High-Throughput Screening: Update on Practices and Success

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

High-Throughput DNA-Encoded Libraries Affinity Selection Platform for Binder Identification with Solid Support Protein Immobilization

DNA-encoded libraries (DELs) have demonstrated to be one of the most powerful technologies within the ligand identification toolbox, widely used either in academia or biotech and pharma companies. DEL methodology utilizes affinity selection (AS) as the approach to interrogate the protein of interest for the identification of binders. Here we present a high-throughput, fully automated AS platform developed to fulfill industrial standards and compatible with different assay formats to improve the reproducibility of the AS process for DEL binders identification. This platform is flexible enough to virtually set aside all kinds of DELs and AS methods and conditions using immobilized proteins. It bears the two main immobilization methods to support of the proteins of interest: magnetic beads or resin tip columns. A combination of a broad variety of protocol options with a wide range of different experimental conditions can be set up with a throughput of 96 samples at the same time. In addition, small modifications of the protocols provide the platform with the versatility to run not only the routine DEL screens, but also test covalent libraries, the successful immobilization of the proteins of interest, and many other experiments that may be required. This versatile AS platform for DEL can be a powerful instrument for direct application of the technology in academic and industry settings.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer

BACKGROUND/OBJECTIVES

Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4.

SUBJECTS/METHODS

In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.

RESULTS

The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.

CONCLUSIONS

Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

Naringin from Coffee Inhibits Foodborne Aspergillus fumigatus via the NDK Pathway: Evidence from an In Silico Study

In the tropics, coffee has been one of the most extensively cultivated economic crops, especially Arabica coffee (Coffea arabica L.). The coffee pulp, which includes phytochemicals with a proven antifungal action, is one of the most insufficiently utilized and neglected byproducts of coffee refining. In the current experiment, we carried out in silico screening of the isolated Arabica coffee phytochemicals for antifungal activity against Aspergillus fumigatus: a foodborne fungus of great public health importance. As determined by the molecular docking interactions of the library compounds indicated, the best interactions were found to occur between the nucleoside-diphosphate kinase protein 6XP7 and the test molecules Naringin (-6.771 kcal/mol), followed by Epigallocatechin gallate (-5.687 kcal/mol). Therefore, Naringin was opted for further validation with molecular dynamic simulations. The ligand-protein complex RMSD indicated a fairly stable Naringin-NDK ligand-protein complex throughout the simulation period (2-16 Å). In ADME and gastrointestinal absorbability testing, Naringin was observed to be orally bioavailable, with very low intestinal absorption and a bioavailability score of 0.17. This was further supported by the boiled egg analysis data, which clearly indicated that the GI absorption of the Naringin molecule was obscure. We found that naringin could be harmful only when swallowed at a median lethal dose between 2000 and 5000 mg/kg. In accordance with these findings, the toxicity prediction reports suggested that Naringin, found especially in citrus fruits and tomatoes, is safe for human consumption after further investigation. Overall, Naringin may be an ideal candidate for developing anti-A. fumigatus treatments and food packaging materials. Thus, this study addresses the simultaneous problems of discarded coffee waste management and antifungal resistance to available medications.

Precision Vaccinology Approaches for the Development of Adjuvanted Vaccines Targeted to Distinct Vulnerable Populations

Infection persists as one of the leading global causes of morbidity and mortality, with particular burden at the extremes of age and in populations who are immunocompromised or suffer chronic co-morbid diseases. By focusing discovery and innovation efforts to better understand the phenotypic and mechanistic differences in the immune systems of diverse vulnerable populations, emerging research in precision vaccine discovery and development has explored how to optimize immunizations across the lifespan. Here, we focus on two key elements of precision vaccinology, as applied to epidemic/pandemic response and preparedness, including (a) selecting robust combinations of adjuvants and antigens, and (b) coupling these platforms with appropriate formulation systems. In this context, several considerations exist, including the intended goals of immunization (e.g., achieving immunogenicity versus lessening transmission), reducing the likelihood of adverse reactogenicity, and optimizing the route of administration. Each of these considerations is accompanied by several key challenges. On-going innovation in precision vaccinology will expand and target the arsenal of vaccine components for protection of vulnerable populations.

Using a Quantitative High-Throughput Screening Platform to Identify Molecular Targets and Compounds as Repurposing Candidates for Endometriosis

Endometriosis, defined as the growth of hormonally responsive endometrial-like tissue outside of the uterine cavity, is an estrogen-dependent, chronic, pro-inflammatory disease that affects up to 11.4% of women of reproductive age and gender-diverse people with a uterus. At present, there is no long-term cure, and the identification of new therapies that provide a high level of efficacy and favourable long-term safety profiles with rapid clinical access are a priority. In this study, quantitative high-throughput compound screens of 3517 clinically approved compounds were performed on patient-derived immortalized human endometrial stromal cell lines. Following assay optimization and compound criteria selection, a high-throughput screening protocol was developed to enable the identification of compounds that interfered with estrogen-stimulated cell growth. From these screens, 23 novel compounds were identified, in addition to their molecular targets and in silico cell-signalling pathways, which included the neuroactive ligand-receptor interaction pathway, metabolic pathways, and cancer-associated pathways. This study demonstrates for the first time the feasibility of performing large compound screens for the identification of new translatable therapeutics and the improved characterization of endometriosis molecular pathophysiology. Further investigation of the molecular targets identified herein will help uncover new mechanisms involved in the establishment, symptomology, and progression of endometriosis.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Fast fragment- and compound-screening pipeline at the Swiss Light Source

Over the last two decades, fragment-based drug discovery (FBDD) has emerged as an effective and efficient method to identify new chemical scaffolds for the development of lead compounds. X-ray crystallography can be used in FBDD as a tool to validate and develop fragments identified as binders by other methods. However, it is also often used with great success as a primary screening technique. In recent years, technological advances at macromolecular crystallography beamlines in terms of instrumentation, beam intensity and robotics have enabled the development of dedicated platforms at synchrotron sources for FBDD using X-ray crystallography. Here, the development of the Fast Fragment and Compound Screening (FFCS) platform, an integrated next-generation pipeline for crystal soaking, handling and data collection which allows crystallography-based screening of protein crystals against hundreds of fragments and compounds, at the Swiss Light Source is reported.

Prediction of allosteric sites and signaling: Insights from benchmarking datasets

Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.

Monobodies as tool biologics for accelerating target validation and druggable site discovery

Despite increased investment and technological advancement, new drug approvals have not proportionally increased. Low drug approval rates, particularly for new targets, are linked to insufficient target validation at early stages. Thus, there remains a strong need for effective target validation techniques. Here, we review the use of synthetic binding proteins as tools for drug target validation, with focus on the monobody platform among several advanced synthetic binding protein platforms. Monobodies with high affinity and high selectivity can be rapidly developed against challenging targets, such as KRAS mutants, using protein engineering technologies. They have strong tendency to bind to functional sites and thus serve as drug-like molecules, and they can serve as targeting ligands for constructing bio-PROTACs. Genetically encoded monobodies are effective "tool biologics" for validating intracellular targets. They promote crystallization and help reveal the atomic structures of the monobody-target interface, which can inform drug design. Using case studies, we illustrate the potential of the monobody technology in accelerating target validation and small-molecule drug discovery.

Faster, better, and cheaper: harnessing microfluidics and mass spectrometry for biotechnology

High-throughput screening technologies are widely used for elucidating biological activities. These typically require trade-offs in assay specificity and sensitivity to achieve higher throughput. Microfluidic approaches enable rapid manipulation of small volumes and have found a wide range of applications in biotechnology providing improved control of reaction conditions, faster assays, and reduced reagent consumption. The integration of mass spectrometry with microfluidics has the potential to create high-throughput, sensitivity, and specificity assays. This review introduces the widely-used mass spectrometry ionization techniques that have been successfully integrated with microfluidics approaches such as continuous-flow system, microchip electrophoresis, droplet microfluidics, digital microfluidics, centrifugal microfluidics, and paper microfluidics. In addition, we discuss recent applications of microfluidics integrated with mass spectrometry in single-cell analysis, compound screening, and the study of microorganisms. Lastly, we provide future outlooks towards online coupling, improving the sensitivity and integration of multi-omics into a single platform.

Understanding the Development, Standardization, and Validation Process of Alternative In Vitro Test Methods for Regulatory Approval from a Researcher Perspective

Due to economic, practical, ethical, and scientific reasons, researchers, among others, are pushing for alternative in vitro test methods to replace or reduce existing animal experiments. In order for these tests to be more broadly used by the industrial sector and regulatory bodies, orchestrated efforts are required to show the robustness and reliability of in vitro methods, which can accelerate the use for early screening testing. Another way of increasing the use of alternatives is to coordinate validation studies, that is, multi-laboratory trials, and to gain regulatory approval and instatement as test guidelines or standard method. However, awareness of the exact standardization, validation, and approval process has been a major obstacle for many researchers. Herein, the process has been broken down into three main phases: i) test method development; ii) intra- and inter-laboratory validation; and iii) regulatory acceptance. This general process applies to all alternative methods seeking validation and approval, although the intricacies of different toxicological endpoints and/or chemical sectors may lead to additional work, particularly in the validation stage. The authors' aim is to provide insight in the development process of alternative methods with a focus on in vitro cell culture methods over validation to regulatory acceptance.

Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond

Drug resistance is prevalent across many diseases, rendering therapies ineffective with severe financial and health consequences. Rather than accepting resistance after the fact, proactive strategies need to be incorporated into the drug design and development process to minimize the impact of drug resistance. These strategies can be derived from our experience with viral disease targets where multiple generations of drugs had to be developed to combat resistance and avoid antiviral failure. Significant efforts including experimental and computational structural biology, medicinal chemistry, and machine learning have focused on understanding the mechanisms and structural basis of resistance against direct-acting antiviral (DAA) drugs. Integrated methods show promise for being predictive of resistance and potency. In this review, we give an overview of this research for human immunodeficiency virus type 1, hepatitis C virus, and influenza virus and the lessons learned from resistance mechanisms of DAAs. These lessons translate into rational strategies to avoid resistance in drug design, which can be generalized and applied beyond viral targets. While resistance may not be completely avoidable, rational drug design can and should incorporate strategies at the outset of drug development to decrease the prevalence of drug resistance.

Intein mediated high throughput screening for bispecific antibodies

Bispecific antibodies comprise extremely diverse architectures enabling complex modes of action, such as effector cell recruitment or conditional target modulation via dual targeting, not conveyed by monospecific antibodies. In recent years, research on bispecific therapeutics has substantially grown. However, evaluation of binding moiety combinations often leads to undesired prolonged development times. While high throughput screening for small molecules and classical antibodies has evolved into a mature discipline in the pharmaceutical industry, dual-targeting antibody screening methodologies lack the ability to fully evaluate the tremendous number of possible combinations and cover only a limited portion of the combinatorial screening space. Here, we propose a novel combinatorial screening approach for bispecific IgG-like antibodies to extenuate screening limitations in industrial scale, expanding the limiting screening space. Harnessing the ability of a protein trans-splicing reaction by the split intein Npu DnaE, antibody fragments were reconstituted within the hinge region in vitro. This method allows for fully automated, rapid one-pot antibody reconstitution, providing biological activity in several biochemical and functional assays. The technology presented here is suitable for automated functional and combinatorial high throughput screening of bispecific antibodies.

Use of Natural Products in Leishmaniasis Chemotherapy: An Overview

Leishmaniasis is an infectious parasitic disease that is caused by protozoa of the genus Leishmania, a member of the Trypanosomatidae family. Leishmaniasis is classified by the World Health Organization as a neglected tropical disease that is responsible for millions of deaths worldwide. Although there are many possible treatments for leishmaniasis, these treatments remain mostly ineffective, expensive, and long treatment, as well as causing side effects and leading to the development of resistance. For novel and effective treatments to combat leishmaniasis, many research groups have sought to utilize natural products. In addition to exhibiting potential as therapeutic compounds, natural products may also contribute to the development of new drugs based on their chemical structures. This review presents the most promising natural products, including crude extracts and isolated compounds, employed against Leishmania spp.

Drug Screening and Drug Repositioning as Promising Therapeutic Approaches for Spinal Muscular Atrophy Treatment

Spinal muscular atrophy (SMA) is the most common genetic disease affecting infants and young adults. Due to mutation/deletion of the survival motor neuron (SMN) gene, SMA is characterized by the SMN protein lack, resulting in motor neuron impairment, skeletal muscle atrophy and premature death. Even if the genetic causes of SMA are well known, many aspects of its pathogenesis remain unclear and only three drugs have been recently approved by the Food and Drug Administration (Nusinersen-Spinraza; Onasemnogene abeparvovec or AVXS-101-Zolgensma; Risdiplam-Evrysdi): although assuring remarkable results, the therapies show some important limits including high costs, still unknown long-term effects, side effects and disregarding of SMN-independent targets. Therefore, the research of new therapeutic strategies is still a hot topic in the SMA field and many efforts are spent in drug discovery. In this review, we describe two promising strategies to select effective molecules: drug screening (DS) and drug repositioning (DR). By using compounds libraries of chemical/natural compounds and/or Food and Drug Administration-approved substances, DS aims at identifying new potentially effective compounds, whereas DR at testing drugs originally designed for the treatment of other pathologies. The drastic reduction in risks, costs and time expenditure assured by these strategies make them particularly interesting, especially for those diseases for which the canonical drug discovery process would be long and expensive. Interestingly, among the identified molecules by DS/DR in the context of SMA, besides the modulators of SMN2 transcription, we highlighted a convergence of some targeted molecular cascades contributing to SMA pathology, including cell death related-pathways, mitochondria and cytoskeleton dynamics, neurotransmitter and hormone modulation.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Creating and screening natural product libraries

Covering: up to 2020The National Cancer Institute of the United States (NCI) has initiated a Cancer Moonshot program entitled the NCI Program for Natural Product Discovery. As part of this effort, the NCI is producing a library of 1 000 000 partially purified natural product fractions which are being plated into 384-well plates and provided to the research community free of charge. As the first 326 000 of these fractions have now been made available, this review seeks to describe the general methods used to collect organisms, extract those organisms, and create a prefractionated library. Importantly, this review also details both cell-based and cell-free bioassay methods and the adaptations necessary to those methods to productively screen natural product libraries. Finally, this review briefly describes post-screen dereplication and compound purification and scale up procedures which can efficiently identify active compounds and produce sufficient quantities of natural products for further pre-clinical development.

Drug discovery and chemical probing in Drosophila

Flies are increasingly utilized in drug discovery and chemical probing in vivo, which are novel technologies complementary to genetic probing in fundamental biological studies. Excellent genetic conservation, small size, short generation time, and over one hundred years of genetics make Drosophila an attractive model for rapid assay readout and use of analytical amounts of compound, enabling the experimental iterations needed in early drug development at a fraction of time and costs. Here, we describe an effective drug-testing pipeline using adult flies that can be easily implemented to study several disease models and different genotypes to discover novel molecular insight, probes, quality lead compounds, and develop novel prototype drugs.

Targeting Glycosylation: A New Road for Cancer Drug Discovery

Cancer is a deadly disease that encompasses numerous cellular modifications. Among them, alterations in glycosylation are a proven reliable hallmark of cancer, with most biomarkers used in the clinic detecting cancer-associated glycans. Despite their clear potential as therapy targets, glycans have been overlooked in drug discovery strategies. The complexity associated with the glycosylation process, and lack of specific methodologies to study it, have long hampered progress. However, recent advances in new methodologies, such as glycoengineering of cells and high-throughput screening (HTS), have opened new avenues of discovery. We envision that glycan-based targeting has the potential to start a new era of cancer therapy. In this article, we discuss the promise of cancer-associated glycosylation for the discovery of effective cancer drugs.

Towards reproducible computational drug discovery

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Flaxseed Lignans as Important Dietary Polyphenols for Cancer Prevention and Treatment: Chemistry, Pharmacokinetics, and Molecular Targets

Cancer causes considerable morbidity and mortality across the world. Socioeconomic, environmental, and lifestyle factors contribute to the increasing cancer prevalence, bespeaking a need for effective prevention and treatment strategies. Phytochemicals like plant polyphenols are generally considered to have anticancer, anti-inflammatory, antiviral, antimicrobial, and immunomodulatory effects, which explain their promotion for human health. The past several decades have contributed to a growing evidence base in the literature that demonstrate ability of polyphenols to modulate multiple targets of carcinogenesis linking models of cancer characteristics (i.e., hallmarks and nutraceutical-based targeting of cancer) via direct or indirect interaction or modulation of cellular and molecular targets. This evidence is particularly relevant for the lignans, an ubiquitous, important class of dietary polyphenols present in high levels in food sources such as flaxseed. Literature evidence on lignans suggests potential benefit in cancer prevention and treatment. This review summarizes the relevant chemical and pharmacokinetic properties of dietary polyphenols and specifically focuses on the biological targets of flaxseed lignans. The consolidation of the considerable body of data on the diverse targets of the lignans will aid continued research into their potential for use in combination with other cancer chemotherapies, utilizing flaxseed lignan-enriched natural products.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

A simple fluorescent assay for the discovery of protein-protein interaction inhibitors

Due to the therapeutic potential of targeting protein-protein interactions (PPIs) there is a need for easily executed assays to perform high throughput screening (HTS) of inhibitors. We have developed and optimized an innovative and robust fluorescence-based assay for detecting PPI inhibitors, called FluorIA (Fluorescence-based protein-protein Interaction Assay). Targeting the PPI of RAD52 with replication protein A (RPA) was used as an example, and the FluorIA protocol design, optimization and successful application to HTS of large chemical libraries are described. Here enhanced green fluorescent protein (EGFP)-tagged RAD52 detected the PPI using full-length RPA heterotrimer coated, black microtiter plates and loss in fluorescence intensity identified small molecule inhibitors (SMIs) that displaced the EGFP-tagged RAD52. The FluorIA design and protocol can be adapted and applied to detect PPIs for other protein systems. This should push forward efforts to develop targeted therapeutics against protein complexes in pathological processes.

An Evaluation of the Potential of NMR Spectroscopy and Computational Modelling Methods to Inform Biopharmaceutical Formulations

Protein-based therapeutics are considered to be one of the most important classes of pharmaceuticals on the market. The growing need to prolong stability of high protein concentrations in liquid form has proven to be challenging. Therefore, significant effort is being made to design formulations which can enable the storage of these highly concentrated protein therapies for up to 2 years. Currently, the excipient selection approach involves empirical high-throughput screening, but does not reveal details on aggregation mechanisms or the molecular-level effects of the formulations under storage conditions. Computational modelling approaches have the potential to elucidate such mechanisms, and rapidly screen in silico prior to experimental testing. Nuclear Magnetic Resonance (NMR) spectroscopy can also provide complementary insights into excipient–protein interactions. This review will highlight the underpinning principles of molecular modelling and NMR spectroscopy. It will also discuss the advancements in the applications of computational and NMR approaches in investigating excipient–protein interactions.

Current Screening Methodologies in Drug Discovery for Selected Human Diseases

The increase of many deadly diseases like infections by multidrug-resistant bacteria implies re-inventing the wheel on drug discovery. A better comprehension of the metabolisms and regulation of diseases, the increase in knowledge based on the study of disease-born microorganisms’ genomes, the development of more representative disease models and improvement of techniques, technologies, and computation applied to biology are advances that will foster drug discovery in upcoming years. In this paper, several aspects of current methodologies for drug discovery of antibacterial and antifungals, anti-tropical diseases, antibiofilm and antiquorum sensing, anticancer and neuroprotectors are considered. For drug discovery, two different complementary approaches can be applied: classical pharmacology, also known as phenotypic drug discovery, which is the historical basis of drug discovery, and reverse pharmacology, also designated target-based drug discovery. Screening methods based on phenotypic drug discovery have been used to discover new natural products mainly from terrestrial origin. Examples of the discovery of marine natural products are provided. A section on future trends provides a comprehensive overview on recent advances that will foster the pharmaceutical industry.

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design

Protein dimerization controls many physiological processes in the body. Proteins form homo-, hetero-, or oligomerization in the cellular environment to regulate the cellular processes. Any deregulation of these processes may result in a disease state. Protein-protein interactions (PPIs) can be inhibited by antibodies, small molecules, or peptides, and inhibition of PPI has therapeutic value. PPI drug discovery research has steadily increased in the last decade, and a few PPI inhibitors have already reached the pharmaceutical market. Several PPI inhibitors are in clinical trials. With advancements in structural and molecular biology methods, several methods are now available to study protein homo- and heterodimerization and their inhibition by drug-like molecules. Recently developed methods to study PPI such as proximity ligation assay and enzyme-fragment complementation assay that detect the PPI in the cellular environment are described with examples. At present, the methods used to design PPI inhibitors can be classified into three major groups: (1) structure-based drug design, (2) high-throughput screening, and (3) fragment-based drug design. In this chapter, we have described some of the experimental methods to study PPIs and their inhibition. Examples of homo- and heterodimers of proteins, their structural and functional aspects, and some of the inhibitors that have clinical importance are discussed. The design of PPI inhibitors of epidermal growth factor receptor heterodimers and CD2-CD58 is discussed in detail.

Large-scale virtual screening on public cloud resources with Apache Spark

Background

Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google’s MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark.

Results

We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment.

Conclusion

Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (https://github.com/mcapuccini/spark-vs).Graphical abstract

Advances in capillary electrophoresis and the implications for drug discovery

INTRODUCTION

Many screening platforms are prone to assay interferences that can be avoided by directly measuring the target or enzymatic product. Capillary electrophoresis (CE) and microchip electrophoresis (MCE) have been applied in a variety of formats to drug discovery. CE provides direct detection of the product allowing for the identification of some forms of assay interference. The high efficiency, rapid separations, and low volume requirements make CE amenable to drug discovery. Areas covered: This article describes advances in capillary electrophoresis throughput, sample introduction, and target assays as they pertain to drug discovery and screening. Instrumental advances discussed include integrated droplet microfluidics platforms and multiplexed arrays. Applications of CE to assays of diverse drug discovery targets, including enzymes and affinity interactions are also described. Expert opinion: Current screening with CE does not fully take advantage of the throughputs or low sample volumes possible with CE and is most suitable as a secondary screening method or for screens that are inaccessible with more common platforms. With further development, droplet microfluidics coupled to MCE could take advantage of the low sample requirements by performing assays on the nanoliter scale at high throughput.

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.

Protein-Directed Dynamic Combinatorial Chemistry: A Guide to Protein Ligand and Inhibitor Discovery

Protein-directed dynamic combinatorial chemistry is an emerging technique for efficient discovery of novel chemical structures for binding to a target protein. Typically, this method relies on a library of small molecules that react reversibly with each other to generate a combinatorial library. The components in the combinatorial library are at equilibrium with each other under thermodynamic control. When a protein is added to the equilibrium mixture, and if the protein interacts with any components of the combinatorial library, the position of the equilibrium will shift and those components that interact with the protein will be amplified, which can then be identified by a suitable biophysical technique. Such information is useful as a starting point to guide further organic synthesis of novel protein ligands and enzyme inhibitors. This review uses literature examples to discuss the practicalities of applying this method to inhibitor discovery, in particular, the set-up of the combinatorial library, the reversible reactions that may be employed, and the choice of detection methods to screen protein ligands from a mixture of reversibly forming molecules.

Cellular Assays with a Molecular Endpoint Measured by SAMDI Mass Spectrometry

Cell-based, high-throughput screening (HTS) assays are increasingly important tools used in drug discovery, but frequently rely on readouts of gene expression or phenotypic changes and require development of specialized, labeled reporters. Here a cell-based, label-free assay compatible with HTS is introduced that can report quantitatively on enzyme activities by measuring mass changes of substrates with matrix-assisted laser desorption/ionization mass spectrometry. The assay uses self-assembled monolayers to culture cells on arrays presenting substrates, which serve as reporters for a desired enzyme activity. Each spot of cells is treated with a compound, cultured and lysed, enabling endogenous enzymes to act on the immobilized peptide substrate. It is demonstrated that the assay can measure protein tyrosine phosphatase (PTP) activity from as few as five cells and a screen is described that identifies a compound that reduces PTP activity in cell lysates. This approach offers a valuable addition to the methods available for cell-based screening.

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.

A novel live cell assay to measure diacylglycerol lipase α activity

Diacylglycerol lipase α (DAGLα) hydrolyses DAG to generate the principal endocannabinoid (eCB) 2-arachidonoylglycerol (2-AG) in the central nervous system. DAGLα dependent cannabinoid (CB) signalling has been implicated in numerous processes including axonal growth and guidance, adult neurogenesis and retrograde signalling at the synapse. Recent studies have implicated DAGLα as an emerging drug target for several conditions including pain and obesity. Activity assays are critical to the drug discovery process; however, measurement of diacylglycerol lipase (DAGL) activity using its native substrate generally involves low-throughput MS techniques. Some relatively high-throughput membrane based assays utilizing surrogate substrates have been reported, but these do not take into account the rate-limiting effects often associated with the ability of a drug to cross the cell membrane. In the present study, we report the development of a live cell assay to measure DAGLα activity. Two previously reported DAGLα surrogate substrates, p-nitrophenyl butyrate (PNPB) and 6,8-difluoro-4-methylumbelliferyl octanoate (DiFMUO), were evaluated for their ability to detect DAGLα activity in live cell assays using a human cell line stably expressing the human DAGLα transgene. Following optimization, the small molecule chromogenic substrate PNPB proved to be superior by providing lower background activity along with a larger signal window between transfected and parental cells when compared with the fluorogenic substrate DiFMUO. The assay was further validated using established DAGL inhibitors. In summary, the live cell DAGLα assay reported here offers an economical and convenient format to screen for novel inhibitors as part of drug discovery programmes and compliments previously reported high-throughput membrane based DAGL assays.

Three-Dimensional Cell Culture-Based Screening Identifies the Anthelmintic Drug Nitazoxanide as a Candidate for Treatment of Colorectal Cancer

Because dormant cancer cells in hypoxic and nutrient-deprived regions of solid tumors provide a major obstacle to treatment, compounds targeting those cells might have clinical benefits. Here, we describe a high-throughput drug screening approach, using glucose-deprived multicellular tumor spheroids (MCTS) with inner hypoxia, to identify compounds that specifically target this cell population. We used a concept of drug repositioning-using known molecules for new indications. This is a promising strategy to identify molecules for rapid clinical advancement. By screening 1,600 compounds with documented clinical history, we aimed to identify candidates with unforeseen potential for repositioning as anticancer drugs. Our screen identified five molecules with pronounced MCTS-selective activity: nitazoxanide, niclosamide, closantel, pyrvinium pamoate, and salinomycin. Herein, we show that all five compounds inhibit mitochondrial respiration. This suggests that cancer cells in low glucose concentrations depend on oxidative phosphorylation rather than solely glycolysis. Importantly, continuous exposure to the compounds was required to achieve effective treatment. Nitazoxanide, an FDA-approved antiprotozoal drug with excellent pharmacokinetic and safety profile, is the only molecule among the screening hits that reaches high plasma concentrations persisting for up to a few hours after single oral dose. Nitazoxanide activated the AMPK pathway and downregulated c-Myc, mTOR, and Wnt signaling at clinically achievable concentrations. Nitazoxanide combined with the cytotoxic drug irinotecan showed anticancer activity in vivo. We here report that the FDA-approved anthelmintic drug nitazoxanide could be a potential candidate for advancement into cancer clinical trials.

Drug screen in patient cells suggests quinacrine to be repositioned for treatment of acute myeloid leukemia

To find drugs suitable for repositioning for use against leukemia, samples from patients with chronic lymphocytic, acute myeloid and lymphocytic leukemias as well as peripheral blood mononuclear cells (PBMC) were tested in response to 1266 compounds from the LOPAC¹²⁸⁰ library (Sigma). Twenty-five compounds were defined as hits with activity in all leukemia subgroups (<50% cell survival compared with control) at 10 μM drug concentration. Only one of these compounds, quinacrine, showed low activity in normal PBMCs and was therefore selected for further preclinical evaluation. Mining the NCI-60 and the NextBio databases demonstrated leukemia sensitivity and the ability of quinacrine to reverse myeloid leukemia gene expression. Mechanistic exploration was performed using the NextBio bioinformatic software using gene expression analysis of drug exposed acute myeloid leukemia cultures (HL-60) in the database. Analysis of gene enrichment and drug correlations revealed strong connections to ribosomal biogenesis nucleoli and translation initiation. The highest drug–drug correlation was to ellipticine, a known RNA polymerase I inhibitor. These results were validated by additional gene expression analysis performed in-house. Quinacrine induced early inhibition of protein synthesis supporting these predictions. The results suggest that quinacrine have repositioning potential for treatment of acute myeloid leukemia by targeting of ribosomal biogenesis.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

High-throughput screening platform for natural product-based drug discovery against 3 neglected tropical diseases: human African trypanosomiasis, leishmaniasis, and Chagas disease

African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography-mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites.