Why 90% of clinical drug development fails and how to improve it?

Seminatural environments for rodent behavioral testing: a representative design improving animal welfare and enhancing replicability

The low replicability of scientific studies has become an important issue. One possible cause is low representativeness of the experimental design employed. Already in the 1950's, Egon Brunswick pointed out that experimental setups ideally should be based on a random sample of stimuli from the subjects' natural environment or at least include basic features of that environment. Only experimental designs satisfying this criterion, representative designs in Brunswikian terminology, can produce results generalizable beyond the procedure used and to situations outside the laboratory. Such external validity is crucial in preclinical drug studies, for example, and should be important for replicability in general. Popular experimental setups in rodent research on non-human animals, like the tail suspension test or the Geller-Seifter procedure, do not correspond to contexts likely to be encountered in the animals' habitat. Consequently, results obtained in this kind of procedures can be generalized neither to other procedures nor to contexts outside the laboratory. Furthermore, many traditional procedures are incompatible with current notions of animal welfare. An approximation to the natural social and physical context can be provided in the laboratory, in the form of a seminatural environment. In addition to satisfy the basic demands for a representative design, such environments offer a far higher level of animal welfare than the typical small cages. This perspective article will briefly discuss the basic principles of the generalizability of experimental results, the virtues of representative designs and the coincidence of enhanced scientific quality and animal welfare provided by this kind of design.

Structural insights into interactions of new polymeric (μ-oxo) bridged Cu(II) complexes of taurine with yeast tRNA by spectroscopic and computational approaches and its application towards chemoresistant cancer lines

RNA-targeted drugs are considered as safe treatment option for the cure of many chronic diseases preventing off-targeted delivery and acute toxic manifestations. FDA has approved many such RNA therapies in different phases of clinical trials, validating their use for the treatment of various chronic diseases. We report herein, new water-soluble (μ-oxo) bridged polymeric Cu(II) complexes of taurine (2-aminoethane sulfonic acid) complexes 1 and 2. The therapeutic potency of 1 and 2 was ascertained by studying biophysical interactions with tRNA/ct-DNA. The experimental results demonstrated that the complexes interacted avidly to nucleic acids through intercalation mode depicting a specific preference for tRNA in comparison to ct-DNA and, moreover 2 showed higher binding propensity than 1. The electrophoretic behaviour of the complexes with plasmid pBR322 DNA and tRNA were examined by gel mobility assay that revealed a concentration-dependent activity with complex 2 performing more efficient cleavage as compared to complex 1. Cytotoxicity results on cancer cell strains displayed higher cytotoxicity than complex 1 against treated cancer cells. The synthesized copper(II) taurine complexes have met the basic criteria of anticancer drug design as they are structurally well-characterized, exhibiting good solubility in water, lipophilic in nature with superior intercalating propensity towards tRNA and cytotoxic in nature.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M^pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M^pro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Next-Generation Adjuvants: Applying Engineering Methods to Create and Evaluate Novel Immunological Responses

Adjuvants are a critical component of vaccines. Adjuvants typically target receptors that activate innate immune signaling pathways. Historically, adjuvant development has been laborious and slow, but has begun to accelerate over the past decade. Current adjuvant development consists of screening for an activating molecule, formulating lead molecules with an antigen, and testing this combination in an animal model. There are very few adjuvants approved for use in vaccines, however, as new candidates often fail due to poor clinical efficacy, intolerable side effects, or formulation limitations. Here, we consider new approaches using tools from engineering to improve next-generation adjuvant discovery and development. These approaches will create new immunological outcomes that will be evaluated with novel diagnostic tools. Potential improved immunological outcomes include reduced vaccine reactogenicity, tunable adaptive responses, and enhanced adjuvant delivery. Evaluations of these outcomes can leverage computational approaches to interpret "big data" obtained from experimentation. Applying engineering concepts and solutions will provide alternative perspectives, further accelerating the field of adjuvant discovery.

Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence

This article provides a comprehensive narrative review of the history of antiepileptic drugs (AEDs) and their development over time. Firstly, it explores the significant role of serendipity in the discovery of essential AEDs that continue to be used today, such as phenobarbital and valproic acid. Subsequently, it delves into the historical progression of crucial preclinical models employed in the development of novel AEDs, including the maximal electroshock stimulation test, pentylenetetrazol-induced test, kindling models, and other animal models. Moving forward, a concise overview of the clinical advancement of major AEDs is provided, highlighting the initial milestones and the subsequent refinement of this process in recent decades, in line with the emergence of evidence-based medicine and the implementation of increasingly rigorous controlled clinical trials. Lastly, the article explores the contributions of artificial intelligence, while also offering recommendations and discussing future perspectives for the development of new AEDs.

Emerging trends in organ-on-a-chip systems for drug screening

New drug discovery is under growing pressure to satisfy the demand from a wide range of domains, especially from the pharmaceutical industry and healthcare services. Assessment of drug efficacy and safety prior to human clinical trials is a crucial part of drug development, which deserves greater emphasis to reduce the cost and time in drug discovery. Recent advances in microfabrication and tissue engineering have given rise to organ-on-a-chip, an in vitro model capable of recapitulating human organ functions in vivo and providing insight into disease pathophysiology, which offers a potential alternative to animal models for more efficient pre-clinical screening of drug candidates. In this review, we first give a snapshot of general considerations for organ-on-a-chip device design. Then, we comprehensively review the recent advances in organ-on-a-chip for drug screening. Finally, we summarize some key challenges of the progress in this field and discuss future prospects of organ-on-a-chip development. Overall, this review highlights the new avenue that organ-on-a-chip opens for drug development, therapeutic innovation, and precision medicine.

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Drug-disease association prediction with literature based multi-feature fusion

Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

Co-Delivery of Therapeutics and Bioactive Gas Using a Novel Liposomal Platform for Enhanced Treatment of Acute Arterial Injury

Atherosclerosis is a complex, multi-stage disease characterized by pathological changes across the vascular wall. Endothelial dysfunction, inflammation, hypoxia, and vascular smooth muscle cell proliferation contribute to its progression. An effective strategy capable of delivering pleiotropic treatment to the vascular wall is essential to limit neointimal formation. Echogenic liposomes (ELIP), which can encapsulate bioactive gases and therapeutic agents, have the potential to deliver enhanced penetration and treatment efficacy for atherosclerosis. In this study, liposomes loaded with nitric oxide (NO) and rosiglitazone, a peroxisome proliferator-activated receptor agonist, were prepared using hydration, sonication, freeze-thawing, and pressurization. The efficacy of this delivery system was evaluated in a rabbit model of acute arterial injury induced by balloon injury to the common carotid artery. Intra-arterial administration of rosiglitazone/NO co-encapsulated liposomes (R/NO-ELIP) immediately following injury resulted in reduced intimal thickening after 14 days. The anti-inflammatory and anti-proliferative effects of the co-delivery system were investigated. These liposomes were echogenic, enabling ultrasound imaging to assess their distribution and delivery. R/NO-ELIP delivery exhibited a greater attenuation (88 ± 15%) of intimal proliferation when compared to NO-ELIP (75 ± 13%) or R-ELIP (51 ± 6%) delivery alone. The study demonstrates the potential of echogenic liposomes as a promising platform for ultrasound imaging and therapeutic delivery.

Metabolic Priming as a Tool in Redox and Mitochondrial Theragnostics

Theragnostics is a promising approach that integrates diagnostics and therapeutics into a single personalized strategy. To conduct effective theragnostic studies, it is essential to create an in vitro environment that accurately reflects the in vivo conditions. In this review, we discuss the importance of redox homeostasis and mitochondrial function in the context of personalized theragnostic approaches. Cells have several ways to respond to metabolic stress, including changes in protein localization, density, and degradation, which can promote cell survival. However, disruption of redox homeostasis can lead to oxidative stress and cellular damage, which are implicated in various diseases. Models of oxidative stress and mitochondrial dysfunction should be developed in metabolically conditioned cells to explore the underlying mechanisms of diseases and develop new therapies. By choosing an appropriate cellular model, adjusting cell culture conditions and validating the cellular model, it is possible to identify the most promising therapeutic options and tailor treatments to individual patients. Overall, we highlight the importance of precise and individualized approaches in theragnostics and the need to develop accurate in vitro models that reflect the in vivo conditions.

Endorsement of TNBC Biomarkers in Precision Therapy by Nanotechnology

Breast cancer is a heterogeneous disease which accounts globally for approximately 1 million new cases annually, wherein more than 200,000 of these cases turn out to be cases of triple-negative breast cancer (TNBC). TNBC is an aggressive and rare breast cancer subtype that accounts for 10-15% of all breast cancer cases. Chemotherapy remains the only therapy regimen against TNBC. However, the emergence of innate or acquired chemoresistance has hindered the chemotherapy used to treat TNBC. The data obtained from molecular technologies have recognized TNBC with various gene profiling and mutation settings that have helped establish and develop targeted therapies. New therapeutic strategies based on the targeted delivery of therapeutics have relied on the application of biomarkers derived from the molecular profiling of TNBC patients. Several biomarkers have been found that are targets for the precision therapy in TNBC, such as EGFR, VGFR, TP53, interleukins, insulin-like growth factor binding proteins, c-MET, androgen receptor, BRCA1, glucocorticoid, PTEN, ALDH1, etc. This review discusses the various candidate biomarkers identified in the treatment of TNBC along with the evidence supporting their use. It was established that nanoparticles had been considered a multifunctional system for delivering therapeutics to target sites with increased precision. Here, we also discuss the role of biomarkers in nanotechnology translation in TNBC therapy and management.

Unlocking the potential of snake venom-based molecules against the malaria, Chagas disease, and leishmaniasis triad

Malaria, leishmaniasis and Chagas disease are vector-borne protozoal infections with a disproportionately high impact on the most fragile societies in the world, and despite malaria-focused research gained momentum in the past two decades, both trypanosomiases and leishmaniases remain neglected tropical diseases. Affordable effective drugs remain the mainstay of tackling this burden, but toxicicty, inneficiency against later stage disease, and drug resistance issues are serious shortcomings. One strategy to overcome these hurdles is to get new therapeutics or inspiration in nature. Indeed, snake venoms have been recognized as valuable sources of biomacromolecules, like peptides and proteins, with antiprotozoal activity. This review highlights major snake venom components active against at least one of the three aforementioned diseases, which include phospholipases A2, metalloproteases, L-amino acid oxidases, lectins, and oligopeptides. The relevance of this repertoire of biomacromolecules and the bottlenecks in their clinical translation are discussed considering approaches that should increase the success rate in this arduous task. Overall, this review underlines how venom-derived biomacromolecules could lead to pioneering antiprotozoal treatments and how the drug landscape for neglected diseases may be revolutionized by a closer look at venoms. Further investigations on poorly studied venoms is needed and could add new therapeutics to the pipeline.

A targeted genetic modifier screen in Drosophila uncovers vulnerabilities in a genetically complex model of colon cancer

Received on 16 January 2023; accepted on 21 February 2023Kinases are key regulators of cellular signal transduction pathways. Many diseases, including cancer, are associated with global alterations in protein phosphorylation networks. As a result, kinases are frequent targets of drug discovery efforts. However, target identification and assessment, a critical step in targeted drug discovery that involves identifying essential genetic mediators of disease phenotypes, can be challenging in complex, heterogeneous diseases like cancer, where multiple concurrent genomic alterations are common. Drosophila is a particularly useful genetic model system to identify novel regulators of biological processes through unbiased genetic screens. Here, we report 2 classic genetic modifier screens focusing on the Drosophila kinome to identify kinase regulators in 2 different backgrounds: KRAS TP53 PTEN APC, a multigenic cancer model that targets 4 genes recurrently mutated in human colon tumors and KRAS alone, a simpler model that targets one of the most frequently altered pathways in cancer. These screens identified hits unique to each model and one shared by both, emphasizing the importance of capturing the genetic complexity of human tumor genome landscapes in experimental models. Our follow-up analysis of 2 hits from the KRAS-only screen suggests that classical genetic modifier screens in heterozygous mutant backgrounds that result in a modest, nonlethal reduction in candidate gene activity in the context of a whole animal-a key goal of systemic drug treatment-may be a particularly useful approach to identify the most rate-limiting genetic vulnerabilities in disease models as ideal candidate drug targets.

Microfluidic Liver-on-a-Chip for Preclinical Drug Discovery

Drug discovery is an expensive, long, and complex process, usually with a high degree of uncertainty. In order to improve the efficiency of drug development, effective methods are demanded to screen lead molecules and eliminate toxic compounds in the preclinical pipeline. Drug metabolism is crucial in determining the efficacy and potential side effects, mainly in the liver. Recently, the liver-on-a-chip (LoC) platform based on microfluidic technology has attracted widespread attention. LoC systems can be applied to predict drug metabolism and hepatotoxicity or to investigate PK/PD (pharmacokinetics/pharmacodynamics) performance when combined with other artificial organ-on-chips. This review discusses the liver physiological microenvironment simulated by LoC, especially the cell compositions and roles. We summarize the current methods of constructing LoC and the pharmacological and toxicological application of LoC in preclinical research. In conclusion, we also discussed the limitations of LoC in drug discovery and proposed a direction for improvement, which may provide an agenda for further research.

The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening

Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.

Targeting P21-Activated Kinase-1 for Metastatic Prostate Cancer

Metastatic prostate cancer (mPCa) has limited therapeutic options and a high mortality rate. The p21-activated kinase (PAK) family of proteins is important in cell survival, proliferation, and motility in physiology, and pathologies such as infectious, inflammatory, vascular, and neurological diseases as well as cancers. Group-I PAKs (PAK1, PAK2, and PAK3) are involved in the regulation of actin dynamics and thus are integral for cell morphology, adhesion to the extracellular matrix, and cell motility. They also play prominent roles in cell survival and proliferation. These properties make group-I PAKs a potentially important target for cancer therapy. In contrast to normal prostate and prostatic epithelial cells, group-I PAKs are highly expressed in mPCA and PCa tissue. Importantly, the expression of group-I PAKs is proportional to the Gleason score of the patients. While several compounds have been identified that target group-I PAKs and these are active in cells and mice, and while some inhibitors have entered human trials, as of yet, none have been FDA-approved. Probable reasons for this lack of translation include issues related to selectivity, specificity, stability, and efficacy resulting in side effects and/or lack of efficacy. In the current review, we describe the pathophysiology and current treatment guidelines of PCa, present group-I PAKs as a potential druggable target to treat mPCa patients, and discuss the various ATP-competitive and allosteric inhibitors of PAKs. We also discuss the development and testing of a nanotechnology-based therapeutic formulation of group-I PAK inhibitors and its significant potential advantages as a novel, selective, stable, and efficacious mPCa therapeutic over other PCa therapeutics in the pipeline.

Analysis of intracellular tyrosine phosphorylation in circulating neutrophils as a rapid assay for the in vivo effect of oral tyrosine kinase inhibitors

Tyrosine kinases are crucial signaling components of diverse biological processes and are major therapeutic targets in various malignancies and immune-mediated disorders. A critical step of development of novel tyrosine kinase inhibitors is the transition from the confirmation of the in vitro effects of drug candidates to the analysis of their in vivo efficacy. To facilitate this transition, we have developed a rapid in vivo assay for the analysis of the effect of oral tyrosine kinase inhibitors on basal tyrosine phosphorylation of circulating mouse neutrophils. The assay uses a single drop of peripheral blood without sacrificing the mice. Flow cytometry using intracellular staining by fluorescently labeled anti-phosphotyrosine antibodies revealed robust basal tyrosine phosphorylation in resting circulating neutrophils. This signal was abrogated by the use of isotype control antibodies or by pre-saturation of the anti-phosphotyrosine antibodies with soluble phosphotyrosine amino acids or tyrosine-phosphorylated peptides. Basal tyrosine phosphorylation was dramatically reduced in neutrophils of triple knockout mice lacking the Src-family tyrosine kinases Hck, Fgr, and Lyn. Neutrophil tyrosine phosphorylation was also abrogated by oral administration of the Abl/Src-family inhibitor dasatinib, a clinically used anti-leukemic agent. Detailed dose-response and kinetic studies revealed half-maximal reduction of neutrophil tyrosine phosphorylation by 2.9 mg/kg dasatinib, with maximal reduction observed 2 h after inhibitor administration. Taken together, our assay allows highly efficient analysis of the in vivo effect of orally administered tyrosine kinase inhibitors, and may be used as a suitable alternative to other existing approaches.

Computer-Assisted Design of Peptide-Based Radiotracers

In medical imaging, techniques such as magnetic resonance imaging, contrast-enhanced computerized tomography, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are extensively available and routinely used for disease diagnosis. PET probes with peptide-based targeting are typically composed of small peptides especially developed to have high affinity and specificity for a range of cellular and tissue targets. These probes' key benefits include being less expensive than traditional antibody-based PET tracers and having an effective chemical modification process that allows them to be radiolabeled with almost any radionuclide, making them highly appealing for clinical usage. Currently, as with every pharmaceutical design, the use of in silico strategies is steadily growing in this field, even though it is not part of the standard toolkit used during radiopharmaceutical design. This review describes the recent applications of computational design approaches in the design of novel peptide-based radiopharmaceuticals.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Phytochemical Profiling, Biological Activities, and In Silico Molecular Docking Studies of Causonis trifolia (L.) Mabb. & J.Wen Shoot

Causonis trifolia (L.) Mabb. & J.Wen, commonly known as "fox grape", is an ethnomedicinally important twining herb of the Vitaceae family, and it is used by ethnic communities for its wide range of therapeutic properties. Our research aims to investigate the chemical composition; antioxidant, anti-inflammatory, and antidiabetic activities; and mechanisms of interaction between the identified selective chemical compounds and the target proteins associated with antioxidant, anti-inflammatory, and antidiabetic effects of the optimised phenolic extract of Causonis trifolia (L.) Mabb. & J.Wen, shoot (PECTS) to endorse the plant as a potential drug candidate for a future bioprospecting programme. Here, we employed the response surface methodology (RSM) with a Box-Behnken design to enrich the methanolic extract of C. trifolia shoot with phenolic ingredients by optimising three key parameters: solvent concentration (% v/v, methanol:water), extraction temperature (°C), and extraction duration (hours). From the quantitative phytochemical estimation, it was evident that the PECTS contained good amounts of phenolics, flavonoids, tannins, and alkaloids. During the HPLC analysis, we identified a total of eight phenolic and flavonoid compounds (gallic acid, catechin hydrate, chlorogenic acid, caffeic acid, p-coumaric acid, sinapic acid, coumarin, and kaempferol) and quantified their respective contents from the PECTS. The GC-MS analysis of the PECTS highlighted the presence of 19 phytochemicals. In addition, the bioactivity study of the PECTS showed remarkable potentiality as antioxidant, anti-inflammatory, and antidiabetic agents. In silico molecular docking and computational molecular modelling were employed to investigate the anti-inflammatory, antioxidant, and antidiabetic properties of the putative bioactive compounds derived from the PECTS using the GC-MS technique to understand the drug-receptor interactions, including their binding pattern. Out of the 19 phytocompounds identified by the GC-MS analysis, one compound, ergosta-5,22-dien-3-ol, acetate, (3β,22E), exhibited the best binding conformations with the target proteins involved in anti-inflammatory (e.g., Tnf-α and Cox-2), antioxidant (SOD), and antidiabetic (e.g., α-amylase and aldo reductase) activities. The nontoxic nature of this optimised extract was also evident during the in vitro cell toxicity assay against the Vero cell line and the in vivo acute toxicity study on BALB/c mice. We believe the results of the present study will pave the way for the invention of novel drugs efficacious for several ailments using the C. trifolia plant.

Microfluidic Droplet-Assisted Fabrication of Vessel-Supported Tumors for Preclinical Drug Discovery

High-fidelity in vitro tumor models are important for preclinical drug discovery processes. Currently, the most commonly used model for in vitro drug testing remains the two-dimensional (2D) cell monolayer. However, the natural in vivo tumor microenvironment (TME) consists of extracellular matrix (ECM), supporting stromal cells and vasculature. They not only participate in the progression of tumors but also hinder drug delivery and effectiveness on tumor cells. Here, we report an integrated engineering system to generate vessel-supported tumors for preclinical drug screening. First, gelatin-methacryloyl (GelMA) hydrogel was selected to mimic tumor extracellular matrix (ECM). HCT-116 tumor cells were encapsulated into individual micro-GelMA beads with microfluidic droplet technique to mimic tumor-ECM interactions in vitro. Then, normal human lung fibroblasts were mingled with tumor cells to imitate the tumor-stromal interaction. The tumor cells and fibroblasts reconstituted in the individual GelMA microbead and formed a biomimetic heterotypic tumor model with a core-shell structure. Next, the cell-laden beads were consociated into a functional on-chip vessel network platform to restore the tumor-tumor microenvironment (TME) interaction. Afterward, the anticancer drug paclitaxel was tested on the individual and vessel-supported tumor models. It was demonstrated that the blood vessel-associated TME conferred significant additional drug resistance in the drug screening experiment. The reported system is expected to enable the large-scale fabrication of vessel-supported heterotypic tumor models of various cellular compositions. It is believed to be promising for the large-scale fabrication of biomimetic in vitro tumor models and may be valuable for improving the efficiency of preclinical drug discovery processes.

Anthelmintic Drugs as Emerging Immune Modulators in Cancer

Despite recent advances in treatment approaches, cancer is still one of the leading causes of death worldwide. Restoration of tumor immune surveillance represents a valid strategy to overcome the acquired resistance and cytotoxicity of conventional therapies in oncology and immunotherapeutic drugs, such as immune checkpoint inhibitors and immunogenic cell death inducers, and has substantially progressed the treatment of several malignancies and improved the clinical management of advanced disease. Unfortunately, because of tumor-intrinsic and/or -extrinsic mechanisms for escaping immune surveillance, only a fraction of patients clinically respond to and benefit from cancer immunotherapy. Accumulating evidence derived from studies of drug repositioning, that is, the strategy to identify new uses for approved or investigational drugs that are outside the scope of the original medical indication, has suggested that some anthelmintic drugs, in addition to their antineoplastic effects, exert important immunomodulatory actions on specific subsets of immune cell and related pathways. In this review, we report and discuss current knowledge on the impact of anthelmintic drugs on host immunity and their potential implication in cancer immunotherapy.

Biogenic Synthesis of Cu-Mn Bimetallic Nanoparticles Using Pumpkin Seeds Extract and Their Characterization and Anticancer Efficacy

BACKGROUND

Cancer is a chronic, heterogeneous illness that progresses through a spectrum of devastating clinical manifestations and remains the 2nd leading contributor to global mortality. Current cancer therapeutics display various drawbacks that result in inefficient management. The present study is intended to evaluate the anticancer potential of Cu-Mn bimetallic NPs (CMBNPs) synthesized from pumpkin seed extract against colon adenocarcinoma cancer cell line (HT-29).

METHODS

The CMBNPs were biosynthesized by continuously stirring an aqueous solution of pumpkin seed extract with CuSO4 and manganese (II) acetate tetrahydrate until a dark green solution was obtained. The characteristic features of biogenic CMBNPs were assessed by UV-visible spectrophotometry (UV-vis), X-ray powder diffraction (XRD), energy-dispersive X-ray (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). A battery of biological assays, viz. neutral red uptake (NRU) assay, in vitro scratch assay, and comet assay, were performed for anticancer efficacy evaluation.

RESULTS

The formation of spherical monodispersed bimetallic nanoparticles with an average size of 50 nm was recorded using TEM. We observed dose-dependent cytotoxicity of CMBNPs in the HT-29 cell line with an IC₅₀ dose of 115.2 µg/mL. On the other hand, CMBNPs did not show significant cytotoxicity against normal cell lines (Vero cells). Furthermore, the treatment of CMBNPs inhibited the migration of cancer cells and caused DNA damage with a significant increase in comet tail length.

CONCLUSIONS

The results showed substantial anticancer efficacy of CMBNPs against the studied cancer cell line. However, it is advocated that the current work be expanded to different in vitro cancer models so that an in vivo validation could be carried out in the most appropriate cancer model.

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.

Improving the predictive power of xenograft and syngeneic anti-tumour studies using mice humanised for pathways of drug metabolism

Drug development is an expensive and time-consuming process, with only a small fraction of drugs gaining regulatory approval from the often many thousands of candidates identified during target validation. Once a lead compound has been identified and optimised, they are subject to intensive pre-clinical research to determine their pharmacodynamic, pharmacokinetic and toxicological properties, procedures which inevitably involve significant numbers of animals - mainly mice and rats, but also dogs and monkeys in much smaller numbers and for specific types of drug candidates. Many compounds that emerge from this process, having been shown to be safe and efficacious in pre-clinical studies, subsequently fail to replicate this outcome in clinical trials, therefore wasting time, money and, most importantly, animals. Due to high rates of metabolism and a differing spectrum of metabolites (some pharmacologically active) in rodents, species differences in drug metabolism can be a major impediment to drug discovery programmes and confound the extrapolation of animal data to humans. To circumvent this, we have developed a complex transgenic mouse model – 8HUM - which faithfully replicates human Phase I drug metabolism (and its regulation), and which will generate more human-relevant data from fewer animals in a pre-clinical setting and reduce attrition in the clinic. One key area for the pre-clinical application of animals in an oncology setting – almost exclusively mice - is their use in anti-tumour studies. We now further demonstrate the utility of the 8HUM mouse using a murine melanoma cell line as a syngeneic tumour and also present an immunodeficient version 8HUM_Rag2 -/- - for use in xenograft studies. These models will be of significant benefit not only to Pharma for pre-clinical drug development work, but also throughout the drug efficacy, toxicology, pharmacology, and drug metabolism communities, where fewer animals will be needed to generate more human-relevant data.

CD73 in glioblastoma: Where are we now and what are the future directions?

Glioblastoma (GB) is the most aggressive type of brain tumor with heterogeneity, strong invasive ability, and high resistance to therapy due to immunosuppressive mechanisms. CD73 is an overexpressed enzyme in GB acts via two main mechanisms:(1) CD73 acts as an adhesion protein independent of the enzymatic activity or (2) via the catalyses of AMP to adenosine (ADO) generating a strong modulatory molecule that induces alterations in the tumor cells and in the tumor microenvironment cells (TME). Taken together, CD73 is receiving attention during the last years and studies demonstrated its dual potential benefit as a target to GB therapy. Here, we review the roles of CD73 and P1 receptors (ADO receptors) in GB, the impact of CD73 in the immune interactions between tumor and other immune cells, the proposed therapeutic strategies based on CD73 regulation, and discuss the gap in knowledge and further directions to bring this approach from preclinical to clinical use.

Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods

Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules

Hydrogen bonding is an interaction of great importance in drug discovery and development as it may significantly affect chemical and biological processes including the interaction of small molecules with other molecules, proteins, and membranes. In particular, hydrogen bonding can impact drug-like properties such as target affinity and oral availability which are critical to developing effective pharmaceuticals, and therefore, numerous methods for the calculation of properties such as hydrogen-bond strengths, free energy of hydration, or water solubility have been proposed over time. However, the accessibility to efficient methods for the predictions of such properties is still limited. Here, we present the development of Jazzy, an open-source tool for the prediction of hydrogen-bond strengths and free energies of hydration of small molecules. Jazzy also allows the visualisation of hydrogen-bond strengths with atomistic resolution to support the design of compounds with desired properties and the interpretation of existing data. The tool is described in its implementation, parameter fitting, and validation against two data sets of experimental hydration free energies. Jazzy is also applied against two chemical series of bioactive compounds to show that hydrogen-bond strengths can be used to understand their structure-activity relationships. Results from the validations highlight the strengths and limitations of Jazzy, and suggest its suitability for interactive design, screening, and machine-learning featurisation.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

ATP/ADP biosensor organoids for drug nephrotoxicity assessment

Drug nephrotoxicity is a common healthcare problem in hospitalized patients and a major limitation during drug development. Multi-segmented kidney organoids derived from human pluripotent stem cells may complement traditional cell culture and animal experiments for nephrotoxicity assessment. Here we evaluate the capability of kidney organoids to investigate drug toxicity in vitro. Kidney organoids express renal drug transporters, OAT1, OAT3, and OCT2, while a human proximal tubular cell line shows the absence of OAT1 and OAT3. Tenofovir and aristolochic acid (AA) induce proximal tubular injury in organoids which is ameliorated by an OAT inhibitor, probenecid, without damage to podocytes. Similarly, cisplatin causes proximal tubular damage that can be relieved by an OCT inhibitor, cimetidine, collectively suggesting the presence of functional OATs and OCTs in organoid proximal tubules. Puromycin aminonucleoside (PAN) induced segment-specific injury in glomerular podocytes in kidney organoids in the absence of tubular injury. Reporter organoids were generated with an ATP/ADP biosensor, which may be applicable to high-throughput screening in the future. In conclusion, the kidney organoid is a useful tool for toxicity assessment in the multicellular context and may contribute to nephrotoxicity assessment during drug development.

Majoon chobchini reinstates PDL-1 expression and blocks dendritic cell -T helper 17 pathogenic axis in rheumatoid arthritis animal model

Dendritic cells (DCs) are the critical players in the puzzle of rheumatoid arthritis (RA) disease pathogenesis. Blockade of DC activation has been shown to curtail Th17 cell differentiation and its aberrant function in RA. Recent studies have pointed to the role of the PI3K/AKT signaling axis in the maturation and activation of DCs. However, it is yet to be established how PI3K/AKT inhibition would lead to the abolishment of DC activation and Th17 cell plasticity in RA. Herein, our study decoded whether and how majoon chobchini, an unani compound, abated dendritic cell maturation and regulated the Th17/Treg paradigm in RA. Given our results, majoon chobchini conspicuously restrained MHC II, CD86 expression and, subsequently elevated PDL-1 levels in DCs in-vivo. Of note, inhibition of DC maturation by majoon chobchini, in turn, favoured suppression of the Th17 cell population while driving Treg cell development in adjuvant induced arthritic (AA) rats. Concurrently, majoon chobchini decreased the catabolic effects of IL-17 (Th17 associated cytokine) via a reciprocal increase in IL-10 (Treg associated cytokine) levels in AA rats. Mechanistically, majoon chobchini sustained FoxO1 nuclear localization signaled through dampened PI3K/AKT phosphorylation in-vitro. In concert, PDL-1 expression was heightened in majoon chobchini treated activated DCs that provides a framework for ablation of the DC-Th17 cell pathogenic axis in RA. Notwithstanding, the PI3K inhibitor LY294002 exhibited similar inhibitory effects. In essence, majoon chobchini enhanced PDL-1 expression that abolished DC maturation via regulation of the PI3K/AKT/FoxO1 axis, thereby hindering Th17 differentiation in an animal model of RA. This further warrants a clinical investigation that could validate majoon chobchini as a prospective therapeutic drug in the treatment of RA.

Compound combinations targeting longevity: Challenges and perspectives

Aging is one of the world's greatest concerns, requiring urgent, effective, large-scale interventions to decrease the number of late-life chronic diseases and improve human healthspan. Anti-aging drug therapy is one of the most promising strategies to combat the effects of aging. However, most geroprotective compounds are known to successfully affect only a few aging-related targets. Given this, there is a great biological rationale for the use of combinations of anti-aging interventions. In this review, we characterize the various types of compound combinations used to modulate lifespan, discuss the existing evidence on their role in life extension, and present some key points about current challenges and future prospects for the development of combination drug anti-aging therapy.

Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches

With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibitors have been revealed, utilizing them to search for novel Mpro inhibitors remains limited. Although there were two major groups of Mpro inhibitors, covalent and noncovalent inhibitors, noncovalent inhibitors were our main focus due to the safety concerns with their covalent counterparts. Hence, this study aimed to explore Mpro noncovalent inhibition ability of phytochemicals extracted from Vietnamese herbals by combining multiple structure-based approaches. By closely inspecting 223 complexes of Mpro with noncovalent inhibitors, a 3D-pharmacophore model representing typical chemical features of Mpro noncovalent inhibitors was generated with good validation scores (sensitivity = 92.11%, specificity = 90.42%, accuracy = 90.65%, and goodness-of-hit score = 0.61). Afterward, the pharmacophore model was applied to explore the potential Mpro inhibitors from our in-house Vietnamese phytochemical database, revealing 18 substances, 5 of which were in vitro assayed. The remaining 13 substances were then examined by induced-fit molecular docking, revealing 12 suitable compounds. A machine-learning activity prediction model was developed to rank the hit, suggesting nigracin and calycosin-7-O-β-glucopyranoside as promising Mpro natural noncovalent inhibitors.

A Human Ovarian Tumor & Liver Organ-on-Chip for Simultaneous and More Predictive Toxo-Efficacy Assays

In oncology, the poor success rate of clinical trials is becoming increasingly evident due to the weak predictability of preclinical assays, which either do not recapitulate the complexity of human tissues (i.e., in vitro tests) or reveal species-specific outcomes (i.e., animal testing). Therefore, the development of novel approaches is fundamental for better evaluating novel anti-cancer treatments. Here, a multicompartmental organ-on-chip (OOC) platform was adopted to fluidically connect 3D ovarian cancer tissues to hepatic cellular models and resemble the systemic cisplatin administration for contemporarily investigating drug efficacy and hepatotoxic effects in a physiological context. Computational fluid dynamics was performed to impose capillary-like blood flows and predict cisplatin diffusion. After a cisplatin concentration screening using 2D/3D tissue models, cytotoxicity assays were conducted in the multicompartmental OOC and compared with static co-cultures and dynamic single-organ models. A linear decay of SKOV-3 ovarian cancer and HepG2 liver cell viability was observed with increasing cisplatin concentration. Furthermore, 3D ovarian cancer models showed higher drug resistance than the 2D model in static conditions. Most importantly, when compared to clinical therapy, the experimental approach combining 3D culture, fluid-dynamic conditions, and multi-organ connection displayed the most predictive toxicity and efficacy results, demonstrating that OOC-based approaches are reliable 3Rs alternatives in preclinic.

Incorporation of an Isohexide Subunit Improves the Drug-like Properties of Bioactive Compounds

An enhanced ability to pre-engineer favorable drug-likeness qualities into bioactive molecules would focus and streamline the drug development process. We find that phenols, carboxylic acids, and a purine react with isosorbide ("GRAS" designated) under Mitsunobu coupling conditions to deliver the isoidide conjugates selectively and efficiently. Such conjugates show improved solubility and permeability properties compared with the bare scaffold compounds themselves, and the purine adduct may have applications as a 2'-deoxyadenosine isostere. We anticipate additional benefits, implied by their structures, in metabolic stability and reduced toxicity of the isoidide conjugates as well.

Dual Anticancer and Antibacterial Properties of Silica-Based Theranostic Nanomaterials Functionalized with Coumarin343, Folic Acid and a Cytotoxic Organotin(IV) Metallodrug

Five different silica nanoparticles functionalized with vitamin B12, a derivative of coumarin found in green plants and a minimum content of an organotin(IV) fragment (1-MSN-Sn, 2-MSN-Sn, 2-SBA-Sn, 2-FSPm-Sn and 2-FSPs-Sn), were identified as excellent anticancer agents against triple negative breast cancer, one of the most diagnosed and aggressive cancerous tumors, with very poor prognosis. Notably, compound 2-MSN-Sn shows selectivity for cancer cells and excellent luminescent properties detectable by imaging techniques once internalized. The same compound is also able to interact with and nearly eradicate biofilms of Staphylococcus aureus, the most common bacteria isolated from chronic wounds and burns, whose treatment is a clinical challenge. 2-MSN-Sn is efficiently internalized by bacteria in a biofilm state and destroys the latter through reactive oxygen species (ROS) generation. Its internalization by bacteria was also efficiently monitored by fluorescence imaging. Since silica nanoparticles are particularly suitable for oral or topical administration, and considering both its anticancer and antibacterial activity, 2-MSN-Sn represents a new dual-condition theranostic agent, based primarily on natural products or their derivatives and with only a minimum amount of a novel metallodrug.

OGG1 competitive inhibitors show important off-target effects by directly inhibiting efflux pumps and disturbing mitotic progression

One of the most abundant DNA lesions induced by Reactive oxygen species (ROS) is 8-oxoG, a highly mutagenic lesion that compromises genetic instability when not efficiently repaired. 8-oxoG is specifically recognized by the DNA-glycosylase OGG1 that excises the base and initiates the Base Excision Repair pathway (BER). Furthermore, OGG1 has not only a major role in DNA repair but it is also involved in transcriptional regulation. Cancer cells are particularly exposed to ROS, thus challenging their capacity to process oxidative DNA damage has been proposed as a promising therapeutic strategy for cancer treatment. Two competitive inhibitors of OGG1 (OGG1i) have been identified, TH5487 and SU0268, which bind to the OGG1 catalytic pocket preventing its fixation to the DNA. Early studies with these inhibitors show an enhanced cellular sensitivity to cytotoxic drugs and a reduction in the inflammatory response. Our study uncovers two unreported off-targets effects of these OGG1i that are independent of OGG1. In vitro and in cellulo approaches have unveiled that OGG1i TH5487 and SU0268, despite an unrelated molecular structure, are able to inhibit some members of the ABC family transporters, in particular ABC B1 (MDR1) and ABC G2 (BCRP). The inhibition of these efflux pumps by OGG1 inhibitors results in a higher intra-cellular accumulation of various fluorescent probes and drugs, and largely contributes to the enhanced cytotoxicity observed when the inhibitors are combined with cytotoxic agents. Furthermore, we found that SU0268 has an OGG1-independent anti-mitotic activity-by interfering with metaphase completion-resulting in a high cellular toxicity. These two off-target activities are observed at concentrations of OGG1i that are normally used for in vivo studies. It is thus critical to consider these previously unreported non-specific effects when interpreting studies using TH5487 and SU0268 in the context of OGG1 inhibition. Additionally, our work highlights the persistent need for new specific inhibitors of the enzymatic activity of OGG1.

A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs-the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.

Explore drug-like space with deep generative models

The process of design/discovery of drugs involves the identification and design of novel molecules that have the desired properties and bind well to a given disease-relevant target. One of the main challenges to effectively identify potential drug candidates is to explore the vast drug-like chemical space to find novel chemical structures with desired physicochemical properties and biological characteristics. Moreover, the chemical space of currently available molecular libraries is only a small fraction of the total possible drug-like chemical space. Deep molecular generative models have received much attention and provide an alternative approach to the design and discovery of molecules. To efficiently explore the drug-like space, we first constructed the drug-like dataset and then performed the generative design of drug-like molecules using a Conditional Randomized Transformer approach with the molecular access system (MACCS) fingerprint as a condition and compared it with previously published molecular generative models. The results show that the deep molecular generative model explores the wider drug-like chemical space. The generated drug-like molecules share the chemical space with known drugs, and the drug-like space captured by the combination of quantitative estimation of drug-likeness (QED) and quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI) can cover a larger drug-like space. Finally, we show the potential application of the model in design of inhibitors of MDM2-p53 protein-protein interaction. Our results demonstrate the potential application of deep molecular generative models for guided exploration in drug-like chemical space and molecular design.

Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

In 1997, the 'Rule of Five' (Ro5) suggested physicochemical limitations for orally administered drugs, based on the analysis of chemical libraries from the early 1990s. In this review, we report on the trends in oral drug product development by analyzing products launched between 1994 and 1997 and between 2013 and 2019. Our analysis confirmed that most new oral drugs are within the Ro5 descriptors; however, the number of new drug products of drugs with molecular weight (MW) and calculated partition coefficient (clogP) beyond the Ro5 has slightly increased. Analysis revealed that there is no single scientific or technological reason for this trend, but that it likely results from incremental advances are being made in molecular biology, target diversity, drug design, medicinal chemistry, predictive modeling, drug metabolism and pharmacokinetics, and drug delivery.

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.

Fast Methods for Drug Approval: Research Perspectives for Pandemic Preparedness

Public heath emergencies such as the outbreak of novel infectious diseases represent a major challenge for drug regulatory bodies, practitioners, and scientific communities. In such critical situations drug regulators and public health practitioners base their decisions on evidence generated and synthesised by scientists. The urgency and novelty of the situation create high levels of uncertainty concerning the safety and effectiveness of drugs. One key tool to mitigate such emergencies is pandemic preparedness. There seems to be, however, a lack of scholarly work on methodology for assessments of new or existing drugs during a pandemic. Issues related to risk attitudes, evidence production and evidence synthesis for drug approval require closer attention. This manuscript, therefore, engages in a conceptual analysis of relevant issues of drug assessment during a pandemic. To this end, we rely in our analysis on recent discussions in the philosophy of science and the philosophy of medicine. Important unanswered foundational questions are identified and possible ways to answer them are considered. Similar problems often have similar solutions, hence studying similar situations can provide important clues. We consider drug assessments of orphan drugs and drug assessments during endemics as similar to drug assessment during a pandemic. Furthermore, other scientific fields which cannot carry out controlled experiments may guide the methodology to draw defeasible causal inferences from imperfect data. Future contributions on methodologies for addressing the issues raised here will indeed have great potential to improve pandemic preparedness.

Metabolism-Guided Optimization of Tryptophanol-Derived Isoindolinone p53 Activators

For the first time, the pharmacokinetic (PK) profile of tryptophanol-derived isoindolinones, previously reported as p53 activators, was investigated. From the metabolites' identification, performed by liquid chromatography coupled to high resolution tandem mass spectrometry (LC-HRMS/MS), followed by their preparation and structural elucidation, it was possible to identify that the indole C2 and C3 are the main target of the cytochrome P450 (CYP)-promoted oxidative metabolism in the tryptophanol-derived isoindolinone scaffold. Based on these findings, to search for novel p53 activators a series of 16 enantiopure tryptophanol-derived isoindolinones substituted with a bromine in indole C2 was prepared, in yields of 62-89%, and their antiproliferative activity evaluated in human colon adenocarcinoma HCT116 cell lines with and without p53. Structural optimization led to the identification of two (S)-tryptophanol-derived isoindolinones 3.9-fold and 1.9-fold more active than hit SLMP53-1, respectively. Compounds' metabolic stability evaluation revealed that this substitution led to a metabolic switch, with the impact of Phase I oxidative metabolism being minimized. Through differential scanning fluorimetry (DSF) experiments, the most active compound of the series in cell assays led to an increase in the protein melting temperature (Tm) of 10.39 °C, suggesting an effective binding to wild-type p53 core domain.

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction

Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Carbonic Anhydrase Inhibitors as Novel Antibacterials in the Era of Antibiotic Resistance: Where Are We Now?

Resistance to antibiotic treatment developed by bacteria in humans and animals occurs when the microorganisms resist treatment with clinically approved antibiotics. Actions must be implemented to stop the further development of antibiotic resistance and the subsequent emergence of superbugs. Medication repurposing/repositioning is one strategy that can help find new antibiotics, as it speeds up drug development phases. Among them, the Zn²⁺ ion binders, such as sulfonamides and their bioisosteres, are considered the most promising compounds to obtain novel antibacterials, thus avoiding antibiotic resistance. Sulfonamides and their bioisosteres have drug-like properties well-known for decades and are suitable lead compounds for developing new pharmacological agent families for inhibiting carbonic anhydrases (CAs). CAs are a superfamily of metalloenzymes catalyzing the reversible reaction of CO₂ hydration to HCO₃^- and H⁺, being present in most bacteria in multiple genetic families (α-, β-, γ- and ι-classes). These enzymes, acting as CO₂ transducers, are promising drug targets because their activity influences microbe proliferation, biosynthetic pathways, and pathogen persistence in the host. In their natural or slightly modified scaffolds, sulfonamides/sulfamates/sulamides inhibit CAs in vitro and in vivo, in mouse models infected with antibiotic-resistant strains, confirming thus their role in contrasting bacterial antibiotic resistance.

Translational Nanomedicines Across Human Reproductive Organs Modeling on Microfluidic Chips: State-of-the-Art and Future Prospects

Forecasting the consequence of nanoparticles (NPs) and therapeutically significant molecules before materializing for human clinical trials is a mainstay for drug delivery and screening processes. One of the noteworthy obstacles that has prevented the clinical translation of NP-based drug delivery systems and novel drugs is the lack of effective preclinical platforms. As a revolutionary technology, the organ-on-a-chip (OOC), a coalition of microfluidics and tissue engineering, has surfaced as an alternative to orthodox screening platforms. OOC technology recapitulates the structural and physiological features of human organs along with intercommunications between tissues on a chip. The current review discusses the concept of microfluidics and confers cutting-edge fabrication processes for chip designing. We also outlined the advantages of microfluidics in analyzing NPs in terms of characterization, transport, and degradation in biological systems. The review further elaborates the scope and research on translational nanomedicines in human reproductive organs (testis, placenta, uterus, and menstrual cycle) by taking the advantages offered by microfluidics and shedding light on their potential future implications. Finally, we accentuate the existing challenges for clinical translation and scale-up dynamics for microfluidics chips and emphasize its future perspectives.

The Role of Myeloperoxidase in Clozapine-Induced Inflammation: A Mechanistic Update for Idiosyncratic Drug-Induced Agranulocytosis

The risk of idiosyncratic drug-induced agranulocytosis (IDIAG) markedly constrains the use of clozapine, a neuroleptic with unparalleled efficacy. Most clozapine patients experience an early inflammatory response, likely a necessary step in IDIAG onset. However, most patients do not progress to IDIAG, presumably because of the requirement of specific human leukocyte antigen (HLA) haplotypes, T cell receptors, and other unknown factors. We established that clozapine activates inflammasomes and that myeloperoxidase bioactivation of clozapine generates neoantigens, but the connection between these early mechanistic events remained unknown and, thus, was the aim of this work. We found that the myeloperoxidase inhibitor PF-1355 attenuated myeloperoxidase activity in phorbol myristate acetate (PMA)-differentiated THP-1 macrophages, and it also attenuated clozapine-induced release of inflammatory mediators (e.g., IL-1β, CXCL1, and C-reactive protein). In vivo, pretreatment of Sprague Dawley rats with PF-1355 significantly attenuated clozapine-induced increases in neutrophil mobilization from the bone marrow to the blood and spleen, as determined using differential blood counts and flow cytometry. Moreover, the clozapine-triggered release of inflammatory mediators (e.g., IL-1β, calprotectin, CXCL1, and α-1-acid glycoprotein) from the liver, spleen, and bone marrow was dampened by myeloperoxidase inhibition. These data support the working hypothesis that oxidation of clozapine to a reactive metabolite by myeloperoxidase is critical for induction of the inflammatory response to clozapine. Ultimately, a better mechanistic understanding of the early events involved in the immune response to clozapine may elucidate ways to prevent IDIAG, enabling safer, more frequent therapeutic use of this and potentially other highly efficacious drugs.