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Pharmaceuticals Presented at the Hamner Institutes for Health Sciences Course: Physiologically Based Pharmacokinetic (PBPK) Modeling in Drug Development and Evaluation April 6–10, 2009 Applications of PBPK Modeling in Preclinical Drug Development Micaela Reddy DMPK Department, Modeling & Simulation Group Roche Palo Alto March 30, 2009 1 Pharmaceuticals Goals ■ Present examples of multiple ways that PBPK modeling can be used to address key issues for preclinical projects ■ Describe how PBPK models can be used to generate hypotheses and prioritize additional experiments ■ Provide a method for developing physiologically based oral absorption models and discuss the factors that might impact oral absorption for BCS Class 1, 2, 3 and 4 compounds ■ Illustrate how PBPK/PD modeling can be used to assess the likelihood that a compound will be efficacious in the clinic and to characterize the therapeutic window 2 Donor pH Dependence of Solubility Acceptor conc. 10 1 time 0.1 Calculated Measured Fitted 0.01 0.001 pH Intestinal permeability P-g p vo P In v i formulation K A cti v e p r oc h es s es Protein binding sm So i l o lu b a t bi e lit M y Conc 0 1 2 3 4 5 6 7 8 9 10 Normal Medium with Calcium Ca2+-free Medium with 1 mM EGTA Uptake Cell Lysis and Sampli ng 3 T. Lavé Pharmaceuticals log(Solubility) [mg/mL] PBPK models have many applications in preclinical drug development because of the need to integrate data In silico estimates Pharmaceuticals Outline ■ ■ ■ ■ PBPK models for generating hypotheses Oral absorption modeling PBPK modeling for candidate selection Predicting exposure window using PBPK/PD 4 Pharmaceuticals PBPK modeling is an evolving process ■ New paradigm: See if you can predict in preclinical species. If you cannot, perform appropriate experiments to fill the knowledge gap. ■ Mechanistic knowledge and availability of key data determine the confidence you can have in model predictions. 5 Pharmaceuticals Fundamental value of modeling ■ Models allow you to integrate multiple sources of data and information with a mechanistic description based on your fundamental understanding of the system. ■ If the model prediction is correct, it is useful. ● Instead of making predictions based on rat data, you can take it a step further. After testing your understanding of the system with rat, you can extend that knowledge to make predictions in man. ■ If the model prediction is incorrect, it is also useful. ● The disconnect between the model and data suggests your understanding of the system is flawed. ● The model can be used to rule out hypotheses that are inconsistent with the data and determine additional experimentation that is most likely to be informative. 6 Pharmaceuticals Example: Lack of IVIVC from nonlinear CL Rat PO Profile at 3 mpk Problem: Modeling indicated lack of good IVIVC Concentration (ng/mL) 1000 PBPK Simulation Measured PK 100 10 1 0 10 20 30 0.1 Time Post Dose (h) Experiments: • Rat and dog bile-duct cannulated studies to better understand clearance mechanisms: No significant biliary secretion • Using lower concentration of drug (0.1 µM instead of 1 µM) for clearance measurement in vitro to prevent saturation of metabolic enzymes in hepatocyte or microsome to obtain accurate intrinsic clearance D. Lu 7 Pharmaceuticals Outcome: IVIVC achieved for the compound within 2-fold uncertainty using newly obtained microsome clearance. Proceeded with PBPK simulation in human. Rat PO Profile at 3 mpk Concentration (ng/mL) 1000 PBPK Simulation 100 Measured PK 10 1 0 5 10 15 20 25 30 0.1 Time Post Dose (h) D. Lu 8 Courtesy of Dr. Werner Rubas Pharmaceuticals Example: Impact of enterohepatic circulation (EHC) on PK 9 Pharmaceuticals For this compound, CL is overpredicted and t1/2 is underpredicted by the in vitro model parameters Observed 1000 Modeled Conc. [ng/mL] 100 10 1 0.1 0 5 10 15 20 25 30 Time [h] Hypothesis: Enterohepatic circulation of parent Estimation of fraction EHC = (CLobs – CLpred)/CLobs = 0.25 10 W. Rubas 1000 Pharmaceuticals Inclusion of predicted EHC (25%) improves simulation Observed Modeled w/o EHC Modeled w EHC Conc. [ng/mL] 100 10 1 0.1 0 5 10 15 20 25 30 Time [h] Experiment using bile duct cannulated rats: Fraction EHC was determined to be 0.33. 11 W. Rubas Pharmaceuticals PK profiles are well simulated when enterohepatic circulation is included (33%) PO 2 mg/kg IV 0.5 mg/kg 1000 1000 Observed Observed Modeled w/o EHC Modeled Modeled w EHC 100 Conc. [ng/mL] Conc. [ng/mL] 100 10 10 1 1 0.1 0.1 0 5 10 15 20 25 30 Time [h] Predicted AUC and t1/2 are within 10 % of observed 0 5 10 15 20 25 30 Time [h] Cmin prediction is improved 12 W. Rubas Pharmaceuticals Outline ■ PBPK models for generating hypotheses ■ Oral absorption modeling ● Validating the model ● Using the model ■ Compound prioritization based on early preclinical data ■ Predicting exposure window using PBPK/PD 13 Example: ACAT model in GastroPlus Pharmaceuticals Recently, several powerful PBPK approaches have been developed to predict oral absorption. The model can also include degradation of compound passing through the GI tract, the impact of transporters, and differences in permeability with region. Species differences in regional pH, compartment volume, etc. are included in the model. Other examples: ADAM (advanced dissolution, absorption & metabolism) model (Jamei et al., in press), GI tract described as tube with spatially varying properties (Willman et al., 2003) 14 Data (in vitro/in vivo) Validation model (few compounds) Pharmaceuticals How can M&S increase efficacy and speed in drug discovery? Screening funnel Multiple series Sensitivity analysis Critical parameters Not critical Which one to focus on first? •Metabolic stability? •Protein binding? •Solubility? Stop experiment Clinical lead Front load 15 Pharmaceuticals PBPK modeling for oral absorption ■ PBPK modeling approaches for predicting human oral absorption can be used to simulate ● absorption as a function of time ● the amount absorbed from various regions of the GI tract ● plasma concentrations as a function of time ■ Approach: ● Develop a model in a preclinical species and check it against whatever data is available (e.g., po SDPK data) ● Determine how to improve the model based on mismatches between model and data; perform critical experiments to elucidate key mechanisms ● Develop the human model based on what was learned in the preclinical species 16 Pharmaceuticals Applications of oral absorption modeling ■ Predicting bioavailability ■ Evaluate potential absorption and bioavailability for large ■ ■ ■ ■ number of compounds with very limited data Determine the sensitivity of bioavailability and PK to various input parameters Maximum absorbable dose Predicting whether a food effect is expected Determining an appropriate formulation strategy 17 Pharmaceuticals An absorption model can be developed using permeability and solubility data Physicochemical properties Parameter Early in development (LO) Late in development (CLS, EIGLP) cLogP / Log D @ reference pH In silico Measured Log D-pH profile pKa’s In silico Measured Permeability (human Peff) In silico Optional: cell-based permeability assay (e.g., MDCK, Caco-2) or PAMPA Cell-based permeability assay (e.g., MDCK, Caco-2) or PAMPA Solubility @ reference pH Aqueous; HT Fessif/Fassif for low solubility compounds (e.g., < 20 ug/ml aqu. sol.) pH-solubility profile, including Fessif/Fassif Solubility factor In silico Based on solubility data for range of pH’s 18 Pharmaceuticals Outline ■ PBPK models for generating hypotheses ■ Oral absorption modeling ● Validating the model ● Using the model ■ PBPK modeling for candidate selection ■ Predicting exposure window using PBPK/PD 19 Pharmaceuticals To check your absorption model Simulation results can be compared to: Bioavailability for a single experiment Bioavailability as a function of dose Fraction absorbed into the portal vein Plasma time-course concentrations (requires a PK study) Before using absorption modeling to make decisions, it is good practice to check the model results against PK data at multiple doses and in one or more species if possible. 20 Pharmaceuticals Using absorption modeling to predict bioavailability ■ F = Fabs x Fh x Fg x … where ● Fabs is the fraction that is absorbed into enterocytes ● Fh is the fraction that makes it through the liver ● Fg is the fraction that makes it through the enterocytes without being metabolized ■ There could be additional sources of loss (e.g., metabolism by microflora in the gut, instability in the stomach, etc.). ■ Absorption modeling provides a prediction of fraction absorbed, but to predict in vivo bioavailability, first-pass processes must also be incorporated. 21 Pharmaceuticals Definition of Bioavailability (F) ■ Bioavailability (F) is the fraction (or percent) of administered dose that reaches systemic circulation intact. AUCpo AUCiv ■ Bioavailability is typically calculated F= by comparing AUC from iv PK data D po D iv to AUC from po PK data. ■ Often bioavailability is < 1 because loss can occur from factors like ● incomplete absorption (e.g., from low permeability or solubility) ● first pass extraction by the gut and liver ● chemical degradation in the lumen ■ Sometimes estimated F is > 1 than because of factors such as ● saturable metabolism ● enterohepatic circulation ● incomplete administration of iv dose 22 ■ One mechanism that often has a large impact on bioavailability is first-pass hepatic extraction. ■ At a minimum, the effect of first-pass extraction (Eh) should be combined with Fabs for estimating bioavailability. (Assume Fg=1 if no phenotype data are available.) F = Fh x Fabs Fh = fraction that makes it past the liver, 1 - Eh Fabs = fraction absorbed ■ When can Fabs be compared directly to bioavailability? ● For compounds that are cleared by mechanisms other than ● hepatic metabolism or biliary excretion For low-clearance compounds (CL < 0.1 x QL) 23 Pharmaceuticals Comparing Fabs to Bioavailability ■ Fh can be calculated as: Fh = 1 – Eh = 1 – CL / QL ■ Eh can be calculated from hepatocyte or microsome in vitro data ■ Eh can also be estimated using CL from an iv SDPK study (CL / QL) ● Caution: Is the mechanism of CL hepatic metabolism? ■ Which method to use? Do scaled hepatocyte or microsome data: ● predict IV CL? Either method can be used. ● underpredict IV CL? Metabolism is probably not the only mechanism of CL, and so scaled hepatocyte or microsome data should be used. ● overpredict IV CL? Most CL is probably from metabolism, and the IV CL can be used as the more accurate CL estimate. CL = CLplasma / BPR where BPR = blood to plasma ratio In some cases (e.g., high extraction compounds that do not partition into red blood cells) it is critical to have BPR data for interpretation of PO PK data! 24 Pharmaceuticals Methods for calculating Fh Pharmaceuticals Checking model prediction of bioavailability Selected drug properties and F from rat SDPK data with Fabs from GastroPlus and Fh from an appropriate method. Solubility, µg/ml a Caco2 Peff, cm/sx10-6 ER F Fabs Fh FabsxFh Compound 1 145 (Aq) 13.2 1.2 0.26 0.96 0.23 0.22 Compound 2 21.2 (Aq) 16.3 1.1 0.34 0.55 0.34 0.19 Compound 3 2 (Aq) 15.3 0.77 0.46 0.079 0.31 0.024 Compound 3 38.7 (Fa) 15.3 0.77 0.46 0.78 0.24 Compound 4 6.1 (Aq) 1.52 8.4 0.38 0.077 0.37 0.029 Compound 4 32.9 (Fa) 1.52 8.4 0.38 0.35 0.37 0.13 Compound 4 32.9 (Fa) 13.2 (w/ elac.b) 0.87 0.38 0.62 0.37 0.23 a Aq = aqueous solubility at pH=6.5. Fa = FaSSIF solubility at pH=6.5. b Elacridar is a P-gp inhibitor. 0.31 25 Multiple mechanisms affect exposure as a function of dose. However, in terms of Fabs, one would generally expect the following: ■ BCS Class 2 and 4 may exhibit dose-proportional or lessthan-dose-proportional increases in AUC and Cmax as the dose increases. ● If the model predicts that dose-limited absorption occurs (i.e., simulated Fabs decreases with increasing dose), there should be a less-than-dose-proportional increases in AUC and Cmax as the dose increases in po SDPK studies. ■ BCS Class 1 and 3 may exhibit dose-proportional or greaterthan-dose-proportional increases in AUC and Cmax as the dose increases in po SDPK studies. 26 Pharmaceuticals Checking bioavailability prediction for a range of doses Pharmaceuticals Checking fraction absorbed into the portal vein * ■ The Fabs into the portal vein will not reflect the effects of first-pass Cumulative Aabs, mg hepatic metabolism (although other first-pass processes, such as metabolism by enterocytes, might still have an impact). ■ Therefore, Fabs into mg/kg POPOdose 700100 mg ITMN-191 Dose the portal vein can be 300 used more easily to 250 check the model 200 prediction of Fabs. 150 100 KFA KFB KFC KFD GP sim 50 0 0 4 8 12 16 20 24 28 time, h * Useful for compounds with high hepatic extraction! 27 Pharmaceuticals Calculation: Amount Absorbed ■ The amount absorbed as a function of time can be calculated from the concentrations in the portal and peripheral vein. d Aabs = Qpv x (Cpv – Cp) x BPR dt Aabs = amount absorbed t = time Qpv = portal vein blood flow rate (~85% of liver blood flow) Cpv = plasma concentration in portal vein Cp = plasma concentration in peripheral vein BPR = blood-to-plasma ratio 28 Pharmaceuticals Checking the model against plasma time-course concentrations Rat, 20 mg/kg in Suspension Rat, 2 mg/kg in Suspension 100000 Cp, ng/ml 1000 Rat 1 Rat 2 100 Rat 3 Sim 10 10000 Cp, ng/ml 10000 Rat 1 1000 Rat 2 100 Rat 3 Sim 10 1 1 0 4 8 12 16 20 24 0 4 8 time, h 16 20 24 time, h Monkey, 20 mg/kg Monkey, 2 mg/kg KF 1 10000 KF 2 KF 3 1000 Sim Cp, ng/ml 100000 100000 Cp, ng/ml 12 KF 1 10000 KF 2 KF 3 1000 Sim 100 100 0 4 8 12 time, h 16 20 24 0 4 8 12 time, h 16 20 24 29 ■ For rat PO PK, first use a compartment model to simulate iv PK to remove one source of uncertainty and check the absorption prediction. ● Assumption: linear kinetics ● First-pass Eh will need to be calculated and included ● After checking absorption model, redo calculation to check the PO PK profile using the PBPK model. 0.5 mg/kg iv PK Compartment model for systemic PK Change to po exposure, parameterize oral absorption model 2 mg/kg po PK 30 Pharmaceuticals Approach: Reduce confounding factors by using a compartment model for systemic PK. Pharmaceuticals Next, switch to the PBPK model for systemic PK to verify that the model is still predictive. ■ After checking the absorption model, redo the calculation to check the PO PK profile using the PBPK model. 0.5 mg/kg iv PK PBPK model for systemic PK Change to po exposure, parameterize oral absorption model 2 mg/kg po PK 31 Pharmaceuticals What do you do if the model does not match the data? Several avenues of exploration ■ User error ● Double check input, paying close attention to units ● Are solubility, permeability, and compound properties entered correctly? ■ Inappropriate input for BCS Class 2, 3 or 4 ● BCS Class 2, 4: need appropriate solubility data ● BCS Class 3, 4: carefully consider best way to estimate Peff ■ Important mechanism impacting absorption or PK not yet identified (e.g., metabolism by gut, nonlinear metabolism) ● Need to design appropriate experiments 32 Pharmaceuticals Absorption models for BCS Class 1 compounds tend to be reliable, but issues can still arise. Example: Dissolution data was necessary to simulate absorption of diltiazem (BCS Class I) ■ The initial model overpredicted Cmax and underpredicted tmax. Dissolution data was required to simulate absorption. ■ Input: Dissolution data, solubility = 3000 µg/ml @pH = 6.4, PAMPA Peff = 1.9x10-6 cm/s, hepatic extraction = 54% Weibull function Dog PO PK, Teva IR 120 mg Tablet Formulation=IR, Teva 1600 100 Cp, ng/mL 1200 Dog 1 80 Dog 2 Dog 3 800 Mean Sim, generic 400 Sim, disso data 0 60 Observed 40 Predicted 20 0 0 4 8 12 16 Time (hours) 20 24 0 50 100 time (min) 150 200 33 Pharmaceuticals For human simulation, dissolution data and first-pass extraction by the gut had to be incorporated. Weibull function Cardizem IR - 120 mg dose Formulation=IR, Cardizem 120 100 Cp, ng/ml 100 80 60 40 No GFJ GFJ 80 Sim, no GFJ 60 Sim, GFJ 40 Observed Predicted 20 0 0 4 8 12 time, h 16 20 24 Data from Christensen et al. 2002 20 0 0 500 1000 1500 time (min) One way to determine the impact of first pass extraction by the gut is to do a grapefruit juice (GFJ) study because it inhibits CYP3A4 in the gut (Yang et al. 2007). Fg < 1 for many CYP3A4 substrates, e.g., midazolam (Fg = 0.57), felodipine (Fg = 0.58), and saquinavir (Fg = 0.67). * Most PBPK software packages include CYP expression in the gut and can predict Fg if reaction phenotyping data is available. 34 Pharmaceuticals Considerations for dissolution data ■ Dissolution data might improve your prediction, depending on how the study is designed and whether dissolution is rate limiting. ■ Considerations for study design: ● Sink conditions ● Physiologically relevant media (FeSSIF, FaSSIF, FeSGF, FaSGF) ● Two-stage design (e.g., pH 1 and then pH 6) Key plot for diagnosing whether dissolution might be rate-limiting Dissolved Absorbed BCS Class II compound 35 Pharmaceuticals BCS Class 2 Low solubility, high permeability ■ For BCS Class 2 solubility is a very sensitive model parameter, ■ ■ ■ ■ and appropriate (i.e., physiologically relevant) solubility data is critical. FeSSIF and FaSSIF solubility data have been shown to dramatically improve the predictive accuracy of the absorption model for low-solubility compounds. If the dosing solution contains solubility enhancers, solubility data with solubility enhancers might also be appropriate. Different batches of drug can have very different solubilities. A mismatch between Fabs in a po SDPK study and the model prediction of Fabs could be because the compound administered to the animals and the compound used to determine the solubility were from different batches. Efflux transporters might be an issue. 36 Pharmaceuticals Verify that sufficient data are available to set solubility If a compound has a physiologically relevant pKa, carefully consider if solubility data have been generated for a sufficient range of pH values. 37 Pharmaceuticals Early stage prediction of bioavailability in rats for BCS Class 2 compounds Used to verify understanding of factors impacting series %F Compounds: -CLogP, pKa from ADMET Predictor; 45.0 -Dose form: IR suspension; 40.0 12 within 2-fold 2 over-predicted 2 under-predicted 35.0 -Solubility: phosphate (if >20 ug/mL) and FaSSIF -particle size 25 µm -Peff from Caco2 AB Physiology: Rat (fasted), Opt LogD Model 30.0 %F (cal) -Particle density 1.2 g/mL y = 1.0x 25.0 20.0 15.0 10.0 5.0 0.0 Simulation: 24h 0 5 10 15 20 25 30 35 40 45 %F (measured) First-pass extraction: In vivo IV clearance 38 Courtesy of Q. Hu ■ For BCS Class 3, the effect of transporters may have a large, and not easily predicted, impact on PK. ● At low doses, the impact of transporters will be largest. ● At high doses, transporters could saturate. ● Solution: include saturable transport into the model. ■ For estimating Peff for Pgp substrates, Caco2 (A>B) data with elacridar is likely more predictive. But for lower solubility or permeability compounds, Caco2 (A>B) data without elacridar could be more predictive. Looking at the Fabs calculation using both Peff values could provide a reasonable range of values. ■ In the intestines (duodenum, proximal jejunum scrapings), CYP3A4 was the most abundant P450 (80% of intestinal P450), followed by CYP2C9 (15%) (Paine et al. 2006). Gut metabolism may be most important for low permeability CYP3A4 substrates (Yang et al. 2007). 39 Pharmaceuticals BCS Class 3 Low permeability, high solubility Pharmaceuticals The gut expresses many efflux and uptake transporters. Expression levels can vary depending on location in GI tract. Figure from: Predicting drug disposition, absorption/elimination/ transporter interplay and the role of food on drug absorption. Custodio JM, Wu CY, Benet LZ (2008). Adv Drug Deliv Rev 60(6): 717-733. 40 Pharmaceuticals Early stage prediction of bioavailability in rats for BCS Class 3 compounds Used to verify understanding of factors impacting series %F 5 over-predicted 8 within 2-fold Data: In silico cLogP, pKa; Caco2 Peff with no inhibitor, THESA solubility, Eh calculated using Mx data Courtesy of S. Larrabee 41 ■ For BCS Class 4, the issues that occur with BCS Class 2 and 3 might both occur. ■ For BCS Class 4, it might be important to do more checking than for the other BCS classes, and to be more careful with interpretation of results, particularly if transporters are involved. ■ Absorption modeling can still be useful, but results should be carefully interpreted. The key is to interpret more qualitatively than quantitatively unless you have validated the model appropriately. 42 Pharmaceuticals BCS Class 4 Low solubility, low permeability Pharmaceuticals Outline ■ PBPK models for generating hypotheses ■ Oral absorption modeling ● Validating the model ● Using the model ■ PBPK modeling for candidate selection ■ Predicting exposure window using PBPK/PD 43 ■ Peters (2008): Assess any “lineshape” mismatch between simulated and observed oral profiles to gain mechanistic insights into processes impacting absorption and PK ● for example, drug-induced delays in gastric emptying and regional variation in gut absorption. ■ Jones et al. (2006): GastroPlus human oral absorption models were established for six compounds. Food effects were predicted for a range of doses and compared to the results from human food effect studies. ● In general, the models were able to predict whether a food effect would be major (i.e., for two compounds) or minor (i.e., for four compounds). 44 Pharmaceuticals Examples from the literature Pharmaceuticals Example: Is a CR formulation feasible? ■ CR formulation development is expensive, but can be necessary for a compound with a short t1/2 in humans. ■ Extensive PK experimentation is typically performed to determine whether a CR formulation is feasible (i.e., whether the compound can be absorbed in the colon) before resources are spent on the effort. ● Surgically modified animals with cannulas for administering compound to various parts of the GI tract ● Cross-over IV and PO PK studies for deconvolution to determine the amount absorbed as a function of time ● EnterionTM capsule, Pharmaceutical Profiles ■ Modeling can help to focus and reduce the experimentation. 45 Simulated Human Site-of-Absorption Data 100 mg Furosemide PO 1 PO IJ IC 0.8 Cp, ug/ml Pharmaceuticals Based on the po PK profiles Site-of-absorption study alone, it is not clear where Example: Furosemide (BCS Class 4) absorption occurs. 0.6 0.4 0.2 0 0 4 8 12 16 20 24 time, h IJ IC 46 Simulations performed with GastroPlus demo database. ■ If iv and po PK data are available, numerical deconvolution can be used to estimate the fraction of drug absorbed as a function of time. ● The results are sometimes noisy and difficult to interpret. ● Better results are obtained with a cross-over design so that iv and po data are available in the same animal. ■ This analysis will not necessarily result in clear information on where drug is absorbed (i.e., results may be difficult to interpret). ■ For compounds that are well absorbed in the small intestines, the po data will have no information on the rate of absorption from the colon. 47 Pharmaceuticals Deconvolution of po PK data (an empirical approach) can also be useful for understanding absorption. Pharmaceuticals Example: Ketoprofen (BCS Class 1) Simulation for humans administered a 50 mg dose of ketoprofen: Most ketoprofen absorption occurs in the duodenum and jejunum. The po PK data contains no information on rate of absorption from the large intestines. Simulations can provide useful information about whether deconvolution of po PK data will yield useful information. 48 Simulation performed with GastroPlus demo database. Pharmaceuticals 100 PO 80 60 Peff (x10-6 cm/s) = 40 0.3 1.2 1.7 3.4 20 0 Cumulative Aabs, mg Cumulative Aabs, mg Example: Should a CR formulation be developed for a compound with a short t1/2? 100 Peff (x10-6 cm/s) = 80 IC 0.3 1.2 1.7 3.4 60 40 20 0 0 4 8 12 16 time, h 20 24 0 4 8 12 16 20 24 time, h For this compound, Caco2 Peff data had been measured and exhibited 10-fold variability. Simulations indicate that the intracolonic route in humans may result in 31-46% of the PO AUC. Sensitivity analysis shows that Peff is critical for making the prediction. 49 Pharmaceuticals Model refinement with new data suggests that CR development would be very challenging. ■ Peff was a sensitive model parameter. ■ Additional experimentation revealed that the compound had permeability in the low end of the range. ● Refined simulations indicated that the IC route in humans would likely result in 31% of the PO AUC or less. ■ In a recent PharmaProfiles poster by Connor et al. (2008), CR development is classified as very challenging for compounds with a relative bioavailability of < 30% upon administration to the colon. ■ Based on this assessment, CR development would likely be very challenging. 50 Pharmaceuticals Outline ■ ■ ■ ■ ■ PBPK models for generating hypotheses Oral absorption modeling PBPK modeling for a parent compound and metabolite PBPK modeling for candidate selection Predicting exposure window using PBPK/PD 51 Pharmaceuticals PBPK For Candidate Selection ■ Aim: to use physiologically based simulation tools to combine the available PK and PD data and assist in selection of the optimal clinical candidate ■ Steps taken: ● Verification: compare simulated and observed PK in rat ● Predict PK: estimate human PK ● Predict PD: effective concentrations in human ● Estimation: clinical doses and exposures ■ Outcome: ● Balanced comparison of PK/PD incorporating variability and uncertainty to be weighed with other factors (early tox, synthetic tractability, formulation ease etc…) N. Parrott 52 Pharmaceuticals PBPK For Candidate Selection 1.5 40 1 30 0.5 20 0 10 Clint human hepatocytes [uL/min/M cells] 0 CL Rat (ml/min/kg) 40 30 20 10 0 Ki rat (nM) Ki human (nM) Available data for 5 compounds •Physicochemical 0.40 •In vitro hepatocytes rat & man 0.30 •Protein binding rat & man 0.20 0.10 •In vivo PK i.v. and p.o. in rat 0.00 •Effect versus concentration in rat fu% rat fu% man 6 4 2 0 ID90 rat (mg/kg) N. Parrott 53 Pharmaceuticals PBPK For Candidate Selection Simulated Cmax in human for a dose of 25mg including variability in both CL and V based upon in vitro and in vivo data Mean with 95% limits shown 400 Cmax [ng/mL] 350 300 250 200 150 100 50 ROCHE_5 ROCHE_4 ROCHE_3 ROCHE_2 N. Parrott ROCHE_1 0 54 Pharmaceuticals PBPK For Candidate Selection 1000 Variability is based on the observed variability in the rat 100 10 N. Parrott ROCHE_5 ROCHE_4 ROCHE_3 ROCHE_2 1 ROCHE_1 Conc for 90% reversal [ng/mL] Effective concentration in human estimated from rat PD data rat and in vitro data in both species (Fu% rat & human, Ki rat & human) 55 40 Prediction in Human 30 20 10 0 CL Rat (ml/min/kg) Simulation 1000 Pharmaceuticals Multiple Discovery Data 1.5 100 1 0.5 10 0 Clint human hepatocytes [uL/min/M cells] 0.40 0.30 0.20 •Integrates multiple PK and PD data 1 Conc. for 90% effect in human (ng/mL) 0.10 0.00 fu% rat fu% man 40 30 20 10 •Aids rational and balanced decision 80 60 40 0 Ki rat (nM) Ki human (nM) •Focuses on expected human PK/PD 6 4 2 0 ID90 rat (mg/kg) 20 0 Daily dose [mg] 56 N. Parrott Pharmaceuticals Outline ■ ■ ■ ■ ■ PBPK models for generating hypotheses Oral absorption modeling PBPK modeling for a parent compound and metabolite PBPK modeling for candidate selection Predicting exposure window using PBPK/PD 57 Pharmaceuticals Hypothetical example Predicting therapeutic window for drug with safety issue and nonlinear PK ■ A BCS Class II compound has dose-limited absorption. ■ Efficacy for the compound is related to percent inhibition, which can be described using a simple Emax model with EC50 = 40 ng/ml. ■ The compound has an off-target effect, and the PD for this effect also can be described using a simple Emax model with EC50 = 2000 ng/ml. ■ A simple modeling approach can be used to understand whether efficacy can be achieved while avoiding the PD that is a safety concern. 58 Pharmaceuticals Even at low doses, dose-limited absorption is expected to occur. Cp, ug/ml 0.16 20 mg 40 mg 0.12 Fabs = 31% 0.08 60 mg 80 mg 100 mg 0.04 Fabs = 62% 0 0 4 8 12 16 20 24 time, h 59 Pharmaceuticals Dose-limited absorption might make it difficult to achieve efficacious Cp Efficacy, % of Emax ■ For efficacy, a minimum of 50% inhibition is required. 100 80 20 mg 40 mg 60 40 60 mg 80 mg 20 100 mg 0 0 4 8 12 16 20 24 time, h 60 Pharmaceuticals At a 100 mg dose, the safety PD would likely be detectable. ■ The safety effect cannot be determined as different from Safety PD, % change from baseline the baseline for < 5% response, and so doses up to 60 mg might be acceptable. 7 6 5 4 3 2 1 0 20 mg 40 mg 60 mg 80 mg 100 mg 0 4 8 12 16 20 24 time, h 61 Pharmaceuticals 100 0.16 100 mg dose Cp, ug/ml 0.14 80 0.12 0.1 60 0.08 40 0.06 0.04 20 0.02 0 Efficacy/safety PD, % Despite a relatively low peak-to-trough ratio, the drug does not have a therapeutic window. Cp Efficacy PD Safety PD 0 0 4 8 12 16 20 24 time, h 62 ■ PBPK modeling can be useful for multiple applications throughout the preclinical development process. ● Generating hypotheses and prioritizing experimentation ● Verifying that processes impacting absorption are understood ● Selecting the most efficacious compound to move forward ■ The key is to use the model appropriately for the amount of validation that was possible. ■ PBPK modeling can be used to integrate multiple forms of preclinical data and to make a prediction of human PK/PD for both efficacy and safety end points. 63 Pharmaceuticals Summary Pharmaceuticals Acknowledgements ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ Neil Parrott Thierry Lave Werner Rubas Ying Ou Dan Lu Pam Berry Paul Weller Karen Olocco Dimitrios Stefanidis Qingyan Hu Susan Larrabee 64 Pharmaceuticals References ■ Christensen H, Anders S, Holmboe AB, and Berg KJ. (2002). ■ ■ ■ ■ Coadministration of grapefruit juice increases systemic exposure of diltiazem in healthy volunteers. Eur J Clin Pharmacol, 58: 515520. Connor, A, King, G, and Jones, K. “Evaluation of Human Regional Bioavailability to Assess Whether Modified Release Development is Feasible” AAPS 2008 Poster M1378 Hinderling, PH. (1997). Red blood cells: A neglected compartment in pharmacokinetics and pharmacodynamics. Pharmacological Reviews 49: 279-295. Jamei M, Turner D, Yang J, Neuhoff S, Polak S., RostamiHodjegan A, and Tucker G. (In press 2009). Population-Based Mechanistic Prediction of Oral Drug Absorption. AAPS J. Jones, HM, Parrott, N, Ohlenbusch, G, and Lavé, T. (2006). Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling. Clin. Pharmacokinet., 45, 1213-1226. 65 Pharmaceuticals References ■ Martinez, MN, and Amidon, GL. (2002). A mechanistic approach to ■ ■ ■ ■ ■ understanding the factors affecting drug absorption: A review of fundamentals. J Clin Pharmacol, 42:620-643. Masimirembwa CM, Bredberg U, Andersson TB. (2003). Metabolic stability for drug discovery and development - Pharmacokinetic and biochemical challenges. Clin Pharmacokinet 42: 515-528. Paine MF, Hart HL, Ludington SS, Haining RL, Rettie AE, and Zeldin DC. (2006). The human intestinal cytochrome P450 “pie”. DM&D 34(5):880-886. Peters, S. (2008). Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin. Pharmacokinet., 47: 261-275. Willmann, S, Schmitt, W, Keldenich, J, and Dressman, JB. (2003). A physiologic model for simulating gastrointestinal flow and drug absorption in rats. Pharm Res 20: 1766-1771. Yang J, Jamei M, Rowland-Yeo K, Tucker G, and Rostami-Hodjegan A. (2007). Prediction of Intestinal First-Pass Drug Metabolism. Current Drug Metabolism, 8: 676-684. 66