WITHDRAWN--a resource for withdrawn and discontinued drugs

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds. In addition, most existing atom-based graph neural network methods focus on attributing model predictions to individual nodes or edges rather than chemically meaningful fragments or substructures. In current studies, we develop a novel fragment-based graph transformer network (FGTN) approach to generate the QSAR model of human reproductive toxicity by considering internal topological structure information of compounds. In the FGTN model, the compound is represented by a graph architecture using fragments to be nodes and bonds linking two fragments to be edges. A super molecule-level node is further proposed to connect all fragment nodes by undirected edges, obtaining global molecular features from fragment embeddings. The FGTN model achieved an accuracy (ACC) of 0.861 and an area under the receiver operating characteristic curve (AUC) value of 0.914 on nonredundant blind tests, outperforming traditional fingerprint-based machine learning models and atom-based GCN model. The FGTN model can attribute toxic predictions to fragments, generating specific structural alerts for the positive compound. Moreover, FGTN may also have the capability to distinguish various chemical isomers. We believe that FGTN can be used as a reliable and effective tool for human reproductive toxicity prediction in contribution to the advancement of chemical safety assessment.

Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: a reproducibility study

OBJECTIVE

Previous studies have revealed a substantial between-centre variability in DCE-MRI biomarkers of hepatocellular function in rats. This study aims to identify the main sources of variability by comparing data measured at different centres and field strengths, at different days in the same subjects, and over the course of several months in the same centre.

MATERIALS AND METHODS

13 substudies were conducted across three facilities on two 4.7 T and two 7 T scanners using a 3D spoiled gradient echo acquisition. All substudies included 3-6 male Wistar-Han rats each, either scanned once with vehicle (n = 76) or twice with either vehicle (n = 19) or 10 mg/kg of rifampicin (n = 13) at follow-up. Absolute values, between-centre reproducibility, within-subject repeatability, detection limits, and effect sizes were derived for hepatocellular uptake rate (Ktrans) and biliary excretion rate (kbh). Sources of variability were identified using analysis of variance and stratification by centre, field strength, and time period.

RESULTS

Data showed significant differences between substudies of 31% for Ktrans (p = 0.013) and 43% for kbh (p < 0.001). Within-subject differences were substantially smaller for kbh (8%) but less so for Ktrans (25%). Rifampicin-induced inhibition was safely above the detection limits, with an effect size of 75 ± 3% in Ktrans and 67 ± 8% in kbh. Most of the variability in individual data was accounted for by between-subject (Ktrans = 23.5%; kbh = 42.5%) and between-centre (Ktrans = 44.9%; kbh = 50.9%) variability, substantially more than the between-day variation (Ktrans = 0.1%; kbh = 5.6%). Significant differences in kbh were found between field strengths at the same centre, between centres at the same field strength, and between repeat experiments over 2 months apart in the same centre.

DISCUSSION

Between-centre bias caused by factors such as hardware differences, subject preparations, and operator dependence is the main source of variability in DCE-MRI of liver function in rats, closely followed by biological between-subject differences. Future method development should focus on reducing these sources of error to minimise the sample sizes needed to detect more subtle levels of inhibition.

ProTox 3.0: a webserver for the prediction of toxicity of chemicals

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

Chemical Derivatization Leads to the Discovery Of Novel Analogs of Azotochelin, a Natural Siderophore, as Promising Anticancer Agents

Siderophores are structurally unique medicinal natural products and exhibit considerable therapeutic potential. Herein, we report the design and synthesis of azotochelin, a natural siderophore, and an extensive library of azotochelin analogs and their anticancer properties. We modified the carboxylic acid and the aromatic ring of azotochelin using various chemical motifs. We evaluated the cytotoxicity of the compounds against six different cancer cell lines (KB-3-1, SNB-19, MCF-7, K-562, SW-620, and NCI-H460) and a non-cancerous cell line (HEK-293). Among the twenty compounds tested, the IC50 values of nine compounds (14, 32, 35-40, and 54) were between 0.7 and 2.0 μM against a lung cancer cell line (NCI-H460). Moreover, several compounds showed good cytotoxicity profile (IC50 <10 μM) against the tested cancer cell lines. The flow cytometry analysis showed that compounds 36 and 38 induced apoptosis in NCI-H460 in a dose-dependent manner. The cell cycle analysis indicated that compounds 36 and 38 significantly arrested the cell cycle at the S phase to block cancer cell proliferation in the NCI-H460 cell line. The study has produced various novel azotochelin analogs that are potentially effective anticancer agents and lead compounds for further synthetic and medicinal chemistry exploration.

In silico off-target profiling for enhanced drug safety assessment

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

In-depth mechanistic analysis including high-throughput RNA sequencing in the prediction of functional and structural cardiotoxicants using hiPSC cardiomyocytes

BACKGROUND

Cardiotoxicity remains one of the most reported adverse drug reactions that lead to drug attrition during pre-clinical and clinical drug development. Drug-induced cardiotoxicity may develop as a functional change in cardiac electrophysiology (acute alteration of the mechanical function of the myocardium) and/or as a structural change, resulting in loss of viability and morphological damage to cardiac tissue.

RESEARCH DESIGN AND METHODS

Non-clinical models with better predictive value need to be established to improve cardiac safety pharmacology. To this end, high-throughput RNA sequencing (ScreenSeq) was combined with high-content imaging (HCI) and Ca2+ transience (CaT) to analyze compound-treated human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).

RESULTS

Analysis of hiPSC-CMs treated with 33 cardiotoxicants and 9 non-cardiotoxicants of mixed therapeutic indications facilitated compound clustering by mechanism of action, scoring of pathway activities related to cardiomyocyte contractility, mitochondrial integrity, metabolic state, diverse stress responses and the prediction of cardiotoxicity risk. The combination of ScreenSeq, HCI and CaT provided a high cardiotoxicity prediction performance with 89% specificity, 91% sensitivity and 90% accuracy.

CONCLUSIONS

Overall, this study introduces mechanism-driven risk assessment approach combining structural, functional and molecular high-throughput methods for pre-clinical risk assessment of novel compounds.

Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients

MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.

The reprotoxic adverse side effects of neurogenic and neuroprotective drugs: current use of human organoid modeling as a potential alternative to preclinical models

The management of neurological disorders heavily relies on neurotherapeutic drugs, but notable concerns exist regarding their possible negative effects on reproductive health. Traditional preclinical models often fail to accurately predict reprotoxicity, highlighting the need for more physiologically relevant systems. Organoid models represent a promising approach for concurrently studying neurotoxicity and reprotoxicity, providing insights into the complex interplay between neurotherapeutic drugs and reproductive systems. Herein, we have examined the molecular mechanisms underlying neurotherapeutic drug-induced reprotoxicity and discussed experimental findings from case studies. Additionally, we explore the utility of organoid models in elucidating the reproductive complications of neurodrug exposure. Have discussed the principles of organoid models, highlighting their ability to recapitulate neurodevelopmental processes and simulate drug-induced toxicity in a controlled environment. Challenges and future perspectives in the field have been addressed with a focus on advancing organoid technologies to improve reprotoxicity assessment and enhance drug safety screening. This review underscores the importance of organoid models in unraveling the complex relationship between neurotherapeutic drugs and reproductive health.

Reproductive chemical database: a curated database of chemicals that modulate protein targets regulating important reproductive biological processes

Recent studies have shifted the spotlight from adult disease to gametogenesis and embryo developmental events, and these are greatly affected by various environmental chemicals, such as drugs, metabolites, pollutants, and others. Growing research has highlighted the critical importance of identifying and understanding the roles of chemicals in reproductive biology. However, the functions and mechanisms of chemicals in reproductive processes remain incomplete. We developed a comprehensive database called the Reproductive Chemical Database (RCDB) ( https://yu.life.sjtu.edu.cn/ChenLab/RCDB ) to facilitate research on chemicals in reproductive biology. This resource is founded on rigorous manual literature extraction and precise protein target prediction methodologies. This database focuses on the delineation of chemicals associated with phenotypes, diseases, or endpoints intricately associated with four important reproductive processes: female and male gamete generation, fertilization, and embryo development in human and mouse. The RCDB encompasses 93 sub-GO processes, and it revealed 1447 intricate chemical-biological process interactions. To date, the RCDB has meticulously cataloged and annotated 830 distinct chemicals, while also predicting 614 target proteins from a selection of 3800 potential candidates. Additionally, the RCDB offers an online predictive tool that empowers researchers to ascertain whether specific chemicals play discernible functional roles in these reproductive processes. The RCDB is an exhaustive, cross-platform, manually curated database, which provides a user-friendly interface to search, browse, and use reproductive processes modulators and their comprehensive related information. The RCDB will help researchers to understand the whole reproductive process and related diseases and it has the potential to promote reproduction research in the pharmacological and pathophysiological areas.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Targeting bladder cancer with Trigonella foenum-graecum: a computational study using network pharmacology and molecular docking

Trigonella foenum-graecum (TF-graecum), known as Hulba or Fenugreek, is one of the oldest known medicinal plants. It has been found to have antimicrobial, antifungal, antioxidant, wound-healing, anti-diarrheal, hypoglycemic, anti-diabetic, and anti-inflammatory activities. In our current report, we have collected and screened the active compounds of TF-graecum and their potential targets via different pharmacology platforms. Network construction shows that eight active compounds may act on 223 potential bladder cancer targets. The pathway enrichment analysis for the seven potential targets of the eight compounds selected, based on KEGG pathway analysis, was conducted to clarify the potential pharmacological effects. Finally, molecular docking and molecular dynamics simulation showed the stability of protein-ligand interactions. This study highlights the need for increased research into the potential medical benefits of this plant.Communicated by Ramaswamy H. Sarma.

3D-QSAR, molecular docking, simulation dynamic and ADMET studies on new quinolines derivatives against colorectal carcinoma activity

Cancer is the uncontrolled spread of abnormal cells that results in abnormal tissue growth in the affected organ. One of the most important organs is exposed to the growth of colon cancer cells, which start in the large intestine (colon) or the rectum. Several therapeutic protocols were used to treat different kinds of cancer. Recently, several studies have targeted tubulin and microtubules due to their remarkable prefoliation. Also, recent research shows that quinoline compounds have significant efficacy against human colorectal cancer. So, the present work investigated the potential of thirty quinoline compounds as tubulin inhibitors using computational methods. A 3D-QSAR approach using two contours (CoMFA and CoMSIA), molecular docking simulation to determine the binding type of the complexes (ligand-receptor), molecular dynamics simulation and identifying pharmacokinetic characteristics were used to design molecules. For all compounds designed (T1-5), molecular docking was used to compare the stability by type of binding. The ADMET has been utilized for molecules with good stability in molecular docking (T1-3); these compounds have good medicinal characteristics. Furthermore, a molecular dynamics simulation (MD) at 100 ns was performed to confirm the stability of the T1-3 compounds; the molecules (T1-3) remained the most stable throughout the simulation. The compounds T1, T2 and T3 are the best-designed drugs for colorectal carcinoma treatments.Communicated by Ramaswamy H. Sarma.

Vascular organs-on-chip made with patient-derived endothelial cells: technologies to transform drug discovery and disease modeling

INTRODUCTION

Vascular diseases impart a tremendous burden on healthcare systems in the United States and across the world. Efforts to improve therapeutic interventions are hindered by limitations of current experimental models. The integration of patient-derived cells with organ-on-chip (OoC) technology is a promising avenue for preclinical drug screening that improves upon traditional cell culture and animal models.

AREAS COVERED

The authors review induced pluripotent stem cells (iPSC) and blood outgrowth endothelial cells (BOEC) as two sources for patient-derived endothelial cells (EC). They summarize several studies that leverage patient-derived EC and OoC for precision disease modeling of the vasculature, with a focus on applications for drug discovery. They also highlight the utility of patient-derived EC in other translational endeavors, including ex vivo organogenesis and multi-organ-chip integration.

EXPERT OPINION

Precision disease modeling continues to mature in the academic space, but end-use by pharmaceutical companies is currently limited. To fully realize their transformative potential, OoC systems must balance their complexity with their ability to integrate with the highly standardized and high-throughput experimentation required for drug discovery and development.

Heart-on-a-chip systems: disease modeling and drug screening applications

Cardiovascular disease (CVD) is the leading cause of death worldwide, casting a substantial economic footprint and burdening the global healthcare system. Historically, pre-clinical CVD modeling and therapeutic screening have been performed using animal models. Unfortunately, animal models oftentimes fail to adequately mimic human physiology, leading to a poor translation of therapeutics from pre-clinical trials to consumers. Even those that make it to market can be removed due to unforeseen side effects. As such, there exists a clinical, technological, and economical need for systems that faithfully capture human (patho)physiology for modeling CVD, assessing cardiotoxicity, and evaluating drug efficacy. Heart-on-a-chip (HoC) systems are a part of the broader organ-on-a-chip paradigm that leverages microfluidics, tissue engineering, microfabrication, electronics, and gene editing to create human-relevant models for studying disease, drug-induced side effects, and therapeutic efficacy. These compact systems can be capable of real-time measurements and on-demand characterization of tissue behavior and could revolutionize the drug development process. In this review, we highlight the key components that comprise a HoC system followed by a review of contemporary reports of their use in disease modeling, drug toxicity and efficacy assessment, and as part of multi-organ-on-a-chip platforms. We also discuss future perspectives and challenges facing the field, including a discussion on the role that standardization is expected to play in accelerating the widespread adoption of these platforms.

Investigating the association between CYP2J2 inhibitors and QT prolongation: a literature review

Drug withdrawal post-marketing due to cardiotoxicity is a major concern for drug developers, regulatory agencies, and patients. One common mechanism of cardiotoxicity is through inhibition of cardiac ion channels, leading to prolongation of the QT interval and sometimes fatal arrythmias. Recently, oxylipin signaling compounds have been shown to bind to and alter ion channel function, and disruption in their cardiac levels may contribute to QT prolongation. Cytochrome P450 2J2 (CYP2J2) is the predominant CYP isoform expressed in cardiomyocytes, where it oxidizes arachidonic acid to cardioprotective epoxyeicosatrienoic acids (EETs). In addition to roles in vasodilation and angiogenesis, EETs bind to and activate various ion channels. CYP2J2 inhibition can lower EET levels and decrease their ability to preserve cardiac rhythm. In this review, we investigated the ability of known CYP inhibitors to cause QT prolongation using Certara's Drug Interaction Database. We discovered that among the multiple CYP isozymes, CYP2J2 inhibitors were more likely to also be QT-prolonging drugs (by approximately 2-fold). We explored potential binding interactions between these inhibitors and CYP2J2 using molecular docking and identified four amino acid residues (Phe61, Ala223, Asn231, and Leu402) predicted to interact with QT-prolonging drugs. The four residues are located near the opening of egress channel 2, highlighting the potential importance of this channel in CYP2J2 binding and inhibition. These findings suggest that if a drug inhibits CYP2J2 and interacts with one of these four residues, then it may have a higher risk of QT prolongation and more preclinical studies are warranted to assess cardiovascular safety.

Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data

One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.

DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles

Evaluation of chemical drug-likeness is essential for the discovery of high-quality drug candidates while avoiding unwarranted biological and clinical trial costs. A high-quality drug candidate should have promising drug-like properties, including pharmacological activity, suitable physicochemical and ADMET properties. Hence, in silico prediction of chemical drug-likeness has been proposed while being a challenging task. Although several prediction models have been developed to assess chemical drug-likeness, they have such drawbacks as sample dependence and poor interpretability. In this study, we developed a novel strategy, named DBPP-Predictor, to predict chemical drug-likeness based on property profile representation by integrating physicochemical and ADMET properties. The results demonstrated that DBPP-Predictor exhibited considerable generalization capability with AUC (area under the curve) values from 0.817 to 0.913 on external validation sets. In terms of application feasibility analysis, the results indicated that DBPP-Predictor not only demonstrated consistent and reasonable scoring performance on different data sets, but also was able to guide structural optimization. Moreover, it offered a new drug-likeness assessment perspective, without significant linear correlation with existing methods. We also developed a free standalone software for users to make drug-likeness prediction and property profile visualization for their compounds of interest. In summary, our DBPP-Predictor provided a valuable tool for the prediction of chemical drug-likeness, helping to identify appropriate drug candidates for further development.

InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism

Safety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88-0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81-0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

How to fix a broken heart-designing biofunctional cues for effective, environmentally-friendly cardiac tissue engineering

Cardiovascular diseases bear strong socioeconomic and ecological impact on the worldwide healthcare system. A large consumption of goods, use of polymer-based cardiovascular biomaterials, and long hospitalization times add up to an extensive carbon footprint on the environment often turning out to be ineffective at healing such cardiovascular diseases. On the other hand, cardiac cell toxicity is among the most severe but common side effect of drugs used to treat numerous diseases from COVID-19 to diabetes, often resulting in the withdrawal of such pharmaceuticals from the market. Currently, most patients that have suffered from cardiovascular disease will never fully recover. All of these factors further contribute to the extensive negative toll pharmaceutical, biotechnological, and biomedical companies have on the environment. Hence, there is a dire need to develop new environmentally-friendly strategies that on the one hand would promise cardiac tissue regeneration after damage and on the other hand would offer solutions for the fast screening of drugs to ensure that they do not cause cardiovascular toxicity. Importantly, both require one thing-a mature, functioning cardiac tissue that can be fabricated in a fast, reliable, and repeatable manner from environmentally friendly biomaterials in the lab. This is not an easy task to complete as numerous approaches have been undertaken, separately and combined, to achieve it. This review gathers such strategies and provides insights into which succeed or fail and what is needed for the field of environmentally-friendly cardiac tissue engineering to prosper.

In silico identification of potential inhibitors of vital monkeypox virus proteins from FDA approved drugs

The World Health Organization (WHO) recently declared the monkeypox outbreak 'A public health emergency of international concern'. The monkeypox virus belongs to the same Orthopoxvirus genus as smallpox. Although smallpox drugs are recommended for use against monkeypox, monkeypox-specific drugs are not yet available. Drug repurposing is a viable and efficient approach in the face of such an outbreak. Therefore, we present a computational drug repurposing study to identify the existing approved drugs which can be potential inhibitors of vital monkeypox virus proteins, thymidylate kinase and D9 decapping enzyme. The target protein structures of the monkeypox virus were modelled using the corresponding protein structures in the vaccinia virus. We identified four potential inhibitors namely, Tipranavir, Cefiderocol, Doxorubicin, and Dolutegravir as candidates for repurposing against monkeypox virus from a library of US FDA approved antiviral and antibiotic drugs using molecular docking and molecular dynamics simulations. The main goal of this in silico study is to identify potential inhibitors against monkeypox virus proteins that can be further experimentally validated for the discovery of novel therapeutic agents against monkeypox disease.

Production of Highly Uniform Midbrain Organoids from Human Pluripotent Stem Cells

Brain organoids have been considered as an advanced platform for in vitro disease modeling and drug screening, but numerous roadblocks exist, such as lack of large-scale production technology and lengthy protocols with multiple manipulation steps, impeding the industrial translation of brain organoid technology. Here, we describe the high-speed and large-scale production of midbrain organoids using a high-throughput screening-compatible platform within 30 days. Micro midbrain organoids (µMOs) exhibit a highly uniform morphology and gene expression pattern with minimal variability. Notably, µMOs show dramatically accelerated maturation, resulting in the generation of functional µMOs within only 30 days of differentiation. Furthermore, individual µMOs display highly consistent responsiveness to neurotoxin, suggesting their usefulness as an in vitro high-throughput drug toxicity screening platform. Collectively, our data indicate that µMO technology could represent an advanced and robust platform for in vitro disease modeling and drug screening for human neuronal diseases.

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

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

CSL-Tox: an open-source analytical framework for the comparison of short-term and long-term toxicity end points and assessing the need of chronic studies in drug development

In-vivo toxicity assessment is an important step prior to clinical development and is still the main source of data for overall risk assessment of a new molecular entity (NCE). All in-vivo studies are performed according to regulatory requirements and many efforts have been exerted to minimize these studies in accordance with the (Replacement, Reduction and Refinement) 3Rs principle. Many aspects of in-vivo toxicology packages can be optimized to reduce animal use, including the number of studies performed as well as study durations, which is the main focus of this analysis. We performed a statistical comparison of adverse findings observed in 116 short-term versus 78 long-term in-house or in-house sponsored Contract Research Organizations (CRO) studies, in order to explore the possibility of using only short-term studies as a prediction tool for the longer-term effects. All the data analyzed in this study was manually extracted from the toxicology reports (in PDF formats) to construct the dataset. Annotation of treatment related findings was one of the challenges faced during this work. A specific focus was therefore put on the summary and conclusion sections of the reports since they contain expert assessments on whether the findings were considered adverse or were attributed to other reasons. Our analysis showed a general good concordance between short-term and long-term toxicity findings for large molecules and the majority of small molecules. Less concordance was seen for certain body organs, which can be named as "target organ systems' findings". While this work supports the minimization of long-term studies, a larger-scale effort would be needed to provide more evidence. We therefore present the steps performed in this study as an open-source R workflow for the Comparison of Short-term and Long-term Toxicity studies (CSL-Tox). The dataset used in the work is provided to allow researchers to reproduce such analysis, re-evaluate the statistical tools used and promote large-scale application of this study. Important aspects of animal research reproducibility are highlighted in this work, specifically, the necessity of a reproducible adverse effects reporting system and utilization of the controlled terminologies in-vivo toxicology reports and finally the importance of open-source analytical workflows that can be assessed by other scientists in the field of preclinical toxicology.

Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data

Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.

Pretrained transformer models for predicting the withdrawal of drugs from the market

MOTIVATION

The process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse drug reactions (ADRs) frequently occur, some of which may even result in death. Early identification of potential ADRs is critical to improve the efficiency and safety of the drug development process.

RESULTS

In this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns. Our method achieved an area under the curve (AUC) of over 0.75 through cross-database validation, outperforming classical machine learning models and graph-based models. Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets.

AVAILABILITY AND IMPLEMENTATION

The code and datasets are available at https://github.com/eyalmazuz/DrugWithdrawn.

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.

Integrating Liver-Chip data into pharmaceutical decision-making processes

INTRODUCTION

Drug-induced liver injury (DILI) is a potentially lethal condition that heavily impacts the pharmaceutical industry, causing approximately 21% of drug withdrawals and 13% of clinical trial failures. Recent evidence suggests that the use of Liver-Chip technology in preclinical safety testing may significantly reduce DILI-related clinical trial failures and withdrawals. However, drug developers and regulators would benefit from guidance on the integration of Liver-Chip data into decision-making processes to facilitate the technology's adoption.

AREAS COVERED

This perspective builds on the findings of the performance assessment of the Emulate Liver-Chip in the context of DILI prediction and introduces two new decision-support frameworks: the first uses the Liver-Chip's quantitative output to elucidate DILI severity and enable more nuanced risk analysis; the second integrates Liver-Chip data with standard animal testing results to help assess whether to progress a candidate drug into clinical trials.

EXPERT OPINION

There is now strong evidence that Liver-Chip technology could significantly reduce the incidence of DILI in drug development. As this is a patient safety issue, it is imperative that developers and regulators explore the incorporation of the technology. The frameworks presented enable the integration of the Liver-Chip into various stages of preclinical development in support of safety assessment.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

Essential Oil Derived from Underutilized Plants Cymbopogon khasianus Poses Diverse Biological Activities against "Aspergillosis" and "Mucormycosis"

Palmrosa essential oil (PEO) from Cymbopogon khasianus, is used as complementary and traditional medicine worldwide. The present study aimed at compositional profiling of PEO and molecular docking of PEO bioactive compound geraniol against fungal enzymes chitin synthase (CS), UDP-glycosyltransferase (UDPG) and glucosamine-6-phosphate synthase (GPS), as apposite sites for drug designing against "Aspergillosis" and "Mucormycosis" and in vitro confirmation. Compositional profile of PEO was completed by GC-FID analysis. For molecular docking, Patch-dock tool was conducted. Ligand-enzyme 3D interactions were also calculated. ADMET properties (absorption, distribution, metabolism, excretion and toxicity) were also calculated. GC-FID discovered the occurrence of geraniol as a major component in PEO, thus nominated for docking analysis. Docking analysis specified active binding of geraniol to GPS, CS and UDPG fungal enzymes. Wet-lab authentication was achieved by three fungal strains Aspergillus niger, A. oryzae and Mucor sp. Docking studies revealed that ligand geraniol exhibited intercations with GPS, CS and UDPG fungal enzymes by H-bond and hydrophobic interactions. Geraniol obeyed LIPINSKY rule, and exhibited adequate bioactivity. Wet lab results indicated that PEO was able to inhibit fungal growth against "Aspergillosis" and "Mucormycosis".

Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT

Currently, numerous potent chemotherapeutic agents are available in the market but most of them show poor pharmacokinetics, lethal effects and drug resistance during their enduring use. The increased cancer cases, deaths and need of better treatment stimulates us to give newer lifesaving anticancer drugs. The phthalimide derivatives are structurally diverse and exert potential anticancer activity. In this regard, the 3D QSAR Pharmacophore model was developed and validated using fifty-eight phthalimide derivatives. The validation parameters corroborated the reliability and statistical robustness of CEASER Hypo 1. Three databases-NCI Open, Drug Bank, and Asinex were submitted to ADMET and drug-like filtering; 117893 drug-like compounds were mapped on CEASER Hypo 1; and 362 hits with IC50 <1 µM were discovered. These hits were docked on VEGFR2-TK, and in the form of results fifteen hits exhibited greater affinity than sorafenib. The top lead ASN 03206926 was subjected for MD simulation (100 ns) and RMSD, Rg, RMSF, number of hydrogen bonds, and SASA verified that the complex was stable, rigid and highly compact. Results demonstrated GLU885, PHE918, CYS919, LYS920, HIS1026, CYS1045, ASP1046 are the essential residues for favourable interactions. The binding free energy calculations support the affinity and stability revealed by docking and MD simulation. The DFT calculations, negative binding energy and lower HOMO-LUMO band gap revealed that the process is spontaneous and ASN 03206926 is very reactive. Following extensive analysis we suggest that the ASN 03206926 might be employed as a new VEGFR2-TK inhibitor for the treatment of breast and VEGFR2-TK associated cancers.Communicated by Ramaswamy H. Sarma.

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features

BACKGROUND

Drug development productivity has been declining lately due to elevated costs and reduced discovery rates. Therefore, pharmaceutical companies have been seeking alternative ways to determine and evaluate drug candidates.

RESEARCH DESIGN AND METHODS

In this work, we proposed a new computational approach to directly predict the regulatory approval of drug candidates, and implemented it as a method called 'DrugApp.' To accomplish this task, we employed multiple types of features including molecular and physicochemical properties of drug candidates, together with clinical trial and patent-related features, which are then processed by random forest classifiers to train our disease group-specific approval prediction models.

RESULTS

Our evaluations indicated DrugApp has a high and robust prediction performance. Within a use-case study, we showed our method can predict phase IV trial drugs that are later withdrawn from the market due to severe side effects. Finally, we used DrugApp models to forecast the approval of drug candidates that are currently in phases I/II/III of clinical trials.

CONCLUSIONS

We hope that our study will aid the research community in terms of evaluating and improving the process of drug development. The datasets, source code, results, and pre-trained models of DrugApp are freely available at https://github.com/HUBioDataLab/DrugApp.

Assessing the transferability and reproducibility of 3D in vitro liver models from primary human multi-cellular microtissues to cell-line based HepG2 spheroids

To reduce, replace, and refine in vivo testing, there is increasing emphasis on the development of more physiologically relevant in vitro test systems to improve the reliability of non-animal-based methods for hazard assessment. When developing new approach methodologies, it is important to standardize the protocols and demonstrate the methods can be reproduced by multiple laboratories. The aim of this study was to assess the transferability and reproducibility of two advanced in vitro liver models, the Primary Human multicellular microtissue liver model (PHH) and the 3D HepG2 Spheroid Model, for nanomaterial (NM) and chemical hazard assessment purposes. The PHH model inter-laboratory trial showed strong consistency across the testing sites. All laboratories evaluated cytokine release and cytotoxicity following exposure to titanium dioxide (TiO₂) and zinc oxide (ZnO) nanoparticles. No significant difference was observed in cytotoxicity or IL-8 release for the test materials. The data were reproducible with all three laboratories with control readouts within a similar range. The PHH model ZnO induced the greatest cytotoxicity response at 50.0 μg/mL and a dose-dependent increase in IL-8 release. For the 3D HepG2 spheroid model, all test sites were able to construct the model and demonstrated good concordance in IL-8 cytokine release and genotoxicity data. This trial demonstrates the successful transfer of new approach methodologies across multiple laboratories, with good reproducibility for several hazard endpoints.

Nonclinical Safety Signals in PharmaPendium Improve the Predictability of Human Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a leading cause of candidate attrition during drug development in the pharmaceutical industry. This study evaluated liver toxicity signals for 249 approved drugs (114 of "most-DILI concern" and 135 of "no-DILI concern") using PharmaPendium and assessed the association between nonclinical and clinical injuries using contingency table analysis. All animal liver findings were combined into eight toxicity categories based on nature and severity. Together, these analyses revealed that cholestasis [odds ratio (OR): 5.02; 95% confidence interval (CI) 1.04-24.03] or liver aminotransferase increases (OR: 1.86; 95% CI 1.09-3.09) in rats and steatosis (OR-1.9; 95% CI 1.03-3.49) or liver aminotransferase increases (OR-2.57; 95% CI 1.4-4.7) in dogs were significant predictors of human liver injury. The predictive value further improved when the liver injury categories were combined into less severe (steatosis, cholestasis, liver aminotransferase increase, hyperbilirubinemia, or jaundice) and more-severe (liver necrosis, acute liver failure, or hepatotoxicity) injuries. In particular, less-severe liver injuries in the following pairs of species predicted human hepatotoxicity {[dog and mouse] (OR: 2.70; 95% CI 1.25-5.84), [dog and rat] (OR-2.61; 95% CI 1.48-4.59), [monkey and mouse] (OR-4.22; 95% CI 1.33-13.32), and [monkey and rat] (OR-2.45; 95% CI 1.15-5.21)} were predictive of human hepatotoxicity. Meanwhile, severe liver injuries in both [dog and rat] (OR-1.9; 95% CI 1.04-3.49) were significant predictors of human liver toxicity. Therefore, we concluded that the occurrence of DILI in humans is highly likely if liver injuries are observed in one rodent and one nonrodent species and that liver aminotransferase increases in dogs and rats can predict DILI in humans. Together, these findings indicate that the liver safety signals observed in animal toxicity studies indicate potential DILI risk in humans and could therefore be used to prioritize small molecules with less potential to cause DILI in humans.

Contractility assessment of human iPSC-derived cardiomyocytes by using a motion vector system and measuring cell impedance

Predicting drug-induced cardiotoxicity during the non-clinical stage is important to avoid severe consequences in the clinical trials of new drugs. Human iPSC-derived cardiomyocytes (hiPSC-CMs) hold great promise for cardiac safety assessments in drug development. To date, multi-electrode array system (MEA) has been a widely used as a tool for the assessment of proarrhythmic risk with hiPSC-CMs. Recently, new methodologies have been proposed to assess in vitro contractility, such as the force and velocity of cell contraction, using hiPSC-CMs. Herein, we focused on an imaging-based motion vector system (MV) and an electric cell-substrate impedance sensing system (IMP). We compared the output signals of hiPSC-CMs from MV and IMP in detail and observed a clear correlation between the parameters. In addition, we assessed the effects of isoproterenol and verapamil on hiPSC-CM contraction and identified a correlation in the contractile change of parameters obtained with MV and IMP. These results suggest that both assay systems could be used to monitor hiPSC-CM contraction dynamics.

Trends in small molecule drug properties: A developability molecule assessment perspective

Developability molecule assessment is a key interfacial capability across the biopharmaceutical industry, screening and staging molecules discovered by medicinal chemists for successful chemistry manufacturing controls (CMC) development and launch. The breadth of responsibility and expertise such teams possess puts them in a unique position to understand the impact of the physicochemical properties of a drug during its initial discovery and subsequent development. However, most of the publications describing trends in physicochemical properties are written from a medicinal chemistry perspective with the aim to identify molecules with better ADMET profiles that are either lead-like or drug-like, failing to describe the impact these properties have on CMC development. To systematically uncover knowledge obtained from recent trends in physicochemical properties and the corresponding impact on CMC development, a comprehensive analysis was conducted on molecules in the drug repurposing hub dataset. The only physicochemical property that seems to have been preserved in FDA-approved oral molecules over the decades (1900-2020) is a constant H-bond donor count, highlighting the importance this property has on cell permeability and lattice energy. Pharmaceutical attrition analysis suggests that partition-distribution coefficient, H-bond acceptors, polar surface area and the fraction of sp³ carbons are properties that are associated with compound attrition. Looking at pharmaceutical attrition asynchronously with the temporal analysis of FDA-approved oral molecules highlights the opposing trends, risks and diminishing effects some of these physiochemical properties (cLogP, cLogD and Fsp3) have on describing compound attrition during the past decade. Trellising the dataset by target class suggests that certain formulation and drug delivery strategies can be anticipated or put into place based on target class of a molecule. For example, molecules binding to nuclear hormone receptors are amenable to lipid-based drug delivery systems with proven commercial success. Although the poor solubility of kinase inhibitors is a combination of hydrophobicity (due to aromaticity) required to bind to its target and high lattice energy (melting point), they are a challenging target class to formulate. The influence of drug targets on physicochemical properties and the temporal nature of these properties is highlighted when comparing molecules in the drug repurposing dataset to those developed at Amgen. An improved understanding of the impact of molecular properties on performance attributes can accelerate decisions and facilitate risk assessments during candidate selection and development.

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.

Artificial intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation

Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative model by combining a semisupervised variational autoencoder (SSVAE) with an MGA toxicity predictor. Our aim is to generate molecules with low toxicity, good drug-like properties, and structural diversity. For multiobjective optimization, we have developed a method with hierarchical constraints on the toxicity space of small molecules to generate drug-like small molecules, which can also minimize the effect on the diversity of generated results. The evaluation results of the metrics indicate that the developed model has good effectiveness, novelty, and diversity. The generated molecules by this model are mainly distributed in low-toxicity regions, which suggests that our model can efficiently constrain the generation of toxic structures. In contrast to simply filtering toxic ones after generation, the low-toxicity molecular generative model can generate molecules with structural diversity. Our strategy can be used in target-based drug discovery to improve the quality of generated molecules with low-toxicity, drug-like, and highly active properties.

Development of binary classification models for assessment of drug-induced liver injury in humans using a large set of FDA-approved drugs

Drug-induced liver injury (DILI) has been identified as one of the major causes for drugs withdrawn from the market, and even termination during the late stages of development. Therefore, it is imperative to evaluate the DILI potential of lead compounds during the research and development process. Although various computational models have been developed to predict DILI, most of which applied the DILI data were extracted from preclinical sources. In this investigation, the in silico prediction models for DILI were constructed based on 1140 FDA-approved drugs by using naïve Bayes classifier approach. The genetic algorithm method was applied for the molecular descriptors selection. Among these established prediction models, the NB-11 model based on eight molecular descriptors combined with ECFP_18 showed the best prediction performance for DILI, which gave 91.7% overall prediction accuracy for the training set, and 68.9% concordance for the external test set. Therefore, the established NB-11 prediction model can be used as a reliable virtual screening tool to predict DILI adverse effect in the early stages of drug design. In addition, some new structural alters for DILI were identified, which could be used for structural optimization in the future drug design by medicinal chemists.

An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors

Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs or chemicals, so the pharmaceutical and chemical industries demand reliable and precise computational tools to assess the respiratory toxicity of compounds. The purpose of this study is to develop quantitative structure-activity relationship models for a large dataset of chemical compounds associated with respiratory system toxicity. First, several feature selection techniques are explored to find the optimal subset of molecular descriptors for efficient modeling. Then, eight different machine learning algorithms are utilized to construct respiratory toxicity prediction models. The support vector machine classifier outperforms all other optimized models in 10-fold cross-validation. Additionally, it outperforms the prior study by 2% in prediction accuracy and 4% in MCC. The best SVM model achieves a prediction accuracy of 86.2% and a MCC of 0.722 on the test set. The proposed SVM model predictions are explained using the SHapley Additive exPlanations approach, which prioritizes the relevance of key modeling descriptors influencing the prediction of respiratory toxicity. Thus, our proposed model would be incredibly beneficial in the early stages of drug development for predicting and understanding potential respiratory toxic compounds.

Simultaneous triple-parametric optical mapping of transmembrane potential, intracellular calcium and NADH for cardiac physiology assessment

Investigation of the complex relationships and dependencies of multiple cellular processes that govern cardiac physiology and pathophysiology requires simultaneous dynamic assessment of multiple parameters. In this study, we introduce triple-parametric optical mapping to simultaneously image metabolism, electrical excitation, and calcium signaling from the same field of view and demonstrate its application in the field of drug testing and cardiovascular research. We applied this metabolism-excitation-contraction coupling (MECC) methodology to test the effects of blebbistatin, 4-aminopyridine and verapamil on cardiac physiology. While blebbistatin and 4-aminopyridine alter multiple aspects of cardiac function suggesting off-target effects, the effects of verapamil were on-target and it altered only one of ten tested parameters. Triple-parametric optical mapping was also applied during ischemia and reperfusion; and we identified that metabolic changes precede the effects of ischemia on cardiac electrophysiology.

Population-based high-throughput toxicity screen of human iPSC-derived cardiomyocytes and neurons

In this study, we establish a population-based human induced pluripotent stem cell (hiPSC) drug screening platform for toxicity assessment. After recruiting 1,000 healthy donors and screening for high-frequency human leukocyte antigen (HLA) haplotypes, we identify 13 HLA-homozygous "super donors" to represent the population. These "super donors" are also expected to represent at least 477,611,135 of the global population. By differentiating these representative hiPSCs into cardiomyocytes and neurons we show their utility in a high-throughput toxicity screen. To validate hit compounds, we demonstrate dose-dependent toxicity of the hit compounds and assess functional modulation. We also show reproducible in vivo drug toxicity results using mouse models with select hit compounds. This study shows the feasibility of using a population-based hiPSC drug screening platform to assess cytotoxicity, which can be used as an innovative tool to study inter-population differences in drug toxicity and adverse drug reactions in drug discovery applications.

J. Khojah H, Susan Dewey R. Spontaneous adverse drug reaction reporting by community pharmacists: preparedness and barriers

Background

Adverse drug reactions (ADRs) are undesired, unintended responses to drugs, and are significantly underreported. Pharmacists are drug experts recognized as custodians of drug safety, who are expected to be prepared for and knowledgeable about ADR reporting.

Objectives

To identify Egyptian community pharmacists’ preparedness for and perceived barriers to spontaneous ADR reporting.

Methods

This cross-sectional study recruited a sample of community pharmacists across Egypt, who were invited to complete a self-administrated questionnaire during April 2020.

Results

A total of 923 pharmacists across Egypt responded to the questionnaire. Most pharmacists were knowledgeable about the definition of ADRs (93.9 %) and indicated they felt reporting ADRs benefits the patients (82.2%). Despite recognizing their public health value, only a small percentage of participants conveyed familiarity with the reporting process for both paper (19.2%) and electronic (30.4%) forms, indeed 56.6% of participants did not remember what the ADR report form looked like. Moreover, 75.4% of respondents said they felt that community pharmacies are not the right place for reporting, with 49% suggesting that reporting was the responsibility of physicians. However, only 32.1% reported having insufficient time being a barrier to ADR reporting.

Conclusions

Community pharmacists in Egypt are not well prepared for spontaneous ADR reporting due to a lack of knowledge about the formal process and not acknowledging their responsibility, although time was not a major barrier. Therefore, this highlights a clear opportunity for improvement likely involving targeted education.

Rapid Detection of Direct Compound Toxicity and Trailing Detection of Indirect Cell Metabolite Toxicity in a 96-Well Fluidic Culture Device for Cell-Based Screening Environments: Tactics in Six Sigma Quality Control Charts

Microfluidic screening tools, in vitro, evolve amid varied scientific disciplines. One emergent technique, simultaneously assessing cell toxicity from a primary compound and ensuing cell-generated metabolites (dual-toxicity screening), entails in-line systems having sequentially aligned culture chambers. To explore dual-tox screens, we probe the dissemination of nutrients involving 1-way transport with upstream compound dosing, midstream cascading flows, and downstream cessation. Distribution of flow gives rise to broad concentration ranges of dosing compound (0→ICcompound100) and wide-ranging concentration ranges of generated cell metabolites (0→ICmetabolites100). Innately, single-pass unidirectional flow retains 1st pass informative traits across the network, composed of nine interconnected culture wells, preserving both compound and cell-secreted byproducts as data indicators in each adjacent culture chamber. Thereafter, to assess effective compound hepatotoxicity (0→ECcompound100) and simultaneously classify for cell-metabolite toxicity (0→ECmetabolite100), we reveal utility by analyzing culture viability against ramping exposures of acetaminophen (APAP) and nefazodone (NEF), compounds of hepatic significance. We then discern metabolite generation with an emphasis on amplification across µchannel multiwell sites. Lastly, using conventional cell functions as indicator tools to assess dual toxicity, we investigate a non-drug induced liver injury (non-DILI) compound and DILI compound. The technology is for predictive evaluations of new compound formulations, new chemical entities (NCE), or drugs that have previously failed testing for unresolved reasons.

Targeting UDP-Glycosyltransferase, Glucosamine-6-Phosphate Synthase and Chitin Synthase by Using Bioactive 1,8 Cineole for “Aspergillosis” Fungal Disease Mutilating COVID-19 Patients: Insights from Molecular Docking, Pharmacokinetics and In-vitro Studies

SARS-CoV-2 (COVID-19)-associated co-infections like “Aspergillosis”, has recently baffled the world. Due to its key role in cell wall synthesis, in the present study UDP-glycosyltransferase, glucosamine-6-phosphate synthase and chitin synthase have been chosen as appropriate targets for molecular docking. The objective of the present study was molecular docking of eucalyptus essential oil component 1,8 cineole against cell wall enzymes followed by in vitro validation. For molecular docking, patch-dock web based online tool was used. Ligand–Protein 2D and 3D Interactions were also studied. Drug likeliness, toxicity profile and cancer cell line toxicity were also studied. Molecular docking results indicated that 1,8 cineole form hydrogen bonding and hydrophobic interactions with UDP-glycosyltransferase, glucosamine-6-phosphate synthase and chitin synthase enzymes. 1,8 cineole also depicted drug likeliness by showing compliance with the LIPINSKY rule, sufficient level of bioactivity and cancer cell line toxicity thus signifying its role as a potent anti-fungal drug.