introduction A

s usual, some of the articles in this number of DDW are devoted to suggestions as to how to improve and accelerate the processes of drug discovery and development and, in

particular, to rectify what in one article is described as “the stagger- ing clinical trial failure rate of experimental drugs”. There are, again as usual, proposals regarding improvements in existing tech- nologies, but also an increasing tendency to suggest the introduc- tion of new technologies and processes with a shift away from those which have been time-honoured for several decades. For example, in the article referred to above it is stated that the

industry is now looking towards artificial intelligence (AI) to bring about quicker, less expensive, paths to clinical trials with fewer fail- ures leading to the introduction of more, and improved, new medicines. According to the authors, about 81 start-ups and 19 pharma companies are already using AI in their drug discovery pro- grammes. They introduce, and describe the use of, an innovative Emergent Intelligence (EI) technology known as Emergent Behavior Analysis (EBA) which, it is claimed, “translates vast amounts of data into actionable insights”. This question of how to deal with the vast quantities of data now

generated within drug discovery laboratories has been the subject of a number of articles in DDW over the past few years and there is general agreement with the author of another of our articles who states that “innovative organisations can optimise their chances of success... through effective use of these data repositories”. He, too, advocates the use of machine learning and AI technologies and sug- gests that biopharma companies would benefit from forming rela- tionships with specialist companies with appropriate technical expertise in these areas. The increasing use of AI and machine learning is also foreseen in

an article by authors from AstraZeneca who describe the new R&D framework within that company which has resulted in an almost five-fold increase in productivity. The emphasis has been, and remains, on the adoption of preclinical models with improved pre- dictability in that they produce results which are more translatable to man and provide data on multiple parameters including toxicity, efficacy and PK/PD determination before compound enter clinical trials. This has involved wider collaboration with more partners. The increasing focus on personalised medicine has led to a number

of articles on that topic in DDW over the past few years. More recently a new term ‘Precision Medicine’ has entered our vocabulary and, in the opinion of the authors of another of our articles, it is important to distinguish between the two terms which are often, mis- takenly, used interchangeably. The models now used more closely resemble the human conditions thus ensuring that candidates which enter the later, and more expensive, stages of clinical development stand a higher chance of success than was previously the case. They state that “Precision Medicine can best be described as an amalgam of Personalised Medicine and modern conventional medicine”. Based on this definition there has been a large increase in approvals by the FDA of Precision Medicine Drugs – from 5% of the total approved in 2005 to 35% in evolving pre-clinical technologies. Antibiotic resistance is now accepted as being one of the major

health unmet issues worldwide and, in another article, the authors report that the WHO describes the current pipeline of potential new

Drug Discovery World Summer 2018

products designed to combat this threat as being inadequate. The authors propose long- term solutions, partic- ularly for carbapen- em-resistant Gram- negative organisms, where they believe that the prospects are “reasonably good”. They consider that antibiotic


and development for resistant Gram-posi- tive bacteria has been successful. Other articles on

technological advan- ces include one on single-cell analysis in which it is pointed out that this technol- ogy should aid in the diagnosis and assessment of prognosis of dis- eases and in planning personalised, targeted, individualised treat- ments. Historically, cellular processes and signalling pathways have been determined in populations of cells and can, therefore, only rep- resent the average response and can give no indication of the degree of heterogeneity in the population The authors of an article in this number of DDW describe advances that have been made in the use of microfluidic platforms that harness picodroplet technology to allow very large numbers of single-cell tests to be carried out daily. Technological advances have also been made in mass spectrometry

which has for a long time, been a valuable tool for drug researchers. Now, there are commercially available instruments (reviewed in one of our articles) which overcome some of the challenges faced by researchers. These include, for example, the minimisation or elimina- tion of some of the complexities of sample preparation. Cell therapies, involving the direct use of whole cells or cellular

material in patients are, according to another author, “poised to play a pivotal role in the development of these precision or person- alised medicines that will transform the lives of millions of patients worldwide”. For this goal to be realised it is essential that there are sources which can reliably preserve the integrity and viability of the cultured cells. Advances which are being made to achieve this end are described and discussed. Therapeutic antibodies are increasingly being used to treat a vari-

ety of diseases. Large numbers of clones can now be produced from modern antibody generation methods and from these large numbers a few promising leads are isolated which can then be engineered to optimise appropriate characteristics including their kinetics, affini- ties and epitope specificities. There have been advances in biophysi- cal tools to achieve this end. Higher-throughput methods are being developed to speed up this so-called library to leads process. Dr Roger Brimblecombe PhD, DSc, FRCPath, FRSB


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