Gastrointestinal toxicities (GITs) in clinical trials often lead to dose-limitations that reduce drug efficacy and delay treatment optimization. Preclinical animal models do not accurately replicate human physiology, leaving few options for early detection of GITs, such as diarrhea, before human studies. Chemotherapeutic agents, known to cause clinical diarrhea, frequently target mitotic cells. Therefore, we hypothesized a model utilizing proliferative cell populations derived from human intestinal crypts would predict clinical diarrhea occurrence with high accuracy. Here, we describe the development of a diarrhea prediction assay utilizing RepliGut® Planar, a primary intestinal stem cell-derived platform. To evaluate the ability of this model to predict clinical diarrhea risk, we assessed toxicity of 30 marketed drugs by measuring cell proliferation (EdU incorporation), cell abundance (nuclei quantification), and barrier formation (TEER) in cells derived from three human donors. Dose response curves were generated for each drug, and the IC15 to Cmax ratio was used to identify a threshold for assay positivity. This model accurately predicted diarrhea potential, achieving an accuracy of 91 % for proliferation, 90 % for abundance, and 88 % for barrier formation. In vitro toxicity screening using primary proliferative cells may reduce clinical diarrhea and ultimately lead to safer and more effective treatments for patients.

The increasing ethical concerns and regulatory restrictions surrounding animal testing have accelerated the development of advanced in vitro models that more accurately replicate human physiology. Among these, stem cell-based systems and organoids have emerged as revolutionary tools, providing ethical, scalable, and physiologically relevant alternatives. This review explores the key trends and driving factors behind the adoption of these models, such as technological advancements, the principles of the 3Rs (Replacement, Reduction, and Refinement), and growing regulatory support from agencies like the OECD and FDA. It also delves into the development and application of various model systems, including 3D reconstructed tissues, induced pluripotent stem cell-derived cells, and microphysiological systems, highlighting their potential to replace animal models in toxicity evaluation, disease modeling, and drug development. A critical aspect of implementing these models is ensuring robust quality control protocols to enhance reproducibility and standardization, which is necessary for gaining regulatory acceptance. Additionally, we discuss advanced strategies for assessing toxicity and efficacy, focusing on organ-specific evaluation methods and applications in diverse fields such as pharmaceuticals, cosmetics, and food safety. Despite existing challenges related to scalability, standardization, and regulatory alignment, these innovative models represent a transformative step towards reducing animal use and improving the relevance and reliability of preclinical testing outcomes.

Idiopathic pulmonary fibrosis (IPF) exhibits extremely high mortality rates. Targeted therapy, which utilizes specific drugs or other substances to identify and attack specific molecular targets in the lesion, holds promise as a potent means of treating IPF. M2 macrophages have been shown to express high levels of osteopontin (OPN) early in the onset of IPF and sustain this high expression to promote the progression of IPF. Intervention in OPN expression can effectively impede the development of fibrosis. While the technology for targeting proteins with siRNA has become increasingly mature, the targeted delivery of siRNA to resident M2 macrophages in the lungs remains challenging. In this study, we developed an engineered self-assembling OPN siRNA carrier complex based on a metal-polyphenol network (luteolin-Zr) and PEG conjugated with an M2 macrophage-targeting peptide (Pery-PEG-M2), termed siOPN@LuZ-M2, for the treatment of pulmonary fibrosis. Consequently, significant therapeutic effects were observed in both bleomycin-induced pulmonary fibrosis mouse models and human precision-cut lung slices (hPCLS) models. Importantly, luteolin, which is slowly released from siOPN@LuZ-M2 within cells, can gradually accumulate in fibrotic lung tissue, exerting an anti-inflammatory effect and further enhancing the treatment of IPF. It is worth mentioning that siOPN@LuZ-M2 can be labeled with 89Zr, allowing for the detection of its in vivo distribution and metabolic behavior via PET-CT. This study presents a promising new image-guided molecular targeting strategy for the treatment of fibrosis.

Pincer-type mono- and poly-nuclear Pd(II) and Pt(II) complexes bearing imidazo[1,5-a]pyridine and imidazolylpyridine moieties were synthesized and characterized using several spectroscopic methods. Determination of molecular structures of these complexes using single crystal X-ray diffraction studies revealed a distorted square planar geometry around the bivalent palladium and platinum in all the complexes. These Pd(II) complexes displayed high catalytic activity in various reactions, such as the Suzuki-Miyaura cross-coupling reaction, transfer hydrogenation reaction, and alkyne homocoupling. The experimental results matched well with the theoretical data of all catalysts. Substantial deviations in the catalytic activity were observed by changing the co-ligand, binding mode of the ligand and the number of metal centres. Under optimal conditions, the Suzuki cross-coupling and transfer hydrogenation reactions were successfully accomplished with a wide range of functional groups by taking only 0.1 mol% of tetranuclear Pd(II) complex (5) as the catalyst. Intermediates in the Suzuki coupling reaction were also detected using mass spectroscopy. Among the studied complexes, the tetranuclear palladium complex exhibited the highest catalytic activity. Further, Pd(II) complexes were tested in a model reaction of the homocoupling of phenylacetylene, and diphenylbutadiyne was produced in excellent yield. Additionally, the interactions of all the complexes with calf thymus DNA (CT-DNA) and bovine serum albumin (BSA) were investigated using electronic spectroscopy. Absorption study showed minor groove binding of DNA with these complexes, while intercalative binding through displacement of ethidium bromide (EB) in EB-DNA was observed in all the complexes, quenching the fluorescence intensity. The complexes also displayed high binding affinity toward BSA, as confirmed by emission, synchronous fluorescence, and steady-state fluorescence anisotropy measurements. Moreover, the pharmacokinetic properties of two bioactive compounds (3s and 3t) obtained from the Suzuki coupling reaction were calculated, and to evaluate their activity as leukotriene A4 hydrolase (LTA4H) inhibitor, these molecules were docked with human LTA4H enzyme.

In this study, we introduce a novel approach for predicting two key drug properties, blood-brain barrier (BBB) permeability and human intestinal absorption via Caco-2 permeability. Our methodology centers around a specialized neural network, the atom transformer-based Message Passing Neural Network (MPNN), which we have combined with contrastive learning techniques to enhance the process of representing and embedding molecular structures for more accurate property prediction. These innovative models focus on predicting BBB and Caco-2 permeability -two critical factors in drug absorption and distribution- which fall under the broader scope of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. The models are readily accessible online through the Enalos Cloud Platform which offers a user-friendly, AI-powered, ready-to-use web service that significantly streamlines the drug design process, enabling users to easily predict and understand the behavior of potential drug compounds within the human body.Scientific Contribution Our study combines an atom-attention Message Passing Neural Network (AA-MPNN) with contrastive learning (CL), which significantly improves predictive accuracy. Our model leverages self-supervised learning to expand the chemical space used in training and self-attention mechanisms to focus on critical molecular features, enhancing both model accuracy and interpretability. Additionally, the ready-to-use web service based on our model democratizes access to predictive tools for the scientific and regulatory communities.

The poor survival of ovarian cancer patients is linked to their high likelihood of relapse. In spite of full apparent macroscopic clearance, tumor recurrences arise from cells that are resistant to primary chemotherapy in the form of minimal residual disease (MRD). MRD exhibits distinct molecular drivers from bulk cancer and therefore necessitates alternative therapeutic strategies. However, there is a lack of 3D models that faithfully recapitulate MRD ex vivo for therapy development. This study constructs microfluidics-based 3D microtumors to generate a clinically-relevant model for ovarian cancer MRD. The microtumors recapitulate the non-genetic heterogeneity of ovarian cancer, capturing the "Oxford Classic" five molecular signatures. Gene expression in the 3D microtumors aligns closely with MRD from ovarian cancer patients and features the upregulation of fatty acid metabolism genes. Finally, the MRD 3D microtumors respond to the approved fatty acid oxidation inhibitor, perhexiline, demonstrating their utility in drug discovery. This system might be used as a drug-testing platform for the discovery of novel MRD-specific therapies in ovarian cancer.

Targeted cancer therapies aim to effectively treat patients with specific biomarker profiles. Nevertheless, these therapies may not always precisely hit their intended targets, leading to uncertainty about the specific subset of patients who will benefit. To address this uncertainty, the identification of sensitive patient subsets in clinical trials becomes crucial. Our proposed phase IIB/III clinical trial design seeks to pinpoint a biomarker signature with precision, ensuring the accurate identification of patients who will respond to a specific treatment. This approach allows for the selective enrollment of sensitive patients to maximize benefits for trial participants. We incorporate Bayesian methodology to facilitate response-adaptive randomization, enhancing the likelihood that each participant receives his/her optimal treatment. Furthermore, our design uses inverse-probability-of-treatment-weighted analysis to avoid selection bias and control for the type I error rate. The evaluation of this trial design is based on four criteria: the statistical power, response rate of all patients participating in the current trial, their individual loss, and probabilities of receiving their optimal treatment for both current trial participants and future patients. Simulations demonstrate the proposed design's potential for maximizing trial participants' benefits with little sacrifice on statistical power. Its key advantages include an improved overall response rate within the trial and a higher percentage of patients receiving the optimal treatment.

Colorectal cancer (CRC) is a leading global cancer with high mortality, especially in metastatic cases, with limited therapeutic options. The tumor microenvironment (TME), a network comprising various immune cells, stromal cells and extracellular (ECM) components plays a crucial role in influencing tumor progression and therapy outcome. The genetic heterogeneity of CRC and the complex TME complicates the development of effective, personalized treatment strategies. The prognosis has slowly improved during the past decades, but metastatic CRC (mCRC) is common among patients and is still associated with low survival. The therapeutic options for CRC differ from those for mCRC and include surgery (mostly for CRC), chemotherapy, growth factor receptor signaling pathway targeting, as well as immunotherapy. Malignant CRC cells are established in the TME, which varies depending on the primary or metastatic site. Herein, we review the role and interactions of several ECM components in 3D models of CRC and mCRC tumor cells, with an emphasis on how the TME affects tumor growth and treatment. This comprehensive summary provides support for the development of 3D models that mimic the interactions within the TME, which will be essential for the development of novel anticancer therapies.

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class imbalance in classification models and can be categorized as data-level, algorithm-level, and hybrid methods. But to the best of our knowledge, an in-depth analysis of the performance of these techniques against the class ratio is not available in the literature. We have addressed these shortcomings in this study and have performed a detailed analysis of the performance of four different techniques to address imbalanced class distribution using machine learning (ML) methods and AutoML tools. To carry out our study, we have selected four such techniques─(a) threshold optimization using (i) GHOST and (ii) the area under the precision-recall curve (AUPR) curve, (b) internal balancing method of AutoML and class-weight of machine learning methods, and (c) data balancing using SMOTETomek─and generated 27 data sets considering nine different class ratios (i.e., the ratio of the positive class and total samples) from three data sets that belong to the drug discovery and development field. We have employed random forest (RF) and support vector machine (SVM) as representatives of ML classifier and AutoGluon-Tabular (version 0.6.1) and H2O AutoML (version 3.40.0.4) as representatives of AutoML tools. The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. In summary, exploration of multiple data balancing techniques is recommended for classifying imbalanced data sets to achieve optimal performance as neither of the external techniques nor the internal techniques outperform others significantly. The results are specific to the ML methods and AutoML libraries used in this study, and for generalization, a study can be carried out considering a sizable number of ML methods and AutoML libraries.

Drug-induced cardiotoxicity (DICT) is a significant challenge in drug development and public health. DICT can arise from various mechanisms; New Approach Methods (NAMs), including quantitative structure-activity relationships (QSARs), have been extensively developed to predict DICT based solely on individual mechanisms (e.g., hERG-related cardiotoxicity) due to the availability of datasets limited to specific mechanisms. While these efforts have significantly contributed to our understanding of cardiotoxicity, DICT assessment remains challenging, suggesting that approaches focusing on isolated mechanisms may not provide a comprehensive evaluation. To address this, we previously developed DICTrank, the largest dataset for assessing overall cardiotoxicity liability in humans based on FDA drug labels. In this study, we evaluated the utility of DICTrank for QSAR modeling using five machine learning methods─Logistic Regression (LR), K-Nearest Neighbors, Support Vector Machines, Random Forest (RF), and extreme gradient boosting (XGBoost)─which vary in algorithmic complexity and explainability. To reflect real-world scenarios, models were trained on drugs approved before and within 2005 to predict the DICT risk of those approved thereafter. While we observed no clear association between prediction performance and model complexity, LR and XGBoost achieved the best results with DICTrank. Additionally, our significant-feature analyses with RF and XGBoost models provided novel insights into DICT mechanisms, revealing that drug properties associated with descriptors such as "structural and topological", "polarizability", and "electronegativity" contributed significantly to DICT. Moreover, we found that model performance varied by therapeutic category, suggesting the need to tailor models accordingly. In conclusion, our study demonstrated the robustness and reliability of DICTrank for cardiotoxicity prediction in humans using machine learning methods.

This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, with a focus on pharmaceuticals. We employed a clustering strategy to provide a fair assessment of the model performances. By applying the generated model to a set of pharmaceutically relevant molecules, we aim to highlight potential PBT chemicals and extract PBT-relevant substructures. These substructures can serve as structural flags, alerting drug designers to potential environmental issues from the earliest stages of the drug discovery process. Incorporating these findings into pharmaceutical development workflows is expected to drive significant advancements in creating more environmentally friendly drug candidates while preserving their therapeutic efficacy.

The occurrence of spirocyclic motifs in clinical candidates and approved drugs is on the rise. This is related to the improvement of drug-like properties that can be achieved by introducing this sp3-rich system into bioactive compounds. Given the increasing number of synthetic methodologies and building blocks available, spirocycles are becoming widely accessible to medicinal chemists. From restricting conformation to induce a better fit with the target, to modulation of physicochemical and pharmacokinetic properties, spirocycles are being used to address several challenges in drug discovery. This review covers general aspects of the chemistry of spirocycles, highlighting some key strategies for their preparation. As reported in publications over the past five years, we demonstrate that, beyond the exploration of structure-activity relationships (SAR) in medicinal chemistry, the use of spirocycles is an attractive approach for enhancing properties such as potency, selectivity, physicochemistry, and pharmacokinetics.

The gold standard for regulatory approval in oncology is overall survival (OS). Because OS data are initially limited, early drug development decisions are often based on early efficacy endpoints, such as objective response rate and progression-free survival. Tumor size (TS)-OS models provide a framework to support decision-making on potential late-stage success based on early readouts, through leveraging TS data with limited follow-up and treatment-agnostic TS-OS link functions, to predict longer-term OS. Conditional simulations (also known as Bayesian forecasting) with TS-OS models can be used to simulate long-term OS outcomes for an ongoing study, conditional on the available TS and OS data at interim data cuts of the same study. This tutorial provides a comprehensive overview of the steps involved in using such conditional simulations to support better informed drug development decisions in oncology. The tutorial covers the selection of the TS-OS framework model; applying the TS-OS model to the interim data; performing conditional simulations; generating relevant output; as well as correct interpretation and communication of the output for decision making.

Cardiovascular disease remains the leading cause of death worldwide, yet despite massive investment in drug discovery, the progress of cardiovascular drugs from lab to clinic remains slow. It is a complex, costly pathway from drug discovery to the clinic and failure becomes more expensive as a drug progresses along this pathway. The focus has begun to shift to optimisation of in vitro culture methodologies, not only because these must be undertaken are earlier on in the drug discovery pathway, but also because the principles of the 3Rs have become embedded in national and international legislation and regulation. Numerous studies have shown myocyte cell behaviour to be much more physiologically relevant in 3D culture compared to 2D culture, highlighting the advantages of using 3D-based models, whether microfluidic or otherwise, for preclinical drug screening. This review aims to provide an overview of the challenges in cardiovascular drug discovery, the limitations of traditional routes, and the successes in the field of preclinical models for cardiovascular drug discovery. It focuses on the particular role biomimicry can play, but also the challenges around implementation within commercial drug discovery.

In this study, a biothiol-sensitive near-infrared (NIR) fluorescent sensor, BDP-CYS, based on a coumarin-hemicyanine skeleton, was designed and developed based on thiol-halogen SNAr nucleophilic substitution. BDP-CYS was successfully implemented to ratiometrically monitor endogenous and exogenous Cys, Hcy, and GSH in living cells as well as to distinguish between normal and cancer cells. Furthermore, the probe was utilized to detect changes of biothiols in drug-induced hepatotoxicity and evaluate the treatment effectiveness of diabetes-associated liver injury in vivo. The advantages of BDP-CYS's Cys, Hcy, and GSH include practical sensitivity, high selectivity, rapidity of reaction, and stability across a range of pH and temperature conditions, thus introducing a new tool to better understand the roles of biothiols in oxidative stress.

The utilization of predictive methodologies for the assessment of toxicological properties represents an alternative approach that facilitates the identification of safe compounds while concurrently reducing the financial costs associated with the process. The objective of the Tox24 Challenge was to assess the progress in computational methods for predicting the activity of chemical binding to transthyretin (TTR). In order to fulfill the requirements of this task, the data set, measured by the Environmental Protection Agency, consisted of 1512 chemical substances of diverse nature. This paper describes the model that won the Tox24 Challenge and the steps taken for its further improvement. The Transformer convolutional neural network (CNN) model achieved the best performance as a standalone solution. Meanwhile, a multitask model built on a graph CNN, trained using 11 additional acute systemic toxicity data sets with increased weighting on the TTR binding activity, showed comparable results on the blind test set. The winning solution was a consensus model consisting of two catBoost models with OEstate and Mold2 descriptor sets, as well as two transformer-based models. The improvement of this solution involved adding a fifth model based on multitask learning using the graph CNN method, which led to a reduction in RMSE on the blind test set to 20.3%. The winning model was developed using the OCHEM web platform and is available online at https://ochem.eu/article/162082.

The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP). However, the structured format of the pharmacogenomics data poses challenge for the utility of LLM in DSP. Therefore, the objective of this study is multi-fold: to adapt prompt engineering for structured pharmacogenomics data toward optimizing LLM's DSP performance, to evaluate LLM's generalization in real-world DSP scenarios, and to compare LLM's DSP performance against that of state-of-the-science baselines.

We systematically investigated the capability of the Generative Pre-trained Transformer (GPT) as a DSP model on four publicly available benchmark pharmacogenomics datasets, which are stratified by five cancer tissue types of cell lines and encompass both oncology and non-oncology drugs. Essentially, the predictive landscape of GPT is assessed for effectiveness on the DSP task via four learning paradigms: zero-shot learning, few-shot learning, fine-tuning and clustering pretrained embeddings. To facilitate GPT in seamlessly processing the structured pharmacogenomics data, domain-specific novel prompt engineering is employed by implementing three prompt templates (i.e., Instruction, Instruction-Prefix, Cloze) and integrating pharmacogenomics-related features into the prompt. We validated GPT's performance in diverse real-world DSP scenarios: cross-tissue generalization, blind tests, and analyses of drug-pathway associations and top sensitive/resistant cell lines. Furthermore, we conducted a comparative evaluation of GPT against multiple Transformer-based pretrained models and existing DSP baselines.

Extensive experiments on the pharmacogenomics datasets across the five tissue cohorts demonstrate that fine-tuning GPT yields the best DSP performance (28% F1 increase, p-value= 0.0003) followed by clustering pretrained GPT embeddings (26% F1 increase, p-value= 0.0005), outperforming GPT in-context learning (i.e., few-shot). However, GPT in the zero-shot setting had a big F1 gap, resulting in the worst performance. Within the scope of prompt engineering, performance enhancement was achieved by directly instructing GPT about the DSP task and resorting to a concise context format (i.e., instruction-prefix), leading to F1 performance gain of 22% (p-value=0.02); while incorporation of drug-cell line prompt context derived from genomics and/or molecular features further boosted F1 score by 2%. Compared to state-of-the-science DSP baselines, GPT significantly asserted superior mean F1 performance (16% gain, p-value<0.05) on the GDSC dataset. In the cross-tissue analysis, GPT showcased comparable generalizability to the within-tissue performances on the GDSC and PRISM datasets, while statistically significant F1 performance improvements on the CCLE (8%, p-value=0.001) and DrugComb (19%, p-value=0.009) datasets. Evaluation on the challenging blind tests suggests GPT's competitiveness on the CCLE and DrugComb datasets compared to random splitting. Furthermore, analyses of the drug-pathway associations and log probabilities provided valuable insights that align with previous DSP findings.

The diverse experiment setups and in-depth analysis underscore the importance of generative LLM, such as GPT, as a viable in silico approach to guide precision oncology.

https://github.com/bioIKEA/SensitiveCancerGPT.

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.

Repurposing the existing drugs for the management of both common and rare diseases is increasingly appealing due to challenges such as high attrition rates, the economy, and the slow pace of discovering new drugs. Drug repurposing involves the utilization of existing medications to treat diseases for which they were not originally intended. Despite encountering scientific and economic challenges, the pharmaceutical industry is intrigued by the potential to uncover new indications for medications. Medication repurposing is applicable across different stages of drug development, with the greatest potential observed when the drug has undergone prior safety testing. In this review, strategies for repurposing drugs for Parkinson's disease (PD) are outlined, a neurodegenerative disorder predominantly impacting dopaminergic neurons in the substantia nigra pars compacta region. PD is a debilitating neurodegenerative condition marked by an amalgam of motor and non-motor symptoms. Despite the availability of certain symptomatic treatments, particularly targeting motor symptoms, there remains a lack of established drugs capable of modifying the clinical course of PD, leading to its unchecked progression. Although standard drug discovery initiatives focusing on treatments that relieve diseases have yielded valuable understanding into the underlying mechanisms of PD, none of the numerous promising candidates identified in preclinical studies have successfully transitioned into clinically effective medications. Due to the substantial expenses associated with drug discovery endeavors, it is understandable that there has been a notable shift towards drug reprofiling strategies. Assessing the efficacy of an existing medication offers the additional advantage of circumventing the requirement for preclinical safety assessments and formulation enhancements, consequently streamlining the process and reducing both the duration of time and financial investments involved in bringing a treatment to clinical fruition. Furthermore, repurposed drugs may benefit from lower rates of failure, presenting an additional potential advantage. Various strategies for repurposing drugs are available to researchers in the field of PD. Some of these strategies have demonstrated effectiveness in identifying appropriate drugs for clinical trials, thereby providing validation for such strategies. This review provides an overview of the diverse strategies employed for drug reprofiling from approaches that place emphasis on single-gene transcriptional investigations to comprehensive epidemiological correlation analysis. Additionally, instances of previous or current research endeavors employing each strategy have been discussed. For the strategies that have not been yet implemented in PD research, their strategic efficacy is demonstrated using examples involving other disorders. In this review, we assess the safety and efficacy potential of prominent candidates repurposed as potential treatments for modifying the course of PD undergoing advanced clinical trials.

The vascular endothelial growth factor (VEGF) and its cell surface receptor, as well as the human VEGFR-2 domain kinase, are some of the signaling pathways that have received the most attention in this field. This study aimed to identify novel molecules as VEGFR-2 inhibitors using 3D-QSAR modeling based on 1,2,3-triazole. Docking studies and dynamic simulations were performed to analyze novel interactions with the inhibitors and validate the molecular docking, dynamic simulations, and ADMET analyses. The optimized CoMSIA/SEH model showed good statistical results, and molecular docking and molecular dynamics simulations demonstrated stability of M3 ligand with the receptor and provided insight into ligand-receptor interactions. The newly developed compounds performed well in ADMET evaluations and showed promising results using Lipinski's rule of five, suggesting that the molecule M3 could be a useful anti-angiogenesis agent. In conclusion, this study provides insights into the structure-activity relationship of VEGFR-2 inhibitors and identifies M3 as a potential new anti-angiogenesis drug. The methodology used in this study can be applied to other similar drug targets to discover new and potent inhibitors.

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.

The objective of this study was to determine the apparent intrinsic clearance (Clint, app) and fraction unbound in human liver microsomes (fu, mic) of 86 marketed central nervous system (CNS) drugs and to predict the in vivo hepatic blood clearance (CLh, b).

Clint, app in human liver microsomes (HLM) was determined by substrate depletion, and fu, mic was determined by equilibrium dialysis. The relationship between lipophilicity (logP) and unbound intrinsic clearance (Clint, u) was explored using the Biopharmaceutical Drug Disposition Classification System (BDDCS) and Extended Clearance Classification System (ECCS). The predicted hepatic blood clearance by direct scaling, conventional method and Poulin method using well-stirred (WS) and parallel-tube (PT) models were compared with observed values.

The Clint, app in HLM ranged from < 5.8 to 477 µl/min/mg. The fu, mic in HLM ranged from 0.02 to 1.0. The scaled Clint values ranged from < 5 to 4496 ml/min/kg. The metabolic rate increased with an increase in logP (logP ≥ 2.5) of the CNS compounds. The direct scaling and Poulin methods showed comparable results based on the percentage of clearance predictions within a two-fold error. The conventional method resulted in under-predictions of Clint, in vivo or CLh, b using the WS or PT models. The Poulin method is favored over the other methods based on the statistical parameters.

Experimental Clint, app and fu, mic for 86 CNS compounds were successfully determined, and the scaled clearance was used to predict the hepatic blood clearance of 34 drugs. The success of prospective clearance predictions using HLM is expected to be high for most of the lipophilic BDDCS class 1 and class 2 and ECCS class 2 CNS compounds. The Poulin method resulted in more accurate predictions falling within a two-fold error of the observed values using the WS or PT models.

Strategies for recognizing predictive cavities and binding site identification are decisive for drug discovery, molecular docking, and tracing protein-ligand interactions. The two major approaches experimental and computational strive for prognosticating and distinguishing protein's binding sites. Profuse diminutive molecules are associated with the binding sites and influence normal biological functioning. The various structure-based strategies such as molecular dynamics, docking simulations, algorithms for pocket identification, and homology modeling are covered under computational techniques, where they propound the exhaustive comprehension of possible binding pockets hinge on the structure of protein and its physiochemical properties. The various sequence-based approaches rely on the homogeneousness of the sequence and machine learning replicas edified on already known protein and ligand composites to anticipate the interactive sites of novel proteins. The high-resolution structural identification and foot printing of protein to map the confirmational changes attributable to ligand and binding sites can be identified through diverse experimental methods such as NMR spectroscopy, mass spectrometry, and x-ray crystallography. These techniques are pivotal for drug discovery and designing, as the efficiency and specificity of ligands can be amplified through virtual screening and structural-based drug designing. Moreover, the ongoing developments in this domain promise to drive the revolution and efficiency in drug discovery and research in molecular biology.

Molecular Modelling in Drug Designing or Computer Aided Drug Designing (CADD) plays a significant role in new drug identification in the current world. However, it has sensitivity challenges and limitation because theoretical models involve assumption and approximations Computational models are not very accurate, some of the major challenges that face these models include the following. These include, for instance, molecular-docking or molecular-dynamics-simulation models which may not represent an accurate biological system and thus the predictions will be wrong. CADD depends on the availability of accurate, high-quality structural information for target proteins and ligand. Unfortunately, there are instances when experimental structures are not available, and homology models are employed, which can be imprecise. The computational cost is another drawback; only high accuracy simulations call for huge amounts of computational power and time well-suited for screening a multitude of agents. Moreover, they have weaknesses in determining pharmacokinetic and toxicity patterns of compounds that influence drug performance and effectiveness. In other words, even though CADD greatly helps drug discovery, it is still constrained by experimental validation to solve its drawbacks and optimize its foretelling.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

The global spread of monkeypox, caused by the double-stranded DNA monkeypox virus (MPXV), has underscored the urgent need for effective antiviral treatments. In this study, we aim to identify a potent inhibitor for MPXV DNA polymerase (DNAP), a critical enzyme in the virus replication process. Using a computational drug repurposing approach, we performed a virtual screening of 1615 FDA-approved drugs based on drug-likeness and molecular docking against DNAP. Among these, 1430 compounds met Lipinski's rule of five for drug-likeness, with Doxycycline emerging as the most promising competitive inhibitor, binding strongly to the DNAP active site with a binding affinity of - 9.3 kcal/mol. This interaction involved significant hydrogen bonds, electrostatic interactions, and hydrophobic contacts, with Doxycycline demonstrating a stronger affinity than established antivirals for smallpox, including Cidofovir, Brincidofovir, and Tecovirimat. Stability and flexibility analyses through a 200 ns molecular dynamics simulation and normal mode analysis confirmed the robustness of Doxycycline binding to DNAP. Overall, our results suggest Doxycycline as a promising candidate for monkeypox treatment, though additional experimental and clinical studies are needed to confirm its therapeutic potential and clinical utility.

The online version contains supplementary material available at 10.1007/s40203-025-00307-7.

Drug discovery is inherently challenged by a multiple criteria decision making problem. The arduous path from hit discovery through lead optimization and preclinical candidate selection necessitates the evolution of a plethora of molecular properties. In this study, we focus on the hit discovery phase while beginning to address multiple criteria critical to the development of novel therapeutics to treat Mycobacterium tuberculosis infection. We develop a hybrid structure- and ligand-based pipeline for nominating diverse inhibitors targeting the β-ketoacyl synthase KasA by employing a Bayesian optimization-guided docking method and an ensemble model for compound nominations based on machine learning models for in vitro antibacterial efficacy, as characterized by minimum inhibitory concentration (MIC), and mouse pharmacokinetic (PK) plasma exposure. The application of our pipeline to the Enamine HTS library of 2.1M molecules resulted in the selection of 93 compounds, the experimental validation of which revealed exceptional PK (41%) and MIC (19%) success rates. Twelve compounds meet hit-like criteria in terms of MIC and PK profile and represent promising seeds for future drug discovery programs.

Fragment-based quantum chemistry methods offer a means to sidestep the steep nonlinear scaling of electronic structure calculations so that large molecular systems can be investigated using high-level methods. Here, we use fragmentation to compute protein-ligand interaction energies in systems with several thousand atoms, using a new software platform for managing fragment-based calculations that implements a screened many-body expansion. Convergence tests using a minimal-basis semiempirical method (HF-3c) indicate that two-body calculations, with single-residue fragments and simple hydrogen caps, are sufficient to reproduce interaction energies obtained using conventional supramolecular electronic structure calculations, to within 1 kcal/mol at about 1% of the computational cost. We also demonstrate that the HF-3c results are illustrative of trends obtained with density functional theory in basis sets up to augmented quadruple-ζ quality. Strategic deployment of fragmentation facilitates the use of converged biomolecular model systems alongside high-quality electronic structure methods and basis sets, bringing ab initio quantum chemistry to systems of hitherto unimaginable size. This will be useful for generation of high-quality training data for machine learning applications.

While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.

Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cause. In our previous study, we reported the upregulation of the CXC motif chemokine receptor 4 (CXCR4), in Alzheimer's disease (ad) and Parkinson's disease (PD). We reached this conclusion by analysing gene expression patterns of ad and PD patients, compared to healthy individuals of similar age. We used RNA sequencing data from Gene Expression Omnibus to carry out this analysis. Herein, we aim to identify natural compounds that have potential inhibitory activity against CXCR4 through cheminformatics-guided machine learning, to aid drug discovery for neurodegenerative disorders, especially ad and PD. Natural compounds are gaining prominence in the treatment of neurodegenerative disorders due to their biocompatibility and potential neuroprotective properties, including their ability to modulate CXCR4 expression. Recent advances in artificial intelligence (AI) and machine learning (ML) algorithms have opened new avenues for drug discovery research across various therapeutic areas, including neurodegenerative disorders. We aim to produce an ML model using cheminformatics-guided machine learning algorithms using data of compounds with known CXCR4 activity, retrieved from the Binding Database, to analyse various physicochemical attributes of natural compounds obtained from the COCONUT Database and predict their inhibitory activity against CXCR4. Insight Box This work extends our previous study published in Integrative Biology (DOI: 10.1093/intbio/zyad012). We aim to demonstrate the effectiveness of AI and ML in identifying potential treatment options for Alzheimer's and Parkinson's diseases. By analysing vast amounts of data and identifying patterns that may not be apparent to human researchers, AI-powered systems can provide valuable insight into potential treatment options that may have been overlooked through traditional research methods. Our study underscores the significance of interdisciplinary collaboration between computational and experimental scientists in drug discovery and in developing a robust pipeline to identify potential leads for drug development.

Mental health represents a major challenge to our societies. One key difficulty associated with neuropsychiatric drug development is the lack of connection between the underlying biology and the disease. Nevertheless, there is growing optimism in this field with recent drug approvals (the first in decades) and renewed interest from pharmaceutical companies and investors. Here we review some of the most promising drug discovery and development endeavors currently deployed by industry. We also present elements illustrating the renewed interest from key stakeholders in neuropsychiatric drug development and provide potential future directions in this field.

Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties. A total of 41 validation datasets were collected from the literature, curated and used for assessing the models' external predictivity, emphasizing the performance of the models inside the applicability domain. Overall, the results confirmed the adequate predictive performance of the majority of the selected tools, with models for PC properties (R2 average = 0.717) generally outperforming those for TK properties (R2 average = 0.639 for regression, average balanced accuracy = 0.780 for classification). Notably, several of the tools evaluated exhibited good predictivity across different properties and were identified as recurring optimal choices. Moreover, a systematic analysis of the chemical space covered by the external validation datasets confirmed the validity of the collected results for relevant chemical categories (e.g., drugs and industrial chemicals), further increasing the confidence in the overall evaluation. The best performing models were ultimately suggested for each investigated property and proposed as robust computational tools for high-throughput assessment of highly relevant chemical properties. SCIENTIFIC CONTRIBUTION: The present manuscript provides an overview of the state-of-the-art available computational tools for predicting the PC and TK properties of chemicals. The results here offer valuable guidance to researchers, regulatory authorities, and the industry in identifying robust computational tools suitable for predicting relevant chemical properties in the context of chemical design, toxicity and environmental fate assessment.

Drug discovery and development is a complex and costly process, with a substantial portion of the expense dedicated to characterizing the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of new drug candidates. While the advent of deep learning and molecular graph neural networks (GNNs) has significantly enhanced in silico ADMET prediction capabilities, reliably quantifying prediction uncertainty remains a critical challenge. The performance of GNNs is influenced by both the volume and the quality of the data. Hence, determining the reliability and extent of a prediction is as crucial as achieving accurate predictions, especially for out-of-domain (OoD) compounds. This paper introduces a novel GNN model called conformalized fusion regression (CFR). CFR combined a GNN model with a joint mean-quantile regression loss and an ensemble-based conformal prediction (CP) method. Through rigorous evaluation across various ADMET tasks, we demonstrate that our framework provides accurate predictions, reliable probability calibration, and high-quality prediction intervals, outperforming existing uncertainty quantification methods.

The blood-brain barrier (BBB) tightly regulates substance transport between the bloodstream and the brain. Models for the study of the physiological processes affecting the BBB, as well as predicting the permeability of therapeutic substances for neurological and neurovascular pathologies, are highly desirable. Existing models, such as Transwell utilizing-models, do not mimic the extracellular environment of the BBB with their stiff, semipermeable, non-biodegradable membranes. To help overcome this, we engineered electrospun membranes from poly L-lactic acid in combination with a nanometric coating of poly(ethyl acrylate) (PEA) that drives fibrillogenesis of fibronectin, facilitating the synergistic presentation of both growth factors and integrin binding sites. Compared to commercial semi-porous membranes, these membranes significantly improve the expression of BBB-related proteins in brain endothelial cells. PEA-coated membranes in combination with different growth factors and extracellular protein coatings reveal nerve growth factor (NGF) and fibroblast growth factor (FGF-2) caused formation of better barriers in vitro. This BBB model offers a robust platform for studying key biochemical factors influencing barrier formation that marries the simplicity of the Transwell model with the highly tunable electrospun PEA-fibronectin membranes. This enables the generation of high-throughput drug permeability models without the need of complicated co-culture conditions.

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field. The deep forest model, with its adaptively determinable number of cascade levels, can perform exceptionally well even on small-scale data. However, the original deep forest suffers unbalanced multi-grained scanning process and premature stopping of cascade forest training. In this paper, we propose a human oral bioavailability predict method based on an improved deep forest, called balanced multi-grained scanning mapping cascade forest (bgmc-forest). Firstly, the mordred descriptor method is selected to feature extraction, then enhanced features are obtained by the improved balanced multi-grained scanning, which solves the problem of missing features at both ends. And finally, the prediction results are obtained by feature mapping cascaded forests, which is based on principal component analysis and cascade forests, ensures the effectiveness of the cascade forest. The superiority of the model constructed in this paper is demonstrated through comparative experiments, while the effectiveness of the improved module is verified through ablation experiments. Finally the decision-making process of the model is explained by the shapley additive explanations interpretation algorithm.

Addressing drug-induced liver injury is crucial in drug development, often causing Phase III trial failures and market withdrawals. Traditional animal models fail to predict human liver toxicity accurately. Virtual twins of human organs present a promising solution. We introduce the Virtual Hepatic Lobule, a foundational element of the Living Liver, a multi-scale liver virtual twin. This model integrates blood flow dynamics and an acetaminophen-induced injury model to predict hepatocyte injury patterns specific to patients. By incorporating metabolic zonation, our predictions align with clinical zonal hepatotoxicity observations. This methodology advances the development of a human liver virtual twin, aiding in the prediction and validation of drug-induced liver injuries.

The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction.

We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance.

The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds.

The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.

This study focuses on the design and synthesis of 3-substituted-2-oxindole derivatives aimed at developing dual-active molecules with anti-cancer and anti-inflammatory properties. The molecules were designed with diverse structural and functional features while adhering to Lipinski, Veber, and Leeson criteria. Physicochemical properties were assessed using SWISSADME to ensure drug-likeness and favourable pharmacokinetics. Multistep synthetic procedures were employed for molecule synthesis. In vitro evaluations confirmed the dual activity of the derivatives, with specific emphasis on the significance of dialkyl aminomethyl substitutions for potency against various cell lines. 4 a exhibited GI50 value 3.00E-05 against MDA-MB-231, 4 b has shown GI50 value 2E-05 against MDA-MB-231, 4 c has shown GI50 value 6E-05 against VERO, 4 d has shown GI50 value 8E-05 each against both the MDA-MB-231 and MCF-7 and 4 e has shown GI50 values 2E-05 and 5E-05 each against both the MCF-7 and VERO. The analysis indicates that compounds 3 c (71.19 %), 3 e (66.84 %), and 3 g (63.04 %) exhibited significant anti-inflammatory activity. Additionally, in silico binding free energy analysis and interaction studies revealed significant correlations between in vitro and computational data, identifying compounds 4 d, 4 e, 3 b, 3 i, and 3 e as promising candidates. Key residues such as Glu917, Cys919, Lys920, Glu850, Lys838, and Asp1046 were found to play critical roles in ligand binding and kinase inhibition, providing valuable insights for designing potent VEGFR2 inhibitors. The Quantum Mechanics-based Independent Gradient Model analysis further highlighted the electronic interaction landscape, showing larger attractive peaks and higher electron density gradients for compounds 4 d and 4 e compared to Sunitinib, suggesting stronger and more diverse attractive forces. These findings support the potential of these compounds for further development and optimization in anticancer drug design.

Centella asiatica Linn Urban (C. asiatica), aka Mandukparni, is one of the flagship herbs used in traditional medicines to effectively manage neurological problems. Although this plant has a wealth of comprehensive preclinical pharmacological profiles, further clinical research and execution of its molecular mode of action are still required. We searched electronic databases (Google Scholar, SciFinder, MEDLINE, Scopus, EMBASE, Science Direct, and PubMed) using relevant key words to retrieve information pertaining to C. asiatica till June 2023 and performed network pharmacology to understand the mechanism related to their neurobiological roles. This study extensively analyses its pharmacological properties, nutritional profile, ethnomedical uses, safety, and mechanistic role in treating neurological and neurodegenerative disorders. Additionally, a network pharmacology study was performed which suggests that its phytomolecules are involved in different neuroactive ligand-receptor pathways, glial cell differentiation, gliogenesis, and astrocyte differentiation. Hopefully, this report will lead to a paradigm shift in medical practice, research, and the creation of phytopharmaceuticals derived from C. asiatica that target the central nervous system.

The proliferation of multidrug-resistant and biofilm-forming pathogenic bacteria poses a serious threat to public health. The limited effectiveness of current antibiotics motivates the search for new antibacterial compounds. In this study, a novel strain, RG-5, was isolated from desert soil. This strain exhibited potent antibacterial and antibiofilm properties against multidrug-resistant and biofilm-forming pathogenic bacteria. Through phenotypical characterizations, 16S rRNA gene sequence and phylogenetic analysis, the strain was identified as Streptomyces pratensis with 99.8% similarity. The active compound, RG5-1, was extracted, purified by reverse phase silica column HPLC, identified by ESI-MS spectrometry, and confirmed by 1H and 13C NMR analysis as 2,5-Piperazinedione, 3,6-bis(2-methylpropyl), belonging to cyclic peptides. This compound showed interesting minimum inhibitory concentrations (MICs) of 04 to 15 µg/mL and minimum biofilm inhibitory concentrations (MBICs 50%) of ½ MIC against the tested bacteria. Its molecular mechanism of action was elucidated through a molecular docking study against five drug-protein targets. The results demonstrated that the compound RG5-1 has a strong affinity and interaction patterns with glucosamine-6-phosphate synthase at - 6.0 kcal/mol compared to reference inhibitor (- 5.4 kcal/mol), medium with penicillin-binding protein 1a (- 6.1 kcal/mol), and LasR regulator protein of quorum sensing (- 5.4 kcal/mol), confirming its antibacterial and antibiofilm activities. The compound exhibited minimal toxicity and favorable physicochemical and pharmacological properties. This is the first report that describes its production from Streptomyces, its activities against biofilm-forming and multidrug-resistant bacteria, and its mechanism of action. These findings indicate that 2,5-piperazinedione, 3,6-bis(2-methylpropyl) has the potential to be a promising lead compound in the treatment of antibiotic-resistant and biofilm-forming pathogens.

Our knowledge of the roles of individual cytochrome P450 (P450) enzymes in drug metabolism has developed considerably in the past 30 years, and this base has been of considerable use in avoiding serious issues with drug interactions and issues due to variations. Some newer approaches are being considered for "phenotyping" metabolism reactions with new drug candidates. Endogenous biomarkers are being used for noninvasive estimation of levels of individual P450 enzymes. There is also the matter of some remaining "orphan" P450s, which have yet to be assigned reactions. Practical problems that continue in drug development include predicting drug-drug interactions, predicting the effects of polymorphic and other P450 variations, and evaluating interspecies differences in drug metabolism, particularly in the context of "metabolism in safety testing" regulatory issues ["disproportionate (human) metabolites"]. SIGNIFICANCE STATEMENT: Cytochrome P450 enzymes are the major catalysts involved in drug metabolism. The characterization of their individual roles has major implications in drug development and clinical practice.

Objective: The development of pharmaceutical agents relies heavily on optimizing their pharmacodynamics, pharmacokinetics, and toxicological properties, collectively known as ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity). Accurate assessment of these properties during the early stages of drug development is challenging due to resource-intensive experimental evaluation and limited comprehensive data availability. To overcome these obstacles, there has been a growing reliance on computational and predictive tools, leveraging recent advancements in machine learning and graph-based methodologies. This study presents an innovative approach that harnesses the power of optimal transport (OT) theory to construct three graph kernels for predicting drug ADMET properties. This approach involves the use of graph matching to create a similarity matrix, which is subsequently integrated into a predictive model. Results: Through extensive evaluations on 19 distinct ADMET datasets, the potential of this methodology becomes evident. The OT-based graph kernels exhibits exceptional performance, outperforming state-of-the-art graph deep learning models in 9 out of 19 datasets, even surpassing the most impactful Graph Neural Network (GNN) that excels in 4 datasets. Furthermore, they are very competitive in 2 additional datasets. Conclusion: Our proposed novel class of OT-based graph kernels not only demonstrates a high degree of effectiveness and competitiveness but also, in contrast to graph neural networks, offers interpretability, adaptability and generalizability across multiple datasets.