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Any novel drug need to undergo exhaustive and accurate preclinical testing and also the clinical phase followed by the approval by EMEA, MHRA and FDA, which is an ultimate phase prior to marketing. Most preclinical compounds tend to fail the ultimate phase i.e. approval by regulatory due to the only reason that they produce induced organ toxicity. Among these preclinical candidate compounds (PCCs), around 50% are discontinued due to hepatotoxicity. Thus, hepatotoxicity is the highly mentioned reason for withdrawal of most approved drugs (J. Ozer et al., 2008). The potential liver injury due to the novel therapeutics in humans could be assessed which depends highly on preclinical studies, in particular the rigorous study of endpoints of hepatotoxicity. Even then, in the cases where clinical trials were performed, calculations show that only around 50% of the adverse events caused in human liver correlate to the present preclinical regimes (Olson et al., 2000). With the highly sophisticated industrial efforts, novel biomarkers which diagnose the histomorphologic difference of liver in preclinical studies which could be correlated to human beings are under evaluation, development and improvement. Therefore, new biomarkers are aimed at lowering the occurrence of spurious negative results, which in turn lead to more exact estimation of drug-induced hepatotoxicity in preclinical studies and finally clinical studies. In the pharmaceutical industries also these fake positive preclinical results of liver injury could affect largely the developmental process. Usually, fake negative indicators show greater impact upon safety in comparison with fake positive indicators. An ideal biomarker to measure hepatotoxicity must be organ specific mainly for liver, must be able to strongly correlate with clearly shown liver histomorphologic differences with an added data to serum alanine aminotransferase (ALT) along with aspartate aminotransferase (AST). It must also be adaptable by screening assays in order to serve large throughput techniques which are available in a commercial scale. An ideal biomarker should also be accessible for the techniques which include blood collection and also must be clinically derivable and must be able for correlation to the humans from other preclinical species. As and when the novel biomarkers are found, they undergo strenuous technical review or cross-checking and biological validation in order for broad acceptance by all the scientific communities. But in fact none of the biomarkers till date has passed all these strenuous evaluations (J. Ozer et al., 2008).
Currently available biomarkers and their flaws:
The current preclinical techniques are few combinations of substances present in the serum. All of them are shown in the Table 1. Some of these techniques are more sensitive than others. Here an apt example would be the serum AST activity. Usually it is related with hepatotoxicity, but there are chances of its elevation in response to skeletal muscle or cardiac injury and in the same manner serum ALT is related with multiple necrosis (Burhop et al., 2004).
Table 1: Currently available biomarkers for hepatotoxicity measurement (J. Ozer et al.,
Alanine aminotransferase ââ‚¬" the gold standard:
Serum ALT is the very commonly looked for component in case of liver injury. To a large extent it is accurate but sometimes tends to show spuriously negative data of liver hepatotoxicity and also sometimes show fake positive results to a limited extent and hence it is taken as gold standard of hepatotoxicity. Even though the altogether use of ALT is excellent, it may not relate properly with preclinical data at times. Here comes the need for other biomarkers to help ALT signals to become perfect and these could be mentioned as bridge biomarkers (Amacher, 2002).
ALT assay ââ‚¬" Could it be better?
Since ALT is a very sensitive and specific measure, there is a need for us to think in a way that since ALT has 2 isozymes, which were found basing on the structure of molecule and tissue specificity, and that this data could be used to make new and sophisticated ALT assay. The two isozymes for ALT that are present in the human beings are ALT1 and ALT2 and they are around 70% look alike (Yang et al., 2002). The response of ALT1 and ALT2 were differentiated in a new immunoassay and this was done in the view of drug induced liver injury. This differentiated could be exploited for clinical and preclinical research (Lindblom et al., 2007).
Other biomarkers and scope for novel improvement:
Novel safety biomarkers are indicators of hepatotoxicity both in vivo and in vitro. Genome expression profiling have been used since long for biomarker discovery but proteomics and metabolite profiling are emerging technologies and are used extensively in discovery of biomarkers. Research conducted by Ozer et al. using proteome analysis led to the discovery of a few hepatotoxicity biomarkers which are Malate dehydrogenase, purine nucleoside phosphorylase, and paraoxanase 1. These biomarkers are indicator of hepatic histopathologic changes (J. Ozer et al., 2008).
Figure 1: Process of biomarker discovery. (A) Series of steps followed in idiosyncratic hepatotoxicity biomarker discovery. (B) Designating a subset of hepatotoxicity markers. (J.Gao et al., 2005)
Even though it is well known that serum ALT is the most sensitive biomarker of liver injury, it could be improved as it has got a few other factors which alter its activity and this could be a major problem during drug development either it could be a preclinical or clinical study. This elevation in the ALT levels could be taken as fake positive data and could lead to unnecessary measures. If more and more data is available from novel bridge biomarkers then this elevated ALT data could become clear and be useful. The novel biomarkers such as sorbital, glutamate and malate dehydrogenases, F protein etc. could be used to correlate the data available currently in the development of drugs. Thus there is every need for the novel biomarkers in the context of correlating the present biomarkers. These novel biomarkers could be hence termed as bridge biomarkers as they form a bridge of data correlation (J. Ozer et al., 2008).