Lab Automation In-Depth Focus 2018
In this issue: how lab automation has accelerated pharmaceutical research by simplifying operations, reducing manual tasks and increasing the efficiency, quality and reproducibility of results.
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Articles
Informatics In-Depth Focus 2018
In this In-Depth Focus: enabling research on understudied and unstudied targets, and using multi-omics data to detail drug responses in the human metabolic network.
Assays In-Depth Focus 2018
In this In-Depth Focus: high throughput strategies for antibody library screening, and the latest in vitro 3D cell and tissue culture model systems in HTS.
Drug Target Review – Issue #1 2018
In this issue: how customised cell engineering advances immunotherapy, how insights into auto-immunity are providing new opportunities for immune-oncology, and advances in lab automation and robotics are accelerating the pace of antimicrobial therapy.
Rational drug design: How far have we come?
From the early use of Hansch parameters and Topliss Trees to today’s computational structure–activity techniques, medicinal chemists have long sought to rationalise drug design to find the quickest and most resource-efficient route to market. But while modern strategies are more reliant on statistical algorithms and vast data libraries, these founding…
Artificial intelligence in drug discovery: how long before we see the real impact?
A new race is well underway involving big pharma and big data companies to see who can most effectively mine the new massive data using artificial intelligence (AI). The aim: reducing costs by using targeted in silico analysis, reducing in vitro and in vivo screening, and reviewing huge quantities of…
Proteogenomics research – on the frontier of precision medicine
Proteogenomics is the systematic and comprehensive integration of proteomics with genomics and transcriptomics. Proteogenomics is opening new hallmarks in biomedical research. Recently, several studies have demonstrated the relevance of proteogenomics in cancer research. This article provides a brief review of the advantages of proteogenomics in precision medicine.
Expert view: Conquering late stage failures with advanced hit-to-lead techniques
Failing early in drug discovery is the primary driving force for new techniques in the hit-to-lead phase.
Identification of hits and leads for human African trypanosomiasis
Neglected tropical diseases (NTD) affect more than one billion people and new targets and drugs are needed to tackle these infections. The multi-disciplinary NMTrypI consortium has identified a number of different compound classes which could be effective against these diseases. This article reviews some of these compounds...
Informatics infrastructure for public-private collaborations in neglected disease research
Efforts to develop new medicines for diseases of the developing world (DDW) have been somewhat fragmented in the past and progress has been limited, despite considerable investment. Public-private partnership (PPP) is becoming an essential model for research in neglected disease areas. However, collaboration on this scale presents unique challenges, some…
Patient-derived neural progenitor cells as a drug discovery model for mitochondrial diseases
Mitochondrial DNA (mtDNA) mutations cause severe disorders that are untreatable and mostly affect the nervous system. The difficulty in funding therapies may also be explained by the lack of viable modelling systems...
Expert view: Optimising the hit-to-lead workflow
Hits identified in high-throughput screens are evaluated within the hit-to-lead phase of drug discovery, where they undergo an iterative optimisation process employing a variety of techniques to identify promising lead compounds to move forward to the lead optimisation phase.
Using bioinformatics sequence similarities to optimise repurposing activities
A significant amount of selectivity and potency data originating from screening of drug targets is generated each year and deposited in public databases. This can be exploited to accelerate drug discovery, in particular, for a variety of repurposing activities...
Machine learning approaches as tools to accelerate drug discovery
Computational methods based on machine learning approaches are being introduced increasingly widely to screen the large number of molecules that have never participated in the drug discovery process, but which might have significant drug development potential. This article considers the latest advances in machine learning as applied to drug discovery...
Q&A: Implementing ADCC assays in QC: when donor variability isn’t an option
DiscoverX recently introduced the single donor-derived KILR CD16 Effector Cells to ensure assay reproducibility for screening, characterisation, and QC lot release of antibody drugs.
