In recent years, several new fluorinated functional groups have been employed in medicinal chemistry. This review will highlight some recent developments in this area. We draw attention to useful ...synthetic advances for the installation of fluorine-containing groups. In addition, we examine the application of some fluorinated functional groups that have recently been gaining popularity in drug discovery. We use matched-pair analysis to assemble aggregate data on the impact on potency of one of these groups, pentafluorosulfanyl, as compared to trifluoromethyl. We further used matchedpair analysis to identify some interesting effects on in vitro ADME properties of replacing H by F on certain moieties.
Background
In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting ...the genotoxicity of reagent-sized aryl-amines (ArNH
2
), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.
Results
In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.
Conclusions
Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.
Blockade of the hERG potassium channel prolongs the ventricular action potential (AP) and QT interval, and triggers early after depolarizations (EADs) and torsade de pointes (TdP) arrhythmia. ...Opinions differ as to the causal relationship between hERG blockade and TdP, the relative weighting of other contributing factors, definitive metrics of preclinical proarrhythmicity, and the true safety margin in humans. Here, we have used in silico techniques to characterize the effects of channel gating and binding kinetics on hERG occupancy, and of blockade on the human ventricular AP. Gating effects differ for compounds that are sterically compatible with closed channels (becoming trapped in deactivated channels) versus those that are incompatible with the closed/closing state, and expelled during deactivation. Occupancies of trappable blockers build to equilibrium levels, whereas those of non-trappable blockers build and decay during each AP cycle. Occupancies of ~83% (non-trappable) versus ~63% (trappable) of open/inactive channels caused EADs in our AP simulations. Overall, we conclude that hERG occupancy at therapeutic exposure levels may be tolerated for nontrappable, but not trappable blockers capable of building to the proarrhythmic occupancy level. Furthermore, the widely used Redfern safety index may be biased toward trappable blockers, overestimating the exposure-IC50 separation in nontrappable cases.
Overexpression of the antiapoptotic members of the Bcl-2 family of proteins is commonly associated with cancer cell survival and resistance to chemotherapeutics. Here, we describe the structure-based ...optimization of a series of N-heteroaryl sulfonamides that demonstrate potent mechanism-based cell death. The role of the acidic nature of the sulfonamide moiety as it relates to potency, solubility, and clearance is examined. This has led to the discovery of novel heterocyclic replacements for the acylsulfonamide core of ABT-737 and ABT-263.
The human cardiac sodium channel (hNav1.5, encoded by the SCN5A gene) is critical for action potential generation and propagation in the heart. Drug-induced sodium channel inhibition decreases the ...rate of cardiomyocyte depolarization and consequently conduction velocity and can have serious implications for cardiac safety. Genetic mutations in hNav1.5 have also been linked to a number of cardiac diseases. Therefore, off-target hNav1.5 inhibition may be considered a risk marker for a drug candidate. Given the potential safety implications for patients and the costs of late stage drug development, detection, and mitigation of hNav1.5 liabilities early in drug discovery and development becomes important. In this review, we describe a pre-clinical strategy to identify hNav1.5 liabilities that incorporates in vitro, in vivo, and in silico techniques and the application of this information in the integrated risk assessment at different stages of drug discovery and development.
In breast cancer, estrogen receptor alpha (ERα) positive cancer accounts for approximately 74% of all diagnoses, and in these settings, it is a primary driver of cell proliferation. Treatment of ERα ...positive breast cancer has long relied on endocrine therapies such as selective estrogen receptor modulators, aromatase inhibitors, and selective estrogen receptor degraders (SERDs). The steroid-based anti-estrogen fulvestrant (5), the only approved SERD, is effective in patients who have not previously been treated with endocrine therapy as well as in patients who have progressed after receiving other endocrine therapies. Its efficacy, however, may be limited due to its poor physicochemical properties. We describe the design and synthesis of a series of potent benzothiophene-containing compounds that exhibit oral bioavailability and preclinical activity as SERDs. This article culminates in the identification of LSZ102 (10), a compound in clinical development for the treatment of ERα positive breast cancer.
We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ ...quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.
The concepts of activity cliffs and matched molecular pairs (MMP) are recent paradigms for analysis of data sets to identify structural changes that may be used to modify the potency of lead ...molecules in drug discovery projects. Analysis of MMPs was recently demonstrated as a feasible technique for quantitative structure-activity relationship (QSAR) modeling of prospective compounds. Although within a small data set, the lack of matched pairs, and the lack of knowledge about specific chemical transformations limit prospective applications. Here we present an alternative technique that determines pairwise descriptors for each matched pair and then uses a QSAR model to estimate the activity change associated with a chemical transformation. The descriptors effectively group similar transformations and incorporate information about the transformation and its local environment. Use of a transformation QSAR model allows one to estimate the activity change for novel transformations and therefore returns predictions for a larger fraction of test set compounds. Application of the proposed methodology to four public data sets results in increased model performance over a benchmark random forest and direct application of chemical transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA).
The prediction of human pharmacokinetics early in the drug discovery cycle has become of paramount importance, aiding candidate selection and benefit-risk assessment. We present herein computational ...models to predict human volume of distribution at steady state (VD(ss)) entirely from in silico structural descriptors. Using both linear and nonlinear statistical techniques, partial least-squares (PLS), and random forest (RF) modeling, a data set of human VD(ss) values for 669 drug compounds recently published ( Drug Metab. Disp. 2008 , 36 , 1385 - 1405 ) was explored. Descriptors covering 2D and 3D molecular topology, electronics, and physical properties were calculated using MOE and Volsurf+. Model evaluation was accomplished using a leave-class-out approach on nine therapeutic or structural classes. The models were assessed using an external test set of 29 additional compounds. Our analysis generated models, both via a single method or consensus which were able to predict human VD(ss) within geometric mean 2-fold error, a predictive accuracy considered good even for more resource-intensive approaches such as those requiring data generated from studies in multiple animal species.
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