Employing an adverse outcome pathway framework for weight-of-evidence assessment with application to the ICH S1B guidance addendum.

Across industry, there is a paradigm shift occurring for carcinogenicity testing, with the focus moving from long term animal studies to alternative approaches. Based on the explorative work done in recent years, the International Council for Harmonization (ICH) recently published a draft addendum to the S1B guidance, which allows for a weight-of-evidence (WoE) assessment to be conducted based on data gathered throughout the pharmaceutical development process and literature to mitigate some testing in rodents if the body of evidence clearly shows undertaking an animal lifetime study would not add value to the risk assessment. While several alternative approaches already exist, and other new approach methodologies (NAMs) are being explored, all of which can contribute to this WoE, it is important that all the evidence can be combined in a meaningful and consistent way to reach a conclusion. Adverse outcome pathways have been advocated as a framework for organising evidence in an integrated approach to testing and assessment, which gives context to data and can aid reaching a conclusion as to the adverse outcome (AO). This approach can be combined with a reasoning methodology to give a prediction for an AO and applied to the factors which need to be considered for the ICH S1B WoE to predict for carcinogenicity. Using this approach to the WoE assessment, consistent, scientifically robust, and transparent calls can be made as to whether conducting an animal carcinogenicity study would add value to a human risk assessment and mitigate the need to run animal studies unnecessarily.

Comparison of Three Frailty Scales for Prediction of Adverse Outcomes among Older Adults: A Prospective Cohort Study.

OBJECTIVES: To compare the ability of Frailty Phenotype (FP), FRAIL and Frailty Index (FI) to predict adverse outcomes. DESIGN: A prospective cohort study. SETTING: A senior community in Beijing, China. PARTICIPANTS: A total of 188 older adults aged 65 years or older (mean age 84.0 ± 4.4 years, 58.5% female). MEASUREMENTS: Frailty was evaluated by FP, FRAIL and FI. The agreement between scales was assessed by Cohen kappa coefficient. The predictive value of the three scales for adverse outcomes during one-year follow-up period were analyzed using decision curve analysis(DCA) and receiver operating characteristic curve (ROC) analysis. RESULTS: Frailty ranged from 25% (FRAIL) to 42.6% (FI). The agreement between scales was moderate to good (Cohen's kappa coefficient 0.44~0.61). DCA showed though the curves of the scales overlapped across all relevant risk thresholds, clinical treating had a higher net benefit than "treat all" and "treat none" when risk of unplanned hospital visits ≥30%, risk of functional decline or falls ≥15%. The three scales had similar predictive value for unplanned hospital visits (area under ROC, AUC 0.63, 0.64 and 0.69). FRAIL and FI had similar predictive value for functional decline (AUC 0.63,0.65). FI had predictive value for falls (AUC 0.65). CONCLUSIONS: All three scales showed clinical utility but FRAIL may be best in practice because it is simple. Multidimensional measures of frailty are better than unidimensional for prediction of adverse outcomes among older adults.

Everything safe? ­ Risk situations in advanced home care from the point of view of ventilated patients and their relatives / Alles sicher? ­ Risikosituationen in der häuslichen Intensivpflege aus Sicht beatmeter Patienten und ihrer Angehörigen.

Background: The number of home mechanically ventilated (HMV) patients has been growing for years. However, little is known about requirements, processes and effects of advanced home care, provided in distance from clinics and doctors. To date, safety related aspects of the above mentioned issues have scarcely been examined. Aim: Users of advanced home care were asked about their experiences and about situations in which they felt safe or unsafe. The aim was to gain insights into the daily care provision, explore safety risks from the users' point of view, and to develop new approaches to enhance patient safety in home care for the severely ill. Method: A qualitative explorative study has been carried out, based on semi-structured interviews (ventilated patients N = 21; relatives N = 15). Sampling, data collecting and data analysis were guided by principles of Grounded Theory. Results: Risk situations occur when (non-)verbal communication offers of HMV patients are overseen or misunderstood, patient- or technology related monitoring tasks are neglected, if coordination and collaboration requirements are undervalued and if negotiation processes as well as education and supervision needs are disregarded. Furthermore, nurses' lack of competence, self-confidence and professionalism may produce risk situations. Conclusion: Listen carefully to patients and relatives can help to identify quality shortcomings in advanced home care, to prevent risk situations and to develop patient-centered safety concepts for this particular setting.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.