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This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of 'good practice' recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines. [ABSTRACT FROM AUTHOR]

Six male volunteers (mean age 24 years) received a single oral dose of 0.025 mg CQP201-403 and placebo in a randomised double-blind crossover design. Fifteen plasma samples were collected over 48 h and were assayed by radioimmunoassay for drug substance and prolactin (PRL). Three of the samples were drawn during sleep on the first study day. The pharmacological effect (E%) of CQP201-403 was expressed as reduction in plasma PRL levels. The pharmacokinetic (PK)- pharmacodynamic (PD) model consisted of two kinetic compartments and an effect compartment linked to the central compartment. A sigmoid Emax model (Hill equation) described the relationship between the drug concentration in the effect compartment and E%. Curve-fitting of PK and PD data provided individual parameter estimates which served to generate computer-simulated PK and PD profiles after single and multiple doses in order to: investigate the in vivo concentration- effect relationship; evaluate the consequence of dosage reduction on the steady-state PD profile; and study the robustness of the response to changes in drug potency and bioavailability. [ABSTRACT FROM AUTHOR]

Pharmacometricians are virtually nonexistent in Africa and the developing world. The unrelenting burden of infectious diseases, which are often treated using medicines with narrow effectiveness and safety dose ranges, and the growing prevalence and recognition of non-communicable diseases represent significant threats for the patients, although affording an opportunity for advancing science. This article outlines the case for pharmacometricians to redirect their expertise to focus on the disease burden affecting the developing world.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e69; doi:10.1038/psp.2013.45; published online 28 August 2013.

The Animal Health Modeling & Simulation Society (AHM&S) is a newly founded association (2012) that aims to promote the development, application, and dissemination of modeling and simulation techniques in the field of Veterinary Pharmacology and Toxicology. The association is co-chaired by Pr. Johan Gabrielsson (Europe), Pr. Jim Riviere (USA), and secretary Dr. Jonathan Mochel (Switzerland). This short communication aims at presenting the membership, rationale and objectives of this group., (© 2013 John Wiley & Sons Ltd.)

Our objective was to show, using two examples, that a pharmacokinetic (PK) similarity analysis can be performed using nonlinear mixed-effects models (NLMEM). We used two studies that compared different biosimilars: a three-way crossover trial with somatropin and a parallel-group trial with epoetin-α. For both data sets, the results of NLMEM-based analysis were compared with those of noncompartmental analysis (NCA). For the latter analysis, we performed an NLMEM-based equivalence Wald test on secondary parameters of the model: the area under the curve and the maximal concentration. Somatropin PK was described by a one-compartment model and epoetin-α PK by a two-compartment model with linear and Michaelis-Menten elimination. For both studies, similarity of PK was demonstrated by means of both NCA and NLMEM, and both methods led to similar results. Therefore, for establishing similarity, PK data can be analyzed by either of the methods. NCA is an easier approach because it does not require data modeling; however, NLMEM leads to a better understanding of the underlying biological system.

The aim of this conceptual framework paper is to contribute to the further development of the modelling of effects of drugs or toxic agents by an approach which is based on the underlying physiology and pathology of the biological processes. In general, modelling of data has the purpose (1) to describe experimental data, (2a) to reduce the amount of data resulting from an experiment, e.g. a clinical trial and (2b) to obtain the most relevant parameters, (3) to test hypotheses and (4) to make predictions within the boundaries of experimental conditions, e.g. range of doses tested (interpolation) and out of the boundaries of the experimental conditions, e.g. to extrapolate from animal data to the situation in man. Describing the drug/xenobiotic-target interaction and the chain of biological events following the interaction is the first step to build a biologically based model. This is an approach to represent the underlying biological mechanisms in qualitative and also quantitative terms, thus being inherently connected in many aspects to systems biology. As the systems biology models may contain variables in the order of hundreds connected with differential equations, it is obvious that it is in most cases not possible to assign values to the variables resulting from experimental data. Reduction techniques may be used to create a manageable model which, however, captures the biologically meaningful events in qualitative and quantitative terms. Until now, some success has been obtained by applying empirical pharmacokinetic/pharmacodynamic models which describe direct and indirect relationships between the xenobiotic molecule and the effect, including tolerance. Some of the models may have physiological components built in the structure of the model and use parameter estimates from published data. In recent years, some progress toward semi-mechanistic models has been made, examples being chemotherapy-induced myelosuppression and glucose-endogenous insulin-antidiabetic drug interactions. We see a way forward by employing approaches to bridge the gap between systems biology and physiologically based kinetic and dynamic models. To be useful for decision making, the 'bridging' model should have a well founded mechanistic basis, but being reduced to the extent that its parameters can be deduced from experimental data, however capturing the biological/clinical essential details so that meaningful predictions and extrapolations can be made.

Physiologically based modelling of pharmacodynamics/toxicodynamics requires an a priori knowledge on the underlying mechanisms causing toxicity or causing the disease. In the context of cancer, the objective of the expert meeting was to discuss the molecular understanding of the disease, modelling approaches used so far to describe the process, preclinical models of cancer treatment and to evaluate modelling approaches developed based on improved knowledge. Molecular events in cancerogenesis can be detected using 'omics' technology, a tool applied in experimental carcinogenesis, but also for diagnostics and prognosis. The molecular understanding forms the basis for new drugs, for example targeting protein kinases specifically expressed in cancer. At present, empirical preclinical models of tumour growth are in great use as the development of physiological models is cost and resource intensive. Although a major challenge in PKPD modelling in oncology patients is the complexity of the system, based in part on preclinical models, successful models have been constructed describing the mechanism of action and providing a tool to establish levels of biomarker associated with efficacy and assisting in defining biologically effective dose range selection for first dose in man. To follow the concentration in the tumour compartment enables to link kinetics and dynamics. In order to obtain a reliable model of tumour growth dynamics and drug effects, specific aspects of the modelling of the concentration-effect relationship in cancer treatment that need to be accounted for include: the physiological/circadian rhythms of the cell cycle; the treatment with combinations and the need to optimally choose appropriate combinations of the multiple agents to study; and the schedule dependence of the response in the clinical situation.

The plasma concentration-time profile of a drug is essential to explain the relationship between the administered dose and the kinetics of drug action. However, in some cases such as in pre-clinical pharmacology or phase-III clinical studies where it is not always possible to collect all the required PK information, this relationship can be difficult to establish. In these circumstances several authors have proposed simple models that can analyse and simulate the kinetics of the drug action in the absence of PK data. The present work further develops and evaluates the performance of such an approach. A virtual compartment representing the biophase in which the concentration is in equilibrium with the observed effect is used to extract the (pharmaco)kinetic component from the pharmacodynamic data alone. Parameters of this model are the elimination rate constant from the virtual compartment (KDE), which describes the equilibrium between the rate of dose administration and the observed effect, and the second parameter, named EDK(50) which is the apparent in vivo potency of the drug at steady state, analogous to the product of EC(50), the pharmacodynamic potency, and clearance, the PK "potency" at steady state. Using population simulation and subsequent (blinded) analysis to evaluate this approach, it is demonstrated that the proposed model usually performs well and can be used for predictive simulations in drug development. However, there are several important limitations to this approach. For example, the investigated doses should extend from those producing responses well below the EC(50) to those producing ones close to the maximum response, optimally reach steady state response and followed until the response returns to baseline. It is shown that large inter-individual variability on PK-PD parameters will produce biases as well as large imprecision on parameter estimates. It is also clear that extrapolations to dosage routes or schedules other than those used to estimate the parameters should be undertaken with great caution (e.g., in case of non-linearity or complex drug distribution). Consequently, it is advised to apply this approach only when the underlying structural PD and PK are well understood. In any case, K-PD model should definitively not be substituted for the gold standard PK-PD model when correct full model can and should be identified.

Introduction: Ibandronate is a potent, nitrogen-containing bisphosphonate that is licensed as a once-monthly oral preparation and is currently in clinical development as a novel intermittent intravenous (i.v.) injection in osteoporosis. Ibandronate pharmacokinetic (PK) data were used to develop a PK model that could ultimately be incorporated into a PK pharmacodynamic (PD) model to assist the ibandronate development program through computer-assisted trial design. This manuscript reports the use of non-linear mixed-effects modeling to characterize the PK of ibandronate, to examine the possible influence of ethnicity on the disposition of ibandronate and to develop an appropriate population PK model for ibandronate., Methods: A retrospective, cross-study population PK analysis was performed using PK data from five phase I studies with i.v. ibandronate (0.125 - 2.0 mg) conducted in Caucasian and Japanese healthy male volunteers, postmenopausal Caucasian women without osteopenia and postmenopausal Japanese women with osteopenia. The following covariates were investigated to establish their influence on the central volume of distribution (V1) and drug clearance (CL): age, body weight, gender, disease status (healthy versus osteopenic), creatinine clearance (CLCR), and ethnicity (Japanese versus Caucasian). Serum concentrations of ibandronate were quantified by GC-MS or ELISA, and data were modeled using non-linear mixed-effects modeling implemented by the software program NONMEM., Results: The PK of ibandronate was adequately described by a linear 3-compartment model. Disease status, body weight, gender and CLCR significantly influenced ibandronate CL (10 34%) and the latter 3 also influenced V1 (20 29%). Ethnicity was not a determinant for ibandronate PK in the final model. Although gender was the most influential covariate, differences in V1 and CL between the sexes were modest (29 and 34%, respectively) and the overall effects on ibandronate exposure (Cmax and AUC) were not clinically relevant. The final model described the observed PK of ibandronate well, and all PK parameters were estimated with an acceptable degree of precision (SE < 13%)., Conclusion: The PK of i.v. ibandronate was well described by a linear 3-compartment population PK model that included disease status, body weight, gender and CLCR as covariates, but without greatly affecting ibandronate exposure (Cmax and AUC). Ethnicity did not influence ibandronate PK and was not included in the final model.

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940-2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making.

To help identify the role of modelling and simulation in the development of anti-cancer agents, their main advantages and the obstacles to their rational use, an expert meeting was organized by COST B15. This manuscript presents a synthesis of views expressed at that meeting and indicates future directions. The manuscript also shows some examples where modelling and simulation have proven to be of relevant value in the drug development process for anti-cancer agents.

Aims: Ibandronate, a highly potent nitrogen-containing bisphosphonate, is the subject of an ongoing clinical development programme that aims to maximize the potential of simplified, less frequent oral and intravenous (i.v.) administration in osteoporosis. A modelling and simulation project was undertaken to characterize further the clinical pharmacology of ibandronate and identify convenient intermittent oral and i.v. regimens for clinical evaluation., Methods and Results: Using selected data from clinical studies involving 174 women with postmenopausal osteoporosis (PMO), a classical multicompartmental pharmacokinetic-pharmacodynamic (PK-PD) model was developed that accurately described the PK of i.v. ibandronate in plasma and urine and urinary excretion of the C-telopeptide of the alpha chain of type I collagen (uCTX), a sensitive biomarker of PD response to ibandronate. To reduce processing times, the classical PK-PD model was simplified using a "kinetics of drug action" or kinetic (K)-PD model (i.e. a dose-response model as opposed to a dose-concentration-response model). The performance of the K-PD model was evaluated by fitting data simulated with the PK-PD model under various dosing regimens. The simplified model produced a virtually indistinguishable fit of the data from that of the PK-PD model. The K-PD model was extended to consider the influence of supplemental therapy (calcium with or without vitamin D) on the PD response and validated by retrospectively simulating the uCTX response in a prior Phase III and Phase II/III study of i.v. ibandronate, given once every 3 months, in 3380 women with PMO. The observed median uCTX responses at the scheduled assessment points in the completed studies were within the distribution of the simulated responses. The K-PD model for i.v. ibandronate was extended further to allow simultaneous fitting of uCTX responses after i.v. and oral administration in 676 postmenopausal women with osteoporosis, and validated by retrospectively simulating the data observed in a Phase I study of oral daily ibandronate in 180 women with PMO. The K-PD model adequately described the uCTX response after oral dosing., Conclusions: This validated K-PD model is currently being used to evaluate a range of novel intermittent oral and i.v. ibandronate regimens in an ongoing clinical development programme.

Preclinical studies, along with Phase I, II, and III clinical trials demonstrate the pharmacokinetics, pharmacodynamics, safety and efficacy of a new drug under well controlled circumstances in relatively homogeneous populations. However, these types of studies generally do not answer important questions about variability in specific factors that predict pharmacokinetic and pharmacodynamic (PKPD) activity, in turn affecting safety and efficacy. Semi-physiological and clinical PKPD modeling and simulation offer the possibility of utilizing data obtained in the laboratory and the clinic to make accurate characterizations and predictions of PKPD activity in the target population, based on variability in predictive factors. Capecitabine is an orally administered pro-drug of 5-fluorouracil (5-FU), designed to exploit tissue-specific differences in metabolic enzyme activities in order to enhance efficacy and safety. It undergoes extensive metabolism in multiple physiologic compartments, and presents particular challenges for predicting pharmacokinetic and pharmacodynamic activity in humans. Clinical and physiologically based pharmacokinetic (PBPK) and pharmacodynamic models were developed to characterize the activity of capecitabine and its metabolites, and the clinical consequences under varying physiological conditions such as creatinine clearance or activity of key metabolic enzymes. The results of the modeling investigations were consistent with capecitabine's rational design as a triple pro-drug of 5-FU. This paper reviews and discusses the PKPD and PBPK modeling approaches used in capecitabine development to provide a more thorough understanding of what the key predictors of its PBPK activity are, and how variability in these predictors may affect its PKPD, and ultimately, clinical outcomes.

Aims: To assess the relationship between systemic exposure to capecitabine metabolites and parameters of efficacy and safety in patients with advanced or metastatic colorectal cancer from two phase III studies., Methods: Concentration-effect analyses were based on data from 481 patients (248 males, 193 females; age range 27-86 years) in two phase III studies. Plasma concentration-time data for 5'-deoxy-5-fluorouridine (5'-DFUR), 5-fluorouracil (5-FU) and alpha-fluoro-beta-alanine (FBAL) were obtained from sparse blood samples collected within the time windows 0.5-1.5 h, 1.5-3.0 h, and 3.0-5.0 h after capecitabine administration (1250 mg m(-2)) on the first day of cycles 2 (day 22) and 4 (day 64), respectively. Systemic exposure based on plasma concentrations of capecitabine and its metabolites was determined using individual parameter estimates derived from a population pharmacokinetic model constructed for this purpose in NONMEM. Logistic regression analysis was conducted for selected safety parameters (all treatment-related grade 3-4 adverse events, treatment-related grade 3-4 diarrhoea, grade 3 hand-foot syndrome (HFS) and grade 3-4 hyperbilirubinaemia) and for tumour response. Cox regression analysis was used for the analysis of time-to-event data (time to disease progression and duration of survival)., Results: Statistically significant relationships between covariates and PK parameters were found as follows. A doubling of alkaline phosphatase activity was associated with a 11% decrease in 5-FU clearance and a 12% increase in its AUC. A 50% decrease in creatinine clearance was associated with a 35% decrease in FBAL clearance, a 53% increase in its AUC, a 24% decrease in its volume of distribution, and a 41% increase in its Cmax. A 30% increase in body surface was associated with a 24% increase in the volume of distribution of FBAL and a 19% decrease in its Cmax. There was a broad overlap in systemic drug exposure between patients regardless of the occurrence of treatment-related grade 3-4 adverse events or response to treatment, leading to weak relationships between systemic exposure to capecitabine metabolites and the safety and efficacy parameters. Of 42 concentration-effect relationships investigated, only five achieved statistical significance. Thus, we obtained a positive association between the AUC of FBAL and grade 3-4 diarrhoea (P = 0.035), a positive association between the AUC of 5-FU and grade 3-4 hyperbilirubinaemia (P = 0.025), a negative association between the Cmax of FBAL and grade 3-4 hyperbilirubinaemia (P = 0.014), a negative association between the AUC of 5-FU (in plasma) and time to disease progression (hazard ratio (HR) = 1.626, P = 0.0056), and a positive association between the Cmax of 5'-DFUR and survival (HR = 0.938, P = 0.0048). Additionally, there were inconsistencies when concentration-effect relationships were compared across the two studies., Conclusions: Systemic exposure to capecitabine and its metabolites in plasma is poorly predictive of safety and efficacy. The present results have no clinical implications for the use of capecitabine and argue against the value of therapeutic drug monitoring for dosage adjustment.

Capecitabine has been developed as an orally administered tumor selective fluoropyrimidine for use in the treatment of breast and colorectal cancer. The metabolic pathway for capecitabine includes 5'-deoxy-5-fluorocytidine (5'-DFCR) and 5'-deoxy-5-fluorouridine (5'-DFUR), which is then converted to the pharmacologically active agent 5-fluorouracil (5-FU). A previous analysis showed that systemic exposure to 5'-DFUR and alpha-fluoro-beta-alanine (FBAL), a catabolite of 5-FU, was predictive of dose limiting toxicities. Therefore, a multi-response population pharmacokinetic (PK) model for the description of plasma concentrations of 5'-DFUR, 5-FU and FBAL following oral administration of capecitabine was developed using NONMEM. PK data from a bioequivalence study in 24 patients with various solid tumors were used to develop the PK structural part of the population PK model. The 5'-DFUR, 5-FU and FBAL plasma concentrations were described by a linear disposition PK model with first order absorption and lag time. Sparse plasma concentration data from 54 phase II breast cancer patients were added to the bioequivalence data and the influence of covariates on the apparent oral clearances of 5'-DFUR, 5-FU and FBAL and on the apparent volume of distribution of FBAL was investigated. This was conducted by including all significant (p < 0.05) single covariate-PK parameter pairs in the full PK model, followed by one by one deletion (p < 0.001) from the population model. Statistically significant effects were found for the influence of gender, body surface area and total bilirubin on 5'-DFUR clearance and the influence of creatinine clearance on FBAL clearance. However, none of these effects were considered to have clinical relevance.

Although the use of pharmacokinetic/pharmacodynamic modelling and simulation (M&S) in drug development has increased during the last decade, this has most notably occurred in patient studies using the population approach. The role of M&S in Phase I, although of longer history, does not presently have the same impact on drug development. However, trends such as the increased use of biomarkers and clinical trial simulation as well as adoption of the learn/confirm concept can be expected to increase the importance of modelling in Phase I. To help identify the role of M&S, its main advantages and the obstacles to its rational use, an expert meeting was organised by COST B15 in Brussels, January 10-11, 2000. This article presents the views expressed at that meeting. Although it is clear that M&S occurs in only a minority of Phase I clinical trials, it is used for a large number of different purposes. In particular, M&S is considered valuable in the following situations: censoring because of assay limitation, characterisation of non-linearity, estimating exposure-response relationship, combined analyses, sparse sampling studies, special population studies, integrating PK/PD knowledge for decision making, simulation of Phase II trials, predicting multiple dose profile from single dose, bridging studies and formulation development. One or more of the following characteristics of M&S activities are often present and severely impede its successful integration into clinical drug development: lack of trained personnel, lack of protocol and/or analysis plan, absence of pre-specified objectives, no timelines or budget, low priority, inadequate reporting, no quality assurance of the modelling process and no evaluation of cost-benefit. The early clinical drug development phase is changing and if these implementation aspects can be appropriately addressed, M&S can fulfill an important role in reshaping the early trials by more effective extraction of information from studies, better integration of knowledge across studies and more precise predictions of trial outcome, thereby allowing more informed decision making.

Objective: To use pharmacostatistical models to evaluate the overall exposure of patients with Parkinson's disease to levodopa in the presence and absence of tolcapone., Methods: Four hundred twelve patients with Parkinson's disease with fluctuating and nonfluctuating responses to levodopa participated in three multicentered, parallel, double-blind, placebo-controlled dose-finding studies and received either placebo or tolcapone in addition to levodopa-decarboxylase inhibitor therapy. Sparse blood samples were obtained from 393 patients for levodopa and 3-O-methyldopa assay, and the data were analyzed with use of the NONMEM program., Results: The fraction of levodopa metabolized to 3-O-methyldopa was substantially reduced by the co-administration of tolcapone (by 65%, 74%, and 84% with tolcapone doses of 50, 200, and 400 mg, respectively, in fluctuators, and by 50% and 90% with doses of 200 and 400 mg, respectively, in nonfluctuators). This led to an overall reduction in levodopa clearance (CL) of approximately 15% to 25% in fluctuators and 20% to 30% in nonfluctuators. Because this was partly compensated for by a reduction in levodopa dose in these studies, the total daily exposure of patients to levodopa was only slightly increased (11% to 16%). The peak-trough fluctuations of plasma levodopa (Cmax-Cmin) were reduced in both populations in a dose-dependent fashion., Conclusions: Tolcapone effectively inhibited the formation of 3-O-methyldopa and resulted in a decrease in levodopa CL. The consequent increase in levodopa bioavailability was mostly offset by reductions in levodopa dose. It is possible that decreased fluctuations in plasma levodopa concentrations rather than increased levodopa exposure may explain the clinical benefits obtained with tolcapone.

Aims: To use pharmacostatistical models to characterize tolcapone's pharmacokinetics in parkinsonian patients, and to identify any demographic subpopulations which may be at risk of either under- or over-exposure to this catechol-O-methyltransferase (COMT) inhibitor., Methods: Four hundred and twelve patients participated in three multicentre, parallel, double-blind, placebo-controlled, dose-finding studies and received either placebo or tolcapone (50, 200 or 400 mg three times daily) in addition to levodopa/decarboxylase inhibitor therapy. Sparse blood samples were obtained from 275 patients for tolcapone assay and the concentrations (1414 in total) were analysed using the NONMEM program., Results: The pharmacokinetic model which best described the data was a two-compartment open model with first-order absorption and possibly a lag-time. Tolcapone pharmacokinetics were shown to be stable, with no systematic trend between 2 and 6 weeks of treatment. The absorption of the drug was shown to be rapid and concomitant food intake had only a minor effect on the relative bioavailability (10-20% reduction compared with fasting). The overall clearance of tolcapone could be estimated with good precision (approximately 4. 5-5 l h-1 ), and none of the investigated covariates (e.g. sex, age, body weight) had any clinically significant influence on this parameter. The volume of distribution showed relatively high variability and was calculated to be approximately 30 l, leading to an estimated half-life in patients of approximately 5-8 h., Conclusions: Using sparse concentrations and mixed effect-effects modelling analysis it is possible to describe the pharmacokinetics of tolcapone in parkinsonian populations. The parameter estimates obtained agreed with those obtained from conventional pharmacokinetic studies and no subpopulation was shown to be at risk of either under- or over-exposure to tolcapone.

We propose a framework for considering the role of pharmacokinetic/pharmacodynamic modeling in drug development and an appraisal of its current and potential impact on that activity. After some introduction, definitions, and background information on drug development, we discuss subject-matter models that underlie pharmacokinetic/pharmacodynamic modeling and show how they determine appropriate statistical models. We discuss the broad role modeling can play in drug development, enhancing primarily the "learning" steps, i.e. acquiring the information needed for the label and for planning efficient confirmatory clinical trials. Examples of past applications of modeling to drug development are presented in tabular form, followed by a discussion of some practical issues in application. Modeling will not reach its potential utility until it is manifest as a visible and separate work unit within a drug development program. We suggest that that work unit is the "in numero" study: a protocol-driven exercise designed to extract additional information, and/or answer a specific drug-development question, through an integrated model-based (meta-) analysis of existent raw data, often pooled across separate (clinical) studies.

There is broad recognition within the pharmaceutical industry that the drug development process, especially the clinical part of it, needs considerable improvement to cope with rapid changes in research and health care environments. Modelling and simulation are mathematically founded techniques that have been used extensively and for a long time in other areas than the pharmaceutical industry (e.g. automobile, aerospace) to design and develop products more efficiently. Both modelling and simulation rely on the use of (mathematical and statistical) models which are essentially simplified descriptions of complex systems under investigation. It has been proposed to integrate pharmacokinetic (PK) and pharmacodynamic (PD) principles into drug development to make it more rational and efficient. There is evidence from a survey on 18 development projects that a PK/PD guided approach can contribute to streamline the drug development process. This approach extensively relies on PK/PD models describing the relationships among dose, concentration (and more generally exposure), and responses such as surrogate markers, efficacy measures, adverse events. Well documented empirical and physiologically based PK/PD models are becoming available more and more, and there are ongoing efforts to integrate models for disease progression and patient behavior (e.g. compliance) as well. Other types of models which are becoming increasingly important are population PK/PD models which, in addition to the characterization of PK and PD, involve relationships between covariates (i.e. patient characteristics such as age, body weight) and PK/PD parameters. Population models allow to assess and to quantify potential sources of variability in exposure and response in the target population, even under sparse sampling conditions. As will be shown for an anticancer agent, implications of significant covariate effects can be evaluated by computer simulations using the population PK/PD model. Stochastic simulation is widely used as a tool for evaluation of statistical methodology including for example the evaluation of performance of measures for bioequivalence assessment. Recently, it was suggested to expand the use of simulations in support of clinical drug development for predicting outcomes of planned trials. The methodological basis for this approach is provided by (population) PK/PD models together with random sampling techniques. Models for disease progression and behavioral features like compliance, drop-out rates, adverse event dependent dose reductions, etc. have to be added to population PK/PD models in order to mimic the real situation. It will be shown that computer simulation helps to evaluate consequences of design features on safety and efficacy assessment of the drug, enabling identification of statistically valid and practically realisable study designs. For both modelling and simulation a guidance on 'best practices' is currently worked out by a panel of experts comprising representatives from academia, regulatory bodies and industry, thereby providing a necessary condition that model-based analysis and simulation will further contribute to streamlining pharmaceutical drug development processes.

Objective: The aim of this study was to confirm the most appropriate dosage of a new soft gelatin capsule (SGC) formulation of the HIV protease inhibitor saquinavir by investigating the relationships between systemic (plasma) exposure to saquinavir and plasma HIV RNA and CD4+ cell counts using empirical mathematical modelling., Design and Setting: A randomised, non-blind, multicentre, dose-ranging 8-week study of monotherapy with 400, 800 or 1200 mg of saquinavir-SGC or 600 mg of the hard gelatin capsule (HGC) formulation, both administered 3 times daily, was carried out in protease inhibitor-naive, HIV-positive adults. Two surrogate markers of response, plasma HIV RNA level and CD4+ cell count, were fitted to 2 measures of systemic drug exposure, the area under the plasma concentration-time curve (AUC) and trough plasma concentration (Cmin), using 6 exposure-response models of progressively increasing complexity. Akaike and Schwarz model selection criteria were applied to determine the most effective pharmacokinetic-pharmacodynamic relationship., Results: A total of 88 patients were randomised; pharmacokinetic and pharmacodynamic data were available for 84 patients. In terms of plasma HIV RNA, pharmacokinetic-pharmacodynamic relationships were best described by a 2-parameter maximum effect (Emax) model, which predicted a typical maximum reduction in viral load of 1.94 log10 copies/ml [coefficient of variation (CV) 12%], with a half-maximal antiviral response occurring at a Cmin of 50 micrograms/L (CV 40%). Saquinavir-SGC 1200 mg administered 3 times daily produced a median AUC to 24 hours (AUC24) of approximately 20,000 micrograms/L.h, corresponding to 85% of the maximum achievable antiviral effect as defined by the model. None of the models yielded a satisfactory fit for CD4+ cell count., Conclusion: Empirical mathematical modelling confirmed that, when administered 3 times daily, the optimum dose of saquinavir-SGC is 1200 mg, corresponding to 3600 mg/day.

Computer simulations have been successfully applied in various industries (e.g. automobile, aerospace) to make product development more efficient. Just recently, it was suggested to use simulations in support of clinical drug development for predicting clinical outcomes of planned trials. The methodological basis for this approach is provided by pharmacokinetic and pharmacodynamic mathematical models together with Monte Carlo techniques. In the present paper, the basic notions of clinical trial simulation are introduced and illustrated with the example of an oral anticancer drug. It is shown that computer simulation helps to evaluate consequences of design features on safety and efficacy assessment of the drug which are not easily obtained otherwise. An overview of existing simulation resources with respect to training and software is provided.

The integration of pharmacokinetic and pharmacodynamic principles into drug development has been proposed as a way of making it more rational and efficient. The use of these principles in drug development to make scientific and strategic decisions is defined as the 'pharmacokinetic-pharmacodynamic guided approach to drug development'. The objectives of this survey were: (i) to assess the extent the pharmacokinetic-pharmacodynamic guided approach to drug development has been used in a large multinational pharmaceutical company: (ii) to evaluate the impact of pharmacokinetic and/or pharmacodynamic results on clinical drug development; and (iii) to identify factors which prevented the full application of the pharmacokinetic-pharmacodynamic guided approach. This was done by looking at 18 projects in the current development portfolio at Hoffman La Roche and evaluating the use of this approach by interviewing the responsible clinical pharmacologist using a standardised questionnaire. (i) Benefits from using the pharmacokinetic-pharmacodynamic guided approach were reported in every project, independent of development phase and therapeutic area. This approach was more extensively used in the recent projects. The selection of dosages in clinical studies was found to be the most important application of pharmacokinetic-pharmacodynamic results in terms of an impact on drug development. (ii) Time savings, up to several months, could be quantified in 8 projects during the entry-into-man studies and in 6 projects during the phase II or III studies. In 4 projects, 1 clinical study was avoided. (iii) The most important scientific factor preventing the full application of the approach was the lack of knowledge on the predictive value of the pharmacodynamic or surrogate marker for effect (6 projects). The results of the survey have shown that the use of the pharmacokinetic-pharmacodynamic guided approach has contributed to making clinical drug development more rational and more efficient. Opportunities to apply the pharmacokinetic-pharmacodynamic approach should be identified in each project and a project specific strategy for the pharmacokinetic-pharmacodynamic guided approach should be defined during phase 0 of drug development.

In support of animal toxicity testing of new drugs, toxicokinetics is designed to assess the systemic exposure of the animals to the drug across dose levels, genders, and periods of the study. In small rodents, repeated sampling may alter the health of the animals and jeopardize the toxicity evaluation. One conventional way to circumvent this limitation is to collect serial samples from satellite animals maintained as the main study animals but not monitored for toxicity. We evaluated, on a real example, whether the exposure could be assessed in the main animals from sparse samples. The only acceptable designs consisted of one single sample per animal repeated on two or three study days. In the rat 13-week oral toxicity study of a new chemical entity, both serial sampling in the satellite animals and sparse sampling in the main animals were applied. Similar measures of exposure and qualitative conclusions were derived from the two groups of animals. The very sparse design applied to the main group yielded adequate estimation of the animal exposure, even with a very simple non-compartmental approach. The population pharmacokinetics analysis of the sparse data with NONMEM provided additional information about drug disposition and the influence of the covariates.

Currently, there is an increasing focus on the implementation of pharmacokinetic-pharmacodynamic (PK-PD) studies and modelling as essential tools for drug development. Strategies involving specifically the population approach, which are based on relatively recent statistical methodology (e.g. nonlinear mixed effects modelling, NONMEM) have been advocated for investigating pharmacokinetic and pharmacodynamic variability as well as dose-concentration-effect relationships. The present article outlines this approach, and discusses how it can be implemented within the framework of the studies currently performed as part of the clinical phases of new drug development. It also considers study design and performance, based on real-life experiences. Population approaches, if designed carefully and early, as part of the planning of the drug development programme, are expected to play a significant role at every phase of the programme and to contribute to providing information that is valuable for registration purposes. Statistical methodology and software are now widely available. However, practical issues such as integration of the population approach within existing protocols, quality control of the data, timing of laboratory and statistical analyses, as well as resource allocation, remain legitimate concerns to be considered in prospective studies.

The immunosuppressant, SDZ IMM 125 (IMM), is a derivative of cyclosporin A (CyA). The disposition kinetics of IMM in plasma, blood cells, and various tissues of the rat was characterized by a physiologically based pharmacokinetic (PBPK) model; the model was then applied to predict the disposition kinetics in dog and human. Accumulation of IMM in blood cell is high (equilibrium blood cell/plasma ratio = 8), although the kinetics of drug transference between plasma and blood cell is moderately slow, taking approximately 10 min to reach equilibrium, implying a membrane-limited distribution into blood cells. A local PBPK model, assuming blood-flow limited distribution and tissue/blood partition coefficient (KP) data, failed to adequately describe the observed kinetics of distribution, which were slower than predicted. A membrane transport limitation is therefore needed to model dynamic tissue distribution data. Moreover, a slowly interacting intracellular pool was also necessary to adequately describe the kinetics of distribution in some organs. Three elimination pathways (metabolism, biliary secretion, and glomerular filtration) of IMM were assessed at steady state in vivo and characterized independently by the corresponding clearance terms. A whole-body PBPK model was developed according to these findings, which described closely the IMM concentration-time profiles in arterial blood as well as 14 organs/tissues of the rat after intravenous administration. The model was then scaled up to larger mammals by modifying physiological parameters, tissue distribution and elimination clearances; in vivo enzymatic activity was considered in the scale-up of metabolic clearance. The simulations agreed well with the experimental measurements in dog and human, despite the large interspecies difference in the metabolic clearance, which does not follow the usual allometric relationship. In addition, the nonlinear increase in maximum blood concentration and AUC with increasing dose, observed in healthy volunteers after intravenous administration, was accommodated quantitatively by incorporating the known saturation of specific binding of IMM to blood cells. Overall, the PBPK model provides a promising tool to quantitatively link preclinical and clinical data.

A physiologically based mathematical model was built to describe the pharmacodynamic effects in response to the administration of intravenous (iv) dihydropyridine drugs in healthy volunteers. This model incorporates a limited number of hemodynamic variables, namely, mean arterial blood pressure (MAP), cardiac output (CO) or heart rate (HR), stroke volume (SV), and total peripheral resistance (TPR), into a closed-loop system supposed to represent essential features of the cardiovascular regulation. We also defined an additional auxiliary control variable (U) which is thought to represent primarily the role of the baroreceptor reflex. It was assumed that the variable U was related to MAP changes through both deviation- and rate-sensitive mechanisms. Other model parameters are the baseline levels for MAP, CO (or HR), and TPR, as well as time constants to account for further temporal aspects of the regulation. Finally, TPR was assumed to be linked to the plasma concentrations of dihydropyridine drugs via a conventional pharmacokinetic/pharmacodynamic (PK/PD) model, relying upon an effect compartment and a linear, hyperbolic, or sigmoidal relationship between the reduction in TPR and the drug concentrations at the effect site. The model characteristics were explored by studying the influence of various parameters, including baseline levels and deviation- and rate-sensitive control parameters, on the hemodynamic responses to a fictive constant rate i.v. infusion of a vasodilator drug. Attempts were also made to mimic literature data with nifedipine, following i.v. administration under both constant and exponentially decreasing infusion rates. The applicability of the model was demonstrated by fitting hemodynamic data following i.v. infusion of nicardipine to healthy volunteers, under experimental conditions similar to those described above for nifedipine. The effect model for the action of nicardipine on TPR, combined with the physiological model including a feedback control loop, allowed an adequate quantitative description of time profiles for both cardiac output and mean arterial pressure. The suggested model is a useful tool for integrated data analysis of hemodynamic responses to vasodilator drugs in healthy volunteers. Computer simulations suggest that a graded variation of a few model parameters--including baseline levels of TPR and MAP and the deviation-sensitive parameter of the arterial pressure control--would also be able to account for the pattern of hemodynamic response observed in hypertensive patients, which is qualitatively different to that seen in normotensive subjects. Extrapolation of drug response from the healthy volunteer to the hypertensive patient is allowed by our model. Its usefulness for an early evaluation of drug efficacy during drug development is under current investigation.

There is current emphasis for extended integration of pharmacokinetics (PK) and pharmacodynamics (PD) into all phases of new drug development, including large-scale clinical trials. In this paper, we focus on study design and data analysis issues for the investigation of pharmacokinetic/pharmacodynamic and blood level/effect relationships in patients. The application of descriptive and model-based regression statistical methodology for including sparse drug systemic concentration data in the analysis of efficacy and safety is illustrated by examples chosen from diverse therapeutic areas. The population approach, based on mixed-effects modelling, is one such methodology, which also provides new tools for analysis of response vs dose and response vs time data. The existence of a variety of statistical techniques for handling complex PK/PD time-varying data should increase the impact of such data analysis on future drug development.

Drug level monitoring during routine clinical visits in the course of phase III trials provides a means to document pharmacokinetic variability in a patient population. Such a pharmacokinetic screen was performed for the new calcium antagonist isradipine. A total of 697 blood samples were collected at any time after the morning dose from 252 patients who had received oral doses of isradipine. Three approaches of data analysis based on exploratory (graphical and statistical) techniques were used to relate plasma level to patient demographic data and laboratory parameters. The pharamacokinetics of isradipine seemed to be influenced by the demographic variables of age (already detected in conventional studies) and weight, as well as by the blood serum levels of inorganic phosphorous, uric acid, alkaline phosphatase, and bilirubin, but only to a small, clinically irrelevant extent. The findings from the three approaches were complementary. They suggest that a pharmacokinetic screening in clinical trials is feasible at reasonable experimental cost and effort and provides useful data on interindividual and intraindividual pharmacokinetic variability in patients.

We propose a general pharmacokinetic-pharmacodynamic model that integrates the rhythmic fluctuation of hormone secretion for the description of the hormone-lowering effect of a drug. The mathematical model takes into account the variation in response observed after administration of a placebo and the drug. It is assumed that the change with time in the physiological response during the placebo period results from fluctuations in the concentration of hypothetical endogenous molecules. The mathematical formulation for predicting the response after drug intake is derived assuming competitive interaction of these "molecules" with the active species for binding to receptors. The suggested "fluctuation model" was implemented in order to describe the time course of the prolactin (PRL) plasma level after administration of two oral doses (2.5 and 5.0 mg) of the dopaminomimetic compound DCN 203-922 (DCN) to 9 healthy male subjects. Its performance was compared with that of conventional modeling approaches, in which the circadian changes after placebo are neglected and the hormone baseline is assumed to be constant. The new model provided a better description of the time course of PRL in most subjects. It was used for prediction of the amplitude and duration of the PRL suppressant effect after single and chronic administration of DCN at various dosage regimens as well as after changes in drug absorption.

A compartmental model of the kinetics of 5-fluorouracil (5-FU) and its catabolites in humans is proposed. This model was developed using data from a previous study in which plasma levels and urinary amounts of unchanged drug and metabolites were quantitated after i.v. bolus injection of 500 mg/m2 5-FU in ten patients. Biliary excretion was also quantified in two subjects. The different processes, biochemical transformations, and urinary and biliary excretion were adequately described by first-order kinetics. The technique of multiresponse modelling was used for global fitting of all data for each patient. Satisfactory agreement was achieved between measured and predicted values. This model enabled accurate evaluation of pharmacokinetic parameters that could not be adequately calculated using a model-free analysis. The total clearance and elimination half-life of 5-FU and its catabolites are reported for all subjects. The estimated mean half-life was 6.9 +/- 3.9 min for unchanged 5-FU and 225 +/- 352, 7.6 +/- 4, and 9.6 +/- 7.7 min, respectively, for the three measured catabolites dihydrofluorouracil (FUH2), alpha-fluoro-beta-ureidopropionic acid (FUPA), and alpha-fluoro-beta-alanine (FBAL). The percentage of anabolic, catabolic, urinary, and biliary elimination in total clearance was also quantitated. Anabolic clearance accounted for 39% +/- 14% of total 5-FU clearance, with substantial variation occurring among patients. Urinary clearance represented 6.5% +/- 3.2%, 0.8% +/- 0.9%, 13.2% +/- 4.7%, and 98.2% +/- 2.5% of total clearance for 5-FU, FUH2, FUPA, and FBAL, respectively. The model was also satisfactorily fitted to the data of a patient deficient in dihydropyrimidine dehydrogenase, an enzyme previously thought to be the rate-limiting step for 5-FU catabolism. In this case, catabolism was highly reduced and urinary excretion of 5-FU increased up to 64% of total drug clearance. This first global model of the kinetics of 5-FU and all of its catabolites in patients given an i.v. bolus infusion of 500 mg/m2 5-FU represents a further step toward detailed comprehensive modeling of the kinetics of this drug.

Albendazole pharmacokinetic parameters were determined in lambs after iv, oral, and intraruminal single administrations. The parent drug and two metabolites, albendazole sulfoxide and albendazole sulfone, were simultaneously determined in whole blood, plasma, and urine using an HPLC method. The parent drug was only recovered in plasma when injected intravenously. For other routes, only the two metabolites were detectable; they were present in red blood cells and plasma at equal concentrations. The pharmacokinetic parameters were determined by using compartmental models which simultaneously described the two oxidative steps and the urinary excretion of the sulfoxide derivative. Dose-dependent pharmacokinetics was studied in the dose range 0.95-3.8 mg/kg. The results showed that clearance remained constant within the tested dose range since the area under the curve normalized to the dose was similar in the cases of sulfoxide and sulfone metabolites, whatever the route of administration. The drug appeared to be extensively metabolized in the body regardless of the route of administration. Sulfoxidation probably took place in liver, but other tissues seemed to be responsible for the formation of the sulfoxide which has been described as the major anthelmintic derivative of albendazole.

The sublingual absorption of vincamine used as tracer occurs in two successive absorption steps: true sublingual absorption and absorption in the gastrointestinal tract of the drug dissolved in the saliva and not absorbed through the buccal mucosa. This is confirmed by a pharmacokinetic study and simulation. These two successive absorptions can explain the increase in the amounts of drug absorbed.

Individual pharmacokinetic parameters can be viewed as independent realizations of a random variable. The probability density function of the variable is assumed to be specified by its first two moments (mean vector and covariance matrix), and these moments then characterize the distribution of the parameters in the population. The following methods are presented for estimation of population characteristics from a set of pharmacokinetic measurements in a sample of subjects: The Global Two-Stage Approach (GTS) uses estimates (and their covariances) of individual parameters obtained after separate fitting of each individual's data. The Iterated Two-Stage Approach (ITS) makes the GTS procedure iterative, using refined bayesian estimates of individual parameters at each step. The Nonlinear Filtering Approach (NLF) also relies on individual parameter estimates produced by using an optimal filter on each subject's data. The three methods give exact results (maximum likelihood estimates), as does NONMEM (the Nonlinear Mixed-Effect Model Approach), when the individual pharmacokinetic model is linear with respect to the parameters and when the distributions of the pharmacokinetic parameters and of the measurement noise in the individual data are both multivariate normal. When the individual pharmacokinetic model is statistically nonlinear (the usual case), the methods differ with respect to: (1) their strategy for handling nonlinearity, (2) their ability to deal with any type of data (experimental and/or routine), and (3) their sensitivity to the amplitude of random effects. With regard to computational aspects, both the computer memory storage requirements and the amount of computation required for the GTS approach are much smaller than for the three other methods. Contrasting considerations as well as results of simulations suggest that GTS, ITS, and, in future, NLF may be valuable alternatives to NONMEM or modifications of it for estimation of population characteristics of pharmacokinetic parameters.

Adrenalectomized rat kidney is commonly used for the study of mineralocorticoid mechanism of action in mammals. In this model, aldosterone is known to bind to two classes of binding sites: type I (mineralocorticoid) and type II (glucocorticoid). The study of the aldosterone binding in normal rat kidney requires the elimination of endogenous hormones bound to each type of receptor. Thus, a suitable technique was developed using in situ perfusion of the kidneys. The efficacy of this method was of about 85 to 90% at the level of both cytoplasm and nucleus. Aldosterone binding capacity was checked in normal rat kidney after in situ perfusion and was found to be 300 to 500% lower than in adrenalectomized rat kidney, both in cytoplasm and nuclei. Computer analysis of aldosterone binding parameters in the cytoplasm (30,000 X g supernatant) of rat kidney suggested that adrenalectomy might induce an important rise in the number of mineralocorticoid receptors (congruent to 260%). An increase in the number of glucocorticoid receptors was also observed but appeared to be lower. Aldosterone, when perfused during 24 h in adrenalectomized rats, lowered the number of type I sites to the same level as observed in normal rat kidney. This effect was fully reversible after interruption of aldosterone perfusion. These results suggested an aldosterone-induced down regulation of mineralocorticoid receptors.

A two-compartment model with time lag is proposed to describe the pharmacokinetics of drugs subject to enterohepatic circulation. The basic model, including two compartments for body and GI tract, respectively, with elimination occurring from both compartments, was previously proposed. The assumption that the reabsorption of a drug molecule is delayed after its biliary excretion is expressed by the addition of a time lag in the transfer from the first to the second compartment. Computer simulation of the model for intravenous bolus injection and oral intake of the drug was performed through first-order numerical integration. Several qualitative results concerning changes in pharmacokinetics due to modifications in biliary excretion, in reabsorption, or in elimination are identical with predictions using the basic model. However, several qualitative and quantitative results were significantly different. The pharmacokinetics, though remaining linear, are no longer biexponential. Initial decay after intravenous injection was not affected by modifications in reabsorption or elimination from intestine. Predictions based on the time-delay model agree with existing experimental evidence concerning pharmacokinetics of substances undergoing enterohepatic cycling. Delayed recirculation may lead to rebounds in plasma level profiles as well as after intravenous and oral administration. The half-life of the drug is significantly prolonged even when the kinetic processes involved in recirculation remain unchanged.

In order to perform a quantification of the hepatic RES function, a mathematical model of the colloids phagocytosis was constructed and validated in normal and partially hepatectomised rats. The experimental design consisted of the collection of successive blood samples for the measurement of radiocolloids time courses after the injection of different doses of gelatin colloids (0.075--5 mg of gelatin/100 g body wt). The unknown parameters were estimated by method of maximum likelihood using a second order algorithm. A good fit between experimental and simulated data was obtained for a large range of injected doses using a single set of parameters. Comparisons of parameters values between normal and hepatectomised rats were found to be -onsistent with the hepatectomy ratio. This computerised estimation of parameters provides a determination of both the total number of Kupffer cells and the mean time of a complete phagocytosis cycle which cannot be obtained by classical approaches.

Lithium has a narrow therapeutic index and exhibits a wide pharmacokinetic variability. Individual dosage regimen adjustment is necessary to warrant the efficacy and safety of long-term treatment. We propose the "renal clearance method" for rapid determination of the lithium carbonate daily dose for chronic therapy. After the first intake of drug by a manic-depressive patient, a four-hour trial is performed. It involves two blood samplings and two urine collections, in which lithium and creatine are assayed. Comparison of observed creatinine with a value predicted according to age, morphological characteristics, sex and serum creatinine of the patients allows the interpretation of conflicting results. The estimation of lithium and creatinine clearances of each patient is performed using a computerized or manual method which unfolds a decision procedure. The daily dosage (1.5 to 6 250 mg tablets in two or three daily intakes) is deduced from the according lithium renal clearance (0.4 to over 2 l/h) by means of a nomogram established in previous studies on about 50 patients. The clearance method has been investigated in routine hospital care on a 40 patients sample. The range of satisfactory lithium serum levels during patients monitoring was 0.6-0.9 mmol/l. Accurate dosage regimen forecasting is obtained in 92% of the patients. The percentage observed in a subset of 13 patients with the C24 method, which relies on a unique blood sample 24 hours after the first dose, was much lower (54%). The renal clearance method appears as a robust and reliable technique for individual lithium dosage regimen adjustment in routine care.