ABSTRACT
American Society for Clinical Pharmacology and Therapeutics Abstracts of papers, *Quantitative Systems Pharmacology: Multiscale Model-Based Drug Development Through Integrating Systems Biology and Pharmacometrics Pre-conference Hyatt Regency, New Orleans, Louisiana, March 3, 2015 Abstracts of papers, *2015 Annual Meeting Hyatt Regency, New Orleans, Louisiana, March 4–7, 2015
QP-01 MODELING and SIMULATION-GUIDED RATIONAL DRUG DISCOVERY AND DEVELOPMENT: A CASE STUDY OF MAVRILIMUMAB. B. Wang,1 C. Wu,1 L. Roskos2; 1AstraZenica/MedImmune, Mountain View, CA, 2AstraZenica/MedImmune, Gaithersburg, MD. B. Wang: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/ Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. C. Wu: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. L. Roskos: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/ Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. BACKGROUND: The development of a new drug is a lengthy and costly process with low probability of success. Continuum of “learn/confirm/predict” by translational and clinical modeling and simulations (M&S) was implemented at every decision point for mavrilimumab, a human monoclonal antibody targeting GM-CSFRa. Mavrilimumab is currently being developed for the treatment of rheumatoid arthritis. METHODS: At discovery stage, a translational model integrating biology, receptor internalization kinetics and endogenous IgG kinetics was utilized to set an antibody affinity goal. The binding affinity of the lead molecule and nonhuman primate data were incorporated in the model to predict the pharmacokinetics (PK) and receptor occupancy (RO) in humans for first-time-in-human (FIH) starting dose recommendation. Mechanistic PK modeling facilitated the transition from single weightbased intravenous dosing for FIH to multiple fixed subcutaneous (SC) dosing of mavrilimumab for a Proof-of-Principle (PoP) study. Efficacy modeling and stochastic simulations identified 150 mg as the top dose for a Proof-of-Concept (PoC) study. RESULTS: The lead molecule, mavrilimumab, met the affinity goal and the PK in RA patients was well predicted. PK and efficacy outcome from the PoP study confirmed appropriate selection of SC doses and every-two-week dosing interval. Although 150 mg was not evaluated in the PoP study, improved efficacy was observed at this dose level in PoC as predicted by a priori clinical simulations. Informative
dropouts were further included in the PoC efficacy model for selection of optimal Phase III dose. CONCLUSION: By rational recommendations of an antibody affinity goal, safety margin and clinical dosing regimens, M&S greatly facilitated the discovery and development of mavrilimumab.
QP-02 ASSESSING SYNERGY OF DRUG AGONISTS USING A SURFACE RESPONSE ANALYSIS IN R. G. Vlasakakis,1 R.L. O’Connor-Semmes,2 M.A. Young2; 1GLaxoSmithKline, London, United Kingdom, 2GlaxoSmithKline, Research Triangle Park, NC. G. Vlasakakis: None. R.L. O’Connor-Semmes: None. M.A. Young: None.
*Abstracts appear in presentation number order. QP denotes those abstracts selected to be presented at the Quantitative Systems Pharmacology: Multiscale Model-Based Drug Development Through Integrating Systems Biology and Pharmacometrics Pre-conference, March 3, 2015. PT denotes those abstracts submitted by trainees that were selected to receive Presidential Trainee Awards; these will be on display on Wednesday, March 4, at the Showcase of Top Trainee Abstracts, and during ASCPT poster session hours Thursday and Friday, March 5 and 6, 2015. OI, OII, OIII denotes abstracts selected for oral presentation. Oral Sessions are scheduled for Thursday, March 5 and Saturday, March 7. PI and PII denote Poster Session I (Thursday, March 5), Poster Session II (Friday, March 6). PW denote Poster Walk Session I, (Thursday, March 5) and Poster Walk Session III and IV (Friday, March 6). Late-breaking abstracts and encore abstracts are not published in this Supplement. They will be available in the Final Program. CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 97 SUPPLEMENT 1 | FEBRUARY 2015
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ABSTRACT BACKGROUND: The assessment of synergy between drug entities is critical in therapeutic areas such as HIV, oncology, anaesthesiology, diabetes, and obesity where combination therapies are commonly used to maximize drug effect and minimize toxicity. A major component aiding research in this direction is the utilization of modern in silico resources and surface response analyses to identify regions of synergistic efficacy among molecules by using modeling and visualization tools. The objective of this work was to illustrate a surface response analysis tool for the assessment of drug synergy implemented in the R computing environment. METHODS: A surface response is a mathematical 3D representation that relates a dependent variable, such as drug effect, to two (or more) independent inputs, such as doses or drug concentrations (Kern, 2004). In this example, a surface response was constructed by simulating dosing combinations for two agonists in order to assess their maximum synergistic efficacy. RESULTS: Figure CONCLUSION: A surface response analysis is part of a library of functions in R (“rgl”) which makes it easy to understand, adjust and debug. Surface response analyses support informed decision-making by providing a method to fit and visualize additive, synergistic data and identify regions of clinical interest.
QP-03 APPLICATION OF PBPK AND BAYESIAN MODELING FOR PREDICTION OF THE LIKELIHOOD OF INDIVIDUAL PATIENTS EXPERIENCING SERIOUS ADVERSE REACTIONS TO A STANDARD DOSE OF EFAVIRENZ. M. Chetty, T. Cain, M. Jamei, A. Rostami; Simcyp, Sheffield, United Kingdom. M. Chetty: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. T. Cain: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. M. Jamei: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. A. Rostami: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. BACKGROUND: Serious adverse reactions to a standard 600 mg of efavirenz have been reported in poor metabolizers (PMs) of CYP2B6, the major enzyme responsible for efavirenz metabolism. The objective of this study was to determine whether a standard dose of efavirenz can be useful as a probe drug in identifying PMs, when genotyping is unavailable. METHODS: Prior in vitro data were used to develop a physiologically based pharmacokinetic (PBPK) model to simulate the pharmacokinetics of a 600 mg dose of efavirenz in extensive metabolizers (EMs), intermediate metabolizers (IMs) and PMs of CYP2B6, using the Simcyp population-based simulator (V13 R2). After verifying the performance of the models with observed data, the models were used to simulate 5,000 virtual individuals in each category. Simulated concentration-time data were then used as a training set in a Bayesian model that was developed to determine the probability of identifying each phenotype based on plasma concentrations of efavirenz. Sampling times of 2 hr, 4 hr, 8 hr, 12 hr and 24 hr for a single dose and 24 hr post dose for multiple dosing were tested to determine which sampling time was associated with the highest probability of identifying PMs. The predictive capacity of the final Bayesian model was tested using published clinical data. RESULTS: Performance of the developed models was acceptable. Application of the Bayesian model suggested that there was a good likelihood of differentiating between the three phenotypes and in particular the probability of correctly identifying a PM phenotype was 0.82, corresponding to the 24 hr single dose sample. CONCLUSION: This approach may be useful in identifying patients who are at risk of experiencing serious adverse reactions and require dosage adjustments.
QP-04 INTEGRATING METABOLOMICS AND GENOMICS REVEALS NOVEL BIOMARKERS OF HYDROCHLOROTHIAZIDE RESPONSE IN PHARMACOGENOMIC EVALUATION OF ANTIHYPERTENSIVE RESPONSES (PEAR) STUDY. M.H. Shahin,1 D.M. Rotroff,2 Y. Gong,1 T. Langaee,1 C.W. McDonough,1 A.L. Beitelshees,3 T.J. Garrett,4 A.B. Chapman,5 J.G. Gums,1 S.T. Turner,6 A. Motsinger-Reif,2 R.F. Frye,1 S.E. Scherer,7 W. Sadee,8 O. Fiehn,9 R.M. Cooper-DeHoff,1 R. Kaddurah-Daouk,10 J.A. Johnson1; 1Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, 2Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 3Department of Medicine, University of Maryland, Baltimore, MD, 4Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 5Department of Medicine, Emory University, Atlanta, GA, 6College of Medicine, Mayo Clinic, Rochester, MN, 7 Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 8 Program in Pharmacogenomics, Department of Pharmacology, The Ohio State University, Columbus, OH, 9Genome Center, University of California at Davis, Davis, CA, 10Department of Psychiatry and Behavioral Sciences, Duke University, S2
Durham, NC. M.H. Shahin: None. D.M. Rotroff: None. Y. Gong: None. T. Langaee: None. C.W. McDonough: None. A.L. Beitelshees: None. T.J. Garrett: None. A.B. Chapman: None. J.G. Gums: None. S.T. Turner: None. A. Motsinger-Reif: None. R.F. Frye: None. S.E. Scherer: None. W. Sadee: None. O. Fiehn: None. R.M. Cooper-DeHoff: None. R. Kaddurah-Daouk: None. J.A. Johnson: None. BACKGROUND: Hydrochlorothiazide (HCTZ) is among the most commonly prescribed antihypertensives in the US, yet less than 50% of HCTZ treated patients achieve blood pressure (BP) control. In this study, we used a genomics-metabolomics integrative approach to identify novel biomarkers of HCTZ BP response. METHODS: This study included 228 White hypertensive PEAR participants with BP determined at baseline and after 9 weeks of HCTZ treatment. Genomewide genotyping was determined via Illumina Omni 1M Quad Chip. Untargeted metabolomics analysis was performed on fasting plasma samples using a GC TOF MS platform. Pathway analysis was conducted to integrate the BP GWAS signals at P < 1x10-4 with the metabolomics findings. Gene expression was also tested using RNA-Seq in extreme BP responders (25 responders and 25 non-responders). RESULTS: Metabolomics analysis revealed 212 known metabolites, of which 13 were significantly associated with systolic and diastolic BP response (FDR < .05). Integrative pathway analysis identified metabolites in the platelet activation pathway (p 5 .009) and Rho Kinase 1 gene (ROCK1) as a potential factor influencing HCTZ BP response. ROCK1 rs8085654 variant carriers had a poor BP response vs. non carriers (DSBP/DDBP: -5.8/-2.7 vs. -10.7/-5.9 mmHg, respectively, DSBP p 5 5x10-5 and DDBP p 5 9x10-4). Additionally, ROCK1 baseline expression levels were significantly different between HCTZ BP responders vs. non responders (23.765.8 vs 20.063.2 FPKM, respectively, p 5 .01). CONCLUSION: These results align with recent animal studies showing ROCK1 contribution to increased BP. Moreover, this study highlights the strength of using different omics to identify novel biomarkers of drug response, and suggests that ROCK1 might be an important determinant of HCTZ BP response.
QP-05 SYSTEMS PHARMACOLOGY MODELING OF HYPOMETHYLATING AGENTS DECITABINE & SGI-110 FOR EVALUATION OF AML TREATMENT BY TARGETING S-PHASE WITH PROLONGED PHARMACOKINETIC EXPOSURES. A. Oganesian,1 O. Demin, Jr.,2 A. Nikitich,2 O. Demin,2 M. Azab1; 1Astex Pharmaceuticals, Dublin, CA, 2Institute for Systems Biology, Moscow, Russian Federation. A. Oganesian: 2. I am a paid consultant/employee for; Company/Drug; Astex Pharmaceuticals, Inc. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Still investigational. O. Demin, Jr.: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/ Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. A. Nikitich: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. O. Demin: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. M. Azab: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Astex Pharmaceuticals, Inc. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. BACKGROUND: Treatment for acute myeloid leukemia (AML) targets reduction of abnormal cell proliferation rate. Decitabine (DAC), a well characterized hypomethylating agent (HMA), is incorporated into DNA during the S-phase; inhibits methylation; helps re-expression of tumor suppressor genes; and induces G2/ M arrest. SGI-110, a second generation HMA given subcutaneously (SQ), was designed to increase in vivo exposure of its active metabolite DAC. This work explored how changes in exposure window of DAC affect DNA demethylation and tumor cell proliferation in AML patients. METHODS: A systems pharmacology model was developed to characterize myeloblasts transition between cell cycle phases and proliferation of healthy neutrophils; PK of DAC after IV infusion or SQ SGI-110; LINE-1 demethylation; and progression of AML. Model parameters were calculated using literature data or fitted against experimental data. RESULTS: The model satisfactorily reproduced in vitro data with myeloblasts cell lines showing proliferation and distribution between cell cycle in the presence or absence of DAC; LINE-1 demethylation after IV DAC and SQ SGI-110; blast levels VOLUME 97 SUPPLEMENT 1 | FEBRUARY 2015 | www.wileyonlinelibrary/cpt
ABSTRACT in blood during AML progression of patients with and without treatment by HMAs. The model shows that administration of SGI-110 SQ at 60 mg/m2 (Days 1-5 of a 28-day cycle) induces LINE-1 demethylation at a higher level compared to IV DAC due to wider exposure window of the active metabolite. In simulation of 6 treatment cycles in low and highly proliferative virtual AML patients, longer DAC exposure window results in a more pronounced effect on myeloblast proliferation in bone marrow and reduction of blasts in peripheral blood. CONCLUSION: Increased exposure window of decitabine post treatment with SGI-110 SQ may result in higher efficacy in treatment of AML.
QP-06 PHARMACOKINETIC/PHARMACODYNAMIC MODELING OF HUMAN ANTI-FGF23 ANTIBODY (KRN23) AND SERUM PHOSPHORUS IN ADULTS WITH X-LINKED HYPOPHOSPHATEMIA. X. Zhang,1 N.H. Gosselin,2 J. Marier,2 T. Peyret,2 T. Ito,1 E. Imel,3 T.O. Carpenter4; 1Kyowa Hakko Kirin Pharma Inc., Princeton, NJ, 2Pharsight-A Certara Company, Montreal, QC, Canada, 3Indiana University School of Medicine, Indianapolis, IN, 4Yale University School of Medicine, New Haven, CT. X. Zhang: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/ employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. N.H. Gosselin: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. J. Marier: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. T. Peyret: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. T. Ito: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. E. Imel: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. T.O. Carpenter: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc., Ultragenyx. 3. I received honoraria from; Company/Drug; Pfizer. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; KRN23. BACKGROUND: X-linked hypophosphatemia (XLH) is an inherited metabolic bone disease caused by mutations of PHEX, with abnormally elevated serum FGF23 resulting in low renal maximum threshold for phosphate reabsorption, low serum phosphorus (inorganic, Pi), and inappropriately normal 1,25 dihydroxyvitamin D levels with subsequent development of short stature and skeletal deformities. KRN23 is an anti-FGF23 antibody being developed for the treatment of XLH. The objective was to assess the pharmacokinetic/pharmacodynamic (PK/PD) relationship between KRN23 concentrations and change from baseline serum phosphorus (DPi) levels in adults with XLH. METHODS: Serum Pi concentrations were collected during escalating dosing subcutaneous regimen of KRN23 every 28 days over an initial 4-month period (0.05 to 0.6 mg/kg, N=28) and a subsequent 12-month titrated dosing period (0.1 to 1.0 mg/kg, N=22). KRN23 concentrations were derived from a KRN23 pharmacokinetic model to pair with the observed serum Pi data. Multiple PK/PD models and quality of fit were evaluated using graphical and statistical estimators. RESULTS: Mean DPi increased and reached a plateau of effect between the 6th and 10th doses of KRN23, and slightly decreased thereafter. A PK/PD model with maximum effect (Emax) and a time-varying concentration to reach 50% of maximal effect (EC50) described the data adequately. Typical Emax and EC50 at the start of treatment were 1.5 mg/dL and 1780 ng/mL, respectively. Typical EC50 values at week 32 and 72 increased to 4102.4 and 5998.7 ng/mL, respectively CONCLUSION: An Emax model with time-varying EC50 accurately described the time-course of DPi after every 28 days dosing of KRN23 up to 16 doses. The PK/ PD model can be used to perform trial simulations of KRN23 in adults with XLH.
BACKGROUND: Besides the anti-glycemic effect, metformin has beneficial effects on the cardiovascular system, polycystic ovary syndrome, and tumor prevention. Here, we used metabolomic approaches to investigate the global changes of metabolites in plasma in response to metformin treatment in healthy volunteers. The significantly changed metabolites lead to new insights into metformin mechanism. Also, these metabolic signatures may be used as early biomarkers of metformin response. METHODS: a) 33 healthy African-American subjects were given two oral doses of metformin (1000 and 850 mg). b) Fasting baseline and two plasma samples at two times after metformin dosing were selected. c) Measurement was used mass spectrometry based GC-TOF. d) Metabolites and metformin exposure analysis: Wilcoxon paired t-test, Spearman’s rank correlation, and linear regression; Pathway analysis: Human Metabolome Database (v3.5). RESULTS: Compared to baseline, 39 metabolites were significantly changed at peak metformin concentrations in plasma. 26 were structurally identified. The top known metabolites were hypoxanthine, maltose, citrulline, ribose, tyrosine, and ornithine (q value
American Society for Clinical Pharmacology and Therapeutics Abstracts of papers, *Quantitative Systems Pharmacology: Multiscale Model-Based Drug Development Through Integrating Systems Biology and Pharmacometrics Pre-conference Hyatt Regency, New Orleans, Louisiana, March 3, 2015 Abstracts of papers, *2015 Annual Meeting Hyatt Regency, New Orleans, Louisiana, March 4–7, 2015
QP-01 MODELING and SIMULATION-GUIDED RATIONAL DRUG DISCOVERY AND DEVELOPMENT: A CASE STUDY OF MAVRILIMUMAB. B. Wang,1 C. Wu,1 L. Roskos2; 1AstraZenica/MedImmune, Mountain View, CA, 2AstraZenica/MedImmune, Gaithersburg, MD. B. Wang: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/ Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. C. Wu: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. L. Roskos: 1. This research was sponsored by; Company/Drug; MedImmune. 2. I am a paid consultant/employee for; Company/ Drug; MedImmune. 5. I am a significant stockholder for; Company/Drug; AstraZenica/MedImmune. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Mavrilimumab. BACKGROUND: The development of a new drug is a lengthy and costly process with low probability of success. Continuum of “learn/confirm/predict” by translational and clinical modeling and simulations (M&S) was implemented at every decision point for mavrilimumab, a human monoclonal antibody targeting GM-CSFRa. Mavrilimumab is currently being developed for the treatment of rheumatoid arthritis. METHODS: At discovery stage, a translational model integrating biology, receptor internalization kinetics and endogenous IgG kinetics was utilized to set an antibody affinity goal. The binding affinity of the lead molecule and nonhuman primate data were incorporated in the model to predict the pharmacokinetics (PK) and receptor occupancy (RO) in humans for first-time-in-human (FIH) starting dose recommendation. Mechanistic PK modeling facilitated the transition from single weightbased intravenous dosing for FIH to multiple fixed subcutaneous (SC) dosing of mavrilimumab for a Proof-of-Principle (PoP) study. Efficacy modeling and stochastic simulations identified 150 mg as the top dose for a Proof-of-Concept (PoC) study. RESULTS: The lead molecule, mavrilimumab, met the affinity goal and the PK in RA patients was well predicted. PK and efficacy outcome from the PoP study confirmed appropriate selection of SC doses and every-two-week dosing interval. Although 150 mg was not evaluated in the PoP study, improved efficacy was observed at this dose level in PoC as predicted by a priori clinical simulations. Informative
dropouts were further included in the PoC efficacy model for selection of optimal Phase III dose. CONCLUSION: By rational recommendations of an antibody affinity goal, safety margin and clinical dosing regimens, M&S greatly facilitated the discovery and development of mavrilimumab.
QP-02 ASSESSING SYNERGY OF DRUG AGONISTS USING A SURFACE RESPONSE ANALYSIS IN R. G. Vlasakakis,1 R.L. O’Connor-Semmes,2 M.A. Young2; 1GLaxoSmithKline, London, United Kingdom, 2GlaxoSmithKline, Research Triangle Park, NC. G. Vlasakakis: None. R.L. O’Connor-Semmes: None. M.A. Young: None.
*Abstracts appear in presentation number order. QP denotes those abstracts selected to be presented at the Quantitative Systems Pharmacology: Multiscale Model-Based Drug Development Through Integrating Systems Biology and Pharmacometrics Pre-conference, March 3, 2015. PT denotes those abstracts submitted by trainees that were selected to receive Presidential Trainee Awards; these will be on display on Wednesday, March 4, at the Showcase of Top Trainee Abstracts, and during ASCPT poster session hours Thursday and Friday, March 5 and 6, 2015. OI, OII, OIII denotes abstracts selected for oral presentation. Oral Sessions are scheduled for Thursday, March 5 and Saturday, March 7. PI and PII denote Poster Session I (Thursday, March 5), Poster Session II (Friday, March 6). PW denote Poster Walk Session I, (Thursday, March 5) and Poster Walk Session III and IV (Friday, March 6). Late-breaking abstracts and encore abstracts are not published in this Supplement. They will be available in the Final Program. CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 97 SUPPLEMENT 1 | FEBRUARY 2015
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ABSTRACT BACKGROUND: The assessment of synergy between drug entities is critical in therapeutic areas such as HIV, oncology, anaesthesiology, diabetes, and obesity where combination therapies are commonly used to maximize drug effect and minimize toxicity. A major component aiding research in this direction is the utilization of modern in silico resources and surface response analyses to identify regions of synergistic efficacy among molecules by using modeling and visualization tools. The objective of this work was to illustrate a surface response analysis tool for the assessment of drug synergy implemented in the R computing environment. METHODS: A surface response is a mathematical 3D representation that relates a dependent variable, such as drug effect, to two (or more) independent inputs, such as doses or drug concentrations (Kern, 2004). In this example, a surface response was constructed by simulating dosing combinations for two agonists in order to assess their maximum synergistic efficacy. RESULTS: Figure CONCLUSION: A surface response analysis is part of a library of functions in R (“rgl”) which makes it easy to understand, adjust and debug. Surface response analyses support informed decision-making by providing a method to fit and visualize additive, synergistic data and identify regions of clinical interest.
QP-03 APPLICATION OF PBPK AND BAYESIAN MODELING FOR PREDICTION OF THE LIKELIHOOD OF INDIVIDUAL PATIENTS EXPERIENCING SERIOUS ADVERSE REACTIONS TO A STANDARD DOSE OF EFAVIRENZ. M. Chetty, T. Cain, M. Jamei, A. Rostami; Simcyp, Sheffield, United Kingdom. M. Chetty: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. T. Cain: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. M. Jamei: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. A. Rostami: 2. I am a paid consultant/employee for; Company/Drug; Simcyp Ltd. BACKGROUND: Serious adverse reactions to a standard 600 mg of efavirenz have been reported in poor metabolizers (PMs) of CYP2B6, the major enzyme responsible for efavirenz metabolism. The objective of this study was to determine whether a standard dose of efavirenz can be useful as a probe drug in identifying PMs, when genotyping is unavailable. METHODS: Prior in vitro data were used to develop a physiologically based pharmacokinetic (PBPK) model to simulate the pharmacokinetics of a 600 mg dose of efavirenz in extensive metabolizers (EMs), intermediate metabolizers (IMs) and PMs of CYP2B6, using the Simcyp population-based simulator (V13 R2). After verifying the performance of the models with observed data, the models were used to simulate 5,000 virtual individuals in each category. Simulated concentration-time data were then used as a training set in a Bayesian model that was developed to determine the probability of identifying each phenotype based on plasma concentrations of efavirenz. Sampling times of 2 hr, 4 hr, 8 hr, 12 hr and 24 hr for a single dose and 24 hr post dose for multiple dosing were tested to determine which sampling time was associated with the highest probability of identifying PMs. The predictive capacity of the final Bayesian model was tested using published clinical data. RESULTS: Performance of the developed models was acceptable. Application of the Bayesian model suggested that there was a good likelihood of differentiating between the three phenotypes and in particular the probability of correctly identifying a PM phenotype was 0.82, corresponding to the 24 hr single dose sample. CONCLUSION: This approach may be useful in identifying patients who are at risk of experiencing serious adverse reactions and require dosage adjustments.
QP-04 INTEGRATING METABOLOMICS AND GENOMICS REVEALS NOVEL BIOMARKERS OF HYDROCHLOROTHIAZIDE RESPONSE IN PHARMACOGENOMIC EVALUATION OF ANTIHYPERTENSIVE RESPONSES (PEAR) STUDY. M.H. Shahin,1 D.M. Rotroff,2 Y. Gong,1 T. Langaee,1 C.W. McDonough,1 A.L. Beitelshees,3 T.J. Garrett,4 A.B. Chapman,5 J.G. Gums,1 S.T. Turner,6 A. Motsinger-Reif,2 R.F. Frye,1 S.E. Scherer,7 W. Sadee,8 O. Fiehn,9 R.M. Cooper-DeHoff,1 R. Kaddurah-Daouk,10 J.A. Johnson1; 1Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, 2Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 3Department of Medicine, University of Maryland, Baltimore, MD, 4Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 5Department of Medicine, Emory University, Atlanta, GA, 6College of Medicine, Mayo Clinic, Rochester, MN, 7 Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 8 Program in Pharmacogenomics, Department of Pharmacology, The Ohio State University, Columbus, OH, 9Genome Center, University of California at Davis, Davis, CA, 10Department of Psychiatry and Behavioral Sciences, Duke University, S2
Durham, NC. M.H. Shahin: None. D.M. Rotroff: None. Y. Gong: None. T. Langaee: None. C.W. McDonough: None. A.L. Beitelshees: None. T.J. Garrett: None. A.B. Chapman: None. J.G. Gums: None. S.T. Turner: None. A. Motsinger-Reif: None. R.F. Frye: None. S.E. Scherer: None. W. Sadee: None. O. Fiehn: None. R.M. Cooper-DeHoff: None. R. Kaddurah-Daouk: None. J.A. Johnson: None. BACKGROUND: Hydrochlorothiazide (HCTZ) is among the most commonly prescribed antihypertensives in the US, yet less than 50% of HCTZ treated patients achieve blood pressure (BP) control. In this study, we used a genomics-metabolomics integrative approach to identify novel biomarkers of HCTZ BP response. METHODS: This study included 228 White hypertensive PEAR participants with BP determined at baseline and after 9 weeks of HCTZ treatment. Genomewide genotyping was determined via Illumina Omni 1M Quad Chip. Untargeted metabolomics analysis was performed on fasting plasma samples using a GC TOF MS platform. Pathway analysis was conducted to integrate the BP GWAS signals at P < 1x10-4 with the metabolomics findings. Gene expression was also tested using RNA-Seq in extreme BP responders (25 responders and 25 non-responders). RESULTS: Metabolomics analysis revealed 212 known metabolites, of which 13 were significantly associated with systolic and diastolic BP response (FDR < .05). Integrative pathway analysis identified metabolites in the platelet activation pathway (p 5 .009) and Rho Kinase 1 gene (ROCK1) as a potential factor influencing HCTZ BP response. ROCK1 rs8085654 variant carriers had a poor BP response vs. non carriers (DSBP/DDBP: -5.8/-2.7 vs. -10.7/-5.9 mmHg, respectively, DSBP p 5 5x10-5 and DDBP p 5 9x10-4). Additionally, ROCK1 baseline expression levels were significantly different between HCTZ BP responders vs. non responders (23.765.8 vs 20.063.2 FPKM, respectively, p 5 .01). CONCLUSION: These results align with recent animal studies showing ROCK1 contribution to increased BP. Moreover, this study highlights the strength of using different omics to identify novel biomarkers of drug response, and suggests that ROCK1 might be an important determinant of HCTZ BP response.
QP-05 SYSTEMS PHARMACOLOGY MODELING OF HYPOMETHYLATING AGENTS DECITABINE & SGI-110 FOR EVALUATION OF AML TREATMENT BY TARGETING S-PHASE WITH PROLONGED PHARMACOKINETIC EXPOSURES. A. Oganesian,1 O. Demin, Jr.,2 A. Nikitich,2 O. Demin,2 M. Azab1; 1Astex Pharmaceuticals, Dublin, CA, 2Institute for Systems Biology, Moscow, Russian Federation. A. Oganesian: 2. I am a paid consultant/employee for; Company/Drug; Astex Pharmaceuticals, Inc. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; Still investigational. O. Demin, Jr.: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/ Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. A. Nikitich: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. O. Demin: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Institute for Systems Biology. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. M. Azab: 1. This research was sponsored by; Company/Drug; Astex Pharmaceuticals, Inc. 2. I am a paid consultant/employee for; Company/Drug; Astex Pharmaceuticals, Inc. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; still investigational. BACKGROUND: Treatment for acute myeloid leukemia (AML) targets reduction of abnormal cell proliferation rate. Decitabine (DAC), a well characterized hypomethylating agent (HMA), is incorporated into DNA during the S-phase; inhibits methylation; helps re-expression of tumor suppressor genes; and induces G2/ M arrest. SGI-110, a second generation HMA given subcutaneously (SQ), was designed to increase in vivo exposure of its active metabolite DAC. This work explored how changes in exposure window of DAC affect DNA demethylation and tumor cell proliferation in AML patients. METHODS: A systems pharmacology model was developed to characterize myeloblasts transition between cell cycle phases and proliferation of healthy neutrophils; PK of DAC after IV infusion or SQ SGI-110; LINE-1 demethylation; and progression of AML. Model parameters were calculated using literature data or fitted against experimental data. RESULTS: The model satisfactorily reproduced in vitro data with myeloblasts cell lines showing proliferation and distribution between cell cycle in the presence or absence of DAC; LINE-1 demethylation after IV DAC and SQ SGI-110; blast levels VOLUME 97 SUPPLEMENT 1 | FEBRUARY 2015 | www.wileyonlinelibrary/cpt
ABSTRACT in blood during AML progression of patients with and without treatment by HMAs. The model shows that administration of SGI-110 SQ at 60 mg/m2 (Days 1-5 of a 28-day cycle) induces LINE-1 demethylation at a higher level compared to IV DAC due to wider exposure window of the active metabolite. In simulation of 6 treatment cycles in low and highly proliferative virtual AML patients, longer DAC exposure window results in a more pronounced effect on myeloblast proliferation in bone marrow and reduction of blasts in peripheral blood. CONCLUSION: Increased exposure window of decitabine post treatment with SGI-110 SQ may result in higher efficacy in treatment of AML.
QP-06 PHARMACOKINETIC/PHARMACODYNAMIC MODELING OF HUMAN ANTI-FGF23 ANTIBODY (KRN23) AND SERUM PHOSPHORUS IN ADULTS WITH X-LINKED HYPOPHOSPHATEMIA. X. Zhang,1 N.H. Gosselin,2 J. Marier,2 T. Peyret,2 T. Ito,1 E. Imel,3 T.O. Carpenter4; 1Kyowa Hakko Kirin Pharma Inc., Princeton, NJ, 2Pharsight-A Certara Company, Montreal, QC, Canada, 3Indiana University School of Medicine, Indianapolis, IN, 4Yale University School of Medicine, New Haven, CT. X. Zhang: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/ employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. N.H. Gosselin: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. J. Marier: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. T. Peyret: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Pharsight-A Certara Company. T. Ito: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. E. Imel: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc. T.O. Carpenter: 1. This research was sponsored by; Company/Drug; Kyowa Hakko Kirin Pharma Inc. 2. I am a paid consultant/employee for; Company/Drug; Kyowa Hakko Kirin Pharma Inc., Ultragenyx. 3. I received honoraria from; Company/Drug; Pfizer. 6. The following product discussed is not labeled for the use under discussion or is still investigational; Company/Drug; KRN23. BACKGROUND: X-linked hypophosphatemia (XLH) is an inherited metabolic bone disease caused by mutations of PHEX, with abnormally elevated serum FGF23 resulting in low renal maximum threshold for phosphate reabsorption, low serum phosphorus (inorganic, Pi), and inappropriately normal 1,25 dihydroxyvitamin D levels with subsequent development of short stature and skeletal deformities. KRN23 is an anti-FGF23 antibody being developed for the treatment of XLH. The objective was to assess the pharmacokinetic/pharmacodynamic (PK/PD) relationship between KRN23 concentrations and change from baseline serum phosphorus (DPi) levels in adults with XLH. METHODS: Serum Pi concentrations were collected during escalating dosing subcutaneous regimen of KRN23 every 28 days over an initial 4-month period (0.05 to 0.6 mg/kg, N=28) and a subsequent 12-month titrated dosing period (0.1 to 1.0 mg/kg, N=22). KRN23 concentrations were derived from a KRN23 pharmacokinetic model to pair with the observed serum Pi data. Multiple PK/PD models and quality of fit were evaluated using graphical and statistical estimators. RESULTS: Mean DPi increased and reached a plateau of effect between the 6th and 10th doses of KRN23, and slightly decreased thereafter. A PK/PD model with maximum effect (Emax) and a time-varying concentration to reach 50% of maximal effect (EC50) described the data adequately. Typical Emax and EC50 at the start of treatment were 1.5 mg/dL and 1780 ng/mL, respectively. Typical EC50 values at week 32 and 72 increased to 4102.4 and 5998.7 ng/mL, respectively CONCLUSION: An Emax model with time-varying EC50 accurately described the time-course of DPi after every 28 days dosing of KRN23 up to 16 doses. The PK/ PD model can be used to perform trial simulations of KRN23 in adults with XLH.
BACKGROUND: Besides the anti-glycemic effect, metformin has beneficial effects on the cardiovascular system, polycystic ovary syndrome, and tumor prevention. Here, we used metabolomic approaches to investigate the global changes of metabolites in plasma in response to metformin treatment in healthy volunteers. The significantly changed metabolites lead to new insights into metformin mechanism. Also, these metabolic signatures may be used as early biomarkers of metformin response. METHODS: a) 33 healthy African-American subjects were given two oral doses of metformin (1000 and 850 mg). b) Fasting baseline and two plasma samples at two times after metformin dosing were selected. c) Measurement was used mass spectrometry based GC-TOF. d) Metabolites and metformin exposure analysis: Wilcoxon paired t-test, Spearman’s rank correlation, and linear regression; Pathway analysis: Human Metabolome Database (v3.5). RESULTS: Compared to baseline, 39 metabolites were significantly changed at peak metformin concentrations in plasma. 26 were structurally identified. The top known metabolites were hypoxanthine, maltose, citrulline, ribose, tyrosine, and ornithine (q value
