Inappropriate polypharmacy, especially in older people, imposes a substantial burden of adverse drug events, ill health, disability, hospitalization, and even death. The single most important predictor of inappropriate prescribing and risk of adverse drug events in older patients is the number of prescribed drugs. Deprescribing is the process of tapering or stopping drugs, aimed at minimizing polypharmacy and improving patient outcomes. Evidence of efficacy for deprescribing is emerging from randomized trials and observational studies. A deprescribing protocol is proposed comprising 5 steps: (1) ascertain all drugs the patient is currently taking and the reasons for each one;(2) consider overall risk of drug-induced harm in individual patients in determining the required intensity of deprescribing intervention; (3) assess each drug in regard to its current or future benefit potential compared with current or future harm or burden potential; (4) prioritize drugs for discontinuation that have the lowest benefit-harm ratio and lowest likelihood of adverse withdrawal reactions or disease rebound syndromes; and (5) implement a discontinuation regimen and monitor patients closely for improvement in outcomes or onset of adverse effects. Whereas patient and prescriber barriers to deprescribing exist, resources and strategies are available that facilitate deliberate yet judicious deprescribing and deserve wider application.

Optimizing the prescription of antimicrobials is required to improve clinical outcome from infections and to reduce the development of antimicrobial resistance. One such method to improve antimicrobial dosing in individual patients is through application of therapeutic drug monitoring (TDM). The aim of this manuscript is to review the place of TDM in the dosing of antimicrobial agents, specifically the importance of pharmacokinetics (PK) and pharmacodynamics (PD) to define the antimicrobial exposures necessary for maximizing killing or inhibition of bacterial growth. In this context, there are robust data for some antimicrobials, including the ratio of a PK parameter (e.g. peak concentration) to the minimal inhibitory concentration of the bacteria associated with maximal antimicrobial effect. Blood sampling of an individual patient can then further define the relevant PK parameter value in that patient and, if necessary, antimicrobial dosing can be adjusted to enable achievement of the target PK/PD ratio. To date, the clinical outcome benefits of a systematic TDM programme for antimicrobials have only been demonstrated for aminoglycosides, although the decreasing susceptibility of bacteria to available antimicrobials and the increasing costs of pharmaceuticals, as well as emerging data on pharmacokinetic variability, suggest that benefits are likely.

Background & Aims We performed a genome-wide association study (GWAS) to identify genetic risk factors for drug-induced liver injury (DILI) from licensed drugs without previously reported genetic risk factors. Methods We performed a GWAS of 862 persons with DILI and 10,588 population-matched controls. The first set of cases was recruited before May 2009 in Europe (n = 137) and the United States (n = 274). The second set of cases were identified from May 2009 through May 2013 from international collaborative studies performed in Europe, the United States, and South America. For the GWAS, we included only cases with patients of European ancestry associated with a particular drug (but not flucloxacillin or amoxicillin-clavulanate). We used DNA samples from all subjects to analyze HLA genes and single nucleotide polymorphisms. After the discovery analysis was concluded, we validated our findings using data from 283 European patients with diagnosis of DILI associated with various drugs. Results We associated DILI with rs114577328 (a proxy for A*33:01 a HLA class I allele; odds ratio [OR], 2.7; 95% confidence interval [CI], 1.9−3.8; P = 2.4 × 10⁻⁸) and with rs72631567 on chromosome 2 (OR, 2.0; 95% CI, 1.6−2.5; P = 9.7 × 10⁻⁹). The association with A*33:01 was mediated by large effects for terbinafine-, fenofibrate-, and ticlopidine-related DILI. The variant on chromosome 2 was associated with DILI from a variety of drugs. Further phenotypic analysis indicated that the association between DILI and A*33:01 was significant genome wide for cholestatic and mixed DILI, but not for hepatocellular DILI; the polymorphism on chromosome 2 was associated with cholestatic and mixed DILI as well as hepatocellular DILI. We identified an association between rs28521457 (within the lipopolysaccharide-responsive vesicle trafficking, beach and anchor containing gene) and only hepatocellular DILI (OR, 2.1; 95% CI, 1.6−2.7; P = 4.8 × 10⁻⁹). We did not associate any specific drug classes with genetic polymorphisms, except for statin-associated DILI, which was associated with rs116561224 on chromosome 18 (OR, 5.4; 95% CI, 3.0−9.5; P = 7.1 × 10⁻⁹). We validated the association between A*33:01 terbinafine- and sertraline-induced DILI. We could not validate the association between DILI and rs72631567, rs28521457, or rs116561224. Conclusions In a GWAS of persons of European descent with DILI, we associated HLA-A*33:01 with DILI due to terbinafine and possibly fenofibrate and ticlopidine. We identified polymorphisms that appear to be associated with DILI from statins, as well as 2 non−drug-specific risk factors.