A Semi-automated Approach to Create Purposeful Mechanistic Datasets from Heterogeneous Data: Data Mining Towards the in silico Predictions for Oestrogen Receptor Modulation and Teratogenicity

Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways-namely key events (KEs) and key event relationships-to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.

Electroresponsive Silk-Based Biohybrid Composites for Electrochemically Controlled Growth Factor Delivery

Stimuli-responsive materials are very attractive candidates for on-demand drug delivery applications. Precise control over therapeutic agents in a local area is particularly enticing to regulate the biological repair process and promote tissue regeneration. Macromolecular therapeutics are difficult to embed for delivery, and achieving controlled release over long-term periods, which is required for tissue repair and regeneration, is challenging. Biohybrid composites incorporating natural biopolymers and electroconductive/active moieties are emerging as functional materials to be used as coatings, implants or scaffolds in regenerative medicine. Here, we report the development of electroresponsive biohybrid composites based on Bombyx mori silkworm fibroin and reduced graphene oxide that are electrostatically loaded with a high-molecular-weight therapeutic (i.e., 26 kDa nerve growth factor-β (NGF-β)). NGF-β-loaded composite films were shown to control the release of the drug over a 10-day period in a pulsatile fashion upon the on/off application of an electrical stimulus. The results shown here pave the way for personalized and biologically responsive scaffolds, coatings and implantable devices to be used in neural tissue engineering applications, and could be translated to other electrically sensitive tissues as well.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting

In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.