Challenging the Norm: A Multidisciplinary Perspective on Intravenous to Subcutaneous Bridging Strategies for Biologics.

The transition from intravenous (i.v.) to subcutaneous (s.c.) administration of biologics is a critical strategy in drug development aimed at improving patient convenience, compliance, and therapeutic outcomes. Focusing on the increasing role of model-informed drug development (MIDD) in the acceleration of this transition, an in-depth overview of the essential clinical pharmacology, and regulatory considerations for successful i.v. to s.c. bridging for biologics after the i.v. formulation has been approved are presented. Considerations encompass multiple aspects beginning with adequate pharmacokinetic (PK) and pharmacodynamic (i.e., exposure-response) evaluations which play a vital role in establishing comparability between the i.v. and s.c. routes of administrations. Selected key recommendations and points to consider include: (i) PK characterization of the s.c. formulation, supported by the increasing preclinical understanding of the s.c. absorption, and robust PK study design and analyses in humans; (ii) a thorough characterization of the exposure-response profiles including important metrics of exposure for both efficacy and safety; (iii) comparability studies designed to meet regulatory considerations and support approval of the s.c. formulation, including noninferiority studies with PK and/or efficacy and safety as primary end points; and (iv) comprehensive safety package addressing assessments of immunogenicity and patients' safety profile with the new route of administration. Recommendations for successful bridging strategies are evolving and MIDD approaches have been used successfully to accelerate the transition to s.c. dosing, ultimately leading to improved patient experiences, adherence, and clinical outcomes.

Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment.

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

The European Medicines Agency Experience With Pediatric Dose Selection.

Getting the right dose regimen for children and adolescents is important but poses great scientific, practical, and ethical challenges. At the same time, the availability of data in adults is a huge advantage and needs to be used optimally when designing studies in children and analyzing pediatric data. Furthermore, the processes of maturation and growth are always key when selecting doses for children. All the above make study adaptations and model-informed approaches imperative for dose exposure-response characterization and dose selection in children. This article summarizes the experience gained in the European Medicines Agency on this topic and proposes some general guiding principles for defining objectives, study designs, and methodology tools for pediatric dose selection.

Model informed dosing of hydroxycholoroquine in COVID-19 patients: Learnings from the recent experience, remaining uncertainties and gaps.

AIMS: In the absence of a commonly agreed dosing protocol based on pharmacokinetic (PK) considerations, the dose and treatment duration for hydroxychloroquine (HCQ) in COVID-19 disease currently vary across national guidelines and clinical study protocols. We have used a model-based approach to explore the relative impact of alternative dosing regimens proposed in different dosing protocols for hydroxychloroquine in COVID-19. METHODS: We compared different PK exposures using Monte Carlo simulations based on a previously published population pharmacokinetic model in patients with rheumatoid arthritis, externally validated using both independent data in lupus erythematous patients and recent data in French COVID-19 patients. Clinical efficacy and safety information from COVID-19 patients treated with HCQ were used to contextualize and assess the actual clinical value of the model predictions. RESULTS: Literature and observed clinical data confirm the variability in clinical responses in COVID-19 when treated with the same fixed doses. Confounding factors were identified that should be taken into account for dose recommendation. For 80% of patients, doses higher than 800 mg day on day 1 followed by 600 mg daily on following days might not be needed for being cured. Limited adverse drug reactions have been reported so far for this dosing regimen, most often confounded by co-medications, comorbidities or underlying COVID-19 disease effects. CONCLUSION: Our results were clear, indicating the unmet need for characterization of target PK exposures to inform HCQ dosing optimization in COVID-19. Dosing optimization for HCQ in COVID-19 is still an unmet need. Efforts in this sense are a prerequisite for best benefit/risk balance.

Population Pharmacokinetics of Hydroxychloroquine in COVID-19 Patients: Implications for Dose Optimization.

BACKGROUND AND OBJECTIVE: In the absence of characterization on pharmacokinetics and reference concentrations for hydroxychloroquine in COVID-19 patients, the dose and treatment duration for hydrochloroquine are currently empirical, mainly based on in vitro data, and may vary across national guidelines and clinical study protocols. The aim of this paper is to describe the pharmacokinetics of hydroxychloroquine in COVID-19 patients, considered to be a key step toward its dosing optimization. METHODS: We have developed a population pharmacokinetic model for hydroxychloroquine in COVID-19 patients using prospectively collected pharmacokinetic data from patients either enrolled in a clinical trial or treated with hydroxychloroquine as part of standard of care in two tertiary Belgian hospitals. RESULTS: The final population pharmacokinetic model was a one-compartment model with first-order absorption and elimination. The estimated parameter values were 9.3/h, 860.8 L, and 15.7 L/h for the absorption rate constant, the central compartment volume, and the clearance, respectively. The bioavailability factor was fixed to 0.74 based on previously published models. Model validations by bootstraps, prediction corrected visual predictive checks, and normalized prediction distribution errors gave satisfactory results. Simulations were performed to compare the exposure obtained with alternative dosing regimens. CONCLUSION: The developed models provide useful insight for the dosing optimization of hydroxychloroquine in COVID-19 patients. The present results should be used in conjunction with exposure-efficacy and exposure-safety data to inform optimal dosing of hydroxychloroquine in COVID-19.

Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

AIMS: The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications. METHODS: A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies. RESULTS: A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation. CONCLUSION: A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Towards a Generic Tool for Prediction of Meropenem Systemic and Infection-Site Exposure: A Physiologically Based Pharmacokinetic Model for Adult Patients with Pneumonia.

OBJECTIVE: The objective of this study was to develop a physiologically based pharmacokinetic model for meropenem using a retrograde approach, which could serve as a basis for prediction of the systemic and infection-site drug exposures in different populations and indications. We intended this model to be a useful tool to inform (local) pharmacokinetic-based optimal dosing of meropenem in different settings. METHODS: We developed a reduced physiologically based pharmacokinetic model with NONMEM software using a top-down approach. We used historical (previously published) data for model development and qualification. We used steady-state systemic and infection-site concentrations from 60 adult patients diagnosed with severe lung infection for model development and internal evaluation. The data included rich plasma and sparse epithelial lining fluid samples. We based the internal validation of the model on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness-of-fit plot with no indication of bias, and acceptable performance of visual predictive checks. We performed external validation by fitting the model to independent data from five previously published studies: four studies in patients with pneumonia, with different grades of renal impairment, and one study in morbidly obese patients. RESULTS: We successfully fitted a reduced physiologically based pharmacokinetic model with six compartments (arterial and venous pools, infection site [lungs], liver, kidneys and rest of the body) to the data and adequately estimated model parameters. We successfully qualified the model (internally and externally) using established methods. Estimated values for tissue-to-plasma partition coefficients were 0.2629 and 0.1946 for lungs and non-fat tissues (kidneys and liver), respectively. Estimated total clearance was 8.174 L/h for a typical patient with a glomerular filtration rate of 65 mL/min. Consistent with the known mechanism of meropenem elimination and previously published models, renal clearance accounted for 70% of total clearance. The model had good predictive performances on data from five different sources including populations with different characteristics with regard to body size, renal function and morbidity. CONCLUSIONS: We successfully developed a physiologically based pharmacokinetic model for meropenem in adult patients to be used as a basis for prediction of concentrations in different groups of patients, and eventually for effective dose individualisation in different subgroups of the population.

Prediction of paraquat exposure and toxicity in clinically ill poisoned patients: a model based approach.

AIMS: Paraquat poisoning is a medical problem in many parts of Asia and the Pacific. The mortality rate is extremely high as there is no effective treatment. We analyzed data collected during an ongoing cohort study on self-poisoning and from a randomized controlled trial assessing the efficacy of immunosuppressive therapy in hospitalized paraquat-intoxicated patients. The aim of this analysis was to characterize the toxicokinetics and toxicodynamics of paraquat in this population. METHODS: A non-linear mixed effects approach was used to perform a toxicokinetic/toxicodynamic population analysis in a cohort of 78 patients. RESULTS: The paraquat plasma concentrations were best fitted by a two compartment toxicokinetic structural model with first order absorption and first order elimination. Changes in renal function were used for the assessment of paraquat toxicodynamics. The estimates of toxicokinetic parameters for the apparent clearance, the apparent volume of distribution and elimination half-life were 1.17 l h(-1) , 2.4 l kg(-1) and 87 h, respectively. Renal function, namely creatinine clearance, was the most significant covariate to explain between patient variability in paraquat clearance.This model suggested that a reduction in paraquat clearance occurred within 24 to 48 h after poison ingestion, and afterwards the clearance was constant over time. The model estimated that a paraquat concentration of 429 µg l(-1) caused 50% of maximum renal toxicity. The immunosuppressive therapy tested during this study was associated with only 8% improvement of renal function. CONCLUSION: The developed models may be useful as prognostic tools to predict patient outcome based on patient characteristics on admission and to assess drug effectiveness during antidote drug development.

Population pharmacokinetic analysis of tacrolimus early after pediatric liver transplantation.

BACKGROUND: Tacrolimus (TAC) pharmacokinetics (PKs) show considerable unexplained variability, particularly in the early period after transplantation. Therefore, TAC is a good candidate for therapeutic drug monitoring. The main objective of the present work was to propose a robust PK model for TAC in the early period after transplantation, with the final goal to provide practitioners with a tool for dose individualization in pediatric patients. METHODS: TAC concentration data were obtained from 82 pediatric liver allograft recipients during the first 2 weeks after transplantation. Previously published models, and a model recently developed by our group for pediatrics early after pediatric liver transplantation, were fitted to the data and their predictive performance compared with the performances of a model developed using the data from 82 pediatric patients. RESULTS: During the data-driven analysis, the PKs of TAC were best described by a 1-compartment model with time-varying first order elimination. Apparent volume of distribution and blood clearance estimates were 283 L and 10 L/h, respectively. The absorption was also considered to be a first order process, with a first order rate fixed to 4.45 hours. Parameters were estimated with good precision and accuracy. Although hematocrit levels, time after transplantation, liver weight, and body weight influenced the clearance, body weight was the only covariate retained on volume of central and peripheral compartments. Two of the 5 previous models showed acceptable predictive performances using the observed data. CONCLUSIONS: Time after transplantation, body weight, and hematocrit levels were shown to influence TAC PK in the early pediatric post-liver transplantation period and should be considered, besides therapeutic drug monitoring, by clinicians for the TAC posology prescription and adaptation.

Statistical tools for dose individualization of mycophenolic acid and tacrolimus co-administered during the first month after renal transplantation.

AIM: To predict simultaneously the area under the concentration-time curve during one dosing interval [AUC(0,12 h)] for mycophenolic acid (MPA) and tacrolimus (TAC), when concomitantly used during the first month after transplantation, based on common blood samples. METHODS: Data were from two different sources, real patient pharmacokinetic (PK) profiles from 65 renal transplant recipients and 9000 PK profiles simulated from previously published models on MPA or TAC in the first month after transplantation. Multiple linear regression (MLR) and Bayesian estimation using optimal samples were performed to predict MPA and TAC AUC(0,12 h) based on two concentrations. RESULTS: The following models were retained: AUC(0,12 h) = 16.5 + 4.9 × C1.5 + 6.7 × C3.5 (r(2) = 0.82, rRMSE = 9%, with simulations and r(2) = 0.66, rRMSE = 24%, with observed data) and AUC(0,12 h) = 24.3 + 5.9 × C1.5 + 12.2 × C3.5 (r(2) = 0.94, rRMSE = 12.3%, with simulations r(2) = 0.74, rRMSE = 15%, with observed data) for MPA and TAC, respectively. In addition, bayesian estimators were developed including parameter values from final models and values of concentrations at 1.5 and 3.5 h after dose. Good agreement was found between predicted and reference AUC(0,12 h) values: r(2) = 0.90, rRMSE = 13% and r(2) = 0.97, rRMSE = 5% with simulations for MPA and TAC, respectively and r(2) = 0.75, rRMSE = 11% and r(2) = 0.83, rRMSE = 7% with observed data for MPA and TAC, respectively. CONCLUSION: Statistical tools were developed for simultaneous MPA and TAC therapeutic drug monitoring. They can be incorporated in computer programs for patient dose individualization.

The revised EMA guideline for the investigation of bioequivalence for immediate release oral formulations with systemic action.

On August 1, 2010, a revised guidance regarding bioequivalence (BE) assessment for the approval of innovator (bridging studies, variations, line extensions) and generic medicinal products in the EU came into effect (EMA Guideline on the Investigation of Bioequivalence, CPMP/EWP/QWP/1401/98 Rev. 1/Corr**, London, 20 January 2010). This guideline specifies the requirements for BE assessment for immediate release oral dosage forms with systemic action. Compared to the previous BE guideline of the EMA, clearer guidance is now given on several topics including BE assessment of highly variable drugs/drug products (HVDs/HVDPs), the use of metabolite data, acceptance criteria for narrow therapeutic index drugs (NTIDs), BCS-based biowaivers, and dose strength to be used in case of application for marketing authorization of several strengths. However, the health authorities of the various EU member states do not necessarily apply the same rules as far as substitution and switchability between medicinal products are concerned. Moreover, differences still exist between the BE guidelines of the major health authorities (FDA, EMA, NIHC, ...) on topics such as HVDs/HVDPs, NTIDs and BCS-based biowaivers. Global harmonization should be the next logical step to guarantee accessibility to safe and efficacious drug products for patients in all parts of the world.

Population pharmacokinetics of four ß-lactams in critically ill septic patients comedicated with amikacin.

OBJECTIVES: The study aimed to characterize the pharmacokinetics (PK) of four ß-lactams (piperacillin, ceftazidime, cefepime, and meropenem) in patients comedicated with amikacin (AMK), and to confirm the predictive performance of AMK data, obtained from therapeutic drug monitoring (TDM), on these PK, using a population modeling approach. DESIGN AND METHODS: Serum samples were collected in 88 critically ill septic patients. For each ß-lactam, the covariate model was optimized using renal function. Furthermore, predictive performance of AMK concentrations and PK parameters was assessed on ß-lactam PK. RESULTS: A two-compartment model with first-order elimination best fitted the ß-lactam data. Results supported the superiority of AMK concentrations, over renal function and AMK PK parameters, to assess the ß-lactam PK. CONCLUSION: The study confirmed the significant link between the exposure to AMK and to ß-lactams, and presented population models able to guide ß-lactam dosage adjustments using renal biomarkers or TDM-related aminoglycoside data.

Population pharmacokinetic modeling and optimal sampling strategy for Bayesian estimation of amikacin exposure in critically ill septic patients.

Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, the present study aimed to develop a population PK model for amikacin (AMK) in severe sepsis and to subsequently propose an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters. Concentration-time profiles for AMK were obtained from 88 critically ill septic patients during the first 24 hours of antibiotic treatment. The population PK model was developed using a nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, and comedication. Optimal sampling times were selected based on a robust Bayesian design criterion. Taking into account clinical constraints, a two-point sampling approach was investigated. A two-compartment model with first-order elimination best fitted the AMK concentrations. Population PK estimates were 19.2 and 9.34 L for the central and peripheral volume of distribution and 4.31 and 2.21 L/h for the intercompartmental and total body clearance. Creatinine clearance estimated using the Cockcroft-Gault equation was retained in the final model. The two optimal sampling times were 1 hour and 6 hours after onset of the drug infusion. Predictive performance of individual Bayes estimates computed using the proposed optimal sampling strategy was reported: mean prediction errors were less than 5% and root mean square errors were less than 30%. The present study confirmed the significant influence of the creatinine clearance on the PK disposition of AMK during the first hours of treatment in critically ill septic patients. Based on the population estimates, an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters was developed, meeting the need of clinical practice.

Empirical models for dosage optimization of four beta-lactams in critically ill septic patients based on therapeutic drug monitoring of amikacin.

OBJECTIVES: The study aims to develop empirical models able to predict the pharmacokinetics (PK) of four beta-lactams using the amikacin (AMK) therapeutic drug monitoring (TDM), in order to optimize their dosage regimens. DESIGN AND METHODS: 69 critically ill septic patients were included. All received a first dose of AMK combined with piperacillin/tazobactam, ceftazidime, cefepime or meropenem. A multivariate analysis was performed to predict the beta-lactam PK using AMK PK parameters estimated from TDM and using pathophysiological variables. RESULTS: An optimal prediction model was identified for each PK parameter of each beta-lactam. The best predictor of each model was one of the AMK PK parameters estimated from TDM. Other variables included colloid solution, renal and hepatic biomarkers, age and body weight. CONCLUSION: PK of the four beta-lactams could be easily and rapidly predicted in critically ill septic patients using the AMK TDM. These predictions could improve the beta-lactam dosages in clinical practice.

Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus.

BACKGROUND AND OBJECTIVE: Mycophenolate mofetil is a prodrug of mycophenolic acid (MPA), an immunosuppressive agent used in combination with corticosteroids and calcineurin inhibitors or sirolimus for the prevention of acute rejection after solid organ transplantation. Although MPA has a rather narrow therapeutic window and its pharmacokinetics show considerable intra- and interindividual variability, dosing guidelines recommend a standard dosage regimen of 0.5-1.0 g twice daily in adult renal, liver and cardiac transplant recipients. The main objective of the present study was to develop a method to predict the MPA area under the plasma concentration-time curve during one 12-hour dosing interval (AUC(12)) by using multiple linear regression models and maximum a posteriori (MAP) Bayesian estimation methods in patients co-medicated with ciclosporin or sirolimus, aiming to individualize the dosage regimen of mycophenolate mofetil. PATIENTS AND METHODS: Pharmacokinetic profiles of MPA and mycophenolic acid glucuronide (MPAG), the main metabolite of MPA, were obtained from 40 stable adult renal allograft recipients on three different occasions: the day before switching from ciclosporin to sirolimus co-medication (+/-7.4 months post-transplantation; period I) and at 60 days and 270 days after the switch (periods II and III). Blood samples for determination of MPA and MPAG concentrations in plasma were taken at 0 hours (pre-dose) and at 0.33, 0.66, 1.25, 2, 4, 6, 8 and 12 hours after oral intake of mycophenolate mofetil. The MPA AUC(12) was calculated by the trapezoidal method (the observed AUC(12)). Patients were randomly divided into (i) a model-building test group (n = 27); and (ii) a model-validation group (n = 13). Multiple linear regression models were developed, based on three sampling times after drug administration. Subsequently, a population pharmacokinetic model describing MPA and MPAG plasma concentrations was developed using nonlinear mixed-effects modelling and a Bayesian estimator based on the population pharmacokinetic model was used to predict the MPA AUC(12) based on three sampling times taken within 2 hours following dosing. RESULTS: Fifty-two percent of the observed AUC(12) values (three periods) in the 40 patients receiving a fixed dose of mycophenolate mofetil 750 mg twice daily were outside the recommended therapeutic range (30-60 microg x h/mL). The failure of the standard dose to yield an AUC(12) value within the therapeutic range was especially pronounced during the first study period. Of the multiple linear regression models that were tested, the equation based on the 0-hour (pre-dose), 0.66- and 2-hour sampling times showed the best predictive performance in the validation group: r2 = 0.79, relative root mean square error (rRMSE) = 14% and mean relative prediction error (MRPE) = 0.9%. The pharmacokinetics of MPA and MPAG were best described by a two-compartment model with first-order absorption and elimination for MPA, plus a compartment for MPAG, also including a gastrointestinal compartment and enterohepatic cycling in the case of sirolimus co-medication. The ratio of aminotransferase liver enzymes (AST and ALT) and the glomerular filtration rate significantly influenced MPA glucuronidation and MPAG renal excretion, respectively. Bayesian estimation of the MPA AUC(12) based on 0-, 1.25- and 2-hour sampling times predicted the observed AUC(12) values of the patients in the validation group, with the following predictive performance characteristics: r2 = 0.93, rRMSE = 12.4% and MRPE = -0.4%. CONCLUSION: Use of the developed multiple linear regression equation and Bayesian estimator, both based on only three blood sampling times within 2 hours following a dose of mycophenolate mofetil, allowed an accurate prediction of a patient's MPA AUC(12) for therapeutic drug monitoring and dose individualization. These findings should be validated in a randomized prospective trial.

Pharmacokinetics and dosage adjustment in patients with renal dysfunction.

INTRODUCTION: Chronic kidney disease is a common, progressive illness that is becoming a global public health problem. In patients with kidney dysfunction, the renal excretion of parent drug and/or its metabolites will be impaired, leading to their excessive accumulation in the body. In addition, the plasma protein binding of drugs may be significantly reduced, which in turn could influence the pharmacokinetic processes of distribution and elimination. The activity of several drug-metabolizing enzymes and drug transporters has been shown to be impaired in chronic renal failure. In patients with end-stage renal disease, dialysis techniques such as hemodialysis and continuous ambulatory peritoneal dialysis may remove drugs from the body, necessitating dosage adjustment. METHODS: Inappropriate dosing in patients with renal dysfunction can cause toxicity or ineffective therapy. Therefore, the normal dosage regimen of a drug may have to be adjusted in a patient with renal dysfunction. Dosage adjustment is based on the remaining kidney function, most often estimated on the basis of the patient's glomerular filtration rate (GFR) estimated by the Cockroft-Gault formula. Net renal excretion of drug is a combination of three processes: glomerular filtration, tubular secretion and tubular reabsorption. Therefore, dosage adjustment based on GFR may not always be appropriate and a re-evaluation of markers of renal function may be required. DISCUSSION: According to EMEA and FDA guidelines, a pharmacokinetic study should be carried out during the development phase of a new drug that is likely to be used in patients with renal dysfunction and whose pharmacokinetics are likely to be significantly altered in these patients. This study should be carried out in carefully selected subjects with varying degrees of renal dysfunction. In addition to this two-stage pharmacokinetic approach, a population PK/PD study in patients participating in phase II/phase III clinical trials can also be used to assess the impact of renal dysfunction on the drug's pharmacokinetics and pharmacodynamics. CONCLUSION: In conclusion, renal dysfunction affects more that just the renal handling of drugs and/or active drug metabolites. Even when the dosage adjustment recommended for patients with renal dysfunction are carefully followed, adverse drug reactions remain common.