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

CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability

The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated NaV1.5 and CaV1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, NaV1.5 and CaV1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source at https://github.com/gregory-kyro/CardioGenAI to facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows.Scientific contributionThis work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, NaV1.5 and CaV1.2 channel activity, which can function independently as effective components of virtual screening pipelines.

Old drugs, new challenges: reassigning drugs for cancer therapies

The "War on Cancer" began with the National Cancer Act of 1971 and despite more than 50 years of effort and numerous successes, there still remains much more work to be done. The major challenge remains the complexity and intrinsic polygenicity of neoplastic diseases. Furthermore, the safety of the antitumor therapies still remains a concern given their often off-target effects. Although the amount of money invested in research and development required to introduce a novel FDA-approved drug has continuously increased, the likelihood for a new cancer drug's approval remains limited. One interesting alternative approach, however, is the idea of repurposing of old drugs, which is both faster and less costly than developing new drugs. Repurposed drugs have the potential to address the shortage of new drugs with the added benefit that the safety concerns are already established. That being said, their interactions with other new drugs in combination therapies, however, should be tested. In this review, we discuss the history of repurposed drugs, some successes and failures, as well as the multiple challenges and obstacles that need to be addressed in order to enhance repurposed drugs' potential for new cancer therapies.

Large-Scale Compartmental Model-Based Study of Preclinical Pharmacokinetic Data and Its Impact on Compound Triaging in Drug Discovery

Reliable and robust human dose prediction plays a pivotal role in drug discovery. The prediction of human dose requires proper modeling of preclinical intravenous (IV) pharmacokinetic (PK) data, which is usually achieved either through noncompartmental analysis (NCA) or compartmental analysis. While NCA is straightforward, it loses valuable information about the shape of the PK curves. In contrast, compartmental analysis offers a more comprehensive interpretation but poses challenges in scaling up for high-throughput applications in discovery. To address this challenge, we developed computational frameworks, termed compartmental PK (CPK) and automated dose prediction (ADP), to enable automated compartmental model-based IV PK data modeling, translation, and simulation for human dose prediction in compound triaging and optimization. With CPK and ADP, we analyzed compounds with data collected at the MRL between 2013 and 2023 to quantitatively characterize the impact of different PK modeling and simulation methods on human dose prediction. Our study revealed that despite minimal impact on estimating animal PK parameters, different methods significantly impacted predicted human dose, exposure, and Cmax, driven more by different simulation assumptions than by the PK modeling itself. CPK-ADP therefore enables us to efficiently perform complex human dose predictions on a large scale while integrating the latest and best information available on absorption, distribution, and clearance to support decision-making in discovery.

Transforming Drug Discovery with Miniaturized Predictive Tissue Models

Drug development is a lengthy and expensive process that involves screening thousands of potential candidates in vitro, followed by pre-clinical efficacy, pharmacokinetic, and pharmacodynamic studies in relevant animal models, before evaluating the safety and efficacy of a drug in clinical trials [...].

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations

The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse side effects of existing DPP4 medications. The present study implemented a combined approach to machine learning and structure-based virtual screening to identify DPP4 inhibitors. Two ML models were trained based on DPP4 IC50 datasets. The ML models random forest (RF) and multilayer perceptron (MLP) neural network showed good accuracy, with the area under the curve being 0.93 and 0.91, respectively. The natural compound library was screened through ML models, and 1% (217) of compounds were selected for further screening. Structure-based virtual screening was performed along with positive control sitagliptin to obtain more specific and selective leads for DPP4. Based on binding affinity, drug-likeness properties, and interaction with DPP4, Z-614 and Z-997 compounds showed high binding affinity and specificity in the catalytic pocket of DPP4. Finally, the stability conformation of the DPP4 enzyme complex was checked by a molecular dynamics (MD) simulation. The MD simulation showed that both compounds bind better in the catalytic pocket, but the Z-614 compound altered the DPP4 native conformation. Therefore, Z-614 showed a high deviation in the backbone. This combined approach (ML and structure-based) study reported that Z-997 binds most stably to DPP4 in their catalytic pocket with a binding free energy of -70.3 kJ/mol, suggesting its therapeutic potential as a treatment option for T2DM disease.Communicated by Ramaswamy H. Sarma.

New indolin-2-ones, possessing sunitinib scaffold as HDAC inhibitors and anti-cancer agents with potential VEGFR inhibition activity; design, synthesis and biological evaluation

New series of indolin-2-ones possessing sunitinib scaffold and a hydroxamic acid moiety were designed and synthesized as inhibitors of HDAC, demonstrating significant anti-cancer properties with potential VEGFR inhibition, using sunitinib and vorinostat as the lead compounds. The newly synthesized compounds incorporate the sunitinib framework along with functional groups derived from vorinostat, thus they can be named the rigid analogs of vorinostat. The cytotoxic effects of these compounds were assessed against two cancer cell lines, HCT116 (human colon cancer) and HT29 (human colon adenocarcinoma), as well as NIH (a normal fibroblast cell line). A majority of the compounds displayed notable cytotoxicity towards HT-29 and HCT-116, with IC50 values ranging from 1.78 to 38.54 µM notably, compound 13c exhibited the highest anti-proliferative effect against HT-29, with an IC50 of 1.78 µM, comparable to or exceeding that of the reference drugs, sunitinib and vorinostat. This compound reduced the expression levels of VEGFR-2 and phosphorylated VEGFR-2 (pVEGFR-2) by approximately 80 % and inhibited the HDAC1 enzyme (IC50 = 1.07 µM), indicating its anticancer activity through the targeting of these enzymes. Further cellular mechanism investigations revealed that compound 13c induced substantial apoptosis in HCT-116 cells, with a total apoptotic cell percentage of 41.1 % in treated cells (2.59 µM), compared to negative control (3.68 %)). The CAM assay also indicated that 13c possesses antiangiogenic property similar to that of sunitinib. Additionally, a molecular docking simulation supported the initial design strategy and suggested a common mode of interaction of compound 13c at the binding sites of VEGFR-2 and HDAC1. These findings suggested that 13c could be as a promising lead targeting VEGFR-2 and HDAC1. Therefore, it deserved further investigation for cancer treatment.

Gigantol, a promising natural drug for inflammation: a literature review and computational based study

Gigantol, a bibenzyl compound extracted from various medicinal plants, has shown a number of biological activities, making it an attractive candidate for potential medical applications. This systematic review aims to shed light on gigantol's promising role in inflammation treatment and its underlying mechanisms. Gigantol exhibits potential anti-inflammatory properties in pre-clinical pharmacological test systems. It effectively reduced the levels of pro-inflammatory markers and arachidonic acid metabolites through various pathways, such as NF-κB, AKT, PI3K, and JNK/cPLA2/12-LOX. The in-silico investigations demonstrated that the MMP-13 enzyme served as the most promising target for gigantol with highest binding affinity (docking score = -8.8 kcal/mol). Encouragingly, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis of gigantol confirmed its compatibility with the necessary physiochemical, pharmacokinetic, and toxicity properties, bolstering its potential as a drug candidate. Gigantol, with its well-documented anti-inflammatory properties, could be a promising agent for treating inflammation in the near future.

Shared molecular, cellular, and environmental hallmarks in cardiovascular disease and cancer: Any place for drug repurposing?

Cancer and cardiovascular disease (CVD) are the 2 biggest killers worldwide. Specific treatments have been developed for the 2 diseases. However, mutual therapeutic targets should be considered because of the overlap of cellular and molecular mechanisms. Cancer research has grown at a fast pace, leading to an increasing number of new mechanistic treatments. Some of these drugs could prove useful for treating CVD, which realizes the concept of cancer drug repurposing. This review provides a comprehensive outline of the shared hallmarks of cancer and CVD, primarily ischemic heart disease and heart failure. We focus on chronic inflammation, altered immune response, stromal and vascular cell activation, and underlying signaling pathways causing pathological tissue remodeling. There is an obvious scope for targeting those shared mechanisms, thereby achieving reciprocal preventive and therapeutic benefits. Major attention is devoted to illustrating the logic, advantages, challenges, and viable examples of drug repurposing and discussing the potential influence of sex, gender, age, and ethnicity in realizing this approach. Artificial intelligence will help to refine the personalized application of drug repurposing for patients with CVD. SIGNIFICANCE STATEMENT: Cancer and cardiovascular disease (CVD), the 2 biggest killers worldwide, share several underlying cellular and molecular mechanisms. So far, specific therapies have been developed to tackle the 2 diseases. However, the development of new cardiovascular drugs has been slow compared with cancer drugs. Understanding the intersection between pathological mechanisms of the 2 diseases provides the basis for repurposing cancer therapeutics for CVD treatment. This approach could allow the rapid development of new drugs for patients with CVDs.

A UHPLC-MS/MS Method Reveals the Pharmacokinetics of Deacetyl Asperulosidic Acid Methyl Ester in Rats

In the current study, a simple ultra-high performance liquid chromatography-tandem mass spectrometry method was developed and fully validated for the quantitation of deacetyl asperulosidic acid methyl ester in rat plasma. The plasma sample was precipitated with acetonitrile and then separated on the Waters ACQUITY UPLC HSS T3 column. The mobile phases, water and acetonitrile, were added with 0.1% formic acid. The mass spectrometry detection was performed in negative-ion multiple reaction monitoring. In the range of 1-1000 ng/mL, the linearity meets the requirements with correlation coefficient more than 0.99. The parameters of accuracy, precision, carryover, matrix effect, extraction recovery, stability, and dilution integrity are within accepted ranges. The validated method has been successfully used for pharmacokinetic study of deacetyl asperulosidic acid methyl ester in rats. After oral administration, deacetyl asperulosidic acid methyl ester was quickly absorbed into blood and reached the maximum plasma drug concentration of 4047.49 ng/mL at 2 h. The half-life of deacetyl asperulosidic acid methyl ester is 5.6 h, which suggests that it has a moderate metabolic process. Since the absolute bioavailability of deacetyl asperulosidic acid methyl ester is only 3.74%, its gastrointestinal stability, first-pass effect, and transmembrane properties remain to be studied.

Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features

Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. Concerning the characterization of the employed Feature Genes, the models were interpreted, and the mechanisms of bioactive compounds with a narrow therapeutic index (NTI) can also be analyzed. In summary, the models established in this research exhibit superior capacity to those of previous studies; these models enable accurate high-safety substance screening via cytotoxicity prediction across cell lines. Moreover, for the first time, Cytotoxicity Signature (CTS) genes were identified, which could provide additional clues for further study of mechanisms of action (MOA), especially for NTI compounds.

PET imaging of the anticancer drug candidate [11C]trimebutine in a rat glioma model

PURPOSE

Preclinical studies suggest that trimebutine could be a potential treatment for glioblastoma. The aim of this study was to investigate the distribution, kinetics and tumor accumulation of [11C]trimebutine.

METHOD

A proliferation assay and cell scratch healing assay were performed to confirm the antitumor effects of trimebutine on C6 glioma cells in-vitro. Trimebutine was subsequently labeled with 11C. The distribution and kinetics of [11C]trimebutine in health rats and rats with an orthotopic C6 glioma were evaluated by ex-vivo gamma counting and positron emission tomography, respectively. Blocking experiments with an excess of unlabeled trimebutine or the μ-opioid receptor ligand cyprodime were employed to determine if trimebutine exhibits saturable binding in the brain. In addition, plasma stability of the tracer was assessed.

RESULTS

The proliferation assay and cell scratch healing assay confirmed that trimebutine has anti-tumor effects in-vitro. [11C]Trimebutine with a radiochemical purity >98 % was synthesized in 15 ± 5 % radiochemical yield. In peripheral organs, the highest accumulation of the tracer was detected in excretion organs. In the brain, the highest tracer uptake was observed in the brainstem and the lowest in the hypothalamus, although differences between regions were small. PET imaging showed rapid brain uptake of [11C]trimebutine, followed by a gradual washout. Administration of an intravenous dose of trimebutine (10 mg/kg) significantly decreased the uptake in all brain regions (p < 0.05), except midbrain. Likewise, administration of cyprodime (2 mg/kg) significantly reduced [11C]trimebutine uptake in the brain (p < 0.01). However, uptake of [11C]trimebutine in the tumor was not significantly different from its brain uptake in rats bearing an orthotopic C6 glioma. The percentage of intact [11C]trimebutine at 60 min post injection was only 1.7 ± 0.6 %.

CONCLUSION

Trimebutine exhibits inhibitory effects on the growth and migration of glioma cells in a dose- and time-dependent manner. [11C]Trimebutine was able to penetrate the blood-brain barrier in rats and tracer uptake could be significantly reduced by administration of a μ-opioid receptor antagonist. However, [11C]trimebutine failed to selectively accumulate in orthotopic C6 glioma, which could be caused by low expression levels of the drug target in these tumors, or by fast metabolism of the tracer.

Accelerating antiviral drug discovery: early hazard detection with a dual zebrafish and cell culture screen of a 403 compound library

The constant emergence of new viral pathogens underscores the need for continually evolving, effective antiviral drugs. A key challenge is identifying compounds that are both efficacious and safe, as many candidates fail during development due to unforeseen toxicity. To address this, the embryonic zebrafish morphology, mortality, and behavior (ZBE) screen and the SYSTEMETRIC® Cell Health Screen (CHS) were employed to evaluate the safety of 403 compounds from the Cayman Antiviral Screening Library. Of these compounds, 114 were FDA-approved, 17 were discontinued, and 97 remained on the market. CHS identified 25% (104 compounds) as toxic, with a Cell Health Index™ (CHI) > 0.5. The embryonic zebrafish model identified an additional 20% as toxic (79), bringing the total to 183. ZBEscreen flagged 19 toxic hits among compounds still on the market, seven of which were also identified by CHS. The combined use of CHS and zebrafish models enhanced hazard detection. Together, CHS and ZBEscreen identified 45.5% of the library as potentially hazardous. Notably, the zebrafish non-hazardous compounds correlated strongly with over-the-counter or prescribed antiviral drugs, confirming their known safety profile. Over 130 hazard-associated compounds warranted further investigation. Using self-organizing maps, six distinct neighborhoods of compound similarity were identified. This dual approach streamlined the early detection of hazards associated with promising leads and is expected to facilitate faster, safer antiviral discovery.

Adeno-Associated Virus Gene Therapy Development: Early Planning and Regulatory Considerations to Advance the Platform Vector Gene Therapy Program

Gene therapy development presents multiple challenges, and early planning is vital in the successful implementation of such programs. The Platform Vector Gene Therapy (PaVe-GT) program is a National Institutes of Health (NIH) initiative developing adeno-associated virus (AAV) gene therapies for four low-prevalence rare diseases. Utilizing the platform-based approach, the program aims to incorporate efficiencies throughout the preclinical and clinical development processes followed by public dissemination of scientific and regulatory learnings. Early in development, the establishment of a Target Product Profile (TPP) by the research team is a critical step to guide product development and align preclinical studies to clinical objectives. Based on the specific needs of the investigational product as defined in the TPP, an overall regulatory strategy can then be outlined to meet the regulatory requirements for the first-in-human clinical trials. During the preclinical phase of development, sponsors may request meetings with the Food and Drug Administration (FDA) to gather feedback on the planned studies and regulatory strategy. To pave the way for PaVe-GT's first investigational AAV gene therapy lead candidate, AAV9-hPCCA, we sought early feedback from the FDA utilizing an INitial Targeted Engagement for Regulatory Advice on CBER/CDER ProducTs (INTERACT) meeting. Here, we elaborate on the value of establishing a TPP and the FDA INTERACT meeting by including our initial AAV9-hPCCA TPP, detailing our INTERACT meeting experience, providing all corresponding regulatory documentation, and highlighting lessons learned. The regulatory documents along with templates developed by our program can also be found on the PaVe-GT website (https://pave-gt.ncats.nih.gov/). This communication aims to provide stakeholders with resources that can be applied to drug development programs in establishing a viable regulatory path to clinical trial initiation.

Refined ADME Profiles for ATC Drug Classes

Background: Modern generative chemistry initiatives aim to produce potent and selective novel synthetically feasible molecules with suitable pharmacokinetic properties. General ranges of physicochemical properties relevant for the absorption, distribution, metabolism, and excretion (ADME) of drugs have been used for decades. However, the therapeutic indication, dosing route, and pharmacodynamic response of the individual drug discovery program may ultimately define a distinct desired property profile. Methods: A methodological pipeline to build and validate machine learning (ML) models on physicochemical and ADME properties of small molecules is introduced. Results: The analysis of publicly available data on several ADME properties presented in this work reveals significant differences in the property value distributions across the various levels of the anatomical, therapeutic, and chemical (ATC) drug classification. For most properties, the predicted data distributions agree well with the corresponding distributions derived from experimental data across fourteen drug classes. Conclusions: The refined ADME profiles for ATC drug classes should be useful to guide the de novo generation of advanced lead structures directed toward specific therapeutic indications.

Designing novel cabozantinib analogues as p-glycoprotein inhibitors to target cancer cell resistance using molecular docking study, ADMET screening, bioisosteric approach, and molecular dynamics simulations

Introduction

One of the foremost contributors to mortality worldwide is cancer. Chemotherapy remains the principal strategy for cancer treatment. A significant factor leading to the failure of cancer chemotherapy is the phenomenon of multidrug resistance (MDR) in cancer cells. The primary instigator of MDR is the over expression of P-glycoprotein (P-gp), a protein that imparts resistance and facilitates the ATP-dependent efflux of various anticancer agents. Numerous efforts have been made to inhibit P-gp function with the aim of restoring the effectiveness of chemotherapy due to its broad specificity. The main objective has been to create compounds that either serve as direct P-gp inhibitors or interact with cancer therapies to modulate transport. Despite substantial in vitro achievements, there are currently no approved drugs available that can effectively "block" P-gp mediated resistance. Cabozantinib (CBZ), a multi-kinase inhibitor, is utilized in the treatment of various carcinomas. CBZ has been shown to inhibit P-gp efflux activity, thereby reversing P-gp mediated MDR. Consequently, P-gp has emerged as a critical target for research in anti-cancer therapies.

Methods

The purpose of this study was to computationally identify new andsafer analogues of CBZ using bioisosteric approach, focusing on improved pharmacokinetic properties andreduced toxicity. The physicochemical, medicinal, and ADMET profiles of generated analogues were computed using the ADMETLab 3.0 server. We also predicted the drug likeness (DL) and drug score (DS) of analogues. The molecular docking studies of screened analogues against the protein (PDB ID: 3G5U) were conducted using AutoDock Vina flowing by BIOVIA Discovery Studio for visualizing interactions.Molecular dynamics (MD) simulation of docked ligands was done using Schrödinger suite.

Results and Discussion

The docking scores for the ligands CBZ01, CBZ06, CBZ11, CBZ13, CBZ25, CBZ34, and CBZ38 ranged from -8.0 to -6.4 kcal/mol against the protein (PDB ID: 3G5U). A molecular dynamics (MD) simulation of CBZ01, CBZ13, and CBZ38 was conducted using the Schrödinger suite, revealing that these complexesmaintained stability throughout the 100 ns simulation.

Conclusion

An integrated computational approach combining bioisosteric approach, molecular docking, drug likeness calculations, and MD simulations highlights the promise of ligands CBZ01 and CBZ13 as candidates for the development of potential anticancer agents for the treatment of various cancers.

Exploring a new paradigm for serum-accessible component rules of natural medicines using machine learning and development and validation of a direct predictive model

In the field of pharmaceutical research, Lipinski's Rule of Five (RO5) was once widely regarded as the prevailing standard for the development of novel drugs. Despite the fact that an increasing number of recently approved drugs no longer adhere to this rule, it continues to serve as a valuable guiding principle in the field of drug discovery. The present study aims to establish a set of rules specifically for the serum-accessible components of natural medicines. A comprehensive literature review was conducted to collect data on serum-accessible components of natural medicines, and machine learning methods were then applied to analyse and screen molecular features distinguishing serum-accessible components from non-serum-accessible ones. The most critical rules for serum-accessible components of natural medicines were identified, and these were named the "Natural Medicine's Rule of 5 (NMRO5)." We then compared the molecular property distributions and predictive performance of NMRO5 with RO5. Then, we developed a predictive model capable of directly assessing the possibility of a molecule being serum-accessible. This model was validated using in vivo experiments on multiple natural medicines. Furthermore, we performed molecular modifications on serum-accessible components to "violate" NMRO5, conducting both forward and reverse validations to confirm the reliability of NMRO5. The results obtained revealed that NMRO5 is characterised by the following: higher TPSA, MaxEState, and PEOE VSA1 values, and lower LogP and MinEState values. This indicates that natural medicine components with these properties are more likely to be serum-accessible or remain in plasma rather than being rapidly eliminated. The investigation revealed significant disparities among the five molecular properties of NMRO5, and the predictive performance of eight models based on NMRO5 consistently outperformed those based on RO5. This finding suggests that NMRO5 provides a more reliable framework for determining whether a molecule is serum-accessible compared to RO5. Finally, we developed a direct predictive model for serum-accessible components, achieving an accuracy of 0.7257, an F1 score of 0.7223, and an AUC of 0.7553.

Preclinical Retinal Disease Models: Applications in Drug Development and Translational Research

Retinal models play a pivotal role in translational drug development, bridging preclinical research and therapeutic applications for both ocular and systemic diseases. This review highlights the retina as an ideal organ for studying advanced therapies, thanks to its immune privilege, vascular and neuronal networks, accessibility, and advanced imaging capabilities. Preclinical retinal disease models offer unparalleled insights into inflammation, angiogenesis, fibrosis, and hypoxia, utilizing clinically translatable bioimaging tools like fundoscopy, optical coherence tomography, confocal scanning laser ophthalmoscopy, fluorescein angiography, optokinetic tracking, and electroretinography. These models have driven innovations in anti-inflammatory, anti-angiogenic, and neuroprotective strategies, with broader implications for systemic diseases such as rheumatoid arthritis, Alzheimer's, and fibrosis-related conditions. By emphasizing the integration of the 3Rs principles and novel imaging modalities, this review highlights how retinal research not only enhances therapeutic precision but also minimizes ethical concerns, paving the way for more predictive and human-relevant approaches in drug development.

Short Scalable Route to Bis-morpholine Spiroacetals and Oxazepane Analogues: Useful 3D-Scaffolds for Compound Library Assembly

sp3-Rich molecular scaffolds incorporating nitrogen heterocycles represent important starting points for assembling compound screening libraries and drug discovery. Herein, we report a four-step synthesis of a conformationally well-defined sp3-rich scaffold incorporating two morpholine rings embedded within a spiroacetal framework. The synthesis involves the intermediacy of a 2-chloromethyl-substituted morpholine, accessed from epichlorohydrin and readily available β-aminoalcohols. Base-mediated dehydrochlorination affords an exocyclic enol ether, from which the second morpholine ring is constructed in two steps. Scaffold synthesis is high-yielding and can be performed on a large scale. The methodology allows ready substitution of one-or both- of the morpholine rings for 1,4-oxazepanes and the generation of 6,7- and 7,7-spiroacetal analogues, which are virtually unexplored in drug discovery. Substituted 6,6-systems can be prepared and, in some instances, undergo acid-mediated anomerization to deliver the scaffolds in high diastereoselectivity. The two amine functionalities embedded in the 6,6- and 6,7-spiroacetal scaffolds were sequentially functionalized to provide a diverse physical compound library. These library compounds occupy a similar chemical space to small-molecule drugs that have been approved for clinical application by the Food and Drug Administration yet are structurally dissimilar and may therefore act upon novel targets, representing attractive starting materials for drug discovery.

Current challenges and future perspectives of drug discovery in China

INTRODUCTION

China's pharmaceutical industry, which is historically centered around generic medicines, has largely transformed from imitation to innovation over the past three decades. Despite unprecedented progress, critical challenges remain such as insufficient indigenous research funding, underdeveloped academia-industry relationships, and significant barriers to market access.

AREAS COVERED

This perspective examines the evolving pharmaceutical landscape of China, focusing on its participation in global clinical trials and the resultant new drug approvals. Data for this analysis was sourced from several databases (e.g. PharmCube, NextPharma, and PharmaGO), academic reports, and published literature, covering data up to 2024 (unless otherwise specified). This perspective highlights ongoing regulatory challenges, such as inconsistencies in product standards, and the approval processes relative to the U.S.A. and the European Union. There is also an urgent demand for sustained international investment and recognition, partially due to the recent changes in the geopolitical environment. This perspective also discusses China's efforts to implement accelerated approval pathways and foster multilateral development collaborations.

EXPERT OPINION

China must align its regulatory policies more closely to the international norm to generate robust trial data that will be readily acceptable to the FDA and EMA. Continued investment in biologics as well as cell and gene therapy and artificial intelligence will drive innovation and enhance competitiveness. Additionally, strengthening the academia-industry collaboration is crucial to obtaining new leads through translational research. Ultimately, structural reforms are required to solidify the country's goal of becoming a major player in the global pharmaceutical market.

Discovery of Novel Pyridin-2-yl Urea Inhibitors Targeting ASK1 Kinase and Its Binding Mode by Absolute Protein-Ligand Binding Free Energy Calculations

Apoptosis signal-regulating kinase 1 (ASK1), a key component of the mitogen-activated protein kinase (MAPK) cascades, has been identified as a promising therapeutic target owing to its critical role in signal transduction pathways. In this study, we proposed novel pyridin-2-yl urea inhibitors exhibiting favorable physicochemical properties. The potency of these compounds was validated through in vitro protein bioassays. The inhibition (IC50) of compound 2 was 1.55 ± 0.27 nM, which was comparable to the known clinical inhibitor, Selonsertib. To further optimize the hit compounds, two possible binding modes were initially predicted by molecular docking. Absolute binding free energy (BFE) calculations based on molecular dynamics simulations further discriminated the binding modes, presenting good tendency with bioassay results. This strategy, underpinned by BFE calculations, has the great potential to expedite the drug discovery process in the targeting of ASK1 kinase.

AI-directed formulation strategy design initiates rational drug development

Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such an inefficient screening manner not only wastes research investments but also threatens the safety of clinical volunteers and patients. A design-oriented paradigm for formulation strategy determination is urgently needed to initiate rational drug development. Herein, we introduce FormulationDT, the first data-driven and knowledge-guided artificial intelligence (AI) platform for rational formulation strategy design. Learning from approved drug formulations, FormulationDT devised a comprehensive formulation strategy design system containing 12 decisions for both oral and injectable administration. Utilizing PU-Decide, our specialized partially supervised learning framework designed for positive-unlabeled (PU) scenarios, FormulationDT developed precise and interpretable classification models for each decision, achieving area under the receiver operating characteristic curve (ROC_AUC) scores ranging from 0.78 to 0.98, with an average above 0.90. Incorporating extensive domain knowledge, FormulationDT is now accessible through a user-friendly web platform (http://formulationdt.computpharm.org/). Moreover, FormulationDT demonstrates its value by showcasing its application in proteolysis targeting chimeras (PROTACs) and recent drug approvals. Overall, this study created the first approved drug formulation dataset and tailored the PU-Decide framework to develop a high-performance, interpretable, and user-friendly AI formulation strategy design platform, which holds promise for driving risk reduction and efficiency gains across the life cycle of drug discovery and development.

Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation

In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the effective delivery and therapeutic performance of rivaroxaban, as its low aqueous solubility limits oral bioavailability and necessitates innovative approaches for drug formulation. The study explored the role of primary alcohols combined with dichloromethane in improving solubility, emphasizing their industrial relevance in crystallization, purification, and drug manufacturing processes. To complement experimental insights, machine learning models were employed to predict rivaroxaban solubility based on temperature, solvent type, and mass fraction of dichloromethane. Three models-AdaBoost Gaussian process regression (ADAGPR), AdaBoost multilayer perceptron (ADAMLP), and AdaBoost LASSO regression (ADALASSO)-were evaluated using [Formula: see text], RMSE, and MAPE metrics. Among these, ADAGPR demonstrated superior performance with an R² score of [Formula: see text], outperforming ADAMLP [Formula: see text] and [Formula: see text]. It also achieved the lowest total RMSE [Formula: see text] and MAPE [Formula: see text], confirming its predictive precision and reliability. Optimal solubility conditions were identified at [Formula: see text] with a mass fraction of 0.8190 in a dichloromethane-methanol mixture, yielding a predicted solubility of [Formula: see text]. These findings highlight the potential of combining chemical engineering principles with advanced predictive modeling to optimize solubility in complex solvent systems, offering significant value to pharmaceutical development and process optimization.

An approach to produce thousands of single-chain antibody variants on a SPR biosensor chip for measuring target binding kinetics and for deep characterization of antibody paratopes

Drug discovery continues to face a staggering 90% failure rate, with many setbacks occurring during late-stage clinical trials. To address this challenge, there is an increasing focus on developing and evaluating new technologies to enhance the "design" and "test" phases of antibody-based drugs (e.g., monoclonal antibodies, bispecifics, CAR-T therapies, ADCs) and biologics during early preclinical development, with the goal of identifying lead molecules with a higher likelihood of clinical success. Artificial intelligence (AI) is becoming an indispensable tool in this domain, both for improving molecules identified through traditional approaches and for the de novo design of novel therapeutics. However, critical bottlenecks persist in the "build" and "test" phases of AI-designed antibodies and protein binders, impeding early preclinical evaluation. While AI models can rapidly generate thousands to millions of putative drug designs, technological and cost limitations mean that only a few dozen candidates are typically produced and tested. Drug developers often face a tradeoff between ultra-high-throughput wet lab methods that provide binary yes/no binding data and biophysical methods that offer detailed characterization of a limited number of drug-target pairs. To address these bottlenecks, we previously reported the development of the Sensor-integrated Proteome On Chip (SPOC®) platform, which enables the production and capture-purification of 1,000 - 2,400 folded proteins directly onto a surface plasmon resonance (SPR) biosensor chip for measuring kinetic binding rates with picomolar affinity resolution. In this study, we extend the SPOC technology to the expression of single-chain antibodies (sc-antibodies), specifically scFv and VHH constructs. We demonstrate that these constructs are capture-purified at high levels on SPR biosensors and retain functionality as shown by the binding specificity to their respective target antigens, with affinities comparable to those reported in the literature. SPOC outputs comprehensive kinetic data including quantitative binding (R max ), on-rate ( k a ), off-rate ( k d ), affinity ( K D ), and half-life ( t 1/2 ), for each of thousands of on-chip sc-antibodies. Additionally, we present a case study showcasing single amino acid mutational scan of the complementarity-determining regions (CDRs) of a HER2 VHH (nanobody) paratope. Using 92 unique mutated variants from four different amino acid substitutions, we pinpoint critical residues within the paratope that could further enhance binding affinity. This study serves as a demonstration of a novel high-throughput approach for biophysical screening of hundreds to thousands of single chain antibody sequences in a single assay, generating high affinity resolution kinetic data to support antibody discovery and AI-enabled pipelines.

Neuroprotective thiazole sulfonamides against 6-OHDA-induced Parkinsonian model: in vitro biological and in silico pharmacokinetic assessments

The limitations of currently existing medications in delaying or halting the development of Parkinson's disease (PD) remain dramatically problematic, making it the second most prevalent neurodegenerative disorder. Moreover, it is expected that the number of PD cases will double within the next 30 years. Herein, to discover a novel neuroprotective therapeutic strategy, a series of multifunctional thiazole sulfonamides underwent preliminary assessment owing to their neuroprotective capabilities against 6-hydroxydopamine (6-OHDA)-induced damage in human neuronal SH-SY5Y cells. Pretreatment with novel synthetic hybrids, including 1, 2, and 8, significantly improved cell viability, reduced lactate dehydrogenase (LDH) leakage, prevented mitochondrial dysfunction, and mitigated intracellular oxidative stress. Insight molecular mechanisms and potential targets of these compounds were elucidated through their activation and binding interaction with sirtuin 1 (SIRT1), suggesting their influencing roles on relevant downstream cascades of PD. Furthermore, in silico pharmacokinetic analysis revealed the drug-likeness of these three hybrids, which are capable of being distributed into the central nervous system (CNS) with slight toxicity. Therefore, these novel neuroprotective thiazole sulfonamides are promising candidates for further development (i.e., in vivo and clinical trials) of effective PD therapy.

Deep learning: A game changer in drug design and development

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

6H-Indolo-[2,3-b]-quinoxaline derivatives as promising bifunctional SHP1 inhibitors

Dysfunction in the SHP1 enzyme can cause cancers and many diseases, so it is of great significance to develop novel small molecule SHP1 inhibitors. Through continuous monitoring of metabolic and targeted processes of SHP1 inhibitors in real-time, we can evaluate the effectiveness and toxicity of the inhibitors, further optimize drug design, and explore SHP1 biology. Indoloquinoxaline is an important class of N-containing heterocycle, which has been studied and applied in the pharmacological field and in optoelectronic materials. In this work, the potential Src homology 2 domain-containing phosphatase 1 (SHP1) inhibitor 5a was developed with the help of the structural fusion and scaffold hop of a fluorophore, 6H-indolo-[2,3-b]-quinoxaline, and a bio-active skeleton, thieno[2,3-b]quinoline-procaine. Compound 5a selectively inhibited the SHP1PTP enzyme abilities (IC50 = 2.34 ± 0.06 μM), exhibited a significant fluorescence response (P = 0.007) in response to SHP1PTP activity, and emitted strong blue/green fluorescence in MDA-MB-231 cells. Furthermore, compound 5a showed irreversible binding with SHP1PTP in simulations and dialysis experiments. Altogether, compound 5a serves as a bifunctional SHP1 inhibitor, combining imaging and therapeutic functionalities, enhancing our understanding of SHP1 biological mechanisms, and positively impacting novel drug development.

SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases

MOTIVATION

Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).

RESULTS

This study presents a novel machine learning approach using multimodal vector-quantized variational autoencoders (VQ-VAEs) for predicting therapeutic target molecules across diseases. To address the lack of known therapeutic target-disease associations, we incorporate the information on uncharacterized diseases without known targets or uncharacterized proteins without known indications (applicable diseases) in the semi-supervised learning (SSL) framework. The method integrates disease-specific and protein perturbation profiles with genetic perturbations (e.g. gene knockdowns and gene overexpressions) at the transcriptome level. Cross-cell representation learning, facilitated by VQ-VAEs, was performed to extract informative features from protein perturbation profiles across diverse human cell types. Concurrently, cross-disease representation learning was performed, leveraging VQ-VAE, to extract informative features reflecting disease states from disease-specific profiles. The model's applicability to uncharacterized diseases or proteins is enhanced by considering the consistency between disease-specific and patient-specific signatures. The efficacy of the method is demonstrated across three practical scenarios for 79 diseases: target repositioning for target-disease pairs, new target prediction for uncharacterized diseases, and new indication prediction for uncharacterized proteins. This method is expected to be valuable for identifying therapeutic targets across various diseases.

AVAILABILITY AND IMPLEMENTATION

Code: github.com/YamanishiLab/SSL-VQ and Data: 10.5281/zenodo.14644837.

ROC-guided virtual screening, molecular dynamics simulation, and bioactivity validation assessment Z195914464 as a 3CL Mpro inhibitor

Discovering novel class anti-SARS-CoV-2 compounds with novel backbones is essential for preventing and controlling SARS-CoV-2 transmission, which poses a substantial threat to the health and social sustainable development of the global population because of its high pathogenicity and high transmissibility. Although the potential mutation of SARS-CoV-2 might diminish the therapeutic efficacy of drugs, 3CL Mpro is the target highly conservative in contrast with other targets. It is an essential enzyme for coronavirus replication. Based on this, this study utilized the drug discovery strategy of Knime molecular filtering framework, ROC-guided virtual screening, clustering analysis, binding mode analysis, and activity evaluation approaches to identify compound Z195914464 (IC50: 7.19 μM) is a novel class inhibitor of anti-SARS-CoV-2 against the 3CL Mpro target. In addition, based on molecular dynamics simulations and MMPBSA analyses, discovered that compound Z195914464 can interact with more key residues and lower bonding energies, which explains why it exhibited more activity than the other three compounds. In summary, this study developed a method for the rapid and accurate discovery of active compounds and can also be applied in the discovery of active compounds in other targets.

The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing

Metronomic chemotherapy (MC), long-term continuous administration of anticancer drugs, is gaining attention as an alternative to the traditional maximum tolerated dose (MTD) chemotherapy. By combining MC with other treatments, the therapeutic efficacy is enhanced while minimizing toxicity. MC employs multiple mechanisms, making it a versatile approach against various cancers. However, drug resistance limits the long-term effectiveness of MC, necessitating ongoing development of anticancer drugs. Traditional drug discovery is lengthy and costly due to processes like target protein identification, virtual screening, lead optimization, and safety and efficacy evaluations. Drug repurposing (DR), which screens FDA-approved drugs for new uses, is emerging as a cost-effective alternative. Both experimental and computational methods, such as protein binding assays, in vitro cytotoxicity tests, structure-based screening, and several types of association analyses (Similarity-Based, Network-Based, and Target Gene), along with retrospective clinical analyses, are employed for virtual screening. This review covers the mechanisms of MC, its application in various cancers, DR strategies, examples of repurposed drugs, and the associated challenges and future directions.

Anxiolytic Efficacy of Indirubin: In Vivo Approach Along with Receptor Binding Profiling and Molecular Interaction with GABAergic Pathways

Anxiety is a natural response to stress, characterized by feelings of worry, fear, or unease. The current research was conducted to investigate the anxiolytic effect of indirubin (IND) in different behavioral paradigms in Swiss albino mice. To observe the animal's behavioural response to assess anxiolytic activity, different tests were performed, such as the open-field (square cross, grooming, and rearing), swing, dark-light, and hole cross tests. The experimental mice were administered IND (5 and 10 mg/kg, p.o.), where diazepam (DZP) and vehicle were used as positive and negative controls, respectively. In addition, a combination treatment (DZP+IND-10) was provided to the animals to determine the modulatory effect of IND on DZP. Molecular docking approach was also conducted to determine the binding energy of IND with the GABAA receptor (α2 and α3 subunits) and pharmacokinetics were also estimated. The findings revealed that IND dose-dependently significantly (p<0.05) reduced the animal's movement exerting calming behavior like DZP. IND also demonstrated the highest docking score (-7.7 kcal/mol) against the α3 subunit, while DZP showed a lower docking value (-6.4 kcal/mol) than IND. The ADMET analysis revealed that IND has proper drug-likeness and pharmacokinetic characteristics. In conclusion, IND exerted anxiolytic effects through GABAergic Pathways.

ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning

Drug registration requires risk assessment of new active pharmaceutical ingredients or excipients to ensure they are safe for human health and the environment. However, traditional risk assessment is expensive and relies heavily on animal testing. Machine learning (ML) has been used as a risk assessment tool, providing less time, money, and involved animals than in vivo experiments. Despite that, the ML models often rely on a single model, which may introduce bias and unreliable prediction. Stacking ensemble learning is an ML framework that makes predictions based on multimodal outcomes. This framework performs well in quantitative structure-activity relationship (QSAR) studies. In this study, we developed ToxSTK, a multi-target toxicity assessment using stacking ensemble learning. We aimed to create an ML tool that facilitates toxicity assessments more affordably with reduced reliance on animal models. We focused on four key targets generally assessed in early-stage drug development: hERG toxicity, mTOR toxicity, PBMCs toxicity, and mutagenicity. Our model integrated 12 molecular fingerprints with 3 ML algorithms, generating 36 novel predictive features (PFs). These PFs were then combined to construct the final meta-decision model. Our results demonstrated that the ToxSTK model surpasses standard regression and classification metrics, ensuring it is highly reliable and accurate in predicting chemical toxicities within its application domain. This model passed the y-randomization test, confirming that the identified QSAR is robust and not due to random chance. Additionally, this model outperforms the existing ML methods for these endpoints, suggesting its effectiveness for risk assessment applications. We recommend incorporating this stacking ensemble learning framework into the chemical risk assessment pipeline to improve model generalization, accuracy, robustness, and reliability.

Utilizing Tissues Self-Assembled in Fiber Optic-Based "Chinese Guzheng Strings" for Contractility Sensing and Drug Efficacy Evaluation: A Practical Approach

Recent advances in drug design and compound synthesis have highlighted the increasing need for effective methods of toxicity evaluation. A specialized force sensor, known as the light wavelength-encoded "Chinese guzheng" is developed. This innovative sensor is equipped with optical fiber strings and utilizes a wavelength-encoded fiber Bragg grating (FBG) that is chemically etched to reduce its diameter. This design allows the sensor to detect minimal forces as low as l µN. This sensor is successfully applied to monitor human-induced pluripotent stem cell-derived human-engineered heart tissue (hEHT) models that can self-assemble and contact optical fiber-based strings. The sensor detects micro newton contraction forces in real-time by measuring the wavelength drift resulting from hEHT contractions. In addition, the sensor is precise and durable, exhibiting a fatigue resistance of up to 800 000 cycles, making it suitable for long-term monitoring. The device effectively measured the contractile force of the hEHTs under various physiological conditions, including natural contraction, electrical stimulation, and stretching. Moreover, multichannel detection enables the study and demonstration of short- and long-term effectiveness of multiple drugs. This breakthrough sensor addresses the critical need for high-precision real-time monitoring in drug evaluation and provides a solid foundation for screening drugs to treat cardiomyopathy.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

The importance of preclinical models for cholangiocarcinoma drug discovery

INTRODUCTION

Biliary tract cancer (BTC) comprises a clinically diverse and genetically heterogeneous group of tumors along the intra- and extrahepatic biliary system (intrahepatic and extrahepatic cholangiocarcinoma) and gallbladder cancer with the common feature of a poor prognosis, despite increasing molecular knowledge of associated genetic aberrations and possible targeted therapies. Therefore, the search for even more precise and individualized therapies is ongoing and preclinical tumor models are central to the development of such new approaches.

AREAS COVERED

The models described in the current review include simple and advanced in vitro and in vivo models, including cell lines, 2D monolayer, spheroid and organoid cultures, 3D bioprinting, patient-derived xenografts, and more recently, machine-perfusion platform-based models of resected liver specimens. All these models have individual advantages, disadvantages and limitations that need to be considered depending on the desired application.

EXPERT OPINION

In addition to potential cost limitations, availability of BTC cell types, time required for model establishment and growth success rate, the individual models differently reflect relevant characteristics such as tumor heterogeneity, spatial tumor-stroma microenvironment interactions, metabolic and nutritional gradients and immunological interactions. Therefore, a consequent combination of different models may be required to improve clinical study outcomes by strengthening the preclinical data basis.

Exploring Drug Repurposing for Interstitial Cystitis/Bladder Pain Syndrome: Defining Novel Therapeutic Targets

INTRODUCTION

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating pain condition of unknown etiology. Effective therapies for this condition could not have been developed in the last century. Drug repurposing is a practical strategy for enhancing patient access to successful therapies. It is an approach for discovering novel applications for licensed or investigational pharmaceuticals that extend beyond the initial medical indication. This work aims to identify repurposable medications through bioinformatics to discover potential drugs or compounds that can reverse the IC/BPS disease signature.

METHODS AND MATERIAL

The analysis involved examining the differentially expressed genes in IC/BPS patients with two distinct disease phenotypes (Hunner's lesion disease, non-Hunner's lesion disease) and controls using the datasets GSE11783, GSE28242, and GSE57560. The goal was to assess the reversal of the disease signature on the L1000CDS2 and cMAP platforms.

RESULTS

Twenty-one compounds were repurposed, consisting of 11 small molecules, 10 chemical compounds, 3 natural products, and 6 FDA-approved drugs, currently used for clinical indications such as cancer, myelofibrosis, and diabetes.

DISCUSSION

Bioinformatics can be useful for identifying therapeutic agents for IC/BPS by accessing and processing big data on molecular and cellular levels. Prospective in vivo experiments must validate repurposed drugs. The expansion of large-scale genome sequencing, gene expression studies, and clinical data for IC/BPS will improve successful drug selection.

Site-Specific Competitive Kinase Inhibitor Target Profiling Using Phosphonate Affinity Tags

Protein kinases are prime targets for drug development due to their involvement in various cancers. However, selective inhibition of kinases, while avoiding off-target effects remains a significant challenge for the development of protein kinase inhibitors. Activity-based protein profiling (ABPP), in combination with pan-kinase activity-based probes (ABPs) and mass spectrometry-based proteomics, enables the identification of kinase drug targets. Here, we extend existing ABPP strategies for kinase profiling with a site-specific analysis, allowing for protein kinase inhibitor target engagement profiling with amino acid specificity. The site-specific approach involves highly efficient enrichment of ABP-labeled peptides, resulting in a less complex peptide matrix, straightforward data analysis, and the screening of over ∼100 kinase active sites in a single LC-MS analysis. The complementary use of both trypsin and pepsin in parallel to generate the ABP-labeled peptides considerably expanded the coverage of kinases and pinpoint the exact binding sites. Using the site-specific strategy to examine the on- and off-targets of the Ephrin receptor (Eph) B4 inhibitor NVP-BHG712 showed binding to EphA2 with an IC50 of 17 nM and EphB4 with an IC50 of 20 nM. Next to the known targets, EphA2 and EphB4, NVP-BHG712 bound to the discoidin domain-containing receptor 1 with an IC50 of 2.1 nM, suggesting that a discoidin domain-containing receptor 1-targeting regio-isomer of NVP-BHG712 was used. The promiscuity of XO44 toward ATP-binding pockets on nonkinase proteins facilitated the screening of additional off-target sites, revealing inosine-5'-monophosphate dehydrogenase 2 as a putative off-target. Expanding the search to other amino acids revealed that XO44, in addition to 745 lysines, also covalently linked 715 tyrosines, which significantly expands the competitive ABPP search space and highlights the added value of the site-specific method. Therefore, the presented approach, which can be fully automated with liquid handling platforms, provides a straightforward, valuable new approach for competitive site-specific kinase inhibitor target profiling.

Liver organoids: Current advances and future applications for hepatology

The creation of self-organizing liver organoids represents a significant, although modest, step toward addressing the ongoing organ shortage crisis in allogeneic liver transplantation. However, researchers have recognized that achieving a fully functional whole liver remains a distant goal, and the original ambition of organoid-based liver generation has been temporarily put on hold. Instead, liver organoids have revolutionized the field of hepatology, extending their influence into various domains of precision and molecular medicine. These 3D cultures, capable of replicating key features of human liver function and pathology, have opened new avenues for human-relevant disease modeling, CRISPR gene editing, and high-throughput drug screening that animal models cannot accomplish. Moreover, advancements in creating more complex systems have led to the development of multicellular assembloids, dynamic organoid-on-chip systems, and 3D bioprinting technologies. These innovations enable detailed modeling of liver microenvironments and complex tissue interactions. Progress in regenerative medicine and transplantation applications continues to evolve and strives to overcome the obstacles of biocompatibility and tumorigenecity. In this review, we examine the current state of liver organoid research by offering insights into where the field currently stands, and the pivotal developments that are shaping its future.

Quest for discovering novel CDK12 inhibitor

CDK12 is essential for cellular processes like RNA processing, transcription, and cell cycle regulation, inhibiting cancer cell growth and facilitating macrophage invasion. CDK12 is a significant oncogenic factor in various cancers, including HER2-positive breast cancer, Anaplastic thyroid carcinoma, Hepatocellular carcinoma, prostate cancer, and Ewing sarcoma. It is also regarded as a potential biomarker, emphasizing its broader significance in oncology. Targeting CDK12 offers a promising strategy to develop therapy. Various monoclonal antibodies have drawn wide attention, but they are expensive compared to small-molecule inhibitors, limiting their accessibility and affordability for patients. Consequently, this research aims to identify effective CDK12 inhibitors using comprehensive high-throughput virtual screening. RASPD protocol has been employed to screen three different databases against the target followed by drug-likeness, molecular docking, ADME, toxicity, Consensus molecular docking, MD Simulation, and in-vitro studies MTT assay. The research conducted yielded one compound ZINC11784547 has demonstrated robust binding affinity, favorable ADME features, less toxicity, remarkable stability, and cytotoxic effect. The identified compound holds promise for promoting cancer cell death through CDK12 inhibition.

Analyzing Oxygen Atom Distribution in FDA-Approved Drugs to Enhance Drug Discovery Strategies

Despite advancements in molecular design rules and understanding biochemical processes, the field of drug design and discovery seeks to minimize the number and duration of synthesis-testing cycles to convert lead compounds into drug candidates. A promising strategy involves gaining insightful understanding of key heteroatoms such as oxygen and nitrogen. This work presents a comprehensive analysis of oxygen atoms in approved drugs, aiming to streamline drug design and discovery efforts. The study examines the frequency, distribution, prevalence, and diversity of oxygen atoms in a dataset of 2049 small molecules approved by the FDA and other agencies. The analysis focuses on various types of oxygen atoms, including sp3, sp2-hybridized, ring, and nonring. In general, existence of sp3-O slightly outperforms sp2-O, which is associated with balancing various factors such as flexibility, solubility, stability, and pharmacokinetics, in addition to activity and selectivity. In approved drugs, majority of oxygen atoms are present within 4 Å from the COM of the molecule. This analysis offers valuable understanding of oxygen distribution, which could be used during the multiparameter optimization process, facilitating the transformation of a hit/lead compound into a potential drug candidate.

The importance of preclinical models in cholangiocarcinoma

Cholangiocarcinoma (CCA) is an adenocarcinoma of the hepatobiliary system with a grim prognosis. Incidence is rising globally and surgery is currently the only curative treatment, but is only available for patients who are fit and diagnosed in an early-stage of disease progression. Great importance has been placed on developing preclinical models to help further our understanding of CCA and potential treatments to improve therapeutic outcomes. Preclinical models of varying complexity and cost have been established, ranging from more simplistic in vitro 2D CCA cell lines in culture, to more complex in vivo genetically engineered mouse models. Currently there is no single model that faithfully recaptures the complexities of human CCA and the in vivo tumour microenvironment. Instead a multi-model approach should be used when designing preclinical trials to study CCA and potential therapies.

Inhibition of naproxen crystallization by polymers: The role of topology and chain length of polyvinylpyrrolidone macromolecules

This paper presents an innovative approach that utilizes self-synthesized homopolymers of polyvinylpyrrolidone (PVP) with different architectures as effective matrices for inhibiting the crystallization of naproxen (NAP). We have thoroughly investigated amorphous solid dispersions containing NAP and (i) self-synthesized linear PVP, (ii) self-synthesized three-armed star-shaped PVP, and (iii) self-synthesized linear PVP with a mass (Mn) corresponding to the length of one arm of the star polymer, as well as (iv) commercial linear PVP K30 as a reference. Differential scanning calorimetry, X-ray diffraction, and infrared spectroscopy studies, as well as molecular dynamics simulations were conducted to gain comprehensive insights into the thermal and structural properties, as well as intermolecular interactions in the NAP-PVP systems. The main purpose of all experiments was to assess the impact of macromolecule structure (topology, molecular weight) on the kinetics of the crystallization of NAP - a drug that is very difficult to vitrify. Our studies clearly showed that the most effective matrix in inhibiting the NAP crystallization is linear, self-synthesized PVP with higher molecular weight (Mn) similar to that of the commercial PVP K30, but lower, strictly controlled dispersity. We also found that crystallization of API proceeds at a similar pace for the binary mixture composed of a star-shaped PVP and linear polymer with Mn corresponding to Mn of one arm of the star-shaped macromolecule in the vicinity of the Tg. The obtained data highlight the key role of polymer structure in designing new pharmaceutical formulations.

Distinguishing classes of neuroactive drugs based on computational physicochemical properties and experimental phenotypic profiling in planarians

Mental illnesses put a tremendous burden on afflicted individuals and society. Identification of novel drugs to treat such conditions is intrinsically challenging due to the complexity of neuropsychiatric diseases and the need for a systems-level understanding that goes beyond single molecule-target interactions. Thus far, drug discovery approaches focused on target-based in silico or in vitro high-throughput screening (HTS) have had limited success because they cannot capture pathway interactions or predict how a compound will affect the whole organism. Organismal behavioral testing is needed to fill the gap, but mammalian studies are too time-consuming and cost-prohibitive for the early stages of drug discovery. Behavioral medium-throughput screening (MTS) in small organisms promises to address this need and complement in silico and in vitro HTS to improve the discovery of novel neuroactive compounds. Here, we used cheminformatics and MTS in the freshwater planarian Dugesia japonica-an invertebrate system used for neurotoxicant testing-to evaluate the extent to which complementary insight could be gained from the two data streams. In this pilot study, our goal was to classify 19 neuroactive compounds into their functional categories: antipsychotics, anxiolytics, and antidepressants. Drug classification was performed with the same computational methods, using either physicochemical descriptors or planarian behavioral profiling. As it was not obvious a priori which classification method was most suited to this task, we compared the performance of four classification approaches. We used principal coordinate analysis or uniform manifold approximation and projection, each coupled with linear discriminant analysis, and two types of machine learning models-artificial neural net ensembles and support vector machines. Classification based on physicochemical properties had comparable accuracy to classification based on planarian profiling, especially with the machine learning models that all had accuracies of 90-100%. Planarian behavioral MTS correctly identified drugs with multiple therapeutic uses, thus yielding additional information compared to cheminformatics. Given that planarian behavioral MTS is an inexpensive true 3R (refine, reduce, replace) alternative to vertebrate testing and requires zero a priori knowledge about a chemical, it is a promising experimental system to complement in silico cheminformatics to identify new drug candidates.

A Novel Tumor on Chip Mimicking the Breast Cancer Microenvironment for Dynamic Drug Screening

In light of the emerging breakthroughs in cancer biology, drug discovery, and personalized medicine, Tumor-on-Chip (ToC) platforms have become pivotal tools in current biomedical research. This study introduced a novel rapid prototyping approach for the fabrication of a ToC device using laser-patterned poly(methyl methacrylate) (PMMA) layers integrated with a polylactic acid (PLA) electrospun scaffold, enabling dynamic drug delivery and the assessment of therapeutic efficacy in cancer cells. Traditional drug screening methods, such as conventional cell cultures, mimic certain aspects of cancer progression but fail to capture critical features of the tumor microenvironment (TME). While animal models offer a closer approximation of tumor complexity, they are limited in their ability to predict human drug responses. Here, we evaluated the ability of our ToC device to recapitulate the interactions between cancer and TME cells and its efficacy in evaluating the drug response of breast cancer cells. The functional design of the proposed ToC system offered substantial potential for a wide range of applications in cancer research, significantly accelerating the preclinical assessment of new therapeutic agents.

Protective effects of herbal compounds against cyclophosphamide-induced organ toxicity: a pathway-centered approach

Cyclophosphamide is a key component of numerous chemotherapeutic protocols, demonstrating broad-spectrum efficacy against various malignancies and non-cancerous conditions. This review examines CPM's metabolic pathways, therapeutic applications, and its resulting organ-specific toxicities. Despite its clinical benefits in treating nephrotic syndrome, encephalomyelitis, breast cancer, ovarian cancer, and other diseases, CPM is associated with significant adverse effects on the kidneys, liver, heart, lungs, and intestines. The discussion delves into the molecular mechanisms underlying these toxicities, highlighting dysregulation in key signaling pathways, including Nrf2, NF-κB, MAPK/ERK, and AKT. In addressing these challenges, recent studies have identified various herbal drugs and phytochemicals capable of mitigating CPM-induced toxicity. Notable compounds such as cinnamaldehyde, baicalin, quercetin, and curcumin have demonstrated protective effects. Integrating these herbal formulations with CPM therapy is proposed to enhance patient safety and treatment efficacy. This review underscores the influence of CPM on apoptosis and inflammation pathways, which lead to alterations in organ-specific biomarkers. Phytochemicals may exert protective effects by restoring disrupted signaling pathways and normalizing altered biomarkers. The compilation of phytochemicals presented in this review serves as a valuable resource for researchers exploring other herbal products with potential protective effects against CPM toxicity. A significant gap in the current literature is the lack of clinical trials evaluating phytochemicals that mitigate CPM toxicity in vivo. Rigorous clinical studies are necessary to establish the efficacy and safety of herbal formulations in cancer treatment. Such research will clarify the role of natural remedies in complementing conventional therapies, ultimately improving patient outcomes.

Revitalizing the heart: strategies and tools for cardiomyocyte regeneration post-myocardial infarction

Myocardial infarction (MI) causes the loss of millions of cardiomyocytes, and current treatments do not address this root issue. New therapies focus on stimulating cardiomyocyte division in the adult heart, inspired by the regenerative capacities of lower vertebrates and neonatal mice. This review explores strategies for heart regeneration, offers insights into cardiomyocyte proliferation, evaluates in vivo models, and discusses integrating in vitro human cardiac models to advance cardiac regeneration research.

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing

Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback (and potentially additional data on drugs and diseases) has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. In particular, an uncontrolled bias on unknown associations might lead to severe data leakage and a misestimation of the model's true performance. Moreover, in drug repurposing, the ability of a model to extrapolate beyond its training distribution is crucial and should also be assessed. Finally, we identified a subcategory of collaborative filtering that seems efficient and robust to distribution shifts. Benchmarks constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods.

(E)-1-(3-(3-Hydroxy-4-Methoxyphenyl)-1-(3,4,5-Trimethoxyphenyl)allyl)-1H-1,2,4-Triazole and Related Compounds: Their Synthesis and Biological Evaluation as Novel Antimitotic Agents Targeting Breast Cancer

Background/Objectives: The synthesis of (E)-1-(1,3-diphenylallyl)-1H-1,2,4-triazoles and related compounds as anti-mitotic agents with activity in breast cancer was investigated. These compounds were designed as hybrids of the microtubule-targeting chalcones, indanones, and the aromatase inhibitor letrozole. Methods: A panel of 29 compounds was synthesized and examined by a preliminary screening in estrogen receptor (ER) and progesterone receptor (PR)-positive MCF-7 breast cancer cells together with cell cycle analysis and tubulin polymerization inhibition. Results: (E)-5-(3-(1H-1,2,4-triazol-1-yl)-3-(3,4,5-trimethoxyphenyl)prop-1-en-1-yl)-2-methoxyphenol 22b was identified as a potent antiproliferative compound with an IC50 value of 0.39 mM in MCF-7 breast cancer cells, 0.77 mM in triple-negative MDA-MB-231 breast cancer cells, and 0.37 mM in leukemia HL-60 cells. In addition, compound 22b demonstrated potent activity in the sub-micromolar range against the NCI 60 cancer cell line panel including prostate, melanoma, colon, leukemia, and non-small cell lung cancers. G2/M phase cell cycle arrest and the induction of apoptosis in MCF-7 cells together with inhibition of tubulin polymerization were demonstrated. Immunofluorescence studies confirmed that compound 22b targeted tubulin in MCF-7 cells, while computational docking studies predicted binding conformations for 22b in the colchicine binding site of tubulin. Compound 22b also selectively inhibited aromatase. Conclusions: Based on the results obtained, these novel compounds are suitable candidates for further investigation as antiproliferative microtubule-targeting agents for breast cancer.