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Journal Papers

[1] Burt, H. J., S. Neuhoff, L. Almond, L. Gaohua, M. Harwood, M. Jamei, A. Rostami-Hodjegan, G. T. Tucker , K. Rowland-Yeo, (2016), "Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug–drug interactions", European Journal of Pharmaceutical Sciences.

Abstract:
Metformin is used as a probe for OCT2 mediated transport when investigating possible DDIs with new chemical entities. The aim of the current study was to investigate the ability of physiologicallybased pharmacokinetic (PBPK) models to simulate the effects of OCT and MATE inhibition by cimetidine on metformin kinetics. PBPK models were developed, incorporating mechanistic kidney and liver sub-models for metformin (OCT and MATE substrate) and a mechanistic kidney sub-model for cimetidine. The models were used to simulate inhibition of the MATE1, MATE2-K, OCT1 and OCT2 mediated transport of metformin by cimetidine. Assuming competitive inhibition and using cimetidine Ki values determined in vitro, the predicted metformin AUC ratio was 1.0 compared to an observed value of 1.46. The observed AUC ratio could only be recovered with this model when the cimetidine Ki for OCT2 was decreased 1000-fold or the Ki’s for both OCT1 and OCT2 were decreased 500-fold. An alternative description of metformin renal transport by OCT1 and OCT2, incorporating electrochemical modulation of the rate of metformin uptake together with 8-18-fold decreases in cimetidine Ki’s for OCTs and MATEs, allowed recovery of the extent of the observed effect of cimetidine on metformin AUC. While the final PBPK model has limitations, it demonstrates the benefit of allowing for the complexities of passive permeability combined with active cellular uptake modulated by an electrochemical gradient and active efflux.
[2] Gill, K. L., I. Gardner, L. Li , M. Jamei, (2016), " A Bottom-Up Whole-Body Physiologically Based Pharmacokinetic Model to Mechanistically Predict Tissue Distribution and the Rate of Subcutaneous Absorption of Therapeutic Proteins", The AAPS Journal, Vol. & (NO) 18 (1), pp. 156-70.

Abstract:
The ability to predict subcutaneous (SC) absorption rate and tissue distribution of therapeutic proteins (TPs) using a bottom-up approach is highly desirable early in the drug development process prior to clinical data being available. A whole-body physiologically based pharmacokinetic (PBPK) model, requiring only a few drug parameters, to predict plasma and interstitial fluid concentrations of TPs in humans after intravenous and subcutaneous dosing has been developed. Movement of TPs between vascular and interstitial spaces was described by considering both convection and diffusion processes using a 2-pore framework. The model was optimised using a variety of literature sources, such as tissue lymph/plasma concentration ratios in humans and animals, information on the percentage of dose absorbed following SC dosing via lymph in animals and data showing loss of radiolabelled IgG from the SC dosing site in humans. The resultant model was used to predict t max and plasma concentration profiles for 12 TPs (molecular weight 8-150 kDa) following SC dosing. The predicted plasma concentration profiles were generally comparable to observed data. t max was predicted within 3-fold of reported values, with one third of the predictions within 0.8-1.25-fold. There was no systematic bias in simulated C max values, although a general trend for underprediction of t max was observed. No clear trend between prediction accuracy of t max and TP isoelectric point or molecular size was apparent. The mechanistic whole-body PBPK model described here can be applied to predict absorption rate of TPs into blood and movement into target tissues following SC dosing.
[3] Johnson, T. N., M. Jamei , K. Rowland-Yeo, (2016), "How does the In Vivo Biliary Elimination of Drugs Change with Age? Evidence from In Vitro and Clinical Data Using a Systems Pharmacology Approach", Drug Metabolism and Disposition.

Abstract:
Information on the developmental changes of biliary excretion (BE) of drugs is sparse. The aims of this study were to collate literature data on the pharmacokinetics of biliary excreted drugs used in pediatrics and to apply a Physiologically Based Pharmacokinetic (PBPK) model to predict their systemic clearance (CL) with a view to elucidating age-related changes of biliary excretion. Drug parameters for azithromycin, ceftriaxone, digoxin administered intravenously (IV) and buprenorphine (IV and sublingual) were collated from the literature and used in the Simcyp Simulator to predict adult CL values which were then validated against observed data. The change in CL with age was simulated in the pediatric model and compared to observed data; where necessary, the ontogeny function associated with BE was applied to recover the age-related CL. For azithromycin a fraction of adult BE activity of 15% was necessary to predict the CL in neonates (26 weeks GA) whilst 100% activity was apparent by 7 months. For ceftriaxone and digoxin full BE activity appeared to be present at term birth, for the latter an adult BE activity of 10% was needed to predict the CL in premature neonates (30 weeks GA). The CL of buprenorphine with age was described by the ontogeny of the major elimination pathways (CYP3A4 and UGT1A1) with no ontogeny assumed for the biliary component. Thus, the ontogeny of BE for all four drugs appears to be rapid and attain adult levels at birth or within the first few months of postnatal age.
[4] Chetty, M., L. Li, R. Rose, K. Machavaram, M. Jamei, A. Rostami-Hodjegan , I. Gardner, (2015), " Prediction of the pharmacokinetics, pharmacodynamics and efficacy of a monoclonal antibody, using a physiologically based pharmacokinetic FcRn model", Frontiers in Immunology, Vol. & (NO) 5.

Abstract:
Although advantages of physiologically based pharmacokinetic models (PBPK) are now well established, PBPK models that are linked to pharmacodynamic (PD) models to predict pharmacokinetics (PK), PD, and efficacy of monoclonal antibodies (mAbs) in humans are uncommon. The aim of this study was to develop a PD model that could be linked to a physiologically based mechanistic FcRn model to predict PK, PD, and efficacy of efalizumab. The mechanistic FcRn model for mAbs with target-mediated drug disposition within the Simcyp population-based simulator was used to simulate the pharmacokinetic profiles for three different single doses and two multiple doses of efalizumab administered to virtual Caucasian healthy volunteers. The elimination of efalizumab was modeled with both a target-mediated component (specific) and catabolism in the endosome (non-specific). This model accounted for the binding between neonatal Fc receptor (FcRn) and efalizumab (protective against elimination) and for changes in CD11a target concentration. An integrated response model was then developed to predict the changes in mean Psoriasis Area and Severity Index (PASI) scores that were measured in a clinical study as an efficacy marker for efalizumab treatment. PASI scores were approximated as continuous and following a first-order asymptotic progression model. The reported steady state asymptote (Yss) and baseline score [Y (0)] was applied and parameter estimation was used to determine the half-life of progression (Tp) of psoriasis. Results suggested that simulations using this model were able to recover the changes in PASI scores (indicating efficacy) observed during clinical studies. Simulations of both single dose and multiple doses of efalizumab concentration-time profiles as well as suppression of CD11a concentrations recovered clinical data reasonably well. It can be concluded that the developed PBPK FcRn model linked to a PD model adequately predicted PK, PD, and efficacy of efalizumab.
[5] Hamon, J., M. Renner, M. Jamei, A. Lukas, A. Kopp-Schneider , F. Y. Bois, (2015), "Quantitative in vitro to in vivo extrapolation of tissues toxicity", Toxicology in Vitro, Vol. & (NO) 20 (1, Part A), pp. 203-216.

Abstract:
Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose–response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project’s partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.
[6] Bonner, J. J., P. Vajjah, K. Abduljalil, M. Jamei, A. Rostami-Hodjegan, G. T. Tucker , T. N. Johnson, (2015), "Does age affect gastric emptying time? A model-based meta-analysis of data from premature neonates through to adults", Biopharmaceutics and Drug Disposition, Vol. & (NO) 36 (4), pp. 245-57.

Abstract:
PURPOSE: Gastric emptying (GE) is often reported to be slower and more irregular in premature neonates than in older children and adults. The aim of this study was to investigate the impact of age and other covariates on the rate of GE. METHODS: The effect of age on the mean gastric residence times (MGRT) of liquid and solid food was assessed by analysing 49 published studies of 1457 individuals, aged from 28 weeks gestation to adults. The data were modelled using the nonlinear mixed-effects approach within NONMEM version 7.2 (ICON, Dublin, Ireland), with evaluation of postnatal age, gestational age and meal type as covariates. A double Weibull function was selected as a suitable model since it could account for the typical biphasic nature of GE. RESULTS: Age was not a significant covariate for GE but meal type was. Aqueous solutions were associated with the fastest emptying time (mean simulated gastric residence time of 45 min) and solid food was associated with the slowest (98 min). CONCLUSIONS: These findings challenge the assertion that GE is different in neonates, as compared with older children and adults due to age, and they reinforce the significance of food type in modulating GE.
[7] Musther, H., K. L. Gill, M. Chetty, A. Rostami-Hodjegan, M. Rowland , M. Jamei, (2015), " Are Physiologically Based Pharmacokinetic Models Reporting the Right C(max)? Central Venous Versus Peripheral Sampling Site", The AAPS Journal, Vol. & (NO) 17 (5), pp. 1268-79.

Abstract:
Physiologically based pharmacokinetic (PBPK) models can over-predict maximum plasma concentrations (C(max)) following intravenous administration. A proposed explanation is that invariably PBPK models report the concentration in the central venous compartment, rather than the site where the samples are drawn. The purpose of this study was to identify and validate potential corrective models based on anatomy and physiology governing the blood supply at the site of sampling and incorporate them into a PBPK platform. Four models were developed and scrutinised for their corrective potential. All assumed the peripheral sampling site concentration could be described by contributions from surrounding tissues and utilised tissue-specific concentration-time profiles reported from the full-PBPK model within the Simcyp Simulator. Predicted concentrations for the peripheral site were compared to the observed C(max). The models results were compared to clinical data for 15 studies over seven compounds (alprazolam, imipramine, metoprolol, midazolam, omeprazole, rosiglitazone and theophylline). The final model utilised tissue concentrations from adipose, skin, muscle and a contribution from artery. Predicted C(max) values considering the central venous compartment can over-predict the observed values up to 10-fold whereas the new sampling site predictions were within 2-fold of observed values. The model was particularly relevant for studies where traditional PBPK models over-predict early time point concentrations. A successful corrective model for C(max) prediction has been developed, subject to further validation. These models can be enrolled as built-up modules into PBPK platforms and potentially account for factors that may affect the initial mixing of the blood at the site of sampling.
[8] Gaohua, L., J. Wedagedera, B. G. Small, L. Almond, K. Romero, D. Hermann, D. Hanna, M. Jamei , I. Gardner, (2015), " Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs", CPT: pharmacometrics and systems pharmacology, Vol. & (NO) 4 (10), pp. 605-13.

Abstract:
Achieving sufficient concentrations of antituberculosis (TB) drugs in pulmonary tissue at the optimum time is still a challenge in developing therapeutic regimens for TB. A physiologically based pharmacokinetic model incorporating a multicompartment permeability-limited lung model was developed and used to simulate plasma and pulmonary concentrations of seven drugs. Passive permeability of drugs within the lung was predicted using an in vitro-in vivo extrapolation approach. Simulated epithelial lining fluid (ELF):plasma concentration ratios showed reasonable agreement with observed clinical data for rifampicin, isoniazid, ethambutol, and erythromycin. For clarithromycin, itraconazole and pyrazinamide the observed ELF:plasma ratios were significantly underpredicted. Sensitivity analyses showed that changing ELF pH or introducing efflux transporter activity between lung tissue and ELF can alter the ELF:plasma concentration ratios. The described model has shown utility in predicting the lung pharmacokinetics of anti-TB drugs and provides a framework for predicting pulmonary concentrations of novel anti-TB drugs.
[9] Patel, N., S. Polak, M. Jamei, A. Rostami-Hodjegan , D. B. Turner, (2014), "Quantitative prediction of formulation-specific food effects and their population variability from in vitro data using the physiologically-based ADAM model: A case study using the BCS/BDDCS Class II drug Nifedipine", European Journal of Pharmaceutical Sciences, Vol. & (NO) 57, pp. 240-249.

Abstract:
Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimizing the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE = -29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.
[10] Jamei, M. , F. Bajot, S. Neuhoff, Z. Barter, J. Yang, A. Rostami-Hodjegan and K. Rowland-Yeo, (2014), " A Mechanistic Framework for In Vitro - In Vivo Extrapolation of Liver Membrane Transporters: Prediction of Drug-Drug Interaction between Rosuvastatin and Cyclosporine", Clinical Pharmacokinetics, Vol. & (NO) 53 (1), pp. 73-87.

Abstract:
Background and Objectives: The interplay between liver metabolising enzymes and transporters is a complex process involving system related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug-drug interactions (mDDI and tDDI) add to the complexity of this interplay. Thus gaining meaningful insight on the impact of each element to the disposition of a drug and accurately predicting DDIs becomes very challenging. To address this, an In Vitro-In Vivo Extrapolation (IVIVE)-linked mechanistic physiologically-based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented. Methods: An IVIVE technique for liver transporters is described and a full body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDI involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic anionic transporting polypeptides OATP1B1, OAT1B3, and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide NTCP and breast cancer resistant protein BCRP) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies which had not been used for development of the rosuvastatin model. Results: The simulated area under the plasma concentration curve (AUC), maximum concentration (Cmax) and the time of reaching maximum concentration (tmax) values of rosuvastatin over the dose range of 10 to 80 mg, were within two-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine, an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers. Conclusion: The results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.
[11] Kostewicz, E. S., L. Aarons, M. Bergstrand, M. B. Bolger, A. Galetin, O. Hatley, M. Jamei, R. Lloyd, X. Pepin, A. Rostami, E. Sjogren, C. Tannergren, D. B. Turner, C. Wagner, W. Weitschies , J. Dressman, (2014), "PBPK models for the prediction of in vivo performance of oral dosage forms", Eur J Pharm Sci, Vol. & (NO) in press.

Abstract:
Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.
[12] Li, L., I. Gardner, M. Dostalek , M. Jamei, (2014), " Simulation of Monoclonal Antibody Pharmacokinetics in HumansUsing a Minimal Physiologically Based Model", The AAPS Journal.

Abstract:
Compared to small chemical molecules, monoclonal antibodies and Fc-containing derivatives (mAbs) have unique pharmacokinetic behaviour characterised by relatively poor cellular permeability, minimal renal filtration, binding to FcRn, target-mediated drug disposition, and disposition via lymph. A minimal physiologically based pharmacokinetic (PBPK) model to describe the pharmacokinetics of mAbs in humans was developed. Within the model, the body is divided into three physiological compartments; plasma, a single tissue compartment and lymph. The tissue compartment is further sub-divided into vascular, endothelial and interstitial spaces. The model simultaneously describes the levels of endogenous IgG and exogenous mAbs in each compartment and sub-compartment and, in particular, considers the competition of these two species for FcRn binding in the endothelial space. A Monte-Carlo sampling approach is used to simulate the concentrations of endogenous IgG and mAb in a human population. Existing targeted-mediated drug disposition (TMDD) models are coupled with the minimal PBPK model to provide a general platform for simulating the pharmacokinetics of therapeutic antibodies using primarily pre-clinical data inputs. The feasibility of utilising pre-clinical data to parameterise the model and to simulate the pharmacokinetics of adalimumab and an anti-ALK1 antibody (PF-03446962) in a population of individuals was investigated and results were compared to published clinical data.
[13] Mishra, H., S. Polak, M. Jamei , A. Rostami-Hodjegan, (2014), " Interaction Between Domperidone and Ketoconazole: Toward Prediction of Consequent QTc Prolongation Using Purely In Vitro Information", CPT Pharmacometrics Syst. Pharmacol., Vol. & (NO) 3 (8), pp. e130.

Abstract:
We aimed to investigate the application of combined mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation in predicting the domperidone (DOM) triggered pseudo-electrocardiogram modification in the presence of a CYP3A inhibitor, ketoconazole (KETO), using in vitro–in vivo extrapolation. In vitro metabolic and inhibitory data were incorporated into physiologically based pharmacokinetic (PBPK) models within Simcyp to simulate time course of plasma DOM and KETO concentrations when administered alone or in combination with KETO (DOM+KETO). Simulated DOM concentrations in plasma were used to predict changes in gender-specific QTcF (Fridericia correction) intervals within the Cardiac Safety Simulator platform taking into consideration DOM, KETO, and DOM+KETO triggered inhibition of multiple ionic currents in population. Combination of in vitro–in vivo extrapolation, PBPK, and systems pharmacology of electric currents in the heart was able to predict the direction and magnitude of PK and PD changes under coadministration of the two drugs although some disparities were detected.
[14] Chetty, M., R. H. Rose, K. Abduljalil, N. Patel, G. Lu, T. Cain, M. Jamei , A. Rostami-Hodjegan, (2014), " Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability", Frontiers in pharmacology, Vol. & (NO) 5, pp. 258.

Abstract:
This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.
[15] Li, L., I. Gardner, R. Rose , M. Jamei, (2014), "Incorporating Target Shedding Into a Minimal PBPK-TMDD Model for Monoclonal Antibodies", CPT: pharmacomet. syst. pharmacol., Vol. & (NO) 3, pp. e96.

Abstract:
Shedding of a pharmacological target from cells, giving rise to a soluble target that can also bind therapeutic proteins, is a common phenomenon. In this study, a minimal physiologically based pharmacokinetic model was used to simulate the pharmacokinetics of trastuzumab and the simultaneous binding of the compound to soluble (in blood and tissue interstitial space) and membrane-bound (in the tissue interstitial space) forms of human epidermal growth factor receptor 2 (HER2). The parameter values describing binding of trastuzumab to HER2 were largely derived from in vitro data, and the effects of varying HER2 levels, the affinity difference between membrane-bound HER2 and shed antigen, and slow binding kinetics were investigated. The model simulates a sharp decrease in trough drug concentrations at concentrations of soluble target between 500 and 1,000[thinsp]ng/ml in plasma. This corresponds with the clinical concentration range of soluble target wherein changes in half-life of trastuzumab have been observed.
[16] Rose, R. H., S. Neuhoff, K. Abduljalil, M. Chetty, A. Rostami-Hodjegan , M. Jamei, (2014), "Application of a Physiologically Based Pharmacokinetic Model to Predict OATP1B1-Related Variability in Pharmacodynamics of Rosuvastatin", CPT: pharmacometrics and systems pharmacology, Vol. & (NO) 3, pp. e124.

Abstract:
Typically, pharmacokinetic-pharmacodynamic (PK/PD) models use plasma concentration as the input that drives the PD model. However, interindividual variability in uptake transporter activity can lead to variable drug concentrations in plasma without discernible impact on the effect site organ concentration. A physiologically based PK/PD model for rosuvastatin was developed that linked the predicted liver concentration to the PD response model. The model was then applied to predict the effect of genotype-dependent uptake by the organic anion-transporting polypeptide 1B1 (OATP1B1) transporter on the pharmacological response. The area under the plasma concentration-time curve (AUC0-infinity) was increased by 63 and 111% for the c.521TC and c.521CC genotypes vs. the c.521TT genotype, while the PD response remained relatively unchanged (3.1 and 5.8% reduction). Using local concentration at the effect site to drive the PD response enabled us to explain the observed disconnect between the effect of the OATP1B1 c521T>C polymorphism on rosuvastatin plasma concentration and the cholesterol synthesis response.
[17] Abduljalil, K., M. Jamei, A. Rostami-Hodjegan , T. N. Johnson, (2014), " Changes in Individual Drug-Independent System Parameters during Virtual Paediatric Pharmacokinetic Trials: Introducing Time-Varying Physiology into a Paediatric PBPK Model", The AAPS Journal, Vol. & (NO) 16 (3), pp. 568-576.
[18] Wu, F., L. Gaohua, P. Zhao, M. Jamei, S.-M. Huang, E. Bashaw , S.-C. Lee, (2014), "Predicting Nonlinear Pharmacokinetics of Omeprazole Enantiomers and Racemic Drug Using Physiologically Based Pharmacokinetic Modeling and Simulation: Application to Predict Drug/Genetic Interactions", Pharmaceutical Research, Vol. & (NO) 31 (8), pp. 1919-1929.

Abstract:
Purpose The objective of this study is to develop a physiologically-based pharmacokinetic (PBPK) model for each omeprazole enantiomer that accounts for nonlinear PK of the two enantiomers as well as omeprazole racemic drug. Methods By integrating in vitro, in silico and human PK data, we first developed PBPK models for each enantiomer. Simulation of racemic omeprazole PK was accomplished by combining enantiomer models that allow mutual drug interactions to occur. Results The established PBPK models for the first time satisfactorily predicted the nonlinear PK of esomeprazole, R-omeprazole and the racemic drug. The modeling exercises revealed that the strong time-dependent inhibition of CYP2C19 by esomeprazole greatly altered the R-omeprazole PK following administration of racemic omeprazole as in contrast to R-omeprazole given alone. When PBPK models incorporated both autoinhibition of each enantiomer and mutual interactions, the ratios between predicted and observed AUC following single and multiple dosing of omeprazole were 0.97 and 0.94, respectively. Conclusions PBPK models of omeprazole enantiomers and racemic drug were developed. These models can be utilized to assess CYP2C19-mediated drug and genetic interaction potential for omeprazole and esomeprazole
[19] Ellens, H. , S. Deng, J. Coleman, J. Bentz, M. E. Taub, I. Ragueneau-Majlessi, S. Chung, K. Heredi-Szabo, S. Neuhof, J. E. Palm, P. Balimane, L. Zhang, M. Jamei, ... and C. A. Lee, (2013), "Application of Receiver Operating Characteristic (ROC) Analysis to Refine the Prediction of Potential Digoxin Drug Interactions", Drug Metabolism and Disposition, Vol. & (NO) 41 (7), pp. 1367-1374.

Abstract:
In the 2012 FDA draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits P-glycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when [I1]/IC50 is ≥ 0.1 or [I2]/IC50 is ≥10. In this manuscript, refined criteria are presented, determined by receiver operating characteristic (ROC) analysis, utilizing IC50 values generated by 23 laboratories. P-gp probe substrates were digoxin for polarized cell-lines and N-methyl quinidine or vinblastine for MDR1 over-expressed vesicles. Inhibition of probe substrate transport was evaluated using 15 known P-gp inhibitors. Importantly, the criteria derived in this manuscript take into account variability in IC50 values. Moreover, they are statistically derived based on the highest degree of accuracy in predicting true positive and true negative digoxin DDI results. The refined criteria of ([I1]/IC50 > 0.03 and [I2]/IC50 > 45) and FDA criteria were applied to a test set of 101 in vitro-in vivo digoxin DDI pairs collated from the literature. The number of false negatives (none predicted but DDI observed) were similar, 10 and 12%, while the number of false positives (DDI predicted but not observed) substantially decreased, from 51% to 40%, relative to the FDA criteria. Based on estimated overall variability in IC50 values a theoretical 95% confidence interval calculation was developed for single laboratory IC50's, translating into a range of [I1]/IC50 and [I2]/IC50 values. The extent by which this range falls above the criteria is a measure of risk associated with the decision, due to variability in IC50 values.
[20] Bentz, J. E. , M. O'Connor, D. Bednarczyk, J. Coleman, C. A. Lee, J. E. Palm, A. Pak, E. S. Perloff, E. L. Reyner, P. Balimane, M. Brannstrom, X. Chu, C. Funk, A. Guo, I. H. Hanna, K. Heredi-Szabo, K. M. Hillgren, L. Li, E. Hollnack-Pusch, M. Jamei, ... and H. Ellens, (2013), "Variability in P-Glycoprotein Inhibitory Potency (IC50) Using Various In Vitro Experimental Systems: Implications for Universal Digoxin DDI Risk Assessment Decision Criteria", Drug Metabolism and Disposition, Vol. & (NO) 41 (7), pp. 1347-1366.

Abstract:
A P-glycoprotein (P-gp) IC50 working group was established with twenty-three participating pharmaceutical and contract research laboratories and one academic institution to assess inter-laboratory variability in P-gp IC50 determinations. Each lab followed their in-house protocol to determine in vitro IC50 values for sixteen inhibitors using four different test systems: Caco-2 (11 labs), MDCKII-MDR1 (6 labs) and LLC-PK1-MDR1 (4 labs) cells, and membrane vesicles containing human P-gp (5 labs). For cell models, various equations to calculate remaining transport activity (e.g. efflux ratio, unidirectional flux, net secretory flux) were also evaluated. The difference in IC50 values for each of the inhibitors across all test systems and equations ranged from a minimum of 20- and 24-fold between lowest and highest IC50 values for sertraline and isradipine, to a maximum of 407- and 796-fold for telmisartan and verapamil, respectively. For telmisartan and verapamil, variability was greatly influenced by data from one laboratory in each case. Excluding these two data sets brings the range in IC50 values for telmisartan and verapamil down to 69- and 159-fold. The efflux ratio based-equation generally resulted in several fold lower IC50 values compared to unidirectional or net-secretory flux equations. Statistical analysis indicated that variability in IC50 values was mainly due to inter-laboratory variability, rather than an implicit systematic difference between test systems. Potential reasons for variability are discussed and the simplest, most robust experimental design for P-gp IC50 determination proposed. The impact of these findings on drug-drug interaction risk assessment is discussed in the companion paper and recommendations are provided.
[21] Machavaram, K. K. , L. M. Almond, A. Rostami-Hodjegan, I. Gardner, M. Jamei, S. Tay, S. Wong, A. Joshi and J. R. Kenny, (2013), "A Physiologically-Based Pharmacokinetic Modelling Approach to Predict Disease-Drug Interactions: Suppression of CYP3A by IL-6", Clin Pharmacol Ther, Vol. & (NO) 94 (2), pp. 260-8.

Abstract:
Elevated cytokine levels can down-regulate expression of cytochrome P450 enzymes (CYPs) and suppress their activity. Treatment of inflammation or infection with cytokine-modulating therapeutic proteins (TP) could reverse suppression manifesting as therapeutic protein-drug drug interactions (TP-DDI). A physiologically-based pharmacokinetic model was used to quantitatively predict the impact of IL-6 on sensitive CYP3A4 substrates. Elevated simvastatin AUC in virtual rheumatoid arthritis patients, following 100 pg/mL of IL-6 was comparable to observed clinical data (59% versus 58%). In virtual bone marrow transplant (BMT) patients, 500 pg/ml of IL-6 resulted in increase in cyclosporine AUC that was in good agreement with the observed data (45% versus 39%). In a different group of BMT patients treated with cyclosporine, the magnitude of interaction with IL-6 was under predicted ~3-fold. The complexity of TP-DDI highlights underlying pathophysiological factors to be considered but these simulations provide valuable first steps towards predictions of TP-DDI risk.
[22] Zamek-Gliszczynski , M. J., C. A. Lee, A. Poirier, J. Bentz, X. Chu, H. Ellens, T. Ishikawa, M. Jamei, J. C. Kalvass, S. Nagar, K. S. Pang, K. Korzekwa, P. W. Swaan, M. E. Taub, P. Zhao and A. Galetin, (2013), "ITC Recommendations for Transporter Kinetic Parameter Estimation and Translational Modeling of Transport-Mediated PK and DDIs in Humans", Clin Pharmacol Ther, Vol. & (NO) 94 (1), pp. 64-79.

Abstract:
This white paper provides a critical analysis of methods for estimating transporter kinetics and recommendations on proper parameter calculation in various experimental systems. Rational interpretation of transporter-knockout animal findings and application of static and dynamic physiologically based modeling approaches for prediction of human transporter-mediated pharmacokinetics and drug–drug interactions (DDIs) are presented. The objective is to provide appropriate guidance for the use of in vitro, in vivo, and modeling tools in translational transporter science.
[23] Rowland Yeo, K. , M. Jamei and A. Rostami-Hodjegan, (2013), "Predicting drug - drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach", Expert Review of Clinical Pharmacology, Vol. & (NO) 6 (2), pp. 143-157.

Abstract:
The development of in vitro–in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug–drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.
[24] Neuhoff, S. , K. Rowland Yeo, Z. Barter, M. Jamei, D. Turner A. and Rostami-Hodjegan, (2013), "Application of permeability-limited physiologically-based pharmacokinetic models: Part I - Digoxin pharmacokinetic incorporating P-Glycoprotein-mediated efflux", Journal of Pharmaceutical Sciences, Vol. & (NO) 102 (9), pp. 3145-60.

Abstract:
A prerequisite for the prediction of the magnitude of P-glycoprotein (P-gp)-mediated drug- drug interactions between digoxin and P-gp inhibitors (such as verapamil and its metabolite norverapamil) or P-gp inducers (such as rifampicin) is a predictive pharmacokinetic model for digoxin itself. Thus, relevant in vitro metabolic, transporter and inhibitory data incorporated into permeability-limited models, such as the “Advanced Dissolution, Absorption and Metabolism” (ADAM) module and the permeability-limited liver (PerL) module, integrated with a mechanistic physiologically-based pharmacokinetic (PBPK) model such as that of the Simcyp Simulator (Version 12) are necessary. Simulated concentration- time profiles of digoxin generated using the developed model were consistent with observed data across 31 independent studies (13 intravenous single dose (SD), 12 per oral SD and 6 multiple dose studies). The fact that predicted t max and C max of oral digoxin were similar to observed values indicated that the relative contributions of permeation and P-gp- mediated efflux in the model were appropriate. There was no indication of departure from dose proportionality over the dose range studied (0.25 to 1.5 mg). All dose normalised AUCs for the 0.25, 0.5, 0.75 and 1 mg doses resembled each other. Thus, PBPK modelling in conjunction with mechanistic absorption and distribution models and reliable in vitro transporter data can be used to assess the impact of dose on P-gp mediated efflux (or otherwise).
[25] Jamei, M , S Marciniak, D Edwards, K Wragg, K Feng, A Barnett, A Rostami-Hodjegan, (2013), " The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance", In Silico Pharmacology, Vol. & (NO) 1 (1), pp. 9.

Abstract:
Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R and D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.
[26] Neuhoff, S. , K. Rowland Yeo, Z. Barter, M. Jamei, D. Turner A. and Rostami-Hodjegan, (2013), "Application of permeability-limited physiologically-based pharmacokinetic models: Part II - Prediction of P-glycoprotein mediated drug-drug interactions with digoxin", Journal of Pharmaceutical Sciences, Vol. & (NO) 102 (9), pp. 3161-73.

Abstract:
For Peer Review 2 Abstract Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp) mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a CYP3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in AUC and C max values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and C max values of digoxin following administration of rifampicin due to P-gp induction were 1.57 (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.
[27] Darwich, A. s. , D. Pade, K. Rowland-Yeo, M. Jamei, A. A. Asberg, H. Christensen, D. M. Ashcroft and A. Rostami-Hodjegan, (2013), "Evaluation of an In Silico PBPK Post-Bariatric Surgery Model through Simulating Oral Drug Bioavailability of Atorvastatin and Cyclosporine", CPT: PSP, Vol. & (NO) 2, pp. e47.

Abstract:
An increasing prevalence of morbid obesity has led to dramatic increases in the number of bariatric surgeries performed. Altered gastrointestinal physiology following surgery can be associated with modified oral drug bioavailability (Foral). In the absence of clinical data, an indication of changes to Foral via systems pharmacology models would be of value in adjusting dose levels after surgery. A previously developed virtual “post-bariatric surgery” population was evaluated through mimicking clinical investigations on cyclosporine and atorvastatin after bariatric surgery. Cyclosporine simulations displayed a reduced fraction absorbed through gut wall (fa) and Foral after surgery, consistent with reported observations. Simulated atorvastatin Foral postsurgery was broadly reflective of observed data with indications of counteracting interplay between reduced fa and an increased fraction escaping gut wall metabolism (FG). Inability to fully recover observed atorvastatin exposure after biliopancreatic diversion with duodenal switch highlights the current gap regarding the knowledge of associated biological changes.
[28] Polak, S. , C. Ghobadi, H. Mishra, M. Ahamadi, N. Patel, M. Jamei, A. Rostami-Hodjegan, (2012), "Prediction of concentration-time profile and its inter-individual variability following the dermal drug absorption", J Pharm Sci, Vol. & (NO) 101 (7), pp. 2584-2595.

Abstract:
Estimation of systemic exposure after absorption of any xenobiotic from the skin is very important in development of dermal pharmaceutical products as well as assessing un-intended exposures due to cosmetic products or environmental and occupational compounds. Historically, animal models have been used to evaluate dermal drug absorption before conducting human trials. However, occasional disparity between the animal and human data plus rising public interest and regulatory requirements to reduce animal usage in research combined with high cost and time-consuming attributes of animal experiments have prompted many academic and industrial researchers to develop economically viable and scientifically robust in silico and in vitro methods to assess dermal drug absorption. There are a number of in silico models available in literature from quantitative structure-activity relationship to semi-mechanistic to physiologically based mechanistic models. Nonetheless, to the best of our knowledge, so far, there has been no attempt to combine mechanistic skin absorption model with database of physiological variability to simulate the inter- and intra-individual variability observed in human trials. Thus, we report here mechanistic dermal absorption model with formulation, stratum corneum, viable epidermis-dermis and blood compartments along with datab"ase of human dermal physiological variability including gender, ethnic and site of application variations. The developed model is incorporated into the Simcyp simulator which is a 'bottom-up' platform and database for mechanistic modelling and simulation of the drug disposition process using full body physiologically based pharmacokinetics model. The built model is validated using the clinical pharmacokinetic data from five different topical formulations of diclofenac. The effect of penetration enhancers, site of application, gender and ethnic variations were incorporated to simulate the clinical trials. The applied mechanistic dermal absorption model when combined with skin physiological database was able to recover well the observed clinical pharmacokinetics and population variability in all the five validation studies.
[29] Gaohua, L. , K. Abduljalil, M. Jamei, T. N. Johnson and A. Rostami-Hodjegan, (2012), "A Pregnancy Physiologically-Based Pharmacokinetic (p-PBPK) Model for Disposition of Drugs Metabolized by CYP1A2, CYP3A4 and CYP2D6", British Journal of Clinical Pharmacology, Vol. & (NO) 74 (5), pp. 873-885.

Abstract:
The physiological changes that occur in the maternal body and the placental-foetal unit during pregnancy influence the absorption, distribution, metabolism, and excretion (ADME) of xenobiotics. These include drugs that are prescribed for therapeutic reasons or chemicals to which women are exposed unintentionally from the surrounding environment. The pregnancy physiologically-based pharmacokinetic (p-PBPK) models developed for theoretical assessment of the kinetics of xenobiotics during pregnancy should take into account all the dynamic changes of the maternal and embryonic/foetal physiological functions. A number of p-PBPK models have been reported for pregnant animals and humans in the past 3 decades which have mainly been applied in the risk assessment of various environmental chemicals. The purpose of this review is to critically evaluate the current state of the art in p-PBPK modelling and to recommend potential steps that could be taken to improve model development and its application particularly in drug discovery and development for pregnant women, with potential implications for optimal drug treatment in pregnancy. The pregnancy-induced changes in physiology and pharmacokinetics, including metabolism, are reviewed to illustrate the basic alterations essential for pregnancy model development. A systemic search of the literature for existing p-PBPK models is carried out and the model structures, governing equations, methods of modelling growth, model validation/verification as well as model applications are highlighted. This review discusses benefits and limitations of the reported p-PBPK models so far and suggests areas for model improvement. The need for establishing databases on the system-related (biological, anatomical and physiological) and drug-related (physio-chemical, affinity to enzymes and transpoorters) parameters for healthy and unhealthy pregnancies is particularly emphasized.
[30] Darwich, A. , D. Pade, B. Ammori., M. Jamei, D. Ashcroft and A. Rostami-Hodjegan, (2012), "A mechanistic pharmacokinetic model to assess modified oral drug bioavailability post bariatric surgery in morbidly obese patients: Interplay between CYP3A gut wall metabolism, permeability and dissolution", JPP, Vol. & (NO) 64 (7), pp. 1008-1024.

Abstract:
Due to the multi-factorial physiological implications of bariatric surgery, attempts to explain trends in oral bioavailability following bariatric surgery using singular attributes of drugs or simplified categorisations such as the biopharmaceutics classification system have been unsuccessful. Pharmacokinetic post bariatric surgery models were created for Roux-en-Y gastric bypass, biliopancreatic diversion with duodenal switch, sleeve gastrectomy and jejunoileal bypass, through altering the ‘Advanced Dissolution Absorption and Metabolism’ (ADAM) model incorporated into the Simcyp® Simulator. Post to pre surgical simulations were carried out for five drugs with varying characteristics regarding their gut wall metabolism, dissolution and permeability (simvastatin, omeprazole, diclofenac, fluconazole and ciprofloxacin). Results suggest that the trends in oral bioavailability pre to post surgery to be dependent on a combination of drug parameters as well as the surgical procedure carried out. In the absence of clinical studies, the ability to project the direction and the magnitude of changes in bioavailability of drug therapy, using evidence-based mechanistic pharmacokinetic in silico models would be of significant value in guiding prescribers to make the necessary adjustments to dosage regimens for an increasing population of patients who are undergoing bariatric surgery.
[31] Lu, G. , , K. Abduljalil, M. Jamei, T. Johnson, H. Soltani and A. Rostami-Hodjegan, (2012), "Physiologically-based Pharmacokinetic (PBPK) models for assessing the kinetics of xenobiotics during pregnancy: Achievements and shortcomings", Current Drug Metabolism, Vol. & (NO) 13 (6), pp. 695-720.

Abstract:
The physiological changes that occur in the maternal body and the placental-foetal unit during pregnancy influence the absorption, distribution, metabolism, and excretion (ADME) of xenobiotics. These include drugs that are prescribed for therapeutic reasons or chemicals to which women are exposed unintentionally from the surrounding environment. The pregnancy physiologically-based pharmacokinetic (p-PBPK) models developed for theoretical assessment of the kinetics of xenobiotics during pregnancy should take into account all the dynamic changes of the maternal and embryonic/foetal physiological functions. A number of p-PBPK models have been reported for pregnant animals and humans in the past 3 decades which have mainly been applied in the risk assessment of various environmental chemicals. The purpose of this review is to critically evaluate the current state of the art in p-PBPK modelling and to recommend potential steps that could be taken to improve model development and its application particularly in drug discovery and development for pregnant women, with potential implications for optimal drug treatment in pregnancy. The pregnancy-induced changes in physiology and pharmacokinetics, including metabolism, are reviewed to illustrate the basic alterations essential for pregnancy model development. A systemic search of the literature for existing p-PBPK models is carried out and the model structures, governing equations, methods of modelling growth, model validation/verification as well as model applications are highlighted. This review discusses benefits and limitations of the reported p-PBPK models so far and suggests areas for model improvement. The need for establishing databases on the system-related (biological, anatomical and physiological) and drug-related (physio-chemical, affinity to enzymes and transpoorters) parameters for healthy and unhealthy pregnancies is particularly emphasized.
[32] Rowland Yeo, K. , R.L. Walsky, M. Jamei, A. Rostami-Hodjegan and G.T. Tucker, (2011), "Prediction of time-dependent CYP3A4 drug-drug interactions by physiologically based pharmacokinetic modelling: Impact of inactivation parameters and enzyme turnover", European Journal of Pharmaceutical Sciences, Vol. & (NO) 43 (3), pp. 160-173.

Abstract:
Predicting the magnitude of time-dependent metabolic drug-drug (mDDIs) interactions involving cytochrome P-450 3A4 (CYP3A4) from in vitro data requires accurate knowledge of the inactivation parameters of the inhibitor (K I, kinact) and of the turnover of the enzyme (k deg) in both the gut and the liver. We have predicted the magnitude of mDDIs observed in 29 in vivo studies involving six CYP3A4 probe substrates and five mechanism based inhibitors of CYP3A4 of variable potency (azithromycin, clarithromycin, diltiazem, erythromycin and verapamil). Inactivation parameters determined anew in a single laboratory under standardised conditions together with data from substrate and inhibitor files within the Simcyp Simulator (Version 9.3) were used to determine a value of the hepatic kdeg (0.0193 or 0.0077 h-1) most appropriate for the prediction of mDDIs involving time-dependent inhibition of CYP3A4. The higher value resulted in decreased bias (geometric mean fold error - 1.05 versus 1.30) and increased precision (root mean squared error - 1.29 versus 2.30) of predictions of mean ratios of AUC in the absence and presence of inhibitor. Depending on the k deg value used (0.0193 versus 0.0077 h-1), predicted mean ratios of AUC were within 2-fold of the observed values for all (100%) and 27 (93%) of the 29 studies, respectively and within 1.5-fold for 24 (83%) and 17 (59%) of the 29 studies, respectively. Comprehensive PBPK models were applied for accurate assessment of the potential for mDDIs involving time-dependent inhibition of CYP3A4 using a hepatic kdeg value of 0.0193 h -1 in conjunction with inactivation parameters determined by the conventional experimental approach.
[33] Ghobadi C. , T.N. Johnson, M. Aarabi, L.M. Almond, A.C. Allabi, K. Rowland-Yeo, M. Jamei and A. Rostami-Hodjegan, (2011), " Application of a Systems Approach to the Bottom-Up Assessment of Pharmacokinetics in Obese Patients: Expected Variations in Clearance", Clin Pharmacokinet, Vol. & (NO) 50 (12), pp. 809-822.

Abstract:
... Conclusion: Extension of a mechanistic predictive pharmacokinetic model to accommodate physiological and biochemical changes associated with obesity and morbid obesity allowed prediction of changes in drug clearance on the basis of in vitro data, with reasonable accuracy across a range of compounds that are metabolized by different enzymes. Prediction of the effects of obesity on drug clearance, normalized by various body size scalars, is of potential value in the design of clinical studies during drug development and in the introduction of dosage adjustments that are likely to be needed in clinical practice.
[34] Rowland Yeo, K. , M. Aarabi, M. Jamei and A. Rostami-Hodjegan, (2011), "Modelling and predicting drug pharmacokinetics in patients with renal impairment", Expert Review of Clinical Pharmacology, Vol. & (NO) 4 (2), pp. 261-274.

Abstract:
Current guidance issued by the Food and Drug Administration to assess the impact of renal impairment on the pharmacokinetics of a drug under development has recently been updated to include evaluation of drugs with non-renal elimination routes. Renal impairment not only affects elimination of the drug in the kidney, but also the non-renal route of drugs that are extensively metabolised in the liver. Renal failure may influence hepatic drug metabolism either by inducing or suppressing hepatic enzymes, or by its effects on other variables such as protein binding, hepatic blood flow and accumulation of metabolites. Prior simulation of the potential exposure of individuals with renal impairment may help in the selection of a safe and effective dosage regimen. In this review, we discuss application of a systems biology approach to simulate drug disposition in subjects with renal impairment.
[35] Rowland Yeo, K. , M Jamei, J Yang, GT Tucker and A Rostami-Hodjegan, (2010), "Physiologically-based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut – the effect of diltiazem on the time-course of exposure to triazolam", European journal of pharmaceutical sciences, Vol. & (NO) 39 (5), pp. 298-309.

Abstract:
Aim: To predict the magnitude of metabolic drug-drug interaction (mDDI) between triazolam and diltiazem and its primary metabolite N-desmethyldiltiazem (MA). Methods: Relevant in vitro metabolic and inhibitory data were incorporated into a mechanistic physiologically based pharmacokinetic model within Simcyp (Version 9.1) to simulate the time course of changes in active CYP3A4 content in gut and liver and plasma concentrations of diltiazem, MA and triazolam in a virtual population with characteristics related to in vivo studies. Results: The predicted median increases in AUC(0,∞) of triazolam, which ranged from 3.9 to 9.5 for 20 simulated trials (median 5.9), were within 1.5-fold of the observed median value (4.4) in 14 of the trials. Considering the effects of diltiazem only and not those of MA, and ignoring auto-inhibition of MA metabolism and inhibition of its metabolism by diltiazem, resulted in lower increases in triazolam exposure (AUC ratios of 1.5 to 2.0 (median 1.7) and 2.7 to 5.3 (median 3.4), respectively). Conclusion: Prediction of mDDIs involving diltiazem requires consideration of both competitive and time-dependent inhibition in gut and liver by both diltiazem and MA, as well as the complex interplay between the two moieties with respect to mutual inhibition of parent compound and its metabolite.
[36] Darwich, A. S. , S. Neuhoff, M. Jamei and A. Rostami-Hodjegan, (2010), "Interplay of Metabolism and Transport in Determining Oral Drug Absorption and Gut Wall Metabolism: A Simulation Assessment Using the "Advanced Dissolution, Absorption, Metabolism (ADAM)" Model", Current Drug Metabolism, Vol. & (NO) 11 (9), pp. 716-729.

Abstract:
Bioavailability of orally administered drugs can be influenced by a number of factors including release from the formulation, dissolution, stability in the gastrointestinal (GI) environment, permeability through the gut wall and first-pass gut wall and hepatic me- tabolism. Although there are various enzymes in the gut wall which may contribute to gut first pass metabolism, Cytochrome P450 (CYP) 3A has been shown to play a major role. The efflux transporter P-glycoprotein (P-gp; MDR1/ABCB1) is the most extensively studied drug efflux transporter in the gut and might have a significant role in the regulation of GI absorption. Although not every CYP3A substrate will have a high extent of gut wall first-pass extraction, being a substrate for the enzyme increases the likelihood of a higher first-pass extraction. Similarly, being a P-gp substrate does not necessarily pose a problem with the gut wall absorption however it may reduce bioavailability in some cases (e.g. when drug has low passive permeability)...
[37] Bois, F. Y. , M Jamei and H. J. Clewell, (2010), "PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals", Toxicology, Vol. & (NO) 278(3), pp. 256-267.

Abstract:
Generic PBPK models, applicable to a large number of substances, coupled to parameter databases and QSAR modules, are now available for predictive modelling of inter-individual variability in the absorption, distribution, metabolism and excretion of environmental chemicals. When needed, Markov chain Monte Carlo methods and multilevel population models can be jointly used for a Bayesian calibration of a PBPK model, to improve our understanding of the determinants of population heterogeneity and differential susceptibility. This article reviews those developments and illustrates them with recent applications to environmentally relevant questions.
[38] Jamei, M. , Steve Marciniak, Kirui Feng, Adrian Barnett, Geoffrey T. Tucker, and Amin Rostami-Hodjegan, (2009), "The Simcyp Population-Based ADME Simulator", Expert Opinion On Drug Metabolism and Toxicology, Vol. & (NO) 5(2), pp. 211-223.

Abstract:
The Simcyp; population-based ADME simulator is a platform and database for 'bottom-up' mechanistic modelling and simulation of the processes of oral absorption, tissue distribution, metabolism and excretion of drugs and drug candidates in healthy and disease populations. It combines experimental data generated routinely during pre-clinical drug discovery and development from in vitro enzyme and cellular systems and relevant physicochemical attributes of compound and dosage form with demographic, physiological and genetic information on different patient populations. This review describes the framework and organisation of the simulator and how it combines the different categories of information.
[39] Jamei, M. , Gemma L Dickinson and Amin Rostami-Hodjegan, (2009), " A Framework for Assessing Inter-individual Variability in Pharmacokinetics Using Virtual Human Populations and Integrating General Knowledge of Physical Chemistry, Biology, Anatomy, Physiology and Genetics: A Tale of 'Bottom-Up' vs 'Top-Down' Recognition of Covariates", Drug Metabolism and Pharmacokinetics, Vol. & (NO) 24 (1), pp. 53-75.

Abstract:
An increasing number of failures in clinical stages of drug development have been related to the effects of candidate drugs in a sub-group of patients rather than the 'average' person. Expectation of extreme effects or lack of therapeutic effects in some subgroups following administration of similar doses requires a full understanding of the issue of variability and the importance of indentifying covariates that determine the exposure to the drug candidates in each individual. In any drug development program the earlier these covariates are known the better. An important component of the drive to decrease this failure rate in drug development involves attempts to use physiologically-based pharmacokinetics 'bottom-up' modeling and simulation to optimize molecular features with respect to the absorption, distribution, metabolism and elimination (ADME) processes. The key element of this approach is the separation of information on the system (i.e. human body) from that of the drug (e.g. physicochemical characteristics determining permeability through membranes, partitioning to tissues, binding to plasma proteins or affinities toward certain enzymes and transporter proteins) and the study design (e.g. dose, route and frequency of administration, concomitant drugs and food). In this review, the classical 'top-down' approach in covariate recognition is compared with the 'bottom-up' paradigm. The determinants and sources of inter-individual variability in the different stages of drug absorption, distribution, metabolism and excretion are discussed in detail. Further, the commonly known tools for simulating ADME properties are introduced.
[40] Almond, L. , J Yang, M Jamei, GT Tucker and A Rostami-Hodjegan, (2009), "Towards a Quantitative Framework for the Prediction of DDIs Arising from Cytochrome P450 Induction", Curr Drug Metab, Vol. & (NO) 10 (4), pp. 420-432.

Abstract:
Although CYP induction is not generally considered to be as clinically relevant as CYP inhibition, there are important examples where induction has caused both therapeutic failure, due to insufficient exposure to parent drug, and toxicity, mediated by increased formation of reactive metabolites. Furthermore, while there has been considerable progress in the extrapolation of in vitro data to predict the in vivo consequences of enzyme inhibition, less attention has been given to the quantitative impact of enzyme induction as a mechanism of drug-drug interaction (DDI) and as a component of compound selection and early drug development. We discuss current approaches in the context of a mechanistic framework for the prediction of the extent and time-course of enzyme induction in vivo based on in vitro experimentation. Factors influencing the extent of DDI due to CYP induction are summarised, and areas deficient in information that would allow more accurate prediction within target populations are highlighted.
[41] Jamei, M. , D Turner, J Yang, S Neuhoff, S Polak, A Rostami-Hodjegan and GT Tucker, (2009), "Population-based Mechanistic Prediction of Oral Drug Absorption", AAPS Journal, Vol. & (NO) 11 (2), pp. 225-237.

Abstract:
Bioavailability of drugs and delivery systems in the gastrointestinal tract is influenced by many physiological factors including fluid dynamic and composition, transit and motility, bacteria and pH, metabolism and transport which can be even further complicate by the gender, race, age, food and disease status. Therefore, oral absorption of low soluble and/or low permeable compounds is often associated with high inter- and intra-individual variability. Recently, there has been a rise in the number of models and algorithms for prediction of average bioavailability; nonetheless the efforts to predict the extent of absorption variability have been limited. Such predictions require mechanistic incorporation of available physiological and pathological knowledge within the physiologically-based absorption models during their development. This paper aims at describing different aspects of such a paradigm and presenting some real world examples.
[42] Mahfouf, M. , Masoud Jamei, D.A. Linkens and J. Tenner, (2008), "Inverse modelling for optimal metal design using fuzzy specified multi-objective fitness functions", Control Engineering Practice, Vol. & (NO) 16 (2), pp. 179-191.

Abstract:
This paper presents the results which relate to the development and application of evolutionary multi-objective optimisation algorithms for the design of alloy steels using crisp as well as fuzzy logic based objective functions. The applied optimisation algorithms aim at determining the optimal heat treatment regime(s) and the required weight percentages for the chemical composites to obtain the pre-defined mechanical properties of steels, such as the ultimate tensile strength (UTS) or better known as tensile strength (TS) and elongation (ELO). During this process, the targeted mechanical properties and the reliability of their predictions are considered simultaneously in the above objective functions. Results show that for the multi-objective case the use of fuzzy logic based functions, as opposed to crisp ones, is beneficial especially when the application can tolerate a relatively low degree of discrimination between the so-called multi-objective pareto solutions.
[43] Yang, J. , Mingxiang Liao, Magang Shou, Masoud Jamei, Karen Rowland Yeo, Geoffrey T. Tucker, and Amin Rostami-Hodjegan, (2008), "Cytochrome P450 Turnover: Regulation of Synthesis and Degradation, Methods for Determining Rates, and Implications for the Prediction of Clinical Drug Interactions", Current Drug Metabolism, Vol. & (NO) 9(5), pp. 384-94.

Abstract:
In vivo enzyme levels are governed by the rates of de novo enzyme synthesis and degradation. A current lack of consensus on values of the in vivo turnover half-lives of human cytochrome P450 (CYP) enzymes places a significant limitation on the accurate prediction of changes in drug concentration-time profiles associated with interactions involving enzyme induction and mechanism (time)-based inhibition (MBI). In the case of MBI, the full extent of inhibition is also sensitive to values of enzyme turnover half-life. We review current understanding of CYP regulation, discuss the pros and cons of various in vitro and in vivo approaches used to estimate the turnover of specific CYPs and, by simulation, consider the impact of variability in estimates of CYP turnover on the prediction of enzyme induction and MBI in vivo. In the absence of consensus on values for the in vivo turnover half-lives of key CYPs, a sensitivity analysis of predictions of the pharmacokinetic effects of enzyme induction and MBI to these values should be an integral part of the modelling exercise, and the selective use of values should be avoided.
[44] Yang, J. , Masoud Jamei, Karen R. Yeo, Amin Rostami-Hodjegan, and Geoffrey T. Tucker, (2007), " Misuse of the Well-Stirred Model of Hepatic Drug Clearance", Drug Metabolism and Disposition, Vol. & (NO) 35, pp. 501-502.
[45] Yang, J. , Masoud Jamei, Karen R. Yeo, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), "Theoretical assessment of a new experimental protocol for determining kinetic values describing mechanism (time)-based enzyme inhibition", European journal of pharmaceutical sciences, Vol. & (NO) 31, pp. 232-241.

Abstract:
We have shownpreviously that the conventional experimental protocol (CEP) used to characterise mechanism-based enzyme inhibition (MBI) of drug metabolism in vitro may introduce substantial bias in estimates of the relevant kinetic parameters. The aim of this study was to develop and assess, by computer simulation, an alternative, mechanistically-based experimental protocol (MEP). This protocol comprises three parts viz. assessment of the metabolism of the mechanism-based enzyme inactivator (MBEI), of its ability to participate in competitive inhibition and its ability to cause time-dependent inhibition. Thus, values of the maximum inactivation rate constant (kinact), the inactivator concentration associated with half-maximal rate of inactivation (KI), the partition ration (r), and the reversible inhibition constant (Ki) of the MBEI are determined by nonlinear optimization of the experimental data using a model that allows for metabolism of both probe substrate and MBEI, the time-course of inactivation of the enzyme, and reversible inhibition of the metabolism of both probe substrate and MBEI. Sensitivity analysis is used to estimate the degree of confidence in the final parameter values. Virtual experiments using the MEP and the CEP were simulated, applying starting kinetic parameters reported for 16 known MBEIs. In the presence of simulated experimental error (5% CV), the MEP recovered accurate estimates of the kinetic values for all compounds, while estimates using the CEP were less accurate and less precise. The MEP promises to improve consistency in the determination of in vitro measures of MBI and, thereby, the quantitative assessment of its in vivo consequences.
[46] Yang, J. , Masoud Jamei, Karen Rowland Yeo, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), "Prediction of intestinal first-pass drug metabolism", Current Drug Metabolism, Vol. & (NO) 8 (7), pp. 676-684.

Abstract:
Despite a lower content of many drug metabolising enzymes in the intestinal epithelium compared to the liver (e.g. intestinal CYP3A abundance in the intestine is 1% that of the liver), intestinal metabolic extraction may be similar to or exceed hepatic extraction. Modelling of events on first-pass through the intestine requires attention to the complex interplay between passive permeability, active transport, binding, relevant blood flows and the intrinsic activity and capacity of enzyme systems. We have compared the predictive accuracy of the "well-stirred" gut model with that of the "Q(Gut)" model. The former overpredicts the fraction escaping first-pass gut metabolism; the latter improves the predictions by accounting for interplay between permeability and metabolism.
[47] Yang, J. , Masound Jamei, Amir Heydari, Karen Yeo, Rafael De La Torre, Magi Farre, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2006), " Mechanism-based inhibition of CYP2D6 by MDMA and its implications for pharmacokinetics and toxicity", Journal of Psychopharmacology, Vol. & (NO) 20, pp. 842-849.

Abstract:
The aim of this study was to model the in vivo kinetic consequences of mechanism-based inhibition (MBI) of CYP2D6 by 3,4- methylenedioxymethamphetamine (MDMA, ecstasy). A model with physiologically-based components of drug metabolism was developed, taking account of change in the hepatic content of active CYP2D6 due to MBI by MDMA. Based on the in vitro information, plasma concentration–time profiles of MDMA after various doses were computed and compared with reported observations. The analysis suggested that a typical recreational MDMA dose could inactivate most hepatic CYP2D6 within an hour, and the return to a basal level of CYP2D6 could take at least 10 days. Thus, the genetic polymorphism of CYP2D6 and coadministration of CYP2D6 inhibitors may have less impact on MDMA pharmacokinetics and the risk of acute toxicity than previously thought. This is consistent with clinical observations that indicate no obvious link between inherited CYP2D6 deficiency and acute MDMA intoxication.
[48] Mahfouf, M. , and M. Jamei, (2005), "Rule-base generation via symbiotic evolution for a Mamdani-type fuzzy control system", Proceedings of the I MECH E Part I Journal of Systems and Control Engineering, Vol. & (NO) 218/8, pp. 621-635.

Abstract:
In this paper, an intelligent-based active suspension system using a Mamdani-type fuzzy logic controller is developed. In order to formulate the rule-base, a new algorithm based on symbiotic evolution is proposed. Because almost all fuzzy rule-base generation algorithms usually produce a structure with redundant and overlapped membership functions, an algorithm that merges such similar membership functions is also integrated within this approach. It is shown that this algorithm leads to a more transparent and more interpretable rule-base with a minimum number of membership functions and a reduced number of rules. As a test-bed an active suspension system was used that was based on a quarter-car model with parameters relating to a Ford Fiesta MK2. The proposed method was compared with a PID (proportional-integral-derivative)-based active suspension system whose parameters were optimized using genetic algorithms (GAs). Simulation results show that the symbiotic evolution-based fuzzy controller achieved better performances in all carried-out investigations.
[49] Mahfouf, M. , M. Jamei and D. A. Linkens, (2005), " Optimal design of alloy steels using multiobjective genetic algorithms", Materials and Manufacturing Processes, Vol. & (NO) 20/3, pp. 553-567.

Abstract:
Determining the optimal heat treatment regimen and the required weight percentages for the chemical composites to obtain the desired mechanical properties of steel is a challenging problem for the steel industry. To tackle what is in essence an optimization problem, several neural network-based models, which were developed in the early stage of this research work, are used to predict the mechanical properties of steel such as the tensile strength (TS), the reduction of area (ROA), and the elongation. Because these predictive models are generally data driven, such predictions should be treated carefully. In this research work, evolutionary multiobjective (EMO) optimization algorithms are exploited not only to obtain the targeted mechanical properties but also to consider the reliability of the predictions. To facilitate the implementation of a broad range of single-objective and multi-objective algorithms, a versatile Windows 2000®-based application is developed. The obtained results from the single-objective and the multiobjective optimization algorithms are presented and compared, and it is shown that the EMO techniques can be effectively used to deal with such optimization problems.
[50] Yang, J. , M. Jamei, K.R. Yeo, G.T. Tucker and A. Rostami-Hodjegan, (2005), "Kinetic values for mechanism-based enzyme inhibition: assessing the bias introduced by the conventional experimental protocol", European Journal of Pharmaceutical Sciences, Vol. & (NO) 26 (3-4), pp. 334-340.

Abstract:
The in vitro characterisation of a mechanism-based enzyme inactivator (MBEI) includes determination of the maximum inactivation rate constant (k(inact)), the inactivator concentration that produces half-maximal rate of inactivation (K(I)), and the partition ratio (r). Conventional experimental protocols (CEPs) assume insignificant metabolism of the MBEI during the "pre-incubation" stage and negligible inactivation of enzyme during the "incubation" stage. The aim of this study was to evaluate the bias in the estimation of kinetic values as a consequence of these assumptions. Ranges of values of k(inact), K(I), and r for reported MBEIs were collated and data for 27 virtual compounds were generated by combining the median, high and low values of each parameter. The kinetics of the virtual compounds and of four reported MBEIs were simulated under CEP, but taking account of enzyme inactivation, metabolism of the MBEI and the probe substrate, and their interaction at relevant stages. The differences between the estimated and starting kinetic values reflect the bias introduced by the CEP in the absence of experimental error. Despite simulating a stringent experimental procedure, 19% of the estimated kinetic values of the 27 virtual MBEIs had greater than 100% bias. Simulations relating to two of the actual MBEIs indicated no bias in k(inact) and 8-33% bias in K(I). However, the bias in K(I) values of the two other compounds exceeded 98% and corresponding bias in k(inact) was greater than 300%. Thus, CEP may introduce substantial bias in estimated kinetic values for mechanism-based inhibition, and the validity of some of the reported kinetic parameters may be questionable.
[51] Jamei, M. , M. Mahfouf and D. A. Linkens, (2004), " Elicitation and Fine-Tuning of Mamdani-Type Fuzzy Rules Using Symbiotic Evolution", Fuzzy Sets and Systems", Fuzzy Sets and Systems, Vol. & (NO) 147:1, pp. 57-74.

Abstract:
In this paper, the ability of Symbiotic Evolution (SE) to elicit a fuzzy rule-base of the Mamdani-type is reviewed. Almost all fuzzy rule-base generation algorithms produce a rule-base with redundant and overlapped membership functions that cause difficulties in understating and computing of the generated rule-base. We address this problem by applying an algorithm to merge any similar membership functions. It is shown that this algorithm leads generally to a more transparent and more interpretable rule-base with a minimum number of membership functions and a reduced number of rules. In addition, a new post-processing approach is proposed for recovering any probable performance lost after membership functions merging. The proposed methodology has been applied successfully for the design of an active control suspension system using a non-linear Bond Graphs (BGs) based quarter-car model with parameters that relate to a Ford Fiesta MK2.

Conference Papers

[1] Mahfouf, M. , M. Jamei and D. A. Linkens, (2004), "Optimal Design of Metals Using Fuzzy Specified Multi-Objective Functions", 11th IFAC Symposium on automation in Mining, Mineral and Metal processing, 8th-10th September , 2004, Nancy, France.
[2] Jamei, M. , M. Mahfouf and D. A. Linkens, (2003), "Optimal steel design using multi-objective optimisation algorithm", International conference on Metal Fabrication and Welding Technology (METFAB - 2003) and European Symposium on Manufacturing and Modelling of Fabricated Structural Components (MMFSC), Nottingham, UK, pp. 295-308.

Abstract:
Determining the optimal heat treatment regime and the required weight percentages for the chemical composites to obtain the desired mechanical properties of steel is a challenging problem for the steel industry. In order to tackle what is in essence an optimisation problem, several neural-network based models, which were developed in the early stage of this research work, are used to predict the mechanical properties of steel such as the Tensile Strength (TS), the Reduction of Area (ROA), and the Elongation. Since these predictive models are generally data-driven, such predictions should be treated carefully. In this research work Evolutionary Multi-Objective (EMO) optimisation algorithms are exploited not only to obtain the targeted mechanical properties but also to consider the reliability of the predictions. In order to facilitate the implementation of a broad range of single-objective and multi-objective algorithms, a versatile Windows 2000 based application is developed. The obtained results from the single objective and the multi-objective optimisation algorithms are presented and compared, and it is shown that the EMO techniques can be effectively used to deal with such optimisation problems.
[3] Jamei, M. , M. Mahfouf and D. A. Linkens, (2001), "Rule-Base Generation via Symbiotic Evolution for a Mamdani-Type Fuzzy Control System", Proceedings of the UK Workshop on Computational Intelligence, Edinburgh, UK, pp. 15-20.
[4] Jamei, M. , M. Mahfouf and D. A. Linkens, (2001), "Elicitation and Fine-Tuning of Mamdani-Type Fuzzy Rules Using Symbiotic Evolution", European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems (EUNITE 2001), Tenerife, Spain.

Abstract:
In this paper, the ability of Symbiotic Evolution (SE) to elicit a fuzzy rule-base of the Mamdani-type is reviewed. Almost all fuzzy rule-base generation algorithms produce a rule-base with redundant and overlapped membership functions that cause difficulties in understating and computing of the generated rule-base. We address this problem by applying an algorithm to merge any similar membership functions. It is shown that this algorithm leads generally to a more transparent and more interpretable rule-base with a minimum number of membership functions and a reduced number of rules. In addition, a new post-processing approach is proposed for recovering any probable performance lost after membership functions merging. The proposed methodology has been applied successfully for the design of an active control suspension system using a non-linear Bond Graphs (BGs) based quarter-car model with parameters that relate to a Ford Fiesta MK2.
[5] Mahfouf, M. , M. Jamei and D. A. Linkens, (2001), "Rule-Base Generation via Symbiotic Evolution for a Mamdani-Type Fuzzy Control System", Proceedings of the 10th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE01), Melbourne, Australia, pp. 396-399.

Abstract:
In this paper, an algorithm for generating the rule-base of fuzzy systems via Symbiotic evolution is proposed. Because the initially generated rules generally reflect an opaque structure with redundant membership functions, we also applied a new algorithm to merge any similar membership functions. It is shown that his algorithm leads generally to a more transparent and more interpretable rule-base with a minimum number of membership functions and a reduced number of rules. The proposed methodology has been applied successfully for the design of an active control suspension system using a non-linear Bond Graphs (BGs) based quarter-car model with parameters that relate to a Ford Fiesta MK2.
[6] Jamei, M. , M. Mahfouf and D. A. Linkens, (2001), "Fuzzy Control Design for a Bond Graph Model of a Non-Linear suspension System", Proceedings of the Western Multi-conference 2001, International Conference on Bond Graph Modelling and Simulation (ICBGM '01), Phoenix, Arizona, USA, pp. 131-136.

Abstract:
This paper represents on a fuzzy-based active control suspension system with a minimum number of rules. In addition, a non-linear Bond Graphs (BGs) representation of a quarter-car is proposed by using parameters of a Ford Fiesta MK2. The suggested 25 rule base fuzzy controller significantly reduces the computation burden in comparison to the widely published 49 rule base fuzzy controller, while obtaining a good ride and handling performances.
[7] Jamei, M. , M. Mahfouf and D. A. Linkens, (2000), "A GA tuned Fuzzy Controller for a Non-Linear Active Suspension System", Proceedings of the 7th UK Workshop on Fuzzy Systems, Sheffield, UK, Vol. & (NO) 2, pp. 143-146.

Abstract:
A fuzzy-based active control suspension system with a minimum number of rules is proposed. The scaling factors of the controller are fine-tuned by using Genetic Algorithms (GAs) and the controller is applied to a non-linear quarter-car. The suggested 25 rule base fuzzy logic controller (FLC) reduces the computation burden significantly in comparison to the widely published 49 rule base fuzzy logic controller, while obtaining a good ride and handling performances.

Posters and Abstracts

[1] Bois, F. Y. , D. Habka, M. Jamei, (Aug 30th to Sep 3rd, 2009), "Predict-IV WP5, An integrated modelling approach for in vitro – in vivo extrapolations", 7th World Congress on Alternative and Animal Use in the Life Sciences, Rome, Italy.
[2] Bois, F. Y. , D. Habka, M. Jamei, (Aug 30th to Sep 3rd, 2009), "Predict-IV WP5, An integrated modelling approach for in vitro – in vivo extrapolations", 7th World Congress on Alternative and Animal Use in the Life Sciences, Rome, Italy.
[3] Polak, S. , M. Ahamadi, C. Ghobadi, H. Mishra, M. Jamei and A. Rostami-Hodjegan, (Aug 30th to Sep 3rd, 2009), "Prediction of human pharmacokinetics following dermal administration: integration of a skin absorption module to the Simcyp Population-Based ADME Simulator with the aim of avoiding animal studies", 7th World Congress on Alternative and Animal Use in the Life Sciences, Rome, Italy.
[4] Yang, J. , Mingxiang Liao, Magang Shou, Masoud Jamei, Karen Rowland Yeo, Geoffrey T. Tucker, and Amin Rostami-Hodjegan, (2008), " Cytochrome P450 Turnover: Regulation of Synthesis and Degradation, Methods for Determining Rates, and Implications for the Prediction of Clinical Drug Interactions", The 2nd Asian Pacific ISSX Meeting, Shanghai, China.
[5] Rowland-Yeo, K. , M Jamei, J Yang, GT Tucker and A Rostami-Hodjegan, (2008), " Prediction of time-dependent CYP3A4-mediated in vivo drug-drug interactions from in vitro data using biological information on enzyme turnover implemented within a physiologically based model", 15th North American ISSX Meeting, San Diego, California, USA.
[6] Neuhoff, S. , J. Yang, M. Jamei, G. T. Tucker, and A. Rostami-Hodjegan, (2008), " Simulation of the Effect of P-glycoprotein on Drug Absorption in the Human Gastrointestinal Tract", 10th European Regional ISSX Meeting, Vienna, Austria.
[7] Polak, S. , M. Jamei, D. B. Turner, J. Yang, S. Neuhoff, G. T. Tucker, and A. Rostami-Hodjegan, (2008), "Prediction of the in vivo Behaviour of Modified Release Formulations of Metoprolol from in vitro Dissolution Profiles Using the ADAM Model (Simcyp V8)", 10th European Regional ISSX Meeting, Vienna, Austria.
[8] Rowland Yeo, K. , Masoud Jamei, Jiansong Yang, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Prediction of in vivo drug interactions from in vitro enzyme kinetic data: time-based versus steady state simulations", PSWC 2007 - 3rd Pharmaceutical Sciences World Congress: Optimising Drung Therapy: an Imperative for World, Amsterdam, The Netherlands.
[9] Yang, J. , Masoud Jamei, Karen R. Yeo, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Theoretical Assessment of a New Experimental Protocol for Determining Kinetic Values Describing Mechanism (Time)-Based Enzyme Inhibition", PSWC 2007 - 3rd Pharmaceutical Sciences World Congress: Optimising Drung Therapy: an Imperative for World, Amsterdam, The Netherlands.
[10] Turner, D. , Masoud Jamei, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Supersaturation Properties of Poorly Soluble Weak Bases are Key Factors in Determining Oral Drug Absorption: A Simulation Study of Nifedipine Using the ADAM Model (Simcyp v7.1)", EUFEPS and COST B25 Conference on Bioavailability (BA) and Bioequivalence (BE), Athens-Greece.
[11] Polak, S. , Masoud Jamei, David Turner, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Impact of Variability in Estimates of Drug Diffusion Coefficient on the Prediction of Fraction Absorbed from the Gut (fa) Using the ADAM Model (Simcyp v7.1)", EUFEPS and COST B25 Conference on Bioavailability (BA) and Bioequivalence (BE), Athens-Greece.
[12] Yang, J. , Masoud Jamei, Karen Rowland Yeo, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Comparison of the well-stirred gut and the QGut models for predicting intestinal first-pass metabolism", 4th World Conference on Drug Absorption, Transport and Delivery (4th WCDATD), Kanazawa, Japan.
[13] Jamei, M , Jiansong Yang, David Turner, Karen Rowland Yeo, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " A Novel Physiologically-Based Mechanistic Model for Predicting Oral Drug Absorption: The Advanced Dissolution, Absorption, and Metabolism (ADAM) Model", 4th World Conference on Drug Absorption, Transport and Delivery (4th WCDATD), Kanazawa, Japan.
[14] Jamei, M. , Anders L. Finnoy, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2007), " Bias in Estimates of Metabolic Constants When Applying the Michaelis-Menten Equation to Drugs Exhibiting Atypical Enzyme Kinetics", PSWC 2007 - 3rd Pharmaceutical Sciences World Congress: Optimising Drung Therapy: an Imperative for World, Amsterdam, The Netherlands.
[15] Rowland Yeo, K. , Masoud Jamei, Jiansong Yang, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2006), " Prediction of the inhibitory effects of ketaconazole and fluconazole on midazolam and zolpidem clearance from in vitro data using physiologically-based pharmacokinetic modelling", 9th International Conference on Drug-Drug Interactions, Seattle, WA, USA.
[16] Jamei, M. , Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2006), " Co-operativity in the in vitro Kinetics of Cytochrome P450 (CYP) Mediated Drug Metabolism Will Have Minimal Impact on in vivo Metabolic Clearance", Drug Metabolism Reviews, Vol. & (NO) 38, Supplement 1, pp. 14.
[17] Jamei, M. , Jiansong Yang, Amin Rostami-Hodjegan and Geoffrey T. Tucker, (2005), " Genetic algorithms and their applications in PK/PD data analysis", Drug Metabolism Reviews, Vol. & (NO) 37, Supplement 1, pp. 48.
[18] Jamei, M. , Jiansong Yang, Geoffrey T. Tucker and Amin Rostami-Hodjegan, (2005), " Inter- and intra-individual variability in gastro-intestinal physiology has significant effects on the prediction of fraction of dose absorbed (fa)", 3rd World Conference on Drug Absorption, Transport and Delivery, Barcelona, Spain.
[19] Jamei, M. , Jiansong Yang, Amin Rostami-Hodjegan and Geoffrey T. Tucker, (2005), "Inter- and intra-individual variability in gastro-intestinal physiology has significant effects on the prediction of fraction of dose absorbed (fa)", 2005 FDA Science Forum.
[20] Jamei, M. , Jiansong Yang and Amin Rostami-Hodjegan, (2004), " Inter and intra-individual variability in physiological parameters of gastro-intestinal tract has significant effects on the predicted fraction of dose absorbed", LogP 2004, The 3rd Lipophilicity Symposium, Zurich, Switzerland.
[21] Jamei, M. , (2001), "Fuzzy Control Design through Symbiotic Evolution (Extended abstract)", The 8th Iranian Students Seminar in Europe (ISS-2001), UMIST, Manchester, UK.
[22] Jamei, M. , M. Mahfouf and D. A. Linkens, (2000), "Fuzzy-Based Controller of a Non-linear Quarter Car Suspension System", The 7th Iranian Students Seminar in Europe (ISS-2000), UMIST, Manchester, UK.
[23] Jamei, M. , (1999), "Bond Graphs and their Application in Car Suspension Modelling (Extended abstract)", The 6th Iranian Students Seminar in Europe (ISS-1999), UMIST, Manchester, UK.

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