Drug dosage adjustment for patients with acute or chronic kidney disease is an accepted standard of practice. The challenge is how to accurately estimate a patient's kidney function in both acute and chronic kidney disease and determine the influence of renal replacement therapies on drug disposition. Kidney Disease: Improving Global Outcomes (KDIGO) held a conference to investigate these issues and propose recommendations for practitioners, researchers, and those involved in the drug development and regulatory arenas. The conference attendees discussed the major challenges facing drug dosage adjustment for patients with kidney disease. In particular, although glomerular filtration rate is the metric used to guide dose adjustment, kidney disease does affect nonrenal clearances, and this is not adequately considered in most pharmacokinetic studies. There are also inadequate studies in patients receiving all forms of renal replacement therapy and in the pediatric population. The conference generated 37 recommendations for clinical practice, 32 recommendations for future research directions, and 24 recommendations for regulatory agencies (US Food and Drug Administration and European Medicines Agency) to enhance the quality of pharmacokinetic and pharmacodynamic information available to clinicians. The KDIGO Conference highlighted the gaps and focused on crafting paths to the future that will stimulate research and improve the global outcomes of patients with acute and chronic kidney disease.

Neointimal thickening of primary arteriovenous fistulas represents a local inflammatory process and appears to be associated with increased protein expression of TGF-beta1 and IGF-I. While local IGF-I is likely to stimulate smooth muscle cell proliferation in this setting, TGF-beta1 may be an important trigger of ECM production and deposition.

Mathematical modeling of drug effects maximizes the information gained from an experiment, provides further insight into the mechanisms of drug effects, and allows for simulations in order to design studies or even to derive clinical treatment strategies. We reviewed modeling of antimicrobial drug effects and show that most of the published mathematical models can be derived from one common mechanism-based PK-PD model premised on cell growth and cell killing processes. The general sigmoid Emax model applies to cell killing and the various parameters can be related to common pharmacodynamics, which enabled us to synthesize and compare the different parameter estimates for a total of 24 antimicrobial drugs from published literature. Furthermore, the common model allows the parameters of these models to be related to the MIC and to a common set of PK-PD indices. Theoretically, a high Hill coefficient and a low maximum kill rate indicate so-called time-dependent antimicrobial effects, whereas a low Hill coefficient and a high maximum kill rate indicate so-called concentration-dependent effects, as illustrated in the garenoxacin and meropenem examples. Finally, a new equation predicting the time to microorganism eradication after repeated drug doses was derived that is based on the area under the kill-rate curve.