the following  equation should 
be used: 
 
% of positive  control = (mRNA fold increase of test drug treated cells - 1)  100/ (mRNA 
fold increase of positive control  1) 
 
2.  Correlation methods:  The sponsor may use correlation methods as described in Figure 3 
to predict the magnitude of a clinical  induction  effect (e.g., AUC ratio of index substrate in 
the presence and absence of inducers) of an investigational  drug according to a calibration 
curve of relative induction  scores (RIS) or I /EC  for a set of known inducers of the 
max,u 50
same enzyme.  If the predicted magnitude is more than a predefined cut-off (e.g., AUC ratio 
 0.8), a drug is considered have induction  potential in vivo. 
 
The calibration  can be established once for one batch of hepatocytes and does not need to be 
determined for each experiment.  Sometimes,  E  or EC  cannot be estimated due to an 
max 50
incomplete in vitro induction  profile (e.g., limited  by solubility  or cytotoxicity  of tested 
drug).  An alternative correlation approach may be used if the method is validated.      
 
Figure 3:  Two Correlation Methods to Assess the Potential of an Investigational Drug to 
Induce Metabolizing Enzymes (Fahmi and Ripp, 2010) 
 
Correlation Method 1:  Calculate a relative induction  score (RIS) using (E   I ) / (EC  + 
max max,u 50
I )  
max,u
OR 
Correlation Method 2:  Calculate I / EC  values  
max,u  50
E is the maximum induction effect determined in vitro. 
max 
EC  is the concentration causing half-maximal effect determined in vitro.  
50
I  is the maximal unbound plasma concentration of the interacting drug at steady state.* 
max,u
*Considering uncertainties in the protein binding measurements, the unbound fraction in plasma should be set to 1% 
(fraction unbound in the plasma (f )= 0.01) if experimentally determined to be < 1%. 
u,p  
 
3. Basic kinetic model:   To use this method, the sponsor should calculate the R value 
(R ) as described in Figure 4 and compare to a predefined cut-off determined from a set 
3
of inducers and non-inducers.  For example, a R  value  0.8 may indicate that the 
3
investigational  drug has induction  potential  in vivo. 
 
Figure 4: An Equation to Calculate the R value for Basic Models of Induction (Kenny, 
Ramsden, et al. 2018) 
 
R  = 1 / [1 + d  ((E   10  I ) / (EC  + 10  I ))] 
3 max max,u 50 max,u
R  is the predicted ratio of intrinsic clearance values of a probe substrate for an enzymatic pathway in the absence 
3
and presence of an inducer. 
d is the scaling factor and is assumed to be 1.  A different value can be used if supported by prior experience with 
the system used (Vermet, Raoust, et al. 2016). 
E is the maximum induction effect determined in vitro. 
max 
I  is the maximal unbound plasma concentration of the interacting drug at steady state.* 
max,u
EC  is the concentration causing half-maximal effect determined in vitro.  
50
*Considering uncertainties in the protein binding measurements, the unbound fraction should be set to 1% if 
experimentally determined to be <1%. 
  6  Contains Nonbinding Recommendations 
 
 
If these methods indicate that the investigational  drug has the potential to induce metabolizing 
enzymes (using specific cutoff values mentioned above or developed by individual  laboratories 
for these methods), the sponsor should further investigate the enzyme induction  potential of the 
investigational  drug by using mechanistic models (see appendix, section VII.C.1) or by 
conducting a clinical  DDI study with a sensitive index substrate.  If the predicted AUCR of a 
sensitive index substrate in the presence and absence of an investigational  drug is  0.8 based on 
static mechanistic models or dynamic mechanistic models (e.g., PBPK models; see appendix, 
section VII.C.1), the sponsor should further investigate potential DDIs by conducting a clinical 
DDI study using a sensitive index substrate. 
 
When static mechanistic models or dynamic mechanistic models (see appendix, section VII.C.1) 
are used for predicting DDIs caused by enzyme induction,  the models should include the 
induction  mechanism only (i.e., the model should not include concurrent inhibition  predictions 
for an investigational  drug that is hypothesized  to be both an inducer and inhibitor)  to assess the 
potential of an investigational  drug to induce metabolizing  enzymes.   
  
3.  Additional Considerations 
 
The AUCR cutoffs of > 0.8 (for induction)  and < 1.25 (for inhibition)  using mechanistic models 
are the suggested default values to indicate that the investigational  drug has no effect on the 
levels of metabolizing  enzymes. 
 
When evaluating whether an investigational  drug is an inhibitor  of multiple  CYP enzymes, the 
sponsor can prioritize  in vivo DDI evaluations for various CYP enzymes with sensitive index 
substrates of respective pathways (see the January 2020 FDA guidance for industry Clinical 
Drug Interaction Studies  Cytochrome P450 Enzyme- and Transporter-Mediated Drug 
Interactions) based on rank-ordered R , R , or the predicted AUCR values, preferably using the 
1 2
in vitro inhibition  parameters obtained in the same study.4  That is, the sponsor may first carry 
out an in vivo study with a sensitive index substrate of the CYP with the largest R or AUCR 
value.  If this in vivo study shows no interaction,  in vivo evaluations  of other CYPs with lower 
potencies (e.g., smaller R or AUCR) are not needed.  However, if this in vivo study shows a 
positive interaction between the drug and the sensitive index CYP substrate, the sponsor should 
conduct additional  in vivo studies for other CYPs, starting with the CYP with the next largest R 
or AUCR value.  Alternatively,  the sponsor can use a mechanistic dynamic model to inform the 
need for additional  studies.  The sponsor should verify and update dynamic models to 
demonstrate that the model can adequately describe the observed findings  from the first in vivo 
study with a sensitive index substrate.  In the presence of inhibitory  metabolites of an 
investigational  drug, their contribution  and rank order of metabolite R values should also be 
considered when determining  what in vivo studies should be conducted.  
 
                                              
4 An orally administered drug may inhibit intestinal metabolic enzymes (e.g., CYP3A) in addition to hepatic 
enzymes.  Therefore, in vivo DDI for CYP3A inhibition should be considered if R  is greater than or equal to 11, 
1,gut
even if R for CYP3A is not the largest value among the major CYPs evaluated. 
1
  
  7  Contains Nonbinding Recommendations 
 
Concurrent prediction  of inhibition  and induction  using mechanistic static models or dynamic 
models (see appendix, section VII.C.1) can be considered for predicting the net effect of an 
investigational  drug that is hypothesized  to be both an inhibitor  and an inducer of metabolizing 
enzymes.  However, there is a concern with concurrent predictions,  as over-prediction  of 
inhibition  may mask the induction  effect leading to a false negative prediction of the overall 
effect (Einolf,  Chen, et al. 2014).  If the induction  potential  is over-predicted, it will  mask the 
inhibition  effect. 
 
In vitro induction  studies may also detect enzyme down-regulation.   However, research in this 
area is presently very limited,  and the mechanisms behind these effects are unclear.  If 
concentration-dependent  down-regulation  is observed in vitro and is not attributable to 
cytotoxicity,  additional  in vitro or in vivo studies may be needed to understand the potential 
clinical  consequences (Hariparsad, Ramsden, et al. 2017). 
 
 
IV.  EVALUATING TRANSPORTER-MEDIATED DRUG INTERACTIONS 
 
Membrane transporters can have clinically  relevant effects on the pharmacokinetics  and 
pharmacodynamics of a drug in various organs and tissues by controlling  its absorption, 
distribution,  and elimination  (Giacomini,  Huang, et al. 2010; Giacomini  and Huang 2013).  In 
contrast to drug metabolizing  enzymes that are largely expressed in the liver and small intestines, 
transporters are expressed in tissues throughout the human body and govern the access of 
endogenous and exogenous substances to various sites in the body.  In concert with metabolizing 
enzymes, transporters can govern a drugs disposition  and pharmacological  action.  Conversely, 
a drug can also modulate transporter expression or activity, resulting in altered disposition  of 
endogenous (e.g., creatinine, glucose) or exogenous substances.   
 
Several transporters interact with drugs in clinical  use (Giacomini,  Huang, et al. 2010; Giacomini 
and Huang 2013), for example: 
 
  P-glycoprotein  (P-gp or Multi-drug  Resistance 1 (MDR1) protein) 
  Breast cancer resistance protein (BCRP) 
  Organic anion transporting polypeptide  1B1/1B3 (OATP1B1/OATP1B3) 
  Organic anion transporter 1/3 (OAT1/OAT3)  
  Multidrug  and toxin extrusion (MATE) proteins (MATE1/MATE2-K) 
  Organic cation transporter 2 (OCT2)  
 
Understanding whether the drug is a substrate or inhibitor  of these key transporters can explain 
some clinical  consequences, such as increased toxicity  or altered efficacy, that result from altered 
tissue distribution  of a drug that is a substrate of a transporter.  This section focuses on 
transporters that have clinical  evidence suggesting their involvement  in drug interactions 
(Giacomini,  Huang, et al. 2010; Brouwer, Keppler, et al. 2013; Giacomini  and Huang 2013; 
Tweedie, Polli,  et al. 2013; Zamek-Gliszczynski,  Lee, et al. 2013).  The sponsor should evaluate 
the interactions between investigational  drugs acting as substrates and/or inhibitors  of these 
transporters as outlined  below.  The timing  of the in vitro evaluation  of each transporter may 
vary depending  on the therapeutic indications  of the investigational  drug.  For example, if the 
  8  Contains Nonbinding Recommendations 
 
intended population  is likely  to use statins, the sponsor should examine the potential of the 
investigational  drug to interact with OATP1B1/1B3 before initiation  of clinical  studies in 
patients.  If in vitro experiments indicate a low potential for an interaction between the 
transporter and investigational  drug, subjects taking statins may be included in clinical  studies to 
better represent the intended patient population.   
 
A.  Determining if the Investigational Drug is a Substrate of the Transporters P-
gp and BCRP 
 
P-gp and BCRP are expressed in various tissues including  the gastrointestinal  tract, liver, kidney, 
and brain.  Thus, both transporters have the potential to impact the oral bioavailability,  the tissue 
distribution,  and the hepatic and renal elimination  of substrates.  
 
1.  Conducting In Vitro Studies 
 
Sponsors should evaluate most investigational  drugs in vitro to determine whether they are 
substrates of P-gp and BCRP using the experimental systems described in the appendix, section 
VII.B.  P-gp and BCRP are generally not expected to impact the oral bioavailability  of highly 
permeable and highly  soluble drugs.  In vitro assessment of these drugs as P-gp or BCRP 
substrates is not suggested unless there are potential safety concerns with the drug distributing 
into tissues (e.g., the kidney and brain).  See the 2017 FDA guidance for industry entitled Waiver 
of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage 
Forms Based on a Biopharmaceutics Classification System to determine if the investigational 
drug can be classified as highly  permeable and/or highly  soluble (e.g., biopharmaceutics 
classification  system class 1 drugs). 
 
2.  Data Analysis and Interpretation 
 
The following  results suggest that an investigational  drug is an in vitro P-gp substrate: 
 
  A net flux ratio (or efflux ratio (ER)) of  2 for an investigational  drug in cells that 
express P-gp (e.g., Caco-2 cells or transfected cells overexpressing P-gp)5 
  
  A flux that is inhibited  by at least one known P-gp inhibitor  at a concentration at least 10 
times its K  or IC  (e.g., the ER decreases to < 50% of the ER in the absence of inhibitor 
i 50
or the flux reduced to unity).   
 
When using Caco-2 cells that express multiple  efflux transporters, the sponsor should use more 
than one P-gp inhibitor  to determine the specificity of the efflux.  The sponsor may use a net flux 
ratio cutoff other than 2 or a specific relative ratio to positive controls if prior experience with 
the cell system justifies these alternative methods.   
 
                                              
5 The ER can be calculated as the ratio of the basal to apical (B-A) transport rate to the apical to basal (A-B) 
transport rate.  The net flux ratio can be calculated as the ratio of the ER between cells expressing the transporter of 
interest to cells not expressing the transporter. 
 
  9  Contains Nonbinding Recommendations 
 
If in vitro studies indicate that a drug is a P-gp substrate, the sponsor should consider whether to 
conduct an in vivo study based on the drugs safety margin, therapeutic index, and likely 
concomitant medications that are known P-gp inhibitors  in the indicated patient population  (see 
the January 2020 FDA guidance for industry entitled Clinical Drug Interaction Studies  
Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions).  
 
The sponsor may also use the above procedures to determine whether the drug is a BCRP 
substrate by using known BCRP inhibitors.   If in vitro studies indicate that a drug is a BCRP 
substrate, the sponsor should consider whether to conduct an in vivo study based on the drugs 
safety margin, therapeutic index, and likely  concomitant medications that are known BCRP 
inhibitors  in the indicated patient population  (see the January 2020 FDA guidance for industry 
entitled Clinical Drug Interaction Studies  Cytochrome P450 Enzyme- and Transporter-
Mediated Drug Interactions).  
 
B.  Determining if the Investigational Drug is a Substrate of the Hepatic 
Transporters OATP1B1 and OATP1B3 
 
OATP1B1 and OATP1B3 are key uptake transporters expressed on the sinusoidal  membrane of 
hepatocytes and play an important  role in the hepatic uptake of various drugs.   
 
1.  Conducting In Vitro Studies 
 
If in vitro studies or human/animal  absorption,  distribution,  metabolism,  and/or excretion 
(ADME) data suggest that an investigational  drugs hepatic uptake or elimination  is significant 
(i.e., the drugs clearance through hepatic metabolism  or biliary  secretion is  25% of the total 
drugs clearance), or the drugs uptake into the liver is clinically  important (e.g., for 
biotransformation  or to exert a pharmacological  effect), the sponsor should evaluate the 
investigational  drug in vitro to determine whether it is a substrate for the hepatic uptake 
transporters OATP1B1 and OATP1B3 (see the appendix, section VII.B).  Other factors to be 
considered include the drugs physiological  properties, e.g., low passive membrane permeability, 
high hepatic concentrations relative to other tissues, organic anion/charged at physiological  pH, 
which support the importance of active uptake of the drug into liver.    
 
2.  Data Analysis and Interpretation 
 
An investigational  drug is considered an in vitro substrate for OATP1B1 or OATP1B3 if:  (1) the 
uptake of the drug in OATP1B1- or OATP1B3-transfected cells is  2-fold of the drugs uptake 
in empty vector-transfected cells; and (2) a known inhibitor  (e.g., rifampin)  can decrease the 
drugs uptake to  50% at a concentration at least 10 times that of the K  or IC .  The sponsor 
i 50
may justify alternative cutoff ratios based on its prior experience with the cell system. 
 
If in vitro studies indicate that a drug is an OATP1B1 or OATP1B3 substrate, the sponsor should 
consider whether to conduct an in vivo study based on the drugs safety margin, therapeutic 
index, and likely  co-medications  that are known OATP1B1 or OATP1B3 inhibitors  in the 
indicated patient populations  (see the 2019 FDA guidance for industry entitled Clinical Drug 
Interaction Studies  Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions). 
  10  Contains Nonbinding Recommendations 
 
 
C.  Determining if the Investigational Drug is a Substrate of the Renal 
Transporters OAT, OCT, and MATE 
 
OAT1, OAT3, and OCT2 are renal transporters expressed on the basolateral membrane of the 
renal proximal  tubule.  MATE1 and MATE2-K are expressed on the brush border membrane.  
All the aforementioned renal transporters can play a role in the active renal secretion of 
investigational  drugs.   
 
1.  Conducting In Vitro Studies 
 
If the investigational  drugs ADME data suggest that active renal secretion is significant  for a 
drug (i.e., active secretion of the parent drug by the kidney is  25% of the systemic clearance), 
the sponsor should evaluate the drug in vitro to determine whether it is a substrate of OAT1/3, 
OCT2, MATE1 and MATE2-K (see appendix, section VII.B).  See Figure 5 for the equation to 
calculate active secretion. 
 
Figure 5: An Equation to Calculate Active Secretion* 
 
Active secretion = CL   (f   GFR) 
r u,p
 
Cl is the renal clearance. 
r 
f is the unbound fraction in plasma. 
u,p 
GFR is the glomerular filtration rate. 
 
*This equation is valid assuming that there is no re-absorption (e.g., no active re-absorption and passive re-
absorption is equal to passive secretion).  The GFR is set as 125 mL/min for subjects with normal renal function if 
the GFR is not measured. 
 
2.  Data Analysis and Interpretation 
 
The investigational  drug is an in vitro substrate for the above renal transporters if:  (1) the ratio 
of the investigational  drugs uptake in the cells expressing the transporter versus the drugs 
uptake in control cells (or cells containing  an empty vector) is  2; and (2) a known inhibitor  of 
the transporter decreases the drugs uptake to  50% at a concentration at least 10 times its K or 
i 
IC .  The sponsor may justify alternative cutoff ratios based on its prior experience with the cell 
50
system. 
 
If in vitro studies indicate that a drug is a substrate of one or more of these renal transporters, the 
sponsor should consider whether to conduct an in vivo study based on the drugs safety margin, 
therapeutic index, and likely  concomitant medications  that are known inhibitors  of these renal 
transporters in the indicated patient populations  (see the January 2020 FDA guidance for 
industry entitled Clinical Drug Interaction Studies  Cytochrome P450 Enzyme- and 
Transporter-Mediated Drug Interactions). 
 
D.  Determining if the Investigational Drug is an Inhibitor of a Transporter  
 
  11  Contains Nonbinding Recommendations 
 
1.  Conducting In Vitro Studies 
 
The sponsor should conduct in vitro studies to evaluate whether an investigational  drug is an 
inhibitor  of P-gp, BCRP, OATP1B1, OATP1B3, OCT2, MATEs (MATE1, MATE2-K), OAT1, 
and OAT3 (see appendix,  section VII.B for considerations regarding in vitro systems).  
 
2.  Data Analysis and Interpretation 
 
P-gp and BCRP:  The sponsor should conduct studies to determine if an investigational  drug 
inhibits  the efflux ratio or net flux of a known P-gp or BCRP substrate in Caco-2, P-gp- or 
BCRP-overexpressed cells or inhibits  uptake of substrate when membrane vesicles are used, and 
determine the drugs inhibition  potency (i.e., IC  or K ).  The investigational  drug has the 
50 i
potential to inhibit  P-gp or BCRP in vivo if the investigational  drug is administered orally,  and 
the I /IC  or K  10 where I  = dose of inhibitor/250  mL.  If a metabolite of the drug is an 
gut  50 i gut
inhibitor  or the investigational  drug is administered by parenteral route, in vivo inhibition  of P-
gp or BCRP may occur if the I /IC  or K   0.1, where I  is the C  of the metabolite or the 
1 50 i 1 max
inhibitor  drug.  These cutoff values are based on a limited  dataset (Zhang, Zhang, et al. 2008; 
Tachibana, Kato, et al. 2009; Agarwal, Arya, et al. 2013; Ellens, Deng, et al. 2013).  The sponsor 
may calibrate its internal in vitro systems with known inhibitors  and non-inhibitors  and propose a 
different cutoff value with proper justification. 
 
If in vitro studies indicate that a drug is a P-gp or BCRP inhibitor,  the sponsor should consider 
whether to conduct an in vivo study based on likely  concomitant medications that are known P-
gp or BCRP substrates in the indicated patient populations  (see the January 2020 FDA guidance 
for industry entitled Clinical Drug Interaction Studies  Cytochrome P450 Enzyme- and 
Transporter-Mediated Drug Interactions).  
 
OATP1B1 and OATP1B3:  The sponsor should conduct studies to determine the inhibition 
potency (i.e., IC  or K ) of the investigational  drug on the uptake of a known OATP1B1 or 
50 i
OATP1B3 substrate in cells overexpressing the relevant transporter.  Time-dependent inhibition 
has been demonstrated for a few OATP1B1/3 inhibitors  (Amundsen, Christensen, et al. 2010; 
Gertz, Cartwright, et al. 2013; Izumi,  Nozaki, et al. 2015; Pahwa, Alam, et al. 2017).  Sponsors 
may consider adding a pre-incubation  step as part of assay validation  when determining  IC  
50
values for an investigational  drug.  The investigational  drug has the potential to inhibit 
OATP1B1/3 in vivo if the R value (as described in Figure 6 below) is > 1.1. 
 
 
 
 
 
 
 
 
 
 
 
  12  Contains Nonbinding Recommendations 
 
 
Figure 6: Equation to Calculate the R Value of the Investigational Drug to Determine the 
Potential to Inhibit OATP1B1/3* 
 
R=1+ ((f   I )/IC ) 1.1 
u,p in,max 50
 
f  is the unbound fraction in plasma. 
u,p
IC  is the half-maximal inhibitory concentration.   
50
I  is the estimated maximum plasma inhibitor concentration at the inlet to the liver.  It is calculated as: 
in,max
 
I  = I  +(F F k Dose)/Q /R  
in,max max a g a h B
 
F is the fraction absorbed.
a     
F  is the intestinal availability. 
g
k  is the absorption rate constant. 
a
Q  is the hepatic blood flow rate. 
h
R  is the blood-to-plasma concentration ratio. 
B
 
*If unknown, Fa= 1, Fg = 1 and k  = 0.1/min can be used as a worst-case estimate.   
a
Considering uncertainties in the protein binding measurements, the unbound fraction (f ) should be set to 1% if 
u,p
experimentally determined to be less than 1%. 
 
The cutoff value described in Figure 6 is based on limited  published  data (Yoshida, Maeda, et al. 
2012; Tweedie, Polli,  et al. 2013; Vaidyanathan, Yoshida, et al. 2016).  Sponsors may calibrate 
their internal in vitro systems with known inhibitors  and non-inhibitors  of these transporter 
systems and propose a specific cutoff value with proper justification. 
 
If in vitro studies indicate that a drug is an OATP1B1 or OATP1B3 inhibitor,  the sponsor should 
consider whether to conduct an in vivo study based on whether the likely  concomitant 
medications used in the indicated patient populations  are known OATP1B1or OATP1B3 
substrates (see the January 2020 FDA guidance for industry entitled Clinical Drug Interaction 
Studies  Cytochrome P450 Enzyme- and Transporter--Mediated Drug Interactions).  
 
OAT, OCT, and MATE:  Sponsors should conduct studies to determine the inhibition  potency 
(i.e., IC  or K ) of the investigational  drug on the uptake of a known substrate for renal 
50 i
transporters (i.e., OAT1, OAT3, OCT2, MATE1, and MATE2-K) in cells overexpressing these 
transporters.  The investigational  drug has the potential to inhibit  these transporters in vivo if the 
I /IC  value is  0.1.6  These cutoff values are based on limited  data (Dong, Yang, et al. 
max,u 50
2016a; Dong, Yang, et al. 2016b).  Sponsors may calibrate their unique in vitro systems with 
known inhibitors  and non-inhibitors  of these transporter systems and propose a different cutoff 
value with proper justification.   Creatinine is also a substrate for OCT2, MATEs, and OAT2 
(Lepist, Zhang, et al. 2014).  Elevated serum creatinine levels observed in clinical studies could 
be due to inhibition  of these transporters by the investigational  drug (Chu, Bleasby, et al. 2016; 
Mathialagan, Rodrigues,  et al. 2017; Arya, Yang, et al. 2014).  Confirmation  of the mechanism 
of an increase in serum creatinine with the investigational  drug requires additional  evidence such 
as clinical  mechanistic studies.  
                                              
6 Considering uncertainties in the protein binding measurements, the unbound fraction should be set to 1% if 
experimentally determined to be less than 1%. 
  13  Contains Nonbinding Recommendations 
 
 
If in vitro studies indicate that a drug is an inhibitor  of these renal transporters, the sponsor 
should consider whether to conduct an in vivo study based on whether the likely  concomitant 
medications used in the indicated patient populations  are known substrates of these renal 
transporters (see the January 2020 FDA guidance for industry entitled Clinical Drug Interaction 
Studies  Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions).   
 
E.  Determining if the Investigational Drug is an Inducer of a Transporter  
 
Certain transporters such as P-gp are induced through mechanisms similar to those for CYP 
enzymes (e.g., by activation of specific nuclear receptors).  Because of these similarities, 
information  from CYP3A induction  studies can inform P-gp induction studies (see the January 
2020 FDA guidance for industry entitled Clinical Drug Interaction Studies  Cytochrome P450 
Enzymes and Transporters-Mediated Drug Interactions).  At this time, the in vitro methods to 
evaluate the induction  of P-gp and other transporters are not well established, therefore 
recommendations for in vitro evaluation of investigational  drugs as transporter inducers are not 
provided. 
 
 
V.  EVALUATING THE DDI POTENTIAL OF METABOLITES 
 
Sponsors should evaluate the DDI potential of an investigational  drugs metabolites for their 
impact on the drugs safety and efficacy using a risk-based assessment that considers safety 
margins, likely  concomitant medications,  and therapeutic indications.   
 
A metabolite with significant  plasma exposure or pharmacological  activities may need to be 
evaluated for its DDI potential as a substrate or as a perpetrator of metabolizing  enzymes (see 
sections below).  In vitro studies normally  use a synthesized or purified metabolite standard.  
Alternative methods are acceptable if the sponsor can justify that the DDI potential of the 
metabolites can be adequately assessed (Callegari, Kalgutkar, et al. 2013; Yu and Tweedie 2013; 
Yu, Balani, et al. 2015).  If basic models suggest that the metabolite(s) may have in vivo DDI 
liability  and a static or dynamic mechanistic modeling  approach (e.g., PBPK) is used for DDI 
assessment of a drug, metabolite(s) should be incorporated in these models.  
 
Published data have shown that some Phase II metabolites can be better substrates (more polar 
than the parent) or inhibitors  of various transporters leading to a higher chance of DDIs than the 
parent drug (Zamek-Gliszczynski  et al, 2014).  Therefore, the DDI potential of a metabolite  as a 
substrate or a perpetrator of major drug transporters should be assessed on a case-by-case basis.  
The same principles  and strategies mentioned above for the parent drug should be applied where 
applicable. 
 
A.  Metabolite as a Substrate 
 
1.  Conducting In Vitro Studies 
 
  14  Contains Nonbinding Recommendations 
 
The risk of a clinically  relevant DDI through altered formation or elimination  of metabolites 
should be investigated if changes in metabolite exposure levels may result in clinically 
meaningful  alteration of efficacy or safety in vivo.  The risk of a DDI when the metabolite acts 
as a substrate should be evaluated for a pharmacologically  active metabolite that contributes to  
50% of the overall activity.  Both the in vitro receptor potency and the in vivo unbound systemic 
exposure (expressed in molar unit) of a metabolite relative to the parent drug need to be taken 
into consideration  when evaluating the contribution  of the metabolite to efficacy.  If plasma 
protein binding  of the parent drug and the metabolite is high, it is preferred to determine their 
protein binding  in the same study to reduce inter-study variability.   If available, data related to 
target tissue distribution  of parent drug and the metabolite may need to be considered when 
evaluating the contribution  of metabolite to in vivo efficacy.  
 
2.  Data Analysis and Interpretation 
 
The sponsor should consider in vivo DDI studies of the metabolite based on in vitro assessments 
using the same strategies as those for the parent drugs (see section III.A). 
 
B.  Metabolite as an Inhibitor 
 
1.  Conducting In Vitro Studies 
 
If in vitro assessments suggest that the parent drug inhibits  major CYP enzymes and transporters 
and in vivo DDI studies are warranted, in vitro assessments of metabolites as enzyme or 
transporter inhibitors  may not be needed because the in vivo inhibition  potential of metabolites 
would be evaluated in vivo along with the parent drug, unless clinically  relevant exposures of the 
metabolite cannot be adequately represented in the in vivo DDI study (i.e., the study duration 
does not allow the metabolite to accumulate).  However, if in vitro assessments suggest that the 
parent drug alone will not inhibit  major CYP enzymes or transporters, in vivo DDIs caused by 
metabolites may still  be possible.  In this situation,  the sponsor should evaluate the in vitro 
inhibition  potential of a metabolite on CYP enzymes taking into account the systemic exposure 
(in molar unit) and polarity (e.g., measured or predicted LogP, the elution  order on the 
chromatogram of reverse phase-high performance liquid  chromatography) of the metabolite 
relative to the parent drug.  The sponsor should conduct an in vitro CYP enzyme inhibition  study 
if:  (1) the metabolite is less polar than the parent drug and the AUC   25% of AUC ; 
metabolite parent
or (2) the metabolite is more polar than the parent dug and the AUC   AUC .  A 
metabolite parent
lower cut-off value for the metabolite-to-parent  AUC ratio may also be considered for 
metabolites with structural alerts for potential mechanism-based inhibition  (Orr, Ripp, et al. 
2012; Yu and Tweedie 2013; Yu, Balani, et al. 2015). 
   
2.  Data Analysis and Interpretation 
 
Based on the results of in vitro DDI assessments of the metabolite,  the sponsor should consider 
an in vivo DDI study of the metabolite using the same strategies as those for the parent drug 
except that R  may not be applicable (see section III.B).   
1,gut
 
 
  15  Contains Nonbinding Recommendations 
 
 
VI.  LABELING RECOMMENDATIONS   
 
The Prescribing Information must include a summary of drug interaction information  that is 
essential for the safe and effective use of the drug product by the health care provider and must 
be based on data derived from human experience whenever possible.7  In the absence of clinical 
information,  the sponsor should include in vitro information  regarding the characterization of 
metabolic and transporter pathways as well as PK interactions between the drug and other 
prescription drugs, over-the-counter drugs, classes of drugs, dietary supplements, and foods or 
juices (including  inhibition,  induction,  and genetic characteristics) in the Prescribing 
Information, if clinically  significant.  In addition,  the results of pertinent in vitro studies that 
establish the absence of an effect must be included.8  In vitro information  that has been 
superseded by clinical  information  should not be included  in the Prescribing Information unless 
it is essential to understanding the clinical  results. 
 
In vitro information  should generally be placed under the 12.3 Pharmacokinetics subsection of 
the CLINICAL PHARMACOLOGY section.  In rare cases, the clinical significance  of the in 
vitro information  may require placement in other sections of the Prescribing Information (e.g., 
BOXED WARNING, CONTRAINDICATIONS, WARNINGS AND PRECAUTIONS, and/or 
DRUG INTERACTIONS sections). 
 
See the following  FDA guidances for industry for labeling  recommendations relevant to drug 
metabolism  and transporter pathways as well as clinical  DDIs: 
 
  Clinical Pharmacology Labeling for Human Prescription Drug and Biological Products 
 Considerations, Content, and Format (December 2016) 
 
  Clinical Drug Interaction Studies  Cytochrome P450 Enzyme- and Transporter-
Mediated Drug Interactions (January 2020) 
 
                                              
7 21 CFR 201.56(a)(3). 
 
8 21 CFR 201.57(c)(13)(c)(i)(C). 
 
  16   
Contains Nonbinding Recommendations 
VII.  APPENDICES 
 
A.  Evaluating Metabolism-Based Drug Interactions In Vitro 
 
Various hepatic in vitro systems can be used to evaluate the drug interaction potential of an 
investigational  drug, including:   
 
(1) Subcellular  human liver tissue fractions such as reconstituted microsomal  systems, 
supernatants after 9000 g centrifugation  of liver homogenate (S9), and cytosol (adding 
appropriate co-factors as necessary) 
 
(2) Recombinant human CYP enzymes in various expression systems that can identify the 
production  of individual  drug metabolites and the involvement  of certain classes of enzymes 
 
(3) Human liver tissues, including  freshly prepared hepatocytes and cryopreserved 
hepatocytes that preserve enzyme architecture and contain the full complement of Phase I 
and Phase II drug metabolizing  enzymes 
 
Although the main focus of this guidance is on CYP and hepatic metabolism,  sponsors should 
consider non-CYP, enzyme-based metabolism  (e.g., Phase II enzymes) and metabolism 
occurring in extra-hepatic tissues when relevant for their investigational  drugs. 
 
1.  Determining if the Investigational Drug is an Enzyme Substrate 
 
Drug metabolizing  enzyme identification  studies, often referred to as reaction phenotyping 
studies, are a set of in vitro experiments that identify the specific enzymes responsible for the 
metabolism  of a drug.  Along with other information  (e.g., in vivo pharmacokinetics,  enzyme 
polymorphism  or DDI data), in vitro phenotyping  data are often used to quantify elimination 
pathways of an investigational  drug.   
 
a.       Conducting metabolic pathway identification  experiments  
 
Metabolic pathway identification  experiments identify the number and structures of metabolites 
produced by a drug and whether the metabolic pathways are parallel or sequential.  These 
experiments use intact human liver systems (e.g., human hepatocytes), human liver 
microsomes, or recombinant enzyme systems.  Data obtained from metabolic pathway 
identification  experiments help to determine whether and how to conduct a reaction 
phenotyping  study.  
 
b.       Identifying  the enzymes that metabolize an investigational  drug 
 
The sponsor should conduct in vitro experiments to identify  specific metabolizing  enzymes that 
are involved in the metabolism  of an investigational  drug, preferably before first-in-human 
studies.  There are two widely used methods for identifying  the individual  CYP enzymes 
responsible for a drug's metabolism:   (1) the first method uses chemicals, drugs, or antibodies  as 
 
17 
01/21/20 
   
Contains Nonbinding Recommendations 
specific enzyme inhibitors  in human liver microsomes or hepatocytes (e.g., a pool of more than 
10 donors); and (2) the second method uses individual  human recombinant CYP enzymes.  The 
sponsor should consider the following  recommendations when performing reaction phenotyping 
experiments: 
 
  The sponsor should use both methods to identify  the specific enzymes responsible for a 
drug's metabolism.  
 
  When using individual  human recombinant CYP enzymes, the sponsor should consider 
the difference in the amount and enzyme activity of CYPs between the recombinant CYP 
enzyme systems and the human liver (Venkatakrishnan, von Moltke, et al. 2000; Chen, 
Liu, et al. 2011).  
 
  The in vitro system for these studies should:  (1) be robust and reproducible; and (2) be 
characterized with in vitro probe substrate to prove the activity of each enzyme.  A list of 
probe substrates can be found on the FDAs Web site on Drug Development and Drug 
Interactions.9   
 
  Whenever possible,  the sponsor should conduct all experiments with drug concentrations 
deemed appropriate by kinetic experiments, relevant to clinical  setting, and under initial 
rate conditions  (linearity  of metabolite  production rates with respect to time and enzyme 
concentrations).  The sponsor should conduct an adequate number of replicates (e.g., 
three or more replicates per drug concentration) in a single study. 
 
  When conducting an in vitro study to examine the contribution  of individual  CYP 
enzymes to the overall metabolism  of an investigational  drug, there are two widely used 
methods: measurement of parent drug depletion;  and measurement of metabolite 
formation.  For the latter method, it is desirable that all of the major metabolites have 
been identified  and quantified in metabolite formation experiments.  
 
  When conducting in vitro studies to examine the contribution  of individual  CYP enzymes 
to the formation of a specific metabolite,  the sponsor should measure the formation rate 
of the metabolite. 
 
  The sponsor should develop validated and reproducible analytical methods to measure 
levels of the parent drug and each metabolite.   
 
  The use of a radiolabeled  drug substrate is advantageous because samples can be 
analyzed using liquid  chromatography coupled with a radioactivity  detector and a mass 
spectrometer to identify and quantify drug-related species. 
                                              
9 A list of probe substrates: 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm0
93664.htm#table1. 
 
 
18 
01/21/20 
   
Contains Nonbinding Recommendations 
 
  The sponsor should separately evaluate individual  isomers of racemic drugs when it is 
important to understand the different disposition  characteristics of each isomer (e.g., 
when two isomers have different pharmacological  activities). 
 
  Most chemical inhibitors  are not specific for an individual  CYP enzyme.  The sponsor 
should verify the selectivity  and potency of inhibitors  in the same experimental 
conditions  using probe substrates for each CYP enzyme.  Commonly  used in vitro CYP 
enzyme inhibitors  can be found on the FDAs Web site on Drug Development and Drug 
Interactions.10  
 
  The sponsor should test the inhibitory  effect of an antibody to a CYP enzyme at 
sufficiently  low and high concentrations to establish a titration  curve and ensure the 
maximal inhibition  of a particular pathway (ideally resulting  in greater than 80 percent 
inhibition).   The sponsor should verify the effect of an antibody using probe substrates of 
each CYP isoform and with the same experimental conditions.   
 
2.  Determining if the Investigational Drug is an Enzyme Inhibitor or Inducer 
 
a.   Conducting in vitro enzyme inhibition  studies 
 
The potential of an investigational  drug to inhibit  CYP enzymes is usually investigated in human 
liver tissue systems using probe substrates to determine the inhibition  mechanisms (e.g., 
reversible or time-dependent inhibition (TDI)) and inhibition  potencies (e.g., K  for reversible 
i
inhibition,  and K  and k  for TDI).  The in vitro systems used for these studies include human 
I inact
liver microsomes, microsomes obtained from recombinant CYP-expression systems, or 
hepatocytes (Bjornsson, Callaghan, et al. 2003). 
 
Kinetic data from in vitro inhibition  studies of an investigational  drug can be used in quantitative 
models to predict the investigational  drugs effects on the pharmacokinetics of other drugs in 
humans.  These analyses inform the decision on whether to conduct an in vivo DDI study using 
sensitive enzyme index substrates (see section III.B.2).   
 
The sponsor should consider the following  recommendations when designing  an in vitro CYP 
inhibition  study: 
 
  A probe substrate should be selective (e.g., predominantly  metabolized  by a single 
enzyme in pooled human liver microsomes or recombinant CYPs) and have simple 
metabolic schemes (ideally,  the drug does not undergo sequential metabolism).  
                                              
10 Examples of in vitro selective inhibitors for P450-mediated metabolism: 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm0
93664.htm#table1-2 
 
 
19 
01/21/20 
   
Contains Nonbinding Recommendations 
Commonly  used in vitro probe substrates and their marker reactions can be found on the 
FDA Web site on Drug Development and Drug Interactions.11 
 
  The sponsor should use a validated and reproducible analytical assay to measure the 
formation of a probe substrates metabolite. 
   
  The in vitro system of choice for enzyme inhibition  should be robust and reproducible 
and include the appropriate strong probe inhibitors  as positive controls (see the FDAs 
Web site on Drug Development and Drug Interactions).12  Kinetic constants (K , IC , 
i 50
K , and/or k ) of the probe inhibitors  should be comparable to the range of literature-
I inact
reported values.  In vitro systems may be pooled human liver microsomes (e.g., pooled 
from more than 10 donors), pooled cryopreserved hepatocytes (e.g., pooled from more 
than 10 donors), or individual  microsomes expressing recombinant CYP enzymes.  To 
obtain inhibition  parameters, the sponsor may consider primary hepatocytes enriched 
with human plasma as an in vitro system that represents physiological  conditions  (Lu, 
Miwa, et al. 2007; Mao, Mohutsky,  et al. 2012). 
 
  The sponsor should first conduct experiments with a high concentration of test drug to 
study its inhibition  potential  on a particular enzyme (e.g., 50 times the unbound  C  or 
max
0.1 times the dose/250 mL).  However, the drug concentration should not exceed the 
drugs solubility  limits or cause deleterious effects in cell models (e.g., cytotoxicity).   If 
the initial  high concentration of the test drug is able to inhibit  a particular enzyme, the 
sponsor should test lower drug concentrations to calculate the drugs IC  or K  value.  
50 i
The sponsor should test at least four different concentrations of the investigational  drug 
with the probe substrate.    
 
  Typical experiments to determine the IC  value of a drug involve  incubating  the 
50
substrate at a concentration at or below its K  to more closely relate the inhibitors  IC  
m 50
to its K .  For K  determinations,  the sponsor should vary both the substrate and inhibitor 
i i
concentrations to cover ranges above and below the substrate's K  and the inhibitor's  K . 
m i
 
  Microsomal protein concentrations are usually less than 1 mg/mL.  The sponsor should 
correct for nonspecific binding  during the incubation  if this binding  is expected to 
influence the analysis of kinetic data.  Nonspecific binding  can be measured 
experimentally  (e.g., using equilibrium  dialysis or ultrafiltration)  (Hallifax and Houston 
                                              
11 Examples of in vitro marker reactions for P450-mediated metabolism and in vitro selective inhibitors for P450-
mediated metabolism: 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm0
93664.htm#table1 
 
12 Examples of in vitro selective inhibitors for P450-mediated metabolism: 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm0
93664.htm#table1-2 
 
 
20 
01/21/20 
   
Contains Nonbinding Recommendations 
2006) or predicted using in silico methods.  It is recommended to experimentally 
determine nonspecific binding  for highly  lipophilic  drugs (Gertz, Kilford, et al. 2008).  
 
  Because buffer strength, type, and pH can all significantly  affect the determination  of 
V  and K , the sponsor should use standardized assay conditions.    
max m
 
  In general, the sponsor should avoid any significant  depletion of the substrate or 
inhibitor.   However, when substrates have a low K , it may be difficult  to avoid substrate 
m
depletion  at low substrate concentrations.  In these circumstances, the sponsor should 
consider substrate depletion when determining  inhibition  kinetics. 
 
  The sponsor should choose an incubation  time and an enzyme amount that result in linear 
formation of the metabolite (at an initial  rate of the metabolite formation). 
 
  The sponsor should use any organic solvents at low concentrations (<1% 
(volume/volume)  and preferably < 0.5%) because some solvents can inhibit  or activate 
enzymes.  The experiment should include a solvent (vehicle) control, and when 
necessary, also a no-solvent control. 
 
  The sponsor can determine inhibition  kinetics according to appropriate mechanisms (e.g., 
reversible inhibition  or TDI).  For a reversible inhibitor,  half of the measured IC  can be 
50
used as an estimate of K , if the substrate concentration used is equal to its K  for the 
i m
enzyme (Haput, Kazmi, et al. 2015).  
 
  The sponsor should routinely  study TDI in standard in vitro screening protocols by pre-
incubating  the investigational  drug (e.g., for at least 30 min) before adding any substrate.  
Any significant  time-dependent and co-factor-dependent (e.g., NADPH for CYPs) loss of 
initial  product formation may indicate TDI.  In these circumstances, the sponsor should 
conduct definitive  in vitro studies to obtain TDI parameters (i.e., k  and K ) (Grimm, 
inact I
Einolf,  et al. 2009).   
 
b.   Evaluating enzyme induction  in vitro 
 
The sponsor can investigate the potential of an investigational  drug to act as an inducer of CYP 
enzymes in plateable, cryopreserved or freshly isolated, human hepatocytes.  Other, alternative in 
vitro systems such as immortalized  hepatic cell lines may be used and are acceptable methods to 
determine the CYP induction  potential of investigational  drugs.  Cell receptor assays may be 
used, but the results from these studies are considered supportive rather than definitive  in nature.  
The sponsor should justify why any alternative in vitro system is appropriate for the study as 
well as the method to interpret the data.  Acceptable study endpoints include mRNA levels 
and/or enzyme activity levels using a probe substrate (Fahmi and Ripp 2010; Einolf,  Chen, et al. 
2014).  A major challenge of measuring only the enzymes activity is that the induction  may be 
masked in the presence of concomitant inhibition.   Transcriptional  analysis through the 
measurement of mRNA levels may address this challenge.  Regardless of which in vitro system 
 
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and endpoint  are chosen, the sponsor should validate the system to show that all major CYP 
enzymes are functional  and inducible  with positive controls.     
 
When using in vitro systems to study enzyme induction,  the sponsor should consider the 
following  recommendations: 
 
  The sponsor should evaluate the ability  of an investigational  drug to induce the major 
CYPs, including  CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4. 
 
  The sponsor should individually  evaluate CYP1A2, CYP2B6, and CYP3A4 first because 
they are induced via different nuclear receptors. 
 
  Activation of a nuclear receptor, PXR, may lead to co-induction  of CYP3A4 and CYP2C 
enzymes.  Thus, a negative in vitro result for CYP3A4 induction  eliminates  the need for 
additional  in vitro or in vivo induction  studies for CYP3A4 and CYP2C enzymes.  If in 
vitro CYP3A4 induction  results are positive and suggest a clinical  DDI study is 
warranted, then the sponsor should evaluate the ability  of the investigational  drug to 
induce CYP2C8, CYP2C9, and CYP2C19 either in vitro or in vivo.   
 
  The in vitro system of choice to evaluate enzyme induction  should be robust and 
reproducible  and include appropriate inducers and non-inducers as positive and negative 
controls (see the FDAs Web site on Drug Development and Drug Interactions).13  Once 
the system is validated,  a non-inducer (as negative control) can be included as optional in 
the test study to evaluate an investigational  drug.  When applicable,  the sponsor should 
conduct pilot experiments to establish a test system (e.g., a particular lot of cryopreserved 
human hepatocytes) for routine studies of CYP induction  (Fahmi, Kish et al. 2010; Fahmi 
and Ripp 2010; Einolf,  Chen et al. 2014).  
 
  Drug concentrations investigated should span the range of therapeutic exposures.  If the 
drug solubility  permits, this range of drug concentrations should include at least one 
concentration that is an order of magnitude greater than the maximum  unbound steady-
state plasma drug concentration in vivo.  The sponsor should conduct three replicate 
experiments per drug concentration.  If the drug is highly  bound to human plasma 
protein, and the medium contains serum (or proteins, e.g., bovine serum albumin),  or if 
the drug may have significant  non-specific binding,  sponsors are encouraged to measure 
the concentration of unbound test drug in the medium of incubation.   Correction for 
binding  may be needed when interpreting the data, to help predict the magnitude of a 
clinical  DDI (Sun, Chothe, et al. 2017; Chang, Yang, et al. 2017).  
                                              
13 For more information, see: 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm0
93664.htm. 
 
 
 
 
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  When primary human hepatocytes are used, the sponsor should use preparations from at 
least three donors.  If the result from at least one donors hepatocytes exceeds the 
predefined threshold, the sponsor should consider the drug an inducer in vitro and 
conduct a follow-up  evaluation. 
 
  The sponsor should demonstrate that the experimental approach can identify the absence 
and presence of the investigational  drugs induction  potential  and avoids false negative 
predictions with the selected system and endpoints. 
 
  Incubation of an investigational  drug usually lasts for 48-72 hours to allow complete 
induction  to occur.  Incubations include a daily addition  of the investigational  drug, and 
the medium containing  the drug is changed regularly.  The optimal  time course for 
incubation  should allow the sponsor to detect enzyme induction  without causing cell 
toxicity.  The sponsor should justify the rationale for shorter incubation  times. 
 
  Actual concentrations of the drug in the system are important  for extrapolating  in vitro 
results to in vivo scenarios.  Sponsors are encouraged to measure concentrations of the 
parent drug in the medium,  preferably at several time points during the last day of the 
incubation (Sun, Chothe, et al. 2017; Chang, Yang, et al. 2017). 
 
B.  Evaluating Transporter-Mediated Drug Interactions In Vitro 
 
In vitro transporter assays can determine whether an investigational  drug is a substrate or 
inhibitor  of a particular transporter.  Coupled with appropriate in vitro-to-in  vivo extrapolation 
methods (see section IV), these assays can determine if the sponsor should conduct an in vivo 
drug interaction study.  Currently, in vitro methods to evaluate transporter induction  are not well 
understood. 
 
1.  General Considerations When Using In Vitro Experimental Systems to Evaluate 
Transporter-Mediated Drug Interactions 
 
a.   Selecting an in vitro test system 
 
The sponsor should choose an in vitro test system that is suitable for a specific transporter, such 
as a membrane vesicle system, a polarized cell-based bidirectional  assay for efflux transporters, 
or a cell-based assay for uptake transporters.  Selecting the in vitro model may depend on the 
purpose of the study and the questions to be addressed.  Table 1 summarizes examples of in vitro 
systems to investigate potential  transporter-mediated drug interactions with an investigational 
drug as either a substrate or an inhibitor  of a specific transporter. 
 
 
 
 
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Table 1.  Examples of In Vitro Systems to Investigate Transporter-Mediated Drug 
Interactions 
Transporter  In Vitro Systems 
ABC Transporters 
BCRP, P-gp  Caco-2 cells, commercial or in-house membrane vesicles, knock-
  out/down cells, transfected cells (MDCK, LLC-PK , etc.) 
1
Solute Carrier (SLC) Transporters 
OATP1B1/3  Hepatocytes, transfected cells (CHO, HEK293, MDCK, etc.) 
OAT1/3, OCT2  Transfected cells (CHO, HEK293, MDCK, etc.)  
MATEs*  Commercial or in-house membrane vesicles, transfected cells (CHO, 
HEK293, MDCK)  
CHO:  Chinese hamster ovary cell 
HEK293:  human embryonic kidney 293 cell 
LLC-PK1:  Lilly Laboratory cancer porcine kidney 1 cell 
MDCK:  Madin-Darby canine kidney cell 
 
*The function of MATEs depends on the driving force from oppositely directed proton gradient; therefore, the 
appropriate pH of MATE assay system should be employed.  
 
Details regarding each in vitro test system to investigate transporter-mediated drug interactions 
are described below:  
 
  Membrane vesicles: 
 
-  In vitro systems using inside-out  membrane vesicles evaluate whether an 
investigational  drug is a substrate or inhibitor  of P-gp or BCRP but may fail to 
identify highly  permeable drugs or highly  non-specific binding  drugs as 
substrates. 
 
-  P-gp and BCRP assays using membrane vesicles should directly measure the 
adenosine triphosphate (ATP)-dependent, transporter-mediated uptake of drugs 
with control vesicles for comparisons.   
 
  Bi-directional  transport assays with cell-based systems: 
 
-  Bidirectional  assays evaluate whether an investigational  drug is a substrate or 
inhibitor  of efflux transporters such as P-gp or BCRP. 
 
-  Cell monolayers should be grown on semi-porous filters in a device with apical 
(AP) and basolateral (BL) chambers. 
 
-  The sponsor should add the test drug to either the AP or BL side of the cell 
monolayer and measure the amount of the drug permeating through the cell 
monolayers in the receiver chamber over time. 
 
 
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-  The sponsor should calculate the apparent permeability  (P ) of the drug in both 
app
the APBL (absorption) and BLAP (efflux) directions and calculate an efflux 
ratio from the ratio of BLAP to APBL P  values for the substrate.  
app
 
-  When using transfected cell lines, the sponsor should compare the efflux ratios of 
the transfected cell line with appropriate control conditions  to account for 
endogenous transporter activity and non-specific binding.   One approach is to 
compare the efflux ratios from transfected cell line to the parental or empty 
vector-transfected cell line.  
 
  Uptake assays with cell-based systems: 
 
-  Uptake assays evaluate whether an investigational  drug is a substrate or inhibitor 
of SLC transporters such as OCTs, OATs, OATPs and MATEs. 
 
-  When transfected cell lines are used, the sponsor should compare the drug uptake 
in the transfected cell line to the parental or empty vector-transfected cell line. 
 
-  The sponsor may use human hepatocytes or hepatic cell lines in suspension, 
plated, or sandwich-cultured  assays.  
 
b.   Determining in vitro test conditions 
 
The sponsor should validate the model system and experimental conditions,  including  culture 
and transport assay conditions,  within the same laboratory.  The sponsor should include 
appropriate positive controls in the test study to ensure the validity  of the studys results.  The 
sponsor should consider the following  recommendations during assay development  and 
validation: 
 
  The sponsor should develop and optimize  transport assays to ensure consistent 
transporter function (e.g., uptake, efflux) with control experiments (e.g., positive controls 
for substrates/inhibitors,  non-transfected control cells).  
 
  The sponsor should verify the functionality  of the assay by conducting studies with 
known positive and negative controls (see the FDAs Web site on Drug Development and 
Drug Interactions14). 
 
  The sponsor should characterize the following  conditions  whenever applicable:  the 
source of the membrane vesicles or cells, cell culture conditions  (e.g., cell passage 
number, seeding density, monolayer age), probe substrate/inhibitor  concentrations, 
                                              
14 For more information, see: 
http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm09
3664.htm. 
 
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incubation  time, buffer/pH conditions,  sampling  interval, and methods for calculating 
parameters such as the IC , K , and K .  
50 i m
 
  The sponsor should use any organic solvents at low concentrations (< 1% volume/volume 
and preferably < 0.5%) because some solvents can affect cell integrity  or transporter 
function.  The experiment should include a solvent (vehicle) control, and when necessary, 
also a no-solvent control.  
 
  For both substrate and inhibitor  studies, the sponsor should demonstrate sufficient total 
recovery of the drugs.  If the total recovery falls below a pre-specified boundary set by 
the laboratories, the nature and extent of the effects leading to a decrease of recovery 
should be investigated and considered when evaluating the potential DDI risk of a test 
drug.  The sponsor should attempt to assess the impact of the following  factors:  
 
-  The stability  of the test drug for the duration of study  
-  The effect of nonspecific binding  of the test drug to cells/apparatus  
-  The test drugs solubility  limits 
-  The effect of adding serum or proteins to the media 
 
  The sponsor should conduct transport studies under linear transport rate conditions.   
 
  The sponsor should establish laboratory acceptance criteria for study results (e.g., 
monolayer integrity,  passive permeability,  efflux or uptake of probe substrates, K for a 
m 
probe substrate, IC  for probe inhibitor).   The K  value of a probe substrate or the IC  
50 m 50
value of a probe substrate or inhibitor  should be comparable to literature-reported values.   
 
  The substrate (which may be the test drug) should be readily measured with no 
interference from the assay matrix. 
 
2.  Determining if the Investigational Drug is a Transporter Substrate 
 
When using in vitro systems to study whether an investigational  drug is a substrate of 
transporters, the sponsor should consider the following  factors: 
 
  The sponsor should evaluate concentrations of the test drug in the range of clinically 
relevant concentrations. 
   
  Several factors may limit  test drug concentrations in the in vitro assays, including 
aqueous solubility,  nonspecific binding  to the culture vessel, and cytotoxicity. 
 
  If the in vitro system expresses multiple  transporters, the sponsor should conduct 
additional  experiments to confirm the findings with two or more known potent inhibitors. 
 
 
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3.  Determining if the Investigational Drug is a Transporter Inhibitor 
 
When using in vitro systems to study whether an investigational  drug is an inhibitor  of 
transporters, the following  should be considered: 
 
  The sponsor should start with a high concentration of the test drug, at least an order of 
magnitude higher than the drugs clinically  relevant concentration.  However, the drug 
concentration should not exceed the drugs solubility  limits  or cause deleterious effects 
(e.g., cytotoxicity)  in the cells.  Because transporters are expressed in different locations 
in tissues, the sponsor should consider different clinically  relevant concentrations (e.g., 
the unbound C  for renal uptake transporters or the unbound  maximum hepatic inlet 
max
concentration for hepatic uptake transporters (see Figure 6)).  For apical intestinal 
transporters, the tested drug concentration should cover 0.1  dose/250 mL.  If the test 
drug demonstrates inhibitory  activity,  the sponsor should test additional  concentrations to 
calculate IC  or K  values.  The sponsor should evaluate at least four concentrations of 
50 i
the test drug with the probe substrate.  The sponsor can then compare IC  or K  values to 
50 i
clinical  plasma or estimated intestinal concentrations to predict the potential  for DDIs. 
 
  Experiments should include a probe substrate concentration range that results in linear 
transport of the substrate.  The probe substrate concentration should be at or below its K  
m
for the transporter. 
 
  The sponsor may consider a pre-incubation  step with the test drug for OATP1B1 and 
OATP1B3 inhibition  to evaluate whether this results in a lower IC of the test drug.  For 
50 
example, recent data show that cyclosporine and its metabolite AM1 are time-dependent 
OATP1B inhibitors  (Amundsen, Christensen et al. 2010; Gertz, Cartwright et al. 2013; 
Izumi, Nozaki et al. 2015). 
 
  Inhibition  can be substrate dependent; therefore, the sponsor should determine the 
inhibition  constant of the test drug with a probe substrate that may also be used in later 
clinical  studies.  Alternatively,  the sponsor may use a probe substrate that usually 
generates a lower IC for known inhibitors  to avoid underestimating  the interaction 
50 
potential of the investigational  drug. 
 
  The sponsor may use positive and negative controls to calibrate their internal in vitro 
systems to generate cutoff values to inform potential future clinical  DDI studies. 
 
C.  Using Model-Based Predictions to Determine a Drugs Potential to Cause 
DDIs 
 
Mathematical models can evaluate the results of in vitro and in vivo DDI studies to determine 
whether, when, and how to conduct further clinical  DDI studies in drug development.  In many 
cases, negative findings  from early in vitro or clinical  studies, in conjunction  with model-based 
 
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predictions,  can eliminate the need for additional  clinical  investigations  of an investigational 
drugs DDI potential.   
 
Mathematical models that integrate in vitro findings  and are verified with clinical  PK data can 
play an important  role in predicting  the DDI potential of an investigational  drug under various 
scenarios.  There are several models to consider when evaluating  the drug as a perpetrator of a 
metabolism-based  DDI.  Basic models generally serve simple purposes, such as the identification 
of low levels of inhibition  or induction  of metabolizing  enzymes by an investigational  drug.  
Static mechanistic models can account for the disposition characteristics of both the perpetrator 
and the probe substrate drugs (Fahmi, Hurst, et al. 2009).  Dynamic mechanistic models, 
including  PBPK models that integrate system-dependent parameters (e.g., based on human 
physiology)  and drug-dependent parameters (Zhao, Zhang, et al. 2011) and their time course of 
changes, can support decisions on when and how to conduct a clinical  DDI study.  Furthermore, 
these models can quantitatively  predict the magnitude of DDI in various clinical  situations,  such 
as in patients with renal impairment  or patients with genetic deficiencies in certain metabolizing 
enzymes.   
 
1.  General Considerations When Using Predictive Models to Evaluate Enzyme-
Based DDIs 
 
a.   Basic models to predict the effect of a drug as an enzyme modulator 
 
Evaluating a drug as a potential enzyme inhibitor  or inducer begins with the use of a basic 
model, i.e., R , R  (only for CYP3A), and R  (only for TDI) for inhibition  effect; R , fold of 
1 1,gut 2 3
change, and correlation methods for induction.   
 
The sponsor should compare the calculated R values or fold-change to the recommended cutoff 
criteria to determine whether it is possible to rule out the potential for a DDI.  Sponsors may 
calibrate their internal in vitro systems with known perpetrators and non-perpetrators of an 
enzyme and propose specific cutoff values with proper justification.   If the basic model does not 
rule out the potential for a DDI, the sponsor should further evaluate the DDI potential of the 
investigational  drug by conducting additional  modeling  analyses, using static mechanistic 
models or PBPK models (see below) or by conducting an in vivo DDI study. 
 
b.  Using static mechanistic models to predict the effect of a drug as an 
enzyme modulator 
 
Static mechanistic models incorporate more detailed drug disposition  and drug interaction 
mechanisms for both interacting and substrate drugs (Fahmi, Hurst, et al. 2009).  The following 
equation can be used to calculate the overall effect of the investigational  drug on substrate drugs 
(represented as the AUCR) (see Figure 7). 
 
 
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Figure 7: Equation to Calculate AUCR of the Substrate Drugs (AUC
 plus investigational 
/AUC ) 
drug  minus investigational drug
 
 
 
The equation assumes that the drug has negligible extrahepatic clearance. 
A is the effect of reversible inhibitions. 
B is the effect of TDI. 
C is the effect of induction. 
F  is the fraction available after intestinal metabolism. 
g
f  is the fraction of hepatic clearance of the substrate mediated by the CYP enzyme that is subject to 
m
inhibition/induction. 
Subscripts h denote liver. 
Subscripts g denote gut. 
 
Each value can be estimated with the following equations: 
 
 
[I]  = f  (C  + (F F k Dose)/Q /R ) (Ito, Iwatsubo, et al. 1998) 
h u,p  max a g a h B
[I]  = F k Dose/Q  (Rostami-Hodjegan and Tucker 2004) 
g a a en
f  is the unbound fraction in plasma.  When it is difficult to measure accurately due to high protein binding (i.e., 
u,p
f <0.01) in plasma, a value of 0.01 should be used for f . 
u,p  u,p
C  is the maximal total (free and bound) inhibitor concentration in the plasma at steady state. 
max
F  is the fraction absorbed after oral administration; a value of 1 should be used when the data are not available. 
a
F  is the fraction available after intestinal metabolism; a value of 1 should be used when the data are not available. 
g
k  is the first order absorption rate constant in vivo; a value of 0.1 min-1 (Ito, Iwatsubo, et al. 1998) can be used 
a
when the data are not available.                   
Q  is the blood flow through enterocytes (e.g., 18 L/hr/70 kg (Yang, Jamei, et al. 2007a)). 
en
Q  is the hepatic blood flow (e.g., 97 L/hr/70 kg (Yang, Jamei, et al. 2007b)). 
h
R  is the blood-to-plasma concentration ratio. 
B
d is the scaling factor and is assumed to be 1. A different value can be used if supported by prior experience with the 
system used (Einolf, Chen, et al. 2014; Vermet, Raoust, et al. 2016). 
 
 
In addition  to the combination  of inhibition  and induction,  one should separately use inhibition 
mechanisms (A and B only) to predict a drugs enzyme inhibition  potential (i.e., assuming C is 
equal to 1), and use induction  mechanisms (C only) to predict a drugs enzyme induction 
 
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potential (i.e., assuming A and B are equal to 1).  It should be noted that concurrent prediction 
may lead to a false negative prediction if the inhibition  potential  is over-predicted, thus masking 
the induction  effect.  If the induction  potential is over-predicted, it will mask the inhibition 
effect. 
 
c.  Using PBPK models to predict enzyme-based DDIs  
 
PBPK models can predict the DDI potential of an investigational  drug and/or a metabolite as an 
enzyme substrate or an enzyme perpetrator.  Figure 8 shows a general PBPK model-based 
framework to predict the DDI potential for the purposes of DDI study planning  in clinical 
development.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 8.  A PBPK Model-Based Framework to Explore the DDI Potential Between a 
Substrate Drug and an Interacting Drug (Modified from Zhao, Zhang, et al. 2011)* 
In vitro and in silico human  In vivo human PK data 
ADME data   (compartmental or PopPK)  
Physicochemical: LogP, pKa  Absorption and first pass 
Parameter input 
  metabolism: 
to build initial 
F=FFF, K 
a g h a
Absorption: P , solubility  PBPK models 
eff
Distribution:V  
ss
Distribution: B/P, K, K, f  
p d u,p
Elimination: CL, CL  
R
Metabolism and transport: Km,  Model  PK of metabolite(s) after parent 
Vmax, Jmax, Clint  refinement   drug administration 
 
PK of metabolite(s) after 
DDI: K, K , K, Induction 
i inact I metabolite administration, when 
(EC50, Emax, and )  Final PBPK model  available 
 
Substrate PBPK model   Interacting drug PBPK model  
 
Link two models  
-Include all mechanisms (e.g., reversible inhibition, time-dependent 
inhibition, and induction)  
-Use operating inhibitor/inducer concentration (e.g., unbound target 
tissue concentrations) 
 
Simulate drug-drug interactions 
 
Evaluate drug-drug interaction potential 
- Predict substrate exposure ratio (AUC and C  ) and their variability (exposure could be systemic or tissue levels) 
max
- Consider physiological/biological plausibility and evaluate parameter uncertainty 
   
ADME is the absorption, distribution, metabolism and excretion.  
AUC is the area under the plasma concentration versus time curve.  
B/P is the blood to plasma ratio.  
C  is the maximum concentration.  
max
CL is the clearance. 
CL  is the intrinsic clearance. 
int
CL  is the renal clearance.  
R
DDI is a drug-drug interaction.  
EC  is the concentration causing half maximal effect.  
50
E  is the maximum effect.  
max
F is the bioavailability.  
F  is the fraction absorbed. 
a
F  is the bioavailability in the gut.  
g
F  is the bioavailability in the liver.  
h
                      Continued 
 
 
 
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Figure 8 continued.  A PBPK Model-Based Framework to Explore the DDI Potential 
Between a Substrate Drug and an Interacting Drug (Modified from Zhao, Zhang, et al. 
2011)* 
f  is the unbound fraction in plasma. 
u,p
 is the Hill coefficient.  
IC  the concentration causing half maximal inhibition. 
50
I  is the maximum effect or inhibition. 
max
J  is the maximum rate of transporter-mediated efflux/uptake.  
max
K  is the first-order absorption rate constant. 
a
K  is the dissociation constant of a drug-protein complex.  
d
K is the reversible inhibition constant, concentration causing half maximal inhibition  
i
K  is the apparent inactivation constant, concentration causing half maximum inactivation  
I
k  is the apparent maximum inactivation rate constant.  
inact
K  is the Michaelis-Menten constant, substrate concentration causing half maximal reaction or transport 
m
K  is the tissue to plasma partition coefficient.  
p
LogP is the logarithm of the octanol-water partition coefficient.  
MOA is the mechanism of action.  
PD is the pharmacodynamics of a drug 
P is the jejunum permeability. 
eff 
PK is pharmacokinetics of a drug.  
PopPK is population pharmacokinetics.  
V is the volume of distribution. 
V  is the maximum rate of metabolite formation. 
max
 
*Note: PBPK models for both substrate and interacting drug (inhibitor or inducer) should be constructed separately 
using in vitro and in vivo disposition parameters and be verified before they are linked through appropriate 
mechanisms to predict the degree of DDI. 
 
 
  When using PBPK modeling, the sponsor should provide comprehensive justifications  on 
any model assumptions, the physiological  and biochemical  plausibility  of the model, 
variability,  and uncertainty measures.  Submissions  using advanced models like PBPK 
models should include a description  of the structural model, the sources and justifications 
for both system- and drug-dependent parameters, the types of error models, all model 
outputs, the data analysis, and an adequate sensitivity  analysis (see the 2018 FDA 
guidance for industry Physiologically Based Pharmacokinetic Analyses  Format and 
Content).  When using predefined models (structural and error) from commercially 
available software, the sponsor should specify the software version and list any 
deviations from the predefined models (Zhao, Rowland, et al. 2012).   
 
  When using PBPK modeling to predict the DDI potential of the investigational  drug as an 
enzyme substrate, the sponsor should address the following  questions (Vieira, Kim, et al. 
2014; Wagner, Pan, et al. 2015; Wagner, Pan, et al. 2016): 
 
-  Can the base PBPK model of the investigational  substrate describe the available 
clinical  PK data using different dosing regimens (e.g., a dose proportionality 
study) and dosing routes (e.g., intravenous or oral)? 
 
 
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-  Are elimination  pathways quantitatively  assigned in the substrates model 
according to available in vitro and in vivo data? 
 
-  Are index perpetrator models verified with regard to their modulating  effect on 
enzyme activity in humans? 
 
-  Are there sensitivity  analyses for parameters exhibiting  a high level of 
uncertainty? 
 
-  If complex metabolic and transport mechanisms are expected, do the substrate and 
modulator models include the major disposition  and interaction mechanisms and 
are they verified?  (see also 2.b below for transporters) 
 
The sponsor may use PBPK models to predict the effects of enzyme modulators  on the 
PK of an investigational  substrate if the models can describe the available data on DDIs 
for a strong enzyme inhibitor  or inducer (Wagner, Zhao, et al. 2015; Wagner, Pan, et al. 
2015; Wagner, Pan, et al. 2016).   
 
  When using PBPK modeling to predict the drug interaction potential of an investigational 
drug as an enzyme perpetrator, the sponsor should address the following  questions 
(Vieira, Zhao, et al. 2012; Wagner, Pan, et al. 2015; Wagner, Pan, et al. 2016): 
 
-  Can the base PBPK model of the investigational  perpetrator describe the available 
clinical  PK data using different dosing regimens (e.g., a dose proportionality 
study) and dosing routes (e.g., intravenous or oral)? 
 
-  Are index substrate models verified with regard to the effect of altered enzyme 
activity on its PK in humans? 
 
-  In addition  to the combination  of inhibition  and induction,  were inhibition  and 
induction  mechanisms separately considered to ensure a conservative prediction 
of in vivo enzyme inhibition  or induction? 
  
-  Did the simulation  use the highest clinical  dose of the investigational  perpetrator? 
 
-  Are there sensitivity  analyses for parameters exhibiting  high levels of uncertainty? 
 
2.  General Considerations When Using Predictive Models to Evaluate  
Transporter-Mediated DDIs 
 
a.  Using basic models to predict the effect of a drug as a transporter inhibitor 
 
Evaluating  a drug as a potential transporter inhibitor  begins with the use of a basic model, 
applying the same concepts as for metabolizing  enzymes (see section IV.B).  The predictions 
 
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and predefined cutoff criteria from this basic model approach determine the need to further 
evaluate transporter inhibition  in vivo.  Generally, the sponsor could assume reversible 
inhibition  and use the IC  as a practical substitute for the K .  The basic models described in 
50 i
this document use the gut luminal  concentration, the maximal unbound plasma concentration, 
and/or the estimated maximal  unbound concentration at the hepatic inlet of the interacting 
drug for [I].  The sponsor should compare the calculated R or [I]/IC  values to predefined 
50
cutoff criteria to determine whether it is possible to rule out the potential for a DDI.  If the 
basic model does not rule out the potential for DDIs, the sponsor should further evaluate the 
DDI potential  of the investigational  drug. 
 
b.  Using PBPK models to predict transporter-based DDIs 
 
PBPK models can include ADME processes mediated by transporters as well as passive 
diffusion  and metabolism.   Compared to CYP enzymes, the predictive performance of PBPK 
modeling  for transporter-based DDIs has not been established (Wagner, Zhao, et al. 2015).  
This is largely due to knowledge gaps in transporter biology  and limited  experience in 
determining  and modeling  the kinetics of transporters (Pan, Hsu, et al, 2016).  Recent 
applications  of PBPK models to evaluate the interplay between transporters and enzymes 
suggest that a model of an investigational  drug as a substrate of multiple  transporters and 
enzymes is only adequate for confident predictions of untested DDI scenarios after the model 
has been verified with clinical  data from a wide range of DDI or pharmacogenetic studies for 
the applicable enzyme or transporter (Varma, Lai, et al. 2012; Gertz, Cartwright, et al. 2013; 
Varma, Lai, et al. 2013; Varma, Lin, et al. 2013; Jamei, Bajot, et al. 2014; Varma, Scialis, et 
al. 2014; Snoeys, Beumont, et al. 2015).  For drugs that are potential transporter inhibitors, 
the sponsor should establish and verify models for transporter substrates (Gertz, 
Tsamandouras, et al. 2014; Tsamandouras, Dickinson,  et al. 2015; Snoeys, Beumont, et al. 
2015).   
 
 
 
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VIII.  ABBREVIATIONS AND ACRONYMS 
 
ADME:  absorption,  distribution,  metabolism,  and/or excretion 
AP:  apical 
ATP:  adenosine triphosphate 
AUC:  area under the plasma concentration-time  curve 
AUC :   area under the plasma concentration-time  curve of metabolite  
metabolite
AUC :  area under the plasma concentration-time  curve of parent drug 
parent
AUCR:  area under the plasma concentration-time  curve ratio 
BL:  basolateral 
B/P:  blood to plasma ratio 
BCRP:  breast cancer resistance protein 
CHO:  Chinese hamster ovary cell 
Cl :  intrinsic  clearance 
int
CL :  renal clearance 
r
C :  total maximal concentration in plasma 
max
CYP:  cytochrome P450 
d:  scaling factor 
DDI:  drug-drug interaction 
EC :  concentration causing half maximal  effect determined in vitro 
50
E :  maximum induction  effect determined in vitro 
max
ER:  efflux ratio 
F :  fraction absorbed 
a
F :  intestinal  availability 
g
f :  fraction of systemic clearance of the substrate mediated by the CYP enzyme that is subject 
m
to inhibition/induction. 
FMO:  flavin monooxygenase   
f :  unbound fraction in plasma 
u,p
GFR:  glomerular filtration  rate 
HEK293:  human embryonic kidney 293 cell 
[I]:  concentration of the interacting drug 
IC :  half-maximal  inhibitory  concentration  
50
I :  intestinal  luminal  concentration estimated as dose/250 mL 
gut
I :  estimated maximum plasma inhibitor  concentration at the inlet to the liver 
in,max
I :  maximal unbound plasma concentration of the interacting drug at steady state 
max,u
J :  maximal flux rate 
max
k :  absorption rate constant 
a
k :  dissociation  constant 
d
k :  partition coefficient 
p
k :  apparent first-order degradation rate constant of the affected enzyme 
deg
K :  inhibition  constant 
i
K :  inhibitor  concentration causing half-maximal  inactivation 
I
k :  maximal inactivation  rate constant 
inact
K :  Michaelis-Menton  constant 
m
k :  observed (apparent first order) inactivation  rate of the affected enzyme 
obs
 
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LLC-PK1:  Lewis-lung cancer porcine kidney 1 cell 
LogP:  octanol-water partition  coefficient 
MAO:  monoamine oxidase   
MATE:  multidrug  and toxin extrusion 
MDCK:  Madin-Darby canine kidney cell 
MDR1:  multi-drug  resistance 1 protein 
NADPH:  nicotinamide  adenine dinucleotide  phosphate (reduced form) 
OAT:  organic anion transporter 
OATP:  organic anion transporting polypeptide 
OCT:  organic cation transporter 
P :  apparent permeability 
app
PBPK:  physiologically-based  pharmacokinetic 
PD:  pharmacodynamics 
P-gp:  P-glycoprotein 
PK:  pharmacokinetic 
pKa:  negative logarithm  of the ionization  constant (Ka) of an acid, a measure of the strength of 
an acid 
PXR:  pregnane X receptor 
Q :  blood flow through enterocytes 
en
Q :  hepatic blood flow rate 
h
R:  ratio of victim AUC in the presence and absence of perpetrators (inhibitors  or inducers), 
predicted with basic models 
R :  blood to plasma ratio 
B
S9:  supernatants after 9000 g centrifugation 
SCH:  sandwich cultured hepatocytes 
SLC:  solute carrier 
TDI:  time-dependent inhibition 
UGT:  uridine  diphosphate (UDP)-glucuronosyl  transferase 
V :  maximal rate 
max
V :  steady-state volume of distribution 
ss
XO:  xanthine oxidase 
 
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