Perspectives on current approaches to virtual screening in drug discovery.

Introduction: For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

Prediction of Human Clearance Using In Silico Models with Reduced Bias.

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets.

Crowdsourcing and open innovation in drug discovery: recent contributions and future directions.

The past decade has seen significant growth in the use of 'crowdsourcing' and open innovation approaches to engage 'citizen scientists' to perform novel scientific research. Here, we quantify and summarize the current state of adoption of open innovation by major pharmaceutical companies. We also highlight recent crowdsourcing and open innovation research contributions to the field of drug discovery, and interesting future directions.

In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters.

A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values.

Kinase Crystal Miner: A Powerful Approach to Repurposing 3D Hinge Binding Fragments and Its Application to Finding Novel Bruton Tyrosine Kinase Inhibitors.

Protein kinases represent an important target class for drug discovery because of their role in signaling pathways involved in disease areas such as oncology and immunology. A key element of many ATP-competitive kinase inhibitors is their hinge-binding motif. Here, we describe Kinase Crystal Miner (KCM)-a new approach developed at Boehringer Ingelheim (BI) that harvests the existing crystallographic information on kinase-inhibitor co-crystal structures from internal and external databases.

Automatically updating predictive modeling workflows support decision-making in drug design.

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions.

Crowdsourcing in pharma: a strategic framework.

Conceptually, all organizations can be described as coordinated actors working together to deliver a product(s), or provide a service(s). For organizations to remain competitive, it is important to have processes that look outward for external 'innovations' that could improve how work is done, and what is delivered. We present a comprehensive review of a variety of processes that pharmaceutical companies have used to engage external actors ('the crowd') to provide innovation in the service of delivering novel therapeutic agents.

Discovery of a potent and dissociated non-steroidal glucocorticoid receptor agonist containing an alkyl carbinol pharmacophore.

Synthesis and structure-activity relationship (SAR) of a series of alkyl and cycloalkyl containing non-steroidal dissociated glucocorticoid receptor (GR) agonists is reported. This series of compounds was identified as part of an effort to replace the CF3 group in a scaffold represented by 1a. The study culminated in the identification of compound 14, a t-butyl containing derivative, which has shown potent activity for GR, selectivity against the progesterone receptor (PR) and the mineralocorticoid receptor (MR), in vitro anti-inflammatory activity in an IL-6 transrepression assay, and dissociation in a MMTV transactivation counter-screen.

Identification of highly efficacious glucocorticoid receptor agonists with a potential for reduced clinical bone side effects.

Synthesis and structure-activity relationship (SAR) of a series of nonsteroidal glucocorticoid receptor (GR) agonists are described. These compounds contain "diazaindole" moieties and display different transcriptional regulatory profiles in vitro and are considered "dissociated" between gene transrepression and transactivation. The lead optimization effort described in this article focused in particular on limiting the transactivation of genes which result in bone side effects and these were assessed in vitro in MG-63 osteosarcoma cells, leading to the identification of (R)-18 and (R)-21.

In silico predictions of genotoxicity for aromatic amines.

Insufficient drug safety is one of the major reasons for failure of drug candidates in Phase II and Phase III clinical trials. Determining toxicity early during the drug discovery process can help lower the attrition rate in clinical trials and lead to significant cost savings. In silico approaches can help to prioritize large numbers of compounds quickly and cost effectively in the early phase of drug discovery.

Substituted phenyl as a steroid A-ring mimetic: providing agonist activity to a class of arylsulfonamide nonsteroidal glucocorticoid ligands.

A class of arylsulfonamide glucocorticoid receptor agonists that contains a substituted phenyl group as a steroid A-ring mimetic is reported. The structural design and SAR that provide the functional switching of a GR antagonist to an agonist is described. A combination of specific hydrogen bonding and lipophilic elements on the A-ring moiety is required to achieve potent GR agonist activity.

Function-regulating pharmacophores in a sulfonamide class of glucocorticoid receptor agonists.

A class of α-methyltryptamine sulfonamide glucocorticoid receptor (GR) modulators was optimized for agonist activity. The design of ligands was aided by molecular modeling, and key function-regulating pharmacophoric points were identified that are critical in achieving the desired agonist effect in cell based assays. Compound 27 was profiled in vitro and in vivo in models of inflammation.

Crowd computing: using competitive dynamics to develop and refine highly predictive models.

A recent application of a crowd computing platform to develop highly predictive in silico models for use in the drug discovery process is described. The platform, Kaggle™, exploits a competitive dynamic that results in model optimization as the competition unfolds. Here, this dynamic is described in detail and compared with more-conventional modeling strategies.

Identification of a potent sodium hydrogen exchanger isoform 1 (NHE1) inhibitor with a suitable profile for chronic dosing and demonstrated cardioprotective effects in a preclinical model of myocardial infarction in the rat.

Sodium-hydrogen exchanger isoform 1 (NHE1) is a ubiquitously expressed transmembrane ion channel responsible for intracellular pH regulation. During myocardial ischemia, low pH activates NHE1 and causes increased intracellular calcium levels and aberrant cellular processes, leading to myocardial stunning, arrhythmias, and ultimately cell damage and death. The role of NHE1 in cardiac injury has prompted interest in the development of NHE1 inhibitors for the treatment of heart failure.

DemQSAR: predicting human volume of distribution and clearance of drugs.

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds.

Non-steroidal dissociated glucocorticoid agonists: indoles as A-ring mimetics and function-regulating pharmacophores.

We report a SAR of non-steroidal glucocorticoid mimetics that utilize indoles as A-ring mimetics. Detailed SAR is discussed with a focus on improving PR and MR selectivity, GR agonism, and in vitro dissociation profile. SAR analysis led to compound (R)-33 which showed high PR and MR selectivity, potent agonist activity, and reduced transactivation activity in the MMTV and aromatase assays.

Nonsteroidal dissociated glucocorticoid agonists containing azaindoles as steroid A-ring mimetics.

Syntheses and structure-activity relationships (SAR) of nonsteroidal glucocorticoid receptor (GR) agonists are described. These compounds contain azaindole moieties as A-ring mimetics and display various degrees of in vitro dissociation between gene transrepression and transactivation. Collagen induced arthritis studies in mouse have demonstrated that in vitro dissociated compounds (R)-16 and (R)-37 have steroid-like anti-inflammatory properties with improved metabolic side effect profiles, such as a reduced increase in body fat and serum insulin levels, compared to steroids.

An in silico method for predicting Ames activities of primary aromatic amines by calculating the stabilities of nitrenium ions.

In this paper, we describe an in silico first principal approach to predict the mutagenic potential of primary aromatic amines. This approach is based on the so-called "nitrenium hypothesis", which was developed by Ford et al. in the early 1990s.

5-Aminomethylbenzimidazoles as potent ITK antagonists.

Benzamide 1 demonstrated good potency as a selective ITK inhibitor, however the amide moiety was found to be hydrolytically labile in vivo, resulting in low oral exposure and the generation of mutagenic aromatic amine metabolites. Replacing the benzamide with a benzylamine linker not only addressed the toxicity issue, but also improved the cellular and functional potency as well as the drug-like properties. SAR studies around the benzylamines and the identification of 10n and 10o as excellent tools for proof-of-concept studies are described.

Discovery of potent inhibitors of interleukin-2 inducible T-cell kinase (ITK) through structure-based drug design.

Interleukin-2 inducible T-cell kinase (ITK) is a member of the Tec kinase family and is involved with T-cell activation and proliferation. Due to its critical role in acting as a modulator of T-cells, ITK inhibitors could provide a novel route to anti-inflammatory therapy. This work describes the discovery of ITK inhibitors through structure-based design where high-resolution crystal structural information was used to optimize interactions within the kinase specificity pocket of the enzyme to improve both potency and selectivity.

2-Aminobenzimidazoles as potent ITK antagonists: trans-stilbene-like moieties targeting the kinase specificity pocket.

Based on the information from molecular modeling and X-ray crystal structures, the kinase specificity pocket of ITK could be occupied upon extension of the right-hand-side of the 2-benzimidazole core of the inhibitors. 2-Aminobenzimidazoles with a trans-stilbene-like extension were designed and synthesized as novel ITK antagonists. Significant improvement on binding affinity and cellular activity were obtained through the trans-stilbene-like antagonists.

Quinol-4-ones as steroid A-ring mimetics in nonsteroidal dissociated glucocorticoid agonists.

We report on the nuclear receptor binding affinities, cellular activities of transrepression and transactivation, and anti-inflammatory properties of a quinol-4-one and other A-ring mimetic containing nonsteroidal class of glucocorticoid agonists.

Trifluoromethyl group as a pharmacophore: effect of replacing a CF3 group on binding and agonist activity of a glucocorticoid receptor ligand.

Compound 1, a potent glucocorticoid receptor ligand, contains a quaternary carbon bearing trifluoromethyl and hydroxyl groups. This paper describes the effect of replacing the trifluoromethyl group on binding and agonist activity of the GR ligand 1. The results illustrate that replacing the CF3 group with a cyclohexylmethyl or benzyl group maintains the GR binding potency.

Combining computational and experimental screening for rapid optimization of protein properties.

We present a combined computational and experimental method for the rapid optimization of proteins. Using beta-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of approximately equal 200,000 mutants.