Global structure–activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

Abstract

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

Extrahepatic carcinogenicity of oral nucleos(t)ide analogues in chronic hepatitis B carriers: A 35,000‐Korean outcome study

“…Considering the low incidence and time dependence of cancer events, as well as the implication of various host factors on cancer occurrence, we believe that the present larger and longer study, with a balanced cohort in terms of sex, age, hyperglycemia, dyslipidemia, fatty liver and chronic HCV infection, including many participants treated with ETV ( n = 3502) and TDF ( n = 2571), which are the NAs most suspected of being carcinogenic, provides more definitive evidence on this relevant issue. Furthermore, the comparative approach across different chemical classes of NA, rather than a focus on individual drugs, should ensure more reliable conclusions concerning carcinogenicity in light of the concept of ‘Structure Activity Relationship’ that underpins toxicological investigations 14–16 …”

“…However, since there was a lack of data on TDF, the most recent agent, that evidence was neither complete nor conclusive. Moreover, given that the chemical structure of pharmaceuticals could lead to tumorigenesis in specific organs, it was important to examine the potential damaging effects of the complete range of oral antiviral structures 14–16 …”

“…Moreover, given that the chemical structure of pharmaceuticals could lead to tumorigenesis in specific organs, it was important to examine the potential damaging effects of the complete range of oral antiviral structures. [14][15][16] As serious concerns about carcinogenicity increased and NA treatments became longer in the present century, particularly in the case of ETV and TDF, we embarked on a large-scale, long-term comprehensive study using the inverse-probability-of-treatmentweighted (IPTW) method to examine the extrahepatic carcinogenic activity by organ site of the three chemical classes of oral NAs in patients with CHB.…”

Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions

“…The Ames test is useful for identifying mutagenic carcinogens with an accuracy of 80% [3,4]. However, 48% of Ames-negative chemicals are carcinogens [5] and additional bioassays do not help in detecting carcinogens from Ames-negative chemicals [6]. It is desirable to develop alternative methods for assessing carcinogenicity of Ames-negative chemicals.…”

Structure–activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity

“…Using a novel SAR modeling method in which ligand-receptor interactions were used as descriptors in the modeling process, we demonstrated that this novel receptor-based method was able to predict non-mutagenic carcinogens with a higher efficiency than the traditional fragment model. In fact, the ligand model produced a concordance value of 71% as compared to 55% for the fragment model [42]. …”