The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates

Design, synthesis and investigation of new imidazole derivatives with biological activities and antifungal effects

Fungal infections are important types of infection that annually cause the death of many people around the world. Therefore, new antifungal agents that are more effective and less toxic are constantly needed. In this study, new imidazole derivatives were synthesized and their antifungal activities were investigated. Compound 5d showed antifungal activity against Candida albicans, Candida parapsilosis and Candida krusei with a minimum inhibitory concentration (MIC50) of 0.98 µg/mL. While compound 5e showed antifungal effects against C. albicans and C. parapsilosis with MIC50 of 0.98 µg/mL, it displayed potent antifungal activity against C. krusei with MIC50 of 1.96 µg/mL. Compound 5h exhibited antifungal activity against C. albicans and C. parapsilosis with MIC50 of 1.96 and 0.98 µg/mL, respectively. It is known that azole group antifungals inhibit ergosterol biosynthesis by inhibiting the 14α-demethylase enzyme. For this reason, in the present study in silico studies were performed on 14α-demethylase enzyme crystal (PDB ID: 1EA1). Molecular docking and dynamics studies were conducted to examine the binding modes of the active compounds (5d, 5e and 5h). The results of the in silico studies agreed with the biological activity results.

Cardiac-Focused Multi-Organ Chips: Advanced Disease Modeling, Drug Testing, and Inter-Organ Communication

Heart disease remains a leading cause of mortality worldwide, posing a significant challenge to global healthcare systems. Traditional animal models and cell culture techniques are instrumental in advancing the understanding of cardiac pathophysiology. However, these methods are limited in their ability to fully replicate the heart's intricate functions. This underscores the need for a deeper investigation into the fundamental mechanisms of heart disease. Notably, cardiac pathology is often influenced by systemic factors, with conditions in other organs contributing to disease onset and progression. Cardiac-focused multi-organ chip technology has emerged to better elucidate these complex inter-organ communications and address the limitations of current in vitro models. This technology offers a novel approach by recreating the cardiac microenvironment and integrating it with other organ systems, thereby enabling more precise disease modeling and drug toxicity assessment. This review provides a comprehensive overview of the heart's structure and function, explores the advancements in cardiac organ chip development, and highlights the applications of cardiac-focused multi-organ chips in medical research. Finally, the future potential of this technology in enhancing disease modeling and therapeutic evaluation is discussed.

Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity

Although the antiallergic properties of compounds such as CAPE, Melatonin, Curcumin, and Vitamin C have been poorly discussed by experimental studies, the antiallergic properties of these famous molecules have never been discussed with calculations. The histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediate allergies and other pathophysiological diseases. In this study, pharmacological activities of FDA-approved second generation H1 antihistamines (Levocetirizine, desloratadine and fexofenadine) and molecules such as CAPE, Melatonin, Curcumin, Vitamin C, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, density functional theory (DFT), molecular docking, biological targets and activities were compared by calculating. Since drug development is an extremely risky, costly and time-consuming process, the data obtained in this study will facilitate and guide future studies. It will also enable researchers to focus on the most promising compounds, providing an effective design strategy. Their pharmacological activity was carried out using computer-based computational techniques including DFT, molecular docking, ADMET analysis, biological targeting, and activity methods. The best binding sites of Desloratadine, Levocetirizine, Fexofenadine, CAPE, Quercetin, Melatonin, curcumin, Vitamin C ligands to Desmoglein 1, Human Histamine H1 receptor, IgE and IL13 protons were determined by molecular docking method and binding energy and interaction states were analyzed. Fexofenadine and Quercetin ligand showed the most effective binding affinity. Melatonin had the best Caco-2 permeability PPB values of Quercetin, CAPE and Curcumin were at optimal levels. On the OATP1B1 and OATP1B3 of curcumin and CAPE, Quercetin was found to have strong inhibition effects on BCRP. Melatonin and CAPE were found to have the highest inhibition values on CYP1A2, while CAPE had the highest inhibition values on CYP2C19 and CYP2C9. Vitamin C and Quercetin were found to be safer in terms of cardiac toxicity and mutagenic risks, while Desloratadine and Levocetirizine carried high risks of neurotoxicity and hematotoxicity, while CAPE was noted for its high enzyme inhibitory activities and low toxicity profiles, while the hERG blockade, DILI, and cytotoxicity values of other compounds pointed to various safety concerns. This study demonstrated the potential of machine learning methods in understanding and discovering H1 receptor blockers. The results obtained provide important clues in the development of important strategies in the clinical use of H1 receptor blockers. In the light of these data, CAPE and Quercetin are remarkable molecules.

Screening of Natural Compounds as Inhibitor of Mpro SARS-CoV-2 Protein; A Molecular Dynamics Approach

BACKGROUND

New strains of SARS-CoV-2 are continually emerging worldwide. Recently, WHO warned of a severe new wave in Europe. Current vaccines cannot fully prevent reinfection in vaccinated individuals.

AIM

Given this issue, recent research focuses on new antiviral candidates with high efficacy and minimal side effects.

OBJECTIVES

Screen natural compounds as inhibitors of Mpro SARS-CoV-2 protein using molecular dynamics.

METHODS

In this study, we have screened the potential of plant-based natural anti-viral compounds. A library of the 579 compounds was generated using currently available literature and online databases. All these compounds were screened based on their binding affinities as predicted by molecular docking analysis and compounds having binding affinity values ≤ -10 Kcal/mol were considered for analysis. Furthermore, from physicochemical assessment, drug-likeness initially nine compounds were identified as the antiviral targets for the selected viral proteins. After ADMET analysis and simulations, the compound 9064 with the lowest RMSD, Coul-SR interaction energy (-71.53 kJ/mol), and LJ-SR energy (-95.32 kJ/mol) was selected as the most stable drug candidate against COVID-19 main protease Mpro.

RESULTS

The ΔG value, calculated using MMGBSA also revealed strong binding of the compound with Mpro. The selected antiviral compound 9064 is an antioxidant flavonoid (Catechin or Cianidanol), which was previously known to have significant immunomodulatory, anti-inflammatory, and antioxidant properties.

CONCLUSION

Considering the limitations of currently available vaccines, our study may provide new insight into potential drugs that may prevent SARS-CoV-2 infection in humans.

A Comprehensive Review of Antimicrobial Agents Against Clinically Important Bacterial Pathogens: Prospects for Phytochemicals

Antimicrobial resistance (AMR) hinders the effective treatment of a range of bacterial infections, posing a serious threat to public health globally, as it challenges the currently available antimicrobial drugs. Among the various modes of antimicrobial action, antimicrobial agents that act on membranes have the most promising efficacy. However, there are no consolidated reports on the shortcomings of these drugs, existing challenges, or the potential applications of phytochemicals that act on membranes. Therefore, in this review, we have addressed the challenges and focused on various phytochemicals as antimicrobial agents acting on the membranes of clinically important bacterial pathogens. Antibacterial phytochemicals comprise diverse group of agents found in a wide range of plants. These compounds have been found to disrupt cell membranes, inhibit enzymes, interfere with protein synthesis, generate reactive oxygen species, modulate quorum sensing, and inhibit bacterial adhesion, making them promising candidates for the development of novel antibacterial therapies. Recently, polyphenolic compounds have been reported to have proven efficacy against nosocomial multidrug-resistant pathogens. However, more high-quality studies, improved standards, and the adoption of rules and regulations are required to firmly confirm the clinical efficacy of phytochemicals derived from plants. Identifying potential challenges, thrust areas of research, and considering viable approaches is essential for the successful clinical translation of these compounds.

Trans-Ferulic acid reduces the sedative activity of diazepam in thiopental sodium-induced sleeping mice: A potential GABAergic transmission

trans-Ferulic acid (TFA), a bioactive compound found in many plants, has been recognized for its diverse pharmacological activities, including potential neurological benefits. Previous studies suggest that TFA exerts anxiolytic effects via GABAergic pathways. This study aimed to investigate the sedative effects of TFA and its possible molecular mechanisms through in vivo and in silico approaches. Adult Swiss mice were randomly divided into six groups (n = 7): control (vehicle), standard (DZP: Diazepam at 3 mg/kg, p.o.), three test groups (TFA at 25, 50, and 75 mg/kg, p.o.), and a combination group (TFA: 50 mg/kg with DZP: 3 mg/kg, p.o.). Thirty minutes post-treatment, thiopental sodium (TS) at 40 mg/kg was administered to induce sedation, and latency as well as duration of sleep, were observed for up to 4 h. In silico studies were conducted with GABAA receptor subunits (α1 and β2) to elucidate the possible molecular interactions. The results demonstrated that TFA significantly reduced latency and extended sleep duration in a dose-dependent manner compared to the control. Additionally, TFA combined with DZP further significantly (p < 0.001) enhanced these effects. In silico analysis revealed that TFA and DZP exhibited strong binding affinities with the GABAA receptor subunits (α1 and β2) in the identical binding sites, with binding energies of -6.8 and - 8.7 kcal/mol, respectively. Collectively, TFA exerted a mild sedative effect in TS-induced sleeping mice and modulated the activity of DZP, likely through interactions with GABAA receptors. TFA showed promising activity as a potential candidate for managing sleep disorders such as insomnia.

Peronema canescens as a Source of Immunomodulatory Agents: A New Opportunity and Perspective

Immunomodulators are pivotal in managing various health conditions by regulating the immune response by either enhancing or suppressing it to maintain homeostasis. The growing interest in natural sources of immunomodulatory agents has spurred the investigation of numerous medicinal plants, including Peronema canescens, commonly known in Asia as sungkai. Traditionally used for its medicinal properties in Southeast Asia, Peronema canescens belongs to the Verbenaceae family and has garnered significant attention. This review discusses the immunomodulatory activity of the active compounds in Peronema canescens and explores the potential directions for future research.

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

Deep active learning with high structural discriminability for molecular mutagenicity prediction

The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model's predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates

Objective

Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy.

Methods

A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits.

Results

The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation.

Conclusion

Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

Hepatitis A Virus Infection in Cynomolgus Monkeys Confounds the Safety Evaluation of a Drug Candidate

In a 3-month toxicity study in cynomolgus monkeys at a European contract laboratory, animals were infected with HAV, initially resulting in hepatic injury being incorrectly attributed to the test compound. Elevated serum ALT/AST/GLDH (5- to 10-fold) were noted in individual animals from all groups including controls, with no apparent dose, exposure, or time-related relationship. Liver histopathology revealed minimal to slight inflammatory cell accumulation in periportal zones of most animals, and minimal to slight hepatocyte degeneration/necrosis in 10/42 animals from all groups. As these findings were more pronounced in 6 drug-treated animals, including 2/6 in the low dose group, the draft report concluded: "treatment-related hepatotoxicity at all dose levels precluded determination of a NOAEL." However, the unusual pattern of hepatotoxicity suggested a factor other than drug exposure might have caused the hepatic effects. Therefore, snap-frozen liver samples were tested for hepatitis viruses using a PCR method. Tests for hepatitis B, C, and E virus were negative; however, 20/42 samples were positive for hepatitis A virus (HAV). Infection was strongly associated with increased serum ALT/GLDH, and/or hepatocyte degeneration/necrosis. Re-evaluation of the study in light of these data concluded that the hepatic injury was not drug-related. A subsequent 6-month toxicology study in HAV-vaccinated cynomolgus monkeys confirmed the absence of hepatotoxicity. Identification of HAV infection supported progression of the drug candidate into later clinical trials. Although rarely investigated, subclinical HAV infection has occasionally been reported in laboratory primates, including those used for toxicology studies and it may be more prevalent than the literature indicates.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Regioselective Partial Hydrogenation and Deuteration of Tetracyclic (Hetero)aromatic Systems Using a Simple Heterogeneous Catalyst

The introduction of added '3-dimensionality' through late-stage functionalisation of extended (hetero)aromatic systems is a powerful synthetic approach. The abundance of starting materials and cross-coupling methodologies to access the precursors allows for highly diverse products. Subsequent selective partial reduction can alter the core structure in a manner of interest to medicinal chemists. Herein, we describe the precise, partial reduction of multicyclic heteroaromatic systems using a simple heterogeneous catalyst. The approach can be extended to introduce deuterium (again at late-stage). Excellent yields can be obtained using simple reaction conditions.

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

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

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo

Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.

The association of medications and supplements with human male reproductive health: a systematic review

Some medications used to treat comorbidities and conditions in reproductive-aged individuals could have a negative impact on fertility. This may occur through hormonal disruption, toxicity to germ cells and spermatozoa, functional impact on the sperm, teratogenicity potential, or ejaculatory abnormalities. Having knowledge of these potential interactions between medications and reproductive potential is important for clinicians to be aware of and guide the patient, along with their treating clinicians, to reproductively favorable alternatives when available. This review aims to summarize the state of the literature regarding medication interactions with human male reproduction using the Anatomical Therapeutic Chemical Classification System of medications.

Patient-derived organoids for precision oncology: a platform to facilitate clinical decision making

BACKGROUND

Despite recent advances in research, there are still critical lacunae in our basic understanding of the cause, pathogenesis, and natural history of many cancers, especially heterogeneity in patient response to drugs and mediators in the transition from malignant to invasive phenotypes. The explication of the pathogenesis of cancer has been constrained by limited access to patient samples, tumor heterogeneity and lack of reliable biological models. Amelioration in cancer treatment depends on further understanding of the etiologic, genetic, biological, and clinical heterogeneity of tumor microenvironment. Patient-derived organoids recapitulate the basic features of primary tumors, including histological complexity and genetic heterogeneity, which is instrumental in predicting patient response to drugs.

METHODS

Human iPSCs from healthy donors, breast and ovarian cancer patients were successfully differentiated towards isogenic hepatic, cardiac, neural and endothelial lineages. Multicellular organoids were established using Primary cells isolated from tumor tissues, histologically normal tissues adjacent to the tumors (NATs) and adipose tissues (source of Mesenchymal Stem Cells) from ovarian and breast cancer patients. Further these organoids were propagated and used for drug resistance/sensitivity studies.

RESULTS

Ovarian and breast cancer patients' organoids showed heterogeneity in drug resistance and sensitivity. iPSCs-derived cardiomyocytes, hepatocytes and neurons showed donor-to-donor variability of chemotherapeutic drug sensitivity in ovarian cancer patients, breast cancer patients and healthy donors.

CONCLUSION

We report development of a novel integrated platform to facilitate clinical decision-making using the patient's primary cells, iPSCs and derivatives, to clinically relevant models for oncology research.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

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

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

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models

BACKGROUND

Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats.

METHODS

PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models.

RESULTS

PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools.

CONCLUSION

PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.

Cryopreservation of Liver-Cell Spheroids with Macromolecular Cryoprotectants

Spheroids are a powerful tool for basic research and to reduce or replace in vivo (animal) studies but are not routinely banked nor shared. Here, we report the successful cryopreservation of hepatocyte spheroids using macromolecular (polyampholyte) cryoprotectants supplemented into dimethyl sulfoxide (DMSO) solutions. We demonstrate that a polyampholyte significantly increases post-thaw recovery, minimizes membrane damage related to cryo-injury, and remains in the extracellular space making it simple to remove post-thaw. In a model toxicology challenge, the thawed spheroids matched the performance of fresh spheroids. F-actin staining showed that DMSO-only cryopreserved samples had reduced actin polymerization, which the polyampholyte rescued, potentially linked to intracellular ice formation. This work may facilitate access to off-the-shelf and ready-to-use frozen spheroids, without the need for in-house culturing. Readily accessible 3-D cell models may also reduce the number of in vivo experiments.

The application of zebrafish patient-derived xenograft tumor models in the development of antitumor agents

The cost of antitumor drug development is enormous, yet the clinical outcomes are less than satisfactory. Therefore, it is of great importance to develop effective drug screening methods that enable accurate, rapid, and high-throughput discovery of lead compounds in the process of preclinical antitumor drug research. An effective solution is to use the patient-derived xenograft (PDX) tumor animal models, which are applicable for the elucidation of tumor pathogenesis and the preclinical testing of novel antitumor compounds. As a promising screening model organism, zebrafish has been widely applied in the construction of the PDX tumor model and the discovery of antineoplastic agents. Herein, we systematically survey the recent cutting-edge advances in zebrafish PDX models (zPDX) for studies of pathogenesis mechanisms and drug screening. In addition, the techniques used in the construction of zPDX are summarized. The advantages and limitations of the zPDX are also discussed in detail. Finally, the prospects of zPDX in drug discovery, translational medicine, and clinical precision medicine treatment are well presented.

Effects of β-eudesmol and atractylodin on target genes and hormone related to cardiotoxicity, hepatotoxicity, and endocrine disruption in developing zebrafish embryos

Atractylodes lancea, commonly known as Kod-Kamao in Thai, a traditional medicinal herb, is being developed for clinical use in cholangiocarcinoma. β-eudesmol and atractylodin are the main active components of this herb which possess most of the pharmacological properties. However, the lack of adequate toxicity data would be a significant hindrance to their further development. The present study investigated the toxic effects of selected concentrations of β-eudesmol and atractylodin in the heart, liver, and endocrine systems of zebrafish embryos. Study endpoints included changes in the expression of genes related to Na/K-ATPase activity in the heart, fatty acid-binding protein 10a and cytochrome P450 family 1 subfamily A member 1 in the liver, and cortisol levels in the endocrine system. Both compounds produced inhibitory effects on the Na/K-ATPase gene expressions in the heart. Both also triggered the biomarkers of liver toxicity. While β-eudesmol did not alter the expression of the cytochrome P450 family 1 subfamily A member 1 gene, atractylodin at high concentrations upregulated the gene, suggesting its potential enzyme-inducing activity in this gene. β-eudesmol, but not atractylodin, showed some stress-reducing properties with suppression of cortisol production.

Exploiting pretrained biochemical language models for targeted drug design

MOTIVATION

The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabelled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation and (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target-specific training. We also compare two decoding strategies to generate compounds: beam search and sampling.

RESULTS

The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials (i.e., data, models, and outputs) are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phosphorescent Ir(III) Complexes for Biolabeling and Biosensing

Cyclometalated Ir(III) complexes exhibit strong phosphorescence emission with lifetime of submicroseconds to several microseconds at room temperature. Their synthetic versatility enables broad control of physical properties, such as charge and lipophilicity, as well as emission colors. These favorable properties have motivated the use of Ir(III) complexes in luminescent bioimaging applications. This review examines the recent progress in the development of phosphorescent biolabels and sensors based on Ir(III) complexes. It begins with a brief introduction about the basic principles of the syntheses and photophysical processes of cyclometalated Ir(III) complexes. Focus is placed on illustrating the broad imaging utility of Ir(III) complexes. Phosphorescent labels illuminating intracellular organelles, including mitochondria, lysosomes, and cell membranes, are summarized. Ir(III) complexes capable of visualization of tumor spheroids and parasites are also introduced. Facile chemical modification of the cyclometalating ligands endows the Ir(III) complexes with strong sensing ability. Sensors of temperature, pH, CO₂, metal ions, anions, biosulfur species, reactive oxygen species, peptides, and viscosity have recently been added to the molecular imaging tools. This diverse utility demonstrates the potential of phosphorescent Ir(III) complexes toward bioimaging applications.

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.

Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept

Conventional methods of drug design require compromise in the form of side effects to achieve sufficient efficacy because targeting drugs to specific organs remains challenging. Thus, new strategies to design organ-specific drugs that induce little toxicity are needed. Based on characteristic tissue niche-mediated drug distribution (TNMDD) and patterns of drug metabolism into specific intermediates, we propose a strategy of distribution- and metabolism-based drug design (DMBDD); through a physicochemical property-driven distribution optimization cooperated with a well-designed metabolism pathway, SH-337, a candidate potassium-competitive acid blocker (P-CAB), was designed. SH-337 showed specific distribution in the stomach in the long term and was rapidly cleared from the systemic compartment. Therefore, SH-337 exerted a comparable pharmacological effect but a 3.3-fold higher no observed adverse effect level (NOAEL) compared with FDA-approved vonoprazan. This study contributes a proof-of-concept demonstration of DMBDD and provides a new perspective for the development of highly efficient, organ-specific drugs with low toxicity.

Assessing Drug-Induced Mitochondrial Toxicity in Cardiomyocytes: Implications for Preclinical Cardiac Safety Evaluation

Drug-induced cardiotoxicity not only leads to the attrition of drugs during development, but also contributes to the high morbidity and mortality rates of cardiovascular diseases. Comprehensive testing for proarrhythmic risks of drugs has been applied in preclinical cardiac safety assessment for over 15 years. However, other mechanisms of cardiac toxicity have not received such attention. Of them, mitochondrial impairment is a common form of cardiotoxicity and is known to account for over half of cardiovascular adverse-event-related black box warnings imposed by the U.S. Food and Drug Administration. Although it has been studied in great depth, mitochondrial toxicity assessment has not yet been incorporated into routine safety tests for cardiotoxicity at the preclinical stage. This review discusses the main characteristics of mitochondria in cardiomyocytes, drug-induced mitochondrial toxicities, and high-throughput screening strategies for cardiomyocytes, as well as their proposed integration into preclinical safety pharmacology. We emphasize the advantages of using adult human primary cardiomyocytes for the evaluation of mitochondrial morphology and function, and the need for a novel cardiac safety testing platform integrating mitochondrial toxicity and proarrhythmic risk assessments in cardiac safety evaluation.

Highly Sensitive Two-Photon Lipid Droplet Tracker for In Vivo Screening of Drug Induced Liver Injury

Lipid droplets (LDs) are lipid-abundant organelles found in most cell lines and primarily consist of neutral lipids. They serve as a repository of various lipids and are associated with many cellular metabolic processes, including energy storage, membrane synthesis, and protein homeostasis. LDs are prominent in a variety of diseases related to lipid regulation, including obesity, fatty liver disease, diabetes, and atherosclerosis. To monitor LD dynamics in live samples, we developed a highly selective two-photon fluorescent tracker for LDs (LD1). It exhibited outstanding sensitivity with a remarkable two-photon-action cross section (Φδ_max > 600 GM), photostability, and low cytotoxicity. In human hepatocytes and in vivo mouse liver tissue imaging, LD1 showed very bright fluorescence with high LD selectivity and minimized background signal to evaluate the stages of nonalcoholic fatty liver disease. Interestingly, we demonstrated that the liver sinusoid morphology became narrower with increasing LD size and visualized the dynamics including fusion of the LDs in vivo. Moreover, real-time and dual-color TPM imaging with LD1 and a two-photon lysosome tracker could be a useful predictive screening tool in the drug development process to monitor impending drug-induced liver injury inducing drug candidates.

Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLMs), and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff), and P-glycoprotein (P-gp) substrate identification (P-gp substrate) in the software ADMET Predictor (AP) from Simulations Plus. The dataset consisted of a total of 4,794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R2= 0.79), guiding the synthesis of new compounds and for prioritizing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability, and permeability by using artificial neural network (ANN) models in the optional Modeler module of AP. Predictions of the test sets were performed with both the global and the local models, and the R2 values for linear regression for predicted versus measured HLM in vitro intrinsic clearance (CLint) based on logarithmic data were 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and laboratory-specific assay conditions for parameters that require biologic assay systems. SIGNIFICANCE STATEMENT: AP is useful early in projects for predicting and categorizing LogD, metabolic stability, and permeability, to guide the synthesis of new compounds, and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can yield improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.

A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein

Toxicity is one of the most important factors limiting the success of new drug development. In this paper, we built a fast and convenient new method (Carrier protein binding information-toxicity relationship, CPBITR) for predicting drug acute toxicity based on the perspective of binding information with carrier protein. First, we studied the binding information between carbamate pesticides and human serum albumin (HSA) through various spectroscopic methods and molecular docking. Then a total of 16 models were established to clarify the relationship between binding information with HSA and drug toxicity. The results showed that the binding information was related to toxicity. Finally we obtained the effective toxicity prediction model for carbamate pesticides. And the "Platform for Predicting Drug Toxicity Based on the Information of Binding with Carrier Protein" was established with the Back-propagation neural network model. We proposed and proved that it was feasible to predict drug toxicity from this new perspective: binding with carrier protein. According to this new perspective, toxicity prediction model of other drugs can also be established. This new method has the advantages of convenience and fast, and can be used to screen out low-toxic drugs quickly in the early stage. It is helpful for drug research and development.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Physicochemical characterization, cytotoxic effect and toxicity evaluation of nanostructured lipid carrier loaded with eucalyptol

Background

Eucalyptol is an active compound of eucalyptus essential oil and was reported to have many medical attributes including cytotoxic effect on breast cancer cells. However, it has low solubility in aqueous solutions which limits its bioavailability and cytotoxic efficiency. In this study, nanostructured lipid carrier loaded with eucalyptol (NLC-Eu) was formulated and characterized and the cytotoxic effect of NLC-Eu towards breast cancer cell lines was determined. In addition, its toxicity in animal model, BALB/c mice was also incorporated into this study to validate the safety of NLC-Eu.

Methods

Eucalyptol, a monoterpene oxide active, was used to formulate the NLC-Eu by using high pressure homogenization technique. The physicochemical characterization of NLC-Eu was performed to assess its morphology, particle size, polydispersity index, and zeta potential. The in vitro cytotoxic effects of this encapsulated eucalyptol on human (MDA MB-231) and murine (4 T1) breast cancer cell lines were determined using the MTT assay. Additionally, acridine orange/propidium iodide assay was conducted on the NLC-Eu treated MDA MB-231 cells. The in vivo sub-chronic toxicity of the prepared NLC-Eu was investigated using an in vivo BALB/c mice model.

Results

As a result, the light, translucent, milky-colored NLC-Eu showed particle size of 71.800 ± 2.144 nm, poly-dispersity index of 0.258 ± 0.003, and zeta potential of − 2.927 ± 0.163 mV. Furthermore, the TEM results of NLC-Eu displayed irregular round to spherical morphology with narrow size distribution and relatively uniformed particles. The drug loading capacity and entrapment efficiency of NLC-Eu were 4.99 and 90.93%, respectively. Furthermore, NLC-Eu exhibited cytotoxic effects on both, human and mice, breast cancer cells with IC50 values of 10.00 ± 4.81 μg/mL and 17.70 ± 0.57 μg/mL, respectively at 72 h. NLC-Eu also induced apoptosis on the MDA MB-231 cells. In the sub-chronic toxicity study, all of the studied mice did not show any signs of toxicity, abnormality or mortality. Besides that, no significant changes were observed in the body weight, internal organ index, hepatic and renal histopathology, serum biochemistry, nitric oxide and malondialdehyde contents.

Conclusions

This study suggests that the well-characterized NLC-Eu offers a safe and promising carrier system which has cytotoxic effect on breast cancer cell lines.

Current Models for Predicting Drug-induced Cholestasis: The Role of Hepatobiliary Transport System

Drug-induced cholestasis is the main type of liver disorder accompanied by high morbidity and mortality. Evidence for the role of hepatobiliary pumps in the cholestasis patho-mechanism is constantly increasing. Recognition of the interactions of chemical agents with these transporters at the initial phases of drug discovery can help develop new drug candidates with low cholestasis potential. This review delivers an outline of the role of these transport proteins in bile creation. It addresses the pathophysiological mechanism for drug-induced cholestasis. In-vitro models, including cell-based and membrane-based approaches and In-vivo models such as genetic knockout animals, are considered. The benefits and restrictions of each model are discussed in this review. Current understandings into the cellular and molecular process that control the activity of hepatobiliary pumps have directed to a better understanding of the pathophysiology of drug-induced cholestasis. A combination of in-vitro monitoring for transport interaction, in-silico predicting systems, and consideration of and metabolic and physicochemical properties must cause more effective monitoring of possible liver problems.

Machine learning - Predicting Ames mutagenicity of small molecules

In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested and the results are not always accurate and reproducible. Therefore, it would be advantageous to develop robust in silico models which can accurately predict the mutagenicity of a compound prior to synthesis to overcome the inadequacies of the Ames test. After curation of a previously defined compound mutagenicity library, over 5000 molecules had their chemical fingerprints and molecular properties calculated. Using 8 classification modelling algorithms, including support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB), a total of 112 predictive models have been constructed. Their performance has been assessed using 10-fold cross validation and a hold-out test set and some of the top performing models have been assessed using the y-randomisation approach. As a result, we have found SVM and XGB models to have good performance during the 10-fold cross validation (AUROC >0.90, sensitivity >0.85, specificity >0.75, balanced accuracy >0.80, Kappa >0.65) and on the test set (AUROC >0.65, sensitivity >0.65, specificity >0.60, balanced accuracy >0.65, Kappa >0.30). We have also identified molecular properties that are the most influential for mutagenicity prediction when combined with chemical molecular fingerprints. Using the Class A mutagenic compounds from the Ames/QSAR International Challenge Project, we were able to verify our models perform better, predicting more mutagens correctly then the StarDrop Ames mutagenicity prediction and TEST mutagenicity prediction.

Deep-tissue real-time imaging of drug-induced liver injury with peroxynitrite-responsive 19F MRI nanoprobes

Peroxynitrite is an important biomarker for assessing drug-induced liver injury (DILI), which is critical for the development and use of drugs. Herein, we report the development of peroxynitrite-responsive self-assembled ¹⁹F MRI nanoprobes, which enable the sensitive imaging of peroxynitrite in L02 cells subjected to oxidative stress and living mice with DILI.

The emergence of imaging mass spectrometry in drug discovery and development: Making a difference by driving decision making

The pharmaceutical industry is a dynamic, science-driven business constantly under pressure to innovate and morph into a higher performing organization. Innovations can include the implementation of new technologies, adopting new scientific methods, changing the decision-making process, compressing timelines, or making changes to the organizational structure. The drivers for the constant focus on performance improvement are the high cost of R&D as well as the lengthy timelines required to deliver new medicines for unmet needs. Successful innovations are measured against both the quality and quantity of potential new medicines in the pipeline and the delivery to patients. In this special feature article, we share our collective experience implementing matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology as an innovative approach to better understand the tissue biodistribution of drugs in the early phases of drug discovery to establish pharmacokinetic-pharmacodynamic (PK-PD) relationships, as well as in the development phase to understand pharmacology, toxicology, and disease pathogenesis. In our experience, successful implementation of MALDI IMS in support of therapeutic programs can be measured by the impact IMS studies have on driving decision making in pipeline progression. This provides a direct quantifiable measurement of the return to the organization for the investment in IMS. We have included discussion not only on the technical merits of IMS study conduct but also the key elements of setting study objectives, building collaborations, data integration into the medicine progression milestones, and potential pitfalls when trying to establish IMS in the pharmaceutical arena. We categorized IMS study types into five groups that parallel pipeline progression from the earliest phases of discovery to late stages of preclinical development. We conclude the article with some perspectives on how we see MALDI IMS maintaining relevance and becoming further embedded as an essential tool in the constantly changing environment of the pharmaceutical industry.

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA)

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood–brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure–activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel “desirability scoring function” was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores.

Influence of Physicochemical Characteristics and Stability of Gold and Silver Nanoparticles on Biological Effects and Translocation across an Intestinal Barrier-A Case Study from In Vitro to In Silico

A better understanding of their interaction with cell-based tissue is a fundamental prerequisite towards the safe production and application of engineered nanomaterials. Quantitative experimental data on the correlation between physicochemical characteristics and the interaction and transport of engineered nanomaterials across biological barriers, in particular, is still scarce, thus hampering the development of effective predictive non-testing strategies. Against this background, the presented study investigated the translocation of gold and silver nanoparticles across the gastrointestinal barrier along with related biological effects using an in vitro 3D-triple co-culture cell model. Standardized in vitro assays and quantitative polymerase chain reaction showed no significant influence of the applied nanoparticles on both cell viability and generation of reactive oxygen species. Transmission electron microscopy indicated an intact cell barrier during the translocation study. Single particle ICP-MS revealed a time-dependent increase of translocated nanoparticles independent of their size, shape, surface charge, and stability in cell culture medium. This quantitative data provided the experimental basis for the successful mathematical description of the nanoparticle transport kinetics using a non-linear mixed effects modeling approach. The results of this study may serve as a basis for the development of predictive tools for improved risk assessment of engineered nanomaterials in the future.

Mining Toxicity Information from Large Amounts of Toxicity Data

Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.

Mirroring Action Potentials: Label-Free, Accurate, and Noninvasive Electrophysiological Recordings of Human-Derived Cardiomyocytes

The electrophysiological recording of action potentials in human cells is a long-sought objective due to its pivotal importance in many disciplines. Among the developed techniques, invasiveness remains a common issue, causing cytotoxicity or altering unpredictably cell physiological response. In this work, a new approach for recording intracellular signals of outstanding quality and with noninvasiveness is introduced. By taking profit of the concept of mirror charge in classical electrodynamics, the new proposed device transduces cell ionic currents into mirror charges in a microfluidic chamber, thus realizing a virtual mirror cell. By monitoring mirror charge dynamics, it is possible to effectively record the action potentials fired by the cells. Since there is no need for accessing or interacting with the cells, the method is intrinsically noninvasive. In addition, being based on optical recording, it shows high spatial resolution and high parallelization. As shown through a set of experiments, the presented methodology is an ideal candidate for the next generation devices for the reliable assessment of cardiotoxicity on human-derived cardiomyocytes. More generally, it paves the way toward a new family of in vitro biodevices that will lay a new milestone in the field of electrophysiology.

Techno-Economic Analysis of Automated iPSC Production

Induced pluripotent stem cells (iPSC) open up the unique perspective of manufacturing cell products for drug development and regenerative medicine in tissue-, disease- and patient-specific forms. iPSC can be multiplied almost without restriction and differentiated into cell types of all organs. The basis for clinical use of iPSC is a high number of cells (approximately 7 × 107 cells per treatment), which must be produced cost-effectively while maintaining reproducible and high quality. Compared to manual cell production, the automation of cell production offers a unique chance of reliable reproducibility of cells in addition to cost reduction and increased throughput. StemCellFactory is a prototype for a fully automated production of iPSC. However, in addition to the already tested functionality of the system, it must be shown that this automation brings necessary economic advantages. This paper presents that fully automated stem cell production offers economic advantages in addition to increased throughput and better quality. First, biological and technological basics for a fully automated production of iPSC are presented. Second, the basics for profitability calculation are presented. Third, profitability of both manual and automated production are calculated. Finally, different scenarios effecting the profitability of manual and automated production are compared.

Fluorescent probe for the imaging of superoxide and peroxynitrite during drug-induced liver injury

Drug-induced liver injury (DILI) is an important cause of potentially fatal liver disease. Herein, we report the development of a molecular probe (LW-OTf) for the detection and imaging of two biomarkers involved in DILI. Initially, primary reactive oxygen species (ROS) superoxide (O₂˙^-) selectively activates a near-infrared fluorescence (NIRF) output by generating fluorophore LW-OH. The C[double bond, length as m-dash]C linker of this hemicyanine fluorophore is subsequently oxidized by reactive nitrogen species (RNS) peroxynitrite (ONOO^-), resulting in cleavage to release xanthene derivative LW-XTD, detected using two-photon excitation fluorescence (TPEF). An alternative fluorescence pathway can occur through cleavage of LW-OTf by ONOO^- to non-fluorescent LW-XTD-OTf, which can react further with the second analyte O₂˙^- to produce the same LW-XTD fluorescent species. By combining NIRF and TPEF, LW-OTf is capable of differential and simultaneous detection of ROS and RNS in DILI using two optically orthogonal channels. Probe LW-OTf could be used to detect O₂˙^- or O₂˙^- and ONOO^- in lysosomes stimulated by 2-methoxyestradiol (2-ME) or 2-ME and SIN-1 respectively. In addition, we were able to monitor the chemoprotective effects of tert-butylhydroxyanisole (BHA) against acetaminophen (APAP) toxicity in living HL-7702 cells. More importantly, TPEF and NIRF imaging confirmed an increase in levels of both O₂˙^- and ONOO^- in mouse livers during APAP-induced DILI (confirmed by hematoxylin and eosin (H&E) staining).

Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery

Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery. ATOM is developing and validating a precompetitive, preclinical, small molecule drug discovery platform that simultaneously optimizes pharmacokinetics, toxicity, protein-ligand interactions, systems-level models, molecular design, and novel compound generation. To achieve this, the ATOM Modeling Pipeline (AMPL) has been developed to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. This modular pipeline has been designed to couple with a generative algorithm that optimizes multiple parameters necessary for drug discovery. ATOM's approach is to consider the full pharmacology and therapeutic window of the drug concurrently, through computationally-driven design, thereby reducing the number of molecules that are selected for experimental validation. Here, we discuss the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of open-source tools for drug discovery.

A Methodological Safe-by-Design Approach for the Development of Nanomedicines

Safe-by-Design (SbD) concepts foresee the risk identification and reduction as well as uncertainties regarding human health and environmental safety in early stages of product development. The EU's NANoREG project and further on the H2020 ProSafe initiative, NanoReg2, and CALIBRATE projects have developed a general SbD approach for nanotechnologies (e.g., paints, textiles, etc.). Based on it, the GoNanoBioMat project elaborated a methodological SbD approach (GoNanoBioMat SbD approach) for nanomedicines with a focus on polymeric nanobiomaterials (NBMs) used for drug delivery. NBMs have various advantages such as the potential to increase drug efficacy and bioavailability. However, the nanoscale brings new challenges to product design, manufacturing, and handling. Nanomedicines are costly and require the combination of knowledge from several fields. In this paper, we present the GoNanoBioMat SbD approach, which allows identifying and addressing the relevant safety aspects to address when developing polymeric NBMs during design, characterization, assessment of human health and environmental risk, manufacturing and handling, and combines the nanoscale and medicine field under one approach. Furthermore, regulatory requirements are integrated into the innovation process.

A Dual-Emissive Phosphorescent Polymeric Probe for Exploring Drug-Induced Liver Injury via Imaging of Peroxynitrite Elevation In Vivo

Drug-induced liver injury (DILI) is a widespread clinical problem. The pathophysiological mechanisms of DILI are complicated, and the traditional diagnostic methods for DILI have their limitations. Owing to its convenient operation, high sensitivity, and high specificity, luminescent sensing and imaging as an indispensable tool in biological research and clinical trials may provide an important means for DILI study. Herein, we report the rational design and preparation of a near-infrared dual-phosphorescent polymeric probe (P-ONOO) for exploring the DILI via specific imaging of peroxynitrite (ONOO^-) elevation in vivo, which was one of early markers of DILI and very difficult to be detected due to its short half-life and high reactive activity. With the utilization of P-ONOO, the raised ONOO^- was visualized successfully in the drug-treated hepatocytes with a high signal-to-noise ratio via ratiometric and time-resolved photoluminescence imaging. Importantly, the ONOO^- boost in the acetaminophen-induced liver injury in real time was verified, and the direct observation of the elevated ONOO^- production in ketoconazole-induced liver injury was achieved for the first time. Our findings may contribute to understanding the exact mechanism of ketoconazole-induced hepatotoxicity that is still ambiguous. Notably, this luminescent approach for revealing the liver injury works fast and conveniently.