Introduction


introduction R


ecent issues of DDWhave included a number of articles discussing how drug research and development organisations are having to learn how to take maximum benefit from the ever-growing


sophistication of the technologies which are now becoming available to them in their quest for leads to new drugs. For example, in the most recent (Summer 2019) number there was a summary of a report from the Pistoia Alliance – a non-profit-making organisation including interested parties from life-science companies, academia, publishers, suppliers, etc with the aim of “lowering barriers in life-science R&D and healthcare”. This report was concerned with the application of bioinformatics in sup- port of personalised medicine. In this edition there is an article, emanating from the same source,


which discusses, among other things, how pharmaceutical R&D compa- nies may look in 2030 and, in particular, how they may have been affect- ed by the increasing sophistication of the technologies which will be developed over the next decade or so allowing, among other things, the replacement of ‘one size fits all’ drugs by personalised medicines. It is clear that Artificial Intelligence (AI) and Machine Learning(ML) will play a significant role in this transformation but some caution is expressed and there are concerns that automated diagnosis using these technologies may, not by 2030, have lived up to all expectations and may even have resulted in what are described as “stupid” errors, possibly leading to loss of life. There is also some concern that a fundamental requirement of AI/ML – access to sufficient data of sufficient quality – may not have been achieved, although steps are in hand to remedy this and other challenges. AI is the subject of another article herein in which it is stated that it


could “create a streamlined, automated approach to drug discovery”. The ultimate goal is a technology which can select viable candidate molecules from very large databases and predict synthetic routes for them. Reaching that goal is acknowledged as requiring “significant vision”, but there are useful applications which can give benefits en route to the ultimate goal. AI can, for example, be used to scan large bodies of medical literature and genetic databases in a search for gene-disease asso- ciations and thus identify new targets. The article also contains a discus- sion of the “mindset change” required in moving to becoming an AI-driv- en organisation which apparently entails a “willingness to take risks and step into new areas”. A third article on AI discusses ways in which it can be used to unlock


the value of early or inconclusive clinical trial data. As the authors point out, “data is increasingly king in our information-driven world”. They quote again the somewhat depressing statistic that, according to the most recent estimate, only 13.8% of development programmes progress suc- cessfully from Phase I to approval and believe that this percentage can, and should, be improved by using Machine Learning (ML) and Deep Learning (DL) techniques not only in current clinical trials but by re- exploring the vast quantities of data from earlier clinical trials deemed at the time to show lack of efficacy. In another article on phenotypic screening of drug candidates, the rel-


ative merits of single-cell analysis versus 3D multi-cell analysis are debat- ed, again in the context of the high failure rate of compounds when they are subjected to clinical testing. Both techniques have, according to the author, their advantages and disadvantages, and currently there are advances being made rapidly in both screening methods. However, there appears to have been no direct comparison of the two, something the author considers should be undertaken, especially now that cell and tissue banks of cell populations from patients are being established and are screening and profiling potential new drugs. Screening is also the topic of another article dealing with high through-


put screening –a technique now used in virtually all discovery laboratories. Again, the underlying theme is the desire to identify ‘genuine’ leads, ie those


Drug Discovery World Fall 2019


which stand a high chance of success by passing through the pre-clinical and clinical stages of development rather than falling by the wayside, by which time much money and resources may have been devoted to them. So, as well as identify- ing these genuine leads, the screen should, if possible, reject any false positives. The authors describe the concept of Pan-Assay Interference Compounds (PAINS), whereby some com- pounds which appear frequently act as activa- tors or inhibitors of multiple targets via unproductive or non- specific mechanisms. The authors discuss the use of a “robustness set” of such nuisance com- pounds which act in this way and describe how they can be used in assays to reduce the likelihood of false positives entering development. Another approach which it is hoped will increase the probability of


early in vitro results giving more reliable predictions of clinical responses, is the use of live-cell imaging systems to replace the traditional use of fixed or artificially-labelled cells. These live cells are more physiological- ly-relevant and the technique can save both time and money. The key commercial products now available are reviewed in one of our articles. The potential impact of the microbiome on drug discovery is now


attracting considerable interest and is the subject of another of our arti- cles where it is stated that as well as impacting the reproducibility of stud- ies, the microbiome may affect the efficacy of drugs or even serve as a therapeutic agent in its own right. A key issue is the development of a suitable pre-clinical model. Germ-free models, ie ones where the micro- biome is, by definition, absent, are already being used and one of the developments from this is to colonise such models with specified microflora and to study, for example, what impact this may have on response to potential therapeutic agents. The author suggests that advancing researchers’ understanding of the microbiome could take dis- ease treatments to a new level. Non-alcoholic fatty liver disease (NAFLD) is now, according to the


authors of another article, one of the most common causes of chronic liver disease in the modern world. They describe it as “a silent epidemic” which represents a substantial opportunity for pharmaceutical compa- nies. Its very aggressive inflammatory manifestation – non-alcoholic steatohepatitis (NASH) – is estimated to have the potential for a peak drug market size which could be as high as $40 billion. The basic prob- lem in developing effective therapies appears to be the lack of predictive pre-clinical models and our authors review the various approaches which have been, or are being, explored. They consider that we are entering “a new era of game-changing NASH models” and believe that current research will lead to effective therapies.


Dr Roger Brimblecombe PhD, DSc, FRCPath, FRSB 7


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