Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules

Abstract

Computational models are currently being used by regulatory agencies and within the pharmaceutical industry to predict the mutagenic potential of new chemical entities. These models rely heavily, although not exclusively, on bacterial mutagenicity data of nonpharmaceutical-type molecules as the primary knowledge base. To what extent, if any, this has limited the ability of these programs to predict genotoxicity of pharmaceuticals is not clear. In order to address this question, a panel of 394 marketed pharmaceuticals with Ames Salmonella reversion assay and other genetic toxicology findings was extracted from the 2000-2002 Physicians' Desk Reference and evaluated using MCASE, TOPKAT, and DEREK, the three most commonly used computational databases. These evaluations indicate a generally poor sensitivity of all systems for predicting Ames positivity (43.4-51.9% sensitivity) and even poorer sensitivity in prediction of other genotoxicities (e.g., in vitro cytogenetics positive; 21.3-31.9%). As might be expected, all three programs were more highly predictive for molecules containing carcinogenicity structural alerts (i.e., the so-called Ashby alerts; 61% +/- 14% sensitivity) than for those without such alerts (12% +/- 6% sensitivity). Taking all genotoxicity assay findings into consideration, there were 84 instances in which positive genotoxicity results could not be explained in terms of structural alerts, suggesting the possibility of alternative mechanisms of genotoxicity not relating to covalent drug-DNA interaction. These observations suggest that the current computational systems when applied in a traditional global sense do not provide sufficient predictivity of bacterial mutagenicity (and are even less accurate at predicting genotoxicity in tests other than the Salmonella reversion assay) to be of significant value in routine drug safety applications. This relative inability of all three programs to predict the genotoxicity of drugs not carrying obvious DNA-reactive moieties is discussed with respect to the nature of the drugs whose positive responses were not predicted and to expectations of improving the predictivity of these programs. Limitations are primarily a consequence of incomplete understanding of the fundamental genotoxic mechanisms of nonstructurally alerting drugs rather than inherent deficiencies in the computational programs. Irrespective of their predictive power, however, these programs are valuable repositories of structure-activity relationship mutagenicity data that can be useful in directing chemical synthesis in early drug discovery.

Derivation and Validation of Toxicophores for Mutagenicity Prediction

“…While in the initial validation studies error percentages ranging from 15% to 35% were achieved [15][16][17] for the investigated datasets, in subsequent studies no predictive method was reported to correctly predict mutagenicity with an error percentage smaller than 24% 29,30,[35][36][37] with the sole exception of one dataset for which an error percentage of 19% was obtained. 30 On the other hand, a recent analysis 38 has demonstrated that such methods identified mutagenic pharmaceuticals with a maximal sensitivity of only 52% and pointed to the need of developing additional toxicophores.…”

Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods

“…A total of 860 GT+ and GT-agents with known genotoxicity test results are selected from several sources including the 1999-2002 Physician's Desk Reference, National Toxicology Program, and a number of publications (5,(11)(12)(13)32). Genotoxicity tests for generating these data include the pre-ICH four standard batteries (Ames test, in vitro cytogenetics, in vivo cytogenetics, and mouse lymphoma assay) and the salt-overly-sensitive (SOS) chromotest (which is a rapid alternative genotoxicity test based on the detection of the DNA damage through the SOS pathway) (33,34).…”

“…However, these methods have been developed and tested by using no more than 394 GT+ and GTagents (5), which is significantly smaller in number and diversity than the 860 known GT+ and GT-agents found from our recent literature search. Therefore, there is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules.…”

“…The molecular mechanisms of genotoxicity include DNA intercalation by aromatic ring of a drug, DNA methylation, DNA adduct formation and strand break, and unscheduled DNA synthesis (4). Some genotoxic (GT+) compounds require metabolic activation, and their GT+ effects are mediated via N-dialkylation (5). These events subsequently result in chromosomal aberrations, micronuclei, sister chromatid exchanges, and cell death, which contribute to drug ADRs (4).…”

On the Use of In Silico Tools for Prioritising Toxicity Testing of the Low‐Volume Industrial Chemicals in REACH

“…A particular focus is put on models that could provide information indicating whether the substance could be classified as carcinogenic, mutagenic or toxic to reproduction (CMR) according to Dangerous Substances Directive [9] or CLP Regulation [10]. To our knowledge, previous studies on the utility of non-experimental approaches to predicting CMR properties focused on comparing the results of specific assays to those obtained using selected in silico models rather than the use of in silico results for CLP classification and the prioritisation of industrial compounds for further testing according to the REACH legislation [11][12][13][14][15][16][17][18][19][20][21].…”