Hit diffusion: limitations to drug discovery and structure-based design.

Modern drug discovery employs a 'screening funnel' to pick compounds worthy of advancing to the clinic, a multi-step process linking a series of assays. Molecules which are active in in vitro assays are passed to a cell-based assay, etc. Each pair of assays may be discordant, due to their measuring similar but not identical properties. This can create an enormous potential to overlook the best molecules, which we highlight here through an understanding of relationships we call 'hit diffusion'. Understanding hit diffusion has important implications for structure-based design, and drug discovery overall. The biophysical bases for assay discordance are outlined, and some strategies for ameliorating the hit diffusion problem are described.

Clinical stage molecule PT150 is a modulator of glucocorticoid and androgen receptors with antiviral activity against SARS-CoV-2.

PT150 is a clinical-stage molecule, taken orally, with a strong safety profile having completed Phase 1 and Phase 2 clinical trials for its original use as an antidepressant. It has an active IND for COVID-19. Antiviral activities have been found for PT150 and other members of its class in a variety of virus families; thus, it was now tested against SARS-CoV-2 in human bronchial epithelial lining cells and showed effective 90% inhibitory antiviral concentration (EC90) of 5.55 µM. PT150 is a member of an extended platform of novel glucocorticoid receptor (GR) and androgen receptor (AR) modulating molecules. In vivo, their predominant net effect is one of systemic glucocorticoid antagonism, but they also show direct downregulation of AR and minor GR agonism at the cellular level. We hypothesize that anti-SARS-CoV-2 activity depends in part on this AR downregulation through diminished TMPRSS2 expression and modulation of ACE2 activity. Given that hypercortisolemia is now suggested to be a significant co-factor for COVID-19 progression, we also postulate an additive role for its potent immunomodulatory effects through systemic antagonism of cortisol.

Cryo-EM as a powerful tool for drug discovery.

The recent revolution in cryo-EM has produced an explosion of structures at near-atomic or better resolution. This has allowed cryo-EM structures to provide visualization of bound small-molecule ligands in the macromolecules, and these new structures have provided unprecedented insights into the molecular mechanisms of complex biochemical processes. They have also had a profound impact on drug discovery, defining the binding modes and mechanisms of action of well-known drugs as well as driving the design and development of new compounds. This review will summarize and highlight some of these structures. Most excitingly, the latest cryo-EM technology has produced structures at 1.2 Å resolution, further solidifying cryo-EM as a powerful tool for drug discovery. Therefore, cryo-EM will play an ever-increasing role in drug discovery in the coming years.

Author Correction: Discovery of a selective catalytic p300/CBP inhibitor that targets lineage-specific tumours.

In the originally published version of this Letter, the authors Arthur F. Kluge, Michael A. Patane and Ce Wang were inadvertently omitted from the author list. Their affiliations are: I-to-D, Inc., PO Box 6177, Lincoln, Massachusetts 01773, USA (A.F.K.); Mitobridge, Inc. 1030 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA (M.A.P.); and China Novartis Institutes for BioMedical Research, No. 4218 Jinke Road, Zhangjiang Hi-Tech Park, Pudong District, Shanghai 201203, China (C.W.). These authors contributed to the interpretation of results and design of compounds. In addition, author 'Edward A. Kesicki' was misspelled as 'Ed Kesicki'. These errors have been corrected online.

Discovery of Spiro Oxazolidinediones as Selective, Orally Bioavailable Inhibitors of p300/CBP Histone Acetyltransferases.

p300 and its paralog CBP can acetylate histones and other proteins and have been implicated in a number of diseases characterized by aberrant gene activation, such as cancer. A novel, highly selective, orally bioavailable histone acetyltransferase (HAT) domain inhibitor has been identified through virtual ligand screening and subsequent optimization of a unique hydantoin screening hit. Conformational restraint in the form of a spirocyclization followed by substitution with a urea led to a significant improvement in potency. Replacement of the hydantoin moiety with an oxazolidinedione followed by fluoro substitution led to A-485, which exhibits potent cell activity, low clearance, and high oral bioavailability.

Discovery of a selective catalytic p300/CBP inhibitor that targets lineage-specific tumours.

The dynamic and reversible acetylation of proteins, catalysed by histone acetyltransferases (HATs) and histone deacetylases (HDACs), is a major epigenetic regulatory mechanism of gene transcription and is associated with multiple diseases. Histone deacetylase inhibitors are currently approved to treat certain cancers, but progress on the development of drug-like histone actyltransferase inhibitors has lagged behind. The histone acetyltransferase paralogues p300 and CREB-binding protein (CBP) are key transcriptional co-activators that are essential for a multitude of cellular processes, and have also been implicated in human pathological conditions (including cancer). Current inhibitors of the p300 and CBP histone acetyltransferase domains, including natural products, bi-substrate analogues and the widely used small molecule C646, lack potency or selectivity. Here, we describe A-485, a potent, selective and drug-like catalytic inhibitor of p300 and CBP. We present a high resolution (1.95 Å) co-crystal structure of a small molecule bound to the catalytic active site of p300 and demonstrate that A-485 competes with acetyl coenzyme A (acetyl-CoA). A-485 selectively inhibited proliferation in lineage-specific tumour types, including several haematological malignancies and androgen receptor-positive prostate cancer. A-485 inhibited the androgen receptor transcriptional program in both androgen-sensitive and castration-resistant prostate cancer and inhibited tumour growth in a castration-resistant xenograft model. These results demonstrate the feasibility of using small molecule inhibitors to selectively target the catalytic activity of histone acetyltransferases, which may provide effective treatments for transcriptional activator-driven malignancies and diseases.

A Maltose-Binding Protein Fusion Construct Yields a Robust Crystallography Platform for MCL1.

Crystallization of a maltose-binding protein MCL1 fusion has yielded a robust crystallography platform that generated the first apo MCL1 crystal structure, as well as five ligand-bound structures. The ability to obtain fragment-bound structures advances structure-based drug design efforts that, despite considerable effort, had previously been intractable by crystallography. In the ligand-independent crystal form we identify inhibitor binding modes not observed in earlier crystallographic systems. This MBP-MCL1 construct dramatically improves the structural understanding of well-validated MCL1 ligands, and will likely catalyze the structure-based optimization of high affinity MCL1 inhibitors.

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.

Protein folding, protein homeostasis, and cancer.

Proteins fold into their functional 3-dimensional structures from a linear amino acid sequence. In vitro this process is spontaneous; while in vivo it is orchestrated by a specialized set of proteins, called chaperones. Protein folding is an ongoing cellular process, as cellular proteins constantly undergo synthesis and degradation. Here emerging links between this process and cancer are reviewed. This perspective both yields insights into the current struggle to develop novel cancer chemotherapeutics and has implications for future chemotherapy discovery.

Navigating structure-activity landscapes.

The problem of how to explore structure-activity relationships (SARs) systematically is still largely unsolved in medicinal chemistry. Recently, data analysis tools have been introduced to navigate activity landscapes and to assess SARs on a large scale. Initial investigations reveal a surprising heterogeneity among SARs and shed light on the relationship between 'global' and 'local' SAR features. Moreover, insights are provided into the fundamental issue of why modeling tools work well in some cases, but not in others.

Assessing how well a modeling protocol captures a structure-activity landscape.

We introduce the notion of structure-activity landscape index (SALI) curves as a way to assess a model and a modeling protocol, applied to structure-activity relationships. We start from our earlier work [ J. Chem. Inf. Model., 2008, 48, 646-658], where we show how to study a structure-activity relationship pairwise, based on the notion of "activity cliffs"--pairs of molecules that are structurally similar but have large differences in activity. There, we also introduced the SALI parameter, which allows one to identify cliffs easily, and which allows one to represent a structure-activity relationship as a graph. This graph orders every pair of molecules by their activity. Here, we introduce the new idea of a SALI curve, which tallies how many of these orderings a model is able to predict. Empirically, testing these SALI curves against a variety of models, ranging over two-dimensional quantitative structure-activity relationship (2D-QSAR), three-dimensional quantitative structure-activity relationship (3D-QSAR), and structure-based design models, the utility of a model seems to correspond to characteristics of these curves. In particular, the integral of these curves, denoted as SCI and being a number ranging from -1.0 to 1.0, approaches a value of 1.0 for two literature models, which are both known to be prospectively useful.

Structure--activity landscape index: identifying and quantifying activity cliffs.

A new method for analyzing a structure-activity relationship is proposed. By use of a simple quantitative index, one can readily identify "structure-activity cliffs": pairs of molecules which are most similar but have the largest change in activity. We show how this provides a graphical representation of the entire SAR, in a way that allows the salient features of the SAR to be quickly grasped. In addition, the approach allows us view the SARs in a data set at different levels of detail. The method is tested on two data sets that highlight its ability to easily extract SAR information. Finally, we demonstrate that this method is robust using a variety of computational control experiments and discuss possible applications of this technique to QSAR model evaluation.

Computer-aided drug design: the next 20 years.

This perspectives article has been taken from a talk the author gave at the symposium in honor of Yvonne C. Martin's retirement, held at the American Chemical Society spring meeting in Chicago on March 25, 2007. The talk was intended as a somewhat lighthearted attempt to gaze into the future; inevitably, in print, things will come across more seriously than was intended. As we all know-the past is rarely predictive of the future.

Inhibitors of hepatitis C virus NS3.4A protease. Effect of P4 capping groups on inhibitory potency and pharmacokinetics.

Reversible tetrapeptide-based compounds have been shown to effectively inhibit the hepatitis C virus NS3.4A protease. Inhibition of viral replicon RNA production in Huh-7 cells has also been demonstrated. We show herein that the inclusion of hydrogen bond donors on the P4 capping group of tetrapeptide-based inhibitors result in increased binding potency to the NS3.4A protease. The capping groups also impart significant effects on the pharmacokinetic profile of these inhibitors.

Inhibitors of hepatitis C virus NS3.4A protease. Part 3: P2 proline variants.

We recently described the identification of an optimized alpha-ketoamide warhead for our series of HCV NS3.4A inhibitors. We report herein a series of HCV protease inhibitors incorporating 3-alkyl-substituted prolines in P(2). These compounds show exceptional enzymatic and cellular potency given their relatively small size. The marked enhancement of activity of these 3-substituted proline derivatives relative to previously reported 4-hydroxyproline derivatives constitutes additional evidence for the importance of the S(2) binding pocket as the defining pharmacophore for inhibition of the NS3.4A enzyme.

Inhibitors of hepatitis C virus NS3.4A protease 2. Warhead SAR and optimization.

The alpha-ketoamide warhead (e.g., 15) was found to be a practical replacement for aliphatic aldehydes in a series of HCV NS3.4A protease inhibitors. Structure-activity relationships and prime side optimization are discussed.

Inhibitors of hepatitis C virus NS3.4A protease 1. Non-charged tetrapeptide variants.

Tetrapeptide-based peptidomimetic compounds have been shown to effectively inhibit the hepatitis C virus NS3.4A protease without the need of a charged functionality. An aldehyde is used as a prototype reversible electrophilic warhead. The SAR of the P1 and P2 inhibitor positions is discussed.

Pharmacophore discovery--lessons learned.

Pharmacophore discovery is one of the major elements of molecular modeling in the absence of X-ray structural data. While pharmacophores initially made their debut as a means for lead discovery, more recent refinements have brought them into the domain of lead optimization, e.g. as a means to define the molecular alignment in 3D-QSAR. In this review, the experiences of over a decade of confronting and solving the challenges of pharmacophore discovery applied to actual drug discovery are summarized. Also, practical tips are described for using the author's methodology for pharmacophore discovery, DANTE.

Enhancement of binary QSAR analysis by a GA-based variable selection method.

Binary quantitative structure-activity relationship (QSAR) is an approach for the analysis of high throughput screening (HTS) data by correlating structural properties of compounds with a "binary" expression of biological activity (1 = active and 0 = inactive) and calculating a probability distribution for active and inactive compounds in a training set. Successfully deriving a predictive binary or any QSAR model largely depends on the selection of a preferred set of molecular descriptors that can capture the chemico-biological interaction for a particular biological target. In this study, a genetic algorithm (GA) was applied as a variable selection method in binary QSAR analysis. This GA-based variable selection method was applied to the analysis of three diverse sets of compounds, estrogen receptor (ER) ligands, carbonic anhydrase II inhibitors, and monoamine oxidase (MAO) inhibitors. Out of a variable pool of 150 molecular descriptors, predictive binary QSAR models were obtained for all three sets of compounds within a reasonable number of GA generations. The results indicate that the GA is a very effective variable selection approach for binary QSAR analysis.