Data standards in drug discovery: A long way to go.

Each year, millions to trillions of data points are generated to evaluate the response of chemicals and biologicals to human cells in vitro and in vivo using various technologies and endpoints. Despite the vast amount of data available, the development process has not become significantly more efficient in recent years. Given the increasing use of more complex physiological models, which are time-consuming and significantly more expensive, it is crucial to maximize the value of these valuable data through improved standardization.

Towards policies that capture the expected value of biomolecular diversity for drug discovery, human health, and well-being.

This paper aims to help policy makers with a characterization of the intrinsic value of biodiversity and its role as a critical foundation for sustainable development, human health, and well-being. Our objective is to highlight the urgent need to overcome economic, disciplinary, national, cultural, and regional barriers, in order to work out innovative measures to create a sustainable future and prevent the mutual extinction of humans and other species. We emphasize the pervasive neglect paid to the cross-dependency of planetary health, the health of individual human beings and other species. It is critical that social and natural sciences are taken into account as key contributors to forming policies related to biodiversity, conservation, and health management. We are reaching the target date of Nagoya treaty signatories to have accomplished measures to prevent biodiversity loss, providing a unique opportunity for policy makers to make necessary adjustments and refocus targets for the next decade. We propose recommendations for policy makers to explore novel avenues to halt the accelerated global loss of biodiversity. Beyond the critical ecological functions biodiversity performs, its enormous untapped the repertoire of natural molecular diversity is needed for solving accelerating global healthcare challenges.

Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization.

Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.

Acoustic Ejection Mass Spectrometry: A Fully Automatable Technology for High-Throughput Screening in Drug Discovery.

Acoustic droplet ejection (ADE)-open port interface (OPI)-mass spectrometry (MS) has recently been introduced as a versatile analytical method that combines fast and contactless acoustic sampling with sensitive and accurate electrospray ionization (ESI)-MS-based analyte detection. The potential of the technology to provide label-free measurements in subsecond analytical cycle times makes it an attractive option for high-throughput screening (HTS). Here, we report the first implementation of ADE-OPI-MS in a fully automated HTS environment, based on the example of a biochemical assay aiming at the identification of small-molecule inhibitors of the cyclic guanosine monophosphate-adenosine monophosphate (GMP-AMP) synthase (cGAS). First, we describe the optimization of the method to enable sensitive and accurate determination of enzyme activity and inhibition in miniaturized 1536-well microtiter plate format. Then we show both results from a validation single-concentration screen using a test set of 5500 compounds, and the subsequent concentration-response testing of selected hits in direct comparison with a previously established matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) readout. Finally, we present the development of an in-line OPI cleaning procedure aiming to match the instrument robustness required for large-scale HTS campaigns. Overall, this work points to critical method development parameters and provides guidance for the establishment of integrated ADE-OPI-MS as HTS-compatible technology for early drug discovery.

Luminescence Energy Transfer-Based Screening and Target Engagement Approaches for Chemical Biology and Drug Discovery.

Luminescence is characterized by the spontaneous emission of light resulting from either chemical or biological reactions. Because of their high sensitivity, reduced background interference, and applicability to numerous situations, luminescence-based assay strategies play an essential role in early-stage drug discovery. Newer developments in luminescence-based technologies have dramatically affected the ability of researchers to investigate molecular binding events. At the forefront of these developments are the nano bioluminescence resonance energy transfer (NanoBRET) and amplified luminescent proximity homogeneous assay (Alpha) technologies. These technologies have opened up numerous possibilities for analyzing the molecular biophysical properties of complexes in environments such as cell lysates. Moreover, NanoBRET enables the validation and quantitation of the interactions between therapeutic targets and small molecules in live cells, representing an essential benchmark for preclinical drug discovery. Both techniques involve proximity-based luminescence energy transfer, in which excited-state energy is transferred from a donor to an acceptor, where the efficiency of transfer depends on proximity. Both approaches can be applied to high-throughput compound screening in biological samples, with the NanoBRET assay providing opportunities for live-cell screening. Representative applications of both technologies for assessing physical interactions and associated challenges are discussed.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery.

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

Anticancer drug discovery from Iranian Chrysanthemum cultivars through system pharmacology exploration and experimental validation.

Breast cancer is the most common carcinoma in women, and natural products would be effective preventing some side effects of cancer treatment. In the present study, cytotoxic activities of different Iranian Chrysanthemum morifolium cultivars were evaluated in human breast cancer cell lines (MCF-7) and human lymphocytes. A systems pharmacology approach was employed between major compounds of these cultivars (chlorogenic acid, luteolin, quercetin, rutin, ferulic acid, and apigenin) and known breast cancer drugs (tucatinib, methotrexate, tamoxifen, and mitomycin) with 22 breast cancer-related targets to analyze the mechanism through which Chrysanthemum cultivars act on breast cancer. Target validation was performed by the molecular docking method. The results indicated that Chrysanthemum extracts inhibited the proliferation of MCF7 cells in a dose- and cultivar-dependent manner. In all studied cultivars, the most effective extract concentration with the lowest viability of MCF-7 cells, was as much as 312 µg ml-1. Also, higher concentrations of the extracts (> 1000 µg ml-1) reduced the lymphocyte cell viability, demonstrating that these doses were toxic. The gene ontology analysis revealed the therapeutic effects of Chrysanthemum's active compounds on breast cancer by regulating the biological processes of their protein targets. Moreover, it has been documented that rutin, owing to its anticancer effects and several other health benefits, is a promising multi-targeted herbal ingredient. Finally, the present study compared different Iranian Chrysanthemum cultivars to provide new insights into useful pharmaceutical applications.

The Communication of Hit Quality Using Natural History Visualizations (NHVs).

High-throughput screening (HTS) often yields a list of compounds that requires prioritization before further work is performed. Prioritization criteria typically include activity, selectivity, physicochemical properties, and other absolute or calculated measurements of compound "value." One critical method of compound prioritization is often not discussed in published accounts of HTS. We have referred to this oft-overlooked metric as "compound natural history." These natural histories are observational evaluations of how a compound has been reported in the historical literature or compound databases. The purpose of this work was to develop a useful natural history visualization (NHV) that could form a standard, important part of hit reporting and evaluation. In this case report, we propose an efficient and effective NHV that will assist in the prioritization of active compounds and demonstrate its utility using a retrospective analysis of reported hits. We propose that this method of compound natural history evaluation be adopted in HTS triage and become an integral component of published reports of HTS outcomes.

Brief Guide: Experimental Strategies for High-Quality Hit Selection from Small-Molecule Screening Campaigns.

Small-molecule screening is a powerful approach to identify modulators of either specific biological targets or cellular pathways with phenotypic endpoints. A myriad of assay technologies are available to assess the activity of enzymes, monitor protein-protein interactions, measure transcription factor activity in reporter assays, or detect cellular features and activities using high-content imaging. A common challenge during small-molecule screening is, however, the presence of hit compounds generating assay interference, thereby producing false-positive hits. Thus, efforts are needed to uncover such interferences to prioritize high-quality hits for further analysis. This process encompasses (1) computational approaches to flag undesirable compounds, and (2) the use of experimental approaches like counter, orthogonal, and cellular fitness screens to identify and eliminate artifacts. In this brief guide, we provide an overview for first-time users, highlighting experimental screening strategies to prioritize high-quality bioactive hits from high-throughput screening/high-content screening (HTS/HCS) campaigns.

A Novel High-Throughput FLIPR Tetra-Based Method for Capturing Highly Confluent Kinetic Data for Structure-Kinetic Relationship Guided Early Drug Discovery.

Target engagement by small molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationships. It is becoming clearer, however, that understanding the kinetics of the interaction between a small-molecule inhibitor and the biological target [structure-kinetic relationship (SKR)] is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in a high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in-depth kinetic studies are often carried out on only a small number of compounds, and usually at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process, but the throughput limitations of traditional methods preclude this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent data points for accurate parameter estimation from time course analysis. Here, we describe the use of the fluorescent imaging plate reader (FLIPR), a charge-coupled device (CCD) camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition, and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation. In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on hundreds of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.

Reducing False Positives through the Application of Fluorescence Lifetime Technology: A Comparative Study Using TYK2 Kinase as a Model System.

The predominant assay detection methodologies used for enzyme inhibitor identification during early-stage drug discovery are fluorescence-based. Each fluorophore has a characteristic fluorescence decay, known as the fluorescence lifetime, that occurs throughout a nanosecond-to-millisecond timescale. The measurement of fluorescence lifetime as a reporter for biological activity is less common than fluorescence intensity, even though the latter has numerous issues that can lead to false-positive readouts. The confirmation of hit compounds as true inhibitors requires additional assays, cost, and time to progress from hit identification to lead drug-candidate optimization. To explore whether the use of fluorescence lifetime technology (FLT) can offer comparable benefits to label-free-based approaches such as RapidFire mass spectroscopy (RF-MS) and a superior readout compared to time-resolved fluorescence resonance energy transfer (TR-FRET), three equivalent assays were developed against the clinically validated tyrosine kinase 2 (TYK2) and screened against annotated compound sets. FLT provided a marked decrease in the number of false-positive hits when compared to TR-FRET. Further cellular screening confirmed that a number of potential inhibitors directly interacted with TYK2 and inhibited the downstream phosphorylation of the signal transducer and activator of transcription 4 protein (STAT4).

Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries.

Over the past 20 years, the toolbox for discovering small-molecule therapeutic starting points has expanded considerably. Pharmaceutical researchers can now choose from technologies that, in addition to traditional high-throughput knowledge-based and diversity screening, now include the screening of fragment and fragment-like libraries, affinity selection mass spectrometry, and selection against DNA-encoded libraries (DELs). Each of these techniques has its own unique combination of advantages and limitations that makes them more, or less, suitable for different target classes or discovery objectives, such as desired mechanism of action. Layered on top of this are the constraints of the drug-hunters themselves, including budgets, timelines, and available platform capacity; each of these can play a part in dictating the hit identification strategy for a discovery program. In this article, we discuss some of the factors that we use to govern our building of a hit identification roadmap for a program and describe the increasing role that DELs are playing in our discovery strategy. Furthermore, we share our learning during our initial exploration of DEL and highlight the approaches we have evolved to maximize the value returned from DEL selections. Topics addressed include the optimization of library design and production, reagent validation, data analysis, and hit confirmation. We describe how our thinking in these areas has led us to build a DEL platform that has begun to deliver tractable matter to our global discovery portfolio.

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data.

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

An Automated High-Throughput Fluorescence In Situ Hybridization (FISH) Assay Platform for Use in the Identification and Optimization of siRNA-Based Therapeutics.

Since the revolutionary discovery of RNA interference (RNAi) more than 20 years ago, synthetic small interfering RNAs (siRNAs) have held great promise as therapeutic agents for treating human diseases by the specific knockdown of disease-causing gene products. To facilitate the development of siRNA therapeutics, a robust, high-throughput in vitro assay for measuring gene silencing is imperative during the initial siRNA lead sequence identification and, later, during the lead optimization with chemically modified siRNAs. There are several potential assays for measuring gene expression. Quantitative reverse transcription PCR (qRT-PCR) has been widely used to quantitate messenger RNA (mRNA). This method has a few disadvantages, however, such as the requirement for RNA isolation, complementary DNA (cDNA) generation, and PCR reaction, which are labor-intensive, limit the assay throughput, and introduce variability. We chose a high-content imaging assay, bDNA FISH, that combines the branched DNA (bDNA) technology with fluorescence in situ hybridization (FISH) to measure gene silencing by siRNAs because it is sensitive and robust with a short reagent procurement and assay development time. We also built a fully automated liquid-handling platform for executing bDNA FISH assays to increase throughput, and the system has a capacity of generating 192 concentration-response curves in a single run. We have successfully developed and executed the bDNA FISH assays for multiple targets using this automated platform to identify and optimize siRNA candidate molecules. Examples of the bDNA FISH assay for selected targets are presented.

Hit Discovery for Public Target Programs in the European Lead Factory: Experiences and Output from Assay Development and Ultra-High-Throughput Screening.

The European Lead Factory (ELF) consortium provides European academics and small and medium enterprises access to ~0.5 million unique compounds, a state-of-the-art ultra-high-throughput screening (u-HTS) platform, and industrial early drug discovery (DD) expertise with the aim of delivering innovative DD starting points. From 2013 to 2018, 154 proposals for eight target classes in seven therapeutic areas were submitted to the ELF consortium, 88 of which were accepted by the selection committee. During this period, 76 primary assays based on seven different readout technologies were optimized and mainly miniaturized to 1536-well plates. In total, 72 u-HTS campaigns were carried out, and follow-up work including hit triage through orthogonal, deselection, selectivity, and biophysical assays were finalized. This ambitious project showed that besides the quality of the compound library and the primary assay, the success of centralized u-HTS of large compound libraries across many target classes, various assay types, and different readout technologies is also largely dependent on the capacity and flexibility of the automation on one hand and the hit-triaging phase on the other, particularly because of undesired compound-assay interference. Thus far, the delivered hit lists from the ELF consortium have resulted in spinoffs, patents, in vivo proof of concepts, preclinical development programs, peer-reviewed publications, PhD theses, and much more, demonstrating early success indications.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology.

The compound optimization monitor (COMO) approach was originally developed as a diagnostic approach to aid in evaluating development stages of analog series and progress made during lead optimization. COMO uses virtual analog populations for the assessment of chemical saturation of analog series and has been further developed to bridge between optimization diagnostics and compound design. Herein, we discuss key methodological features of COMO in its scientific context and present a deep learning extension of COMO for generative molecular design, leading to the introduction of DeepCOMO. Applications on exemplary analog series are reported to illustrate the entire DeepCOMO repertoire, ranging from chemical saturation and structure-activity relationship progression diagnostics to the evaluation of different analog design strategies and prioritization of virtual candidates for optimization efforts, taking into account the development stage of individual analog series.

A painful lesson from the COVID-19 pandemic: the need for broad-spectrum, host-directed antivirals.

While the COVID-19 pandemic has spurred intense research and collaborative discovery worldwide, the development of a safe, effective, and targeted antiviral from the ground up is time intensive. Therefore, most antiviral discovery efforts are focused on the re-purposing of clinical stage or approved drugs. While emerging data on drugs undergoing COVID-19 repurpose are intriguing, there is an undeniable need to develop broad-spectrum antivirals to prevent future viral pandemics of unknown origin. The ideal drug to curtail rapid viral spread would be a broad-acting agent with activity against a wide range of viruses. Such a drug would work by modulating host-proteins that are often shared by multiple virus families thereby enabling preemptive drug development and therefore rapid deployment at the onset of an outbreak. Targeting host-pathways and cellular proteins that are hijacked by viruses can potentially offer broad-spectrum targets for the development of future antiviral drugs. Such host-directed antivirals are also likely to offer a higher barrier to the development and selection of drug resistant mutations. Given that most approved antivirals do not target host-proteins, we reinforce the need for the development of such antivirals that can be used in pre- and post-exposure populations.

Genomic, microbial and environmental standardization in animal experimentation limiting immunological discovery.

BACKGROUND: The use of inbred mice housed under standardized environmental conditions has been critical in identifying immuno-pathological mechanisms in different infectious and inflammatory diseases as well as revealing new therapeutic targets for clinical trials. Unfortunately, only a small percentage of preclinical intervention studies using well-defined mouse models of disease have progressed to clinically-effective treatments in patients. The reasons for this lack of bench-to-bedside transition are not completely understood; however, emerging data suggest that genetic diversity and housing environment may greatly influence muring immunity and inflammation. RESULTS: Accumulating evidence suggests that certain immune responses and/or disease phenotypes observed in inbred mice may be quite different than those observed in their outbred counterparts. These differences have been thought to contribute to differing immune responses to foreign and/or auto-antigens in mice vs. humans. There is also a growing literature demonstrating that mice housed under specific pathogen free conditions possess an immature immune system that remarkably affects their ability to respond to pathogens and/or inflammation when compared with mice exposed to a more diverse spectrum of microorganisms. Furthermore, recent studies demonstrate that mice develop chronic cold stress when housed at standard animal care facility temperatures (i.e. 22-24 °C). These temperatures have been shown alter immune responses to foreign and auto-antigens when compared with mice housed at their thermo-neutral body temperature of 30-32 °C. CONCLUSIONS: Exposure of genetically diverse mice to a spectrum of environmentally-relevant microorganisms at housing temperatures that approximate their thermo-neutral zone may improve the chances of identifying new and more potent therapeutics to treat infectious and inflammatory diseases.