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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 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.
Failing early in drug discovery is the primary driving force for new techniques in the hit-to-lead phase.
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...
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…
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...
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.
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...
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...
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.
Progressing actives from a high-throughput screen (HTS) of a target protein into suitable starting points for lead identification is fraught with pitfalls. The volume and quality of the HTS output is a function of the quality of the screening library, as well as specific properties of both the target and…
One of the biggest challenges facing drug discovery across all therapeutic modalities (small molecule, biologics, and cellular) remains the balance between physiological relevance and throughput.
Drug discovery from concept to drug candidate has become a collective effort where multi-disciplinary project teams from CROs work with drug companies and academia to deliver clinical candidates, overcoming multi-factorial research obstacles along the way.
With access challenges even after successful but costly development programmes, the industry is under pressure to speed up the clinical development process and produce more for less. Adaptive trial designs, more collaborative working and data sharing could provide a solution in the area of cancer immunotherapy, says Jacqueline Karmel, principal…
Declining R&D productivity is a key challenge in the pharmaceutical industry today.
