The calcineurin inhibitors (CNIs) cyclosporin A and tacrolimus are immunosuppressive drugs used extensively in allograft recipients. These drugs show large interindividual pharmacokinetic variation and are associated with severe adverse affects, including nephrotoxicity and cardiovascular disease. In current practice, CNIs are combined with other immunosuppressive drugs such as steroids and mycophenolate mofetil. Dosage is titrated based on blood concentration measurement. For further optimization of calcineurin (CN) inhibition therapy, new monitoring strategies are required. Pharmacodynamic-monitoring strategies constitute novel approaches for optimization of CNIs therapy. This review focuses on the general aspects of immunosuppressive drug pharmacodynamic monitoring and describes the methodologies used for monitoring CN inhibition therapy. Two different types of pharmacodynamic-monitoring strategies can be distinguished: (1) enzymatic strategies, which monitor inhibition of drug-target enzyme activity, and (2) immunologic strategies, which measure cellular responsiveness after in vitro simulated immunologic responses. Enzymatic tests are drug type-specific markers in which CN activity is directly determined. Immunologic strategies measure immune responsiveness at several levels, such as mRNA transcripts (intracellular) concentrations/excretion of cytokines, expression of surface activation markers, and cell proliferation. This review also discusses analytical issues and clinical experience with these techniques. The call for new methodologies to evaluate immunosuppressive therapy has led to the development of a large variety of pharmacodynamic-monitoring strategies. The first reports of their clinical relevance are available, but further understanding of the analytical and clinical variables involved are required for the development of accurate, reproducible, and clinically relevant markers.

Bias detection curves and MA validation charts are useful tools for optimization and validation of MA procedures.

For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of “conventional” QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.

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