Machine learning for target discovery in drug development.

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant 807281) and FCT/FEDER (02/SAICT/2017, Grant 28333) for funding. G.J.L.B. is a Royal Society University Research Fellow (URF\R\180019) and a FCT Investigator (IF/00624/2015)

Boosting BCG with recombinant modified vaccinia ankara expressing antigen 85A: Different boosting intervals and implications for efficacy trials

Objectives. To investigate the safety and immunogenicity of boosting BCG with modified vaccinia Ankara expressing antigen 85A (MVA85A), shortly after BCG vaccination, and to compare this first with the immunogenicity of BCG vaccination alone and second with a previous clinical trial where MVA85A was administered more than 10 years after BCG vaccination. Design. There are two clinical trials reported here: a Phase I observational trial with MVA85A; and a Phase IV observational trial with BCG. These clinical trials were all conducted in the UK in healthy, HIV negative, BCG naı¨ve adults. Subjects were vaccinated with BCG alone; or BCG and then subsequently boosted with MVA85A four weeks later (short interval). The outcome measures, safety and immunogenicity, were monitored for six months. The immunogenicity results from this short interval BCG prime–MVA85A boost trial were compared first with the BCG alone trial and second with a previous clinical trial where MVA85A vaccination was administered many years after vaccination with BCG. Results. MVA85A was safe and highly immunogenic when administered to subjects who had recently received BCG vaccination. When the short interval trial data presented here were compared with the previous long interval trial data, there were no significant differences in the magnitude of immune responses generated when MVA85A was administered shortly after, or many years after BCG vaccination. Conclusions. The clinical trial data presented here provides further evidence of the ability of MVA85A to boost BCG primed immune responses. This boosting potential is not influenced by the time interval between prior BCG vaccination and boosting with MVA85A. These findings have important implications for the design of efficacy trials with MVA85A. Boosting BCG induced anti-mycobacterial immunity in either infancy or adolescence are both potential applications for this vaccine, given the immunological data presented here. Trial Registration. ClinicalTrials.Oxford University was the sponsor for all the clinical trials reported here

Natural product modulators of transient receptor potential (TRP) channels as potential anti-cancer agents.

Treatment of cancer is a significant challenge in clinical medicine, and its research is a top priority in chemical biology and drug discovery. Consequently, there is an urgent need for identifying innovative chemotypes capable of modulating unexploited drug targets. The transient receptor potential (TRPs) channels persist scarcely explored as targets, despite intervening in a plethora of pathophysiological events in numerous diseases, including cancer. Both agonists and antagonists have proven capable of evoking phenotype changes leading to either cell death or reduced cell migration. Among these, natural products entail biologically pre-validated and privileged architectures for TRP recognition. Furthermore, several natural products have significantly contributed to our current knowledge on TRP biology. In this Tutorial Review we focus on selected natural products, e.g. capsaicinoids, cannabinoids and terpenes, by highlighting challenges and opportunities in their use as starting points for designing natural product-inspired TRP channel modulators. Importantly, the de-orphanization of natural products as TRP channel ligands may leverage their exploration as viable strategy for developing anticancer therapies. Finally, we foresee that TRP channels may be explored for the selective pharmacodelivery of cytotoxic payloads to diseased tissues, providing an innovative platform in chemical biology and molecular medicine.We thank FCT Portugal (FCT Investigator to G. J. L. B.), the EU (Marie-Curie CIG and Marie-Curie ITN Protein Conjugates to G. J. L. B.), Deutsche Forschungsgemeinschaft (Postdoctoral Fellowship to F. S.), the EPSRC and MRC for funding. G. J. L. B. is a Royal Society University Research Fellow and the recipient of an European Research Council Starting Grant (TagIt)

Computational advances in combating colloidal aggregation in drug discovery.

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding

Translocation t(X;20)(q13;q13.3) as a secondary chromosome abnormality in a patient with 5q-: a case report

Case report of a translocation : Translocation t(X;20)(q13;q13.3) as a secondary chromosome abnormality in a patient with 5q-: a case report

Intramuscular EMG-driven musculoskeletal modelling: towards implanted muscle interfacing in spinal cord injury patients

OBJECTIVE: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. METHODS: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. RESULTS: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. CONCLUSION: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. SIGNIFICANCE: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation