Use of clinical-grade human induced pluripotent stem cell (iPSC) lines as a starting material for the generation of cellular therapeutics requires demonstration of comparability of lines derived from different individuals and in different facilities. This requires agreement on the critical quality attributes of such lines and the assays that should be used. Working from established recommendations and guidance from the International Stem Cell Banking Initiative for human embryonic stem cell banking, and concentrating on those issues more relevant to iPSCs, a series of consensus workshops has made initial recommendations on the minimum dataset required to consider an iPSC line of clinical grade, which are outlined in this report. Continued evolution of this field will likely lead to revision of these guidelines on a regular basis.

The DDW-J and CIOMS/RUCAM algorithms were equivalent for identifying the DILI cases, confirming the utility of our DILI detection method using MIDs. This study provides evidence supporting the use of MID analyses to improve pharmacovigilance.

No abstract

The current analysis demonstrates the effectiveness of two regulatory actions. The results of the current study indicate that MID research can contribute to assessing and improving pharmacovigilance activities.

The regulatory action led to increased lactate measurement in the overall metformin users, but did not affect metformin prescription rate in the elderly patients. Our findings probably reflect the doctors' judgement that the benefits of metformin use outweigh the risk of lactic acidosis if lactate testing is performed regularly.

What is known and Objective: Demonstration of the utility of electronic medical records (EMRs) for pharmacovigilance (PV) has been highly anticipated. Analysis using appropriately selected EMRs should enable accurate estimation of adverse drug event (ADE) frequencies and thus promote appropriate regulatory actions. Statin-induced myopathy (SIM) is a clinically important ADE, but pharmacoepidemiological methodology for detecting this ADE with high predictability has not yet been established. This study aimed to develop a detection algorithm, highly selective for SIM using EMRs. Methods: We collected EMRs on prescriptions, laboratory tests, diagnoses and medical practices from the hospital information system of Kobe University Hospital, Japan, for a total of 5109 patients who received a statin prescription from April 2006 to March 2009. The current algorithm for extracting SIM-suspected patients consisted of three steps: (i) event detection: increase in creatine kinase (CK) and subsequent statin discontinuation, (ii) filtration by exclusion factors (disease diagnosis/medical practices) and (iii) refinement by the time course of CK values (baseline, event and recovery). A causal relationship between the event and statin prescription (probable/possible/unlikely) was judged by review of patient medical charts by experienced pharmacists. The utility of the current algorithm was assessed by calculating the positive predictive value (PPV). In a comparative analysis, subjects screened in step 1 were extracted by the diagnostic term/code for 'myopathy/rhabdomyolysis', and the PPV of this diagnostic data approach was also estimated. Results and Discussion: Five subjects with suspected SIM were identified using our proposed algorithm, giving a frequency of 0Á1% for the adverse event. Review of the medical charts revealed that the causal association of SIM with statin use was judged as 'Likely (probable/possible)' for all five suspected patients; thus, the PPV was estimated as 100% (95% confidence interval: 56Á6-100%). The higher utility of the current algorithm compared with the diagnostic data approach was also shown by assessing the PPV (100 vs. 33Á3%). What is new and Conclusion:We report on a detection algorithm with high predictability for SIM using EMRs. Combined use of exclusion criteria for disease, medical practice data and time course of CK values contributes to better prediction of SIM. The utility of the proposed algorithm should be further confirmed in a larger study.

No abstract

Candesartan cilexetil is an angiotensin II receptor antagonist, and candesartan, its active metabolite, is metabolized by CYP2C9. However, the effect of CYP2C9*3 on candesartan metabolism is not established. We characterized the kinetics of candesartan by CYP2C9*1/*1 and CYP2C9*1/*3 in human liver microsomes. The difference between the two was not significant. Subsequently, CYP2C9*1 and CYP2C9*3 (Leu 359 ) were expressed in yeast, and the kinetics of candesartan were determined. The wild-type showed the lower K m (345 vs 439 M; 3/4) and higher V max /K m (1/3) than the Leu 359 variant. Also, we investigated potential interaction between candesartan and warfarin with both the wild-type and the Leu 359 variant. Candesartan had no effect on S-warfarin 7-hydroxylation. In contrast, S-warfarin inhibited candesartan metabolism by the wild-type (K i = 17 M) greater than by the Leu 359 variant (K i = 36 M). These findings suggest that CYP2C9*3 may change not only the metabolic activity but also the inhibitory susceptibility compared with