SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours

SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules.

SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15-20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch)

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside.

Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other "omics" data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other "omics" into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called "holomics", will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists

Axillary Sentinel Lymph Node Biopsy for Breast Cancer and Melanoma Patients after Previous Axillary Surgery: A Systematic Review

Objective: Sentinel lymph node biopsy (SLNB) is a validated staging technique for breast carcinoma. Some women are exposed to have a second SLNB due to breast cancer recurrence or a second neoplasia (breast or other). Due to modi- fied anatomy, it has been claimed that previous axillary surgery represents a contra-indication to SLNB. Our objective was to analyse the literature to assess if a second SLNB is to be recommended or not. Methods: For the present study, we performed a review of all published data during the last 10 years on patients with previous axilla surgery and second SLNB. Results: Our analysis shows that second SLNB is feasible in 70%. Extra-axillary SNs rate (31%) was higher after radical lymph node dissection (ALND) (60% - 84%) than after SLNB alone (14% - 65%). Follow-up and com- plementary ALND following negative and positive second SLNB shows that it is a reliable procedure. Conclusion: The review of literature confirms that SLNB is feasible after previous axillary dissection. Triple technique for SN mapping is the best examination to highlight modified lymphatic anatomy and shows definitively where SLNB must be per- formed. Surgery may be more demanding as patients may have more frequently extra-axillary SN only, like internal mammary nodes. ALND can be avoided when second SLNB harvests negative SNs. These conclusions should however be taken with caution because of the heterogeneity of publications regarding SLNB and surgical technique

SwissTargetPrediction: a web server for target prediction of bioactive small molecules.

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein-small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch

Novel targets for immune-checkpoint inhibition in cancer.

Immune-checkpoint inhibitors have revolutionized cancer therapy, yet many patients either do not derive any benefit from treatment or develop a resistance to checkpoint inhibitors. Intrinsic resistance can result from neoantigen depletion, defective antigen presentation, PD-L1 downregulation, immune-checkpoint ligand upregulation, immunosuppression, and tumor cell phenotypic changes. On the other hand, extrinsic resistance involves acquired upregulation of inhibitory immune-checkpoints, leading to T-cell exhaustion. Current data suggest that PD-1, CTLA-4, and LAG-3 upregulation limits the efficacy of single-agent immune-checkpoint inhibitors. Ongoing clinical trials are investigating novel immune-checkpoint targets to avoid or overcome resistance. This review provides an in-depth analysis of the evolving landscape of potentially targetable immune-checkpoints in cancer. We highlight their biology, emphasizing the current understanding of resistance mechanisms and focusing on promising strategies that are under investigation. We also summarize current results and ongoing clinical trials in this crucial field that could once again revolutionize outcomes for cancer patients

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs

Intratumoural immunotherapies for unresectable and metastatic melanoma: current status and future perspectives.

The emergence of human intratumoural immunotherapy (HIT-IT) is a major step forward in the management of unresectable melanoma. The direct injection of treatments into melanoma lesions can cause cell lysis and induce a local immune response, and might be associated with a systemic immune response. Directly injecting immunotherapies into tumours achieves a high local concentration of immunostimulatory agent while minimising systemic exposure and, as such, HIT-IT agents are associated with lower toxicity than systemic immune checkpoint inhibitors (CPIs), enabling their potential use in combination with other therapies. Consequently, multiple HIT-IT agents, including oncolytic viruses, pattern-recognition receptor agonists, injected CPIs, cytokines and immune glycolipids, are under investigation. This review considers the current clinical development status of HIT-IT agents as monotherapy and in combination with systemic CPIs, and the practical aspects of administering and assessing the response to these agents. The future of HIT-IT probably lies in its use in combination with systemic CPIs; data from Phase 2 trials indicate a synergy between HIT-IT and CPIs. Data also suggest that the addition of HIT-IT to a CPI might generate responses in CPI-refractory tumours, thereby overcoming resistance and addressing a current unmet need in unresectable and metastatic melanoma for treatment options following progression after CPI treatment