Adrian Fretland, Lilly Research Laboratories, spoke to SLAS ADMET Special Interest Group members at SLAS2013, Orlando.

1. Drug Interaction Investigations: Impact of
Recent Guidance for Industry on Early ADME
Testing in vitro
Adrian Fretland, Ph.D.
Lilly Research Laboratories
Eli Lilly and Company
SLAS ADMET SPECIAL INTEREST GROUP
Moderator:
David M. Stresser, Ph.D.
Corning® GentestSM Contract Research Services
Life Sciences 1

2. 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.
Life Sciences 2

3. Screening for Drug-Drug Interactions
- Assays, strategies, and impact of regulatory guidance
Adrian J. Fretland, Ph.D.

4. 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!)

5. Introduction
What Do the Regulatory Agencies Say?
What Does It Mean For Us?
Putting It All Together
Conclusions

6. 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

7. Classic example of P450 inhibition-based DDI
- the “perfect storm”
• Terfenadine:
• Potent inhibitor of hERG channel
• CYP3A4 inhibitors raise terfenadine
levels and cause QTc prolongation Blocked by
CYP3A4 inhibitors
• Introduced as Seldane (Marion Mibefradil
Ketoconazole
Merrell Dow) in 1985, withdrawn in
1997
Honig et al., Clin. Pharmacol. Ther., (1992) B. P. Monahan et al., J. Amer. Med. Assoc., 1990

8. Potential drug interactions involving P450s are
common
• A large number of commonly used Partial list of clinically relevant P450 substrates
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

9. “Modern day” metabolism based DDIs
- boosting PK in life threatening diseases
• Use of HIV protease inhibitor ritonavir
has become standard as a boosting Interaction of saquinavir and ritonavir
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 Kempf et al., Ant. Agents Chemo., 1997

10. What can we do pre-clinically?
• Screen, screen, and screen some more
Lead Lead Clinical Lead EIH-Enabling/
Identification Optimization Selection EIH/Beyond
Assessment of Development of Assessment of Translational
Liabilities SAR Risk Strategies
• 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

11. 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
0.04
Systemic Concentration (mg/L)
• In the most complex dynamic and physiologically- 0.04
0.03
based pharmacokinetic (PBPK) models it predicts 0.03
0.02
PK profiles of inhibitor/substrate interactions 0.02
0.01
0.01
0.00
• With the increase in complexity, more and more 0 24 48 72 96 120 144 168 192
Time (h)
robust data is required along with a greater
understanding of a drugs disposition/PK

12. Introduction
What Do the Regulatory Agencies Say?
What Does It Mean For Us?
Putting It All Together
Conclusions

13. 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

14. What do we have now…

15. 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

16. Tiered approach

17. Introduction
What Do the Regulatory Agencies Say?
What Does It Mean For Us?
Putting It All Together
Conclusions

18. P450 Inhibition

20. 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

21. 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

22. 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

23. 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?

24. 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!

25. 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”

26. 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?

27. Recombinant P450s in P450 inhibition screening
– Correlation of HLM and recombinant IC50 values
Good correlation Moderate correlation No correlation
From: Fowler & Zhang, AAPS J , 2008
• 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?

28. 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
n=100
• Approximately 15 hours for 200
compounds, 3 isoforms, 8
concentrations
IC50 values between analytical systems correlate well
n=200

29. What would a CYP inhibition screening strategy look like?
- Screen Smart
Lead Lead Clinical Lead EIH-Enabling/
Identification Optimization Selection EIH/Beyond
Assessment of Development of Assessment of Translational
Liabilities SAR Risk Strategies
Fluorescent screening – IC50
HLM screening – IC50
Regulatory assays
• 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?

30. Streamlining P450 inhibition with rapid analytics
- Screen Smart
Lead Lead Clinical Lead EIH-Enabling/
Identification Optimization Selection EIH/Beyond
Assessment of Development of Assessment of Translational
Liabilities SAR Risk Strategies
Fluorescent screening – IC50
HLM Rapid Fire screening – IC50
HLM screening – IC50
Regulatory assays
Fast analytics can decrease the amount of resources
needed for screening

31. 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.

32. 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

33. P450 Time Dependent Inhibition

34. 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
Interaction of diltiazem with midazolam
0.04 0.20
AUCi/AUC
Systemic Concentration (mg/L)
Systemic Concentration (mg/L)
0.03 Day 1 Day 6 - no inhibitor Day 6 with inhibitor
Plasma concentration (ng/mL)
0.04 0.18
0.16
0.03 0.03 0.14
Day 1 – 1.3
0.03 0.12
0.02
0.02 0.10
0.02 0.02 0.08
0.06
0.01 0.01 0.04
0.01
0.00
0.01 Day 6 – 3.4 0.02
0.00
0 24 0.00 72
48 96 120 144 168 192 0 24 48 72 96 120 144 168 192
0 Time (h) 6 12 18 24 Time (h)
MDZ MDZ+ DIL
Time (hr) Day 8 – 2.0 DIL

36. 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

37. 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!

38. 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”

39. 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
100 Increase pre-
incubation time
% CONTROL ACTIVITY
0
INHIBITOR CONC.
• Advantage – can be combined with competitive inhibition screening, good for
rank ordering compounds
• Disadvantage – difficultly in defining what is a “relevant” shift (risk assessment)

40. 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
• Pre-incubation loss of activity assays
0.08uM
100 • Can be single point or multipoint concentration depending on needs
0.16uM
0.32uM
0.6uM
1.25uM V erapamil
2.5uM 0.075
5uM
%Activity remaining
10uM
0.08uM
20uM
0.16uM
100 40uM
0.32uM
0.6uM 0.050
1.25uM
KI
S lo p e
2.5uM
5uM
%Activity remaining
10uM
10 20uM
40uM
0.025 kinact
10 15 20 25 30
0.000
10 Pre-incubation Time (min)
0 10 20 30 40 50
uM
• Advantage – multi-concentration assay is very informative
10 15 20 25 30
Pre-incubation Time (min)
• Disadvantage – time and resource intensive for multipoint assay, single point
assays difficult to interpret and define relevance

41. But what about risk assessment with TDI?
• With many DDI prediction algorithms for A
100
competitive P450 inhibition, the observed Erythromycin
Diltiazem
Verapmil
versus predicted is good (within two-fold)
.
Predicted DDI (HLM)
• However, when assessing risk for DDIs with 10
TDI, there is often a systematic over
prediction of magnitude of effect
• May lead to discarding compounds with no or little
DDI risk 1
1 10 100
• Resource intensive follow up assays Observed DDI
• Would hepatocytes be a better matrix for
assessing TDI?
• More physiologic system
• Incorporates more complex systems, e.g. protein
degradation, etc.

42. Is there an increase in the accuracy of the DDI
predictions with human hepatocyte data?
A
•
100
Erythromycin
Diltiazem
Verapmil
The accuracy of the
prediction of AUC increase
.
Predicted DDI (HLM)
10 with CYP3A4 TDI is better
using kinetic parameters
from human hepatocytes
when compared to HLM
1
1 10 100
B
Observed DDI
100
Erythromycin Verapmil
•
Diltiazem
Why?
.
Predicted DDI (HH add method)
• More physiological system
10
• Are their additional explanations?
1
1 10 100
Observed DDI

43. 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

44. What would a P450 TDI inhibition screening strategy look like?
Lead Lead Clinical Lead EIH-Enabling/
Identification Optimization Selection EIH/Beyond
Assessment of Development of Assessment of Translational
Liabilities SAR Risk Strategies
Single concentration or IC50 shift
Kinetics determination
Regulatory assays
• Tiered approach with increasing data robustness
• Kinetic determination for risk assessment and further evaluation
• Incorporate human hepatocytes for more complex interactions

45. 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

46. P450 Induction

48. 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

49. 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

50. How do we screen for inducers of P450 metabolism?
- Important considerations
• Assay types
• Ligand binding assay
• Reporter gene (transactivation) assay
• Hepatocytes
• Read outs

51. Mechanism of receptor-mediated induction
- PXR-mediated CYP3A4 induction
SRC-1 CYP3A4
mRNA
RXRPXR Transcription
TF’s RNA pol II
XREM Promoter CYP3A4 gene
Ligand Translation
Ethinyl estradiol Efavirenz Warfarin
Erythromycin Cyclosporin Tamoxifen Drug
CYP3A4
Atorvastatin Carbamazepine Doxorubicin
Indinavir Midazolam
Drug-OH

52. 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?

53. 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

54. Why the change to mRNA?
- Example of ritonavir
•Why does ritonavir
have an activity
CYP3A4 Activity much lower than
Fold Change (Over DMSO)
vehicle control?
8
7 •Is this correct?
6
5 Does the mRNA
4
3 correlate?
2
1
0
ir
ir
r
ir
r
ir
n
r
ir
vir
n
n
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vi
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Am avi
av
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en
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0.
0.
1
10
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0.
0.
At first glance, ritonavir, amprenavir, and saquinavir appear to have
no CYP induction liabilities.

55. Fold Change (Over DMSO)
D
0
2
4
6
8
10
12
0. M
1 SO
uM
1 R
uM if am
10 R pi
n
uM if am
Ri pin
fa
m
0. pi
1 n
uM
- Example of ritonavir
1 Rit
uM on
10 R av
uM it on ir
Ri avi
0. to r
1 na
uM vi
r
Why the change to mRNA?
1 Am
uM p
CYP3A4 mRNA
10 A ren
uM mp avi
r r
Am en
a
inducers of CYP3A4. pr vir
0. en
1 av
uM ir
1 Sa
uM qu
10 S ina
uM aqu vir
Sa ina
qu vir
in
av
mRNA data suggest ritonavir, amprenavir, and saquinavir are
ir
55

56. 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?

57. 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

58. Introduction
What Do the Regulatory Agencies Say?
What Does It Mean For Us?
Putting It All Together
Conclusions

59. 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…

60. Changes in PK in compounds with inhibition and induction
- Interaction of ritonavir and midazolam
0.05
12.00 Interaction driven by inhibition
Systemic Concentration (mg/L)
0.04
10.00
0.04
AUC Ratio
8.00
Interaction a net effect of both 0.03
6.00
inhibition and induction 0.03
4.00 0.02
2.00 0.02
0.00 0.01
0 24 48 72 96 120 144 168 0.01
Time (Hours) 0.00
0 24 48 72 96 120 144 168
Median Population Rss values Induction/Interaction Threshold Time - Substrate (h)
CSys CSys with Interaction
• 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?

61. What about a compound that is a weak inducer
and inhibitor?
Interaction of Compound X with midazolam
2.00
Inhibition Induction
AUC Ratio
1.50
1.00
0.50
0.00
0 24 48 72 96 120 144
Time (Hours)
Median Population Rss values Induction/Interaction Threshold
• 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

62. 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?

64. 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!!!

65. 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

66. 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.)

67. 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 0.02
Plasma concentration (mg/mL)
Day 6 - no inhibitor
0.02 Day 6 - with inhibitor
• pAUC ratio = 1.47
0.01
• oAUC ratio = 4.0
0.01
0.00
0 6 12 18 24
Time - hr
• If however, the primary metabolite, desmethyldiltiazem, is
included the predicted versus the observed fits much
0.03
better
Plasma concentration (mg/mL)
0.03 Day 6 - no…
0.02 Day 6 with …
• pAUC ratio = 3.41 0.02
• oAUC ratio = 4.0 0.01
0.01
0.00
0 6 12 18 24
Time (hr)

68. 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

69. Introduction
What Do the Regulatory Agencies Say?
What Does It Mean For Us?
Putting It All Together
Conclusions

70. 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
from Luo et al., DMD 2002 PHARMACOLOGISTS HAVE IT EASY!

71. 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]??

73. Classic example of P450 induction-DDI
- the classic example rifampin
from Niemi et al., Clin Pharmacokinet 2003.

74. Determination of CYP3A TDI in human hepatocytes
Hepatocyte HLM
diltiazem
0.020
Mean Mean
0.015
Kobs (1/min)
Diltiazem
0.010
KI 8.9 1.53
0.005
kinact 0.0228 0.024
kinact/KI 0.0026 0.016 0.000
0 20 40 60 80 100
Erythromycin Concentration (M)
KI 67.9 5.33 0.08
Erythromycin
kinact 0.079 0.061
0.06
Kobs (1/min)
kinact / KI 0.0012 0.013
0.04
In general, the inactivation
kinetic parameters are 0.02
higher in hepatocytes when 0.00
0 100 200 300
compared to HLM Concentration (M)

75. Drug interactions are precipitated in multiple
manners
• Compounds as • Compounds as
substrates (victims) inhibitors/inducers
(perpetrators)
• Not only P450s…
• UGTs
• Transporters
• Can also be
pharmacodynamic
From: Williams et al, DMD 2004