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Objectives: To describe the population genetics and antibiotic resistance gene distribution of carbapenem-resistant Acinetobacter baumannii (CRAB) isolates causing infections in three Mediterranean countries.Methods: Isolates were collected during the 2013-17 AIDA clinical trial in six hospitals in Israel, Greece and Italy. WGS, bioinformatic characterization and antibiotic resistance profiling were performed.Results: In the 247 CRAB isolates characterized in this study, ST distribution varied by country: 29/31 (93.5%) Greek isolates, 34/41 (82.9%) Italian isolates and 70/175 (40.0%) Israeli isolates belonged to ST2. The identified ST2 isolates included eight distinct clades: 2C, 2D and 2H were significantly more common in Italy, while 2F was unique to Greece. The uncommon ST3 was not present among Greek isolates and constituted only 5/41 (12%) Italian isolates. On the other hand, it was much more common among Israeli isolates: 78/175 (44.6%) belonged to ST3. The vast majority of isolates, 240/247 (97.2%), were found to harbour acquired carbapenemases, primarily blaOXA-23. The chromosomal oxaAb (blaOXA-51-like) and ampC genes characteristic of this organism were also ubiquitous. Most (96.4%) ST3 isolates carried a broad-host-range plasmid IncP1α.Conclusions: The geographical differences in CRAB populations support the theory that clonal spread of CRAB leads to endemicity in hospitals and regions. The close association between antibiotic resistance genes and clades, and between plasmids and STs, suggest that de novo creation of MDR A. baumannii is rare. The clustering of antibiotic resistance genes and plasmids that is unique to each clade/ST, and nearly uniform within clades/STs, suggests that horizontal transmission is rare but crucial to the clade's/ST's success. [ABSTRACT FROM AUTHOR]

Background: The β-lactam antibiotic piperacillin (in combination with tazobactam) is commonly chosen for empirical treatment of suspected bacterial infections. However, pharmacokinetic variability among patient populations and across ages leads to uncertainty when selecting a dosing regimen to achieve an appropriate pharmacodynamic target.Objectives: To guide dosing by establishing a population pharmacokinetic model for unbound piperacillin in febrile children receiving cancer chemotherapy, and to assess pharmacokinetic/pharmacodynamic target attainment (100% fT > 1×MIC and 50% fT > 4×MIC) and resultant exposure, across body weights.Methods: Forty-three children admitted for 89 febrile episodes contributed 482 samples to the pharmacokinetic analysis. The typical doses required for target attainment were compared for various dosing regimens, in particular prolonged infusions, across MICs and body weights.Results: A two-compartment model with inter-fever-episode variability in CL, and body weight included through allometry, described the data. A high CL of 15.4 L/h (70 kg) combined with high glomerular filtration rate (GFR) values indicated rapid elimination and hyperfiltration. The target of 50% fT > 4×MIC was achieved for an MIC of 4.0 mg/L in a typical patient with extended infusions of 2-3 (q6h) or 3-4 (q8h) h, at or below the standard adult dose (75 and 100 mg/kg/dose for q6h and q8h, respectively). Higher doses or continuous infusion were needed to achieve 100% fT > 1×MIC due to the rapid piperacillin elimination.Conclusions: The licensed dose for children with febrile neutropenia (80 mg/kg q6h as a 30 min infusion) performs poorly for attainment of fT>MIC pharmacokinetic/pharmacodynamic targets. Given the population pharmacokinetic profile, feasible dosing regimens with reasonable exposure are continuous infusion (100% fT > 1×MIC) or prolonged infusions (50% fT > 4×MIC). [ABSTRACT FROM AUTHOR]

Background: Pharmacokinetic/pharmacodynamic (PKPD) models developed based on data from in vitro time-kill experiments have been suggested to contribute to more efficient drug development programmes and better dosing strategies for antibiotics. However, for satisfactory predictions such models would have to show good extrapolation properties.Objectives: To evaluate if a previously described mechanism-based PKPD model was able also to predict drug efficacy for higher bacterial densities and across bacterial strains.Methods: A PKPD model describing the efficacy of ciprofloxacin on Escherichia coli was evaluated. The predictive performance of the model was evaluated across several experimental conditions with respect to: (i) bacterial start inoculum ranging from the standard of ∼106 cfu/mL up to late stationary-phase cultures; and (ii) efficacy for seven additional strains (three laboratory and four clinical strains), not included during the model development process, based only on information regarding their MIC. Model predictions were performed according to the intended experimental protocol and later compared with observed bacterial counts.Results: The mechanism-based PKPD model structure developed based on data from standard start inoculum experiments was able to accurately describe the inoculum effect. The model successfully predicted the time course of drug efficacy for additional laboratory and clinical strains based on only the MIC values. The model structure was further developed to better describe the stationary phase data.Conclusions: This study supports the use of mechanism-based PKPD models based on preclinical data for predictions of untested scenarios. [ABSTRACT FROM AUTHOR]

Background:In view of the paucity of clinical evidence,in vitrostudies are needed to find antibiotic combinations effective against multidrug-resistant Gram-negative bacteria. Interpretation ofin vitroeffects is usually based on bacterial growth after 24 h in time-kill and checkerboard experiments. However, the clinical relevance of the effects observedin vitrois not established. In this study we explored alternative output parameters to assess the activities of colistin and meropenem againstPseudomonas aeruginosaandAcinetobacter baumannii. Methods:Four strains each ofP. aeruginosaandA. baumanniiwere exposed to colistin and meropenem, alone and in combination, in 8 h dynamic time-kill experiments. Initial (1 h), maximum and 8 h bacterial reductions and the area under the bacterial time-kill curve were evaluated. Checkerboards, interpreted based on fractional inhibitory concentration indices after 24 h, were performed for comparison. Results:In the time-kill experiments, the combination resulted in enhanced 1 h, maximum and 8 h bacterial reductions against 2, 3 and 5 of 8 strains, respectively, as compared to the single drugs. A statistically significant reduction in the area under the time-kill curve was observed for three strains. In contrast, the checkerboards did not identify synergy for any of the strains. Conclusions:Combination effects were frequently found with colistin and meropenem againstP. aeruginosaand A.baumanniiin time-kill experiments but were not detected with the checkerboard method. We propose that the early dynamics of bacterial killing and growth, which may be of great clinical importance, should be considered in futurein vitrocombination studies. [ABSTRACT FROM PUBLISHER]

Objectives: For antibiotics, extensive animal PKPD studies are often performed to evaluate the PK/PD driver for subsequent use when recommending dosing regimens. The aim of this work was to evaluate a PKPD model, developed based on in vitro time-kill data for colistin, in predicting the relationships between PK/PD indices and the bacterial killing previously observed in mice.Methods: An in silico PKPD model for Pseudomonas aeruginosa exposed to colistin was previously developed based on static in vitro time-kill data. The model was here applied to perform an in silico replication of an in vivo study where the effect of colistin on P. aeruginosa was studied in the thigh infection model. Concentration-time profiles of unbound colistin were predicted and used as input to drive the bacterial killing in the PKPD model. The predicted bacterial count at 24 h was related to each of the PK/PD indices and the results were compared with reported observations in vivo.Results: The model was found to adequately predict in vivo results from mice; both in terms of which PK/PD index best correlates to effect (fAUC/MIC) as well as the magnitude needed for a 2 log kill. The fAUC/MIC needed to achieve a 2 log reduction in bacterial counts after 24 h was here predicted to be 9 compared with 31 previously reported in vivo.Conclusions: This study provides further support that PKPD models based on longitudinal data can be a useful tool to make drug development more efficient within the infectious diseases area. [ABSTRACT FROM AUTHOR]

Objectives: Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa.Methods: In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem.Results: A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h.Conclusions: The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance. [ABSTRACT FROM AUTHOR]

Objectives: In silico pharmacokinetic/pharmacodynamic (PK/PD) models can be developed based on data from in vitro time-kill experiments and can provide valuable information to guide dosing of antibiotics. The aim was to develop a mechanism-based in silico model that can describe in vitro time-kill experiments of Escherichia coli MG1655 WT and six isogenic mutants exposed to ciprofloxacin and to identify relationships that may be used to simplify future characterizations in a similar setting.Methods: In this study, we developed a mechanism-based PK/PD model describing killing kinetics for E. coli following exposure to ciprofloxacin. WT and six well-characterized mutants, with one to four clinically relevant resistance mutations each, were exposed to a wide range of static ciprofloxacin concentrations.Results: The developed model includes susceptible growing bacteria, less susceptible (pre-existing resistant) growing bacteria, non-susceptible non-growing bacteria and non-colony-forming non-growing bacteria. The non-colony-forming state was likely due to formation of filaments and was needed to describe data close to the MIC. A common model structure with different potency for bacterial killing (EC50) for each strain successfully characterized the time-kill curves for both WT and the six E. coli mutants.Conclusions: The model-derived mutant-specific EC50 estimates were highly correlated (r(2) = 0.99) with the experimentally determined MICs, implying that the in vitro time-kill profile of a mutant strain is reasonably well predictable by the MIC alone based on the model. [ABSTRACT FROM AUTHOR]

In oncology trials, overall survival ( OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic-pharmacodynamic ( PKPD) modelling and parametric time-to-event ( TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed. [ABSTRACT FROM AUTHOR]

Our professional and academic community lost a great pioneer in the field of pharmacokinetics and pharmacometrics when Dr. Hartmut Derendorf, distinguished professor at the University of Florida College of Pharmacy passed away unexpectedly on November 23, 2020, at the age of 67. A native of Germany, Dr. Derendorf received his BS and PhD in pharmacy from the University of Münster and then joined University of Florida (UF) as a postdoctoral fellow in 1981. Dr. Hartmut Derendorf, PhD, a world-renowned expert in pharmacokinetics and pharmacometrics (1953-2020). [Extracted from the article]

One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity. [ABSTRACT FROM AUTHOR]

Abstract: Chemotherapy dosing in anticancer treatment is a balancing act between achieving concentrations that are effective towards the malignancy and that result in acceptable side-effects. Neutropenia is one major side-effect of many antitumor agents, and is related to an increased risk of infection. A model capable of describing the time-course of myelosuppression from administered drug could be used in individual dose selection. In this paper we describe the transfer of a previously developed semi-mechanistic model for myelosuppression from NONMEM to a dosing tool in MS Excel, with etoposide as an example. The tool proved capable to solve a differential equation system describing the pharmacokinetics and pharmacodynamics, with estimation performance comparable to NONMEM. In the dosing tool the user provides neutrophil measures from a previous treatment course and request for the dose that results in a desired nadir in the upcoming course through a Bayesian estimation procedure. [Copyright &y& Elsevier]

Objective: To develop guidelines for the management of QT prolongation after citalopram overdose, including decontamination with single-dose activated charcoal (SDAC) and cardiac monitoring.Design: Simulation study using a previously developed pharmacokinetic-pharmacodynamic (PKPD) model which predicted the time-course of QT prolongation and the effect of citalopram dose and use of SDAC on QT prolongation.Main Measures and Results: The previously developed PKPD model was used to address the following in patients following citalopram overdose: (1) Above what dose should patients be decontaminated? (2) Above what dose should patients have cardiac monitoring? (3) For what period of time should patients be monitored? The primary outcome was QT,RR combinations above an abnormal threshold as a surrogate predictor of torsades de pointes. Simulations were performed using MATLAB for an overdose patient with typical demographics: 30-year-old female with a heart rate of 79 bpm taking citalopram therapeutically. The simulations showed: (1) There was significant benefit associated with the administration of SDAC to patients following citalopram overdose ingesting >600 mg; (2) With citalopram overdoses >1,000mg it was advisable to give SDAC and cardiac monitor the patient; (3) The risk of developing future abnormal QT,RR combinations was less than 1% in patients with normal QT,RR combinations up to 13 h post-dose, so the minimum monitoring time for citalopram overdoses >1,000mg should be 13h. Recommended dose levels for intervention should be lowered in older patients and patients with tachycardia, while men are less sensitive to QT prolongation.Conclusions: Guidelines for the management of QT prolongation after citalopram overdose were developed. We believe the model will help clinicians to decide which patients to decontaminate and monitor. [ABSTRACT FROM AUTHOR]

Aims To develop a pharmacokinetic–pharmacodynamic model describing the time-course of QT interval prolongation after citalopram overdose and to evaluate the effect of charcoal on the relative risk of developing abnormal QT and heart-rate combinations. Methods Plasma concentrations and electrocardiograph (ECG) data from 52 patients after 62 citalopram overdose events were analysed in WinBUGS using a Bayesian approach. The reported doses ranged from 20 to 1700 mg and on 17 of the events a single dose of activated charcoal was administered. The developed pharmacokinetic–pharmacodynamic model was used for predicting the probability of having abnormal combinations of QT-RR, which was assumed to be related to an increased risk for torsade de pointes (TdP). Results The absolute QT interval was related to the observed heart rate with an estimated individual heart-rate correction factor [α = 0.36, between-subject coefficient of variation (CV) = 29%]. The heart-rate corrected QT interval was linearly dependent on the predicted citalopram concentration (slope = 40 ms l mg−1, between-subject CV = 70%) in a hypothetical effect-compartment (half-life of effect-delay = 1.4 h). The heart-rate corrected QT was predicted to be higher in women than in men and to increase with age. Administration of activated charcoal resulted in a pronounced reduction of the QT prolongation and was shown to reduce the risk of having abnormal combinations of QT-RR by approximately 60% for citalopram doses above 600 mg. Conclusion Citalopram caused a delayed lengthening of the QT interval. Administration of activated charcoal was shown to reduce the risk that the QT interval exceeds a previously defined threshold and therefore is expected to reduce the risk of TdP. [ABSTRACT FROM AUTHOR]

Abstract: N-(6-Chlorophenoxyhexyl)-N′-cyano-N″-4-pyridylguanidine (CHS 828) is a novel anticancer agent that shows schedule-dependent effects in vitro and in vivo, as well as in Phase I clinical trials. A rat hollow fibre model was used to investigate whether this dependency is due to pharmacokinetic and/or pharmacodynamic factors. The effect on two cell lines, MDA-MB-231 (breast cancer) and CCRF-CEM (leukaemia) were studied after CHS 828 was administered orally as a single dose or in a 5-day schedule, at different total dose levels. The 5-day schedules were associated with greater effects on both cell lines compared with single doses. A one-compartment pharmacokinetic model, with a half-life of 2.3h and a consecutive zero- and first-order process to describe dissolution and absorption, fit the concentration data. Pharmacokinetics were dose-dependent, as the fraction absorbed decreased with increasing dose. Clearance increased with accumulative exposure. Twenty hours after administration, concentrations started to increase again, probably due to coprophagy. Pharmacokinetic–pharmacodynamic models characterized the cell growth and cell kill over time and showed that schedule-dependent antitumour effects were present also when the dose-dependent pharmacokinetics were accounted for. The prolonged schedules of CHS 828 were therefore associated with greater antitumour effects than single doses of the same total exposure. [Copyright &y& Elsevier]

Purpose: Based on the previously published hollow-fibre assay mainly used for early in vivo anticancer drug screening, we wanted to develop an extended hollow-fibre model in which antitumour activity, haematological toxicity and pharmacokinetics could be studied in the same animal.Method: The breast cancer cell lines MDA-MB-231 and MCF-7 were cultured in semipermeable hollow fibres. The fibres were implanted subcutaneously into immunocompetent male Sprague Dawley rats, and the rats were treated with 5-fluorouracil (5-FU, 125 mg/kg), epirubicin (EPI, 10 mg/kg) or cyclophosphamide (CP, 120 mg/kg) intraperitoneally, the new cyanoguanidine CHS 828 (375 mg/kg or 75 mg/kg x 5) orally, or vehicle only. After 6 days the fibres were retrieved and the cell density was evaluated. Haematological parameters were monitored and two to four samples per animal were drawn to determine the pharmacokinetic parameters in NONMEM.Results: Drug treatment had generally low effects on the tumour cells. Of the standard drugs (5-FU, EPI and CP), only CP exerted a statistically significant antiproliferative effect. CHS 828 had only a minor effect as a single dose, but divided into five daily doses had a pronounced effect on both cell lines. 5-FU, EPI and CP all caused a marked decrease in leucocytes, platelets and haemoglobin, while CHS 828 did not seem to affect these parameters. The pharmacokinetics of 5-FU and EPI were in accordance with previously established pharmacokinetic models. The pharmacokinetics of CP and CHS 828 were both described by one-compartment models.Conclusions: This study illustrates the possibility of measuring antitumour effect, haematological toxicity and pharmacokinetics in the same animal using the hollow-fibre model. [ABSTRACT FROM AUTHOR]

New drugs and methods to efficiently fight carbapenem-resistant gram-negative pathogens are sorely needed. In this study, we characterized the preclinical pharmacokinetics (PK) and pharmacodynamics of the clinical stage drug candidate apramycin in time kill and mouse lung infection models. Based on in vitro and in vivo data, we developed a mathematical model to predict human efficacy. Three pneumonia-inducing gram-negative species Acinetobacter baumannii , Pseudomonas aeruginosa , and Klebsiella pneumoniae were studied. Bactericidal kinetics were evaluated with time-kill curves; in vivo PK were studied in healthy and infected mice, with sampling in plasma and epithelial lining fluid after subcutaneous administration; in vivo efficacy was measured in a neutropenic mouse pneumonia model. A pharmacokinetic-pharmacodynamic model, integrating all the data, was developed and simulations were performed. Good lung penetration of apramycin in epithelial lining fluid (ELF) was shown (area under the curve (AUC) ELF /AUC plasma = 88%). Plasma clearance was 48% lower in lung infected mice compared to healthy mice. For two out of five strains studied, a delay in growth (∼5 h) was observed in vivo but not in vitro. The mathematical model enabled integration of lung PK to drive mouse PK and pharmacodynamics. Simulations predicted that 30 mg/kg of apramycin once daily would result in bacteriostasis in patients. Apramycin is a candidate for treatment of carbapenem-resistant gram-negative pneumonia as demonstrated in an integrated modeling framework for three bacterial species. We show that mathematical modelling is a useful tool for simultaneous inclusion of multiple data sources, notably plasma and lung in vivo PK and simulation of expected scenarios in a clinical setting, notably lung infections. [ABSTRACT FROM AUTHOR]

• One hundred and thirty-two publications on pharmacokinetic/pharmacodynamic (PKPD) models for time courses of antibiotic effects were reviewed. • Points to consider when developing such PKPD models are discussed. • Models describing bacterial growth, regrowth, drug effects and interactions are shown. • An interactive online table enables the review of PKPD models from 1963 to 2021. Pharmacokinetic/pharmacodynamic (PKPD) models have emerged as valuable tools for the characterization and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics such as minimum inhibitory concentration and PKPD indices, PKPD models enable description of the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. A literature review was conducted to identify PKPD models based on: (i) antibiotic compounds; (ii) Gram-positive or Gram-negative pathogens; and (iii) in-vitro or in-vivo longitudinal colony-forming unit data. In total, 132 publications were identified that were released between 1963 and 2021, including models based on exposure to single antibiotics (n =92) and drug combinations (n =40), as well as different experimental settings (e.g. static/traditional dynamic/hollow-fibre/animal time-kill models, n =90/27/32/11). An interactive, fully searchable table summarizes the details of each model, namely variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects and interactions. Furthermore, the review highlights the main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings, and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models will assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen–antibiotic combinations over time, and ultimately facilitate model-informed antibiotic translation, dosing and drug development. [ABSTRACT FROM AUTHOR]

This mini-review discusses the pharmacodynamics of immune-related biomarkers in the area of bacterial infectious diseases that could be of interest from a pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) perspective in the evaluation of treatment effects. The host response to an infection is often poorly defined both in preclinical assessments and in clinical practice when it comes to characterisation of PK and PK/PD relationships. Through population modelling, the time courses and variability of immune response variables can be quantified. Incorporation of such biomarker information into PK and PK/PD models may guide the evaluation of individual response to treatment (right antibiotic, more antibiotic, less antibiotic) and when to stop treatment. Furthermore, translation of results from preclinical systems to clinical scenarios may be improved with the incorporation of biomarker information. Potential biomarkers for these purposes are discussed and a few modelling examples are provided. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]