FGF19 signaling through the FGFR4/β-klotho receptor complex has been shown to be a key driver of growth and survival in a subset of hepatocellular carcinomas, making selective FGFR4 inhibition an attractive treatment opportunity. A kinome-wide sequence alignment highlighted a poorly conserved cysteine residue within the FGFR4 ATP-binding site at position 552, two positions beyond the gate-keeper residue. Several strategies for targeting this cysteine to identify FGFR4 selective inhibitor starting points are summarized which made use of both rational and unbiased screening approaches. The optimization of a 2-formylquinoline amide hit series is described in which the aldehyde makes a hemithioacetal reversible-covalent interaction with cysteine 552. Key challenges addressed during the optimization are improving the FGFR4 potency, metabolic stability, and solubility leading ultimately to the highly selective first-in-class clinical candidate roblitinib.

The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship models describing the firing of the alerts from the rest of the molecules. Recently, TIMES has been used to model bacterial mutagenicity [Mekenyan, O., Dimitrov, S., Serafimova, R., Thompson, E., Kotov, S., Dimitrova, N., and Walker, J. (2004) Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem. Res. Toxicol. 17 (6), 753-766]. The original model was derived for a single tester strain, Salmonella typhimurium (TA100), using the Ames test by the National Toxicology Program (NTP). The model correctly identified 82% of the primary acting mutagens, 94% of the nonmutagens, and 77% of the metabolically activated chemicals in a training set. The identified high correlation between activities across different strains changed the initial strategic direction to look at the other strains in the next modeling developments. In this respect, the focus of the present work was to build a general mutagenicity model predicting mutagenicity with respect to any of the Ames tester strains. The use of all reactivity alerts in the model was justified by their interaction mechanisms with DNA, found in the literature. The alerts identified for the current model were analyzed by comparison with other established alerts derived from human experts. In the new model, the original NTP training set with 1341 structures was expanded by 1626 proprietary chemicals provided by BASF AG. Eventually, the training set consisted of 435 chemicals, which are mutagenic as parents, 397 chemicals that are mutagenic after S9 metabolic activation, and 2012 nonmutagenic chemicals. The general mutagenicity model was found to have 82% sensitivity, 89% specificity, and 88% concordance for training set chemicals. The model applicability domain was introduced accounting for similarity (structural, mechanistic, etc.) between predicted chemicals and training set chemicals for which the model performs correctly.

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.

Mathematical chemistry has afforded a variety of research areas with important tools to understand and predict the behavior of chemicals without having to consider the complexities of three-dimensional conformations of molecules. Predictive toxicology, an area of increasing importance to toxicity assessments critical to molecular design and risk management, must be based on more explicit descriptions of structure, however. Minimum energy conformations are often used for convenience due, in part, to the difficulty of computing a representative population of conformers in all but rigid structures. Such simplifying assumptions fail to reveal the variance of the stereoelectronic nature of molecules as well as the misclassification of chemicals which initiate receptor-based toxicity pathways. Because these errors impact both the success in discovering new lead and the identification of possible hazards, it is important that mathematical chemistry develop additional tools for conformational analysis. This paper presents a new system for automated 2D-3D migration of chemicals in large databases with conformer multiplication. The main advantages of this system are its straightforward performance, reasonable execution time, simplicity and applicability to building large 3D chemical inventories. The module for conformer multiplication within the 2D-3D migration system is based on a new formulation of the genetic algorithm for computing populations of possible conformers. The performance of the automated 2D-3D migration system in building a centralized 3D database for all chemicals in commerce worldwide is discussed. The applicability of the 3D database in assessing the impact of molecular flexibility on identifying active conformers in QSAR analysis and assessing similarity between chemicals is illustrated.

Modeling the potential of chemicals to induce chromosomal damage has been hampered by the diversity of mechanisms which condition this biological effect. The direct binding of a chemical to DNA is one of the underlying mechanisms that is also responsible for bacterial mutagenicity. Disturbance of DNA synthesis due to inhibition of topoisomerases and interaction of chemicals with nuclear proteins associated with DNA (e.g., histone proteins) were identified as additional mechanisms leading to chromosomal aberrations (CA). A comparative analysis of in vitro genotoxic data for a large number of chemicals revealed that more than 80% of chemicals that elicit bacterial mutagenicity (as indicated by the Ames test) also induce CA; alternatively, only 60% of chemicals that induce CA have been found to be active in the Ames test. In agreement with this relationship, a battery of models is developed for modeling CA. It combines the Ames model for bacterial mutagenicity, which has already been derived and integrated into the Optimized Approach Based on Structural Indices Set (OASIS) tissue metabolic simulator (TIMES) platform, and a newly derived model accounting for additional mechanisms leading to CA. Both models are based on the classical concept of reactive alerts. Some of the specified alerts interact directly with DNA or nuclear proteins, whereas others are applied in a combination of two- or three-dimensional quantitative structure-activity relationship models assessing the degree of activation of the alerts from the rest of the molecules. The use of each of the alerts has been justified by a mechanistic interpretation of the interaction. In combination with a rat liver S9 metabolism simulator, the model explained the CA induced by metabolically activated chemicals that do not elicit activity in the parent form. The model can be applied in two ways: with and without metabolic activation of chemicals.

The molecular modeling is traditionally based on analysis of minimum energy conformers. Such simplifying assumptions could doom to failure the modeling studies given the significant variation of the geometric and electronic characteristics across the multitude of energetically reasonable conformers representing the molecules. Moreover, it has been found that the lowest energy conformers of chemicals are not necessarily the active ones with respect to various endpoints. Hence, the selection of active conformers appears to be as important as the selection of molecular descriptors in the modeling process. In this respect, we have developed effective tools for conformational analysis based on a genetic algorithm (GA), published in J. Chem. Inf. Comput. Sci. (1994, 34, 234; 1999, 39 (6), 997) and J. Chem. Inf. Model. (2005, 45 (2), 283). This paper presents a further improvement of the evolutionary algorithm for conformer generation minimizing the sensitivity of conformer distributions from the effect of smoothing parameter and improving the reproducibility of conformer distributions given the nondeterministic character of the genetic algorithm (GA). The ultimate goal of the saturation is to represent the conformational space of chemicals with an optimal number of conformers providing a stable conformational distribution which cannot be further perturbed by the addition of new conformers. The generation of stable conformational distributions of chemicals by a limited number of conformers will improve the adequacy of the subsequent molecular modeling analysis. The impact of the saturation procedure on conformer distributions in a specific structural space is illustrated by selected examples. The effect of the procedure on similarity assessment between chemicals is discussed.

Rapid emergence of tumor resistance via RAS pathway reactivation has been reported from clinical studies of covalent KRAS G12C inhibitors. Thus, inhibitors with broad potential for combination treatment and distinct binding modes to overcome resistance mutations may prove beneficial. JDQ443 is an investigational covalent KRAS G12C inhibitor derived from structure-based drug design followed by extensive optimization of two dissimilar prototypes. JDQ443 is a stable atropisomer containing a unique 5-methylpyrazole core and a spiro-azetidine linker designed to position the electrophilic acrylamide for optimal engagement with KRAS G12C C12. A substituted indazole at pyrazole position 3 results in novel interactions with the binding pocket that do not involve residue H95. JDQ443 showed PK/PD activity in vivo and dose-dependent antitumor activity in mouse xenograft models. JDQ443 is now in clinical development, with encouraging early phase data reported from an ongoing Phase Ib/II clinical trial (NCT04699188).

Skin sensitization is an end point of concern for various legislation in the EU, including the seventh Amendment to the Cosmetics Directive and Registration Evaluation, Authorisation and Restriction of Chemicals (REACH). Since animal testing is a last resort for REACH or banned (from 2013 onward) for the Cosmetics Directive, the use of intelligent/integrated testing strategies (ITS) as an efficient means of gathering necessary information from alternative sources (e.g., in vitro, (Q)SARs, etc.) is gaining widespread interest. Previous studies have explored correlations between mutagenicity data and skin sensitization data as a means of exploiting information from surrogate end points. The work here compares the underlying chemical mechanisms for mutagenicity and skin sensitization in an effort to evaluate the role mutagenicity information can play as a predictor of skin sensitization potential. The Tissue Metabolism Simulator (TIMES) hybrid expert system was used to compare chemical mechanisms of both end points since it houses a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. The evaluation demonstrated that there is a great deal of overlap between skin sensitization and mutagenicity structural alerts and their underlying chemical mechanisms. The similarities and differences in chemical mechanisms are discussed in light of available experimental data. A number of new alerts for mutagenicity were also postulated for inclusion into TIMES. The results presented show that mutagenicity information can provide useful insights on skin sensitization potential as part of an ITS and should be considered prior to any in vivo skin sensitization testing being initiated.