introduction P

recision, or Personalised,Medicine, where the treatment is designed to meet the needs of an individual patient, is an increasingly com- mon topic for discussion in the medical community. Interest among

drug discovery and development organisations is also high and is reflect- ed in several articles submitted to DDWin recent years. In this edition we include a report from the Pistoia Alliance on the subject. This alliance is a global, non-profit organisation including life science companies, tech- nology suppliers, publishers and academic groups which work together “to lower barriers in innovation in life science R&D and healthcare”. The report is concerned with the application of bioinformatics in support of Precision Medicine. It is pointed out that in the last few years there have been rapid developments in Next Generation Screening (NGS) tech- nology. This has resulted in a greatly increased capacity to generate genomic sequence data at a much reduced cost. It is now, for example, possible to sequence the whole genome for less than $1,000 and it expect- ed that this cost will reduce even further to a point where in the next few years whole genome sequencing could become a standard component in patient care. Clearly, drug discovery and development organisations will need to

react to this new situation. There will have to be changes in attitude, prac- tices and procedures, especially in dealing with the masses of data which will be generated. An example is given in the report describing how bioin- formatics have been applied in studies on cystic fibrosis where more than 1,700 variants have been identified in the CFTR gene in patients, some variants being much more common than others. Work has progressed to produce drugs which manage these underlying causes of the disease rather than, as in the past, simply aiming to control the symptoms. A cautionary note is struck by the authors of another article who state

that Precision Medicine must address genetic diversity. They agree that the publication of the first human genome sequence almost 20 years ago “marked the start of a new era in medicine” but they believe that there needs to be a fuller understanding of the genetic variants across the entire global population. They state, for example, that 78% of the people in genome wide association studies are of European descent. In another example, a recent pan-genome analysis of more than 900 individuals of African ancestry identified hundreds of millions of bases which are not represented in the current human reference genome. The authors con- clude that the vast majority of the world’s population is not represented in genetic databases, meaning that they cannot contribute to, or benefit from, advances in genetic medicine to the same extent as people of white European ancestry. The ever-increasing complexity of drug development and the accelerat-

ing approach of personalised medicine represent, according to the authors of another of our articles, opportunities for the use of Artificial Intelligence(AI) and Deep Learning (DL). Some of the largest pharmaceu- tical companies are already “placing big bets on the ability of AI to deliv- er improvements in quality, clinical success rates and reduced costs”. The article goes on to show how recent advances in chemical and biological automation produce enough data and learning to build a DL model pipeline. Another topic which has received a good deal of attention in these

pages is the inability of even the largest and wealthiest organisations to meet all of the numerous requirements to take a drug all the way from its scientific conception to a successful launch into the marketplace. To do that there would be a requirement for what the authors of another of our articles describe as a “cornucopia of employees”. The solution is usually either to contract out large tranches of the work or to form alliances of one sort or another. There is another proposal by our authors who admit that at first sight it may appear “counterintuitive” or even “oxymoron- ic”’. The proposal is to set up a Virtual Pharmaceutical Company (VPC). Their model requires a small number of core management/advisers/board members with the objective of producing a drug in a given disease indi- cation. There would be an efficient and delineated out-sourcing and part- nering plan. Examples are given of companies which are currently trying to undertake this “adventurous task”. Yet another theme which regularly receives attention in these pages is

Drug DiscoveryWorld Summer 2019

the depressingly high, and apparently not improving, attrition rate of drugs in devel- opment, many of them late in the process by which time consider- able resources both financial and human, have been applied to them. This situation is particularly unsatisfac- tory among anti-cancer drugs where there is a requirement for the potential


agent to be selectively cytotoxic against can- cerous tissues, leaving normal tissues unaf- fected. It is stated in another of our articles that overall only about 14% of Phase I drugs reach approval, where- as for anti-cancer drugs the figure is only 3.4%. The basic problem obviously lies in poor selection of candidate drugs for development, and many solutions have been proposed but apparently none have, as yet, proved to be adequately effective. Our authors state that because of the drawbacks with all individual approaches, the proce- dure for putative anti-cancer drugs should involve a combination of approaches and they suggest a “robust validation procedure” which could entail the use of four different tests which they describe. Spending more time, money and other resources at this early stage could reduce the chance of much greater expense in the event of failure at a later stage. In an article on cell therapy, the statement is made that “classical phar-

macology is broken”. This view is based again on the failure of the clas- sical processes of target identification and lead development to “system- atically produce drugs” and the statement is also made that “we under- stand the mechanism of just 30% of small molecule drugs due to their lack in specificity”. It is argued that, in contrast, cells “have been engi- neered by millions of years of evolution to respond to specific needs”. However, despite very considerable investment, few cell therapies have received regulatory approval. The two main challenges which have to be met are bypassing the immune system and manufacturability. Steps which are being taken to overcome these challenges are described and the author states that enormous progress has been made. Cell-based assays are now universally-used tools in drug discovery. In

vitro cellular models that mimic the in vivo environment can give valu- able information about both efficacy and safety of potential new thera- peutic agents. Traditionally, these models were both complex and labour intensive, but automation has now removed or reduced these difficulties and productivity and reproducibility have increased while human error has been removed or reduced to a minimum. The commercial products and services now available for automation are reviewed by another author. The cell lines, which are used as discussed in the previous paragraph,

are, however, very fragile and susceptible to contamination. Methods of safeguarding against this are discussed in a final article.When human cell lines are being used there is also a need to ensure adequate operator safe- ty. Systems which meet, or go beyond, statutory requirements are described.

Dr Roger Brimblecombe PhD, DSc, FRCPath, FRSB 7

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64