Clayton Springer

Cardiac Safety Implications of hNav1.5 Blockade and a Framework for Pre-Clinical Evaluation

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.

An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential

BackgroundIn 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 (ArNH2), 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.ResultsIn 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.ConclusionsDespite 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.

STUDIES IN DRUG ALBUMIN BINDING USING HSA AND RSA AFFINITY METHODS

A HPLC gradient methodology using immobilized human serum albumin (HSA) and rat serum albumin (RSA) has been used as a tool to investigate the protein binding trends of novel discovery compounds. These methods have been set up to support a high throughput approach and have been shown to be complementary to one another. Significant binding differences have been observed with some compounds when both methods have been employed. Discovery chemists are now also able to rank order molecules using these quick “trend analysis” albumin binding screens by submitting a 20 μl sample from the 10 mM stock solution from the inhouse compound archive. Additionally, chemists have the opportunity to predict trends in albumin binding by use of an internal QSAR model based upon a diverse 1200 compound result set, which had been previously analyzed with the HSA methodology.

A fingerprint pair analysis of hERG inhibition data

BackgroundDrugs that bind to the human Ether-a-go-go Related Gene (hERG) potassium channel and block its ion conduction can lead to Torsade de Pointes (TdP), a fatal ventricular arrhythmia. Thus, compounds are screened for hERG inhibition in the drug development process; those found to be active face a difficult road to approval. Knowing which structural transformations reduce hERG binding would be helpful in the lead optimization phase of drug discovery.ResultsTo identify such transformations, we carried out a comprehensive analysis of all approximately 33,000 compound pairs in the Novartis internal database which have IC50 values in the dofetilide displacement assay. Most molecular transformations have only a single example in the data set; however, a few dozen transformations have sufficient numbers for statistical analysis.ConclusionsWe observe that transformations which increased polarity (for example adding an oxygen, or an sp2 nitrogen), decreased lipophilicity (removing carbons), or decreased positive charge consistently reduced hERG inhibition between 3- and 10-fold. The largest observed reduction in hERG was from a transformation from imidazole to methyl tetrazole. We also observe that some changes in aromatic ring substituents (for example hydrogen to methoxy) can also reduce hERG binding in vitro.

Potent and selective 2-naphthylsulfonamide substituted hydroxamic acid inhibitors of matrix metalloproteinase-13.

The matrix metalloproteinase enzyme MMP-13 plays a key role in the degradation of type II collagen in cartilage and bone in osteoarthritis (OA). An effective MMP-13 inhibitor would provide a disease modifying therapy for the treatment of arthritis, although this goal still continues to elude the pharmaceutical industry due to issues with safety. Our efforts have resulted in the discovery of a series of hydroxamic acid inhibitors of MMP-13 that do not significantly inhibit MMP-2 (gelatinase-1). MMP-2 has been implicated in the musculoskeletal side effects resulting from pan-MMP inhibition due to findings from spontaneously occurring human MMP-2 deletions. Analysis of the SAR of hundreds of previously prepared hydroxamate based MMP inhibitors lead us to 2-naphthylsulfonamide substituted hydroxamates which exhibited modest selectivity for MMP-13 versus MMP-2. This Letter describes the lead optimization of 1 and identification of inhibitors exhibiting >100-fold selectivity for MMP-13 over MMP-2.

FOCUS — Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists

Communication of data and ideas within a medicinal chemistry project on a global as well as local level is a crucial aspect in the drug design cycle. Over a time frame of eight years, we built and optimized FOCUS, a platform to produce, visualize, and share information on various aspects of a drug discovery project such as cheminformatics, data analysis, structural information, and design. FOCUS is tightly integrated with internal services that involve-among others-data retrieval systems and in-silico models and provides easy access to automated modeling procedures such as pharmacophore searches, R-group analysis, and similarity searches. In addition, an interactive 3D editor was developed to assist users in the generation and docking of close analogues of a known lead. In this paper, we will specifically concentrate on issues we faced during development, deployment, and maintenance of the software and how we continually adapted the software in order to improve usability. We will provide usage examples to highlight the functionality as well as limitations of FOCUS at the various stages of the development process. We aim to make the discussion as independent of the software platform as possible, so that our experiences can be of more general value to the drug discovery community.

The discovery of potent inhibitors of aldosterone synthase that exhibit selectivity over 11-β-hydroxylase

Aldosterone, the final component of the renin-angiotensin-aldosterone system, plays an important role in the pathophysiology of hypertension and congestive heart failure. Aldosterone synthase (CYP11B2) catalyzes the last three steps of aldosterone biosynthesis, and as such appears to be a target for the treatment of these disorders. A sulfonamide-imidazole scaffold has proven to be a potent inhibitor of CYP11B2. Furthermore, this scaffold can achieve high levels of selectivity for CYP11B2 over CYP11B1, a key enzyme in the biosynthesis of cortisol.

Implications of Dynamic Occupancy, Binding Kinetics, and Channel Gating Kinetics for hERG Blocker Safety Assessment and Mitigation

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.

Quantitative Structure–Activity Relationship Models of Chemical Transformations from Matched Pairs Analyses

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).

Discovery of LSZ102, a Potent, Orally Bioavailable Selective Estrogen Receptor Degrader (SERD) for the Treatment of Estrogen Receptor Positive Breast Cancer

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.