Proceedings of the biosimilars workshop at the International Symposium on Oncology Pharmacy Practice 2019.

The International Society of Oncology Pharmacy Practitioners organized a workshop to create learning opportunities on biosimilars in pharmacy practice on 10 October 2019. The topics that were covered included (i) the development and testing of biosimilars, (ii) the challenges of bringing biosimilars to market, and (iii) real-world data on patient safety and perceptions during biosimilar implementation. The development of biosimilars can take up to eight years and the extensiveness of the process depends on several factors, such as the complexity of the production process and regulatory requirements. Compared to generic products of small-molecule drugs, there is a higher barrier to market entry for biosimilars, explaining the small number of biosimilars in the market. Appraisal of biosimilars for inclusion in hospital formularies is also different from the review process of originator biologics, where the former is usually institution-led and has fewer restrictions on use. When several biosimilar products are available, factors that should be considered besides cost are licensed indications, supply chain confidence, clinical data, and product attributes. Real-world data have shown that biosimilars are well-tolerated and have safety data that are comparable to that of the originator product. Oncology pharmacists from the United Kingdom, Kenya, and Canada also presented their respective experiences with biosimilar use. Different countries at varying stages of biosimilar implementation faced distinct challenges. Nevertheless, resources to assist biosimilar implementation can potentially be shared between different regions. International Society of Oncology Pharmacy Practitioners is well-positioned to foster professional cooperation at an international level to drive biosimilar implementation

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

Forecasting constraints on the no-hair theorem from the stochastic gravitational wave background

Although the constraints on general relativity (GR) from each individual gravitational-wave (GW) event can be combined to form a cumulative estimate of the deviations from GR, the ever-increasing number of GW events used also leads to the ever-increasing computational cost during the parameter estimation. Therefore, in this paper, we will introduce the deviations from GR into GWs from all events in advance and then create a modified stochastic gravitational-wave background (SGWB) to perform tests of GR. More precisely, we use the $\mathtt{pSEOBNRv4HM\_PA}$ model to include the model-independent hairs and calculate the corresponding SGWB with a given merger rate. Then we turn to the Fisher information matrix to forecast the constraints on the no-hair theorem from SGWB at frequency $10[{\rm Hz}]\lesssim f\lesssim10^4[{\rm Hz}]$ detected by the third-generation ground-based GW detectors, such as the Cosmic Explorer. We find that the forecasting constraints on hairs are $\delta\omega_{220}=0\pm0.02$ and $\delta\tau_{220}=0\pm0.04$ at $68\%$ confidence range for the parameter space with only two parameters.Comment: 8 pages, 6 figure