Welcome to the Pharmacokinetics UK 2009 Meeting

Wednesday 25th November – Friday 27

th November

Copthorne Hotel Birmingham Paradise Circus

Birmingham B3 3HJ

Programme and Abstract Book

Wednesday 25th November 12:00 Arrival & lunch

Welcome and session 1: Mechanistic Modelling of Drug Absorption

14:00 Welcome: Steve Toon

14:05 Introduction to the First Session: Geoff Tucker and Terry Shepard

14:10 Kiyohiko Sugano, Pfizer

Computational oral absorption simulation: theoretical framework and recent progresses

14:45 Panos Macheras, University of Athens

From the complexity of drug absorption analysis to the minimal modeling for biopharmaceutical classification purposes: A re-appraisal

15:20 Coffee break

15:50 Martin Bergstrand, Uppsala University

Semi-mechanistic modeling of absorption from extended release formulations - linking in-vitro to in-vivo

16:25 Paul Dickinson, AstraZeneca

Predicting product drug absorption performance: BCS and quality by design; kinetic considerations and regulatory approaches 17:00 Session close

18:30 Poster session & free bar

20:00 Dinner

Thursday 26th November

Session 2: Advances in Modelling Methodology

09:00 Introduction to the Second Session: Alison Thomson and Mike Walker

09:05 C. Anthony Hunt, UCSF

Synthetic modelling and simulation: plausible mechanistic explanations for how disease may alter the hepatic disposition of diltiazem

09:40 Marc Lavielle, INRIA Saclay

Estimation of mixed hidden Markov models with SAEM. Application to daily seizures data

10:15 Coffee break

10:50 Kayode Ogungbenro, University of Manchester

Sample size/power calculations for repeated ordinal measurements in population pharmacodynamic experiments

11:25 Bill Gillespie, Metrum Institute

Baysian pharmacometrics: new tools and current trends

12:00 Session close

12:00 Lunch

Session 3: Modelling Post-registration

13:30 Introduction to the Third Session: Peter Milligan and Pascal Delrat

13:40 Robert Hemmings, MHRA, UK & CHMP

Regulatory initiatives around benefit-risk decision making

14:20 Dyfrig Hughes, Bangor University

The fourth hurdle: Concepts, methods and applications of pharmacoeconomics (concepts)

14:40 Coffee break

15:10 Dyfrig Hughes, Bangor University

The fourth hurdle: Concepts, methods and applications of pharmacoeconomics (applications)

15:50 Jens Grueger, Pfizer

Meeting the evidence needs of payers and their scientific advisers to support market access for innovative therapies

16:30 Session close

The Peter Coates Lecture, with an introduction by Steve Toon

18:00 Malcolm Rowland, Emeritus Professor, University of Manchester

Mechanistic pharmacokinetics: Lighting a path to realisation

20:15 PKUK Banquet

Friday 27th November

Session 4: Open session

09:25 Introduction to the open session: Leon Aarons and Steve Toon

09:30 Robin Ferner, University of Birmingham

Postmortem clinical pharmacology

10:00 Colin Garner, The Hull York Medical School

Something old, something new - what was discovered when nuclear physics met biomedicine

10:20 Eunice Yuen, University of Manchester

A population pharmacokinetic/pharmacodynamic model for duloxetine including methods for handling dropouts in patients with diabetic peripheral neuropathy

10:40 Coffee break

11:00 Damien Cronier, Eli Lilly

A fully integrated PK/IVTI/IVE model in mouse to help design the FHD trial for a cell cycle inhibitor LY

11:20 Masoud Jamei, Simcyp

A new platform for combining the „bottom-Up‟ PBPK modelling and POP-PK data analysis

11:40 Joachin Grevel, AstraZeneca

NONMEM validation: Folly or necessity?

12:00 Final conclusions, closing remarks and lunch

Presentation Abstracts Session 1: Mechanistic Modelling of Drug Absorption

1. Computational oral absorption simulation: a theoretical framework and recent progresses Kiyohiko Sugano Pfizer

Computational oral absorption simulation is anticipated to be a powerful tool in drug research. The central dogma of oral absorption consists of Nernst-Brunner (NBE), general absorption (GAE), classical nucleation theory (CNT), and gastrointestinal transit (GIT) equations. These four equations are coordinated by using the same concept of drug concentration in the GI fluid. In NBE, the effect of solid surface pH, hydrodynamics, bile micelles (BM), etc should be taken into account. GAE enables a fully mechanistic in vitro-in vivo extrapolation taking into account the morphological difference (plica, villi, flatness (volume/surface ratio)), paracellular pathway, BM binding (free fraction), unstirred water layer (UWL) and pH. CNT is required to represent the supersaturation phenomena in the GI tract. When considering the effect of BM in the intestinal fluid, the free fraction is the key concept which affects not only solubility but also the dissolution rate and permeability. The effective diffusion coefficient becomes smaller when a drug binds to BM, resulting in a decrease in the dissolution rate and UWL permeability. In most cases, it would be appropriate to assume that only free monomer molecules can permeate the epithelial membrane. These effects of BM are especially important for the food effect prediction. In the case of permeability limited absorption (roughly corresponds to BCS III), the higher BM concentration in the fed state would result in a negative food effect (if the drug binds to BM). In the solubility-epithelial membrane limited case (BCS IV), little food effect is anticipated since the increase of solubility is canceled out by the decrease of effective permeability (e.g., pranlukast), whereas in the solubility -UWL limited case (BCS II), a positive food effect is anticipated since the bile micelle solubilized portion can pass though the UWL (but not the epithelial membrane).

2. From the complexity of drug absorption analysis to the minimal modeling for biopharmaceutical classification purposes: A re-appraisal

Panos Macheras Laboratory of Biopharmaceutics and Pharmacokinetics Faculty of Pharmacy University of Athens, Athens, Greece

Drug release, dissolution and uptake are the principal components of oral drug absorption. All these processes are taking place in the complex milieu of the gastrointestinal tract and they are influenced by physiological (e.g. intestinal pH, transit time) and physicochemical factors (e.g. dose, particle size, solubility, permeability). Most of these factors such as composition, pH, solubility, and permeability are spatially heterogeneous and are altered by the presence of food, the mechanical conditions imposed by the physiology, such as complex motility and hydrodynamic patterns, and also other factors like feedback mechanisms and the synergistic effects of the interplay of various factors. Due to the enormous complexity involved, the models developed for drug dissolution and release attempt to capture their heterogeneous features. Hence, Monte Carlo simulations in Euclidean or fractal matrices have been utilized since both dissolution and release processes are considered as time evolutions of a population of drug molecules moving from the solid state to the solution. The kinetics of drug release and dissolution was found to be approximated by a Weibull (stretched exponential) function. Also, more recently attempts with differential equations of fractional order, the so called fractional kinetics have appeared to describe anomalous kinetics, where the fractional order of differentiation is also related to the geometry of the space. It is interesting to note that the fractional version of a first-order rate process gives rise to a Mittag-Leffler function (which is quite relevant to Weibull function) solution, when integrated. Therefore, it is plausible that a process which appears to have a first-order rate under well stirred in vitro conditions, becomes anomalous (Mittag-Leffler) under constrained in vivo conditions and this could be described in the context of fractional kinetics by simply changing the order of the derivative in the differential equation. Mathematical models have been also proposed to determine the effect of the physicochemical properties, solubility and permeability on the extent of absorption for regulatory purposes e.g. biopharmaceutics classification system. The regulatory oriented approaches are based on the tube model of the intestinal lumen and take into account, apart from drug‟s physicochemical properties, the formulation parameters dose and particle size. In this context, the importance of drug‟s solubility/dose ratio for i) the dissolution kinetics ii) the biopharmaceutics drug classification and iii) the interpretation of the extensive absorption of several Class II drugs (low solubility, high permeability) will be pointed out. Finally, the recently developed biopharmaceutic classification system for marketed drugs based on the rate of drug dissolution and permeation will be presented.

References: 1. P. Macheras, A. Iliadis. Modeling in Biopharmaceutics, Pharmacokinetics and Pharmacodynamics: Homogeneous and Heterogeneous Approaches. Springer 2006. 2. V. Papadopoulou, K. Kosmidis, M.Vlachou, P. Macheras. On the use of the Weibull function for the discernment of drug release mechanisms. International Journal of Pharmaceutics. 309, 44-50 (2006) 3. A. Dokoumetzidis V. Papaadopoulou, G. Valsami, P. Macheras. Development of a reaction-limited model of dissolution: Application to official dissolution tests experiments. International Journal of Pharmaceutics. 355, 114-125 (2008) 4. V. Papadopoulou, G. Valsami, A. Dokoumetzidis, P. Macheras. Biopharmaceutic classification systems for new molecular entities (BCS-NMEs) and marketed drugs (BCS-MD): Theoretical basis and practical examples. International Journal of Pharmaceutics. 361, 70-77 (2008) 5. A. Dokoumetzidis, P. Macheras. IVIVC of controlled release formulations: Physiological-dynamical reasons for their failure. Journal of Controlled Release. 129, 76-78 (2008) 6. A. Dokoumetzidis, P. Macheras. Fractional kinetics in drug absorption and disposition processes. Journal of Pharmacokinetics and Pharmacodynamics 36, 165-78 (2009)

3. Semi-mechanistic modeling of absorption from extended release formulations - linking in vitro to in vivo Martin Bergstrand1, Erik Söderlind2, Ulf Eriksson2, Werner Weitschies3, Mats O. Karlsson1

1 Department of Pharmaceutical Biosciences, Uppsala University, Sweden. 2 AstraZeneca R&D, Mölndal, Sweden. 3 Institute of Pharmacy, University of Greifswald, Greifswald, Germany.

Background: The FDA guidance on extended release formulations (1) states that “Whatever the method used to establish a Level A IVIVC, the model should predict the entire in vivo time course from the in vitro data.” Most common methods for establishment of IVIVC do not utilize the available relevant information in order to do so. One example is information on regional absorption properties that is frequently obtained in drug development to guide development of extended release formulations. A relatively new method for clinical assessment of regional absorption and in vivo drug release is Magnetic Marker Monitoring (MMM) (2). MMM studies also generate relevant data on tablet GI transit. Aim: A new framework to incorporate relevant clinical data and in vitro data to establish IVIVC by prospective simulations. Methods: Data from an ongoing drug development program has been used for development and testing of the suggested approach. The model building data consisted of in vitro data from a family of HPMC gel matrix tablets and in vivo data from: an MMM study with one solid formulation (tablet transit, in vivo drug release and plasma concentration) and plasma concentration data from other studies following local infusion in colon (Bioperm capsule), i.v. dosing and administration of oral solution. A model validation dataset included plasma concentrations for three formulations for which no in vivo data had been used during model building. A model describing drug release as a function of experimental conditions (pH, RPM and ionic strength) and formulation characteristics (API, tablet size etc) was developed. The in vitro model was later applied to the in vivo drug release data from the MMM study together with prior knowledge on physiological properties throughout the GI tract and the observed tablet position. The model was used to estimate the extent of mechanic stress in different parts of the GI tract, expressed as corresponding RPM in the in vitro experiments. The drug release model was subsequently used as an input function for a PK model including regional absorption and disposition throughout the GI tract. The PK model and a Markov model describing tablet transit patterns for different regiments of concomitant food intake was constructed based on a principle previously described by the authors (2). Two hundred copies of the validation dataset was simulated in two steps, by first simulating individual tablet transit profiles and subsequently the plasma concentrations based on their in vitro estimates and the established models for drug release and PK. Based on the simulated dataset a 95% confidence interval was constructed for the median plasma concentration profile for each formulation and compared to the corresponding observed median. Results: Modeling of the in vivo drug release rendered estimates of regional mechanic stress; upper stomach 93 RPM, lower stomach 130 RPM, small intestine 63 RPM, colon 45 RPM. Furthermore it was found that the mechanic stress was significantly lower during the night (-55%). Differences in rate and extent of absorbed substance over the different parts of the GI tract were described with the PK model. A satisfying predictive performance was demonstrated for both drug release and plasma concentrations with respect to the formulation used for model building (internal validation with VPC). Prospective prediction of

formulations not used for model building was in parts successful and in other parts informative on how the model could be further developed. References: 1. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations;

Guidance for Industry; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), U.S. Government Printing Office: Washington, DC, September 1997.

2. Bergstrand M et al. Mechanistic modeling of a magnetic marker monitoring study linking gastrointestinal tablet transit, in vivo drug release, and pharmacokinetics. Clin Pharmacol Ther2009 Jul;86(1):77-83.

4. Predicting Product Drug Absorption Performance: BCS and Quality by Design; Kinetic Considerations and Regulatory Approaches. Paul A Dickinson AstraZeneca, Alderley Park, Cheshire, UK.

Background: Understanding the impact of product and process variables on in vivo performance is an important foundation of any QbD development. When this understanding is linked to meaningful in vitro tests, it can provide a tool for evaluation of multiple aspects of the proposed Design Space, identification of Critical Quality Attributes and the development of science and risk based specifications. Release of drug from the product can be an important determinant of the pharmacokinetics of a drug product and thus some Regulators have suggested that dissolution specifications should be based on the desired clinical (in vivo) performance1. Other authors have suggested that the Biopharmaceutics Classification System (BCS) could be used as a starting point of the process to establish dissolution tests that control pharmacokinetics2,3. Amidon and co-workers4 suggested that an in vitro in vivo correlation would be expected for a BCS class 2 compound. Polli and co-workers5 performed investigations on this subject and developed a model to show that immediate release products may not be expected to yield Level A correlations. Instead, the degree of correlation has been found to be in a gradual and continuous fashion dependent on the relative rate of first-order permeation and

first-order dissolution co 1)

1) were permeation rate limited. It was

the more rapidly dissolving formulations of piroxicam were below 1 or about 1 indicating that a classical IVIVC would not be possible6. Recently Dickinson and co-workers3 have noted that in some instances dissolution methods for products of BCS 2 and 4 compounds are likely to be „over-discriminating‟ versus testing in humans but can nevertheless be used for the definition of design space. This is because a dissolution specification which ensures the design space imparts the desired exposure/drug absorption can be established by considering in vitro and in vivo data.

Method and Results: An approach to developing a deeper understanding of in vivo risk will be described. Product variants that incorporate the highest risk variables were produced and their performance evaluated. In vitro (dissolution) and clinical pharmacokinetic data from a number of immediate release products containing drug substances that cover varied physicochemical properties and several BCS classes (e.g. BCS Class 2 weak base; BCS Class 2/4 weak acid, BCS Class 4) will be presented Conclusion: The data presented confirms early findings that „Safe Space‟ is a possible outcome for well designed BCS 2/4 product, i.e. dissolution may change to a certain extent without impacting on bioavailability.

References: 1. Selen A, (2009) „Clinically meaningful in vitro drug release/dissolution specification setting as guided by applied

biopharmaceutics and QbD.‟ at FDA Sponsored Workshop: Applied Biopharmaceutics and Quality by Design for Dissolution / Release Specification Setting.

2. Yu XL, (2008) „BCS Biowaiver Extension: Roles of Dissolution.‟ at AAPS Workshop on the Role of Dissolution in QbD and Drug Product Life Cycle.

3. Dickinson et al. AAPS Journal. 10:380-90, 2008. 4. Amidon et al. Pharm. Res. 12:413-20, 1995. 5. Polli et al. J. Pharm. Sci 85:753-760, 1996. 6. Polli and Ginski Pharm. Res. 15:47-52, 1998.

Session 2: Advances in Modelling Methodology

5. Synthetic modelling and simulation: plausible mechanistic explanations for how disease may alter the hepatic disposition of diltiazem C. Anthony Hunt Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA 94143 Contributors: Sunwoo Park, Glen Ropella, Li Yan, Teddy (Tai ning) Lam, Sean Kim, Jon Tang, Jesse Engelberg, Shahab Sheikh-Bahaei, and Michael Roberts. Background: Differences in the hepatic disposition of diltiazem and sucrose in livers from normal, CCl4, and ethanol treated rats (JPET301:1079, 2002; Hepatology36:1180, 2002) are a consequence of compound interactions with micro-architectural features that are influenced by the cause and extent of disease. We need concrete theories for 1) how compound interactions with hepatic micro-architectural features contribute to overall measures of disposition, and 2) how disease progressively alters those features. Achieving both is complicated by feature zonation within hepatic lobules coupled with differences in pathophysiology following CCl4 and ethanol treatments.

Aim: Achieve a degree of validation for in silico livers (ISLs) in which nested micro-mechanisms cause simulated diltiazem and sucrose disposition profiles to match wet-lab counterparts from perfusions of normal and two types of diseased rat livers. Design components and methods suitable for reuse in unraveling a variety of ADME mechanistic complexities. Develop methods to observe, measure, and contrast hierarchical ISL disposition details as they unfold. Offer coarse-grained hypotheses for multilevel disease progression.

Methods: Using the synthetic modeling and simulation method (PharmRes26(11):2369, 2009), we built upon prior efforts (JPET328:294, 2009) by instantiating and validating normal ISLs that achieved a prespecified similarity to wet-lab profiles. We used relational rather metric grounding of parameters and components. We discovered a limited set of changes in normal ISL features and properties sufficient to mimic disposition profiles from two different “diseased” ISLs. They were created independently: normal ISL characteristics were systematically altered until simulated diltiazem and sucrose disposition profiles were experimentally indistinguishable from referent, wet-lab profiles. Disposition details occurring within all six ISL levels are observable. Methods were implemented to trace, measure, and record events. So doing provided a heretofore-unavailable view of how and where micromechanistic events combine to influence disposition within ISLs. Derived measures provided plausible explanations of how and why disposition of diltiazem differed between normal and the two diseased livers. Those explanations, where feasible, were mapped to wet-lab measures of pathophysiology.

Discussion: We hypothesize that details occurring during ISL execution may have had wet-lab hepatic counterparts. Differences in mechanistic details between normal and the two “diseased” ISLs are hypotheses about corresponding differences between the normal and diseased livers. Transformations of a normal to diseased ISLs are abstract, coarse-grained theories of disease progression: similar transformations may have occurred in rats during disease progression. Differences in multilevel details during execution provide plausible, physiologically based explanations of differences between the two disease models and how

they interact with diltiazem and sucrose. Because the new methods are extensible to whole organisms and, eventually, patients, they open a door to new experimental means of testing the plausibility of mechanistic explanations of pharmacological and toxicological phenomena.

6. Estimation of Mixed Hidden Markov Models with SAEM. Application to daily seizures data.

Maud Delattre1, Radojka Savic2, Raymond Miller3, Mats O. Karlsson4, Marc Lavielle1,5

1 Department of Mathematics; University of Paris-Sud; Orsay; France; 2 INSERM U738 and University Denis Diderot – Paris 7, Paris, France; 3 Pfizer Global Research and Development, USA; 4 Department of Pharmaceutical Biosciences, Uppsala University, Sweden; 5 INRIA Saclay, France;

Background: Markov elements, which allow the likelihood of a given future state to be dependent on its present state, are often introduced in categorical and count data models to handle dependency of observations. However, the underlying state variable may not always been known. When there is no data available on a previous state, a hidden Markov model (HMM) can be utilized which still treats system as a Markov process, but the parameters of the model are assessed via some other observed variable influenced by this “hidden state”. HMM has a finite set of states. Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state, an outcome can be generated, according to the associated probability distribution. The state is not directly visible. This methodology has been utilized in other scientific areas; however it has never been extended to a mixed HMM, and therefore never applied within population analysis framework. Aim: To develop, evaluate and apply a new methodology for the estimation of Mixed HMM. Methods: The Baum-Welch algorithm is a well-known EM-type algorithm [2]. It can compute maximum likelihood estimates for the parameters (transition and conditional probabilities) of an individual HMM, when given only emissions as training data. We propose the following methodology: i) the SAEM algorithm is combined with the Baum-Welch algorithm for estimating the population parameters of the model, ii) for each subject, the individual parameters are estimated using the Maximum A Posteriori (MAP) approach, iii) for each subject the most likely sequence of hidden states is computed with the Viterbi algorithm [2]. The performance of the HMM was evaluated using Monte Carlo studies. Further, the novel methodology was applied for modelling of epilepsy data, expressed as a daily seizure counts. The pronounced overdispersion phenomenon and a characteristic transition matrix of this data [1] were modelled using HMM where periods of low and high epileptic activity were treated as hidden states. All analyses were performed using MONOLIX with additional Matlab scripts. Results: The Mixed Hidden Markov Model methodology has successfully been developed using the SAEM algorithm. Monte Carlo studies indicated good performances of the proposed methodology (negligible bias, small RMSE, accurate estimation of parameter SE). Daily seizures count data were successfully described using HMM. The model which consists of a mixture of two Poisson distributions dependent on underlying hidden state, described the overdispersion phenomenon, cumulative marginal distribution and a transition matrix of seizure counts well. Therefore, this novel approach offered further improvements compared to current state-of-the art methodologies [1]. Conclusions: The novel Mixed Hidden Markov Model methodology has successfully been developed combining the SAEM and the Baum-Welch algorithms. The SAEM appeared to be powerful and fast algorithm for estimating the parameters of a mixed HMM. The first results obtained with Monte Carlo simulations and using a real data example are extremely encouraging.

References:

1. Trocóniz I. F., Plan E., Miller R., Karlsson M.O., Modelling Overdispersion and Markovian Features in Count Data ACOP Meeting (2008).

2. Rabiner L. R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol 77 (1989).

3. Kuhn E., Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, vol 49, 1020-1038 (2005).

7. Sample Size/Power Calculations for Repeated Ordinal Measurements in Population Pharmacodynamic Experiments Kayode Ogungbenro Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom

Population pharmacodynamic experiments sometime involve repeated measurements of ordinal random variables at specific time points. Such longitudinal data presents a challenge during modelling due to correlation between measurements within an individual and often mixed-effects modelling approach may be used for the analysis. It is important that these studies are adequately powered by including an adequate number of subjects in order to detect a significant treatment effect. This paper describes a method for calculating sample size for repeated ordinal measurements in population pharmacodynamic experiments based on analysis by a mixed-effects modelling approach. The Wald test is used for testing the significance of treatment effects. This method is fast, simple and efficient. It can also be extended to account for differential allocation of subjects to the groups and unbalanced sampling designs between and within groups. The results obtained from simulation studies using nonlinear mixed-effects modelling software (NONMEM) showed good agreement between the power obtained from simulation and nominal power used for sample size calculations.

8. Bayesian pharmacometrics: new tools and current trends William R. Gillespie Metrum Institute, 2 Tunxis Road, Suite 112, Tariffville, CT 06081, USA Background: Two properties that distinguish Bayesian analysis are: (1) unknown quantities are represented as probability distributions, and (2) a formal mechanism exists for combining prior knowledge and new data. The first provides for rigorous quantitative description of uncertainty in model parameters and predictions. The second permits inferences that consider both prior knowledge and new data. Computational tools: Bayesian PKPD modeling is usually implemented via Markov chain Monte Carlo (MCMC) simulations, a general approach for simulating from posterior distributions of parameters or predictions. MCMC methods for Bayesian modeling have been added recently to some existing PKPD tools, e.g., NONMEM VII and S-Adapt. More general-purpose software tools implementing MCMC for Bayesian modeling include WinBUGS, OpenBUGS, JAGS and the SAS MCMC procedure. BUGSModelLibrary: A prototype PKPD model library (BUGSModelLibrary http://bugsmodellibrary.googlecode.com) is available for WinBUGS. The BUGS model specification language is particularly flexible w.r.t. stochastic structure, e.g., essentially unlimited levels of variability and many built-in distributions. BUGSModelLibrary currently has built-in 1 and 2 compartment models and the ability to specify models based on systems of linear or nonlinear ODE's. The data format uses NONMEM/PREDPP conventions. Model predictions are calculated recursively to permit time-dependent model parameters. Examples are presented to illustrate use of BUGSModelLibrary. Pros and cons of Bayesian modeling: Pros and cons of Bayesian modeling and the use of BUGS in particular are discussed. The pros of Bayesian modeling include (1) the ability to combine prior knowledge with new data in a manner that appropriately accounts for uncertainty in the prior information, (2) inferences about parameters and predictions easily expressed in terms of the posterior distributions, and (3) no approximation of the likelihood. The cons include (1) relatively large computational demands and (2) additional work required to specify and assess sensitivity to prior distributions. The primary additional pro of Bayesian PKPD modeling with BUGS is greater flexibility of model specification compared with standard PKPD modeling tools. A con is lack of a rapid exploratory modeling capability within the same platform, e.g., estimation of posterior modes. Current trends: Bayesian modeling is most beneficial in contexts in which model-based inferences depend on both new data and prior information about the model parameters. This is illustrated by the effective use of Bayesian methods with physiologically-based PK and TK models. Bayesian approaches are being extended to other mechanistic modeling applications, e.g., physiologically-based PD and systems biology models.

Session 3: Modelling Post-Registration

9. Regulatory initiatives around benefit-risk decision making Robert Hemmings Medicines and Healthcare products Regulatory Agency, London, UK and CHMP Overview:

The scope for modelling and simulation approaches in Marketing Authorisation Applications (MAAs) will be reviewed

Current standards and processes for benefit-risk evaluation will be described

The pros and cons of using quantitative approaches in benefit-risk decision making will be explored and some of the tools available will be reviewed

Ongoing research surrounding the integration of quantitative approaches into regulatory decision making will be reviewed

A „vision for the future‟ will be presented, considering the use of quantitative approaches and considering the weight given to „relative‟ benefit-risk

Background: The European Medicines Agency is the European Union body responsible for coordinating the evaluation, supervision and pharmacovigilance of medicinal products. Principal activities of the Agency include providing independent, science-based recommendations on the quality, safety and efficacy of medicines and to implement continuous supervision to ensure that their benefits outweigh their risks. The CHMP is the body responsible for preparing the scientific opinions of the Agency, including decisions on whether a benefit-risk profile can be considered favourable for a particular product proposed for use in a specific clinical situation. CHMP‟s opinions are based on assessment reports summarising the key quantitative information from published scientific literature and the applicant‟s dossier. These reports are drafted by a Rapporteur team comprising experts from multiple disciplines, including pharmacokineticists, clinical pharmacologists and biostatisticians. Inferences based on this quantitative information, and on the perspectives external expert advisors, are then presented to CHMP who give their opinion on the benefit-risk balance. Recital 7 of Council Directive 2001/83/EC provides: “The concept of harmfulness and therapeutic efficacy can only be examined in relation to each other and have only relative significance depending on the progress of scientific knowledge and the use for which the medicinal product is intended. The particulars and documents which must accompany an application for marketing authorisation for a medicinal product demonstrate that potential risks are outweighed by the therapeutic efficacy of the product”.

A working group of CHMP was set up to provide recommendations on ways to improve the methodology and the consistency, transparency and communication of the benefit-risk assessment by the CHMP. A report can be found at www.emea.europa.eu/pdfs/human/brmethods/1540407enfin.pdf. The report concludes that expert judgement is expected to remain the cornerstone of benefit-risk evaluation, nevertheless several features of the benefit-risk analysis methods are of interest. The presentation will summarise the pros and cons of different quantitative approaches and ongoing research projects to optimise the use of such methods for regulatory decision making. In particular, it is hoped that such methods can improve the consistency and transparency of the regulatory decision, can better incorporate patient views and can enhance scientific debate.

In parallel, initiatives are ongoing to investigate the potential synergy of closer interactions between regulatory agencies and health technology agencies. One focus is whether the programme of clinical trials conducted in support of the licensing decision and the health technology decision can be made more efficient. The possibility of joint, or parallel, „Scientific Advice‟ to better plan development programmes is being discussed. A relevant question is whether such closer interaction, or the use of quantitative methods, perhaps giving greater validity to cross-trial comparisons of different medicines, will increase the extent to which information on relative efficacy, safety and benefit-risk impact the licensing decision. The presentation will outline a vision for the future of regulatory decision making.

10. The fourth hurdle: Concepts, methods and applications of pharmacoeconomics Dyfrig Hughes. Centre for Economics & Policy in Health, Bangor University, Bangor, LL57 1UT. Since the establishment of the National Institute for Health and Clinical Excellence ten years ago, pharmacoeconomic evaluations have played a central role in informing decision on the availability of new treatments on the National Health Service (NHS) in England and Wales. Treatments are not approved for use on the NHS if they are considered to represent poor value for money. Estimates of cost-effectiveness are derived from economic evaluations, which usually compare a new medicine with the existing medicine(s) that it might displace, in terms of their costs and consequences. Costs are measured according to the perspective adopted – usually the payer (i.e. NHS). Consequences are the health outcomes, usually measured in terms of quality-adjusted life-years (QALYs) that consider the impact of treatments on both health-related quality of life, and survival. In health economic evaluation, models are typically used where the relevant clinical trials have not been conducted or are impossible for ethical or logistical reasons, or have not been designed to capture relevant data (e.g. specific populations or different comparator). Markov models are often used to extrapolate beyond the time horizon of a clinical trial. They depend on epidemiological data on the natural course of a disease. Pharmacoeconomic models provide a systematic approach to decision-making under conditions of uncertainty. They are (1) explicit, (2) quantitative and (3) a prescriptive approach to decision making in that they:

1. make the analyst structure the decision problem in a framework which captures the key elements of the process under evaluation

2. require each decision and consequence relating to the choice made, to be valued in terms of probability, cost and outcome

3. intend to inform decisions on what should be done under a given set of circumstances

I shall give an overview of the discipline of pharmacoeconomics, describing the methods for calculating QALYs, the different forms of economic evaluation, and modelling. Using a case study of anti-TNF-alphas for Crohn‟s disease, I will provide an overview of how decisions on the use of medicines on the NHS are made in the UK.

11. Meeting the evidence needs of payers and their scientific advisers to support market access for innovative therapies Jens Grueger, PhD Vice President, Head of Global Market Access Pfizer Ltd, Primary Care Business Unit, Walton Oaks, UK While quality, efficacy and safety are the key questions to be addressed during the regulatory process for new pharmaceuticals, relative effectiveness, cost-effectiveness and budget impact are central to market access. The “randomized clinical trial paradigm”, which is optimized for generating the relevant evidence for regulatory approval, is not able to generate all of the evidence that payers and their scientific advisors request in order to inform reimbursement decisions. Although therapies are now reaching the market with extensive evidence from head to head trials, and better understanding of biomarkers and surrogate endpoints allow us to assess response and longterm benefits earlier, there will be important questions that cannot be addressed through the clinical development program. In this context, modelling is a necessity, rather than an excuse for not conducting the relevant clinical trials. Areas where modelling is most relevant are:

- extrapolating treatment response beyond clinical trial duration. - translating from efficacy to effectiveness, specifically assessing the benefits in

varying patient populations and in situations of suboptimal treatment adherence. - indirect comparisons against therapies, that could not be addressed in the clinical

development program.

In this presentation, I will discuss how pharmaceutical companies reach out to payers earlier during the clinical development program to ensure that the evidence package at launch meets the needs of payers and their scientific advisers.

Session 4: Open Session

12. Postmortem clinical pharmacology

R E Ferner City Hospital, Birmingham B18 7QH, and School of Clinical and Experimental Medicine, University of Birmingham Concentrations of drugs measured after death are of great forensic importance. It is a natural to extrapolate from a high concentration found after death to the existence of a high concentration prior to death, that is, poisoning. The postulates of pharmacokinetics in life are concentration in the sampling compartment is related to dose and time; and the effects are related to concentration at the site of action. In practice, it is assumed that venous blood (the usual sampling compartment) is homogeneous, cellular membranes are intact, and barriers between blood and gastrointestinal and urinary tracts are maintained. In the latter part of the 20th century, evidence accumulated the assumptions which hold true in life are no longer true after death. Concentrations differed from one sampling site to another, and from one time after death to another, sometimes by a factor of 20. Many potential reasons exist, including changes in blood; post-mortem synthesis or degradation of the drug or metabolites; loss of integrity of cellular membranes; cessation of active transport; and passive absorption from previously sequestered sites. All this adds up to what Pounder has termed „a toxicological nightmare.‟ Further reading: 1. Ferner RE. Post-mortem clinical pharmacology. Brit J Clin Pharmacol 2008; 66: 430–443. 2. Pélissier-Alicot A-L, Gaulier JM, Champsaur P,Marquet P. Mechanisms underlying postmortem redistribution of drugs: a review. J Anal Toxicol 2003; 27: 533–44. 3. Pounder DJ, Jones GR. Post-mortem drug redistribution – a toxicological nightmare. Forensic Sci Int 1990; 45: 253–63.

13. Something old, something new – what was discovered when nuclear physics met biomedicine

Colin Garner Hull York Medical School, University of York The rate of scientific progress is determined by the tools that scientists have available to them. When a new tool comes along it‟s use is adopted at varying rates depending on complexity, cost and availability. In addition if the tool is the provenance of one scientific discipline, such as nuclear physicists for example, it may or may not be taken up by other scientific disciplines such as biologists. The transfer of technology across disciplines is often determined more by serendipity and the receptive mind than by structured interactions. One example of this chance approach concerns the use of Accelerator Mass Spectrometry (AMS) by biologists to study a multitude of biological processes. AMS was not developed to answer biological problems, but to date archaeological artefacts. In the hands of the biological community, AMS has been used to (i) study the metabolic fate of drugs and other xenobiotics at microdose and therapeutic doses (ii) determine drug metabolite profiles (iii) study biomarkers as surrogates of disease (iv) determine the date at which cells were formed in the human body (v) study bone turnover (vi) study protein and DNA adducts and (vii) study prodrug activation in single cell populations. All of this is achieved by AMS, owing to its ability to measure molecules at the atto- to zeptomole level. I will present in my talk examples of some of these AMS applications. References: 1. Lappin G and Garner R C (2003) Big physics, small doses – the use of AMS and PET in human microdosing of

development drugs. Nature Reviews – Drug Discovery, 2, 233-240

2. Stenström K et al (1996) Application of accelerator mass spectrometry (AMS) for high-sensitivity measurements of 14

CO2

in long term studies of fat metabolism. Appl. Radiat.Isot, 47, 417-422

3. Sarapa N et al (2005) The application of accelerator mass spectrometry to absolute bioavailability studies in humans:

simultaneous administration of 14

C-nelfinavir mesylate solution and oral nelfinavir to healthy volunteers. J Clin

Pharmacol, 45, 1198-1205

4. Cupid B C et al (2004) The formation of AFB1-macromolecular adducts in rats and humans at dietary levels of exposure.

Food Chem Toxicol, 52, 3693-3701

5. Johnson R R et al (1994) Calcium resorption from bone in a human studied by 41

Ca tracing. Nucl Instr Methods Phys

Res B, 92, 483-488

6. Spalding K L et al (2005) Retrospective birth dating of cells in humans. Cell, 122, 133-143

14. A population pharmacokinetic/pharmacodynamic model for duloxetine including methods for handling dropouts in patients with diabetic peripheral neuropathy Eunice Yuen1,2, Ivelina Gueorguieva1, Lorea Bueno-Burgos1,3, Sophie Glatt1,4, Leon Aarons2 1Eli Lilly & Co. Ltd, Windlesham Surrey GU20 6PH, 2Pharmacy department, University of Manchester, 3Pharmacy department, University of Navarra, 4UCB, Slough Berkshire Background: Duloxetine is a selective serotonin and norepinephrine reuptake inhibitor (SNRI) indicated for the treatment of diabetic peripheral neuropathic pain (DPNP). The population pharmacokinetics of duloxetine in patients has been previously described [Lobo et al, Clin Pharmacokinet 48(3):189]. In three phase 3 double-blind placebo-controlled trials, the efficacy of duloxetine was evaluated over a 12 week acute therapy phase. The primary efficacy measure was a weekly mean score calculated from 24 hourly average pain scores on an 11-point Likert scale (no pain to worst possible pain). Patients receiving 60mg QD and 60mg BID duloxetine in these trials have shown a significant improvement in weekly pain scores, showing separation from placebo beginning at week 1 [Wernicke et al, Neurology 67:1411, Raskin et al, Pain Medicine 6(5):346, Goldstein et al, Pain 116:109] Aim: To develop a population pharmacokinetic/pharmacodynamic model in DPNP patients and to compare different methods of imputing missing data for dropouts. Methods: Across the 3 studies, a total of 1139 patients were randomised into the following treatment groups: placebo (n=339), 20mg QD (n=115), 60mg QD (n=344) and 60mg BID (n=341). Pharmacokinetic parameters obtained from literature [Lobo et al] were used to simulate duloxetine concentrations at steady state, and pharmacodynamic measures used were weekly Likert scale painscores averaged from daily scores. Population PK/PD analysis was carried out in NONMEM Version 6. To compare different methods of handling dropouts, various methods of imputing missing data were employed, including simple imputation, multiple imputation and pattern mixture models. Results: A total of 12608 PD observations were available for analyses. The patients were classified as suffering from mild (baseline score 3 to 4), moderate (baseline score 5 to 6) or severe pain (baseline score 7 and above) according to their baseline pain scores per guidance from literature [TEC assessment program 21(11): Oct 2006]. The placebo response was described by an exponential decline model for each pain severity group, whilst drug effect was additive and described by a single Emax model using duloxetine concentrations at steady state. Across the 3 trials, completion rate was approximately 73%. More patients in the 60mg treatment groups discontinued the trial due to adverse events compared to the other 2 treatment groups, whilst more patients in the placebo group discontinued the trial due to lack of efficacy compared to the duloxetine-treated groups. Different methods of imputing missing data for these dropouts were evaluated, including last observation carried forward (LOCF), explicit (model-based) methods for multiple imputation, and the 3 subtypes of pattern mixture models – complete case missing value (CCMV), available case missing value (ACMV) and neighbouring case missing value (NCMV). The PK/PD model obtained earlier was then applied to these various enriched datasets and subsequent PD parameters obtained were compared. Conclusion: The Likert scale pain scores were well described by the population PK/PD model. EC50 was the most sensitive estimate to different methods of imputing missing data, however overall, the different methods of handling dropouts produced comparable PD parameters.

15. A fully integrated PK/IVTI/IVE model in mouse to help design the FHD trial for a cell cycle inhibitor LY Damien Cronier1, Lawrence Gelbert2, Graham Wishart1, Alfonso De Dios2. 1: Eli Lilly and Company, Windlesham, UK ; 2: Eli Lilly and Company, Indidanapolis, USA; Background: Cell cycle targets are a primary focus in the development of new targeted anti-cancer agents. Owing to the specific nature of their activity, a better quantitative understanding of the relationship between in vivo target inhibition (IVTI) and in vivo efficacy (IVE) is a prerequisite to the optimal use of these molecules in patients. In addition, connecting the level of target inhibition to the consequences on the dynamics of the cell cycle in cancer cells, and further connecting the latter to in vivo efficacy would make it possible to bring a proof of mechanism and proof of concept, thereby increasing significantly the level of confidence in the pharmacological hypothesis to be tested in the clinical setting. Aim: To develop an integrated semi-mechanistic PK/PD model describing the effect of compound LY on the cell cycle of Colo-205 cancer cells by means of a multi-biomarker approach allowing a full monitoring of the cell cycle dynamics and to connect this PK/PD model to the level of in vivo efficacy. Methods: An integrated PK/IVTI/IVE model was developed in mouse using a sequential approach. The PK of LY were first connected to the level of in vivo target inhibition (IVTI) in Colo-205 xenograft tumours by means of a PK/IVTI model describing the kinetics of the different phases of the cell cycle using a series of transit compartments. LY-mediated inhibition of cell cycle progression was modelled by means of an indirect response mechanism. In vivo efficacy (IVE) of compound LY in Colo-205 was then described by means of a fully integrated PK/IVTI/IVE model consisting of a modified Gompertz model connected to the PK/IVTI model. The connection between IVTI and IVE was described by using both a cytostatic and a cytotoxic component. Results: The fully integrated PK/IVTI/IVE model could account for the dose-dependency of IVE across a dose range of 25 to 100 mpk and over a dosing period of 21 days. The 3 biomarkers collected along the cell cycle in Colo-205 cancer cells were connected in a mechanistic manner and the time shift observed for the peak of inhibition in the 3 compartment as well as the rebound effect were well accounted for, suggesting a block of the cell cycle associated with a cell synchronisation. The model also made it possible to correlate in vivo efficacy to a minimum level of 30 to 50% IVTI maintained throughout the whole treatment period. Conclusion: The PK/PD relationship of compound LY in Colo-205 tumours was modelled by means of a fully integrated PK/IVTI/IVE model. This model made it possible to understand the determinants of IVE and correlate the latter to the maintaining of a minimum 30-50% IVTI throughout the whole treatment period. The semi-mechanistic connection established between IVTI and IVE also made it possible to bring a proof of mechanism and proof of concept in the preclinical setting, thereby supporting the pharmacological hypothesis to be tested in patients.

16. A new platform for combining the ‘Bottom-Up’ PBPK modelling and POP-PK data analysis Masoud Jamei Simcyp Ltd, Blades Enterprise Centre, John St, Sheffield, S2 4SU, UK Background: Arguably, the two main objectives of population pharmacokinetic (POP-PK) modelling are covariate recognition and parameter estimation from sparse samples. Whilst the role of POP-PK in the latter case has remained un-challenged, better understanding of covariates for interindividual variability in PK might be better served by the use of physiologically-based PK (PBPK) models. The „bottom-up‟ paradigm enables integration of mechanisms by which covariates, such as age, weight, sex, and genetic make-up of drug metabolizing enzymes, may influence PK properties into PBPK models; with implications for optimal study size, selection of covariates and consideration of complexity of models to be used for confirming the relationships. However, the use of such models requires incorporation of extensive physiological knowledge of the human body (system information) as well as considerable in vitro data on drug. Hence combining the the two approaches (PBPK and POP-PK) seems ideal and several research groups are currently striving to reconcile these two paradigms. Although many current modelling platforms allow such combined approach to modelling (with literature examples on practical use) implementation of PBPK models into POP-PK platforms and solving the problems has become time consuming unless a limit is put to the level of complexity and dimension of models used. Moreover, combining these two approaches for data analysis requires more efficient optimisation methods. Aim: To create an integrated framework to incorporate broader knowledge of the human body and the experimental drug dependent data („bottom-up‟) into POP-PK data analyses of clinical trials („top-down‟). This platform facilitates the use of whole body PBPK models and prior knowledge in determining unknown system and drug parameters as well as their inter-correlations and inter-individual variability. Methods: Generally, there are three main elements in any parameter estimation exercise, namely observed clinical data, models and parameters to be estimated and a statistical parameter estimation approach. To enter observed data, dosing regimen and covariates an Excel-based template is developed that supports different administration routes, independent dosing regimen and observations for each subject. The template is designed to be compatible to commonly available formats as much as possible and is maintained in XML format. The framework is founded upon the Simcyp Population-based ADME Simulator which includes numerous models and algorithms for modelling absorption, distribution, metabolism and excretion processes. Both individual and population fitting approaches are implemented. For individual fitting different weighted least square objective functions are incorporated. These objective functions can be minimised using any of Hooke-Jeeves, Nelder-Mead, Genetic Algorithms (GA) or a combination of GA and local search techniques. Population parameters can be estimated using either of Maximum Likelihood (ML) or Maximum A Posterior (MAP) methods. The latter allows the inclusion of any prior knowledge of the design parameters using the Bayesian approach. The Expectation-Maximization (EM) algorithm is employed to optimise either of ML or MAP objective functions. Conclusions: This framework is a bridge between the typical „bottom-up‟ PBPK approach and the common POP-PK analysis of clinical data which accelerates model building and covariate recognition. Further, it facilitates the optimal use of accumulated knowledge in different stages of drug discovery and development along with prior knowledge of system biology information of healthy and disease populations.

17. NONMEM Validation: Folly or Necessity? Joachim Grevel AstraZeneca R&D Charnwood, Clin. Pharmacology and DMPK, Loughborough, LE11 5RH Background: What was once the vision of a select few is now part of the development program in any major drug company: the use of modelling in decision-making. As the profile of modelling has been raised to the point that it is part of most submissions to authorities, the question of its validation can no longer be ignored. Aim: This brief presentation is intended to initiate a discussion on the necessity and scope of validation of the modelling work within drug development. Methods: NONMEM is used here as a synonym for efforts in modelling and simulation using a variety of software of which the NONMEM program is only one: fortran compilers (g77, df, ifort, fl32), statistical packages (R, Splus, SAS), programming languages (Perl, C, Visual Basic), PsN, Wings, Monolix, ADAPT, WinBugs, Trial Simulator. GxP stands for the regulatory framework consisting of GLP, GCP, ICH, FDA, EMEA guidelines. Authorities signify the government agencies that grant marketing approval for new medications. M&S stands for modelling and simulation efforts within the framework of model-based drug development (MBDD). Data are considered to stem primarily from clinical trials. Results: M&S is a process within the clinical development that analyses data in order to facilitate decision-making on the road to submissions to authorities. As such it is subject to the rules of GxP. M&S at the same time defies standardisation as it is strongly influenced by the nature of the data, the interest and culture of the stakeholders, the nature of the question, and the skill of the modeller. While the performance of the software and the validity of the models can be tested, the performance of the process is more difficult to qualify. Still it should be possible to agree upon a set of working practices which reduce the risk of misguiding drug development and ultimately drug labelling. Conclusion: Conclusions are not presented here but should be approached as the result of the discussion.

Poster Session (Wednesday 25th

November)

Visual Predictive Checks for Censored and Categorical data Martin Bergstrand, Andrew C. Hooker, Elodie Plan, Mats O Karlsson Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden Background: Non random censoring of data as in the presence of observations below the quantification limit (BQL) can harm not only parameter estimates but also diagnostic plots such as Visual Predictive checks (VPCs). Treating this type of data as a combination of censored continuous data and categorical data (e.g. BQL) can facilitate unbiased interpretation. VPCs can be adopted for any type of categorical data by plotting the observed and the simulated fraction of observations of each category versus an independent variable. To investigate Markov elements in categorical data the transitions categories rather than the observed category can be plotted. The visual interpretation of a VPC is its strength, however it can be difficult to distinguish if lack of agreement is due to random chance or model misspecification. Calculating non-parametric confidence intervals based on simulated data for different percentiles of continuous data or for the fraction of observations in a certain category for categorical data is likely to improve the interpretability. Aim: To illustrate a new approach for VPCs in the presence of categorised data. Methods: VPCs were created based on different models for ordered categorical data, count data and continuous data with censored low and/or high observations. Each VPC was based on 1000 datasets simulated with obtained parameter estimates. For continuous data the median and 95 % prediction interval for the observed data are plotted together with non-parametric 95 % confidence intervals for the corresponding percentiles calculated from the simulated datasets. To ensure correctly calculated percentiles all censored observations was retained in the original dataset. Percentiles for observed data can only be adequately calculated and presented for percentiles where the censored observations constitute a smaller fraction than the percentile in question. For all categorical data the fraction of observations in each category was compared to a simulation based 95 % confidence interval. In all created VPCs time was chosen as the independent variable. Individual strategies for stratification and binning across the independent variable were adopted for each data-set. Results: The combination of VPCs for both censored data and continuous data was found to more clearly indicate the presence of important model misspecifications than VPCs focusing on only the continuous observations. The handling of Markov elements of categorical data could be diagnosed in a simple and intuitive fashion. The inclusion of confidence intervals for the diagnostic variable (i.e. percentiles or fraction of observations) acts as an effective support to identify actual model misspecifications.

Prediction Corrected Visual Predictive Checks Martin Bergstrand, Andrew C. Hooker, Johan E. Wallin, Mats O. Karlsson Department of Pharmaceutical Biosciences, Uppsala University, Sweden Background: Ideally a Visual Predictive Check (VPC) will diagnose both the fixed and random effects in a mixed effects model. In many cases this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of the main independent variable (e.g. time, dose, etc.). Whenever the predictions within a bin differ due to different values of the independent variable or different covariate values (e.g. dose, weight, etc.) diagnoses may be hampered or misleading. In such cases only a part of the variability observed in a traditional VPC will be caused by the random effects (Example 2). Apart from making it very difficult to use these VPCs to diagnose the random effects, this can also lower the power of detecting a model misspecification in the structural model. For example, when a priori or posteriori dose adaptation has been performed to achieve a certain target concentration; a traditional VPC can be completely uninformative (Examples 1, 3 & 4). These shortcomings of the traditional VPCs have been highlighted before (1,2). The Prediction Corrected VPC (PC-VPC) offers a solution to these described problems while retaining the visual interpretation of the traditional VPC. Aim: To investigate the advantage of PC-VPCs with applications to adaptive designs and variability in independent variables. Methods: Models for two simulated examples and two real datasets have been assessed with the PC-VPC and the traditional (non-corrected) VPC. The examples are chosen to illustrate some situations where the traditional VPC shows poor performance. A PC-VPC differs from a traditional VPC in that both the observations and the model predictions are normalized for the typical model prediction in each bin of independent variables (Equations 1 & 2).

binij ij

ij

PREDypc y

PRED Equation 1 ˆ ˆ binPRED

ypc yij ij PRED

ij

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yij = Observation for the ith individual and jth time point, ŷij = model prediction for the ith individual and jth time point. ypcij = Prediction corrected observation, ŷpcij = Prediction corrected model prediction, PREDij = Typical population prediction for the ith individual and jth time point, PRẼDbin = Median of typical population predictions for the specific bin of independent variables. Both traditional VPCs and PC-VPCs were constructed with 5%, 50% and 95% percentiles for the observed data compared to simulated non-parametric 95% confidence intervals (CI) for the corresponding percentiles (1000 simulated datasets). The confidence intervals are presented to facilitate judgment of whether an observed deviation is likely to be due to random chance. Results: The investigated examples demonstrate that PC-VPCs have an enhanced ability to diagnose model misspecification (especially with respect to random effects models). PC-VPCs are also in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori or posteriori dose adaptations. Conclusions: The advantage with the PC-VPC is that it accounts for variations in independent variables and predictive covariates introduced by binning across observations.

Situations that with standard VPC would have required stratification and/or simulation of an adaptive design could preferably be handled with PC-VPC. References: 1. M.O Karlsson, N Holford. A Tutorial on Visual Predictive Checks. PAGE 17 (2008) Abstr 1434 [www.page-

meeting.org/?abstract=1434] 2. D. D. Wang, S. Zhang. Standardized Visual Predictive Check – How and When to used it in Model Evaluation. PAGE 18

(2009) Abstr 1501 [www.page-meeting.org/?abstract=1501]

Physiologically-Based Pharmacokinetic Modelling of Hepatic Uptake of Pravastatin and Rosuvastatin Sonya Tate1, Hannah Jones2, J. Brian Houston1 and Aleksandra Galetin1

1School of Pharmacy and Pharmaceutical Sciences, University of Manchester,2Pfizer Global Research & Development, Sandwich, UK

Background: Organic Transporting Polypeptide 1B1 (OATP1B1) has been associated with population variance in the disposition of various therapeutically used drugs. The contribution and consequent impact made by hepatic uptake via OATP1B1 to the plasma and tissue concentrations of a substrate drug is not yet fully understood. Physiologically-based pharmacokinetic (PBPK) modelling allows mechanistic assessment of hepatic uptake. Combining physiological parameters with uptake and metabolism data determined in hepatocytes in vitro, the concentration of a drug over time can be simulated in blood and in any tissues of interest. Liver concentrations are of particular interest for all OATP1B1 substrates, especially for the statins, considering that liver represents a target organ for these drugs. Aim: To develop a PBPK model incorporating active uptake and passive diffusion across the sinusoidal membrane in hepatocytes. To simulate the intravenous and oral blood concentration-time profiles in human and the oral liver concentration-time profiles in rat (due to lack of human data) for both for pravastatin and rosuvastatin. Methods: A PBPK model incorporating hepatic active uptake was developed and written in MatLab 7.6.0 (R2008a). In addition to the liver, other tissues (kidney, heart, muscle, brain and „rest of body‟) were considered to be well-stirred compartments and perfusion rate limited kinetics was assumed for each non-hepatic tissue. The Compartmental Absorption and Transit (CAT) model was used to simulate oral absorption in the gastrointestinal tract. Pravastatin and rosuvastatin were selected for initial validation work, as both drugs are subject to hepatic uptake via OATP1B1, with relative metabolic stability. The model was used to simulate human intravenous and oral blood profiles and rat oral liver profiles for both drugs using species-specific physiological data. Uptake data determined in rat hepatocytes were collated and scaled using rat or human hepatocellularity where appropriate. The impact of variability in the in vitro uptake data on the simulated profiles was assessed. Results: Human oral blood AUCs were over-predicted by approximately 2-fold for both statins, in contrast to intravenous profiles where under-prediction was observed to a similar extent. The rat hepatocyte uptake data used for the simulations varied widely in the literature and had a considerable effect on the simulated blood concentrations. The ratio of active uptake to passive diffusion varied from 3 to 130-fold; the high active uptake values produced much lower AUC and Cmax predictions compared to the low active uptake values. Simulations of the orally dosed concentration-time liver profile in rat resulted in good agreement with reported data for both statins; corresponding AUC estimates were within 3-fold of the in vivo values. Conclusion: The PBPK model presented here predicts the AUC of OATP1B1 substrates pravastatin and rosuvastatin within 2-fold of in vivo values after intravenous and oral administration. The liver tissue concentration-time profiles in rat were successfully predicted using these uptake data. Application of rat active uptake and passive diffusion data for the prediction of human profiles resulted in good agreement with in vivo data; further validation of this approach using compounds with both metabolic and transporter contributions is required.

Population Pharmacokinetics of Enoxaparin in Neonates, Infants and Children During Secondary Thromboembolic Prophylaxis Mirjam N Trame1,5, Lesley Mitchell2, Christoph Male3, Jeffrey S Barrett4, Georg Hempel1,5, Ulrike Nowak-Göttl5

1Department of Pharmaceutical and Medical Chemistry – Clinical Pharmacy -, University of Muenster, Germany, 2UAB/Stollery Children‟s Hospital, Edmonton, Canada, 3Department Paediatrics, Medical University of Vienna, Vienna, Austria, 4Division of Clinical Pharmacology & Therapeutics, The Children‟s Hospital of Philadelphia,Philadelphia, USA, 5Department of Paediatric Haematology and Oncology, University Children‟s Hospital Muenster, Germany Introduction: Enoxaparin, a low-molecular-weight-heparin (LMWH), has been extensively studied in adults on its safety and efficacy during prevention of symptomatic thromboembolism when acute anticoagulation or secondary prevention is required due to venous thrombosis or stroke. Enoxaparin is used off-label in children but still little is known on the pharmacokinetics in children. The aim of this investigation was to evaluate whether a once or twice daily dosing regimen would be feasible in children to achieve appropriate plasma levels of enoxaparin. Patients and Methods: The courses of 126 children and adolescents with a median age of 5.9 years and a median weight of 24 kg receiving enoxaparin either as a once or twice daily dosing regimen were analysed retrospectively. All studied patients received enoxaparin during secondary prophylaxis therapy. Children < 12 months of age received a starting dose of 1.5 mg/kg followed by a maintenance dose of 1.3 mg/kg. Children > 12 months of age were started on 1 mg/kg followed by a maintenance dose of 1 mg/kg. Blood samples were drawn after patients reached steady-state on their maintenance dose at baseline prior to the next dose, and at 2, 4, 8 and 12 hours after administration and dosages were adjusted in accordance to the measured anti-factor Xa activity. The median enoxaparin concentration in our population resulted in an anti-factor Xa activity of 0.4 U/ml (range 0 – 1 U/ml anti-factor Xa). By means of population pharmacokinetics using nonlinear mixed-effects modelling (NONMEM) plasma concentration-time data were analysed. Several covariates such as age, body weight and body surface area were tested on their effects on the pharmacokinetic parameters. Results: A two-compartment model resulted to be the best model for adequately describing the enoxaparin kinetics of our population. By using body weight, scaled to the median body weight of the dataset, as covariate for clearance (CL) and central volume of distribution (V1) the best results were obtained. The final population estimates of enoxaparin resulted to be: CL 15.2 ml h-1 kg-1 ± 6.82%, V1 169.58 ml kg-1 ± 17.5%, intercompartimental clearance (Q) 58 ml h-1 ± 40.2%, peripheral volume of distribution (V2) 10.3 l ± 44.8% and absorption rate (ka) 0.414 h-1 ± 30.2% (estimates ± standard errors). Interindividual variability (IIV) was found to be 54% for CL and 42% for V1. Figure 1 shows the predicted activity time-course versus the observed activities for a representative patient. The model is capable of describing all aging and dosing groups of our childhood population (neonates, infants to adolescents).

Conclusion: The high IIV in CL and V1 in our population underlines the need for monitoring the activity and individualising the dose. Further population pharmacokinetic/-dynamic investigations should be conducted to predict target enoxaparin levels or other new antithrombotic drugs for more safety and efficacy during antithrombotic therapy when used in children.

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Figure 1: Model predicted and observed anti-FXa activity for one selected patient (dashed line = individual predicted plasma concentration, solid line = population model predicted plasma concentration, dots

Comparison of in vivo and in silico methods to predict tissue:plasma partition coefficients in rat Helen Graham1, Owen Jones2, Mike Walker2, Aleksandra Galetin1 and Leon Aarons1 1School of Pharmacy and Pharmaceutical Sciences, University of Manchester,2AstraZeneca, Alderley Edge, UK Background: Physiologically-based pharmacokinetic (PBPK) modelling is widely used in the pharmaceutical industry as a way of predicting PK/PD relationships in the early stages of drug development. Partition coefficient (Kp) values are important input parameters for these types of models as they help to describe the distribution of a drug within the body, and therefore can also be used to determine the volume of distribution (Vss) of a compound. Although Kp values can be measured experimentally using both in vivo and in vitro methods, these are expensive and time-consuming. This has led to the generation of a variety of methods that can be used to predict Kp values a priori. Two distinct approaches can be identified: 1) the empirical approach, whereby Kp values are predicted using experimentally-derived in vivo data; and 2) the mechanistic approach, whereby predictions are made in silico using tissue composition data.

Aims: To use five a priori models from the literature to produce predictions for partition coefficients in rat using a chemically-diverse dataset of 33 compounds. Comparison to the experimentally-derived values will be performed and the accuracy of the predictions will be analysed. Assessment will also be performed in relation to the physicochemical properties of the drugs in the dataset and tissue investigated.

Methods: The current study has assessed five different models for the prediction of Kp, including Arundel [1], Berezhkovskiy et al [2], Jansson et al.[3], Poulin et al.[4]. and Rodgers et al[5]. Partition coefficient values were predicted for a common dataset of 33 chemically diverse drugs (10 acids, 20 bases, 2 neutrals and 1 zwitterion) for a range of tissues. Predictions were generated for a total of 9 tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle and skin), except for the Poulin et al. [4] and Berezhkovskiy [2] models, as these are designed to deal with non-excretory tissues only and therefore cannot be used to predict values for kidney or liver.

Results: Analysis of the results showed that the Rodgers et al. [5] model was the most accurate at predicting partition coefficients for the current drug dataset, with 62.5% of the predictions within 2-fold of experimental values, and a mean predicted-to-observed ratio of 1.61 (median = 0.92). The least accurate was the Berezhkovskiy [2] model, with 38.9% of the predictions within 2-fold of experimental values, and a mean predicted-to-observed ratio of 5.80 (median = 0.57). Looking in more detail at the Rodgers et al. [5] model, it is clear that although this model produces a high level of accuracy when predicting into muscle tissue (93.8% of all predictions within 2-fold of the published experimental values; n=32), it is less accurate when predicting into rapidly perfused tissues such as liver (40.6%; n=32), lung (53.1%; n=32) and brain (43.8%; n=32).

Conclusion: The Rodgers et al. [5] model was shown to be the most accurate method for predicting partition coefficients for the current drug dataset when compared to the four other methods. However, this model does not show consistent accuracy across all drug classes and tissues, and so further investigation may help elucidate the reasons for this.

References: 1. Arundel (1997) 3

rd IFAC Symposium, Warwick, UK.

2. Berezhkovskiy (2004) J Pharm Sci 93(6):1628-1640 3. Jansson et al. (2008) J Pharm Sci 97(6):2324-2339 4. Poulin and Theil (2000) J Pharm Sci 89(1):16-35; Poulin et al. (2001) J Pharm Sci 90(4):436-447 5. Rodgers et al. (2005) J Pharm Sci 94(6):1259-1276; Rodgers and Rowland (2006) J Pharm Sci 95(6):1238-1257

Population Pharmacokinetic of Native Escherichia Coli Asparaginase Stephan Borghorst14, Rob Pieters2, Hans-Juergen Kühnel3, Joachim Boos4, Georg Hempel1,4 1Department of Pharmaceutical and Medical Chemistry – Clinical Pharmacy, University of Muenster, Germany, 2Department of Paediatric Oncology and Haematology, Erasmus MC-Sophia Children‟s Hospital, Rotterdam, the Netherlands, 3Medac GmbH, Wedel, Germany, 4Department of Paediatric Haematology and Oncology, University Children's Hospital Muenster, Germany Introduction: Native Escherichia Coli Asparaginase (ASNase) is an essential component in most treatment protocols of acute lymphoblastic leukaemia (ALL) and non-Hodgkin's Lymphoma (NHL). Population pharmacokinetics (PopPK) provides the possibility to divide the overall variability of a population in an inter- and intraindividual element and to develop more precise dosing recommendations. Due to great interindividual variability in treatment intensity of patients treated with the same dose of ASNase a PopPK approach was performed. Patients and Methods: A phase 2 clinical trial randomized 32 patients to receive either ASNase or a new recombinant Asparaginase preparation (ReASNase)i. The model building dataset consisted of the 16 patients (233 samples) receiving 5.000 U/m2 ASNase (Asparaginase Medac®) 8 times according to the DCOG-ALL 10 treatment protocol. The PopPK-model was developed using NONMEM (version VI) with First Order Conditional Estimation (FOCE) method and INTERACTION option. Bioequivalence between ASNase and ReASNase was proven by a comparison of the area under the curve (AUC) after the first doseii. To evaluate bioequivalence the pharmacokinetic parameters estimated by the final PopPK model using data from ASNase and ReASNase were compared. Furthermore a factor (θMED) estimating the deviation between both preparations was included into the model. Results: A linear 2-compartmental model with a combined proportional (0.9%) and additive (48.1U/l) error model described the data sufficiently. The pharmacokinetic parameters estimated were: Total systemic clearance 0.135 ± 12.8% l/h/70kg, volume of distribution of the central compartment 4.27 ± 13.1% l/70kg, volume of distribution in the peripheral compartment 0.83 ± 80.4% l/70kg and intercompartmental clearance 0.058 l/h/70kg (mean ± interindividual variability). Body weight was identified as the most important covariate. As there was no significant difference between the final estimated parameters of the two datasets and the factor of deviation (θMED= 0.008) between the two preparations was negligible small the obtained results supported the previous prove of bioequivalence. Conclusion: This PopPK analysis provides the first step in the development of a PopPK model for ASNase. The final PopPK model described the data sufficiently and provides further evidence of bioequivalence between ASNase and ReASNase.

________________________________ i. R.Pieters et al. Blood. 2008 Dec 15. 112(13):4832-8 ii. R Pieters et al. Blood. 2008 Dec 15. 112(13):4832-8

Population Pharmacokinetic-Pharmacodynamic-Viral Dynamics Modelling of Maraviroc Monotherapy Data Using MONOLIX P. L. S. Chan (1), P. Jacqmin (2), M. Lavielle (3), L. McFadyen (1), B. Weatherley (1) Institution: (1) Global Pharmacometrics, Pfizer Global Research and Development, Sandwich, UK; (2) Exprimo NV, Mechelen, Belgium; (3) INRIA Saclay, France Objectives: A 4-differential equation viral dynamics (VD) model was used to describe the kinetics and interaction of target cells, actively infected cells, latently infected cells and viruses after the human immunodeficiency virus (HIV) infection1. NONMEM has been previously used for fitting pharmacokinetic-pharmacodynamic(PKPD)-VD model to the maraviroc (MVC) monotherapy data2. Not only are computation times very long but there are often convergence problems resulting from numerical difficulties in optimizing the linearized likelihood. Only a few of the parameters can be estimated and it is not feasible to perform simultaneous PKPD-VD modelling. MONOLIX implements a stochastic approximation of the standard expectation maximization (SAEM) algorithm for nonlinear mixed effects models without approximations. The SAEM algorithm replaces the usual estimation step of EM by a stochastic procedure which has been shown to be very efficient with improved convergence toward the maximum likelihood estimates3. This analysis compares population PKPD-VD modelling of monotherapy MVC data using MONOLIX to NONMEM. Methods: Plasma concentration (1247 samples) and viral load (1167 observations) arising from 63 asymptomatic HIV infected patients were available. Patients received 10 days MVC monotherapy with doses range from 25-300mg QD and 50-300mg BID. A 2-compartment disposition model with first-order absorption was used to describe the MVC concentrations. An inhibitory Emax model was used to describe the viral inhibition. The need of an effect compartment and/or a lag time was examined to describe the delay in onset of viral inhibition. Parameter estimation was performed using a 2-stage approach in MONOLIX version 2.4 and NONMEM VI. The predicted PK profile based on the Empirical Bayes Estimates (EBE) obtained from separate PK analysis was used to drive the viral inhibition. A simultaneous approach was also tested with MONOLIX. Results: With a 2-stage approach, the time taken in MONOLIX to generate population and individual estimates including diagnostics (conditional means and standard errors, log likelihood profile, visual predictive checks and normalized prediction distribution errors) was over 50% less than in NONMEM without diagnostics. Parameter estimates were comparable between MONOLIX and NONMEM. Conclusion: The SAEM algorithm allows simultaneous estimation of PKPD and viral dynamics parameters. MONOLIX provides an alternative option to NONMEM when facing lengthy computation time or problems in convergence. References: 1. Funk GA, et al. Quantification of In Vivo Replicative Capacity of HIV-1 in Different Compartments of Infected Cells. J Acquir Immune Defic Syndr. 2001;26(5):397-404 2. Rosario C, et al. A pharmacokinetic-pharmacodynamic model to optimize the phase IIa development program of maraviroc. J Acquir Immune Defic Syndr. 2006;42:183-91. 3 . Kuhn E, Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis. 2005;49:1020-38.

Population Fisher Information Matrix for Discrete Type Data in Population Pharmacodynamic Experiments Kayode Ogungbenro and Leon Aarons

Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom

In recent years, interest in the application of experimental design theories to population pharmacokinetic and pharmacodynamic experiments has increased. The aim is to improve the efficiency and the precision with which parameters are estimated during analysis. The population Fisher information matrix has been described for uniresponse and multiresponse population PK experiments for design evaluation and optimisation. Despite these developments and availability of tools for optimal design of population pharmacokinetic and pharmacodynamic experiments much of the effort has been focused on repeated continuous variable measurements leaving behind repeated discrete type measurements. Discrete type data occur mostly in pharmacodynamics and they are special random variables whose values are distinct from each other and are usually integers e.g. ordinal, nominal, dichotomous or count measurements. This work derives expressions for the population Fisher information matrix for repeated ordinal, dichotomous and count measurements based on analysis by a mixed-effects modelling technique using a first-order approximation. The performance of the expressions was investigated using a simulation study based on repeated count measurements in a group of subjects. The model has two fixed-effects parameters and two random-effects parameters. Data simulated in MATLAB were analysed using NONMEM (Laplace method) and lme4 package in R (Laplace and adaptive Gauss-Hermite quadrature methods). The results obtained show good agreement between the percentage relative standard errors obtained using the population Fisher information matrix and simulations. Implementation of these expressions provide an opportunity for the efficient design of population PD and PK/PD studies that involve discrete type data through design evaluation and optimisation.

A Bayesian POP-PK analysis of exposure data from a Phase IIb clinical trial Henry Pertinez University of Manchester Bayesian methods have been applied in the analysis of a plasma exposure dataset from a phase IIb clinical trial. Simulation of the phase IIb profiles using parameters derived from a 3-compartment model fitted to earlier phase I trial data of the same drug, revealed that while the steady state exposure during the dosing period was adequately described, an extended terminal phase only visible in the longer timecourse was not. It is of particular interest to describe this phase accurately if long term predictions of exposure are required and also for future development of a PBPK model for the drug in question (using an open loop paradigm). A Bayesian analysis using the WinBUGS software package allowed the information derived from the phase I studies to be carried forward to allow modelling of the sparsely sampled and noisy phase IIb data with a 4-compartment empirical model, capturing the terminal phase.

The use of population pharmacokinetic modelling of s-warfarin to evaluate the design of drug-drug interaction study for CYP2C9

In-Sun Knutsson1

, Ivelina Gueorguieva2

, Leon Aarons1

. 1

Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PT; 2 Lilly Research Centre, Global PK/PD, Erl Wood Manor, Sunninghill Road, Windlesham, Surrey GU20 6PH, UK. Background: CYP2C9 makes up about 18 % of the cytochrome P450 protein in liver microsomes and is involved in the biotransformation of ~ 15 % of currently used drugs, including all clinically used vitamin K antagonists. S-warfarin is metabolized by the polymorphic CYP2C9 and is a recommended substrate for investigating drug-drug interactions for CYP2C9 by the FDA. Aim: To evaluate the design of drug-drug interaction studies for CYP2C9 by utilizing a population pharmacokinetic (PK) model of s-warfarin. Methods: S-warfarin plasma concentration data were collected from 83 mostly Caucasian healthy volunteers from the warfarin only arm of 7 drug-drug interaction studies conducted in between 1994-2005. A conventional two-compartment population PK model with first order-absorption was established to characterize the data using NONMEM. The optimal sampling times to estimate individual AUC0-∞ were obtained via non-compartmental approaches using the unconstrained optimization by a quadratic approximation method and simulated annealing, taking account of inter-individual variability. The estimated sampling times were compared to those taken from the model building dataset in terms of the mean squared error (MSE) of the geometric mean of AUC0-∞, taking account of inter-trial, inter-individual and between-subject variability. The powers and type-I error rates of testing the equivalences of AUC0-∞ and of AUC0-∞ and maximum concentration (Cmax) were assessed via simulation for a two-by-two cross-over design using the optimal sampling schemes which consisted of 14-17 sampling points per individual per period. The results were compared to those from three conventional bioequivalence sample size calculations. Results: The estimated population mean apparent clearance (CL), central volume (V1), distributional CL, peripheral volume and absorption rate constant (Ka) were 0.25 (L/hr; SE: 0.01), 6.65 (L; 0.22), 0.95 (L/hr; 0.09), 4.27 (L; 0.20) and 3.15 (h-1; 0.53) respectively. The inter-subject variability for apparent CL, V1 and Ka were 0.09, 0.06 and 1.45 respectively and the proportional within-subject variability was 0.02. Given a single dose of 25 mg racemic warfarin, the optimal sampling time points of size 17 were 0.0, 0.5, 1.0, 2.0, 4.0, 6.0, 10.0, 12.0, 16.0, 24.0, 36.0, 48.0, 60.0, 72.0, 96.0, 120.0, 144.0 hours post dose. The numbers of subjects required per trial to achieve 80 % power and 5 % type I error rate of testing the equivalence of AUC0-∞ and Cmax using the optimal sampling scheme of size 17 were 6-19 when the true AUC0-∞ ratios were between 0.85 and 1.2. When the optimal sampling scheme of size 14 was employed, the upper range of the number of subjects required increased nearly two fold, to 37. The numbers of subjects required of testing the equivalence of AUC0-∞ only and of AUC0-∞ and Cmax were in good agreement with those obtained from bioequivalence sample size calculations assuming AUC0-∞ and Cmax being log-normally distributed, however, those from calculations assuming AUC0-∞ being normally distributed over-estimated the required numbers of subjects by roughly 1.5-4 fold. Conclusion: The two-by-two cross-over drug-drug interaction study design was evaluated using a population PK model of s-warfarin. It was found that the required number of subjects

per trial could vary considerably depending on the PK sampling scheme, even when the plasma concentrations were densely sampled.

Combined model for predicting P450 induction, inactivation & inhibition mediated clinical drug-drug interactions based on physiological PK & enzyme turnover models for hepatic & intestinal P450 Douglas Ferguson DxDMPK, Astrazeneca Charnwood, Bakewell Rd, Loughborough, LE11 5RH Background: Cytochrome P450 dependent metabolism is the dominant route of elimination for the majority of drugs and consequently, many drug-drug interactions (DDIs) result from inhibition, inactivation or induction of P450 enzymes. A variety of approaches towards the prediction of DDI have been published - ranging from simple “cut-off rules” to fully mechanistic models. For induction mediated DDIs the mechanistic models have generally been based upon „static‟ inducer concentrations and the application of „dynamic‟ models (where variation of inducer concentration with time is incorporated) has been extremely limited. Aim: To create a dynamic mechanistic model framework that can account for simultaneous influence of P450 induction, time-dependent inactivation & competitive inhibition, in both the liver and intestine, to provide a prediction of the overall DDI. This model would then provide a mechanism to assess the utility of induction/inhibition data derived from various in-vitro systems with respect to quantitative DDI prediction. Methods: A model was created using Berkeley MadonnaTM software that consists of physiological flow based PK models for „perpetrator‟ and „victim‟ drugs linked by hepatic and intestinal P450 turnover models. Induction was modelled as increased de novo synthesis of enzyme whilst time-dependent inactivation was modelled as an increased (first-order) degradation rate. Intrinsic clearances of both victim and perpetrator drug were linked to relative abundance of active P450 using the parameter „fm‟ describing the fraction of clearance mediated by the affected P450. For hepatic interactions the assumptions of the „well-stirred model‟ were employed (unbound concentration within the liver equates to unbound concentration in liver outlet blood). For intestinal interactions the unbound concentration within the enterocyte was assumed to be equal to the unbound concentration in the hepatic portal vein. Oral absorption was modelled using a simple first order approach with fraction escaping gut metabolism (in absence of inhibitor/inducer) being described by parameter „FG‟. Results: Initial testing of the model focussed on CYP3A4 with Rifampin, Erythromycin and Ketoconazole being selected as example perpetrators of induction, time-dependent inactivation and competitive inhibition mediated DDI (respectively). Combining the dynamic model with in-vitro induction data (EC50, Emax, N) obtained from primary human hepatocyte cultures and competitive inhibition data (Imax, IC50, N) & time-dependent inactivation data (KI, kinact) from recombinant CYP3A4 resulted in accurate prediction of reported DDI for a variety of CYP3A4 substrate victim drugs (Midazolam, Nifedipine, Simvastatin &Triazolam). Conclusions: The model described represents a simple dynamic framework for the prediction of DDI arising from induction, inactivation and inhibition of hepatic and intestinal P450 – avoiding several of the simplifications associated with „static‟ methods that may limit predictive accuracy. The approach is similar to that taken within the SimCYP® simulator (where physiological variables are also incorporated to simulate interindividual variability and outcome in relevant patient populations) and can be used in a complementary fashion in the Drug Discovery setting for a variety of purposes (for example, for handling „non-standard‟ PK or direct linking to pharmacodynamic models etc.)

Facilitating Paediatric PK Studies: Utility of the Dried Blood Spot Technique Parul Patel1, 2,*, Graham Lawson2, Sangeeta Tanna2, Hitesh Pandya1, Hussain Mulla1

1University Hospitals of Leicester NHS Trust, Glenfield, Leicester, UK 2School of Pharmacy, De Montfort University, Leicester, LE1 9BH, UK *Contact: parul.patel3@email.dmu.ac.uk Background: A large proportion of medicines administered to children lack formal efficacy/safety studies and there is little understanding of any age related changes in the PK-PD relationship. As such, the dosing regimens for these drugs are derived from adult data adjusted according to child body weight/BSA, which does not account for on-going developmental changes and is evidently more likely to result in sub-optimal therapeutic outcomes. New EU legislation aims to improve the evidence base for drugs prescribed to children. This only provides a gateway to resolving the issue, the responsibility for which falls to pharmaceutical companies and clinical researchers. Technical obstacles to performing PK-PD studies in children: Blood sampling is a substantial barrier to conducting PK studies in children. Most drug assays require a relatively large blood-volume (~1-10ml) for bioanalysis, rendering them less suited for paediatric application. The problem is most apparent in pre-term neonates due to their comparatively small circulatory volume. In addition, obtaining a blood sample via venepuncture can be difficult to achieve, even in children. Collecting blood via a capillary „stab‟ provides a more robust sampling approach, though obtaining large volumes is often impossible. Micro-sampling methods: The collection of blood onto filter card, known as dried blood spots (DBS) has become well established in screening for in-born metabolic errors of the newborn. Recently, DBS samples have been used in drug quantification for therapeutic drug monitoring, toxicology and clinical studies in adults with some reports of application to paediatric studies. DBS sampling requires a microvolume (≤100µl) capillary blood sample which makes it ideal for children and pre-term neonates. Limited sampling pools and poor recruitment rates are also recognised downfalls in paediatric PK studies. DBS samples can be conveniently stored at room temperature and are easy to transport, therefore facilitating multi-centre, global co-operation. Factors of importance in DBS analysis: DBS analysis is associated with a greater potential for error compared with conventional plasma analysis. Careful consideration is therefore required of factors which may affect drug measurements, including the method of sample application, volume of blood spotted and haematocrit. The reliability of DBS methods have been previously demonstrated by several authors. Our research group has investigated the robustness of filter card as a quantitative tool for test compounds dexamethasone and captopril. Validation data assessed against regulatory guidelines suggest the developed assays are suitable for application to PK studies. Challenges to implementing DBS for paediatric PK studies: DBS sampling provides a simplified sampling method, but will necessitate appropriate training for research staff. Though DBS technology provides a minimally invasive method for drug quantification, the methodology will only be useful for PK-PD studies if it is well accepted by parents and clinical research staff. Conclusions: Analytical methods based on DBS sampling have demonstrated a degree of accuracy and precision comparable to traditional large-volume plasma methods. The sampling process has potential to significantly increase the feasibility of PK studies in children and radically improve therapeutic outcomes.

Evaluating the predictive performance of a population pharmacokinetic model of tacrolimus using a validation dataset based on sparse, adaptive-design data. Johan E. Wallin1, Martin Bergstrand1, Mats O. Karlsson1, Henryk Wilczek2, Christine E. Staatz1,3

1 Department of Pharmaceutical Biosciences, Uppsala University, Sweden, 2 Division of Transplantation Surgery, Karolinska Institute, Stockholm, Sweden 3School of Pharmacy, University of Queensland, Brisbane, Australia. Background: Tacrolimus is a potent immunosuppressant agent used to prevent and treat rejection in paediatric liver transplantation. Tacrolimus has a narrow therapeutic window and displays considerable between and within-subject pharmacokinetic variability, and pharmacokinetics seems to be changing markedly during the initial treatment. A model has been developed with the intent to capture this process. This model was used to suggest a revised initial dosing schedule and form the basis for a dose adaptation tool. Aim: To evaluate the predictability of the population model, in comparison to two previously published models(1, 2), using data collected from an independent group of paediatric liver patients. Methods: Retrospective data on tacrolimus dosage and concentrations were collected from the initial two weeks of therapy in 12 paediatric liver transplantation patients. Population predicted drug concentrations from the three models were compared to measured concentrations using samples drawn prior to TDM associated dosage adaption. Individual predicted drug concentrations based on all data were compared to measured concentrations. Individual predicted drug concentrations based on one or three prior measurements were also compared to measured concentrations. Model predictive performance was compared by calculation of MPE and RMSE. Visual evaluation of the predictive performance was performed with prediction corrected VPC (PC-VPC), where observed and simulated observations are normalized based on the population prediction, useful in application to TDM data (3). Results: The new population model was compared to the Sam and Staatz models through comparison of measured and population predicted concentrations based on samples drawn prior to TDM associated dosage adaption. Accuracy and precision expressed as MPE and RMSE was better for the proposed model compared to the Sam and Staatz model. Graphical diagnostics confirmed the increased predictive capability with the proposed model. Conclusion: The proposed pharmacokinetic model predicted the validation data set reasonably well, and was superior to the previously published models in this early post-transplantation phase. This is of importance when implementing revised dose schedules based on this model. References: 1. Sam WJ, Aw M, Quak SH, et al. Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant

patients. Br J Clin Pharmacol 2000; 50 (6): 531. 2. Staatz CE, Taylor PJ, Lynch SV, Willis C, Charles BG, Tett SE. Population pharmacokinetics of tacrolimus in children

who receive cut-down or full liver transplants. Transplantation 2001; 72 (6): 1056. 3. M Bergstrand, A.C Hooker, J.E Wallin, M.O Karlsson. Prediction Corrected Visual Predictive Checks. ACoP (2009)

Abstr F7. [http://www.go-acop.org/sites/all/assets/webform/Poster_ACoP_VPC_091002_two_page.pdf]