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SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
SLAS ADMET SIG: SLAS2013 Presentation
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SLAS ADMET SIG: SLAS2013 Presentation

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

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

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  • 1. Drug Interaction Investigations: Impact ofRecent Guidance for Industry on Early ADMETesting in vitroAdrian Fretland, Ph.D.Lilly Research LaboratoriesEli Lilly and CompanySLAS ADMET SPECIAL INTEREST GROUPModerator:David M. Stresser, Ph.D.Corning® GentestSM Contract Research Services Life Sciences 1
  • 2. SIGs at SLAS "Its 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 guidanceAdrian 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. IntroductionWhat Do the Regulatory Agencies Say?What Does It Mean For Us?Putting It All TogetherConclusions
  • 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. IntroductionWhat Do the Regulatory Agencies Say?What Does It Mean For Us?Putting It All TogetherConclusions
  • 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. IntroductionWhat Do the Regulatory Agencies Say?What Does It Mean For Us?Putting It All TogetherConclusions
  • 18. P450 Inhibition
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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?
  • 23. 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!
  • 24. 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”
  • 25. 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?
  • 26. 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?
  • 27. 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
  • 28. 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?
  • 29. 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
  • 30. 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.
  • 31. 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
  • 32. P450 Time Dependent Inhibition
  • 33. Time dependent P450 inhibition results in clinicallyrelevant 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
  • 34. 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
  • 35. 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!
  • 36. 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”
  • 37. Microsomal-based assays are the most commonassays 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)
  • 38. Microsomal-based assays are the most commonassays 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
  • 39. 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.
  • 40. Is there an increase in the accuracy of the DDIpredictions 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
  • 41. 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
  • 42. 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
  • 43. 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
  • 44. P450 Induction
  • 45. Breaking it down - Tier I• Large changes on how induction is assessed when comparing the 2006 and 2012 guidances • “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
  • 46. 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
  • 47. How do we screen for inducers of P450 metabolism? - Important considerations• Assay types • Ligand binding assay • Reporter gene (transactivation) assay • Hepatocytes• Read outs
  • 48. 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
  • 49. 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?
  • 50. 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
  • 51. 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 SO vi vi Am avi av av av av pi av pi pi na na na m m m en in in in en enDM to to ifa ito fa fa qu qu qu pr pr pr Ri Ri Ri Ri R R Sa Sa Sa Am Am uM uM uM uM uM uM uM uM uM uM uM uM 1 10 1 1 1 10 0. 0. 1 10 1 1 1 10 0. 0. At first glance, ritonavir, amprenavir, and saquinavir appear to have no CYP induction liabilities.
  • 52. 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 ir55
  • 53. 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?
  • 54. 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
  • 55. IntroductionWhat Do the Regulatory Agencies Say?What Does It Mean For Us?Putting It All TogetherConclusions
  • 56. 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…
  • 57. 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?
  • 58. What about a compound that is a weak inducerand 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
  • 59. 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?
  • 60. 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!!!
  • 61. 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
  • 62. 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.)
  • 63. 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)
  • 64. 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
  • 65. IntroductionWhat Do the Regulatory Agencies Say?What Does It Mean For Us?Putting It All TogetherConclusions
  • 66. 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 scientistfrom Luo et al., DMD 2002 PHARMACOLOGISTS HAVE IT EASY!
  • 67. 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]??
  • 68. Classic example of P450 induction-DDI - the classic example rifampin from Niemi et al., Clin Pharmacokinet 2003.
  • 69. 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)
  • 70. Drug interactions are precipitated in multiplemanners• Compounds as • Compounds as substrates (victims) inhibitors/inducers (perpetrators) • Not only P450s… • UGTs • Transporters • Can also be pharmacodynamic From: Williams et al, DMD 2004

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