Recent advances in phenotypic drug discovery

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a "few-shot learning" challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets. The code for FS-CAP is available at https://github.com/Rose-STL-Lab/FS-CAP.

Drug repurposing for cancer

In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight against malignancies. This book chapter aims to embark on the comprehension of the strategic deployment of approved drugs for repurposing and the meticulous journey of drug repurposing from earlier times to the current era. Moreover, the chapter underscores the multifaceted and complex nature of cancer biology, and the evolving field of cancer drug therapeutics while emphasizing the mandate of drug repurposing to advance cancer therapeutics. Importantly, the narrative explores the latest tools, technologies, and cutting-edge methodologies including high-throughput screening, omics technologies, and artificial intelligence-driven approaches, for shaping and accelerating the pace of drug repurposing to uncover novel cancer therapeutic avenues. The chapter critically assesses the breakthroughs, expanding the repertoire of repurposing drug candidates in cancer, and their major categories. Another focal point of this book chapter is that it addresses the emergence of combination therapies involving repurposed drugs, reflecting a shift towards personalized and synergistic treatment approaches. The expert analysis delves into the intricacies of combinatorial regimens, elucidating their potential to target heterogeneous cancer populations and overcome resistance mechanisms, thereby enhancing treatment efficacy. Therefore, this chapter provides in-depth insights into the potential of repurposing towards bringing the much-needed big leap in the field of cancer therapeutics.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Perspectives on Nuclear Magnetic Resonance Spectroscopy in Drug Discovery Research

The drug discovery landscape has undergone a significant transformation over the past decade, owing to research endeavors in a wide range of areas leading to strategies for pursuing new drug targets and the emergence of novel drug modalities. NMR spectroscopy has been a technology of fundamental importance to these research pursuits and has seen its use expanded both within and outside of traditional medicinal chemistry applications. In this perspective, we will present advancement of NMR-derived methods that have facilitated the characterization of small molecules and novel drug modalities including macrocyclic peptides, cyclic dinucleotides, and ligands for protein degradation. We will discuss innovations in NMR spectroscopy at the chemistry and biology interface that have broadened NMR's utility from hit identification through lead optimization activities. We will also discuss the promise of emerging NMR approaches in bridging our understanding and addressing challenges in the pursuit of the therapeutic agents of the future.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

Discovery of a Small-Molecule Modulator of the Autophagy-Lysosome Pathway That Targets Lamin A/C and LAMP1, Induces Autophagic Flux, and Affects Lysosome Positioning in Neurons

Autophagy is a major catabolic degradation and recycling process that maintains homeostasis in cells and is especially important in postmitotic neurons. We implemented a high-content phenotypic assay to discover small molecules that promote autophagic flux and completed target identification and validation studies to identify protein targets that modulate the autophagy pathway and promote neuronal health and survival. Efficient syntheses of the prioritized compounds were developed to readily access analogues of the initial hits, enabling initial structure-activity relationship studies to improve potency and preparation of a biotin-tagged pulldown probe that retains activity. This probe facilitated target identification and validation studies through pulldown and competition experiments using both an unbiased proteomics approach and western blotting to reveal Lamin A/C and LAMP1 as the protein targets of compound RH1115. Evaluation of RH1115 in neurons revealed that this compound induces changes to LAMP1 vesicle properties and alters lysosome positioning. Dysfunction of the autophagy-lysosome pathway has been implicated in a variety of neurodegenerative diseases, including Alzheimer's disease, highlighting the value of new strategies for therapeutic modulation and the importance of small-molecule probes to facilitate the study of autophagy regulation in cultured neurons and in vivo.

Novel quinazolin-4(3H)-one based Cyclin K degraders regulate alternative polyadenylation activity

Phenotypic screening is gaining attention as a powerful method for identifying compounds that regulate cellular phenotypes of interest through novel mechanisms of action. Recently, a new modality of compounds, called molecular glues, which can induce the degradation of target proteins by forming ternary complexes of E3 ligases, has emerged from phenotypic screening. In this study, using global proteomic analysis, we identified a novel Cyclin K degrader, T4, which was previously discovered through phenotypic screening for alternative polyadenylation regulation. Our detailed mechanistic analysis revealed that T4 induced Cyclin K degradation, leading to the regulation of alternative polyadenylation. Additionally, we generated a more potent Cyclin K degrader, TR-213, through a structure-activity relationship study of T4. T4 and TR-213 are structurally distinct from other Cyclin K degraders and can be used as novel chemical tools to further analyze the degradation of Cyclin K and the regulation of alternative polyadenylation.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping

The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.

Navigating drug repurposing for Chagas disease: advances, challenges, and opportunities

Chagas disease is a vector-borne illness caused by the protozoan parasite Trypanosoma cruzi (T. cruzi). It poses a significant public health burden, particularly in the poorest regions of Latin America. Currently, there is no available vaccine, and chemotherapy has been the traditional treatment for Chagas disease. However, the treatment options are limited to just two outdated medicines, nifurtimox and benznidazole, which have serious side effects and low efficacy, especially during the chronic phase of the disease. Collectively, this has led the World Health Organization to classify it as a neglected disease. To address this problem, new drug regimens are urgently needed. Drug repurposing, which involves the use of existing drugs already approved for the treatment of other diseases, represents an increasingly important option. This approach offers potential cost reduction in new drug discovery processes and can address pharmaceutical bottlenecks in the development of drugs for Chagas disease. In this review, we discuss the state-of-the-art of drug repurposing approaches, including combination therapy with existing drugs, to overcome the formidable challenges associated with treating Chagas disease. Organized by original therapeutic area, we describe significant recent advances, as well as the challenges in this field. In particular, we identify candidates that exhibit potential for heightened efficacy and reduced toxicity profiles with the ultimate objective of accelerating the development of new, safe, and effective treatments for Chagas disease.

Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens

Phenotypic drug discovery (PDD) enables the target-agnostic generation of therapeutic drugs with novel mechanisms of action. However, realizing its full potential for biologics discovery requires new technologies to produce antibodies to all, a priori unknown, disease-associated biomolecules. We present a methodology that helps achieve this by integrating computational modeling, differential antibody display selection, and massive parallel sequencing. The method uses the law of mass action-based computational modeling to optimize antibody display selection and, by matching computationally modeled and experimentally selected sequence enrichment profiles, predict which antibody sequences encode specificity for disease-associated biomolecules. Applied to a phage display antibody library and cell-based antibody selection, ∼10⁵ antibody sequences encoding specificity for tumor cell surface receptors expressed at 10³-10⁶ receptors/cell were discovered. We anticipate that this approach will be broadly applicable to molecular libraries coupling genotype to phenotype and to the screening of complex antigen populations for identification of antibodies to unknown disease-associated targets.

DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery

Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensive, and high experimental costs. In this study, we collected 485,900 compounds involving in 3,919,974 bioactivity records against 426 anticancer targets and 346 cancer cell lines from academic literature, as well as 60 tumor cell lines from NCI-60 panel. A total of 832 classification models (426 target- and 406 cell-based predictive models) were then constructed to predict the inhibitory activity of compounds against targets and tumor cell lines using FP-GNN deep learning method. Compared to the classical machine learning and deep learning methods, the FP-GNN models achieve considerable overall predictive performance, with the highest AUC values of 0.91, 0.88, 0.91 for the test sets of targets, academia-sourced and NCI-60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap and its local version were developed based on these high-quality models, enabling users to perform anticancer drug discovery-related tasks including large-scale virtual screening, profiling prediction of anticancer agents, target fishing, and drug repositioning. We anticipate this platform to accelerate the discovery of anticancer drugs in the field. DeepCancerMap is freely available at https://deepcancermap.idruglab.cn.

Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome

NEU-4438 is a lead for the development of drugs against Trypanosoma brucei, which causes human African trypanosomiasis. Optimized with phenotypic screening, targets of NEU-4438 are unknown. Herein, we present a cell perturbome workflow that compares NEU-4438's molecular modes of action to those of SCYX-7158 (acoziborole). Following a 6 h perturbation of trypanosomes, NEU-4438 and acoziborole reduced steady-state amounts of 68 and 92 unique proteins, respectively. After analysis of proteomes, hypotheses formulated for modes of action were tested: Acoziborole and NEU-4438 have different modes of action. Whereas NEU-4438 prevented DNA biosynthesis and basal body maturation, acoziborole destabilized CPSF3 and other proteins, inhibited polypeptide translation, and reduced endocytosis of haptoglobin-hemoglobin. These data point to CPSF3-independent modes of action for acoziborole. In case of polypharmacology, the cell-perturbome workflow elucidates modes of action because it is target-agnostic. Finally, the workflow can be used in any cell that is amenable to proteomic and molecular biology experiments.

Reinforcement learning for systems pharmacology-oriented and personalized drug design

INTRODUCTION

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complex diseases.

AREAS COVERED

In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed.

EXPERT OPINION

In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.

Maximizing the integration of virtual and experimental screening in hit discovery

INTRODUCTION

Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.

AREAS COVERED

After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.

EXPERT OPINION

Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

Repurposing drugs to treat cardiovascular disease in the era of precision medicine

Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine — computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities — support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities.

Drugs can be repurposed for new therapeutic indications. In this Review, Mercola and colleagues summarize the latest techniques for systematic drug repurposing and re-engineering, which could increase the pace, efficiency and cost-effectiveness of drug discovery for the treatment of cardiovascular disease.

Contemporary technologies are expected to make drug repurposing large-scale, systematic and deliberate rather than opportunistic.

New experimental and computational tools harness patient genomics for drug repurposing.

Discovery of repurposed drugs on the basis of patient genomics has implications for precision prescribing of medicines to treat individual patients.

The treatment of rare, monogenic diseases, which often provide too little return on investment to incentivize conventional drug discovery, might benefit because the molecular aetiologies of these diseases are well suited to the discovery of drug repurposing candidates.

Establishment and Characterization of Brain Cancer Primary Cell Cultures From Patients to Enable Phenotypic Screening for New Drugs

Aim: Desmoplastic infantile ganglioglioma (DIG), is a rare tumor arising mainly during the first 2 years of life. Molecular characterization of these benign yet rapidly proliferating tumors has been limited to evaluating a few mutations in few genes. Our aim was to establish a live cell culture to enable the understanding of the cellular processes driving the non-malignant growth of these tumors.

Methods: Tumor tissue from a rare non-infantile 8-year-old female DIG patient was dissociated and digested using collagenase to establish live cultures. Both 2D monolayer and 3D neurospheres were successfully cultured and characterized for proliferative potential, intrinsic plasticity, presence of cancer stem cells and the expression of stem cell markers. Cells cultured as 3D were embedded as tissue blocks. Immunohistochemistry was performed in both tissue and 3D sections for markers including synaptophysin, vimentin, neurofilament and MIB-1. Mutation analysis by NGS was performed using a-100 gene panel.

Results: Using immunohistochemistry, the 3D cultures were shown to express markers as in the original DIG tumor tissue indicating that the spheroid cultures were able to maintain the heterogeneity found in the original tumor. Cells continued proliferating past passage 10 indicative of immortalization. Enrichment of cancer stem cells was observed in neurospheres by FACS using CD133 antibody and RT-PCR. Mutation analysis indicated the presence of germline mutations in three genes and somatic mutations in two other genes.

Conclusion: A spontaneous cell line-like cell culture with high percentage of stem cells has been established from a DIG tumor for the first time.

Inverse Drug Discovery identifies weak electrophiles affording protein conjugates

Traditional biochemical target-based and phenotypic cell-based screening approaches to drug discovery have produced the current covalent and non-covalent pharmacopoeia. Strategies to expand the druggable proteome include Inverse Drug Discovery, which involves incubating one weak organic electrophile at a time with the proteins of a living cell to identify the conjugates formed. An alkyne substructure in each organic electrophile enables affinity chromatography-mass spectrometry, which produces a list of proteins that each distinct compound reacts with. Herein, we review Inverse Drug Discovery in the context of organic compounds of intermediate complexity harboring Sulfur(VI)-fluoride exchange (SuFEx) electrophiles used to expand the cellular proteins that can be targeted covalently.

Characterizing attributes of innovation of technologies for healthcare: a systematic review

OBJECTIVES

Characterizing and evaluating the holistic value of innovative healthcare technologies (e.g. treatments, services) constitutes a crucial goal to maximize limited resources. However, the characteristics of innovation have not been well identified. This review aims to describe the characteristics of healthcare innovation.

METHODS

We performed a comprehensive systematic search using PubMed, Embase, PsycINFO, and Econlit from inception to July 2022. Articles were included if they described innovation or the characteristics of innovation of the technologies in healthcare. Characteristics or definitions of innovation directly or indirectly described as innovation were extracted from the included articles. Two independent reviewers then conceptualized the identified characteristics of innovation to generate innovation attributes in healthcare.

RESULTS

In total, 103 articles were included in this review. Eight attributes describing innovation, i.e. novelty, step change, substantial benefits, an improvement over existing technologies, convenience and/or adherence, added value, acceptable cost, and uncounted benefits, were conceptualized. Most of the identified innovation attributes were based on the researchers' perspective.

CONCLUSIONS

This study conceptualized innovation attributes in healthcare based on the characteristics of healthcare innovation as defined in the literature. Further research is warranted to obtain a complete understanding of the perspectives of researchers and other stakeholders, including patients, healthcare providers, healthcare payers, and the pharmaceutical industry, on recognizing innovation in healthcare.KEY POINTSThis is the first systematic review to conceptualize attributes of healthcare innovation.We conceptualized eight attributes describing innovation, i.e. novelty, step change, substantial benefits, an improvement over existing technologies, convenience and/or adherence, added value, acceptable cost, and uncounted benefits based on the similar concept.In existing literature, patients' and caregivers' perspectives were less frequently found to describe the innovation attributes.Future research is needed to identify, measure, and value various stakeholders, including patients' and caregivers' perspectives on healthcare innovation.

Two-color fluorescent proteins reporting survivin regulation in breast cancer cells for high throughput drug screening

Reporter gene assay is widely used for high throughput drug screening and drug action mechanism evaluation. In this study, we developed a robust dual-fluorescent reporter assay to detect drugs repressing the transcription of survivin, a cancer biomarker from the inhibitor of apoptosis family, in breast cancer cells cultured in three-dimensional (3D) microbioreactors. Survivin is overexpressed in numerous malignancies but almost silent in normal tissue cells and is considered a lead target for cancer therapy. Breast cancer MCF-7 cells were engineered to express enhanced green fluorescent protein driven by a survivin promoter and red fluorescent protein driven by a cytomegalovirus promoter as internal control to detect changes in survivin expression in cells as affected by drugs. This 3D dual-fluorescent reporter assay was validated with YM155 and doxorubicin, which were known to downregulate survivin in cancer cells, and further evaluated with two widely used anticancer compounds, cisplatin, and epigallocatechin gallate, to evaluate their effects on survivin expression. The results showed that the 3D dual-fluorescent reporter assay was robust for high throughput screening of drugs targeting survivin in breast cancer cells.

Overcoming prostate cancer drug resistance with a novel organosilicon small molecule

A major challenge to the treatment of advanced prostate cancer (PCa) is the development of resistance to androgen-deprivation therapy (ADT) and chemotherapy. It is imperative to discover effective therapies to overcome drug resistance and improve clinical outcomes. We have developed a novel class of silicon-containing compounds and evaluated the anticancer activities and mechanism of action using cellular and animal models of drug-resistant PCa. Five organosilicon compounds were evaluated for their anticancer activities in the NCI-60 panel and established drug-resistant PCa cell lines. GH1504 exhibited potent in vitro cytotoxicity in a broad spectrum of human cancer cells, including PCa cells refractory to ADT and chemotherapy. Molecular studies identified several potential targets of GH1504, most notably androgen receptor (AR), AR variant 7 (AR-v7) and survivin. Mechanistically, GH1504 may promote the protein turnover of AR, AR-v7 and survivin, thereby inducing apoptosis in ADT-resistant and chemoresistant PCa cells. Animal studies demonstrated that GH1504 effectively inhibited the in vivo growth of ADT-resistant CWR22Rv1 and chemoresistant C4-2B-TaxR xenografts in subcutaneous and intraosseous models. These preclinical results indicated that GH1504 is a promising lead that can be further developed as a novel therapy for drug-resistant PCa.

Emerging role of G9a in cancer stemness and promises as a therapeutic target

The histone methyltransferase G9a is well-documented for its implication in neoplastic growth. However, recent investigations have demonstrated a key involvement of this chromatin writer in maintaining the self-renewal and tumor-initiating capacities of cancer stem cells (CSCs). Direct inhibition of G9a’s catalytic activity was reported as a promising therapeutic target in multiple preclinical studies. Yet, none of the available pharmacological inhibitors of G9a activity have shown success at the early stages of clinical testing. Here, we discuss central findings of oncogenic expression and activation of G9a in CSCs from different origins, as well as the impact of the suppression of G9a histone methyltransferase activity in such contexts. We will explore the challenges posed by direct and systemic inhibition of G9a activity in the perspective of clinical translation of documented small molecules. Finally, we will discuss recent advances in drug discovery as viable strategies to develop context-specific drugs, selectively targeting G9a in CSC populations.

Context Matters-Why We Need to Change From a One Size Fits all Approach to Made-to-Measure Therapies for Individual Patients With Pancreatic Cancer

Pancreatic cancer is one of the deadliest cancers and remains a major unsolved health problem. While pancreatic ductal adenocarcinoma (PDAC) is associated with driver mutations in only four major genes (KRAS, TP53, SMAD4, and CDKN2A), every tumor differs in its molecular landscape, histology, and prognosis. It is crucial to understand and consider these differences to be able to tailor treatment regimens specific to the vulnerabilities of the individual tumor to enhance patient outcome. This review focuses on the heterogeneity of pancreatic tumor cells and how in addition to genetic alterations, the subsequent dysregulation of multiple signaling cascades at various levels, epigenetic and metabolic factors contribute to the oncogenesis of PDAC and compensate for each other in driving cancer progression if one is tackled by a therapeutic approach. This implicates that besides the need for new combinatorial therapies for PDAC, a personalized approach for treating this highly complex cancer is required. A strategy that combines both a target-based and phenotypic approach to identify an effective treatment, like Reverse Clinical Engineering® using patient-derived organoids, is discussed as a promising way forward in the field of personalized medicine to tackle this deadly disease.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

New Challenges Resulting From the Loss of Function of Nav1.4 in Neuromuscular Diseases

The voltage-gated sodium channel Na_v1.4 is a major actor in the excitability of skeletal myofibers, driving the muscle force in response to nerve stimulation. Supporting further this key role, mutations in SCN4A, the gene encoding the pore-forming α subunit of Na_v1.4, are responsible for a clinical spectrum of human diseases ranging from muscle stiffness (sodium channel myotonia, SCM) to muscle weakness. For years, only dominantly-inherited diseases resulting from Na_v1.4 gain of function (GoF) were known, i.e., non-dystrophic myotonia (delayed muscle relaxation due to myofiber hyperexcitability), paramyotonia congenita and hyperkalemic or hypokalemic periodic paralyses (episodic flaccid muscle weakness due to transient myofiber hypoexcitability). These last 5 years, SCN4A mutations inducing Na_v1.4 loss of function (LoF) were identified as the cause of dominantly and recessively-inherited disorders with muscle weakness: periodic paralyses with hypokalemic attacks, congenital myasthenic syndromes and congenital myopathies. We propose to name this clinical spectrum sodium channel weakness (SCW) as the mirror of SCM. Na_v1.4 LoF as a cause of permanent muscle weakness was quite unexpected as the Na⁺ current density in the sarcolemma is large, securing the ability to generate and propagate muscle action potentials. The properties of SCN4A LoF mutations are well documented at the channel level in cellular electrophysiological studies However, much less is known about the functional consequences of Na_v1.4 LoF in skeletal myofibers with no available pertinent cell or animal models. Regarding the therapeutic issues for Na_v1.4 channelopathies, former efforts were aimed at developing subtype-selective Na_v channel antagonists to block myofiber hyperexcitability. Non-selective, Na_v channel blockers are clinically efficient in SCM and paramyotonia congenita, whereas patient education and carbonic anhydrase inhibitors are helpful to prevent attacks in periodic paralyses. Developing therapeutic tools able to counteract Na_v1.4 LoF in skeletal muscles is then a new challenge in the field of Na_v channelopathies. Here, we review the current knowledge regarding Na_v1.4 LoF and discuss the possible therapeutic strategies to be developed in order to improve muscle force in SCW.

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.

Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators

The use of phenotypic screening was central to the discovery and development of novel thalidomide analogs, the IMiDs (immunomodulatory drugs) agents. With the discovery that these agents bind the E3 ligase, CRL4^CRBN, and alter its substrate specificity, there has been a great deal of endeavor to discover other small molecules that can modulate alternative E3 ligases. Furthermore, the chemical properties necessary for drug discovery and the rules by which neo-substrates are selected for degradation are being defined in the context of phenotypic alterations in specific cellular systems. This review gives a detailed summary of these recent advances and the methodologies being exploited to understand the mechanism of action of emerging protein degradation therapies.

The future of phenotypic drug discovery

Phenotypic drug discovery (PDD) uses biological systems directly for new drug screening. While PDD has proved effective in the discovery of drugs with novel mechanisms, for broader adoption, key challenges need resolution: progression of poorly qualified leads and overloaded pipelines due to lack of effective tools to process and prioritize hits; and advancement of leads with undesirable mechanisms that fail at more expensive stages of discovery. Here I discuss how human-based phenotypic platforms are being applied throughout the discovery process for hit triage and prioritization, for elimination of hits with unsuitable mechanisms, and for supporting clinical strategies through pathway-based decision frameworks. Harnessing the data generated in these platforms can also fuel a deeper understanding of drug efficacy and toxicity mechanisms. As these approaches increase in use, they will gain in power for driving better decisions, generating better leads faster and in turn promoting greater adoption of PDD.

Versatile and rapid microfluidics-assisted antibody discovery

Recent years have seen unparalleled development of microfluidic applications for antibody discovery in both academic and pharmaceutical research. Microfluidics can support native chain-paired library generation as well as direct screening of antibody secreting cells obtained by rodent immunization or from the human peripheral blood. While broad diversities of neutralizing antibodies against infectious diseases such as HIV, Ebola, or COVID-19 have been identified from convalescent individuals, microfluidics can expedite therapeutic antibody discovery for cancer or immunological disease indications. In this study, a commercially available microfluidic device, Cyto-Mine, was used for the rapid identification of natively paired antibodies from rodents or human donors screened for specific binding to recombinant antigens, for direct screening with cells expressing the target of interest, and, to our knowledge for the first time, for direct broad functional IgG antibody screening in droplets. The process time from cell preparation to confirmed recombinant antibodies was four weeks. Application of this or similar microfluidic devices and methodologies can accelerate and enhance pharmaceutical antibody hit discovery.