Limitations and weaknesses of experimental hepatotoxicity data
High-quality and reliable biological data are essential in order to build predictive models to provide relevant information about the toxicological behavior of a substance. Ideally the data for building a model should be obtained using a unique, well-standardized protocol, in the same laboratory by the same scientists. It is also important that these data refer to a clear and unambiguous endpoint (Cronin and Schultz, 2003). However, this is difficult, especially for hepatotoxicity, since the data are spread out in the literature and databases, refer to several endpoints related to hepatotoxicity (steatosis, colestasis, fibrosis etc.) and are obtained with different laboratory methods. Then, as previously mentioned, there is no a good single standard indicator of DILI with high sensitivity and specificity (Przybylak and Cronin, 2012). Indeed, no well-defined biomarkers exist for the identification of hepatotoxicity in vitro or in vivo. Consequently, the data in the literature refer to different effects and mechanisms of action underlying the endpoint of hepatotoxicity. Here we used one of the largest data sets available for DILI (Fourches et al., 2010). This data set, compiled using the data mining procedure, suffers some limitations. Firstly it does not make any distinction between idiosyncratic and dose-dependent toxicity. Idiosyncratic toxicity refers to an abnormal reaction to a drug that is not connected to its pharmacological activity but is due to individual hypersensitivity (Cheng and Dixon, 2003; Russmann et al., 2009). This toxicity does not follow any specific mode of action, but the adverse reactions to drugs are of unknown etiology and involved only a small proportion of the population (Walgren et al., 2005). Furthermore, in this data set, the compounds labeled as “negative” do not refer to compounds without reported effects, but to those without information in the literature. This means that where information is lacking it has been assumed that the compound was negative. Even if it is true that for well-known and investigated drugs, the lack of information can be taken as negative (Hewitt et al., 2013) this can lead to a large amount of FN in the data set that can in the end interfere with the data modeling.
Concerning the second data set we used, the US FDA Human Liver Adverse Effects Database, other limitations need to be discussed. This data set classifies compounds as active (hepatotoxic) and inactive (non-hepatotoxic) on the basis of reported alterations for five hepatic enzymes (ALP, AST, ALT, LDH and GGT). When the hepatocyte membrane is damaged these enzymes, which are normally located in the cytosol, are released into the bloodstream (Pari and Murugan, 2004). Although the serum transaminases are commonly used as indicators of liver injury and reflect damage to hepatocytes (Ozer et al., 2008), they are not always reliable and specific for the detection of hepatotoxicity. For example, ALT and AST are present in other tissues (heart, brain and skeletal muscle) besides the liver and so they are released into the circulation when there is damage to these tissues. AST mostly increases in case of myocyte damage due to extreme physical effort (Ozer et al., 2008). LDH is another enzyme occasionally used as a biomarker of hepatocellular injury. However, it is not routinely employed since its specificity is questionable (Ramaiah, 2007). More recently genomics, proteomics and metabolomics have been proposed as valuable techniques for discovering biomarkers (Amacher et al., 2005) and gene-expression profiling and microRNAs as more sensitive and specific indicators of DILI (Blomme et al., 2009; Laterza et al., 2009). Despite its limitations, however, in our opinion the US FDA Human Liver Adverse Effects Database is a good choice for modeling since the source (US FDA) is reliable and the results are based on objective laboratory parameters (serum transaminases). However, the most of the datasets is not suitable to be used alone for classification modeling. In conclusion, the data we used for modeling have a certain level of uncertainty due to these points which may have influenced the reliability and performance of the model.
An alternative that could limit the uncertainty linked to hepatotoxicity data is to use in vitro data obtained if possible on the same cell lines and using the same laboratory assay and conditions. However, not much public data is available in the open literature for this purpose and this approach too suffers some limitations such as the influence of genetic and environmental factors in the variations of biochemistry (Przybylak and Cronin, 2012).
Mechanistic explanation of SAs
We propose, when possible, a mechanistic rationale using the information in the literature and in public databases (PubChem https://pubchem.ncbi.nlm.nih.gov/, LiverTox, http://livertox.nih.gov/) for each SA that we manually identified through the chemical category approach.
SA ID 1: N-containing heterocyclic aromatic compounds: pyridine, pyrazine, pyrimidine
This SA, identified by the ID number 1 (Table 1), is a generic chemical structure that may be seen in several different chemical families. In the training set it matches 57 compounds (covering different chemical and therapeutic classes), 41 of them classified as hepatotoxic. Considering the lack of specificity of this SA, it is impossible to highlight any single mechanism of action that may explain the toxicity. In the training set this chemical fragment correctly identified hepatotoxic compounds in 71.4% of cases and that is enough to retain this alert. However, we identified two sub-families in this large group of drugs. The first comprises a group of three drugs used for the treatment of malaria (amodiaquin, primaquine, mefloquine). The hepatotoxicity of these molecules are mainly linked to hypersensitivity reactions (LiverTox database, http://livertox.nih.gov/).
The second includes seven chemical compounds used in the therapy of several cancer types (methotrexate, amsacrine, bortezomib, imatinib mesilate, intoplicine, OSI-461, rubitecan).
Methotrexate is a methyl analog of folic acid, used in the treatment of various neoplastic diseases. A reasonable part of the population treated with this drug reported liver injury. The typical histologic features of methotrexate toxicity are aspecific and comprise steatosis, “glycogen” nuclei, multinucleation, anisonucleosis, and lipofuscin accumulation, chronic inflammation of portal tracts, bile duct damage, ductular reaction, and fibrosis. Thin fibrous septa extending from the portal tracts into the lobules, often in a stellate configuration is the typical pattern of liver fibrosis induced by methotrexate. Persistent fibrosis eventually may lead to cirrhosis (Hytiroglou et al., 2004).
The mechanisms of liver injury of bortezomib are still unclear. It is metabolized in the liver largely through the CYP 3A4 pathway and liver injury may be related to production of a toxic intermediate (LiverTox database).
Therapy with imatinib may lead to three forms of acute liver injury: transient and usually asymptomatic elevations in serum enzymes during treatment, clinically apparent acute hepatitis, and reactivation of an underlying chronic hepatitis B (LiverTox database).
Metabolism of OSI-461 occurs in liver and preclinical repeated dose toxicity studies reported liver injury at higher doses. In a recent study (O'Bryant et al., 2009), treatment OSI-461 caused only mild to moderate and reversible transaminase and bilirubin increase.
SAs ID 2: sulphonamides
This SA identified by the ID number 2 (Table 1) matches chemicals containing a sulfonamide group in their structures. Most of the compounds correctly predicted as hepatotoxic by this SA are sulfonamide antibiotics. Several compounds belonging to this class have been reported to cause mild cholestasis hepatitis, but severe and even fatal cases have occurred (Polson, 2007). Sulfonamide antibiotics are among the most common causes of allergic or hypersensitivity reactions and it has been estimated that 6% of patients treated with this class of antibiotics have shown an immune-mediated event. The percentage rises steeply (60%) for HIV-positive individuals (Brackett, 2007). From a histopathological point of view centrilobular cholestasis is frequent in sulfonamide hepatotoxicity with only mild to moderate mixed portal inflammation of lymphocytes, and small numbers of eosinophils and neutrophils (Murray et al., 2008). Granulomatous hepatitis is uncommon but it can occur with many of the sulfonamides while massive hepatocellular necrosis has been described in fatal cases (Murray et al., 2008).
SA ID3: β-lactam antibiotics (penicillin)
The β-lactam ring, which is shared by many penicillin-like antibiotics, represents the SA identified with ID number 3 (Table 1). This SA matched 12 compounds in the training set, eight of them are labeled as hepatotoxic. Six are β-lactam antibiotics (azlocillin, carbenicillin, amoxicillin, flucloxacillin, oxacillin and penicillin) with the core structure of penicillins and two are used as β-lactamase inhibitors (clavulanic acid and sulbactam). β-lactams have been associated with small increases in serum enzymes (Zimmerman, 1999). However, more severe liver diseases, such as hepatitis and or intrahepatic cholestasis, have been reported: β-lactam and the isoxazolyl penicillins (oxacillin) are the most frequently involved (Olans and Weiner, 1976). Amoxicillin which is normally used in combination with clavulanic acid in order to reduce antibiotic-resistance can cause from mild to moderate hepatitis that rarely leads to liver failure. The mechanism of action that leads to toxicity is still not completely clear. However, several human leukocyte antigen haplotypes have been found to be related with hepatotoxicity, especially in the elderly (Pugh et al., 2009). A few severe reactions, including bile duct damage have been reported (Cundiff and Joe, 2007). Prolonged treatment with flucloxacillin in the elderly has been associated with jaundice (Fairley et al., 1993), and cholestatic liver injury has been described, most often with flucloxacillin (Devereaux et al., 1995).
SA ID 4: nucleoside analogs
This SA, marked with the ID number 4 (Table 1), identifies two classes of compounds in the training set: the antiretrovirals (nucleoside analogs reverse transcriptase inhibitors, NRTI) and anti-cancer nucleoside drugs. Four compounds are found in the first class: lamivudine, stavudine, zidovudine and 2′-fluoro-5-methyl arabinosyl uracil, all used for the treatment of HIV or hepatitis B infection. Some cases of liver injury have been reported in patients taking zidovudine, but stavudine is responsible for more severe hepatotoxicity (Nunez, 2006).
The second class of drugs matched by the SA with ID 8 is nucleoside analogs used for the treatment of malignancies (cytarabine, capecitabine, doxifluridine, uridine, and 5-fluoro-2′-deoxyuridine). They are a family of drugs that inhibit DNA synthesis either directly or through inhibition of DNA precursor synthesis (Diab et al., 2007). Several NRTI induce hepatotoxicity through mitochondrial damage (Nunez, 2006). The mechanisms of this liver damage is explained well in Boelsterli and Lim (2007) and Dykens and Will (2007). Briefly, NRTI inhibit the polymerase that replicates mitochondrial DNA, preventing mitochondrial replication and finally leading to a reduction in mitochondrial function in several tissues (liver and muscle toxicity), and also lipodystrophy and lipoatrophy (Dykens and Will, 2007).
Due to their lack of selectivity toward tumor cells, nucleoside analogs are cytotoxic interfering with the physiological cellular metabolism and deregulating the nucleoside/nucleotide pools in both normal and cancerous cells. These drugs cause several side effects such as myelosuppression, hepatotoxicity, renal toxicity, leucopenia, thrombocytopenia and mucositis (Diab et al., 2007).
SA ID 5: triciclycic antidepressants (TCAs)
We identified the SA with ID 5 (Table 1) starting from nine compounds in the training set and labeled as hepatotoxic. Among these nine, seven are triciclycic antidepressants (TCAs), one is also an antidepressant but with four rings (mianserin) and one is an antihistamine with a tricyclic group (cyproheptadine).
TCAs, developed in the 1950s, are a group of compounds that share similar chemical structures and have antidepressant potential in humans. The use of these drugs as antidepressants dropped steeply with the introduction of selective serotonin-reuptake inhibitors and other new-generation molecules. However, they are still used for several off-label prescriptions. Their decrease has meant that few hepatotoxicity cases have been reported in the past 15 years (DeSanty and Amabile, 2007). However, TCAs have been associated with hepatotoxic and cholestatic reactions (Ilan et al., 1996; de Abajo et al., 2004). Asymptomatic increases in transaminase serum levels are a common side effects with this group of drugs (Price et al., 1983; Ramesh et al., 1990) and severe hepatitis and acute liver failure cases are also reported (Lucena et al., 2003).
As mentioned, this group of drugs shares a similar chemical structure so cross-hepatotoxicity is possible (Larrey et al., 1986; Remy et al., 1995). Some TCA-induced hepatotoxic reactions seemed to be immune-mediated since significant extrahepatic symptoms, eosinophilia, and eosinophilic infiltration of the liver have been reported (Anderson and Henrikson, 1978). Hypothetically TCAs could show the same mechanism of action (cholestatic injury) as chlorpromazine since these compounds present structural similarity (Selim and Kaplowitz, 1999; de Abajo et al., 2004).
SA ID 6: aromatic amines
We identified ten molecules in the training set containing the SA with ID 6 (Table 1), six of them were labeled as hepatototoxic. Three compounds (amsacrine, gefinitib and vandetanib) are antineoplastic agents used for the treatment of several cancer types. The others are one antimalarial agent (amodiaquine), a non-narcotic analgesic (glafenine) and an α-adrenergic blocker (prazosin). The common chemical feature is the aromatic amine. Although no specific mechanism of action for the liver toxicity is described, in vivo studies on animal models indicated that aromatic amines exert liver toxicity by inducing cellular oxidative stress (Hillesheim et al., 1995; Ambs and Neumann, 1996).
SA ID 7: macrolide antibiotics
The SA identified with the ID number 7 (Table 1) correctly identified five hepatotoxic compounds out of the seven chemicals matched. These five are all macrolide antibiotics. The main member of this class is erythromycin. Hepatic dysfunction after treatment with these drugs is occasional. Erythromycin estolate has been associated with cholestatic hepatitis in particular (Hashisaki, 1995). Through their hepatic metabolism, reactive oxygen species (ROS), such as superoxide anion (O2-) and hydrogen peroxide (H2O2), are created, leading to the production of free radicals such as OH•. These hydrogen species bind polyunsaturated fatty acids starting the lipid peroxidation that causes oxidative degradation and inactivation of biomolecules (Pari and Murugan, 2004).
SA ID 8: anti-bacterial agents (fluoroquinolone)
The SA marked with the ID number 8 (Table 1) matched six compounds in the training set, four of them are hepatotoxic. The chemicals correctly identified as hepatotoxic belong to the fluorquinolones class of antibiotics. These are anti-bacterial agents are widely prescribed and used (Orman et al., 2011). Side effects from fluorquinolones are uncommon, though significant adverse effects with this drug category have been reported in the gastrointestinal tract, the central nervous system (CNS), heart, cartilaginous tissues and skin (Stahlmann and Lode, 1999). Numerous cases of severe liver injury were reported only for trovafloxacin that was subsequently withdrawn from the market (Ball et al., 1999; Orman et al., 2011).
Concerning the other members of the drugs family, there are occasional case reports of hepatotoxicity (Orman et al., 2011). However, one investigation (Paterson et al., 2012) reported 88 fatal cases of acute liver injury among 144 patients in hospital after treatment with fluorquinolone and with no evidence of preexisting liver disease.
SA ID 9: cationic amphiphilic drugs (CADs)
The SA with ID number 9 (Table 1) comprises a group of heterogeneous drugs and matched six compounds in the training set, five of which were correctly identified as hepatotoxic (chlorpheniramine, disopyramide, doxapam, methadone, and tolterodine). From a chemical point of view this structure is a typical cationic amphiphilic since it has a hydrophobic ring structure and a hydrophilic side chain with an amino group. With the exception of doxapram, all the other drugs matched by SA number 9 are CADs. This category is known to have the potential to cause phospholipidosis which involves excessive accumulation of phospholipids within cells (Sawada et al., 2005). Any tissue can be potentially affected by phospholipidosis and excessive accumulation is commonly found in the lung, liver, brain, kidney, ocular tissues, heart, adrenal glands, hematopoietic tissue, and circulating lymphocytes (Halliwell, 1997). It has been reported that CADs induce phospholipidosis by inhibiting lysosomal phospholipase activity; however the specific mechanism is still not clear (Sawada et al., 2005).
SA ID 10: retinoids
This SA, identified with the ID number 10 (Table 1), is the typical carbon chain of retinoids. It matches four compounds, three of them are retinoids (etretinate, vitamin A, fenretidine), labeled as hepatotoxic and one, a terpene (astaxanthin), as non-hepatotoxic. It is well-known that excessive intake of retinol (vitamin A) is toxic. Hepatomegaly and cirrhosis have been reported in patients given retinol (Myhre et al., 2003). The toxicity of vitamin A is reviewed in Penniston and Tanumihardjo (2006). The mechanism of toxicity of retinoid derivatives has not yet been clarified, but they may alter glycoprotein synthesis and/or genomic expression, inducing membrane injury in hepatic cells (Fallon and Boyer, 1990).
SA ID 11: nitrosourea compounds
The SA identified by ID number 11 (Table 1) comprises the so-called nitrosourea compounds. We identified it using two compounds in our data set: carmustine and lomustine. Both are alkylating agents used in the therapeutic protocols for many types of cancer. Their hepatotoxic effect is usually transient and is associated with glutathione depletion, which that leads to oxidative injury (King and Perry, 2001; Sümbül and Özyilkan, 2010). These compounds have also been associated with rises in aminotransferase levels, with rare fatalities (Thatishetty et al., 2013).
SA ID 12: steroids
We identified 23 molecules that contain the SA with ID 12 (Table 1). Only in seven cases the molecules in the training set were labeled as hepatotoxic (ursodiol, 2-methoxy estradiol, betulinic acid, estrone, ethynil estradiol, ethinyl estradiol 3-methyl-ether, oxymethodone), so we retained this SA for identifying non-hepatotoxic compounds. Among the compounds labeled as non-hepatotoxic we found three chemicals belonging to the class of steroidal neuromuscular-blocking drugs (pipecuronium, rocuronium and vecuronium) which are mainly employed in anesthetic practice (Sparr et al., 2001). To the best of our knowledge the side effects reported for this class of compounds relate to the cardiac vagus nerve that leads to cardiovascular effects (Larijani et al., 1989; Sparr et al., 2001). Another group of compounds are digitoxin and digoxin that belong to the class of cardiac glycosides, used as cardiotonic (Fabricant and Farnsworth, 2001). No studies reported hepatotoxicity after treatment with these compounds. A recent investigation (Rabadia et al., 2014) indicated that digitoxin and digoxin had hepato-protective activity in albino rats exposed to carbon tetrachloride (CCl4).
Dehydrocholic acid is also present among the compounds labeled as non-hepatotoxic; and in fact this chemical is hepato-protective (Herraez et al., 2009). The other compounds identified by the SA with ID 14 are mainly estrogens (estradiol derivatives). In some cases these are labeled as hepatotoxic and in others non-hepatotoxic. This may depend on the different substitutions that somehow affect their toxicity.
SA ID 13: β-lactam antibiotics (cephalosporins)
The SA identified with the ID number 13 (Table 1) matches 16 compounds in the training set. Only five were labeled as hepatotoxic so we decided to retain this SA for identifying negative (non-hepatotoxic) compounds. The non-hepatotoxic compounds matched by this SA belong to the class of cephalosporin antibiotics. The cephalosporins are a safer class of antibiotics than other commonly used antibiotics such as aminoglycosides, sulfonamides, tetracyclines, and penicillins (SA ID 4) (Neu, 1990). From a chemical point of view, both penicillin and cephalosporin antibiotics have a four-member β-lactam ring, but cephalosporins have a six-member dihydrothiazide ring in place of the five-member thiazolidine ring of penicillin (Ong, 2014). Cephalosporins generally cause few side effects and seem less allergenic than the molecules belonging to the penicillin group (Neu, 1990; Dancer, 2001). They rarely cause idiosyncratic hepatotoxicity (Pugh et al., 2009). Since cephalosporins are mostly excreted in the urine, the main toxicity is related to the kidney where they can cause dose-related nephrotoxicity and hypersensitivity interstitial nephritis (Maher, 2013).
Comparison with other <italic>in silico</italic> models for the prediction of hepatotoxicity and general considerations
Since the prediction of toxicity at organ level is a recent acquisition, so far only a small number of in silico models are available for hepatotoxicity. These models differ considering both the data used (in vitro or in vivo, human or animal models) and the techniques applied (statistical or expert-based). In silico models for the prediction of hepatotoxicity have been reviewed (Cheng and Dixon, 2003; Przybylak and Cronin, 2012). To the best of our knowledge, the only comparison with our model is the technique developed by Greene et al. (2010), who starting from a data set of 1266 compounds, identified 38 SAs related to hepatotoxicity. These SAs were implemented into the commercial software Derek for Windows, developed by Lhasa Limited, and the predictive ability of the model was tested on an external data set (626 compounds). In terms of accuracy this model gave lower performance (56%) in the test set than the one we developed (63% in the test set and 68 in the external validation set). Sensitivity was lower too: 46 vs. 88% (test set) and 80% (external validation set), while specificity was higher for Greene's model (73 vs. 33% of our model). In our model the sensitivity is high because the small number of FN in the training, test and external validation sets: 18, 6, and 9 respectively corresponding to 2.4, 3.1, and 8.9% of the total number of compounds in the three sets. In contrast, the number of FP is high (72, 30, and 10 respectively in the training test and external validation sets) leading to a loss of performance in terms of specificity. However, the poor specificity mainly in the test and external validation sets can be explained by two different aspects. Firstly, the conservative approach we followed. Indeed, since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model followed a conservative architecture. When of a compound is matched by more than one SA and the SAs do not agree (one related to hepatotoxicity and another to non-hepatotoxicity) the final prediction is for positive activity, hence hepatotoxicity. This approach may lead to more of FP, in other words non-hepatotoxic compounds wrongly predicted as hepatotoxic influence the performance of the model, mainly for the specificity. Secondly, the SAs extracted for the negative property performed worst compared to those extracted for the positive activity, leading to a decrease in the ability of the model to correctly identify non-toxic compounds. This may be due to the uncertainty mainly of the negative data as already discussed in Section Limitations and Weaknesses of Experimental Hepatotoxicity Data.
The high number of FP in the present model may have an impact also in the context of drug developmental process. However, two aspects should be taken into account. Firstly, the in silico models should not considered as “black box,” but the expert-based judgment is needed to correctly interpret the model output. Secondly, in silico models should be used in the framework of an integrated testing strategy, in support and addition to other techniques.
Besides the final performance and the availability of the models (commercial for Derek for Windows but free for our model), there are other differences between the two in silico models. One of the main differences from our model is that the SAs implemented into Derek for Windows refer only to hepatotoxicity. This means that if a SA related to hepatotoxicity is identified for a compound, it is predicted as positive (hepatotoxic) otherwise a prediction is not provided by the model. In our model we identified specific SAs for non-hepatotoxicity that are used for the final prediction. If no alerts are identified for a compound, it is not predicted, so is classified as “unknown.” The absence of a specific SA for a compound is not enough proof to classify the compound as negative, hence safe (Przybylak and Cronin, 2012). The identification of SAs for negative activity is a novel approach. To the best of our knowledge, most of the in silico models based on SAs can predict the non-toxicity of a substance only on the basis of a lack of information for toxicity. However, an exception related to human toxicity exists, such as the model for mutagenicity developed by Ferrari et al. (2013).
Another model has been developed for the prediction of hepatotoxicity, described in the literature and based on SAs (Egan et al., 2004). Starting from a training set of 244 compounds, most of which were drugs, 74 SAs were identified for hepatotoxicity. However, no information was reported on the statistical performance of the SAs in the training and test sets, so it is hard to make any comparison on the overall application.
Hewitt et al. (2013) using the Fourches et al. (2010) data set, did not build up a real model but identified a list of 16 SAs for hepatotoxicity using the chemical categories approach. Some of these SAs are the same as those we found. In particular, Hewitt et al. (2013) generated sulfonamides SA (ID number 2) and retonoids SA (ID number 10). They also found steroids derivatives SA (ID number 12) and β-lactam antibiotics SAs (ID numbers 3 and 13), but they were associated to the prediction of hepatotoxicity. We used the same SAs (SA 12 and SA 13) for the prediction of non-hepatotoxicity of compounds, since we distinguished for β-lactam antibiotic SAs between penicillins (labeled as hepatotoxic) and cephalosporins (labeled as non-hepatotoxic). The differences between Hewitt et al. (2013) findings and our may be explained considering that we used the Fourches et al. (2010) data set in addition to the US FDA data set. We also extracted the SAs starting from the positive compounds present in the training set and not on the whole data set as the other authors did. Moreover Hewitt et al. (2013) accepted a threshold of similarity lower than to the one we applied (0.6 vs. 0.7) in order to retain chemical groups.
Beside in silico models, also in vitro approaches are reported for the prediction of hepatotoxicity. Some of them are reviewed in Chen et al. (2014a,b). These in vitro assay-based models use human derived hepatocellular carcinoma cells (HepG2) to assess hepatotoxicity of drug/chemicals. The performance of these models in terms of sensitivity and specificity ranges from 45 to 100% and 82 to 100%, respectively. However, in vitro assays are time and money-consuming compared to in silico models.