JDQ443, a Structurally Novel, Pyrazole-Based, Covalent Inhibitor of KRASG12C for the Treatment of Solid Tumors.

Rapid emergence of tumor resistance via RAS pathway reactivation has been reported from clinical studies of covalent KRASG12C 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 KRASG12C 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 KRASG12C 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).

Discovery of Roblitinib (FGF401) as a Reversible-Covalent Inhibitor of the Kinase Activity of Fibroblast Growth Factor Receptor 4.

FGF19 signaling through the FGFR4/ß-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.

In vitro and in vivo characterization of a novel, highly potent p53-MDM2 inhibitor.

Small molecule inhibitors of the p53-MDM2 protein complex are under intense investigation in clinical trials as anti-cancer agents, including our first generation inhibitor NVP-CGM097. We recently described the rational design of a novel pyrazolopyrrolidinone core as a new lead structure and now we report on the synthesis and optimization of this to provide a highly potent lead compound. This new compound displayed excellent oral efficacy in our preclinical mechanistic in vivo model and marked a significant milestone towards the identification of our second generation clinical candidate NVP-HDM201.

Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity.

We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated database. Genotoxic carcinogenicity was predicted based on positive results from at least two genotoxicity tests: one in vitro and one in vivo (which were associated with mutagenicity categories according to the Globally Harmonized System classification). Substances belonging to double positives mutagenicity categories were assigned to be genotoxic carcinogens. In turn, substances that were positive only in a single mutagenicity test were assigned to be mutagens. Chemicals not classified by the selected genotoxicity endpoints were assigned to be negative genotoxic carcinogens and subsequently evaluated for their capability to elicit non-genotoxic carcinogenicity. However, non-genotoxic carcinogenicity mechanisms were not currently included in the developed IATA. The IATA is docked to the OECD Toolbox and uses measured data for different genotoxicity endpoints when available. Alternatively, the system automatically provides predictions by SAR genotoxicity models using the OASIS Tissue Metabolism Simulator platform. When the developed IATA was tested against the consolidated database, its performance was found to be high, with sensitivity of 74% and specificity of 83%, when measured carcinogenicity data were used along with predictions falling within the models' applicability domains. Performance of the IATA would be slightly changed to a sensitivity of 80% and specificity of 72% when the evaluation by non-genotoxic carcinogenicity mechanisms was taken into account. Copyright © 2016 John Wiley & Sons, Ltd.

A feasibility study: Can information collected to classify for mutagenicity be informative in predicting carcinogenicity?

Carcinogenicity is a complex endpoint of high concern yet the rodent bioassay still used is costly to run in terms of time, money and animals. Therefore carcinogenicity has been the subject of many different efforts to both develop short-term tests and non-testing approaches capable of predicting genotoxic carcinogenic potential. In our previous publication (Mekenyan et al., 2012) we presented an in vitro-in vivo extrapolation workflow to help investigate the differences between in vitro and in vivo genotoxicity tests. The outcomes facilitated the development of new (Q)SAR models and for directing testing. Here we have refined this workflow by grouping specific tests together on the basis of their ability to detect DNA and/or protein damage at different levels of biological organization. This revised workflow, akin to an Integrated Approach to Testing and Assessment (IATA) informed by mechanistic understanding was helpful in rationalizing inconsistent study outcomes and categorizing a test set of carcinogens with mutagenicity data on the basis of regulatory mutagenicity classifications. Rodent genotoxic carcinogens were found to be correctly predicted with a high sensitivity (90-100%) and a low rate of false positives (3-10%). The insights derived are useful to consider when developing future (non-)testing approaches to address regulatory purposes.

Development of a mechanistic SAR model for the detection of phototoxic chemicals and use in an integrated testing strategy.

Phototoxicity is of increasing concern in dermatology, since modern lifestyle is often associated with exposure to sunlight. The most commonly reported process is via oxidative reactions. Therefore characterizing the "photo-pro-oxidant" potential of a compound early in its industrial development is of utmost interest, especially for compounds likely to undergo sunlight exposure in skin. Today there is a need for filtering compounds to be tested in the 3T3 neutral red uptake in vitro test for phototoxicity since testing requires resources. A computational model aiming at predicting the mechanisms that imply the generation of reactive oxygen species was developed using a diverse set of 56 chemicals having 3T3 NRU data. An historical mechanistic (Q)SAR model developed for polycyclic aromatic hydrocarbons was used to derive the new mechanistic model: descriptors were selected upfront to describe the modeled phenomenon. The historical parabolic relationships between phototoxicity and the energy gap (E(GAP)) between energies of the highest occupied molecular orbital and the lowest unoccupied molecular orbital was confirmed. The model predicts chemicals to be "phototoxic or photodegradable", or "non-phototoxic and non-photodegradable". A four-step testing strategy is proposed to enable the reduction of experimental testing with the in silico model implemented as a first screen.

Use of genotoxicity information in the development of integrated testing strategies (ITS) for skin sensitization.

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.

Identifying the structural requirements for chromosomal aberration by incorporating molecular flexibility and metabolic activation of chemicals.

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.

Conformational coverage by a genetic algorithm: saturation of conformational space.

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.

2D-3D migration of large chemical inventories with conformational multiplication. Application of the genetic algorithm.

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.