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Kidney disease not only alters the renal elimination but also the non-renal disposition of drugs that are metabolized by the liver. Indeed, modifications in the expression and activity of intestinal and hepatic drug metabolism enzymes and uptake and efflux transporters have been reported. Accumulated uremic toxins, inflammatory cytokines, and parathyroid hormones may modulate these proteins either directly or by inhibiting gene expression. This can lead to important unintended variations in exposure and response when drugs are administered without dose adjustment for reduced renal function. This review summarizes our current understanding of non-renal clearance in circumstances of chronic and acute renal failure with experimental but also clinical studies. It also evaluates the clinical impact on drug disposition. Predicting the extent of the drug disposition modification is difficult first because of the complex interplay between metabolic enzymes and transport proteins but also because of the differential effects in the different organs (liver, intestines). Recommendations of the US FDA are presented as they may be potentially helpful tools to predict these modifications when no specific pharmacokinetic studies are available.

A continuous quality programme conducted by psychiatrists and pharmacists showed positive impact in reducing doses of benzodiazepine prescribed to prisoner patients and contributing to reduce risk of benzodiazepine-related problems.

In a French prison, most inmates reported not being satisfied with their sleep. Life habits between good and bad sleepers were not significantly different except for television and smoking. The most frequently reported symptom of insomnia was several awakenings at night, and the most frequently cited etiologies were rumination of thoughts and noise. Most patients reported that their sleeping problems began or worsened after incarceration. A quarter of the inmates were following a hypnotic treatment, and most of these treatments began in prison. Only 42% of patients were satisfied with its effectiveness. These observations enabled us to make recommendations for healthy sleep patterns such as respecting normal night-and-day cycles, encouraging to stop smoking, and promoting appropriate use of hypnotic treatments.

Among first-line antituberculosis drugs, isoniazid (INH) displays the greatest early bactericidal activity (EBA) and is key to reducing contagiousness in treated patients. The pulmonary pharmacokinetics and pharmacodynamics of INH have not been fully characterized with modeling and simulation approaches. INH concentrations measured in plasma, epithelial lining fluid, and alveolar cells for 89 patients, including fast acetylators (FAs) and slow acetylators (SAs), were modeled by use of population pharmacokinetic modeling. Then the model was used to simulate the EBA of INH in lungs and to investigate the influences of INH dose, acetylator status, and M. tuberculosis MIC on this effect. A three-compartment model adequately described INH concentrations in plasma and lungs. With an MIC of 0.0625 mg/liter, simulations showed that the mean bactericidal effect of a standard 300-mg daily dose of INH was only 11% lower for FA subjects than for SA subjects and that dose increases had little influence on the effects in either FA or SA subjects. With an MIC value of 1 mg/liter, the mean bactericidal effect associated with a 300-mg daily dose of INH in SA subjects was 41% greater than that in FA subjects. With the same MIC, increasing the daily INH dose from 300 mg to 450 mg resulted in a 22% increase in FA subjects. These results suggest that patients infected with M. tuberculosis with low-level resistance, especially FA patients, may benefit from higher INH doses, while dose adjustment for acetylator status has no significant impact on the EBA in patients with low-MIC strains.

Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.

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