The Safe by Design (SbD) concept aims to ensure the production, use and disposal of materials and products safely. While there is a growing interest in the potential of SbD to support policy... Show moreThe Safe by Design (SbD) concept aims to ensure the production, use and disposal of materials and products safely. While there is a growing interest in the potential of SbD to support policy commitments, such as the EU Green Deal and the Circular Economy Action Plan in Europe, methodological approaches and practical guidelines on SbD are, however, largely missing. The combined use of Life Cycle Assessment (LCA) and Risk Assessment (RA) is considered suitable to operationalize SbD over the whole life-cycle of a product. Here, we explore the potential of the combined use of LCA and RA at Technological Readiness Level (TRL) 1–6. We perform a review of the literature presenting and/or developing approaches that combine LCA and RA at early stages of product design. We identify that basic early-on-evaluations of safety (e.g., apply lifecycle thinking to assess risk hotspots, avoid use of hazardous chemicals, minimize other environmental impacts from chemicals) are more common, while more complex assessments (e.g., ex-ante LCA, control banding, predictive (eco)toxicology) require specialized expertise. The application of these simplified approaches and guidelines aims to avoid some obvious sources of risks and impacts at early stages. Critical gaps need to be addressed for wider application of SbD, including more studies in the product design context, developing tools and databases containing collated information on risk, greater collaboration between RA/LCA researchers and companies, and policy discussion on the expansion from SbD to Safe and Sustainable by Design (SSbD). Show less
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be... Show moreProteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be particularly useful when exploring the selectivity and promiscuity of ligands on different proteins. In this review, we will firstly provide a brief introduction to the main concepts of PCM for readers new to the field. The next part focuses on recent technical advances, including the application of support vector machines (SVMs) using different kernel functions, random forests, Gaussian processes and collaborative filtering. The subsequent section will then describe some novel practical applications of PCM in the medicinal chemistry field, including studies on GPCRs, kinases, viral proteins (e.g.from HIV) and epigenetic targets such as histone deacetylases. Finally, we will conclude by summarizing novel developments in PCM, which we expect to gain further importance in the future. These developments include adding three-dimensional protein target information, application of PCM to the prediction of binding energies, and application of the concept in the fields of pharmacogenomics and toxicogenomics. This review is an update to a related publication in 2011 and it mainly focuses on developments in the field since then. Show less
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be... Show moreProteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be particularly useful when exploring the selectivity and promiscuity of ligands on different proteins. In this review, we will firstly provide a brief introduction to the main concepts of PCM for readers new to the field. The next part focuses on recent technical advances, including the application of support vector machines (SVMs) using different kernel functions, random forests, Gaussian processes and collaborative filtering. The subsequent section will then describe some novel practical applications of PCM in the medicinal chemistry field, including studies on GPCRs, kinases, viral proteins (e.g.from HIV) and epigenetic targets such as histone deacetylases. Finally, we will conclude by summarizing novel developments in PCM, which we expect to gain further importance in the future. These developments include adding three-dimensional protein target information, application of PCM to the prediction of binding energies, and application of the concept in the fields of pharmacogenomics and toxicogenomics. This review is an update to a related publication in 2011 and it mainly focuses on developments in the field since then. Show less
