Incorporating physiological and biochemical mechanisms into pharmacokinetic-pharmacodynamic models: a conceptual framework.

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.

Modelling the genesis and treatment of cancer: the potential role of physiologically based pharmacodynamics.

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.

Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science.

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.

Population pharmacokinetics and concentration-effect relationships of capecitabine metabolites in colorectal cancer patients.

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.

A semimechanistic and mechanistic population PK-PD model for biomarker response to ibandronate, a new bisphosphonate for the treatment of osteoporosis.

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.

Clinical pharmacokinetic/pharmacodynamic and physiologically based pharmacokinetic modeling in new drug development: the capecitabine experience.

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.

Modelling and simulation in the development and use of anti-cancer agents: an underused tool?

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.