Malcolm Young, in the Fall 2008 issue of Drug Discovery World wrote the following:
The success rate of this heuristic approach [to drug development] is very low. For example, the average probability that a candidate emerging from lead optimisation will not make it to be a drug is above 99.8% (European Commission 2008) . . . Hence, a very great deal turns on whether one can in fact predict accurately what the efficacy, safety and deliverability issues of a candidate molecule are – before undertaking expensive testing. Prediction accuracy is now seen by many as the central issue in the research-intensive side of the industry. ‘Predictive de-risking’ has become a sort of Excalibur, mythical, but pretty usefully sharp if found.
It is evident from current attrition rates that the processes implemented in the recent past, and in the present if they differ little from what has been traditionally employed, are not sufficiently accurately predictive to yield the required productivity across the industry. This can be a little dissonant for those used to drawing together extensive preclinical packages for IND submissions, but, in the average, the PK, tox, in vitro and in vivo studies required for an IND plainly do not predict efficacy, safety and deliverability in human patients even nearly well enough. Some of the elements of a full picture of the Signal Detection Theory performance of preclinical packages for new molecules, such as correct identification rate and false positive rate, can be estimated, but correct rejections are presumably hidden among the cases that did not in fact gain IND status, and it is anyone’s guess how many incorrect rejections (misses) there have been in which, for example, a candidate would have worked very beneficially in humans – if only we’d known – but was unfortunately not beneficial to mice.
Predictability starts to be an issue at the very start of a discovery programme. Selection of a protein target is often based on evidence that the specific protein is significant in a pathway relevant to the disease of interest, this evidence perhaps being in the form of a knock-out showing an effect in changing cell physiology, and on evidence that the protein target’s function can be affected by the binding of a drug molecule to it. This approach is very deeply ingrained in the current intellectual furniture in discovery, and is characterised as the basis for ‘rational drug discovery’. Iconoclasts, however, are sometimes disposed to cast core beliefs in science as scientific claims, the better to examine their plausibility. In this case, the targetbased approach essentially makes the scientific claim that (for example) ‘inhibiting this one protein will make the patient better’. Probably there are some diseases in which this claim can seem plausible, but for most diseases, especially the complex diseases that reflect those for which we now need new and effective medicines, it is akin to suggesting that one will fix an ailing economy by deleting one company, or that one will disable the enemy’s command and control network by sniping one ill-fated radio operator. (Young 2008)
For the full article, click here.
According to Drug Discovery World:
Professor Malcolm Young is one of the UK’s leading scientists in informatics. He has recently held a number of senior academic positions, including Director of the Complex Systems Group and Pro- Vice Chancellor for Strategic Development at Newcastle University, following a Royal Society Research Fellowship at the RIKEN Institute in Japan, and at Oxford University. Malcolm’s research experience and interest lies in complex systems analysis and informatics, and his main goals are to understand how biological function arises from structural aspects of complex biological systems . . . Malcolm is one of 18 scientists worldwide nominated by The Sunday Times as the ‘Brains behind the 21st Century’.
I have no idea what Professor Young’s views are on using animals in research in general but I think the above speaks for itself.
European Commission. 2008. Innovative Medicines Initiative: better tools for better medicines. Luxembourg.
Young, Malcolm. 2008. Prediction v Attrition Drug Discovery World (Fall):9-12.