1Life Sciences

Adrian Fretland, Ph.D.Lilly Research LaboratoriesEli Lilly and Company

SLAS ADMET SPECIAL INTEREST GROUP

Moderator: David M. Stresser, Ph.D. Corning® GentestSM Contract Research Services

Drug Interaction Investigations: Impact of Recent Guidance for Industry on Early ADME Testing in vitro

2Life Sciences

SIGs at SLAS

"It's through SIGs that like-minded SLAS members connect, share knowledge and experience, and explore new frontiers."

— Michelle Palmer, Ph.D., The Broad Institute, Cambridge, Massachusetts.

Screening for Drug-Drug Interactions - Assays, strategies, and impact of regulatory guidance

Adrian J. Fretland, Ph.D.

Take home messages

• More in depth guidance for all areas of drug interactions

• Assays in place in most of industry and contract research service groups are more than adequate to cover the requirements of the guidance

• Further emphasizes the need for a detailed understanding of a drugs disposition in defining the risk of a clinical drug interaction

• Likely much more M&S because prediction of PK is more important (impact on screening!)

Introduction

What Do the Regulatory Agencies Say?

What Does It Mean For Us?

Putting It All Together

Conclusions

Drug-drug interaction background

• Adverse drug reactions (ADR) cause 100K deaths (~6% of the hospitalized patients) per year in the U.S.

• DDIs are one of the sources of ADR (~25%)

• Most common DDIs are associated with changes in the activity of P450s

• 40% of all PK-based DDIs are due to P450 inhibition

• Nearly 75% of all drugs undergo P450 oxidation, 50% of which is due to CYP3A4

• Risk assessment as early as possible helps identify risks and risk mitigation strategies for the drug development process

Blocked by CYP3A4 inhibitors

MibefradilKetoconazole

Classic example of P450 inhibition-based DDI

- the “perfect storm”

B. P. Monahan et al., J. Amer. Med. Assoc., 1990

• Terfenadine:• Potent inhibitor of hERG channel• CYP3A4 inhibitors raise terfenadine

levels and cause QTc prolongation• Introduced as Seldane (Marion

Merrell Dow) in 1985, withdrawn in 1997

Honig et al., Clin. Pharmacol. Ther., (1992)

Potential drug interactions involving P450s are common

Partial list of clinically relevant P450 substrates• A large number of commonly used drugs are cleared primarily via the P450 metabolic system

• Inhibition or induction of this clearance pathway often has serious consequences related to toxicity or efficacy of coadministered drugs

• Trazadone and CYP2D6 inhibitors – psychomotor dysfunction

• HMG CoA reductase inhbitors and CYP3A inhibitors – myopathy

• Fentanyl and CYP3A inhibitors – fatal respiratory depression

• Identifying potential issues pre-clinically is an important function of ADME scientists

http://medicine.iupui.edu/clinpharm/DDIs/table.aspx

“Modern day” metabolism based DDIs - boosting PK in life threatening diseases

Kempf et al., Ant. Agents Chemo., 1997

• Use of HIV protease inhibitor ritonavir has become standard as a boosting agent for co-administered HIV protease inhibitors/HIV treatment regimens

• Can it/should it be used for other diseases?• HCV infection – transporter interactions?• Oncology

• In this respect, the prediction of interaction magnitude is different in that it is a prediction for efficacy

• Predictions often times more difficult, interacting drugs are not as clean as probe substrates, e.g. midazolam

Interaction of saquinavir and ritonavir

What can we do pre-clinically?

• Screen, screen, and screen some more

• But, screening is not the only answer• Data in the absence of context is meaningless

• Important to have clear, coherent, and consistent screening strategy

• But, also a risk assessment strategy

LeadIdentification

LeadOptimization

Clinical LeadSelection

EIH-Enabling/EIH/Beyond

Assessment ofLiabilities

Development ofSAR

Assessment ofRisk

TranslationalStrategies

What is DDI risk assessment?

• Can range from simple static to more complex mechanistic models

• In its most simplistic form, it relates an expected plasma concentration to an inhibition parameter and an expected outcome

• In more complex static forms, it predicts an outcome in a PK parameter, e.g. change in AUC ratio

• In the most complex dynamic and physiologically-based pharmacokinetic (PBPK) models it predicts PK profiles of inhibitor/substrate interactions

• With the increase in complexity, more and more robust data is required along with a greater understanding of a drugs disposition/PK

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Introduction

What Do the Regulatory Agencies Say?

What Does It Mean For Us?

Putting It All Together

Conclusions

Where were we? - State of the art c. 2006

• Only gives guidance on competitive inhibition

• [I] is total mean steady state Cmax value

• Conservative?

• Mention of mechanistic models, but no guidance

What do we have now…

What are the most substantive changes? - From my perspective

• Many detailed decision trees to assess drug interactions• Multiple levels for consideration with increasing complexity

• “Detailed” guidance on transporters and UGTs

• Guidance for the interaction of monoclonal antibodies with drug metabolizing enzymes

• Discussion of using physiologically based pharmacokinetic modeling (PBPK) risk assessment

• Large changes in the guidance on assessing P450 induction

• Very restrictive cut offs for what triggers a potential in vivo DDI study

Tiered approach

Introduction

What Do the Regulatory Agencies Say?

What Does It Mean For Us?

Putting It All Together

Conclusions

P450 Inhibition

Breaking it down - Tier I

• Simplistic model predicting changes in AUC

• Essentially, same equation as in previous guidance

• The cut off for R is either 1.1 or 11 (dependent on involvement of CYP3A)

• Key parameter is [I]

• For CYP3A inhibitors, calculated gut concentration, molar dose/250 mL

• For other inhibitors, calculated as total maximal systemic concentration

• Known to be overly conservative and over estimating of DDI risk

𝑅=1+ [ 𝐼 ] /𝐾𝑖

More about Tier I

• Use of total systemic concentration is thought to be overly conservative• Fails to account for potentially higher liver levels• May be aggressive?

• Often stated, that this equation will only help with compounds suspected to be non-inhibitors (IC50 > 50 µM)• Is this a true statement?• Depends on therapeutic area…

• Internal decision processes will most likely drive the application of Tier I in DDI risk assessment

Tier I CYP inhibition assessment - Not all therapeutic areas are created alike

• For some therapeutic areas, doses are quite high, e.g. virology and oncology

• Does Tier 1 apply?

• A recent example:• Vemurafenib – recently approved for the treatment of late-stage

melanoma• Dose is 2,400 mg bid – translates to a total Cmax value of ~ 62 µg/mL =

~150 µM!!• Measuring a Ki value this high is near impossible in today’s chemical

space• Contrast atorvastatin, Cmax value of ~ .045 µg/mL = ~0.1 µM!!

• Be careful with ignoring Tier 1 in the decision tree, it does not always guarantee a “non-inhibitor” (IC50 > 50µM) does not need additional scrutiny

The “net effect” model

• The “net effect” model has been shown to be the most predictive static DDI model in literature to date

• But the key parameter is [I]• How should it be calculated?• Cmax,sys, Cmax,inlet?• There is guidance with regards to calculations,

but there assumptions on these calculations• Can lead to overly conservative or

optimistic assessments…

• But, what is added for the consideration of P450 inhibition?

The “net effect” model and P450 inhibition

• Again, essentially the same parameter as used in all P450 inhibition risk assessments

• Incorporates, fraction metabolized (fm) of victim drug

• Consideration of gut inhibition (important for CYP3A predictions)

• Takes into account protein binding

• But the key parameter is still [I]

• From an assay perspective, little if any impact on P450 inhibition screening!

How do we screen for competitive CYP inhibitors? - Important considerations

• What matrix can be used?• Recombinant P450s• Microsomes• Hepatocytes

• What substrates can be used?• Fluorescent• Radioactive• Drug-like substrates

• Screening strategies• Concentrations, IC50 vs. Ki, etc.• “Screen Smart”

What is the proper matrix?

• Depends on project stage/ screening strategy

• For screening, two common choices• Recombinant P450s• Microsomes

• Choice of matrix is also linked choice of substrate• Because of lack of selectivity, fluorescent substrates are only

appropriate for use in inhibition assays using recombinant P450s• Recombinant P450s very popular until recently

• Where is the field now?

Recombinant P450s in P450 inhibition screening – Correlation of HLM and recombinant IC50 values

Good correlation Moderate correlation No correlation

• For many projects there is a poor correlation between systems

• Requires rescreening in HLM – resource intensive• Possibly due to inappropriate accessory protein expression

• Is there a better way?

From: Fowler & Zhang, AAPS J , 2008

n=100

n=200

Rapid analytical methods for P450 inhibition screening - Rapid Fire analytics

• Fluorescence-based screening became popular because speed and convenience

• Minutes to analyze plate versus hours for LC/MS

• Translates to hours for fluorescence and days for LC/MS in regular screening campaigns

• Advent of Rapid Fire LC technology substantially decreases analysis time

• Approximately 15 hours for 200 compounds, 3 isoforms, 8 concentrations

IC50 values between analytical systems correlate well

What would a CYP inhibition screening strategy look like? - Screen Smart

• Tiered approach with increasing data robustness• IC50 to Ki to mechanism of inhibition• Determination of mechanism and Ki are very time and resource intensive

• Does Rapid Fire screening change the approach?

LeadIdentification

LeadOptimization

Clinical LeadSelection

EIH-Enabling/EIH/Beyond

Assessment ofLiabilities

Development ofSAR

Assessment ofRisk

TranslationalStrategies

Fluorescent screening – IC50

HLM screening – IC50

Regulatory assays

Streamlining P450 inhibition with rapid analytics - Screen Smart

LeadIdentification

LeadOptimization

Clinical LeadSelection

EIH-Enabling/EIH/Beyond

Assessment ofLiabilities

Development ofSAR

Assessment ofRisk

TranslationalStrategies

Fluorescent screening – IC50

HLM screening – IC50

Regulatory assays

HLM Rapid Fire screening – IC50

Fast analytics can decrease the amount of resources needed for screening

Other considerations

• What P450 isoforms should be assayed?• At a minimum CYP2C9, CYP2D6, and CYP3A4 in initial screening• If resources allow, CYP1A2, CYP2B6, CYP2C8, and CYP2C19• All are required for regulatory submissions• Also dependent on therapeutic areas of interest

• For regulatory purposes, a second substrate is required for CYP3A4

• Multiple binding sites• It practical terms, few profound differences between substrates

• What about other matrices, specifically hepatocytes• Some utility shown in literature examples, protein binding effects,

permeability, etc.• May provide useful in complex DDIs, i.e. transporter effects, competing

metabolic pathways, etc.

Summary - Competitive inhibition

• The FDA guidance regarding CYP inhibition has been updated to a more mechanistic approach

• Key to addressing whether a potential new drug possess a rick of clinical DDI is robust input data

• Inhibition kinetics• Input data for prediction of inhibitor concentration

• Current industry standard assays for assessing inhibition kinetics are more than adequate

• Continued increases in throughput and decreases in turnaround time are helpful

P450 Time Dependent Inhibition

Time dependent P450 inhibition results in clinically relevant DDIs

• DDIs resulting from TDI can be more ominous and potentially harmful

• Destruction of enzyme – lower enzymatic levels until synthesis restores normal levels

• Potential toxicities resulting from TDI can be prolonged

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Breaking it down - Tier I

• Really very little guidance related to risk in 2006 guidance

• New guidance uses a simplistic model to predict changes in AUC

• Same cut offs as for competitive inhibition ( 1.1 or 11)

• Again, the key parameter is [I]• For CYP3A inhibitors, calculated for gut concentration, molar

dose/250 mL

• For other inhibitors, calculated as total maximal systemic concentration

• Known to over predict the magnitude of DDI

𝑅=(𝐾 𝑜𝑏𝑠+𝐾 𝑑𝑒𝑔) /𝐾 𝑑𝑒𝑔

𝐾 𝑜𝑏𝑠=𝑘𝑖𝑛𝑎𝑐𝑡∗[ 𝐼 ] /(𝐾 𝐼+ [ 𝐼 ] )

The “net effect” model and P450 TDI - Tier II

• Identical to competitive inhibition in the additional factors considered

• Incorporates, fraction metabolized (fm) of victim drug

• Consideration of gut inhibition (important for CYP3A predictions)

• Takes into account protein binding

• But the key parameter is still [I]

• From an assay perspective, little if any impact on P450 inhibition screening!

How do we screen for time dependent CYP inhibitors? - Important considerations

• What matrix should be used?• Recombinant P450s• Microsomes• Hepatocytes

• What substrates should be used?• Fluorescent – in practice, not commonly utilized • Drug-like substrates

• Screening strategies• Concentrations, KI, IC50 shift, progress curves, etc.• What isoforms should be screened?• “Screen Smart”

Microsomal-based assays are the most common assays for primary screening• Microsomal-based assays are the most common platform

used for TDI screening

• Numerous assay formats exist• IC50 shift assay

• Essentially determination of an IC50 with a pre-incubation phase

• Advantage – can be combined with competitive inhibition screening, good for rank ordering compounds

• Disadvantage – difficultly in defining what is a “relevant” shift (risk assessment)

INHIBITOR CONC.

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0.08uM 0.16uM 0.32uM 0.6uM 1.25uM 2.5uM 5uM 10uM 20uM 40uM

Microsomal-based assays are the most common assays for primary screening

KI

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0.08uM 0.16uM 0.32uM 0.6uM 1.25uM 2.5uM 5uM 10uM 20uM 40uM

• Microsomal-based assays are the most common platform used for TDI screening

• Numerous assay formats exist• Pre-incubation loss of activity assays

• Can be single point or multipoint concentration depending on needs

• Advantage – multi-concentration assay is very informative• Disadvantage – time and resource intensive for multipoint assay, single point

assays difficult to interpret and define relevance

But what about risk assessment with TDI?

• With many DDI prediction algorithms for competitive P450 inhibition, the observed versus predicted is good (within two-fold)

• However, when assessing risk for DDIs with TDI, there is often a systematic over prediction of magnitude of effect

• May lead to discarding compounds with no or little DDI risk

• Resource intensive follow up assays

• Would hepatocytes be a better matrix for assessing TDI?

• More physiologic system• Incorporates more complex systems, e.g. protein

degradation, etc.

A

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Erythromycin Verapmil

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Is there an increase in the accuracy of the DDI predictions with human hepatocyte data?

• The accuracy of the prediction of AUC increase with CYP3A4 TDI is better using kinetic parameters from human hepatocytes when compared to HLM

• Why?• More physiological system• Are their additional explanations?

The caveat about predictions of TDI• Unlike competitive inhibition predictions, TDI predictions

are highly dependent on a system parameter, kdeg

• This parameter cannot be directly measured in vivo, but has been estimated through various methods

• The kdeg value for human CYP3A4 has a very wide range• Controversial as to what is the true value

• Could kdeg be “fitted” for more accurate predictions in HLM?

• Real value of hepatocyte systems is to assess DDI in a more complex system

What would a P450 TDI inhibition screening strategy look like?

• Tiered approach with increasing data robustness

• Kinetic determination for risk assessment and further evaluation

• Incorporate human hepatocytes for more complex interactions

LeadIdentification

LeadOptimization

Clinical LeadSelection

EIH-Enabling/EIH/Beyond

Assessment ofLiabilities

Development ofSAR

Assessment ofRisk

TranslationalStrategies

Single concentration or IC50 shift

Kinetics determination

Regulatory assays

Summary - Time-dependent inhibition

• The 2012 FDA guidance for assessing TDI has been updated significantly when compared to the 2006 guidance

• Despite the update to the guidance, models tend to over predict the magnitude of drug interaction with CYP3A4

• More complex cell-based assays may provide an improvement in predictive power of mechanistic models

P450 Induction

Breaking it down - Tier I

• Large changes on how induction is assessed when comparing the 2006 and 2012 guidance's

• “40%” POC in enzyme activity

• Move towards more pharmacological characterization of induction

• d is a calibration term

• In Tier I always set to 1 (most conservative)

• Cut off is an R<1 (induction = reduction in AUC)

• As with competitive and TDI, only total concentration is considered

• Leads to many false positives

𝑅=1/¿

The “net effect” model and P450 induction - Tier II

• Similar to competitive and TDI• Incorporates, fraction metabolized (fm) of victim

drug• Consideration of gut induction (important for

CYP3A predictions)• Takes into account protein binding

• At this level, d is calibrated against known positive controls for your system (typically <1)

• Again, the key parameter is still [I]

• Unlike inhibition, substantial changes to screening paradigms

How do we screen for inducers of P450 metabolism? - Important considerations

• Assay types• Ligand binding assay• Reporter gene (transactivation) assay• Hepatocytes

• Read outs

Mechanism of receptor-mediated induction - PXR-mediated CYP3A4 induction

SRC-1

TF’s RNA pol II

XREM Promoter CYP3A4 gene

CYP3A4mRNATranscription

Translation

CYP3A4

Ligand

RXRPXR

Drug

Drug-OH

Ethinyl estradiol Efavirenz WarfarinErythromycin Cyclosporin TamoxifenAtorvastatin Carbamazepine DoxorubicinIndinavir Midazolam

How do we screen for inducers of P450 metabolism? - Important considerations

• Assay types• Ligand binding assay• Reporter gene (transactivation) assay• Hepatocytes

• Read outs• IC50

• Reporter gene activity• mRNA• Enzyme activity

• Concentrations??• Guidance is for three• Is that enough to derive a robust EC50?

Paradigm shift in induction screening

• Previous guidance flagged a compound if it showed an increase in enzyme activity that was 40% of the positive control

• New guidance is using mRNA

• And, is more pharmacologically driven

• What impact will this have?• Depends on screening strategy and philosophy• But, it will likely drive the need for many more hepatocyte

experiments

𝑅=1/¿

Why the change to mRNA? - Example of ritonavir

CYP3A4 Activity

012345678

Fo

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ge

(Ove

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At first glance, ritonavir, amprenavir, and saquinavir appear to have no CYP induction liabilities.

• Why does ritonavir have an

activity much lower than

vehicle control?

• Is this correct? Does the mRNA

correlate?

55

Why the change to mRNA? - Example of ritonavir

CYP3A4 mRNA

02468

1012

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mRNA data suggest ritonavir, amprenavir, and saquinavir are inducers of CYP3A4.

Other considerations

• What P450 isoforms should be assayed?• Guidance states that the three most inducible P450 isoforms should be

tested, CYP3A4, CYP1A2, and CYP2B6• Only CYP3A4 is really understood from a molecular perspective AND a

clinically relevant perspective…

• Which assay can be used?• From a regulatory perspective, only hepatocyte data are acceptable• For screening purposes, other assays can be utilized• Reporter gene assays

• Ease and convenience• Not always predictive of hepatocyte data

• Ligand binding assay• Very high throughput• Relevance to hepatocyte data?

Summary - P450 induction

• One of the largest changes to the updated FDA guidance on drug interactions is in the assessment of P450 induction

• Move from largely empirical assessment to a mechanistic assessment

• Change from enzyme activity as a marker of induction to mRNA

• Hepatocytes are the primary screening tool on which all risk assessments are based

• Screening paradigms are being reevaluated and updated to address updated guidance

Introduction

What Do the Regulatory Agencies Say?

What Does It Mean For Us?

Putting It All Together

Conclusions

A word about the net effect model

• As stated previously, the most predictive model for clinical DDIs in the literature

• Importantly, it considers all forms of DDI

• What is it’s impact on predictions?

• Back to ritonavir…

Changes in PK in compounds with inhibition and induction - Interaction of ritonavir and midazolam

• If only the inhibition potential is considered, the true magnitude of effect is over estimated

• But, when both inhibition and induction are incorporated into the assessment, the magnitude of effect is far reduced

• In this example, compound would still be considered an inhibitor of CYP3A • Strong inhibitor of CYP3A4 – Ki = 25 nM• What if the dose was much lower?

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What about a compound that is a weak inducer and inhibitor?

• CYP3A4 Ki = 16 µM

• CYP3A4 EC50 = 7 µM

• In this case, a compound that may be considered a weak inducer may actually have no “net effect” for DDI

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A word about the cut offs• Where did these come from? Are they reasonable?

• Related to bioequivalence• Is this correct?• Is there something better?

• These are regulatory cut offs that will define the need for a clinical study

• Internal decision making may define relevance?

The impact of modeling and simulation

• What is meant by “static” and “dynamic” models?

• Static model = Net effect model

• Dynamic models are essentially M&S programs that incorporate the net effect model into a prediction of PK from in vitro data

• Simcyp• Gastroplus• Others• Can also be carried into PBPK models

• Provide easy and convenient was to predict [I]• Potential “Black Box” trap• DANGER!!!

What does the EMA guidance look like?

• More comprehensive in that it discusses interactions beyond DDIs (food effect, PD, etc.)

• For DDIs, all the same equations are utilized as in the FDA guidance

• However, there is no tiered decision tree• May be more practical?

• Again, it is all about [I]

• All mechanistic and static models utilize unbound concentration with correction using a safety factor

• Either 50- or 250-fold depending on degree of protein binding inhibition

What does it really mean?

• More modeling and simulation!• Depending on therapeutic area, any signal for inhibition by

P450s will lead to a need for M&S to de-risk• Even simple static and mechanistic models require robust input

data• More complex models can require more input data

• A deep understanding of molecules (DDI parameters, metabolism, clearance, etc.)

The true story on our friend diltiazem…

• If simulations are conducted with only the inhibition parameters of the parent molecule, diltiazem, little to no interaction is predicted• pAUC ratio = 1.47• oAUC ratio = 4.0

• If however, the primary metabolite, desmethyldiltiazem, is included the predicted versus the observed fits much better• pAUC ratio = 3.41• oAUC ratio = 4.0

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What does it really mean?

• More modeling and simulation• Depending on therapeutic area, any signal for inhibition by

P450s will lead to a need for M&S to de-risk• Even simple static and mechanistic models require robust input

data• More complex models can require even more input data

• A deep understanding of molecules• Understanding of clearance pathways and metabolites• DDI risk for these metabolites• Increases the complexity for prediction of DDIs

• More physiological systems for DDI screening, but not in true screening mode

Introduction

What Do the Regulatory Agencies Say?

What Does It Mean For Us?

Putting It All Together

Conclusions

Assessing DDIs in vitro in modern drug discovery

As biology and medicinal chemistry has progressed the challenges for DMPK have increased

• Poor solubility• Highly selective and potent compounds• Target pharmacology

Identifying these caveats is important to understand the potential limitations in the in vitro data used to assess DDI potential

These challenges are not going to go away for the vast majority of programs, and will not get easier for the DMPK scientist

PHARMACOLOGISTS HAVE IT EASY!from Luo et al., DMD 2002

Take home messages

• More in depth guidance for all areas of drug interactions

• Assays in place in most of industry and contract research service groups are more than adequate to cover the requirements of the guidance

• Development of more specialized/complex systems is of help, but not for screening – TDI and hepatocytes

• Further emphasizes the need for a detailed understanding of a drugs disposition in defining the risk of a clinical drug interaction

• Ritonavir• Diltiazem

• Likely much more M&S because prediction of PK is more important (impact on screening!)

• WHAT IS [I]??

Classic example of P450 induction-DDI

- the classic example rifampin

from Niemi et al., Clin Pharmacokinet 2003.

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Determination of CYP3A TDI in human hepatocytes

In general, the inactivation kinetic parameters are higher in hepatocytes when compared to HLM

 Hepatocyte

HLM

  Mean Mean

Diltiazem    

KI 8.9 1.53

kinact 0.0228 0.024

kinact/KI 0.0026 0.016

Erythromycin    

KI 67.9 5.33

kinact 0.079 0.061

kinact / KI 0.0012 0.013

Drug interactions are precipitated in multiple manners

• Compounds as substrates (victims)

• Compounds as inhibitors/inducers (perpetrators)

• Not only P450s…• UGTs• Transporters

• Can also be pharmacodynamic

From: Williams et al, DMD 2004