Development and application of a data-driven reaction classification model : comparison of an electronic lab notebook and the medicinal chemistry literature

Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework in order to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for 'dynamic' reaction fingerprinting to maximise the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organises labels in four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction datasets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationship between reaction classes

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules

EFFECTIVENESS OF DIABETIC KETOACIDOSIS TREATMENT WITH LOW-DOSIS VENOUS INSULIN INFUSIONS

No abstrac

Improving the prediction of environmental fate of engineered nanomaterials by fractal modelling

A critical analysis of the available engineered nanomaterials (ENMs) environmental fate modelling approaches indicates that existing tools do not satisfactorily account for the complexities of nanoscale phenomena. Fractal modelling (FM) can complement existing kinetic fate models by including more accurate interpretations of shape and structure, density and collision efficiency parameters to better describe homo- and heteroaggregation. Pathways to including hierarchical symmetry concepts and a route to establishing a structural classification of nanomaterials based on FM are proposed

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Environmental Biolog

Identification of the safe(r) by design alternatives for nanosilver-enabled wound dressings

The use of silver nanoparticles (NPs) in medical devices is constantly increasing due to their excellent antimicrobial properties. In wound dressings, Ag NPs are commonly added in large excess to exert a long-term and constant antimicrobial effect, provoking an instantaneous release of Ag ions during their use or the persistence of unused NPs in the wound dressing that can cause a release of Ag during the end-of-life of the product. For this reason, a Safe-by-Design procedure has been developed to reduce potential environmental risks while optimizing functionality and costs of wound dressings containing Ag NPs. The SbD procedure is based on ad-hoc criteria (e.g., mechanical strength, antibacterial effect, leaching of Ag from the product immersed in environmental media) and permits to identify the best one among five pre-market alternatives. A ranking of the SbD alternatives was obtained and the safer solution was selected based on the selected SbD criteria. The SbD framework was also applied to commercial wound dressings to compare the SbD alternatives with products already on the market. The iterative procedure permitted to exclude one of the alternatives (based on its low mechanical strength) and proved to be an effective approach that can be replicated to support the ranking, prioritisation, and selection of the most promising options early in the innovation process of nano-enabled medical devices as well as to encourage the production of medical devices safer for the environment

Concern-driven integrated approaches to nanomaterial testing and assessment - report of the NanoSafety Cluster Working Group

Abstract Bringing together topic-related European Union (EU)-funded projects, the so-called "NanoSafety Cluster" aims at identifying key areas for further research on risk assessment procedures for nanomaterials (NM). The outcome of NanoSafety Cluster Working Group 10, this commentary presents a vision for concern-driven integrated approaches for the (eco-)toxicological testing and assessment (IATA) of NM. Such approaches should start out by determining concerns, i.e., specific information needs for a given NM based on realistic exposure scenarios. Recognised concerns can be addressed in a set of tiers using standardised protocols for NM preparation and testing. Tier 1 includes determining physico-chemical properties, non-testing (e.g., structure-activity relationships) and evaluating existing data. In tier 2, a limited set of in vitro and in vivo tests are performed that can either indicate that the risk of the specific concern is sufficiently known or indicate the need for further testing, including details for such testing. Ecotoxicological testing begins with representative test organisms followed by complex test systems. After each tier, it is evaluated whether the information gained permits assessing the safety of the NM so that further testing can be waived. By effectively exploiting all available information, IATA allow accelerating the risk assessment process and reducing testing costs and animal use (in line with the 3Rs principle implemented in EU Directive 2010/63/EU). Combining material properties, exposure, biokinetics and hazard data, information gained with IATA can be used to recognise groups of NM based upon similar modes of action. Grouping of substances in return should form integral part of the IATA themselves

RENATE : a pseudo-retrosynthetic tool for synthetically accessible de novo design

Reaction-based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction-based de novo design tool, which is based on a pseudo-retrosynthetic fragmentation of a reference ligand and an inside-out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool

Occupational risk of nano-biomaterials: Assessment of nano-enabled magnetite contrast agent using the BIORIMA Decision Support System

The assessment of the safety of nano-biomedical products for patients is an essential prerequisite for their market authorization. However, it is also required to ensure the safety of the workers who may be unintentionally exposed to the nano-biomaterials (NBMs) in these medical applications during their synthesis, formulation into products and end-of-life processing and also of the medical professionals (e.g., nurses, doctors, dentists) using the products for treating patients. There is only a handful of workplace risk assessments focussing on NBMs used in medical applications. Our goal is to contribute to increasing the knowledge in this area by assessing the occupational risks of magnetite (Fe3O4) nanoparticles coated with PLGA-b-PEG-COOH used as contrast agent in magnetic resonance imaging (MRI) by applying the software-based Decision Support System (DSS) which was developed in the EU H2020 project BIORIMA. The occupational risk assessment was performed according to regulatory requirements and using state-of-the-art models for hazard and exposure assessment, which are part of the DSS. Exposure scenarios for each life cycle stage were developed using data from literature, inputs from partnering industries and results of a questionnaire distributed to healthcare professionals, i.e., physicians, nurses, technicians working with contrast agents for MRI. Exposure concentrations were obtained either from predictive exposure models or monitoring campaigns designed specifically for this study. Derived No-Effect Levels (DNELs) were calculated by means of the APROBA tool starting from in vivo hazard data from literature. The exposure estimates/measurements and the DNELs were used to perform probabilistic risk characterisation for the formulated exposure scenarios, including uncertainty analysis. The obtained results revealed negligible risks for workers along the life cycle of magnetite NBMs used as contrast agent for the diagnosis of tumour cells in all exposure scenarios except in one when risk is considered acceptable after the adoption of specific risk management measures. The study also demonstrated the added value of using the BIORIMA DSS for quantification and communication of occupational risks of nano-biomedical applications and the associated uncertainties