for almost a century , drug discovery was driven by the quest for magic bullets , which act by targeting one critical step in a disease process and elicit a cure with few other consequences . however , this concept is far from biological reality , and even the most successful rationally designed drugs ( such as gleevec ) show a quite promiscuous binding behavior , which has opened novel therapeutic possibilities .
today , the emerging picture is that drugs rarely bind specifically to a single target , and this challenges the concept of a magic bullet .
indeed , recent analyses of drug and drug - target networks show a rich pattern of interactions among drugs and their targets , where drugs acting on a single target seem to be the exception .
drug - repositioning strategies seek to exploit the notion of polypharmacology , together with the high connectivity among apparently unrelated cellular processes , to identify new therapeutic uses for already approved drugs .
the main advantage of this approach is that , since it starts from approved compounds with well - characterized pharmacology and safety profiles , it should drastically reduce the risk of attrition in clinical phases .
there are several successful examples of drug repositioning ( for example , thalidomide to treat leprosy or finasteride for the prevention of baldness ) , although they were all found by serendipity and are not the result of well - thought strategies .
more recently , and following the observation that most novel entities are found by phenotypic profiling techniques , systematic initiatives to find new indications for old drugs have flourished .
these approaches rely mostly on genome - wide transcriptional expression data from cultured human cells treated with small molecules , and pattern - matching algorithms to discover functional connections between drugs , genes and diseases through concerted gene - expression changes .
however , unfortunately , pre - clinical outcomes often do not correlate with therapeutic efficacy ; only approximately 30% of the compounds that work well in cell assays work in animal models and , of these , only 5% work in humans .
being aware of the efficacy gap between pre - clinical and clinical outcomes , bork and collaborators presented , in 2008 , a strategy to link precise molecular data with phenotypic observations . in their seminal work , they established and catalogued the relationship between drugs and side - effects observed in clinical phases , and exploited this information to identify shared target proteins between chemically dissimilar drugs .
sharan and colleagues   built on these molecule - phenotype relationships to develop drug - drug and disease - disease similarity measures that they used to train a machine learning algorithm for inferring novel indications to drugs under development , with potential applications in future personalized medicine . in a recent article published in plos one , yang and agarwal   present a computational method that systematically explores novel potential applications of already marketed drugs in distinct therapeutic areas ( for example , suggesting an antidiabetic effect for an anticonvulsant drug ) .
the rationale behind their approach is that drugs sharing a significant number of side - effects should also have , to some extent , a common mechanism of action . in a way
, the side - effect becomes a type of phenotypic biomarker for each particular disease .
the authors then compiled a list of all known drug - disease and drug - side - effect relationships to build disease - specific side - effect profiles and explored the possibility of using these links as hints to suggest novel indications for drugs sharing the same profiles , but prescribed within different therapeutic areas .
approximately 4% of the 84,680 disease - side - effect associations investigated ( that is , 145 diseases and 584 side - effects ) were found to be informative , and some handpicked examples indicate a common mechanism of action for the drugs with similar side - effect profiles .
the approach has also been used to predict safety indications and suggest mechanisms of action for compounds in clinical development phases . in this case , because the precise safety indications are far less clear , the method had to predict side - effect profiles for each novel compound with a structure - activity relationship approach , including an extra level of uncertainty .
as expected , the applicability and accuracy observed in this case are lower than those achieved for marketed drugs with more precise side - effect information .
fifty years ago , drug discovery was mostly driven by the phenotypical response to the assayed molecules observed in animal models . however , in the early 1980s , owing to the success of molecular biology , the criteria for evaluating the potential of a novel compound shifted from a strict physiological observation to a molecular one , where the best lead chemicals were those that bound strongly to the target protein and had a good specificity profile . retrospectively , in most cases this selection strategy was an enormous mistake , because it attempts to predict the behavior of a complex system , with many and varied emerging properties at each level ( that is , molecular , cellular and systemic ) from its constituent components in isolation . as a shortcut to bridge molecular and clinical observations ,
many cell - based and functional assays in animal models have been developed , but unfortunately making inferences from one complexity level to another is often inaccurate , as shown by the attrition rates in drug discovery pipelines .
analogously , repositioning strategies that rely on chemical structures of drugs ( that is , molecular level ) , or their protein targets and cellular responses ( that is , pre - clinical level ) are also bound to suffer from the same problems ( figure 1 ) .
the drug repositioning strategy presented by yang and argawal   is almost exclusively based on clinical observations , without relying on data collected in different complexity levels , and thus should be able to overcome some of these limitations . bridging the levels of biological complexity .
the left half of the scheme represents the association between drugs and their therapeutic effects measured with different assays . captured biological
side - effects are on the outer level and constitute a signal of similar complexity to the therapeutic effect .
pre - clinical experiments such as disease - gene associations or gene - expression profiles provide biomolecular rationale for a disease , but the underlying mechanism of action ( moa ) upon treatment can only be proposed as a complement .
finally , a structure - activity relationship ( sar ) does not embrace biological understanding because the therapeutic outcome is ciphered within the molecular structure alone .
all this information is used in the right half to guide drug repositioning , where candidates are screened correspondingly .
arrows crossing biological complexity levels imply functional inferences not necessarily supported by the assay design .
although the idea of using drug - specific side - effect profiles to suggest novel indications , as shown by yang and agarwal , presents an attractive alternative to skip the uncertainties of traveling between molecular and clinical observations , there are several aspects that new implementations will have to address . the most important one is , undoubtedly , the need to consider the expected different frequencies of therapeutic and adverse effects in the population . whereas therapeutic effects are supposedly the result of interfering with molecules or processes central to the pathological process and common to most individuals ,
this could be related to the doses or the modes of administration of the drugs considered , in which case the issue could be easily addressed , although higher doses or a distinct formulation might also elicit novel undesired effects . however , some side - effects are possibly caused non - specifically by the chemical compound itself , or its degradation by - products , independently of any mechanism of action or the targets it binds , hampering the main hypothesis behind the study .
future improvements of the method should focus on those side - effects directly related to target proteins shared between the therapeutic indication and the side - effect , and will profit from the availability of information about doses , biodisponibility , and so on , to investigate further whether the mechanism of action is indeed the same , and would certainly improve the accuracy of the predictions .
given the crisis in the pharmaceutical industry arising from the lack of new molecules , drug repositioning seems a valid option to get the most out of the safe compounds that are already approved , and novel strategies to search rationally for new indications for old drugs are already flourishing .
however , all current approaches rely on a very limited set of data available for a few hundreds of compounds on the market , and would greatly benefit from the vast amount of clinical data accumulated by the pharmaceutical industry in pre - clinical and clinical phases .
we understand their fear that releasing these data could reveal unexpected side - effects for approved or experimental products , but it could also provide interesting opportunities to rescue abandoned compounds that showed low efficacy in clinical trials .
this work was partially supported by the spanish ministerio de ciencia e innovacin ( bio2010 - 22073 ) and the european commission under fp7 grant agreement 223101 ( antipathogn ) .
 side - effects are the unintended consequence of therapeutic treatments , but they can also be seen as valuable read - outs of drug effects in humans ; these effects are difficult to infer or predict from pre - clinical models . indeed 
 , some studies suggest that drugs with similar side - effect profiles may also share therapeutic properties through related mechanisms of action . 
 a recent publication exploits this concept to systematically investigate new indications for already marketed drugs , and presents a strategy to get the most out of the tiny portion of chemicals that have proved to be effective and safe . 
None
Getting the most out of side-effects
Future outlook
Competing interests
Acknowledgements