The power of combining phenotypic and target-focused drug discovery

High-throughput prioritization of target proteins for development of new antileishmanial compounds

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

A Natural Small Molecule Mitigates Kidney Fibrosis by Targeting Cdc42-mediated GSK-3β/β-catenin Signaling

Kidney fibrosis is a common fate of chronic kidney diseases (CKDs), eventually leading to renal dysfunction. Yet, no effective treatment for this pathological process has been achieved. During the bioassay-guided chemical investigation of the medicinal plant Wikstroemia chamaedaphne, a daphne diterpenoid, daphnepedunin A (DA), is characterized as a promising anti-renal fibrotic lead. DA shows significant anti-kidney fibrosis effects in cultured renal fibroblasts and unilateral ureteral obstructed mice, being more potent than the clinical trial drug pirfenidone. Leveraging the thermal proteome profiling strategy, cell division cycle 42 (Cdc42) is identified as the direct target of DA. Mechanistically, DA targets to reduce Cdc42 activity and down-regulates its downstream phospho-protein kinase Cζ(p-PKCζ)/phospho-glycogen synthase kinase-3β (p-GSK-3β), thereby promoting β-catenin Ser33/37/Thr41 phosphorylation and ubiquitin-dependent proteolysis to block classical pro-fibrotic β-catenin signaling. These findings suggest that Cdc42 is a promising therapeutic target for kidney fibrosis, and highlight DA as a potent Cdc42 inhibitor for combating CKDs.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Functional human iPSC-derived alveolar-like cells cultured in a miniaturized 96‑Transwell air-liquid interface model

In order to circumvent the limited access and donor variability of human primary alveolar cells, directed differentiation of human pluripotent stem cells (hiPSCs) into alveolar-like cells, provides a promising tool for respiratory disease modeling and drug discovery assays. In this work, a unique, miniaturized 96-Transwell microplate system is described where hiPSC-derived alveolar-like cells were cultured at an air-liquid interface (ALI). To this end, hiPSCs were differentiated into lung epithelial progenitor cells (LPCs) and subsequently matured into a functional alveolar type 2 (AT2)-like epithelium with monolayer-like morphology. AT2-like cells cultured at the physiological ALI conditions displayed characteristics of AT2 cells with classical alveolar surfactant protein expressions and lamellar-body like structures. The integrity of the epithelial barriers between the AT2-like cells was confirmed by applying a custom-made device for 96-parallelized transepithelial electric resistance (TEER) measurements. In order to generate an IPF disease-like phenotype in vitro, the functional AT2-like cells were stimulated with cytokines and growth factors present in the alveolar tissue of IPF patients. The cytokines stimulated the secretion of pro-fibrotic biomarker proteins both on the mRNA (messenger ribonucleic acid) and protein level. Thus, the hiPSC-derived and cellular model system enables the recapitulation of certain IPF hallmarks, while paving the route towards a miniaturized medium throughput approach of pharmaceutical drug discovery.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Andrographis paniculata (Burm. F.) Wall. Ex Nees, Andrographolide, and Andrographolide Analogues as SARS-CoV-2 Antivirals? A Rapid Review

Drug repurposing is commonly employed in the search for potential therapeutic agents. Andrographis paniculata, a medicinal plant commonly used for symptomatic relief of the common cold, and its phytoconstituent andrographolide, have been repeatedly identified as potential antivirals against SARS-CoV-2. In light of new evidence emerging since the onset of the COVID-19 pandemic, this rapid review was conducted to identify and evaluate the current SARS-CoV-2 antiviral evidence for A. paniculata, andrographolide, and andrographolide analogs. A systematic search and screen strategy of electronic databases and gray literature was undertaken to identify relevant primary articles. One target-based in vitro study reported the 3CLpro inhibitory activity of andrographolide as being no better than disulfiram. Another Vero cell-based study reported potential SARS-CoV-2 inhibitory activity for both andrographolide and A. paniculata extract. Eleven in silico studies predicted the binding of andrographolide and its analogs to several key antiviral targets of SARS-CoV-2 including the spike protein-ACE-2 receptor complex, spike protein, ACE-2 receptor, RdRp, 3CLpro, PLpro, and N-protein RNA-binding domain. In conclusion, in silico and in vitro studies collectively suggest multi-pathway targeting SARS-CoV-2 antiviral properties of andrographolide and its analogs, but in vivo data are needed to support these predictions.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.

Single-cell infrared phenomics: phenotypic screening with infrared microspectroscopy

We conceptually demonstrate single-cell infrared phenomics as a novel strategy of phenotypic screening with infrared microspectroscopy. Based on this development, the cancer cell HepG2 glycocalyx was first identified as a potential target of protopanaxadiol, an herbal medicine. These findings provide a powerful tool to accurately evaluate the cell stress response and to largely expand the phenotypic screening toolkit for drug discovery.

Microparticle-Assisted Precipitation Screening Method for Robust Drug Target Identification

While thermal proteome profiling (TPP) shines in the field of drug target screening by analyzing the soluble fraction of the proteome samples treated at high temperature, the counterpart, the insoluble precipitate, has been overlooked for a long time. The analysis of the precipitate is hampered by the inefficient sample processing procedure. Herein, we propose a novel method, termed microparticle-assisted precipitation screening (MAPS), for drug target identification. The MAPS method exploits the principle that drug-bound proteins will be more resistant to thermal unfolding similar to the classic TPP method, but the process of protein precipitation is assisted by microparticles. Upon heating, proteins unfold and aggregate on the surface of the microparticles. The introduction of a microparticle simplifies the whole sample preparation workflow. The proteins that precipitate on the microparticles are subjected to washing, alkylation, and digestion. The whole sample preparation is processed conveniently on the surface of the microparticles without any transfer. With the assistance of microparticles, sample loss is minimized. The MAPS method is compatible with minute amounts of initial proteins. MAPS was applied to screen the targets of several well-studied drugs and the known target proteins were successfully identified with high confidence and specificity. To investigate the specificity of the method, MAPS was applied to screen the targets of the pan-kinase inhibitor, staurosporine, and 32 protein kinases (specificity of 80%) were identified using only 20 μg of initial proteins of each sample. MAPS is an unbiased robust method for drug target screening, filling the vacancy of stability-based target screening using a precipitate.

Recent advances in phenotypic drug discovery

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.

Development of a miniaturized 96-Transwell air-liquid interface human small airway epithelial model

In order to overcome the challenges associated with a limited number of airway epithelial cells that can be obtained from clinical sampling and their restrained capacity to divide ex vivo, miniaturization of respiratory drug discovery assays is of pivotal importance. Thus, a 96-well microplate system was developed where primary human small airway epithelial (hSAE) cells were cultured at an air–liquid interface (ALI). After four weeks of ALI culture, a pseudostratified epithelium containing basal, club, goblet and ciliated cells was produced. The 96-well ALI cultures displayed a cellular composition, ciliary beating frequency, and intercellular tight junctions similar to 24-well conditions. A novel custom-made device for 96-parallelized transepithelial electric resistance (TEER) measurements, together with dextran permeability measurements, confirmed that the 96-well culture developed a tight barrier function during ALI differentiation. 96-well hSAE cultures were responsive to transforming growth factor β1 (TGF-β1) and tumor necrosis factor α (TNF-α) in a concentration dependent manner. Thus, the miniaturized cellular model system enables the recapitulation of a physiologically responsive, differentiated small airway epithelium, and a robotic integration provides a medium throughput approach towards pharmaceutical drug discovery, for instance, in respect of fibrotic distal airway/lung diseases.

A Whole Genome-Wide Arrayed CRISPR Screen in Primary Organ Fibroblasts to Identify Regulators of Kidney Fibrosis

Kidney fibrosis presents a hallmark of chronic kidney disease. With ever-increasing patient numbers and limited treatment options available, novel strategies for therapeutic intervention in kidney disease are warranted. Fibrosis commonly results from a wound healing response to repeated or chronic tissue damage, irrespective of the underlying etiology, and can occur in virtually any solid organ or tissue. In order to identify targets relevant for kidney fibrosis, we aimed to employ CRISPR screening in primary human kidney fibroblasts. We demonstrate that CRISPR technology can be applied in primary kidney fibroblasts and can furthermore be used to conduct arrayed CRISPR screening using a high-content imaging readout in a whole genome-wide manner. Hits coming out of this screen were validated using orthogonal approaches and present starting points for validation of novel targets relevant to kidney disease.

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

A previous phenotypic screening campaign led to the identification of a quinazoline derivative with promising in vitro activity against Schistosoma mansoni. Follow-up studies of the antischistosomal potential of this candidate are presented here. The in vivo studies in a S. mansoni mouse model show a significant reduction of total worms and a complete disappearance of immature eggs when administered concomitantly with praziquantel in comparison with the administration of praziquantel alone. This fact is of utmost importance because eggs are responsible for the pathology and transmission of the disease. Subsequently, the chemical optimisation of the structure in order to improve the metabolic stability of the parent compound was carried out leading to derivatives with improved drug-like properties. Additionally, the putative target of this new class of antischistosomal compounds was envisaged by using computational tools and the binding mode to the target enzyme, aldose reductase, was proposed.

The contribution of multicellular model organisms to neuronal ceroid lipofuscinosis research

The NCLs (neuronal ceroid lipofuscinosis) are forms of neurodegenerative disease that affect people of all ages and ethnicities but are most prevalent in children. Commonly known as Batten disease, this debilitating neurological disorder is comprised of 13 different subtypes that are categorized based on the particular gene that is mutated (CLN1-8, CLN10-14). The pathological mechanisms underlying the NCLs are not well understood due to our poor understanding of the functions of NCL proteins. Only one specific treatment (enzyme replacement therapy) is approved, which is for the treating the brain in CLN2 disease. Hence there remains a desperate need for further research into disease-modifying treatments. In this review, we present and evaluate the genes, proteins and studies performed in the social amoeba, nematode, fruit fly, zebrafish, mouse and large animals pertinent to NCL. In particular, we highlight the use of multicellular model organisms to study NCL protein function, pathology and pathomechanisms. Their use in testing novel therapeutic approaches is also presented. With this information, we highlight how future research in these systems may be able to provide new insight into NCL protein functions in human cells and aid in the development of new therapies.

PromISR-6, a Guanabenz Analogue, Improves Cellular Survival in an Experimental Model of Huntington's Disease

Guanabenz (GBZ), an α₂-adrenergic agonist, demonstrated off-target effects that restored protein homeostasis and ameliorated pathobiology in experimental models of neurodegenerative disease. However, GBZ did not directly activate the integrated stress response (ISR), and its proposed mode of action remains controversial. Utilizing an iterative in silico screen of over 10,000 GBZ analogues, we analyzed 432 representative compounds for cytotoxicity in Wild-type, PPP1R15A-/-, and PPP1R15B-/- mouse embryonic fibroblasts. Nine compounds clustering into three functional groups were studied in detail using cell biological and biochemical assays. Our studies demonstrated that PromISR-6 is a potent GBZ analogue that selectively activated ISR, eliciting sustained eIF2α phosphorylation. ISRIB, an ISR inhibitor, counteracted PromISR-6-mediated translational inhibition and reduction in intracellular mutant Huntingtin aggregates. Reduced protein synthesis combined with PromISR-6-stimulated autophagic clearance made PromISR-6 the most efficacious GBZ analogue to reduce Huntingtin aggregates and promote survival in a cellular model of Huntington's disease.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.