Simultaneous inhibition of DNA-PK and PolÏ´ improves integration efficiency and precision of genome editing.

Genome editing, specifically CRISPR/Cas9 technology, has revolutionized biomedical research and offers potential cures for genetic diseases. Despite rapid progress, low efficiency of targeted DNA integration and generation of unintended mutations represent major limitations for genome editing applications caused by the interplay with DNA double-strand break repair pathways. To address this, we conduct a large-scale compound library screen to identify targets for enhancing targeted genome insertions. Our study reveals DNA-dependent protein kinase (DNA-PK) as the most effective target to improve CRISPR/Cas9-mediated insertions, confirming previous findings. We extensively characterize AZD7648, a selective DNA-PK inhibitor, and find it to significantly enhance precise gene editing. We further improve integration efficiency and precision by inhibiting DNA polymerase theta (PolÏ´). The combined treatment, named 2iHDR, boosts templated insertions to 80% efficiency with minimal unintended insertions and deletions. Notably, 2iHDR also reduces off-target effects of Cas9, greatly enhancing the fidelity and performance of CRISPR/Cas9 gene editing.

Understanding Intracellular Biology to Improve mRNA Delivery by Lipid Nanoparticles.

Poor understanding of intracellular delivery and targeting hinders development of nucleic acid-based therapeutics transported by nanoparticles. Utilizing a siRNA-targeting and small molecule profiling approach with advanced imaging and machine learning biological insights is generated into the mechanism of lipid nanoparticle (MC3-LNP) delivery of mRNA. This workflow is termed Advanced Cellular and Endocytic profiling for Intracellular Delivery (ACE-ID). A cell-based imaging assay and perturbation of 178 targets relevant to intracellular trafficking is used to identify corresponding effects on functional mRNA delivery. Targets improving delivery are analyzed by extracting data-rich phenotypic fingerprints from images using advanced image analysis algorithms. Machine learning is used to determine key features correlating with enhanced delivery, identifying fluid-phase endocytosis as a productive cellular entry route. With this new knowledge, MC3-LNP is re-engineered to target macropinocytosis, and this significantly improves mRNA delivery in vitro and in vivo. The ACE-ID approach can be broadly applicable for optimizing nanomedicine-based intracellular delivery systems and has the potential to accelerate the development of delivery systems for nucleic acid-based therapeutics.

Nuclear fascin regulates cancer cell survival.

Fascin is an important regulator of F-actin bundling leading to enhanced filopodia assembly. Fascin is also overexpressed in most solid tumours where it supports invasion through control of F-actin structures at the periphery and nuclear envelope. Recently, fascin has been identified in the nucleus of a broad range of cell types but the contributions of nuclear fascin to cancer cell behaviour remain unknown. Here, we demonstrate that fascin bundles F-actin within the nucleus to support chromatin organisation and efficient DDR. Fascin associates directly with phosphorylated Histone H3 leading to regulated levels of nuclear fascin to support these phenotypes. Forcing nuclear fascin accumulation through the expression of nuclear-targeted fascin-specific nanobodies or inhibition of Histone H3 kinases results in enhanced and sustained nuclear F-actin bundling leading to reduced invasion, viability, and nuclear fascin-specific/driven apoptosis. These findings represent an additional important route through which fascin can support tumourigenesis and provide insight into potential pathways for targeted fascin-dependent cancer cell killing.

MAIP: a web service for predicting blood-stage malaria inhibitors.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development.

Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.

Pluripotent Stem Cell-Derived Hepatocytes Phenotypic Screening Reveals Small Molecules Targeting the CDK2/4-C/EBPα/DGAT2 Pathway Preventing ER-Stress Induced Lipid Accumulation.

Non-alcoholic fatty liver disease (NAFLD) has a large impact on global health. At the onset of disease, NAFLD is characterized by hepatic steatosis defined by the accumulation of triglycerides stored as lipid droplets. Developing therapeutics against NAFLD and progression to non-alcoholic steatohepatitis (NASH) remains a high priority in the medical and scientific community. Drug discovery programs to identify potential therapeutic compounds have supported high throughput/high-content screening of in vitro human-relevant models of NAFLD to accelerate development of efficacious anti-steatotic medicines. Human induced pluripotent stem cell (hiPSC) technology is a powerful platform for disease modeling and therapeutic assessment for cell-based therapy and personalized medicine. In this study, we applied AstraZeneca's chemogenomic library, hiPSC technology and multiplexed high content screening to identify compounds that significantly reduced intracellular neutral lipid content. Among 13,000 compounds screened, we identified hits that protect against hiPSC-derived hepatic endoplasmic reticulum stress-induced steatosis by a mechanism of action including inhibition of the cyclin D3-cyclin-dependent kinase 2-4 (CDK2-4)/CCAAT-enhancer-binding proteins (C/EBPα)/diacylglycerol acyltransferase 2 (DGAT2) pathway, followed by alteration of the expression of downstream genes related to NAFLD. These findings demonstrate that our phenotypic platform provides a reliable approach in drug discovery, to identify novel drugs for treatment of fatty liver disease as well as to elucidate their underlying mechanisms.

Are State Telehealth Policies Associated With The Use Of Telehealth Services Among Underserved Populations?

Using four years of data from a nationally representative consumer survey, we examined trends in telehealth usage over time and the role state telehealth policies play in telehealth use. Telehealth use increased dramatically during the period 2013-16, with new modalities such as live video, live chat, texting, and mobile apps gaining traction. The rate of live video communication rose from 6.6 percent in June 2013 to 21.6 percent in December 2016. However, underserved populations-including Medicaid, low-income, and rural populations-did not use live video communication as widely as other groups did. Less restrictive state telehealth policies were not associated with increased usage overall or among underserved populations. This study suggests that state efforts alone to remove barriers to using telehealth might not be sufficient for increasing use, and new incentives for providers and consumers to adopt and use telehealth may be needed.

Large-scale assessment of activity landscape feature probabilities of bioactive compounds.

Activity landscape representations integrate pairwise compound similarity and potency relationships and provide direct access to characteristic structure-activity relationship features in compound data sets. Because pairwise compound comparisons provide the foundation of activity landscape design, the assessment of specific landscape features such as activity cliffs has generally been confined to the level of compound pairs. A conditional probability-based approach has been applied herein to assign most probable activity landscape features to individual compounds. For example, for a given data set compound, it was determined if it would preferentially engage in the formation of activity cliffs or other landscape features. In a large-scale effort, we have determined conditional activity landscape feature probabilities for more than 160,000 compounds with well-defined activity annotations contained in 427 different target-based data sets. These landscape feature probabilities provide a detailed view of how different activity landscape features are distributed over currently available bioactive compounds.

Prediction of individual compounds forming activity cliffs using emerging chemical patterns.

Activity cliffs are formed by structurally similar or analogous compounds having large potency differences. In medicinal chemistry, pairs or groups of compounds forming activity cliffs are of interest for structure-activity relationship (SAR) analysis and compound optimization. Thus far, activity cliff assessment has mostly been descriptive, i.e., compound data sets and activity landscape representations have been searched for activity cliffs in the context of SAR analysis. Only recently, first attempts have also been made to depart from descriptive analysis and predict activity cliffs. This has been done by building computational models that distinguish compound pairs forming activity cliffs from non-cliff pairs. However, it is principally more challenging to predict single compounds that participate in activity cliffs. Here, we show that individual compounds having high or low potency can be accurately predicted to form activity cliffs on the basis of emerging chemical patterns.

Conditional probabilities of activity landscape features for individual compounds.

Activity landscape representations aid in the analysis of structure-activity relationships (SARs) of large compound data sets. Landscapes are characterized by features with different SAR information content such as, for example, regions formed by structurally diverse compounds having similar activity or, alternatively, structurally similar compounds with large activity differences, so-called activity cliffs. Modeling of activity landscapes typically requires pairwise comparisons of molecular similarity and potency relationships of compounds in a data set. Consequently, landscape features are generally resolved at the level of compound pairs. Herein, we introduce a methodology to assign feature probabilities to individual compounds. This makes it possible to organize compounds comprising activity landscapes into well-defined SAR categories. Specifically, the calculation of conditional feature probabilities of active compounds provides a balanced and further refined view of activity landscapes with a focus on individual molecules.

Predicting potent compounds via model-based global optimization.

Finding potent compounds for a given target in silico can be viewed as a constraint global optimization problem. This requires the use of an optimization function for which evaluations might be costly. The major task is maximizing the function while minimizing the number of evaluation steps. To solve this problem, we propose a machine learning algorithm, which first builds a statistical QSAR-model of the SAR landscape and then uses the model to identify regions in compound space having a high probability to contain a highly potent compound. For this purpose, we devise the so-called expected potency improvement (EI) criterion to rank candidate compounds with respect to their likelihood to exhibit higher potency than the most active compound in the training data. Therefore, this approach significantly differs from a purely prediction-oriented classical QSAR model. The method is superior to a nearest neighbor approach as significantly fewer evaluation steps are needed to identify the most potent compound for the given target.

Activity Landscapes, Information Theory, and Structure - Activity Relationships.

Activity landscapes provide a comprehensive description of structure-activity relationships (SARs). An information theoretic assessment of their features, namely, activity cliffs, similarity cliffs, smooth-SAR, and featureless regions, is presented based on the probability of occurrence of these features. It is shown that activity cliffs provide highly informative SARs compared to smooth-SAR regions, although the latter are the basis for most QSAR studies. This follows since small structural changes in the former are coupled with relatively large changes in activity, thus pinpointing specific structural features associated with the changes in activity. In contrast, Smooth-SAR regions are typically associated with relatively small changes in both structure and activity. Surprisingly, similarity cliffs, which occur when both compounds in a compound-pair have approximately equal activities but significantly different structures, are the most prevalent feature of activity landscapes. Hence, from an information theoretic point of view, they are the least informative landscape feature. Nevertheless, similarity cliffs do provide SAR information on potentially new active compound classes, and in that sense they are quite useful in drug discovery programs since they provide alternative possibilities should ADMET or other issues arise during the discovery and earlier preclinical development phases of drug research.

Assessing the target differentiation potential of imidazole-based protein kinase inhibitors.

A library of 484 imidazole-based candidate inhibitors was tested against 24 protein kinases. The resulting activity data have been systematically analyzed to search for compounds that effectively differentiate between kinases. Six imidazole derivatives with high kinase differentiation potential were identified. Nearest neighbor analysis revealed the presence of close analogues with varying differentiation potential. Small structural modifications of active compounds were found to shift their inhibitory profiles toward kinases with different functions.

Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map.

The transformation of high-dimensional bioactivity spaces into activity landscape representations is as of yet an unsolved problem in computational medicinal chemistry. High-dimensional activity spaces result from the experimental evaluation of compound sets on large numbers of targets. We introduce a first concept to represent and navigate high-dimensional activity landscapes that is based on a data structure termed ligand-target differentiation (LTD) map. This approach is designed to reduce the complexity of high-dimensional bioactivity spaces and enable the identification and further analysis of compound subsets with interesting activity and structural relationships. Its utility has been demonstrated using a set of more than 1400 inhibitors with exact activity measurements for varying numbers of 172 kinases.

Exploring SAR continuity in the vicinity of activity cliffs.

Activity cliffs are formed by structurally similar compounds with significant differences in potency and represent an extreme form of structure-activity relationships discontinuity. By contrast, regions of structure-activity relationships continuity in compound data sets result from the presence of structurally increasingly diverse compounds retaining similar activity. Previous studies have revealed that structure-activity relationships information extracted from large compound data sets is often heterogeneous in nature containing both continuous and discontinuous structure-activity relationships components. Structure-activity relationships discontinuity and continuity are often represented by different compound series, independent of each other. Here, we have searched different compound data sets for the presence of structure-activity relationships continuity within the vicinity of prominent activity cliffs. For this purpose, we have designed and implemented a computational approach utilizing particle swarm optimization to examine the structural neighborhood of activity cliffs for continuous structure-activity relationships components. Structure-activity relationships continuity in the structural neighborhood of activity cliffs was relatively rarely observed. However, in a number of cases, notable structure-activity relationships continuity was detected in the vicinity of prominent activity cliffs. Exemplary local structure-activity relationships environments displaying these characteristics were analyzed in detail. Thus, the structure-activity relationships environment of activity cliffs must not necessarily be discontinuous in nature, and local structure-activity relationships continuity and discontinuity can occur in a concerted manner in series of structurally related compounds.

Representation of multi-target activity landscapes through target pair-based compound encoding in self-organizing maps.

Activity landscape representations provide access to structure-activity relationships information in compound data sets. In general, activity landscape models integrate molecular similarity relationships with biological activity data. Typically, activity against a single target is monitored. However, for steadily increasing numbers of compounds, activity against multiple targets is reported, resulting in an opportunity, and often a need, to explore multi-target structure-activity relationships. It would be attractive to utilize activity landscape representations to aid in this process, but the design of activity landscapes for multiple targets is a complicated task. Only recently has a first multi-target landscape model been introduced, consisting of an annotated compound network focused on the systematic detection of activity cliffs. Herein, we report a conceptually different multi-target activity landscape design that is based on a 2D projection of chemical reference space using self-organizing maps and encodes compounds as arrays of pair-wise target activity relationships. In this context, we introduce the concept of discontinuity in multi-target activity space. The well-ordered activity landscape model highlights centers of discontinuity in activity space and is straightforward to interpret. It has been applied to analyze compound data sets with three, four, and five target annotations and identify multi-target structure-activity relationships determinants in analog series.

Extraction of discontinuous structure-activity relationships from compound data sets through particle swarm optimization.

The characterization of structure-activity relationship (SAR) features of large compound data sets has been a hot topic in recent years, and different methods for large-scale SAR analysis have been introduced. The exploration of local SAR components and prioritization of compound subsets have thus far mostly relied on graphical analysis methods that capture similarity and potency relationships in a systematic manner. A currently unsolved problem in large-scale SAR analysis is how to automatically select those compound subsets from large data sets that carry most SAR information. For this purpose, we introduce a numerical optimization scheme that is based on particle swarm optimization guided by an SAR scoring function. The methodology is applied to four large compound sets. We demonstrate that compound subsets representing the most discontinuous local SARs are consistently selected through particle swarm optimization.

Molecular mechanism-based network-like similarity graphs reveal relationships between different types of receptor ligands and structural changes that determine agonistic, inverse-agonistic, and antagonistic effects.

Receptor ligands might act as agonists, partial agonists, inverse agonists, or antagonists and it is often difficult to understand structural modifications that alter the mechanism of action. In order to compare ligands that are active against a given receptor but have different mechanisms of action, we have designed molecular networks that mirror similarity relationships and incorporate both mechanism of action information and mechanism-specific SAR features. These network representations make it possible to systematically evaluate relationships between different types of receptor ligands and identify communities of structurally very similar ligands with different mechanisms. From a series of such ligands, structural modifications can often be deduced that lead to "mechanism hops".

SAR monitoring of evolving compound data sets using activity landscapes.

In pharmaceutical research, collections of active compounds directed against specific therapeutic targets usually evolve over time. Small molecule discovery is an iterative process. New compounds are discovered, alternative compound series explored, some series discontinued, and others prioritized. The design of new compounds usually takes into consideration prior chemical and structure-activity relationship (SAR) knowledge. Hence, historically grown compound collections represent a viable source of chemical and SAR information that might be utilized to retrospectively analyze roadblocks in compound optimization and further guide discovery projects. However, SAR analysis of large and heterogeneous sets of active compounds is also principally complicated. We have subjected evolving compound data sets to SAR monitoring using activity landscape models in order to evaluate how composition and SAR characteristics might change over time. Chemotype and potency distributions in evolving data sets directed against different therapeutic targets were analyzed and alternative activity landscape representations generated at different points in time to monitor the progression of global and local SAR features. Our results show that the evolving data sets studied here have predominantly grown around seed clusters of active compounds that often emerged early on, while other SAR islands remained largely unexplored. Moreover, increasing scaffold diversity in evolving data sets did not necessarily yield new SAR patterns, indicating a rather significant influence of "me-too-ism" (i.e., introducing new chemotypes that are similar to already known ones) on the composition and SAR information content of the data sets.

Rationalizing three-dimensional activity landscapes and the influence of molecular representations on landscape topology and the formation of activity cliffs.

Activity landscapes are defined by potency and similarity distributions of active compounds and reflect the nature of structure-activity relationships (SARs). Three-dimensional (3D) activity landscapes are reminiscent of topographical maps and particularly intuitive representations of compound similarity and potency distributions. From their topologies, SAR characteristics can be deduced. Accordingly, idealized theoretical landscape models have been utilized to rationalize SAR features, but "true" 3D activity landscapes have not yet been described in detail. Herein we present a computational approach to derive approximate 3D activity landscapes for actual compound data sets and to analyze exemplary landscape representations. These activity landscapes are generated within a consistent reference frame so that they can be compared across different activity classes. We show that SAR features of compound data sets can be derived from the topology of landscape models. A notable correlation is observed between global SAR phenotypes, assigned on the basis of SAR discontinuity scoring, and characteristic landscape topologies. We also show that different molecular representations can substantially alter the topology of activity landscapes for a given data set and modulate the formation of activity cliffs, which represent the most prominent landscape features. Depending on the choice of molecular representations, compounds forming a steep activity cliff in a given landscape might be separated in another and no longer form a cliff. However, comparison of alternative activity landscapes makes it possible to focus on compound subsets having high SAR information content.