The discovery of first-in-class drugs: origins and evolution

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

Novel drugs approved by the EMA, the FDA, and the MHRA in 2023: A year in review

In 2023, seventy novel drugs received market authorization for the first time in either Europe (by the EMA and the MHRA) or in the United States (by the FDA). Confirming a steady recent trend, more than half of these drugs target rare diseases or intractable forms of cancer. Thirty drugs are categorized as "first-in-class" (FIC), illustrating the quality of research and innovation that drives new chemical entity discovery and development. We succinctly describe the mechanism of action of most of these FIC drugs and discuss the therapeutic areas covered, as well as the chemical category to which these drugs belong. The 2023 novel drug list also demonstrates an unabated emphasis on polypeptides (recombinant proteins and antibodies), Advanced Therapy Medicinal Products (gene and cell therapies) and RNA therapeutics, including the first-ever approval of a CRISPR-Cas9-based gene-editing cell therapy.

A pan-respiratory antiviral chemotype targeting a transient host multi-protein complex

We present a novel small molecule antiviral chemotype that was identified by an unconventional cell-free protein synthesis and assembly-based phenotypic screen for modulation of viral capsid assembly. Activity of PAV-431, a representative compound from the series, has been validated against infectious viruses in multiple cell culture models for all six families of viruses causing most respiratory diseases in humans. In animals, this chemotype has been demonstrated efficacious for porcine epidemic diarrhoea virus (a coronavirus) and respiratory syncytial virus (a paramyxovirus). PAV-431 is shown to bind to the protein 14-3-3, a known allosteric modulator. However, it only appears to target the small subset of 14-3-3 which is present in a dynamic multi-protein complex whose components include proteins implicated in viral life cycles and in innate immunity. The composition of this target multi-protein complex appears to be modified upon viral infection and largely restored by PAV-431 treatment. An advanced analog, PAV-104, is shown to be selective for the virally modified target, thereby avoiding host toxicity. Our findings suggest a new paradigm for understanding, and drugging, the host-virus interface, which leads to a new clinical therapeutic strategy for treatment of respiratory viral disease.

Engineering cell-free systems by chemoproteomic-assisted phenotypic screening

Phenotypic screening is a valuable tool to both understand and engineer complex biological systems. We demonstrate the functionality of this approach in the development of cell-free protein synthesis (CFPS) technology. Phenotypic screening identified numerous compounds that enhanced protein production in yeast lysate CFPS reactions. Notably, many of these were competitive ATP kinase inhibitors, with the exploitation of their inherent substrate promiscuity redirecting ATP flux towards heterologous protein expression. Chemoproteomic-guided strain engineering partially phenocopied drug effects, with a 30% increase in protein yield observed upon deletion of the ATP-consuming SSA1 component of the HSP70 chaperone. Moreover, drug-mediated metabolic rewiring coupled with template optimization generated the highest protein yields in yeast CFPS to date using a hitherto less efficient, but more cost-effective glucose energy regeneration system. Our approach highlights the utility of target-agnostic phenotypic screening and target identification to deconvolute cell-lysate complexity, adding to the expanding repertoire of strategies for improving CFPS.

Repurposing of biologics and biopharmaceuticals

The field of drug repurposing is gaining attention as a way to introduce pharmaceutical agents with established safety profiles to new patient populations. This approach involves finding new applications for existing drugs through observations or deliberate efforts to understand their mechanisms of action. Recent advancements in bioinformatics and pharmacology, along with the availability of extensive data repositories and analytical techniques, have fueled the demand for novel methodologies in pharmaceutical research and development. To facilitate systematic drug repurposing, various computational methodologies have emerged, combining experimental techniques and in silico approaches. These methods have revolutionized the field of drug discovery by enabling the efficient repurposing of screens. However, establishing an ideal drug repurposing pipeline requires the integration of molecular data accessibility, analytical proficiency, experimental design expertise, and a comprehensive understanding of clinical development processes. This chapter explores the key methodologies used in systematic drug repurposing and discusses the stakeholders involved in this field. It emphasizes the importance of strategic alliances to enhance the success of repurposing existing compounds for new indications. Additionally, the chapter highlights the current benefits, considerations, and challenges faced in the repurposing process, which is pursued by both biotechnology and pharmaceutical companies. Overall, drug repurposing holds great promise in expanding the use of existing drugs and bringing them to new patient populations. With the advancements in computational methodologies and the collaboration of various stakeholders, this approach has the potential to accelerate drug development and improve patient outcomes.

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 phenotypic screening platform for chronic pain therapeutics using all-optical electrophysiology

ABSTRACT

Chronic pain associated with osteoarthritis (OA) remains an intractable problem with few effective treatment options. New approaches are needed to model the disease biology and to drive discovery of therapeutics. We present an in vitro model of OA pain, where dorsal root ganglion (DRG) sensory neurons were sensitized by a defined mixture of disease-relevant inflammatory mediators, here called Sensitizing PAin Reagent Composition or SPARC. Osteoarthritis-SPARC components showed synergistic or additive effects when applied in combination and induced pain phenotypes in vivo. To measure the effect of OA-SPARC on neural firing in a scalable format, we used a custom system for high throughput all-optical electrophysiology. This system enabled light-based membrane voltage recordings from hundreds of neurons in parallel with single cell and single action potential resolution and a throughput of up to 500,000 neurons per day. A computational framework was developed to construct a multiparameter OA-SPARC neuronal phenotype and to quantitatively assess phenotype reversal by candidate pharmacology. We screened ∼3000 approved drugs and mechanistically focused compounds, yielding data from over 1.2 million individual neurons with detailed assessment of functional OA-SPARC phenotype rescue and orthogonal "off-target" effects. Analysis of confirmed hits revealed diverse potential analgesic mechanisms including ion channel modulators and other mechanisms including MEK inhibitors and tyrosine kinase modulators. Our results suggest that the Raf-MEK-ERK axis in DRG neurons may integrate the inputs from multiple upstream inflammatory mediators found in osteoarthritis patient joints, and MAPK pathway activation in DRG neurons may contribute to chronic pain in patients with osteoarthritis.

Design, Synthesis, and Target Identification of Novel Phenylalanine Derivatives by Drug Affinity Responsive Target Stability (DARTS) in Xanthomonas oryzae pv Oryzae

The increasing resistance displayed by plant phytopathogenic bacteria to conventional pesticides has heightened the urgency for the exploration of novel antibacterial agents possessing distinct modes of action (MOAs). In this study, a series of novel phenylalanine derivatives with the unique structure of acylhydrazone dithioether have been designed and synthesized. Bioassay results demonstrated that most target compounds exhibited excellent in vitro antibacterial activity against Xanthomonas oryzae pv oryzae (Xoo) and Xanthomonas axonopodis pv citri (Xac). Among them, the EC50 values of L3, L4, L6, L21, and L22 against Xoo were 7.4, 9.3, 6.7, 8.9, and 5.1 μg/mL, respectively, superior to that of bismerthiazol (BT) and thiodiazole copper (TC) (41.5 and >100 μg/mL); the EC50 values of L3, L4, L5, L6, L7, L8, L20, L21, and L22 against Xac were 5.6, 2.5, 6.2, 4.1, 4.2, 6.4, 6.3, 3.6, and 5.2 μg/mL, respectively, superior to that of BT and TC (43.3 and >100 μg/mL). An unmodified drug affinity responsive target stability (DARTS) technology was used to investigate the antibacterial MOAs of active compound L22, and the 50S ribosomal protein L2 (RL2) as an unprecedented target protein in Xoo cells was first discovered. The target protein RL2 was then expressed and purified. Furthermore, the in vitro interactions by microscale thermophoresis (Kd = 0.050 μM) and fluorescence titration (Ka = 1.4 × 105 M-1) experiments also demonstrated a strong binding force between compound L22 and RL2. Overall, these results not only facilitate the development of novel antibacterial agents but also establish a reliable method for exploring the targets of bactericides.

Drug discovery and optimization based on the co-crystal structure of natural product with target

Due to their structural diversities and prevalent biological activities, natural products (NPs) are momentous resources for drug discovery. Although NPs have a wide range of biological activities, many exhibit structural complexity that leads to synthetic difficulties, which combines with inefficient biological activity, toxicity, and unfavorable pharmacokinetic characteristics and ultimately imparts poor safety and efficacy outcomes. Progress in crystallization and computational techniques allow crystallography to have a seasonable influences on drug discovery. By co-crystallizing with proteins, therapeutic targets of NPs in specific diseases can be identified. By analyzing the co-crystal information, the structure-activity relationships (SARs) of NPs targeting specific proteins can be grasped. Under the guidance of co-crystal information, directional structural modification and simplification are powerful strategies for overcoming limitations of NPs, improving the success rate of NP-based drug discovery, and obtaining NP-based drugs with high selectivity, low toxicity and favorable pharmacokinetic characteristics. Here, we review the co-crystal information of a selection of NPs, focusing on the SARs of NPs reflected by co-crystal information and the modification and simplification strategies of NPs, and discuss how to apply co-crystal information in the optimization of NP-based lead compound.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

Idealization in pharmacology and pharmacy: Symbol of the chemical formula of one molecule of a substance and a real pharmaceutical product

The essence of the two levels of information used in modern pharmacy, pharmacology, and medicine for operations related to theoretical reasoning about medicines and the actual practice of their use for treating specific cases is fundamentally different. In particular, studies have analyzed the essence of theoretical information about medicines and the norms of their use in accordance with medical care standards. Information about medicines and standards of medical care, which dominate textbooks, reference books, encyclopedias, scientific articles , and normative and technical documents, is built on the idealized essence of chemically pure substances and their interaction with an idealized virtual patient. Accordingly, in the fields of pharmacy, pharmacology, and chemistry, physics, and materials science, researchers have traditionally represented chemical elements (and drugs) by certain chemical formulas, names, and symbols for their molecules. Moreover, in pharmacy and pharmacology, the structural formula of one molecule of only one chemical substance belonging to the group of the so-called main active substances most often plays this role. Generally, this chemical symbol of its molecule is identified with the real substance itself. It is assumed that the substance in question is of ideal high quality, is completely free of any impurities, is not combined with other substances, and does not represent a certain pharmaceutical product (it is not a tablet, not a solution, not an ointment, not an aerosol, etc.), and is not manufactured by a certain pharmaceutical company according to a certain recipe. Moreover, modern pharmaceutical products are not separate molecules, not pure chemical reagents, but all sorts of mixtures of different substances of different quality in different ratios. In addition, each pharmaceutical product of each manufacturing plant and each series number has inherent and unique mechanical, physical, chemical, and physicochemical properties and quality indicators. Therefore, the idealized essence of drugs is far from that of real pharmaceutical products. The chemical name and chemical formula are symbols of one molecule of a chemical element, reflecting its idealized chemical essence, but not the essence of a real “tablet”, “ampule”, and/or “tube” with it. In turn, the virtual patient of known sex, average age, average health status, and a body weight of approximately 70 kg implied by the standards of medical care is just an idealized object of interaction with an idealized “medicine”. In this regard, the study of the relationship between the idealized and real drugs and patients is a crucial part of the problem of the relationship between theory and reality in pharmacy, pharmacology, and medicine.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

Design and Characterization of a Novel eEF2K Degrader with Potent Therapeutic Efficacy Against Triple-Negative Breast Cancer

Dysregulated eEF2K expression is implicated in the pathogenesis of many human cancers, including triple-negative breast cancer (TNBC), making it a plausible therapeutic target. However, specific eEF2K inhibitors with potent anti-cancer activity have not been available so far. Targeted protein degradation has emerged as a new strategy for drug discovery. In this study, a novel small molecule chemical is designed and synthesized, named as compound C1, which shows potent activity in degrading eEF2K. C1 selectively binds to F8, L10, R144, C146, E229, and Y236 of the eEF2K protein and promotes its proteasomal degradation by increasing the interaction between eEF2K and the ubiquitin E3 ligase βTRCP in the form of molecular glue. C1 significantly inhibits the proliferation and metastasis of TNBC cells both in vitro and in vivo and in TNBC patient-derived organoids, and these antitumor effects are attributed to the degradation of eEF2K by C1. Additionally, combination treatment of C1 with paclitaxel, a commonly used chemotherapeutic drug, exhibits synergistic anti-tumor effects against TNBC. This study not only generates a powerful research tool to investigate the therapeutic potential of targeting eEF2K, but also provides a promising lead compound for developing novel drugs for the treatment of TNBC and other cancers.

Antimicrobial activity of nature-inspired molecules against multidrug-resistant bacteria

Multidrug-resistant bacterial infections present a serious challenge to global health. In addition to the spread of antibiotic resistance, some bacteria can form persister cells which are tolerant to most antibiotics and can lead to treatment failure or relapse. In the present work, we report the discovery of a new class of small molecules with potent antimicrobial activity against Gram-positive bacteria and moderate activity against Gram-negative drug-resistant bacterial pathogens. The lead compound SIMR 2404 had a minimal inhibitory concentration (MIC) of 2 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-intermediate Staphylococcus aureus (VISA). The MIC values against Gram-negative bacteria such as Escherichia coli and Actinobacteria baumannii were between 8-32 μg/mL. Time-kill experiments show that compound SIMR 2404 can rapidly kill tested bacteria. Compound SIMR 2404 was also found to rapidly kill MRSA persisters which display high levels of tolerance to conventional antibiotics. In antibiotic evolution experiments, MRSA quickly developed resistance to ciprofloxacin but failed to develop resistance to compound SIMR 2404 even after 24 serial passages. Compound SIMR 2404 was not toxic to normal human fibroblast at a concentration of 4 μg/mL which is twice the MIC concentration against MRSA. However, at a concentration of 8 μg/mL or higher, it showed cytotoxic activity indicating that it is not ideal as a candidate against Gram-negative bacteria. The acceptable toxicity profile and rapid antibacterial activity against MRSA highlight the potential of these molecules for further studies as anti-MRSA agents.

High-content screening identifies a small molecule that restores AP-4-dependent protein trafficking in neuronal models of AP-4-associated hereditary spastic paraplegia

Unbiased phenotypic screens in patient-relevant disease models offer the potential to detect therapeutic targets for rare diseases. In this study, we developed a high-throughput screening assay to identify molecules that correct aberrant protein trafficking in adapter protein complex 4 (AP-4) deficiency, a rare but prototypical form of childhood-onset hereditary spastic paraplegia characterized by mislocalization of the autophagy protein ATG9A. Using high-content microscopy and an automated image analysis pipeline, we screened a diversity library of 28,864 small molecules and identified a lead compound, BCH-HSP-C01, that restored ATG9A pathology in multiple disease models, including patient-derived fibroblasts and induced pluripotent stem cell-derived neurons. We used multiparametric orthogonal strategies and integrated transcriptomic and proteomic approaches to delineate potential mechanisms of action of BCH-HSP-C01. Our results define molecular regulators of intracellular ATG9A trafficking and characterize a lead compound for the treatment of AP-4 deficiency, providing important proof-of-concept data for future studies.

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.

Ultrasensitive and High-Resolution Protein Spatially Decoding Framework for Tumor Extracellular Vesicles

Proteins localized on the surface or within the lumen of tumor-derived extracellular vesicles (EVs) play distinct roles in cancer progression. However, quantifying both populations of proteins within EVs has been hampered due to the limited sensitivity of the existing protein detection methods and inefficient EV isolation techniques. In this study, the eSimoa framework, an innovative approach enabling spatial decoding of EV protein biomarkers with unmatched sensitivity and specificity is presented. Using the luminal eSimoa pipeline, the absolute concentration of luminal RAS or KRASG12D proteins is released and measured, uncovering their prevalence in pancreatic tumor-derived EVs. The pulldown eSimoa pipeline measured absolute protein concentrations from low-abundance EV subpopulations. The eSimoa assays detected EVs in both PBS and plasma samples, confirming their applicability across diverse clinical sample types. Overall, the eSimoa framework offers a valuable tool to (1) detect EVs at concentrations as low as 105 EV mL-1 in plasma, (2) quantify absolute EV protein concentrations as low as fM, and (3) decode the spatial distribution of EV proteins. This study highlights the potential of eSimoa in identifying disease-specific EV protein biomarkers in clinical samples with minimal pre-purification, thereby driving advancements in clinical translation.

Provenance and Clinical Benefit of Medicines Introduced to the French Market, 2008 to 2018

Importance

Both the commercial sector and academia play a vital role in medicine development. Ongoing debates exist on their contribution and the value of medicinal products entering the market.

Objective

To identify the provenance and clinical benefit of medicines that entered the French market between 2008 and 2018.

Design and Setting

In this cross-sectional study, the provenance of each medicine in the French market was established via a review of multiple sources documenting at least 2 matching findings per product. The clinical benefit was assigned using the matched scale developed from the Prescrire and Haute Autorité de Santé (HAS) gradings. The χ2 test was used to analyze the proportions and frequencies of medicines graded by Prescrire and HAS by origin, therapeutic category, and clinical benefit.

Main outcomes and measures

The origins and therapeutic categories of medicines. Clinical benefit based on Prescrire and HAS grading. Concordance of Prescrire and HAS grading.

Results

Of the 632 medicines that entered the French market between 2008 and 2018, 464 originated (73%) in the commercial sector, and 168 originated (27%) in the academic setting or in collaboration with commercial enterprises. Prescrire graded psychotropic agents (13/14 [93%]), whereas HAS graded respiratory agents (24/25 [96%]) as the highest percentage of medicines that provided no added benefit. Prescrire graded 360 medicines (77.6%) that originated in the industry and 108 medicines (64.3%) that originated in the academic setting (P = .001) to have no added clinical benefit. HAS assigned such grading to 331 ([71.3%] industry) vs 104 ([61.9%] academia) (P = .02). Based on the Prescrire grading, academia invented more medicines delivering some added benefit 57 (33.9%) vs 98 (21.1%) invented by industry (P = .001). HAS grading on some added benefit 51 ([30.4%] academia) vs 121 ([26.1%] industry) did not reach statistical significance (P = .29). However, HAS grading on substantial added clinical benefit reached statistical significance in favor of academia (13 [7.7%] vs 12 [2.6%] in the industry; P = .003), whereas Prescrire grading did not (1.8% academia vs 1.3% industry; P = .64).

Conclusions and Relevance

More than 70% of medicines that entered the French market during the 10-year period originated in the commercial sector. Although most medicines were not graded as providing clinical benefit, medicines originating in the academic setting were more likely to be graded as conferring clinical benefit than those originating in the commercial setting.

Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource

The NCI-60 human tumor cell line panel has proved to be a useful tool for the global cancer research community in the search for novel chemotherapeutics. The publicly available cell line characterization and compound screening data from the NCI-60 assay have significantly contributed to the understanding of cellular mechanisms targeted by new oncology agents. Signature sensitivity/resistance patterns generated for a given chemotherapeutic agent against the NCI-60 panel have long served as fingerprint presentations that encompass target information and the mechanism of action associated with the tested agent. We report the establishment of a new public NCI-60 resource based on the cell line screening of a large and growing set of 175 FDA-approved oncology drugs (AOD) plus >825 clinical and investigational oncology agents (IOA), representing a diverse set (>250) of therapeutic targets and mechanisms. This data resource is available to the public (https://ioa.cancer.gov) and includes the raw data from the screening of the IOA and AOD collection along with an extensive set of visualization and analysis tools to allow for comparative study of individual test compounds and multiple compound sets.

PROTAC derivatization of natural products for target identification and drug discovery: Design of evodiamine-based PROTACs as novel REXO4 degraders

INTRODUCTION

Natural products (NPs) play a crucial role in the development of therapeutic drugs. However, it is still highly challenging to identify the targets of NPs. Besides, NPs usually exert their pharmacological activities via acting on multiple targets or pathways, which also poses great difficulties for the target identification of NPs.

OBJECTIVES

Inspired by our continuous efforts in designing drug-like protein degraders, this study introduced a successful example for the target identification and drug discovery of natural products evodiamine by employing PROTAC technology.

METHODS

Taking advantages of proteolysis targeting chimera (PROTAC), herein an integrated strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of antitumor NP evodiamine.

RESULTS

In this study, both highly potent PROTACs and negative controls were designed for quantitative proteomic analysis. Furthermore, REXO4 was confirmed as a direct target of 3-fluoro-10-hydroxylevodiamine, which induced cell death through ROS. In addition, the PROTAC 13c effectively degraded REXO4 both in vitro and in vivo, leading to potent antitumor activities and reduced toxic side effects.

CONCLUSION

In summary, we developed an integrated strategy for the target identification and drug discovery of NPs, which was successfully applied to the PROTAC derivatization and target characterization of evodiamine. This proof-of-concept study highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery, exhibiting broad application in NP-based drug development.

Physical-chemical repurposing of drugs. History of its formation in Russia

It is reported that the traditional scheme of finding and developing a new drug and conducting the whole complex of preclinical studies requires several thousand chemical compounds, hundreds of millions of US dollars and more than 12 years of work. It is shown that physicochemical pharmacology was born in Russia at the end of the 20th century, which in our days has been transformed into physicochemical repurposing of known medicines. The first successfully repurposed known drug was a solution of 4% potassium chloride, which had previously traditionally belonged to the group of macro- and microelements, used by intravenous injections to regulate acid-base balance and rhythmic activity of the heart. In 1983, it was stated that this medicinal solution, when heated to 3942C and applied topically by irrigation of the bleeding surface, could be classified as a vasoconstrictor and hemostatic drug. Hyperthermia was used as a physico-chemical reprofiling factor, which, according to the Arrhenius law, accelerated and intensified, on the one hand, the spastic action of K+ cations on the gaping blood vessels (formation of hyperkalium contracture in the smooth muscles of the vascular wall) and, on the other hand, the blood clotting process in the wound. In subsequent years, the promise of physicochemical repurposing of known drugs was shown on the example of water, hydrogen peroxide, sodium chloride and sodium bicarbonate by purposefully changing their temperature, acid, osmotic activity, as well as the amount and quality of gas content (passing). A chronology of the physicochemical repurposing of known drug solutions and tablets is described and the essence of such new groups of drugs as bleachers of bruises and pyolytics is given. It is shown that both groups of drugs were discovered in Russia and are intended for local use to bleach bruises (blood stains) and dissolve thick mucus, sputum, pus, blood clots, meconium and other dense biological tissues containing the enzyme catalase. It is pointed out that the advantage and at the same time the limitation of the known drugs repurposed according to this scheme is their local application, since their new pharmacological activity is caused mainly by the physical and chemical principle of action, which is manifested by local interaction with the selected area of the patients organism.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

NIH funding for patents that contribute to market exclusivity of drugs approved 2010-2019 and the public interest protections of Bayh-Dole

Previous studies have shown that National Institutes of Health (NIH) funding contributed >$187 billion for basic or applied research related to the 356 drugs approved 2010-2019. This analysis asks how much of this funding led to patents cited as providing market exclusivity, patents that would be subject to the provisions of the Bayh-Dole Act that promote and protect the public interest. The method involves identifying published research in PubMed related to the approved drugs (applied research) or their targets (basic research). NIH-funded projects (grants) funding these publications and patents arising from these projects were both identified in RePORT. Patents cited as providing market exclusivity were identified in DrugPatentWatch (which incorporates FDA Orange Book). NIH funded basic or applied research related to all 313 FDA-approved drugs 2010-2019 with at least one patent in DrugPatentWatch. This research comprised 350 thousand publications (9% applied research; 91% basic research) supported by 341 thousand fiscal years (project years) of NIH funding and $164 billion in NIH project year costs (17% applied research; 83% basic research). These NIH projects also produced 22,360 patents, 119 of which were cited in DrugPatentWatch as protecting 34/313 drugs. These patents were associated with 769 project years of NIH funding (0.23% total) and project year costs of $0.95 billion (0.59% total). Overall, only 1.5% of total NIH funding for applied research and 0.38% of total NIH funding for basic research was associated with patents in DrugPatentWatch. This analysis shows that very little of the NIH funding for research that contributes to new drug approvals leads to patents that provide market exclusivity and are subject to the provisions of the Bayh-Dole Act that promote the public interest in practical applications of the research, reasonable use and pricing, and a return on this public sector investment. This suggests that the Bayh-Dole Act is limited in its ability to protect the public interest in the pharmaceutical innovations driven by NIH-funded research.

Chronological Analysis of First-in-Class Drugs Approved from 2011 to 2022: Their Technological Trend and Origin

The discovery and development of first-in-class (FIC) drugs are becoming increasingly important due to increasing reimbursement pressure and personalized medication. To investigate the technological trends and origin of FIC drugs, the FIC drugs approved in the U.S. from January 2011 to December 2022 were analyzed. The analysis shows that previous major target families, viz. enzymes, G-protein coupled receptors, transporters, and transcription factors, are no longer considered major in recent years. Instead, the shares of secreted proteins/peptides and mRNAs have continuously increased from 2011-2014 to 2019-2022, suggesting that the target family of FIC drugs has shifted to molecules previously considered challenging as drug targets. Small molecules were predominant in 2011-2014, followed by a large increase in antibody medicines in 2015-2018 and further diversification of antibody medicine modalities in 2019-2022. Nucleic acid medicine has also continuously increased its share, suggesting that diversifying modalities supports the creation of FIC drugs toward challenging target molecules. Over half of FIC drugs were created by small and medium enterprises (SMEs), especially young companies established in the 1990s and 2000s. All SMEs that produced more than one FIC drug approved in 2019-2022 have the strong technological capability in a specific modality. Investment in modality technologies and facilitating mechanisms to translate academic modality technologies to start-ups might be important for enhancing FIC drug development.

High-Content Small Molecule Screen Identifies a Novel Compound That Restores AP-4-Dependent Protein Trafficking in Neuronal Models of AP-4-Associated Hereditary Spastic Paraplegia

Unbiased phenotypic screens in patient-relevant disease models offer the potential to detect novel therapeutic targets for rare diseases. In this study, we developed a high-throughput screening assay to identify molecules that correct aberrant protein trafficking in adaptor protein complex 4 (AP-4) deficiency, a rare but prototypical form of childhood-onset hereditary spastic paraplegia, characterized by mislocalization of the autophagy protein ATG9A. Using high-content microscopy and an automated image analysis pipeline, we screened a diversity library of 28,864 small molecules and identified a lead compound, C-01, that restored ATG9A pathology in multiple disease models, including patient-derived fibroblasts and induced pluripotent stem cell-derived neurons. We used multiparametric orthogonal strategies and integrated transcriptomic and proteomic approaches to delineate putative molecular targets of C-01 and potential mechanisms of action. Our results define molecular regulators of intracellular ATG9A trafficking and characterize a lead compound for the treatment of AP-4 deficiency, providing important proof-of-concept data for future Investigational New Drug (IND)-enabling studies.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

Comparison of Research Spending on New Drug Approvals by the National Institutes of Health vs the Pharmaceutical Industry, 2010-2019

Importance

Government and the pharmaceutical industry make substantive contributions to pharmaceutical innovation. This study compared the investments by the National Institutes of Health (NIH) and industry and estimated the cost basis for assessing the balance of social and private returns.

Objectives

To compare NIH and industry investments in recent drug approvals.

Design, Setting, and Participants

This cross-sectional study of NIH funding associated with drugs approved by the FDA from 2010 to 2019 was conducted from May 2020 to July 2022 and accounted for basic and applied research, failed clinical candidates, and discount rates for government spending compared with analogous estimates of industry investment.

Main Outcomes and Measures

Costs from the NIH for research associated with drug approvals.

Results

Funding from the NIH was contributed to 354 of 356 drugs (99.4%) approved from 2010 to 2019 totaling $187 billion, with a mean (SD) $1344.6 ($1433.1) million per target for basic research on drug targets and $51.8 ($96.8) million per drug for applied research on products. Including costs for failed clinical candidates, mean (SD) NIH costs were $1441.5 ($1372.0) million per approval or $1730.3 ($1657.6) million per approval, estimated with a 3% discount rate. The mean (SD) NIH spending was $2956.0 ($3106.3) million per approval with a 10.5% cost of capital, which estimates the cost savings to industry from NIH spending. Spending and approval by NIH for 81 first-to-target drugs was greater than reported industry spending on 63 drugs approved from 2010 to 2019 (difference, -$1998.4 million; 95% CI, -$3302.1 million to -$694.6 million; P = .003). Spending from the NIH was not less than industry spending considering clinical failures, a 3% discount rate for NIH spending, and a 10.5% cost of capital for the industry (difference, -$1435.3 million; 95% CI, -$3114.6 million to $244.0 million; P = .09) or when industry spending included prehuman research (difference, -$1394.8 million; 95% CI, -$3774.8 million to $985.2 million; P = .25). Accounting for spillovers of NIH-funded basic research on drug targets to multiple products, NIH costs were $711.3 million with a 3% discount rate, which was less than the range of reported industry costs with 10.5% cost of capital.

Conclusions and Relevance

The results of this cross-sectional study found that NIH investment in drugs approved from 2010 to 2019 was not less than investment by the pharmaceutical industry, with comparable accounting for basic and applied research, failed clinical trials, and cost of capital or discount rates. The relative scale of NIH and industry investment may provide a cost basis for calibrating the balance of social and private returns from investments in pharmaceutical innovation.

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit Generation

Hit generation is a crucial step in drug discovery that will determine the speed and chance of success of identifying drug candidates. Many strategies are now available to identify chemical starting points, or hits, and each biological target warrants a tailored approach. In this set of best practices, we detail the essential approaches for target centric hit generation and the opportunities and challenges they come with. We then provide guidance on how to validate hits to ensure medicinal chemistry is only performed on compounds and scaffolds that engage the target of interest and have the desired mode of action. Finally, we discuss the design of integrated hit generation strategies that combine several approaches to maximize the chance of identifying high quality starting points to ensure a successful drug discovery campaign.

Linking chemicals, genes and morphological perturbations to diseases

The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.

Screening a small hydrazide-hydrazone combinatorial library for targeting the STAT3 in monocyte-macrophages with insulated reporter transposons

The Signal Transducer and Activator of Transcription 3 (STAT3) pharmacological targeting is regarded as a prospective approach to treat cancer, autoimmune disorders, or inflammatory diseases. We have developed a series of reporters of the STAT3, NF-κB, Nrf2, metal-responsive transcription factor-1 (MTF-1), and hypoxia-inducible factor 1α (HIF-1α) transcriptional activation in human monocyte-macrophage line THP-1. The reporter lines were employed to test a set of hydrazide-hydrazones as potential STAT3 inhibitors. A hydrazide-hydrazone library composed of 70 binary combinations of 7 carbonyl and 10 hydrazide components, including a STAT3 inhibitor clinical drug nifuroxazide, has been assembled and screened by the reporters. For the library as a whole, significant correlations between responses of the STAT3 and NF-κB or the STAT3 and HIF-1α reporters in THP-1 monocytes were found. For selected inhibitory combinations, respective hydrazide-hydrazones have been prepared and tested individually. The most potent 2-acetylpyridine 4-chlorobenzoylhydrazone exhibited the STAT3 inhibitory potential significantly exceeding that of nifuroxazide (ED50 2 vs 50 μM respectively) in THP-1 cells. We conclude that insulated reporter transposons could be a useful tool for drug discovery applications.

Graphical Abstract

Accelerating inhibitor discovery for deubiquitinating enzymes

Deubiquitinating enzymes (DUBs) are an emerging drug target class of ~100 proteases that cleave ubiquitin from protein substrates to regulate many cellular processes. A lack of selective chemical probes impedes pharmacologic interrogation of this important gene family. DUBs engage their cognate ligands through a myriad of interactions. We embrace this structural complexity to tailor a chemical diversification strategy for a DUB-focused covalent library. Pairing our library with activity-based protein profiling as a high-density primary screen, we identify selective hits against 23 endogenous DUBs spanning four subfamilies. Optimization of an azetidine hit yields a probe for the understudied DUB VCPIP1 with nanomolar potency and in-family selectivity. Our success in identifying good chemical starting points as well as structure-activity relationships across the gene family from a modest but purpose-build library challenges current paradigms that emphasize ultrahigh throughput in vitro or virtual screens against an ever-increasing scope of chemical space.

Compressed phenotypic screens for complex multicellular models and high-content assays

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

Deep learning in image-based phenotypic drug discovery

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

Global trends in the health economics field of PD-1/PD-L1 inhibitors: A bibliometric and visualized study

Inhibitors of programmed cell death protein 1 and its associated ligand (PD-L1) are widely used in cancer treatment. However, medical costs and benefits of PD-1/PD-L1 inhibitors need attention owing to differences in response rates among individuals. This study explored global trends in the health economics field of PD-1/PD-L1 inhibitors to enhance their worldwide development. Bibliometric analysis of all documents currently indexed in Web of Science Core Collection from inception to 2022 was performed. Publication year, authors, countries, institutes, and journals were analyzed by Bibliometrix package (version 3.2.1) in R (version 4.1.3). CiteSpace (version 6.1.R6) and VOSviewer (version 1.6.18) were used to analyze burst words, co-authorship of institutes, co-cited journals, and co-cited references, while figures were mainly drawn by Ggplot2 package (version 3.3.5) in R (version 4.1.3) and SCImago Graphica Beta (version 1.0.23). A total of 2020 documents related to the health economics of PD-1/PD-L1 inhibitors were identified, and 1,204 documents met the selection criteria for inclusion in the study. A rapid increase in the number of publications since 2019 was observed, but this increase stopped in 2022, revealing research saturation in the field. Value in Health (166 publications, 13.79% of total documents) had the most publications, while New England Journal of Medicine (2,890 co-citations) was the most co-cited journal. The United States was the leading contributor in this field with 506 publications and the top two productive institutes globally. The main hot topics included the cost-effectiveness of treatment with PD-1 and/or PD-L1 inhibitors, and the comparison between the cost-effectiveness of PD-/PD-L1 inhibitors and other drugs. There were substantial differences between developed and developing countries in the health economics field of PD-1 and/or PD-L1 inhibitors. The cost-effectiveness analysis of combined treatment with PD-1/PD-L1 inhibitors and other drugs warrants further attention. Findings from this study may provide governments and pharmaceutical companies with a strong reference for future research.

Computational Evaluation of Azadirachta indica-Derived Bioactive Compounds as Potential Inhibitors of NLRP3 in the Treatment of Alzheimer's Disease

BACKGROUND

The development of therapeutic agents against Alzheimer's disease (AD) has stalled recently. Drug candidates targeting amyloid-β (Aβ) deposition have often failed clinical trials at different stages, prompting the search for novel targets for AD therapy. The NLRP3 inflammasome is an integral part of innate immunity, contributing to neuroinflammation and AD pathophysiology. Thus, it has become a promising new target for AD therapy.

OBJECTIVE

The study sought to investigate the potential of bioactive compounds derived from Azadirachta-indica to inhibit the NLRP3 protein implicated in the pathophysiology of AD.

METHODS

Structural bioinformatics via molecular docking and density functional theory (DFT) analysis was utilized for the identification of novel NLRP3 inhibitors from A. indica bioactive compounds. The compounds were further subjected to pharmacokinetic and drug-likeness analysis. Results obtained from the compounds were compared against that of oridonin, a known NLRP3 inhibitor.

RESULTS

The studied compounds optimally saturated the binding site of the NLRP3 NACHT domain, forming principal interactions with the different amino acids at its binding site. The studied compounds also demonstrated better bioactivity and chemical reactivity as ascertained by DFT analysis and all the compounds except 7-desacetyl-7-benzoylazadiradione, which had two violations, conformed to Lipinski's rule of five.

CONCLUSION

In silico studies show that A. indica derived compounds have better inhibitory potential against NLRP3 and better pharmacokinetic profiles when compared with the reference ligand (oridonin). These compounds are thus proposed as novel NLRP3 inhibitors for the treatment of AD. Further wet-lab studies are needed to confirm the potency of the studied compounds.

Deep Mining of Complex Antibody Phage Pools

An important, and rapidly growing class of drugs are antibodies which can be discovered through phage display technology. In this technique, antibodies are typically first enriched through consecutive rounds of selection on a target antigen with amplification in bacteria between each selection round. Thereafter, a subset of random individual clones is analyzed for binding in a screening procedure. This results in discovery of the most abundant antibodies in the pool. However, there are multiple factors affecting the enrichment of antibodies during the selection resulting in a very complex output pool of antibodies. A few antibodies are present in many copies and others only in a few copies, where the most abundant antibodies are not necessarily the functionally best ones. In order to utilize the full potential of the output from a phage display selection, and enable discovery of low abundant, potentially functionally important clones, deep mining technologies are needed. In this chapter, two methods for deep mining of an antibody pool are described, protein depletion and antibody blocking. The methods can be applied both when the target is a single antigen and on complex antigen mixtures such as whole cells and tissues.

Shield-activated two-way imaging nanomaterials for enhanced cancer theranostics

Smart nanomaterials with stimuli-responsive imaging enhancement have been widely developed to meet the requirements of accurate cancer diagnosis. However, these imaging nanoenhancers tend to be always on during circulation, which significantly increases the background signal when assessing the imaging performance. To improve unfavorable signal-to-noise ratios, an effective way is to shield the noise signal of these nanoprobes in non-targeted areas. Fortunately, there is a natural mutual shielding effect between some imaging nanomaterials, which provides the possibility of designing engineered nanomaterials with imaging quenching between two different components at the beginning. Once in the tumor microenvironment, the two components will present activated dual-mode imaging ability because of their separation, designated as two-way imaging tuning. This review highlights the design and mechanism of a series of engineered nanomaterials with two-way imaging tuning and their latest applications in the fields of cancer magnetic resonance imaging, fluorescence imaging, and their combination. The challenges and future directions for the improvement of these engineered nanomaterials towards clinical transformation are also discussed. This review aims to introduce the special constraint relationships of imaging components and provide scientists with simpler and more efficient nanoplatform construction ideas, promoting the development of engineered nanomaterials with two-way imaging tuning in cancer theranostics.

Identification of active natural products that induce lysosomal biogenesis by lysosome-based screening and biological evaluation

Lysosomal biogenesis is an essential adaptive process by which lysosomes exert their function in maintaining cellular homeostasis. Defects in lysosomal enzymes and functions lead to lysosome-related diseases, including lysosomal storage diseases and neurodegenerative disorders. Thus, activation of the autophagy-lysosomal pathway, especially induction of lysosomal biogenesis, might be an effective strategy for the treatment of lysosome-related diseases. In this study, we established a lysosome-based screening system to identify active compounds from natural products that could promote lysosomal biogenesis. The subcellular localizations of master transcriptional regulators of lysosomal genes, TFEB, TFE3 and ZKSCAN3 were examined to reveal the potential mechanisms. More than 200 compounds were screened, and we found that Hdj-23, a triterpene isolated from Walsura cochinchinensis, induced lysosomal biogenesis via activation of TFEB/TFE3. In summary, this study introduced a lysosome-based live cell screening strategy to identify bioactive compounds that promote lysosomal biogenesis, which would provide potential candidate enhancers of lysosomal biogenesis and novel insight for treating lysosome-related diseases.

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.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

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.

A decade of approved first-in-class small molecule orphan drugs: Achievements, challenges and perspectives

In the past decade (2011-2020), there was a growing interest in the discovery and development of orphan drugs for the treatment of rare diseases. However, rare diseases only account for a population of 0.65‰-1‰ which usually occur with previously unknown biological mechanisms and lack of specific therapeutics, thus to increase the demands for the first-in-class (FIC) drugs with new biological targets or mechanisms. Considering the achievements in the past 10 years, a total of 410 drugs were approved by U.S. Food and Drug Administration (FDA), which contained 151 FIC drugs and 184 orphan drugs, contributing to make up significant numbers of the approvals. Notably, more than 50% of FIC drugs are developed as orphan drugs and some of them have already been milestones in drug development. In this review, we aim to discuss the FIC small molecules for the development of orphan drugs case by case and highlight the R&D strategy with novel targets and scientific breakthroughs.

Compounds co-targeting kinases in axon regulatory pathways promote regeneration and behavioral recovery after spinal cord injury in mice

Recovery from spinal cord injury (SCI) and other central nervous system (CNS) trauma is hampered by limits on axonal regeneration in the CNS. Regeneration is restricted by the lack of neuron-intrinsic regenerative capacity and by the repressive microenvironment confronting damaged axons. To address this challenge, we have developed a therapeutic strategy that co-targets kinases involved in both extrinsic and intrinsic regulatory pathways. Prior work identified a kinase inhibitor (RO48) with advantageous polypharmacology (co-inhibition of targets including ROCK2 and S6K1), which promoted CNS axon growth in vitro and corticospinal tract (CST) sprouting in a mouse pyramidotomy model. We now show that RO48 promotes neurite growth from sensory neurons and a variety of CNS neurons in vitro, and promotes CST sprouting and/or regeneration in multiple mouse models of spinal cord injury. Notably, these in vivo effects of RO48 were seen in several independent experimental series performed in distinct laboratories at different times. Finally, in a cervical dorsal hemisection model, RO48 not only promoted growth of CST axons beyond the lesion, but also improved behavioral recovery in the rotarod, gridwalk, and pellet retrieval tasks. Our results provide strong evidence for RO48 as an effective compound to promote axon growth and regeneration. Further, they point to strategies for increasing robustness of interventions in pre-clinical models.

Cytotoxic effects of halogenated tin phosphinoyldithioformate complexes against several cancer cell lines

Organotin complexes are studied as promising alternatives to the anticancer drug cisplatin. We report two monoorganotin(IV) complexes based on a dibenzyl phosphinoyldithioformate (H-DBPTF) ligand, containing either bromide (Sn-DBPTF-1) or chloride (Sn-DBPTF-2) anions. The complexes were characterized by standard analytical techniques and the structural details of these complexes were elucidated by single crystal X-ray diffraction. Sn-DBPTF-1 was cytotoxic at IC50 <10 μg mL-1 against cancer cell lines A549 (lung cancer), Aspc-1 (pancreatic cancer), OVCAR-3 (ovarian cancer), T-47D (breast cancer) and HCT116 (colon cancer), and breast epithelial stem cell line D492. The non-tumorigenic breast epithelial cell line MCF-10 was less sensitive at IC50 = 22 μg mL-1. Sn-DBPTF-2 had limited cytotoxic effect at IC50 13-37 μg mL-1. Sn-DBPTF-1 induced apoptosis and double-strand DNA breaks. Cell cycle arrest in G2 occurred in HCT116 and accumulation in G1 in Aspc-1. The results indicate that the basic effect of Sn-DBPTF-1 is to induce DNA damage, leading to apoptosis and cell cycle arrest depending on the cell line.

Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches

The neglected tropical disease (NTD) leishmaniasis is the collective name given to a diverse group of illnesses caused by ~20 species belonging to the genus Leishmania, a majority of which are vector borne and associated with complex life cycles that cause immense health, social, and economic burdens locally, but individually are not a major global health priority. Therapeutic approaches against leishmaniasis have various inadequacies including drug resistance and a lack of effective control and eradication of the disease spread. Therefore, the development of a rationale-driven, target based approaches towards novel therapeutics against leishmaniasis is an emergent need. The utilization of Artificial Intelligence/Machine Learning methods, which have made significant advances in drug discovery applications, would benefit the discovery process. In this review, following a summary of the disease epidemiology and available therapies, we consider three important leishmanial metabolic pathways that can be attractive targets for a structure-based drug discovery approach towards the development of novel anti-leishmanials. The folate biosynthesis pathway is critical, as Leishmania is auxotrophic for folates that are essential in many metabolic pathways. Leishmania can not synthesize purines de novo, and salvage them from the host, making the purine salvage pathway an attractive target for novel therapeutics. Leishmania also possesses an organelle glycosome, evolutionarily related to peroxisomes of higher eukaryotes, which is essential for the survival of the parasite. Research towards therapeutics is underway against enzymes from the first two pathways, while the third is as yet unexplored.