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. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery.

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

In Silico Positional Analogue Scanning with Amber GPU-TI.

Positional analogue scanning (PAS) is an accepted strategy for multiparameter lead optimization (MPO) in drug discovery. Small structural changes as introduced by PAS can lead to 10-fold changes in binding potency in ∼10-20% of cases, a significant parameter shift irrespective of other MPO objectives. Sometimes performing a complete PAS is challenging due to resource and time constraints, building block availability, or difficulty in synthesis. Calculating relative binding free energies (RBFEs) for all positions can contribute to prioritizing the most promising analogues for synthesis. We tested a well-established RBFE calculation method, Amber GPU-TI, for 20 positional analogue scans in 14 test systems (cyclin-dependent kinase 8 (CDK8), hepatitis C virus nonstructural protein 5B (HCV NS5B), tankyrase, RAC-α serine/threonine-protein kinase (Akt), phosphodiesterase 1B (PDE1B), orexin/hypocretin receptor type 1 (OX1R), orexin/hypocretin receptor type 2 (OX2R), histone acetyltransferase K (lysine) acetyltransferase 6A (KAT6A), peroxisome proliferator-activated receptor γ (PPARγ), extracellular signal-regulated kinases (ERK1/2), coactivator-associated arginine methyltransferase 1 (PRMT4), αvß6, bromodomain 1 (BD1), human immunodeficiency virus-1 (HIV-1) entry) involving nitrogen, methyl, halogen, methoxy, and hydroxyl scans with at least four analogues per set. Among the 66 analogue positions explored, we found that in 18 cases Amber GPU-TI calculations predicted a more than 10-fold change in potency. In all of these cases, the experimentally observed direction of potency changes agreed with the predictions. In 16 cases, more than 10-fold changes in experimental potency were observed. Again, in all of these cases, Amber GPU-TI predicted the direction of the potency changes correctly. In none of these cases would a decision made for or against synthesis based on a 10-fold change in potency have resulted in missing an important analogue. Therefore, in silico RBFE calculations using Amber GPU-TI can meaningfully contribute to the prioritization of positional analogues before synthesis.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Indole Inhibitors of MMP-13 for Arthritic Disorders.

Here, we described the design, by fragment merging and multiparameter optimization, of selective MMP-13 inhibitors that display an appropriate balance of potency and physicochemical properties to qualify as tool compounds suitable for in vivo testing. Optimization of potency was guided by structure-based insights, specifically to replace an ester moiety and introduce polar directional hydrogen bonding interactions in the core of the molecule. By introducing polar enthalpic interactions in this series of inhibitors, the overall beneficial physicochemical properties were maintained. These physicochemical properties translated to excellent drug-like properties beyond potency. In a murine model of rheumatoid arthritis, treatment of mice with selective inhibitors of MMP-13 resulted in a statistically significant reduction in the mean arthritic score vs control when dosed over a 14 day period.

Holistic drug design for multiparameter optimization in modern small molecule drug discovery.

Small molecule drug discovery in the modern era necessitates rapid and simultaneous multiparameter optimization. Holistic drug design entails the strategic use of multiple drug design approaches for multiparameter optimization based on the goal and stage of the drug discovery program, the quantity and quality of data for analyses, and the availability and accuracy of predictive models. By leveraging orthogonal, complementary, or synergistic drug design approaches, holistic drug design may improve the efficiency of multiparameter optimization and increase the chance for success in small molecule drug discovery.

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. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions.

Positional Analogue Scanning: An Effective Strategy for Multiparameter Optimization in Drug Design.

Minimizing the number and duration of design cycles needed to optimize hit or lead compounds into high-quality chemical probes or drug candidates is an ongoing challenge in biomedical research. Small structure modifications to hit or lead compounds can have meaningful impacts on pharmacological profiles due to significant effects on molecular and physicochemical properties and intra- and intermolecular interactions. Rapid pharmacological profiling of an efficiently prepared series of positional analogues stemming from the systematic exchange of methine groups with heteroatoms or other substituents in aromatic or heteroaromatic ring-containing hit or lead compounds is one approach toward minimizing design cycles (e.g., exchange of aromatic or heteroaromatic CH groups with N atoms or CF, CMe, or COH groups). In this Perspective, positional analogue scanning is shown to be an effective strategy for multiparameter optimization in drug design, whereby substantial improvements in a variety of pharmacological parameters can be achieved.

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. About 1000 unique three-dimensional kinase inhibitor hinge binding motifs have been extracted from structures covering more than 180 different protein kinases. These hinge binding motifs along with their attachment vectors have been combined in the KCM for the purpose of scaffold hopping, kinase screening deck design, and interactive structure-based design. Prospective scaffold hopping using the KCM identified two potent and selective Bruton tyrosine kinase (BTK) inhibitors with hinge binding fragments novel to BTK.

Structural studies unravel the active conformation of apo RORγt nuclear receptor and a common inverse agonism of two diverse classes of RORγt inhibitors.

The nuclear receptor retinoid acid receptor-related orphan receptor γt (RORγt) is a master regulator of the Th17/IL-17 pathway that plays crucial roles in the pathogenesis of autoimmunity. RORγt has recently emerged as a highly promising target for treatment of a number of autoimmune diseases. Through high-throughput screening, we previously identified several classes of inverse agonists for RORγt. Here, we report the crystal structures for the ligand-binding domain of RORγt in both apo and ligand-bound states. We show that apo RORγt adopts an active conformation capable of recruiting coactivator peptides and present a detailed analysis of the structural determinants that stabilize helix 12 (H12) of RORγt in the active state in the absence of a ligand. The structures of ligand-bound RORγt reveal that binding of the inverse agonists disrupts critical interactions that stabilize H12. This destabilizing effect is supported by ab initio calculations and experimentally by a normalized crystallographic B-factor analysis. Of note, the H12 destabilization in the active state shifts the conformational equilibrium of RORγt toward an inactive state, which underlies the molecular mechanism of action for the inverse agonists reported here. Our findings highlight that nuclear receptor structure and function are dictated by a dynamic conformational equilibrium and that subtle changes in ligand structures can shift this equilibrium in opposite directions, leading to a functional switch from agonists to inverse agonists.

Computer-aided drug design at Boehringer Ingelheim.

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.

Performance of Dark Chemical Matter in High Throughput Screening.

A statistical analysis of 203 high-throughput screens was conducted studying the propensity of small molecules in the Boehringer Ingelheim screening deck to show biological activity after having tested as inactive previously in a growing number of screening assays. Dark chemical matter (DCM) compounds, which have been tested and found to be inactive in 50 or more assays, exhibit hit rates that are comparable to those of compounds tested in much fewer assays. Only compounds tested as inactive in 125 or more assays started showing a hit rate deterioration of up to 40% compared to compounds tested in less than 25 assays. The observed large number of DCM compounds in the BI screening deck is found to be in line with the expected fraction of DCM calculated based on a probability analysis. The analysis suggests not only that DCM compounds have the chance to occasionally provide valuable hits associated with higher selectivity as recently shown by Novartis ( Nat. Chem. Biol. 2015 , 11 , 958 ) but that there is little compelling reason to exclude DCM compounds from screening decks in favor of previously untested or less tested compounds.

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. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

An overview of molecular fingerprint similarity search in virtual screening.

INTRODUCTION: A central premise of medicinal chemistry is that structurally similar molecules exhibit similar biological activities. Molecular fingerprints encode properties of small molecules and assess their similarities computationally through bit string comparisons. Based on the similarity to a biologically active template, molecular fingerprint methods allow for identifying additional compounds with a higher chance of displaying similar biological activities against the same target - a process commonly referred to as virtual screening (VS). AREAS COVERED: This article focuses on fingerprint similarity searches in the context of compound selection for enhancing hit sets, comparing compound decks, and VS. In addition, the authors discuss the application of fingerprints in predictive modeling. EXPERT OPINION: Fingerprint similarity search methods are especially useful in VS if only a few unrelated ligands are known for a given target and therefore more complex and information rich methods such as pharmacophore searches or structure-based design are not applicable. In addition, fingerprint methods are used in characterizing properties of compound collections such as chemical diversity, density in chemical space, and content of biologically active molecules (biodiversity). Such assessments are important for deciding what compounds to experimentally screen, to purchase, or to assemble in a virtual compound deck for in silico screening or de novo design.

Discovery of 1,3-dihydro-2,1,3-benzothiadiazole 2,2-dioxide analogs as new RORC modulators.

Structure-based and pharmacophore-based virtual screening in combination with combinatorial chemistry and X-ray crystallography led to the discovery of a new class of benzothiadiazole dioxide analogs with functional activity as RORC inverse agonists. The early RORC SAR compound 14 exhibited RORC inhibition in a cell based reporter gene assay of 5.7 µM and bound to RORC with an affinity of 1.6 µM in a fluorescence polarization assay displacing a ligand binding site probe. Crystallography confirmed the binding mode of the compound in the ligand binding domain displaying the engagement of a novel sub pocket close to Ser404. Subsequent optimization yielded compounds with enhanced RORC inverse agonist activity. The most active compound 19 showed an IC50 of 440 nM in a human PBMC assay.

3D virtual screening of large combinatorial spaces.

A new method for 3D in silico screening of large virtual combinatorial chemistry spaces is described. The software PharmShape screens millions of individual compounds applying a multi-conformational pharmacophore and shape based approach. Its extension, PharmShapeCC, is capable of screening trillions of compounds from tens of thousands of combinatorial libraries. Key elements of PharmShape and PharmShapeCC are customizable pharmacophore features, a composite inclusion sphere, library core intermediate clustering, and the determination of combinatorial library consensus orientations that allow for orthogonal enumeration of libraries. The performance of the software is illustrated by the prospective identification of a novel CXCR5 antagonist and examples of finding novel chemotypes from synthesizing and evaluating combinatorial hit libraries identified from PharmShapeCC screens for CCR1, LTA4 hydrolase, and MMP-13.

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. One form of toxicity is genotoxicity due to mutagenicity. In this paper different in silico approaches for predicting mutagenicity, in particular in primary aromatic amines, are reviewed.

Deconstruction of sulfonamide inhibitors of the urotensin receptor (UT) and design and synthesis of benzylamine and benzylsulfone antagonists.

Potent small molecule antagonists of the urotensin receptor are described. These inhibitors were derived via systematically deconstructing a literature inhibitor to understand the basic pharmacophore and key molecular features required to inhibit the protein receptor. The series of benzylamine and benzylsulfone antagonists herein reported display a combination of nanomolar molecular and cellular potency as well as acceptable in vitro permeability and metabolic stability.