MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery.

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.

High-throughput synthesis provides data for predicting molecular properties and reaction success.

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.

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.

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.

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.

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).

Fluorinated compounds in medicinal chemistry: recent applications, synthetic advances and matched-pair analyses.

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.

A fingerprint pair analysis of hERG inhibition data.

BACKGROUND: Drugs 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. RESULTS: To 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. CONCLUSIONS: We 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.

The role of the acidity of N-heteroaryl sulfonamides as inhibitors of bcl-2 family protein-protein interactions.

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.

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.

Lipophilic Isosteres of a π-π Stacking Interaction: New Inhibitors of the Bcl-2-Bak Protein-Protein Interaction.

The discovery of new Bcl-2 protein-protein interaction antagonists is described. We replaced the northern fragment of ABT737 (π-π stacking interactions) with structurally simplified hydrophobic cage structures with much reduced conformational flexibility and rotational freedom. The binding mode of the compounds was elucidated by X-ray crystallography, and the compounds showed excellent oral bioavailability and clearance in rat PK studies.

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

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 (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. 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.

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.

Identification of orally available naphthyridine protein kinase D inhibitors.

A novel 2,6-naphthyridine was identified by high throughput screen (HTS) as a dual protein kinase C/D (PKC/PKD) inhibitor. PKD inhibition in the heart was proposed as a potential antihypertrophic mechanism with application as a heart failure therapy. As PKC was previously identified as the immediate upstream activator of PKD, PKD vs PKC selectivity was essential to understand the effect of PKD inhibition in models of cardiac hypertrophy and heart failure. The present study describes the modification of the HTS hit to a series of prototype pan-PKD inhibitors with routine 1000-fold PKD vs PKC selectivity. Example compounds inhibited PKD activity in vitro, in cells, and in vivo following oral administration. Their effects on heart morphology and function are discussed herein.

Identification of potent and selective amidobipyridyl inhibitors of protein kinase D.

The synthesis and biological evaluation of potent and selective PKD inhibitors are described herein. The compounds described in the present study selectively inhibit PKD among other putative HDAC kinases. The PKD inhibitors of the present study blunt phosphorylation and subsequent nuclear export of HDAC4/5 in response to diverse agonists. These compounds further establish the central role of PKD as an HDAC4/5 kinase and enhance the current understanding of cardiac myocyte signal transduction. The in vivo efficacy of a representative example compound on heart morphology is reported herein.

The discovery of potent inhibitors of aldosterone synthase that exhibit selectivity over 11-beta-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.

In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.

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.

PostDOCK: a structural, empirical approach to scoring protein ligand complexes.

In this work we introduce a postprocessing filter (PostDOCK) that distinguishes true binding ligand-protein complexes from docking artifacts (that are created by DOCK 4.0.1). PostDOCK is a pattern recognition system that relies on (1) a database of complexes, (2) biochemical descriptors of those complexes, and (3) machine learning tools. We use the protein databank (PDB) as the structural database of complexes and create diverse training and validation sets from it based on the "families of structurally similar proteins" (FSSP) hierarchy. For the biochemical descriptors, we consider terms from the DOCK score, empirical scoring, and buried solvent accessible surface area. For the machine-learners, we use a random forest classifier and logistic regression. Our results were obtained on a test set of 44 structurally diverse protein targets. Our highest performing descriptor combinations obtained approximately 19-fold enrichment (39 of 44 binding complexes were correctly identified, while only allowing 2 of 44 decoy complexes), and our best overall accuracy was 92%.

CoMFA and HQSAR of acylhydrazide cruzain inhibitors.

An approach combining CoMFA and HQSAR methods was used to describe QSAR models for a series of cruzain inhibitors having the acylhydrazide framework. A CoMFA study using two alignment orientations (I and II), three different probe atoms and changes of the lattice spacing (1 and 2 A) was performed. Alignment II and an sp3 probe carbon atom yielded good cross-validation (q2=0.688) employing lattice spacing of 1 A. The best HQSAR model was generated using atoms, bond, and connectivity as fragment distinction and fragment size default (4-5) showing similar cross-validated value of CoMFA (q2=0.689). Based upon the information derived from CoMFA and HQSAR, we have identified some key features that may be used to design new acylhydrazide derivatives that may be more potent cruzain inhibitors.